University of Southern California Dissertations and Theses

Aging in a high infection society

(USC Thesis Other)

Aging in a high infection society

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

AGING IN A HIGH INFECTION SOCIETY

by

Sarinnapha Vasunilashorn
________________________________________________________

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(GERONTOLOGY)

December 2010

Copyright 2010 Sarinnapha Vasunilashorn

ii
DEDICATION

This dissertation is dedicated to my parents, who have taught me the value of hard
work and determination. Their unconditional love and support has enabled me to
accomplish my goals.

iii
ACKNOWLEDGMENTS

I am grateful to my thesis committee advisors, Dr. Caleb Finch, Dr. Eileen
Crimmins, and Dr. Fred Sattler. Thank you to Dr. Finch for continually encouraging me
to think critically and tackle the more challenging research questions. Thank you to Dr.
Crimmins for her mentorship and guidance at every step of my graduate student career.
Thank you to Dr. Sattler for providing a much needed clinical research perspective. This
work was supported by the National Institute on Aging T32AG0037 and the University
of Southern California Oakley Fellowship.

iv
TABLE OF CONTENTS

Dedication ii

Acknowledgments iii

List of Tables v

List of Figures vii

Abstract ix

Introduction 1

Chapter 1: The Tsimane Health and Life History Project 6

Chapter 2: Biomarkers of Aging in Two Societies: The U.S. and 14
the Tsimane of Bolivia

Chapter 3: Blood Lipids, Infection and Inflammatory Markers 63
in the Tsimane of Bolivia

Chapter 4: Inflammatory Gene Variants in the Tsimane, 97
An Indigenous Bolivian Population with a High
Inflammatory Load

Chapter 5: Conclusion 120

References 125

Appendix: Map of Tsimane Communities 157

v
LIST OF TABLES

Table 2.1: Mean Blood Pressure and Blood Pressure Change with 21
Age in Various Populations

Table 2.2: Mean Cholesterol Levels and Change with Age in 25
Various Populations

Table 2.3: Mean Body Mass Index and Change with Age in 26
Various Populations

Table 2.4: Mean White Blood Cell Count and Distribution and 30
Change with Age in Two Populations

Table 2.5: Ordinary Least Squares Regression Models Indicating the 39
Effect of One Year of Age on Cardiovascular Measures in
the U.S. NHANES (1999-2004) and Tsimane (2003-2007)

Table 2.6: Ordinary Least Squares Regression Models Indicating the 45
Effect of One Year of Age on Metabolic Measures in the
U.S. NHANES (1999-2004) and Tsimane (2003-2007)

Table 2.7: Ordinary Least Squares Regression Models Indicating the 60
Effect of One Year of Age on Markers of Inflammation and
Infection in the U.S. NHANES (1999-2004) and Tsimane
(2003-2007)

Table 2.8: Ordinary Least Squares Regression Models Indicating the 61
Effect of One Year of Age on White Blood Cell Count and
Distribution in the U.S. NHANES (1999-2004) and Tsimane
(2003-2007)

Table 2.9: Ordinary Least Squares Regression Models Indicating the 62
Effect of One Year of Age on Markers of Organ and System
Functioning in the U.S. NHANES (1999-2004) and Tsimane
(2003-2007)

Table 3.1: Characteristics of Tsimane sample, 20 Years of Age and 83
Older

Table 3.2: Blood Serum Lipids and Hemoglobin for Tsimane Adults 84

vi
Table 3.3: Measures of Infection and Inflammation in the Tsimane and 86
the U.S.

Table 3.4: Parasite Prevalence within Bolivia Sample (N=383) 87

Table 3.5: Presence of Six Cholesterol-Related Parasites, Indicators 88
of Infection, Inflammation, and Lipid Levels

Table 3.6: Regression Models Predicting Total and High-Density 91
Lipoprotein Cholesterol Levels from Markers of Infection,
Inflammation, and Parasite Burden

Table 4.1: Clinical Characteristics of the Tsimane Population 112

Table 4.2: Proinflammatory Genotypes of Apolipoprotein E (apoE), 113
C-reactive protein (CRP), and Interleukin-6 (IL-6) based
on Previous Studies

Table 4.3: Population-based Allele Frequencies at C-reactive protein 114
(CRP)-associated SNPs: rs1417938, rs1800947, rs2093061,
rs3093062, rs3091244, rs1205, and rs2808630; Apolipoprotein
(apoE)-associated SNP: rs405509; Interleukin-6 (IL-6)-
associated SNP: rs1800795

Table 4.4: Frequency (%) of Apolipoprotein E (apoE) and C-reactive 116
Protein (CRP) Polymorphisms by Age Group

Table 4.5: Regression Models Predicting Levels of Log Transformed 117
C-reactive Protein (CRP) from Genotypes of Apolipoprotein E
(apoE) and CRP

vii
LIST OF FIGURES

Figure 0.1: Energy Allocation Model 3

Figure 2.1: Mean Level of Systolic Blood Pressure in U.S. and 37
Tsimane Adults Age 20+

Figure 2.2: Mean Level of Diastolic Blood Pressure in U.S. and 37
Tsimane Adults Age 20+

Figure 2.3: Mean Level of Pulse Pressure in U.S. and Tsimane 38
Adults Age 20+

Figure 2.4: Mean Level of Pulse in U.S. and Tsimane Adults 38
Age 20+

Figure 2.5: Mean Level of Total Cholesterol in U.S. and Tsimane 41
Adults Age 20+

Figure 2.6: Mean Level of High-Density Lipopprotein (HDL) 42
Cholesterol in U.S. and Tsimane Adults Age 20+

Figure 2.7: Mean Level of Total Cholesterol (Total-C)/High- 43
Density Lipoprotein Cholesterol (HDL-C) Ratio in U.S.
and Tsimane Adults Age 20+

Figure 2.8: Mean Level of Low-Density Lipoprotein (LDL) 43
Cholesterol in U.S. and Tsimane Adults Age 20+

Figure 2.9: Mean Level of Triglycerides in U.S. and Tsimane 54
Adults Age 20+

Figure 2.10: Mean Level of Body Mass Index in U.S. and Tsimane 54
Adults Age 20+

Figures 2.11 Mean level of markers of infection and inflammation (C-reactive 55
-2.14: protein[CRP], interleukin-6 [IL-6], erythrocyte sedimentation
rate, and Helicobacter pylori) in U.S. and Tsimane adults age 20+

Figures 2.15 Mean level of markers of infection (White blood cell count, 56
-2.20: neutrophils, lymphocytes, eosinophils, monocytes and basophils)
in U.S. and Tsimane adults age 20+

viii
Figures 2.21 Mean level of indicators of organ and system function (Forced 58
-2.24 Expiratory volume [FEV], creatinine clearance, hemoglobin, and
hematocrit) in U.S. and Tsimane adults age 20+

Figure 3.1: Distribution of Total Cholesterol (total-C) in U.S. and 94
Tsimane adults

Figure 3.2: Distribution of High-Density Lipoprotein Cholesterol 95
(HDL-C) in U.S. and Tsimane Adults

Figure 3.3: Distribution of Low-Density Lipoprotein Cholesterol 96
(LDL-C) in U.S. and Tsimane adults

Figure 4.1: Probability of Surival (lx) byage in 1950-1989 and 118
1990-2000 Among the Tsimane

Figure 4.2: C-reactive Protein (CRP) in Blood Plasma in Association 119
with Alleles of Apolipoprotein E (apoE)

ix
ABSTRACT

Increasing evidence suggests that aging is accelerated by exposure to infections
and inflammation. Previous studies suggest that in environments where infection is high,
the processes associated with aging will accelerate and indicators of the aging process
will be more apparent at earlier ages than in environments where exposure to infection is
relatively low. To further our understanding of aging and how biological and genetic
indicators associated with aging vary in a high infection environment, the Tsimane of
Bolivia are studied. The Tsimane are an indigenous population of forager-farmers with
little access to modern medicine, high infectious morbidity, and high mortality.
Three specific aims were investigated. The first specific aim found that compared
to adults in the U.S., Tsimane adults exhibit higher mean levels of infection and
inflammation but substantially lower levels of blood pressure, cholesterol, and body mass
index. Among individual Tsimane, high infectious morbidity is associated with lower
levels of cholesterol. This finding may reflect a remodeling of lipid profiles, as suggested
by findings from the second specific aim. The Tsimane have high levels of infection and
inflammation, as indicated by their high parasite load and white blood cell count, which
were inversely associated with blood lipid levels. The third specific aim suggests that the
strong force of mortality at an early age may be related to differences in genotype
frequencies exhibited in the Tsimane.
These findings suggest that living in a highly infectious environment similar to
the circumstances of our ancestral past is characterized by a very different biological
profile of aging and potentially different genotype distribution with age.

1
INTRODUCTION

Infection and Inflammation

Studies have suggested that over 200 years ago dramatic demographic and
economic changes resulted in a demographic revolution, or a decline in mortality and
fertility from high to low levels. This demographic and economic revolution gave rise to
a physiological revolution, characterized by increases in height and weight (Fogel &
Costa, 1997), increases in birth weight (Barker, 2004), and declines in inflammation
(Crimmins and Finch, 2005; Finch and Crimmins, 2004).
A substantial increase in life expectancy resulted primarily from a decline in
infectious diseases. Finch and Crimmins have proposed that this reduction in infection
resulted in reductions in lifelong levels of inflammation. Less inflammation should not
only be a mechanism allowing increases in height but also a trait serving to delay the
development of cardiovascular diseases (e.g., the atherosclerotic process).
Humans have evolved in environments characterized not only by high levels of
infection, but also by scarce resources and physically demanding labor. Those
circumstances changed beginning in the nineteenth century with the industrial revolution
and with advances in medical science, public health, agricultural technology, and food
production. Fogel (2005) has described what he terms the technophysio evolution of
health capital that accompanied the industrial revolution. Advances over the last century
have altered living circumstances in most countries of the world such that many more
individuals are able to avoid and control infections, consume sufficient food, and work in

2
sedentary occupations. While health conditions prior to industrialization have generally
been studied using historical data, limited information is available on age-related
physiology during these pre-industrial times.

Infection, Inflammation, and Aging
Inflammation is the body’s immune response to pathogens and tissue response to
injury. A healthy inflammatory response is needed to survive infectious insults (Majno,
1975). Inflammation also may be central to the process of aging and age-associated
conditions like Alzheimer disease, cancer, and atherosclerosis (Finch, 2010). High levels
of circulating blood levels of proinflammatory cytokines (e.g., interleukin-6) and C-
reactive protein have been associated with an increased risk of a number of age-related
conditions including cardiovascular disease, mortality, and declines in physical
functioning (Alley et al., 2007; Harris et al., 1999; Papanicolaou et al., 1998; Reuben et
al., 2002; Ridker et al., 1997, 2000). Hence it has been suggested that a gradual increase
in proinflammatory markers is an important characteristic of the aging process, termed
“inflamm-aging” (Francheschi et al., 2006). This underscores the importance of high
levels of inflammation to fight infection from mortality, which is concentrated in the
younger ages among high infection societies. The adverse effect of high levels of
inflammation at older ages is an example of antagonistic pleiotropy.

3
Energy Allocation Model
As indicated above, the inflammatory response is energy intensive. So the decline
in infection and inflammation occurring with the epidemiological transition should be
accompanied by a reduction in the energy needed for repair and more energy will be
required for growth and development. Figure 0.1 is an energy allocation pyramid taken
from Finch (2007, p. 5). This figure illustrates the differences in energy allocation
between low pathogen burdens of healthy environments compared to high pathogen
burdens of infected environments, with the white arrow under the infected pyramid
indicating a static relationship between energy expended for host defense and energy
allotted for basal metabolism. For the purpose of this dissertation and based on data
availability, I will focus on the relationships between the “growth-repair” and “host
defense, acute phase” aspects of the model.
Figure 0.1 Energy Allocation Model

4
The strategy by which the body allocates energy has evolved to adapt to current,
specific environmental conditions. During periods of high infectious load, the body
reallocates more resources and energy towards host defense and less to growth and
maintenance. Increased energy allotted to host defense mechanisms elicits the acute
phase inflammatory process. One of the characteristic signs of inflammation is heat
(calor) or fever, which burns energy and increases basal metabolism by 25-100% (Roe
and Kinney, 1965; Waterlow, 1984), so that every 1°C increase in temperature during a
fever, basal metabolism increases 10-15%. This reallocation of energy in highly
infectious environments, like that experienced by the Tsimane, is likely to result in very
different profiles of growth and of health and aging trajectories, as compared to
populations experiencing low pathogen loads, like the U.S.
Conversely, opposing forces must be considered. In health-rich societies
(populations that are well-nourished and have low infectious disease, as termed by Finch,
2007), “diseases of civilization” have become prevalent. As used to-date, this term
applies to chronic conditions that represent the dominant health problems of
industrialized nations (Kurylowicz and Kopczynski, 1986), including cancer, high
cholesterol, heart disease, obesity, type 2 diabetes, and hypertension. Some of the age-
related physiological changes occurring in modern societies (e.g., coronary heart disease)
share several common classifications as the “diseases of civilization.” In health-poor
environments, where the body was built to combat infection, the characteristics of these
“diseases of civilization” (for instance, rising cholesterol) could be beneficial to host
defense; however, in well-nourished environments with minimal infectious disease, the

5
high lipid levels, characteristic of some “diseases of civilization,” are no longer beneficial
but rather begin to pose a problem. It is likely that this interplay between resource
allocation during changing environments is responsible for the differences in disease
conditions that are prevalent in un-developed, health-poor societies, as compared to
modern, health-rich nations.

6
CHAPTER 1: THE TSIMANE HEALTH AND LIFE HISTORY PROJECT

Several aspects of aging are accelerated by infections and inflammation. In
environments where exposure to infection is high, it is hypothesized that some processes
associated with aging will be sped up and that indicators of the aging process will
become more apparent at earlier ages than in environments where exposure to infection is
relatively low. This response results from the energetic costs of defending the body
against and repairing the body from exposure to infection and is also because some
mechanisms that fight infection may promote aging.
The purpose of this dissertation is to contribute to our knowledge of age-
associated physiology in highly infectious environments. To study this, I examine the
Tsimane of Bolivia, an indigenous forager-farmer population with high morbidity and
mortality (life expectancy is about 52 years) (Gurven et al., 2007). The dissertation is
divided into three empirical papers addressing different aspects of aging in infectious
societies. The first paper compares the age-specific prevalence of biomarkers associated
with aging in industrialized nations, like the U.S., to those associated with the Tsimane.
The second paper examines the relationship between markers of infection and
inflammation and blood lipid levels in the Tsimane. The third and final paper focuses on
the age-associated distribution of genetic loci for a set of genetic markers associated with
inflammation and infection as well as age-associated conditions linked to inflammation.
Data for all three papers are drawn from the Tsimane Health and Life History Project
(discussed in further detail below). For comparsion, the first paper also examines data

7
from the U.S. National Health and Nutrition Examination Survey, a nationally
representative sample of the U.S. non-institutionalized population (1999-2004).
This dissertation is a beginning step towards expanding our knowledge of aging
under conditions of high infection. It cannot, however, address all of the issues
surrounding health and aging in such an environment. Our understanding of the impact
of infection on health and aging suggests that several additional factors (e.g., energy
expenditure, health behaviors, social characteristics, and other indicators of genetic
predisposition) play a role in the aging process. Indeed, all of these factors are important
in age-associated diseases and outcomes, and it is hoped that future analyses, informed by
the findings from this dissertation, will shed light on and pose additional questions on the
complexities of aging under adverse environmental conditions.

Dissertation Format
This dissertation opens with a brief review of infection and inflammation, its
relationship to aging and age-associated conditions, and how it relates to a general energy
allocation model. The review is followed by a chapter detailing the Tsimane Health and
Life History Project and three research chapters. Each of the research chapters includes
its own literature review and methods section related to the specific aim of the analysis in
the chapter.

8
Tsimane Health and Life History Project
Our knowledge of the human life course and aging can be enhanced through a
greater understanding of the link between physiological conditions and the environmental
conditions occurring when our ancestors evolved. This underscores the idea that the
conditions of survival and disease are involved in the process of aging. Since most of
human evolution occurred during times of hunting and gathering (Boyd and Silk, 1997),
one way to understand our past is to study populations with environmental conditions
similar to the past. As such, this dissertation seeks to evaluate the circumstances that
characterize aging, morbidity, and mortality in a forager-farmer, indigenous population –
the Tsimane of Bolivia.
The University of New Mexico-University of California, Santa Barbara Tsimane
Health and Life History Project, initiated in July 2002, is an anthropology and health
project focused on collecting both cross-sectional and longitudinal data on demography,
social, economic, and health characteristics of over 2200 Tsimane’ residing in 17 villages
(Gurven et al., 2007). This project, funded by the NIH, began in June 2004 and
continued to May 2009, had three primary goals: (1) evaluate age patterns of morbidity
and mortality, physical performance, resource production, and activity profiles; (2) test
and expand different models of human life history evaluation (Lee and Kaplan-Robson
models) (Kaplan and Robson, 2002; Lee, 2003; Robson and Kaplan, 2003); (3) foster
interdisciplinary, collaborative cross-cultural research on aging.
The Tsimane are a population of 9,000 indigenous subsistence forager-
horticulturalists who reside in about 60 small villages of extended family clusters,

9
primarily located along the Maniqui river in the Yacuma and Ballivián provinces within
the Beni region of Bolivia (see Appendix A for a regional map). They also reside in the
forest and savanna regions located between the towns of San Ignacio de Mojos and San
Borja (VAIPO, 1998). Though they were exposed to Jesuit missionaries in the late 17
th

century, they never remained in the missions and are still relatively unacculturated.
The Tsimane share several important characteristics of most traditional
populations and other populations prior to modernization. In this proposal, the shared
characteristics of greatest interest are the high pathogen burden and the high work-load
and low energy balance. Their high exposure to infectious diseases is central to each of
the specific aims to be investigated in the current proposal.
Traditional populations, like the Tsimane, have historically been exposed to
several pathogens, many of which are common among other wild primate species (Nunn
et al., 2004). It is probable that our ancestors were exposed to an array of bacterial, viral,
and parasitic pathogens as a result of meat and fish consumption (Finch and Stanford,
2004). Additionally, exposure to cytomegalovirus, pneumonias, intestinal helminthes,
hepatitis B, and herpes has been found among several traditional Amazonian populations
(Black, 1970; Black, 1975; Salzano and Callegari-Jacques, 1988). Hence, such
indigenous, pre-modern societies experienced a much higher pathogen burden than that
of developed and modern populations (Hurtado et al., 2008; McKeown, 1976).
Aside from the high pathogen load characteristic of traditional subsistence
populations, the ratio of energy expenditure relative to food consumption is also high.
These characteristics affect growth and body maintenance such that growth stunting and

10
low body mass index (BMI) occur in adulthood (Leonard, Katzmarzyk, and Crawford,
1996; Leonard and Robertson, 1992). The Tsimane are also a “natural fertility
population,” characteristic of our ancestors who evolved throughout human history.
Under these conditions, child-bearing women have significant energy demands and
immunologic burdens that may also affect the aging process (Doblhammer, 1999;
Kirkwood and Westendorp, 2001; Menken et al., 2003).

Learning from Traditional Populations
In the last 150 years, demographic, technological and epidemiologic transitions
have dramatically altered disease exposure, onset, and treatment, as well as energy
balance, fertility rates, and mortality risks (Barrett et al., 1998; Riley, 2001).
Improvements in clean water supplies, sanitation efforts, and public health
implementation have markedly reduced exposure to infectious conditions, and modern
medicine and immunizations have greatly lowered the adverse outcomes of infections
and contributed to the decline in mortality rates (Crimmins and Finch, 2006; Gage, 2005;
McNeil, 1989). Moreover, changes in work patterns and technological advances have
resulted in less energy expenditure (and exercise), higher food consumption, lower rates
of reproduction, and increased use of drugs. These transitions have occurred relatively
recently— only in the last 150 years— and it is unlikely that selection has consequently
altered the genome of modern populations, as compared to traditional societies.
This dissertation, along with the general aim of the UNM-UCSB Tsimane Health
and Life History Project, seeks to increase our understanding of how environmental

11
conditions are related to the human life course. While increasing attention has been paid
to the impact of early life conditions and disease exposure on aging and chronic disease
in late life (Blackwell, Hayward, and Crimmins, 2001; Elo and Preston, 1992; Hall and
Peckham, 1997), the mechanisms are not well understood. Studies have indicated that
aging and the onset of chronic diseases may be accelerated as a consequence of poor
nutrition (Costa, 2000; Fogel and Costa, 1997), malnutrition during fetal development
(Barker, 1995; Cameron and Demerath, 2002), and exposure to infectious disease during
childhood and throughout life (Crimmins and Finch, 2006; Finch and Crimmins, 2004).
Finch and Crimmins propose that major declines in cohort mortality at later ages are a
result of a decrease in inflammatory levels experienced across these individuals’ lifetimes.
Based on analysis of historical populations, these studies pave the way for future research
using data from traditional societies with characteristics similar to those of historical
populations. This research will allow for a more direct comparison of data collected from
traditional societies to that of population studies obtained from developed and developing
nations.
The strength behind conducting and analyzing research on the Tsimane rests in
our capacity to use modern techniques to study a population that resembles our human
evolutionary history -- as the life expectancy of the Tsimane resembles that of Sweden
150 years prior (Gurven et al., 2008). Compared to studies using historical data that have
been recorded using methodologies representative of the past (e.g., Costa, 2002; Costa et
al., 2007), the Tsimane Health and Life History Project allows researchers to integrate
longitudinal, demographic, anthropological, epidemiological, and biomedical methods to

12
provide a direct comparison of methods and results utilized in modern and developing
populations of the world.

Specific Research Questions

This dissertation addresses three important questions about the aging process in a
highly infectious society:
1) How do physiological markers link to age in a highly infectious society, like
the Tsimane; and how does this relationship in the Tsimane compare to the link
between physiological markers and age in the U.S., a modern society with little
exposure to infection? Previous research has compared biomarkers of aging among
developed nations and few have compared these indicators to undeveloped,
indigenous populations like the Tsimane. Some specific questions include:
1. How do indicators of cardiovascular, metabolic, and immune activation and
inflammation vary with age in the Tsimane and the U.S.?
2. How do the levels of these indicators compare between the U.S and the Tsimane?
Do they exhibit similar levels for indicators of some physiologic systems and
different levels for others?

2) What is the relationship of blood lipids to infection and inflammatory markers
in the Tsimane? Lipids should interact with levels of infection and inflammation, but
few studies have examined the relationship of various markers of infection and
inflammation to blood cholesterol levels. More specific questions include:

13
1. What is the relationship between markers of inflammation and parasite
prevalence?
2. What is the relationship between levels of inflammation and indicators of general
infection to total cholesterol and high-density lipoprotein cholesterol?
3. What is the relationship of parasite infection to total-C and HDL-C levels?
4. Do the above relationships vary when controlling for diet and/or weight (as
indicated by BMI), past infection (as indicated by height changes or stunting),
hemoglobin levels, and Tsimane village region?

3) How do the allele and genotype frequencies of genetic markers associated with
infection and inflammation as well as aging vary with age in the Tsimane? To my
knowledge, no studies have examined the allele and genotype distributions across the
age range of genetic loci associated with inflammation and infection. Some specific
questions include:
1. Does the strong force of mortality due to the high impact of infection at earliest
ages disproportionately affect those with a genetic predisposition towards
combating infection at young ages?
2. Are there significant differences in allele and genotypes distributions between
persons at the oldest ages compared to the very young and middle aged groups?

14
CHAPTER 2: BIOMARKERS OF AGING IN TWO SOCIETIES:
THE U.S. AND THE TSIMANE OF BOLIVIA

Gerontologists have shifted their focus from studying disease often associated
with aging to studying aging processes in the absence of disease. In attempts to
disentangle characteristics of the aging process from disease, experimental researchers
have studied aging patterns across various disease and non-disease models (e.g., flies,
worms, and mice). Within human populations, such manipulations are not possible and
the directionality of disease to biological aging characteristics cannot be determined from
large-scale population surveys. Studies from undeveloped and indigenous populations,
however, have characterized health outcomes and aging characteristics differently from
those of epidemiological studies of developed nations (Drenos, Westendorp, and
Kirkwood, 2006; Gurven et al. 2008, 2009; May et al., 2009; Westendorp, 2004).
In modern health-rich populations, most mortality after age 60 is associated with
cardiovascular conditions, arterial disease, and cancer. Such risks, including increases in
blood pressure and cholesterol levels, have been reported among modern, industrialized
populations (Franklin et al., 2001). However, studies of traditional societies suggest that
these cardiovascular indicators change little with age and may not be characteristic of our
commonly held beliefs about the aging process. Among several African populations (e.g.,
United Republic of Tanzania and Kenya, for example), blood pressure does not change
with age (Carvalho et al., 1989; Lengani et al., 1994; Vaughan, 1979), and in the Kuna
Indians of Panama, the first traditional population in which blood pressure was

15
investigated, hypertension was rare (Kean, 1944). Advanced arterial disease was largely
absent in the Tsimane, an indigenous population of forager-horticulturalists residing in
lowland forests and savannas of Bolivia (Gurven et al. 2009).
Studying the age-associated biological profiles of currently undeveloped
populations with living conditions similar to that of our ancestral past will likely provide
new perspectives of the fundamental underpinnings of the aging process. To better
understand how biological profiles vary with age in a society where arterial disease is
absent (Gurven et al., 2009), I examine the Tsimane of Bolivia, who are currently being
studied by my collaborators, Michael Gurven and Hillard Kaplan, as a model for aging
under pre-industrial human population. The model of resemblance is based upon their
short lifespans (life expectancy of 42.8 years during 1950-1989), young age structure,
high infectious morbidity, varied energy balance with high workloads, and limited access
to modern medicine (Gurven, Kaplan, and Supa, 2007; Gurven et al. 2008). Biological
indicators of assumed-to-be markers of aging in two populations with different
environmental conditions will be compared: the Tsimane with a high exposure to
infection and the U.S. with a relatively low infectious burden. The current paper
examines age differences in levels within the Tsimane and the U.S. population, but does
not examine age change. Because age differences can be affected by mortality rates
among those with a medical condition, it is important to make this clear distinction.

16
Cardiovascular System Change with Age

Many major structural and functional characteristics of the cardiovascular system
are affected by age (Ferrari, Radaelli, and Centola, 2003). These changes can be
categorized under two circumstances: age changes under resting conditions and age
changes under non-resting conditions.
Although the left ventricle of the heart (where blood is forcefully pumped into the
aorta and on to all parts of the body) becomes less strong with age, no changes in cardiac
output (the amount of blood pumped per beat of the right or left ventricle) occur at rest
(DiGiovanna, 2000). Under non-resting conditions (i.e., during times of cardiac
adaptability), the heart differentially adjusts cardiac output levels to meet various
demands. Vigorous exercise in later ages results in an increase in the amount of blood
remaining in the left ventricle post contraction. This results in a minor inhibition of
blood flow from the lungs and leads to an accumulation of blood in the lungs (pulmonary
congestion). Consequently, increases in lung capillary blood pressure forces fluid out of
the capillary walls, leading to a reduction in lung functioning (pulmonary edema), and
causing older adults to feel out of breath sooner when vigorously exercising.
Another non-compensatory change with age includes the declining efficiency of
the heart. Older hearts that are dilated and thickened consume more oxygen in order to
pump the same amount of blood, as compared to younger hearts. If the coronary arteries
remain normal, this should not become a problem. However, most people do not exhibit
completely normal coronary arteries. In this case, individuals with low coronary blood
flow during exercise are more likely to have a heart attack. Moreover, there is also a

17
general thickening and stiffening of the arterial walls, which leads to a loss of elasticity
and a gradual increase in blood pressure, as observed in developed populations (Drizd et
al., 1986). Increasing age has also been related to atherogenesis, growth of cells and an
accumulation of lipids in the arterial wall. These plaques arise in response to injury
(atheromas) (Ross, 1999) and may occlude arterial blood flow resulting in tissue damage
and insuffificient oxygen availability.
Heart disease is the second leading chronic disease for individuals over age 65.
Consequently, it is the leading cause of medical attention and is a major cause of
disability and an altered lifestyle. Though different forms of heart diseases are
increasingly common and more serious with age, disease of the coronary arteries is the
most common (DiGiovanna, 2000). Atherosclerosis, far more than coronary arterial
disease, is due to formation and enlargement of plaque accumulation on the artery walls.
This outcome causes a reduction in blood flow via the narrowing and stiffening of the
arteries, which reduces their ability to dilate when oxygen is required, and forms clots
that clog arteries and halts blood flow.
Congestive heart failure (CHF), another common heart condition, occurs more
often and to a more serious extent with age, as more than 75% of CHF cases occur in U.S.
adults age 65 and over (Rich, 1997). The underlying problem of CHF relates to years of
overworking the heart. Although an overworked heart initially strengthens itself, over
time the excessive dilating and thickening process weakens the heart. This result
contributes to decreased efficiency of the heart, diminished blood flow, and the
malfunctioning of some body organs.

18
Indigenous Societies, Populations in Transition, and Developed Nations

Here I discuss previous studies that have examined biological and anthropometric
indicators that are described and classified into four groups: cardiovascular indicators,
metabolic indicators, infection/inflammatory measures, and markers of organ and system
function. The cardiovascular measures to be examined include systolic and diastolic
blood pressure (SBP and DBP, respectively), pulse pressure (PP), and pulse rate. The
metabolic measures include total cholesterol (total-C), high-density lipoprotein
cholesterol (HDL-C), ratio of total-C/HDL-C, low-density lipoprotein cholesterol (LDL-
C), triglycerides, and body mass index (BMI). C-reactive protein (CRP), interleukin-6
(IL-6), erythrocyte sedimentation rate (ESR), Helicobacter pylori (H. pylori), total white
blood cell (WBC) count and distribution (% neutrophils, lymphocytes, eosinophils,
monocytes, and basophils) are included in the markers of infection and inflammation.
Among our measures of lung and kidney function, we examine forced expiratory volume
in 1 second (FEV
1
) and serum creatinine clearance, respectively. We also investigate
hemoglobin and hematocrit as indicators of environment.
The following is a summary of findings from populations ranging from
industrialized nations to undeveloped societies to better understand how environmental
differences influence age-associated changes in biological and anthropometric measures.

19
Cardiovascular Measures

Blood Pressure

In developed countries, biomarkers associated with the cardiovascular system
(e.g., blood pressure) have been found to increase with age (Whelton, 1985) and differ in
comparison to several less developed and indigenous populations (Cruickshank and
Beevers, 1985; Dressler, 1999; Fleming-Moran and Coimbra Jr, 1990; Stevenson, 1999;
Waldron et al., 1982). For instance, in the U.S and Europe, blood pressure rises with age
(Boe, Humerfelt, and Wedervang, 1957; Hamilton, Pickering, Roberts, and Sowry, 1954;
Master, Garfield, and Waters, 1952). However, little support for differences in age- and
sex-trends within traditional societies has been found, with these populations exhibiting
lower prevalence rates for hypertension as compared to industrialized nations (Page,
1974). The Kuna Indians, who reside in an isolated island chain off the coast of Panama,
were the first reported population for which hypertension was rare (Kean, 1944).
Hypertension was rare in 39 other populations in the Americas, Africa, Asia, and the
Pacific region (James and Baker, 1995).
Little to no changes in blood pressure was reported with age. Mean blood
pressure did not increase in the Samburu of Kenya (Shaper, Williams, and Spencer,
1961), the Ceyelonese (Bibile et al., 1949), the Caroline Islanders (Murril, 1949), the
Kalahari Bushman (Kaminer and Lutz, 1960), and the Yanomamo (Carvalho et al.,
1989). In contrast, an age-related decline in blood pressure was reported among the
Chimbu of New Guinea and other native New Guinea populations (Maddocks and Rovin,
2005; Whyte, 1958). Some differences in blood pressure patterns with age were

20
reported in other African populations, stating that blood pressure has generally remained
the same with age, with exceptions in later life: SBP increases very slightly if at all and
DBP decreases with age (Lengani et al., 1994). This trend has also been reported in
western populations (e.g., the CArdiovascular STudy in the ELderly, CASTEL, project
of Italians) (Casiglia et al., 1991).
Generally, in comparison to industrialized populations, traditional populations and
less urbanized societies have much lower rates of hypertension (Ostfeld and D’Atri,
1977; Page, 1976; Pavan et al., 1997; Poulter and Sever, 1994; Vaughan, 1978; Waldron
et al., 1982). In the case of Kenyan tribes of east Africa in 1929, no cases of
hypertension were found among 1000 individuals (Donnison, 1929). Several studies of
modern populations have suggested an association between higher mean blood pressure
and increases in social structure complexities within a population (Carvalho et al., 1985;
Cassel, 1975; Epstein and Eckoff, 1967; Lowenstein, 1961; Marmot, 1980; McGarvey
and Baker, 1979; Patrick et al., 1983; Prior and Stanhope, 1980; Waldron et al., 1982).
Specifically, blood pressure and the slopes of blood pressure with age were higher in
people with more involvement in a money economy, greater economic competition, and
more contact with individuals of different beliefs and cultures (Copper et al., 1997;
Waldron et al., 1982). (See Table 2.1).

21
Table 2.1 Mean blood pressure and blood pressure change with age in various populations

Mean (mm Hg)
Population SBP DBP
Change with
age Reference
Samburu of Kenya -
Shaper, Williams, Spencer,
1961
Ceyelonese - Bibile et al., 1949
Caroline Islanders - Murril, 1949
Kalahari Bushman - Kaminer and Lutz, 1960
Yanomamo - Carvalho et al., 1989
Chimbu of New
Guinea
M: 113;
F: 118
M: 73;
F: 74 ↓
Maddocks and Rovin, 2005;
Whyte, 1958
Africans
change in later
age: SBP ↑ Lengani et al., 1994
Tanzanians 144 83 - Pavan et al., 1997
Brazilians 159 95 ↑ Pavan et al., 1997
Italians 155 95
change in later
age: SBP ↑,
DBP ↓
Casiglia et al., 1991; Pavan
et al., 1997
"-" signifies no change with age
SBP = systolic blood pressure
DBP = diastolic blood pressure
M=males; F=females

22
Mean Blood Pressure and Pulse Pressure

Aside from examining mean blood pressure and high-risk levels of blood pressure
(hypertension), investigators have also examined indicators of cardiovascular functioning
that account for both SBP and DBP together. Among the Chimbu of New Guinea,
Maddock and Rovin (2005) use mean blood pressure (calculated by: DBP + [SBP-
DBP/3]) to jointly examine SBP and DBP and found that it closely resembles the
physiological mean of blood pressure. Additionally, studies suggest that pulse pressure
(calculated by: SBP – DBP) is a better indicator of cardiovascular risk than mean SBP or
DBP in older hypertensive European adults (Blacher et al., 2000). Few studies have
investigated this relationship in traditional populations, as most studies have solely
examined mean blood pressure levels. In industrialized nations, age-related increases in
PP have been reported, while PP in traditional societies is more stable with age (Aviv,
2001; Skurnick, Aladjem, and Aviv, 2010).
Pulse
Little age-associated changes in pulse rate have been reported in industrialized
populations (Gillum, 1992). I have not found any study of the pulse rate of adults from
traditional societies. A study of aborigine children and adolescents residing in Aurukun
and Weipa missions in Australia report rapid pulse rates in comparison to normative
values ( ≥90 beats/min at rest) (Galbraith, 1967).

23
Metabolic Measures
Total and High-Density Lipoprotein Cholesterol
Generally, total-C levels increase and HDL-C levels decrease with age in the U.S.
(Keys et al., 1950, 1952; Stevenson, Crook, and Godsland, 1993) (Table 2.2), though
these estimates are confounded by the use of statins after the 1990s. In comparing three
populations with varied levels of industrialization, total-C levels were lowest in a
primitive African population, highest in a modern Italian population, and intermediate in
a Brazilian population beginning to transition from a rural to urban lifestyle (Pavan et al.,
1997). Similar findings were reported when comparing total-C levels among South
Africans, Indians, and Whites, who displayed increasing levels of total-C (average of 153,
186, and 197 mg/dl, respectively) (Rubenstein et al., 1969). Moreover, adults from the
Lupingu village, on the eastern shore of Lake Nyasa in Tanzania, had modest levels of
total-C (mean of 148 mg/dl), HDL-C (mean of 36 mg/dl), and low levels of total-C/HDL-
C (mean of 4.50) (Pauletto et al., 1996) relative to industrialized populations like the U.S.
National Health and Nutrition Examination Survey (NHANES). Similar to rural
Guatemalan adults (Mendez, Tejadad, and Flores, 1962), the Tarahumara Indians of
Mexico had mean total-C levels of 135 (Connor et al., 1978). Although the Tarahumara
are a physically active population, their HDL-C levels (mean of 25 mg/dl) were not
higher than normal values in the U.S (Frederickson and Levy, 1972).
LDL and Triglycerides

In an urban U.S population, LDL-C levels increased with age (Jacobs et al., 1980)
and LDL-C levels vary by populations (Table 2.2). Among the Tarahumara Indians of

24
Mexico, mean LDL-C levels were lower (87 mg/dl) (Connor et al., 1978) than in an
urban U.S population (106 mg/dl) (Jacobs et al., 1980).
There is a general increase in triglycerides with age and, similar to the blood lipid
measures, triglyceride levels vary among populations (Bang, Dyerberg, and Nielsen,
1971). Across most age groups, mean triglyceride levels of Danish adults were nearly
twice as high as those of the Greenlandic west coast Eskimos (males: 114 and 50 mg/dl,
respectively; females: 96 and 39 mg/dl, respectively). Mean triglyceride levels among a
traditional population (Tarahumara Indian men) were also lower (123 mg/dl) (Connor et
al., 1978) compared to that of an urban male population in the U.S (143 mg/dl) (Jacobs et
al., 1980).
Body Mass Index

The general pattern of weight over time is that it increases with age until about
age 60, afterwhich it decreases (Seidell et al., 2000); height does not typically change
much between age 20-50, but height decreases in later life, which, with an increase in
weight over time, causes increases in BMI with age (Table 2.3). In comparing the effect
of environment and level of acculturation and development, within traditional
populations, BMI is lower compared to industrialized populations (Pavan et al., 1997).
Additionally, in New Guinea natives, where consumption of protein and fat is lower than
among Europeans, the New Guinea natives are less obese and more muscular than
Europeans (Whyte, 1958). In traditional, but acculturated populations, BMI is higher

25

Table 2.2 Mean cholesterol levels and change with age in various populations

Mean (mg/dl) Change with age
Population
Total-
C
HDL-
C LDL-C Triglycerides
Total-
C
HDL-
C
LDL-
C Triglycerides References
Americans 185 106 143 ↓ ↓ ↑
Jacobs et al.,
1980; Keys et al.,
1950, 1952;
Stevenson, Crook,
Godsland, 1993
Africans 153
Rubenstein et al.,
1969
Indians 186
Rubenstein et al.,
1969
South African whites 197
Rubenstein et al.,
1969
Tanzanians 160 Pavan et al., 1979
Tanzanians of Lake
Nyasa 148 36
Pauletto et al.,
1996
Tarahumara Indians of
Mexico 135 25 87 123 Connor et al., 1978
Italians 227 Pavan et al., 1979
Brazilians 186 Pavan et al., 1979
Greenlandic Eskimos
M: 114; F:
96 M: 50; F: 39 - ↑
Bang and
Dyerberg, 1971
"-" signifies no change with age
Total-C=total cholesterol; HDL-C=high-density lipoprotein cholesterol; LDL-C=low-density lipoprotein cholesterol
M=males; F=females

26

Table 2.3 Mean body mass index and change with age in various populations

Population Mean BMI Change with age Reference
Norweigens ↑ but ↓ after age 60 Seidell et al., 2000
Tanzanians 20 Pavan et al., 1997
Brazilians 26 Pavan et al., 1997
Italians 27 Pavan et al., 1997
New Guineans 23 Whyte, 1958
Australian
aborigines M: 25; F: 28 ↑ (max age: 64) O'Dea et al., 1993
BMI=body mass index
M=males; F=females

than unacculturated populations: 75% of women and 51% of men from a central
Australian aboriginal community with a long history of acculturation were overweight or
obese (O’Dea et al., 1993).
Inflammation and Infection Measures

C-reactive Protein and Interleukin-6

Levels of inflammatory markers generally increase with age (Ferrucci et al.,
2005). This phenomenon is true of CRP, fibrinogen, and interleukins (e.g., IL-6 and IL-
18), and some receptors (e.g., IL-6r, IL-1ra). In comparing differences in inflammatory
markers between populations, the Tsimane have CRP levels that are higher across all age
groups than those in the U.S. (Gurven et al., 2008). By age 55, the Tsimane have spent
about 10 more years with high CRP than Americans. Elevated levels of CRP (>3 mg/l)
have also been reported in other indigenous populations, including a remote Australian
aboriginal community, with mean values of 8.0 and 6.4 mg/l for females and males,
respectively (McDonald et al., 2004).

27
IL-6 has been primarily studied in modern, industrialized societies and— to our
knowledge–few studies report on IL-6 levels in indigenous and undeveloped populations
(e.g., Indians residing in village near to or in Pune) (Yudkin et al., 1999). IL-6 is a
cytokine with broad cellular roles in health and disease, and studies suggest that levels of
IL-6 and CRP are independently related to several similar outcomes and clinical
cardiovascular risk factors (Bermudez et al., 2002). It may be that the relationship of
level of acculturation, which is likely indicative of lower infection, to IL-6 will vary in a
similar manner to that of CRP.
White Blood Cells
WBC count, an indicator of current infection, is typically higher in less developed
communities, as compared to modern societies (Table 2.4). For instance, in the Wichí
aboriginal community located in Salta, Argentina, approximately 24% had WBC counts
above the U.S normal value (>11,000 cells/mm
3
) (Taranto et al., 2003). In the U.S, less
than 25% of the population had a total WBC count of ≥9,400 cells/mm
3
(Horne et al.,
2005), suggesting that the number of Americans with elevated WBC count is lower than
the number of Wichí aboriginals with elevated WBC count.
WBC Distribution
The distribution of WBC counts varies with age and by population (Meyer et al.,
1998). For instance, in the U.S., total WBC counts decline with age, where the median
WBC count of 9,000 cells/mm
3
in children <1 year-old compares to the 6,000 cells/mm
3

among adults age 18 and older (Erkeller-Yusell et al., 1992). Both high and low WBC
count (>6,000 and <3,5000 cells/mm
3
, respectively) has been associated with higher

28
mortality than individuals with levels within 3,500-6,000 cells/mm
3
(Ruggiero et al.,
2007). In indigenous populations, like the Tsimane, the average percent neutrophils,
lymphocytes, basophils, and monocytes (52%, 28%, 0.1%, and 0.1%, respectively)
coincided with the clinically normal value ranges used in the U.S. (48-73%, 18-48%, 0-
2%, and 0-9%, respectively) (Horne et al., 2005; Vasunilashorn et al., forthcoming).
Mean levels of the percent eosinophils in the Tsimane (20%), however, exceeded the
clinically normal range for the U.S. (<5%). Among Indochinese refugees with low levels
of acculturation, nearly half (47%) had eosinophil levels of ≥4% (Erickson and Hoang,
1980).
Sedimentation Rate
Erythrocyte sedimentation rate (ESR), an indicator of general infection and
inflammation, increases with age (i.e., higher in the age group 65-74, as compared to the
age group 45-64) (Böttiger and Svedberg, 1967) and varies by population. In the
Tsimane, ESR was higher (36 mm/hr) (Vasunilashorn et al., forthcoming) than mean
levels reported in the U.S. (15 mm/hr) (Gillum, Mussolino, and Makuc, 1994).
Helicobacter pylori

Seroprevalence of Helicobacter pylori (H. pylori) is dependent on living
conditions, with individuals residing in rural regions exhibiting moderate levels of
infection (39%) (Mitchell et al., 1992). H. pylori infection is primarily acquired during
childhood and has, over time (1969-1989), become less frequent in South Yorkshire,
United Kingdom (Banatvala et al., 1993). Among middle-aged adults in developing

29
countries, H. pylori infection is over 80%, compared to prevalence (20-50%) among
various industrialized countries (Suerbaum and Michetti, 2002).
Organ and System Function

Forced Expiratory Volume

Forced expiratory volume in 1 second (FEV
1
), a common pulmonary function test
used to measure lung function, is the maximum volume of air that can be exhaled. FEV
1
plateaus with age until about 25, after which it decreases with age (Kerstjens et al., 1997;
Schoenberg, Beck, Bouhuys, and 1978) and is typically higher in men than women
(Jacobs, Jr. et al., 1992), is lower among smokers than non-smokers (Kerstjens et al.,
1997), and is associated with height (Bande, Clément, and Van de Woestinjne, 1980).
Given the same age and height for men and women, FEV
1
values in American and
European populations (3.58 and 3.52 L, respectively) (Cotes et al., 1966; Morris et al.,
1971) were higher than that of the Bantu, Zimbabwe, and Sudanese, (2.85, 3.13, and 2.84
L, respectively) (Cookson et al., 1976; Dufetel et al., 1989; Gahutu and Wane, 2006;
Handkinson, Odencrantz, and Fedan, 1999; Johannesen and Erasmus, 1968; Mengesha
and Mekonnem, 1985; Mustafa, 1977).
Creatinine
Serum creatinine clearance, an indicator of kidney function, generally increases
with age (Epstein, 1996; Paper, 1973; Rowe et al., 1976). On average, creatinine
clearance was low among Pima Indian adults (0.8 mg/dl) (Knowler et al., 1978; Nelson et
al., 1996) compared to U.S. levels (males 0.96 and females 1.16 mg/dl) (Jones et al.,
1998).

30

Table 2.4 Mean White Blood Cell Count and Distribution and Change with Age in Two Populations

Mean (cells/mm3) WBC Distribution (%)
Population
WBC
count
Change
with age Neutrophils Eosinophils Lymphocytes Basophils
Mono-
cytes References
U.S. 5900 ↓ 32
Erkeller-Yell et
al., 1992
Tsimane of
Bolivia 10442 52 20 28 0.1 0.1
Vasunilashorn
et al.,
forthcoming

31
Indicators of the Environment

Hemoglobin

Levels of blood hemoglobin generally remain the same with age for women, but
decline slightly for males (Böttiger and Svedberg, 1967; Salive et al., 1992).
Hemoglobin levels also vary by population, with the Tsimane exhibiting lower levels, as
compared to their U.S. counterparts (Astor et al., 2002; Lindsay et al., 2003;
Vasunilashorn et al., forthcoming).
Hematocrit
Hematocrit is the volumetric proportion of blood that consists of red blood cells,
with low levels indicating anemia (<41% for men; <36% for women) (Braunwald et al.,
2001). In a northern Italian population, mean hematocrit levels decreased with age for
men and increased for women (Cirillo et al., 1992). The same decline with age was
found among Puerto Rican men living in rural and urban areas, with average values
slightly higher among urban-dwelling males, as compared to rural male inhabitants (46.2
and 45.9, respectively) (Sorlie et al., 1981).

Study Purpose and Hypotheses

Biological markers can be used to provide early signs of deteriorating health and
have been associated with age, the aging process, and several age-related conditions
(Crimmins et al., 2008a). To characterize aging profiles in a population with no obvious
arterial disease (the Tsimane) and a population where arterial disease is closely associated
with aging (the U.S.), we compare age-related changes in biological and anthropometric

32
indicators in these two different populations. Overall, this comparison should provide
insight into the pronounced differences in life expectancy between the U.S. and the
Tsimane as part of my attempt to understand differences in aging trajectories, as well as
in to disentangle the differences between non-diseased biological aging profiles and
disease processes that can occur with age.
Based on my knowledge of cardiovascular changes with age and of the literature
from studies of undeveloped, developing, and currently developed populations
(summarized below), I predict that the Tsimane will exhibit lower levels of
cardiovascular and metabolic markers compared to their U.S. counterparts. I believe this
difference is due to the daily physical activity and high workload of the Tsimane. We
also hypothesize that the Tsimane will have higher levels of markers of infection and
inflammation compared to the U.S. because of their minimal access to modern medicine
(although this is rapidly changing) and poor sanitation, including lack of clean water.
Due to the continued exposure to infection, Tsimane adults may also exhibit lower
(poorer) levels of lung and kidney functioning compared to their U.S. counterparts.
With respect to age, we predict that the Tsimane will show little change in blood
pressure and cholesterol levels, with slight declines in body mass index among Tsimane
in late life. I also expect that with age the Tsimane will exhibit increases in indicators of
infection and inflammation. A previous study from my collaborators has found that the
Tsimane spend more years of their adult life (at age 35) with higher CRP than Americans
approaching older adulthood (at age 55) (Gurven et al., 2009). Among the indicators of

33
organ function, I expect that these indicators will decline with age in both the Tsimane
and the U.S. but will be more pronounced in the Tsimane than in the U.S.

Methods
Study Sample
Our study sample includes adult participants (age 20+) from the Tsimane Life
History and Health Project (2003-2007). A detailed description of this study has been
previously published (Gurven et al., 2008). Briefly, it is a joint health and anthropology
project geared towards understanding the influences of ecology and evolution on the life
course, with a focus on health, growth and development, aging, biodemographics, and
economics of these remote villages. This dataset allows researchers to study a population
living in an epidemiological, medical, and ecological environment similar to that of
historical populations. This environment is characterized by the high prevalence of
infectious disease, little contact with modern medicine, and low caloric intake relative to
energy expenditure. While many Tsimane continue to maintain their traditional lifestyle,
a growing number are gradually moving to communities near towns with increasing
access to modern medicine.
To compare biological and anthropometric indicators of aging to a modern,
industrialized society, we use data from the U.S. NHANES 1999-2004. The NHANES is
a cross-sectional study designed to regularly monitor the health and nutritional status of
children and adults in the United States. Every year, approximately 5,000 individuals
complete extensive interviews, as well as medical and laboratory examinations (CDC,

34
2004). With the use of weights, the NHANES sample is representative of the U.S. non-
institutionalized population. Our study sample includes non-institutionalized U.S. adults
age 20+.
To examine aging within and between the two populations, we investigate several
commonly studied biological and anthropometric indicators previously related to aging or
aging-related conditions (Crimmins et al., 2008a). As noted earlier, all measures are
categorized into four groups: cardiovascular, metabolic, inflammation and infection, and
organ and system functioning. Included in the cardiovascular indicators are: SBP
(mmHg), DBP (mmHg), PP, and pulse (beats/min). The average of a maximum of three
SBP and DBP values is used for analysis. PP is calculated as the difference between SBP
and DBP, and blood pressure and pulse rates were taken while seated.
The metabolic indicators examined include total-C (mg/dl), HDL-C (mg/dl), total-
C/HDL-C ratio, LDL-C, triglycerides, and BMI (kg/m
2
). Blood serum was analyzed for
total-C, HDL-C, LDL-C, and triglycerides. BMI was calculated from weight divided
height-squared.
The markers of inflammation and infection include: CRP (mg/l), IL-6 (pg/ml),
WBC count (cells/mm3) and distribution (% neutrophils, lymphocytes, eosinophils, and
basophils), ESR, and H. pylori infection. In the Tsimane, serum hs-CRP and IL-6 were
determined from samples collected and frozen in the field, and assayed at 0.1-150.0 mg/L
and 2.0-1000.0 pg/mL (respectively) at the Tricore Reference Laboratories in
Albuquerque, New Mexico using Immulite 2000 kits. The mean replicate interassay
coefficient variation was 5.6% for hs-CRP and 5.8% for IL-6 (Diagnostics Products

35
Corporation, Siemens, Deerfield, IL). Because IL-6 values for the U.S. were not
available, we utilized published data from the InCHIANTI (“Invecchiare in Chianti,”
meaning aging in the Chianti area) Study for comparative purposes to a modernized
population (Ferrucci et al., 2005). NHANES I (1971-1974) was used for ESR in the U.S.
as this was not measured in NHANES 1999-2004.
Indicators of organ and system function are: FEV
1
(liters), serum creatinine
clearance (mg/dl), hemoglobin (g/dl), and hematocrit (%). For FEV
1
in

the U.S.,
NHANES III (1988-1994) was used.

Analysis
For all indicators, means and 95% confidence intervals (CI) are reported.
Ordinary least squares regressions were used to determine the effect of age on each of the
indicators. All models include age and sex (Models I).
The variables input into Model II vary by the biological or anthropometric
indicator. For the cardiovascular measures, Models II adjusts for age, age-squared, sex,
and BMI. For all metabolic measures— with the exception of BMI, Models II includes
age, age-squared, BMI, hemoglobin, and CRP. For BMI, Models II includes age, age-
squared, sex, and CRP. For all measures of inflammation and infection, Models II
includes age, age-squared, and sex. For FEV
1
, Models II includes age, age-squared, sex,
and weight. For creatinine, Model II includes age, age-squared, sex, height, and weight.
For hemoglobin, Models II includes ages, age-squared, sex, and CRP. For hematocrit,
Models II includes age, age-squared, male, weight, and DBP.

36
In the U.S., a third model was conducted for FEV
1
and creatinine. For FEV
1
,
Model III includes the same covariates as Model II with the addition of smoking status
(unavailable in the Tsimane). For creatinine, Model II adjusts for the same covariates as
Model III plus glycosylated hemoglobin (also unavailable in the Tsimane). Analyses
were conducted using SAS 9.1 (SAS Institute, Inc, Cary, NC).

Results
The average age of U.S. adults age 20+ was 50.5 years (SD 19.5) and 39 years
(SD 15.3) for the Tsimane. The proportion of males to females similar in the two
populations: 48% U.S. males; 49% Tsimane males.
Cardiovascular Indicators
Across most age groups, the Tsimane had lower mean levels of all cardiovascular
risk indicators. For all ages, they exhibited lower mean SBP and DBP compared to the
U.S. (except DBP at ages 60+ is similar in both populations) (Figures 2.1 and 2.2). For
ages 20-49, the Tsimane had lower mean pulse rates compared to the U.S.; for ages 50-59
their pulse rates were about equal, with Tsimane older adults (ages 60+) exhibiting a
higher pulse rate compared to their American counterpart (Figure 2.3). PP was similar in
both populations until age 49, after which the U.S. had higher levels than the Tsimane
(Figure 2.4).
Generally, blood pressure increased for the Tsimane and U.S., with the exception
of a decrease in DBP from ages 60+. Pulse showed different patterns with age in the
Tsimane compared to the U.S.: Tsimane pulse rate remained relatively stable until age

37
Systolic blood pressure
90
100
110
120
130
140
150
20-29 30-39 40-49 50-59 60+
Age
Mean (mm Hg)
Diastolic blood pressure
60
65
70
75
80
20-29 30-39 40-49 50-59 60+
Age
Mean (mm Hg)

Figure 2.1 Mean level of systolic blood pressure in U.S. and Tsimane adults age 20+

Figure 2.2 Mean level of diastolic blood pressure in U.S. and Tsimane adults age 20+

38
Pulse pressure
30
40
50
60
70
80
20-29 30-39 40-49 50-59 60+
Age
Mean
Pulse
65
70
75
80
20-29 30-39 40-49 50-59 60+
Age
Mean (beats/min)

Figure 2.3 Mean level of pulse pressure in U.S. and Tsimane adults age 20+

Figure 2.4 Mean level of pulse in U.S. and Tsimane adults age 20+

39
Table 2.5 Ordinary Least Squares Regression Models indicating the effect of one year of age on cardiovascular measures in the U.S.
NHANES (1999-2004) and Tsimane (2003-2007)

Systolic blood pressure Diastolic blood pressure
US Tsimane US Tsimane
Model I Model II Model I Model II Model I Model II Model I Model II
B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value
Age 0.58 <.01
-
0.24 <.01 0.22 <.01
-
0.07 0.56 0.02 <.01 1.15 <.01 0.09 <.01 0.41 <.01
Age^2 0.01 <.01 0.010.02
-
0.01 <.01
-
0.01 <.01
Male 2.65 <.01 2.78 <.01 6.75 <.01 7.03 <.01 3.20 <.01 3.45 <.01 1.95 <.01 1.73 <.01
BMI 0.49 <.01 0.62 <.01 0.19 <.01 0.35 <.01

Pulse Pressure Pulse
US Tsimane US Tsimane
Model I Model II Model I Model II Model I Model II Model I Model II
B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value
Age 0.55 <.01
-
1.38 <.01 0.13 <.01
-
0.48 <.01
-
0.09 <.01
-
0.23 <.01 0.04 0.03
-
0.35 <.01
Age^2 0.02 <.01 0.01<.01 0.01 <.01 0.01 <.01
Male
-
0.55 0.09
-
0.68 0.06 4.80 <.01 5.31 0.56
-
4.02 <.01
-
3.93 <.01
-
5.82 <.01
-
6.00 <.01
BMI 0.30 <.01 0.26 <.01 0.24 <.01
-
0.05 0.43
B = regression coefficient
BMI = body mass index

40
60, after which an increase in mean pulse rate was observed; however, pulse rate in the
U.S. showed a gradual decline with increasing age. For both populations, PP remained
stable across age groups, with an exception in the U.S. for adults ages 60+.
Table 2.5 displays the effect of one year of age on the four cardiovascular
indicators examined. The regression coefficients displaying the effects of one year of age
on each of the cardiovascular indicators were generally larger for in the U.S. compared to
the Tsimane. In the U.S., with each year increase in age, SBP increased about 0.58
mmHg (p<.01), DBP increased 0.02 mmHg (p<.01), PP increased by 0.55 (p<.01), and
pulse rate increased 0.09 beats/min (p<.01) (Models I). In the Tsimane, there was a
significant relationship between age and each of the cardiovascular indicators. On
average per year, the Tsimane SBP increased about 0.22 mmHg (p<.01), DBP increased
0.09 mmHg (p<.01), PP increasedby 0.13 (p<.01), and pulse increased the least at 0.04
beats/min (p<0.03). All of the cardiovascular indicators resembled an age-squared,
parabolic-shaped pattern.
Metabolic Indicators

Similar to the differences in cardiovascular indicators, the Tsimane also exhibited
lower levels of all blood lipid risk markers compared to the U.S. Across most age groups,
the Tsimane had markedly lower levels of total-C, HDL-C, total-C/HDL-C, LDL-C,
triglycerides, and BMI compared to the U.S. (Figures 2.5-2.10, respectively).
With the exception of HDL-C, all of the examined metabolic indicators resembled an
inverted U-shape curve with age for both the U.S. and the Tsimane. For HDL-C,

41
Total Cholesterol
110
130
150
170
190
210
230
20-29 30-39 40-49 50-59 60+
Age
Mean (mg/dl)
Figure 2.5 Mean level of total cholesterol in U.S. and Tsimane adults age 20+

42
HDL
25
30
35
40
45
50
55
20-29 30-39 40-49 50-59 60+
Age
Mean (mg/dl)
Figure 2.6 Mean level of High-Density Lipopprotein (HDL) Cholesterol in U.S. and
Tsimane adults age 20+

43
Total-C/HDL-C
3
3.5
4
4.5
5
20-29 30-39 40-49 50-59 60+
Age
Mean
LDL
60
80
100
120
140
20-29 30-39 40-49 50-59 60+
Age
Mean (mg/dl)
Figure 2.7 Mean level of total cholesterol (Total-C)/High-Density Lipoprotein
Cholesterol (HDL-C) ratio in U.S. and Tsimane adults age 20+

Figure 2.8 Mean level of Low-Density Lipoprotein (LDL) Cholesterol in U.S. and
Tsimane adults age 20+

44
however, the Tsimane exhibited stable levels across age groups, while HDL-C values in
the U.S. increased with age.
Table 2.6 displays the effect of one year of age on the three metabolic indicators
investigated. For the U.S., with every year increase in age, total-C increased 0.53 mg/dl
(p<.01), HDL-C increased 0.08 mg/dl (p<.01), total-C/HDL-C increased 0.01 (p<.01),
LDL-C increased 0.30 mg/dl (p<.01), triglycerides increased 0.87 mg/dl (p<.01), and
BMI increased 0.03 kg/m
2
(p<.01). In contrast, age was not associated with changes in
blood lipids, but for every year increase in Tsimane age, BMI levels decreased (by 0.12
kg/m2, p<.01). The blood lipids can be modeled by an age-squared (parabolic) function
in the U.S. but not in the Tsimane; however, BMI did model an age-squared pattern for
both the U.S. and the Tsimane. After adjusting for BMI, hemoglobin, and CRP, a yearly
increase in age was associated with a greater change in a given metabolic indicator for
both populations. BMI and hemoglobin levels in the U.S. and Tsimane were also
associated with total-C, HDL-C, and triglycerides.

45
Table 2.6 Ordinary Least Squares Regression Models indicating the effect of one year of age on metabolic measures
in the U.S. NHANES (1999-2004) and Tsimane (2003-2007)

Total cholesterol (Total-C) High-density lipoprotein cholesterol (HDL-C)
US Tsimane US Tsimane
Model I Model II Model I Model II Model I Model II Model I Model II
B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value
Age 0.53 <.01 2.69 <.01 0.15 0.09 0.75 0.18 0.08 <.01 0.43 <.01 0.00 0.91
-
0.05 0.8
Age^2
-
0.02 <.01 -0.01 0.43 -0.01 <.01 0.00 0.77
Male
-
0.98 0.29
-
1.79 0.10
-
7.15 0.01
-
14.58 <.01
-
10.14 <.01
-
10.24 <.01
-
0.28 0.77
-
2.19 0.04
BMI 0.26 0.01 1.27 0.01 -0.69 <.01
-
0.18 0.26
Hemoglobin 3.53 0.01 4.95 <.01 -1.74 <.01 1.57 <.01
CRP
-
1.69 0.01 -0.13 0.07 -0.16 0.49
-
0.05 0.07

46
Table 2.6, Continued
Total cholesterol (Total-C) LDL-C
US Tsimane US Tsimane
Model I Model II Model I Model II Model I Model II Model I Model II
B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value B
p-
value
Age 0.53 <.01 2.69 <.01 0.15 0.09 0.75 0.18 0.3 <.01 2.06 <.01 0.17 0.09 0.71 0.26
Age^2
-
0.02 <.01 -0.01 0.43
-
0.02 <.01
-
0.01 0.45
Male
-
0.98 0.29
-
1.79 0.10
-
7.15 0.01
-
14.58 <.01 4.19 <.01 5.06 <.01
-
10.17 <.01
-
12.2 <.01
BMI 0.26 0.01 1.27 0.01 0.48 <.01 1.13 0.02
Hemoglobin 3.53 0.01 4.95 <.01
-
0.84 0.41 2.15 0.08
CRP
-
1.69 0.01 -0.13 0.07
-
2.23 0.02
-
0.06 0.44

47
Indicators of Inflammation and Infection

While the Tsimane had lower levels of cardiovascular and metabolic indicators
compared to the U.S., Tsimane adults exhibited higher levels of markers of infection and
inflammation than American adults. The Tsimane had higher mean CRP, IL-6, ESR, H.
pylori, WBC count, and % eosinophils compared to American and Italian (compared for
IL-6 only) adults (Figures 2.11-2.15, 2.18). The U.S. had a higher % neutrophils,
monocytes, and basophils (Figures 2.16, 2.19, 2.20); with negligible differences in %
lymphocytes (Figure 2.17).
Across age groups, IL-6 and ESR resembled an age-squared (U-shaped curve)
pattern in the Tsimane but gradually increased among American (and Italian, for IL-6)
adults. The age relationship of CRP and H. pylori infection in the Tsimane is less clear;
as shown, mean CRP levels oscillated across age groups. In contrast, CRP and H. pylori
levels in American adults increased with age. WBC count declined with age in both
populations, whereas % neutrophils increased.
Table 2.7 displays the effect of one year of age on the markers of infection and
inflammation investigated. Every year increase in age for U.S. and Tsimane adults was
associated with an increase in ESR and H. pylori (0.17, p<.01 and 0.01, p<.01;
respectively). In the Tsimane, ESR increased with each year of age (0.28, p<.01). While
we were not able to estimate the effect of age on IL-6 levels in the U.S. or in the Italian
adults participating in the InCHIANTI Study (as displayed in Figure 2.9), our estimates
indicated no significant effect of a one year increase in Tsimane adult age on IL-6 levels

48
(-0.01 pg/ml, p=0.72). An age-squared pattern was observed for IL-6 in the Tsimane and
for ESR in both the U.S. and the Tsimane (Models II).
Table 2.8 displays the effect of one year of age on WBC count and distribution. In
the U.S., changes with age were associated with changes in WBC count and distribution,
with an inverse relationship between increases in age with WBC count and %
lymphocytes (-0.01 and -0.04, respectively; Models I). In the Tsimane, age was
associated with declines in WBC count, % lymphocytes, and % eosinophils (-0.04, -0.07,
and -0.03, respectively).
Organ and System Function Indicators

The Tsimane had lower mean levels of FEV
1
(with little difference in slope: 0.015
in the US and 0.004 in the Tsimane) and hemoglobin compared to their U.S. counterparts
(Figures 2.21 and 2.23); however, for most age groups, the Tsimane had higher levels of
creatinine clearance than the U.S. (Figure 2.22), with negligible differences in hemtocrit
between the two populations (except at the earlier and later age groups) (Figure 2.24).
In both populations, FEV
1
declined with age and creatinine clearance increased
with age. Age changes in hemoglobin and hematocrit resembled inverse U-shapes in the
Tsimane, with little changes seen in the U.S.
Table 2.9 displays the effect of one year of age on these indicators of organ and
system function. Declines in FEV
1
and hemoglobin were associated with increases in age
for the U.S. and the Tsimane (FEV
1
: -0.02 and -0.01, respectively; hemoglobin; -0.01 for
both; Models I). After adjusting for weight and smoke status in the U.S. (Models II and
III), the significant association between FEV
1
and age was maintained. Increases in age

49
were associated with increases in creatinine clearance among the Tsimane; however, an
inverse relationship between the two was found for the U.S. (Models I). After adjusting
for height, weight, and glycosylated hemoglobin (Models II and III), this relationship was
maintained for the U.S. An inverse relationship of age to hematocrit was also found for
both populations (Models I), even after adjusting for weight and DBP (Models II).

Discussion

This study finds marked differences in biological and anthropometric measures
between American and Tsimane adults across most age groups. Generally, the prominent
differences between the two societies were found among metabolic markers and markers
of inflammation and infection. While U.S. adults had much higher cholesterol levels and
BMI, Tsimane adults conversely had higher levels of CRP and WBC. U.S. adults also
exhibited higher blood pressure levels and pulse rates across most age groups compared
to the Tsimane, though these differences were less marked than for indicators of the
metabolic system, inflammation, and infection. Our study also found differences in age-
related changes for the various indicators examined. Generally, the U.S. exhibited linear
age changes for most of the indicators examined; however, Tsimane adults showed linear
age changes for only cardiovascular and infection indicators.
The lower levels of systolic and diastolic blood pressure in the Tsimane,
compared to the U.S., aligns with findings from previous studies that have reported
higher blood pressure among industrialized populations (Ostfeld and D’Atri, 1977; Page,
1976; Pavan et al., 1997; Poulter and Sever, 1994; Vaughan, 1978; Waldron et al., 1982).

50
Within industrialized nations, SBP continues to rise with age while DBP levels off and, in
some populations, begins to decline at age 50. (Aviv et al., 2001; Izzo et al., 2000;
Franklin et al., 1997; Nichols and O’Rourke, 1998; Welton et al., 1994). Several
potential mechanisms for explaining the higher levels of blood pressure in westernized
societies have been suggested (Waldorn et al., 1982). First, sociocultural change may
have indirect effects on increases in blood pressure. Increases in body weight associated
with economic modernization may be responsible for the higher blood pressures apparent
in westernized societies (Ostfeld and D’Atri, 1977). Second, blood pressure levels
closely relate to differences in salt consumption (Freis, 1976), with salt consumption
considered the primary cause of increases in blood pressure (Maddocks and Rovin, 1965;
Oliver, Cohen, Neel, 1975; Poulter et al., 1984; Prior et al., 1968; Truswell et al., 1972).
Third, blood pressure is influenced by parasitic load and pregnancy (Fleming-Moran and
Coimbra, 1990; Kaplan, 1982). Given that the Tsimane are characterized by having a
high parasitic load and high fertility population may be anoother reason for why their
blood pressure levels differ from that of the U.S. Last, the use of hypertensive drugs in
the U.S. likely represents selective mortality such that individuals taking medication have
a survival advantage compared to those with untreated hypertension.
When comparing PP between the two populations, few differences exist until later
ages. In industrialized populations, SBP increases with age, whereas DBP levels off and
may even decline with age (Franklin et al., 1997; Izzo, Levy, and Black, 2000; Nichols
and O’Rourke, 1998; Welton, He, and Klag, 1994). This results in a progressive increase
in PP with age (Aviv, 2001). Increasingly, epidemiological data is focusing more on PP

51
as a predictor of cardiovascular risk (Benetos et al., 1997; Blacher et al., 2000; Izo, Levy,
and Black 2000; O’Rourke and Frohlich, 1999). Given that arterial disease is near absent
in the Tsimane (Gurven et al., 2009), the little increase in PP with age makes sense. This
finding is in contrast to the U.S. increase in PP during later ages, which corresponds to
the increase in cardiovascular events with age (Lakatta and Levy, 2006). There are sex
differences in PP, and significant effects on PP among unaculturated females at age 40
have been reported (Skurnick, Aladjem, and Aviv, 2010). The current study does not
find an increase in PP with age among the unacculturated Tsimane, but this difference
may be due to our inclusion differences in sample age; ours includes individuals age 20+.
Large differences among blood lipids and BMI were also observed between the
Tsimane and the U.S. Compared to industrialized populations, the reported lower levels
of cholesterol in the Tsimane are shared by that of other native populations, including
rural Guatamalan Indians (Méndez, Tejada, and Flores, 1962), Tanzanians (Pavan et al.,
1997), Ugandans (Pavan et al., 1997), and the Tarahumara Indians of Mexico (Connor et
al., 1978). The lower cholesterol levels in these less developed populations, as compared
to more developed nations, are likely associated with a low fat consumption (Tejada,
Gore, 1957; Tejada et al., 1958) and a physically active lifestyle (Pavan et al., 1997).
Interestingly, age patterns of the ratio of total-C/HDL-C in both populations are different
from each of the patterns of total-C and HDL-C. It may be that this ratio is a more
comprehensive indicator of metabolic lipid profiles and their potential risks associated
with cardiovascular events. In studying the progression of coronary atherosclerosis, only

52
total-C/HDL-C correlated with coronary lesion growth (r=0.50, p=0.001) (Arntzenius et
al., 1985).
As reported in other studies, the Tsimane have much higher levels of infection
and inflammation than the U.S. (Gurven et al., 2008; McDade et al., 2007). In the
Tsimane, the exponential increase in erythrocyte sedimentation rate with age was in
contrast to the linear increase exhibited by the U.S. The increase of this non-specific
measure of infection may be a result of the constant burden of living in this highly
infectious environment. Other measures of infection (e.g., WBC) decline with age in the
Tsimane. While these markers of infection seem to change with age and likely influence
the aging process, they are unlikely to be markers of aging per se. The next step of this
analysis is to include these indicators of infection into our consideration of the other
markers of aging investigated here.
It is possible that the high pathogen burden characteristic of the Tsimane may be
one reason why they have lower levels of weight-adjusted FEV
1
, as compared to the U.S.
Annesi and colleagues (1992) found that in cross-sectional and longitudinal studies, an
inverse relationship existed between immunoglobulin E, a marker of allergy and parasite
burden, and FEV
1
in European men. The lower hemoglobin levels in the Tsimane may
also be linked to their high burden of infection, since parasite prevalence (e.g., Ascaris
lumbricoides, Trichuris trichiura, Entabmoeba histolytica) has been previously
associated with anemia (Oguntibeju, 2003; Walter et al., 1997).
While the current study has contributed to our understanding of aging in non-
industrialized and industrialized populations, it has its limitations. First, we cannot be

53
certain that blood samples for determination of triglycerides and LDL-C were of fasted
samples in the Tsimane. Due to this, the levels of both in the Tsimane may be even lower
than in the U.S. Second, the cross-sectional limitations of the current analysis do not
directly measure individual change in biological markers with age. The observed
changes may be due to cohort or period effects that, given the data available, we were not
able to account for. Although the findings are suggestive of some potential changes with
age in the level of a given measure within a population, we cannot dismiss the influences
of additional factors not included in these analyses.
In summary, we conclude that the Tsimane have very different biological profiles
of health and possibly aging compared to the U.S. Whereas the Tsimane have lower
levels of cardiovascular and metabolic markers compared to the U.S., they have higher
levels of infection and inflammation. The age-related increases in blood pressure and
some blood lipids noted in industrialized populations show less of an increase with age
among the Tsimane. This study suggests that living in a high infection environment is
linked to a different health profile and is potentially characterized by a different age
profile than under conditions with little exposure to infectio

54
Body mass index
20
22
24
26
28
30
20-29 30-39 40-49 50-59 60+
Age
Mean (kg/m2)
Triglycerides
80
100
120
140
160
180
200
220
20-29 30-39 40-49 50-59 60+
Age
Mean (mg/dl)
Figure 2.9 Mean Level of Triglycerides in U.S. and Tsimane Adults Age 20+

Figure 2.10 Mean Level of Body Mass Index in U.S. and Tsimane Adults Age 20+

55
CRP
0
2
4
6
8
10
12
14
20-29 30-39 40-49 50-59 60+
Age
Mean (mg/l)
IL-6†
0
2
4
6
8
10
20-29 30-39 40-49 50-59 60+
Age
Mean (pg/ml)
H. pylori
0
0.5
1
1.5
2
2.5
3
3.5
4
20-29 30-39 40-49 50-59 60+
Age
Mean
Sedimentation Rate
5
15
25
35
45
55
20-29 30-39 40-49 50-59 60+
Age
Mean
Figures 2.11-2.14. Mean level of markers of infection and inflammation (C-reactive
protein [CRP], interleukin-6 [IL-6], erythrocyte sedimentation rate, and Helicobacter
pylori) in U.S. and Tsimane adults age 20+

56
White blood cell count
6500
7500
8500
9500
10500
11500
20-29 30-39 40-49 50-59 60+
Age
Mean (per mm3)
Neutrophils
45
50
55
60
20-29 30-39 40-49 50-59 60+
Age
Mean (%)
Lymphocytes
25
26
27
28
29
30
31
32
33
34
35
20-29 30-39 40-49 50-59 60+
Age
Mean (%)
Eosinophils
0
5
10
15
20
25
20-29 30-39 40-49 50-59 60+
Age
Mean (%)
Figures 2.15-2.20. Mean level of markers of infection (White blood cell count,
neutrophils, lymphocytes, eosinophils, and basophils) in U.S. and Tsimane adults age
20+

57
Basophils
0
0.2
0.4
0.6
0.8
1
20-29 30-39 40-49 50-59 60+
Age
Mean (%)
Monocytes
0
1
2
3
4
5
6
7
8
9
20-29 30-39 40-49 50-59 60+
Age
Mean (%)
Figures 2.15-2.20. Continued

58
FEV
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
20-29 30-39 40-49 50-59 60+
Age
Mean (Liters)
Creatinine
0.6
0.65
0.7
0.75
0.8
0.85
0.9
20-29 30-39 40-49 50-59 60+
Age
Mean (mg/dl)
Hemoglobin
11
12
13
14
15
20-29 30-39 40-49 50-59 60+
Age
Mean (g/dl)
Hematocrit
37
38
39
40
41
42
43
44
45
20-29 30-39 40-49 50-59 60+
Age
Mean (%)
Figures 2.21-2.24. Mean level of indicators of organ and system function (Forced expiratory volume [FEV], creatinine
clearance, hemoglobin, and hematocrit) in U.S. and Tsimane adults age 20+

59
Table 2.6 Ordinary least squares regression models indicating the effect of one year of age on metabolic measures in the U.S.
NHANES (1999-2004) and Tsimane (2003-2007)

B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value
Age 0.53 <.01 2.69 <.01 0.15 0.09 0.75 0.18 0.08 <.01 0.43 <.01 0.00 0.91 -0.05 0.8 0.01 <.01 0.43 <.01 0.00 0.53 0.03 0.07
Age^2 -0.02 <.01 -0.01 0.43 -0.01 <.01 0.00 0.77 -0.01 <.01 0.00 0.13
Male -0.98 0.29 -1.79 0.10 -7.15 0.01 -14.58 <.01 -10.14 <.01 -10.24 <.01 -0.28 0.77 -2.19 0.04 0.84 <.01 0.82 <.01 -0.16 0.06 -0.18 0.07
BMI 0.26 0.01 1.27 0.01 -0.69 <.01 -0.18 0.26 0.06 <.01 0.05 <.01
Hemoglobin 3.53 0.01 4.95 <.01 -1.74 <.01 1.57 <.01 0.23 <.01 -0.03 0.47
CRP -1.69 0.01 -0.13 0.07 -0.16 0.49 -0.05 0.07 0.00 0.99 0.00 0.53
B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value
Age 0.3 <.01 2.06 <.01 0.17 0.09 0.71 0.26 0.87 <.01 3.66 <.01 0.09 0.69 1.26 0.38 0.03 <.01 0.28 <.01 -0.12 0.61 0.19 <.01
Age^2 -0.02 <.01 -0.01 0.45 -0.03 <.01 -0.01 0.49 -0.01 <.01 -0.01 <.01
Male 4.19 <.01 5.06 <.01 -10.17 <.01 -12.19 <.01 29.75 <.01 29.44 <.01 7.77 0.27 -5.24 0.53 -0.34 0.04 -0.06 0.71 12.66 0.08 0.30 0.33
BMI 0.48 <.01 1.13 0.02 2.24 <.01 2.53 0.05
Hemoglobin -0.84 0.41 2.15 0.08 29.10 0.01 7.20 0.02
CRP -2.23 0.02 -0.06 0.44 7.03 0.04 -0.18 0.34 2.09 <.01 -0.01 0.63
B = regression coefficient
BMI = body mass index
CRP = C-reactive protein
LDL-C = low-density lipoprotein cholesterol
Tsimane
Model I Model II Model I Model II Model I Model II Model I Model II
Total cholesterol (Total-C)
Tsimane US
Model I Model II Model I Model II
LDL-C
US Tsimane
Tsimane US
High-density lipoprotein cholesterol (HDL-C) Total-C/HDL-C
Model II Model I Model II
Tsimane US
Model II Model I Model II Model I
Triglycerides Body mass index
US
Model I Model II
Tsimane US
Model I Model II Model I

60
Table 2.7 Ordinary least squares regression models indicating the effect of one year of age on markers of inflammation
and infection in the U.S. NHANES (1999-2004) and Tsimane (2003-2007)

B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value
Age 0.00 <.01 0 0.30 0.03 0.61 -0.58 0.10 -0.01 0.72 -0.41 0.01
Age^2 0 0.89 0.01 0.08 0.01 0.01
Male -0.16 <0.1 -0.2 <.01 -2.24 0.24 -1.87 0.33 -1.07 0.23 -0.84 0.35
*Note: Because we do not have authorized access to use individual level data for IL-6 data in the InCHIANTI Study,
no regressions were completed
B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value
Age 0.17 <.01 0.06 0.09 0.28 <.01 -0.57 <.01 0.01 <.01 0.01 0.07 0.01 0.37 0.06 0.14
Age^2 0.01 <.01 0.01 <.01 0.00 0.88 0.00 0.18
Male -7.69 <.01 0.18 <.01 -15.75 <.01 -15.62 <.01 0.06 0.12 0.06 0.12 0.23 0.25 0.21 0.31
B=regression coefficient
*NHANES I (1971-75)
Tsimane US
US Tsimane
US Tsimane
Model I Model II
US* Tsimane
Model I Model II Model I Model II Model I Model II Model I Model II
Helicobacter pylori Erythrocyte sedimentation rate
C-reactive protein Interleukin-6
Model I Model II Model I Model II Model I Model II

61
Table 2.8 Ordinary least squares regression models indicating the effect of one year of age on white blood cell count and
distribution in the U.S. NHANES (1999-2004) and Tsimane (2003-2007)

B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value
Age -0.01 <.01 -0.04 <.01 -0.04 <.01 -0.06 0.02 0.02 <.01 -0.16 <.01 0.11 <.01 0.11 0.27 -0.04 <.01 0.18 <.01 -0.07 <.01 -0.09 0.18
Age^2 0.01 <.01 0.00 0.48 0.01 <.01 0.00 0.97 -0.01 <.01 0.00 0.79
Male -0.22 <.01 -0.22 <.01 -0.70 <.01 -0.70 <.01 -0.75 <.01 -0.71 <.01 -0.09 0.88 -0.09 0.88 -0.71 <.01 -0.75 <.01 -0.35 0.35 -0.34 0.36
B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value
Age 0.01 <.01 0.01 0.36 -0.03 0.04 -0.05 0.60 0.01 <.01 0.01 <.01 0.00 0.49 0.00 0.95 0.02 <.01 -0.04 <.01 0.00 0.21 -0.02 0.12
Age^2 0.00 0.82 0.00 0.88 -0.01 <.01 0.00 0.95 0.01 <.01 0.00 0.18
Male 0.49 <.01 0.49 <.01 0.04 0.94 0.04 0.94 -0.03 <.01 -0.03 <.01 -0.01 0.87 -0.01 0.87 1.00 <.01 1.01 <.01 0.14 0.07 0.15 0.06
B=regression coefficient
US Tsimane
Model I Model II Model I Model II Model I Model II
Tsimane US
Model II Model I Model II
Tsimane US
Eosinophils
Model I Model II Model I
Monocytes
Model I Model II
Tsimane US
Model I Model II Model I Model II
Basophils
White blood cells Neutrophils
US
Model I Model II Model I Model II Model I Model II
Lymphocytes
Tsimane US Tsimane

62
Table 2.9 Ordinary least squares regression models indicating the effect of one year of age on markers of organ and system
function in the U.S. NHANES (1999-2004) and Tsimane (2003-2007)

B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value
Age -0.02 <.01 -0.05 <.01 -0.04 <.01 -0.01 <.01 0.00 0.66 -0.01 <.01 -0.04 <.01 -0.04 <.01 0.01 <.01 0.00 0.42
Age^2 0.01 <.01 0.01 0.02 0.00 0.29 0.01 <.01 0.01 <.01 0.00 0.75
Male 0.93 <.01 0.91 <.01 0.87 <.01 -0.71 <.01 0.58 <.01 0.4 <.01 0.33 <.01 0.34 <.01 0.17 <.01 0.14 <.01
Height 0.01 <.01 0.00 0.17 0.00 0.42
Weight 0.01 0.031 0.00 0.09 0.02 <.01 0.00 0.12 0.01 <.01 0.01 <.01
HbA1c -0.02 0.04
Smoking -0.22 <.01
HbA1c = glycosylated hemoglobin
*NHANES III (1988-1994)
B p-value B p-value B p-value B p-value B p-value B p-value B p-value B p-value
Age -0.01 <.01 0.02 <.01 -0.01 <.01 0.12 <.01 -0.01 <.01 -0.04 0.01 -0.02 0.1 0.2 <.01
Age^2 -0.01 <.01 0.00 <.01 0.00 0.06 -0.01 <.01
Male 1.76 <.01 1.74 <.01 1.36 <.01 1.12 <.01 5.29 <.01 5.26 <.01 4.39 <.01 3.66 <.01
Weight 0.00 0.14 0.09 <.01
DBP 0.05 <.01 0.04 0.05
CRP -0.14 <.01 -0.01 <.01
B = regression coefficient
Creatinine Forced Expiratory Volume
Model I Model II Model I Model I Model II Model I Model II
Model III Model I
Model II
Model I Model II Model II Model I Model II Model I Model II Model III
Tsimane Tsimane US US*
US
n/a
n/a
Tsimane US
Hemoglobin Hematocrit
Tsimane

63
CHAPTER 3: BLOOD LIPIDS, INFECTION AND INFLAMMATORY MARKERS IN
THE TSIMANE OF BOLIVIA

The Tsimane, forager-farmers of the Bolivian Amazon, are a model for aging in pre-
industrial human populations because of their short lifespans, high infectious morbidity,
variable energy balance with high workloads, natural high fertility (Walker et al., 2008), and
limited access to modern medicine. Mortality throughout the lifespan has been high until
recently; their life expectancy at birth of 42.8 years (1950-1989) approximates the
demographics of nineteenth-century European populations, Sweden for example (Gurven et
al., 2007, 2008; McDade et al., 2005).
In addition to short life expectancies, the Tsimane exhibit relatively low levels of
blood cholesterol (blood-C). While these low lipid levels may be attributed to their typically
modest diets with low saturated fat, the Tsimane also have high levels of infection. We
predict that in high infection environments there would be a potential inverse relationship
between markers of infection and inflammation and blood lipid levels.
The Tsimane have a high prevalence of elevated blood C-reactive protein (CRP) at
all ages (Gurven et al., 2008; McDade et al., 2007), with about 25% having a CRP value of
greater than 10 mg/dl, a level that indicates acute or chronic infections. In modern industrial
nations with longer life expectancy and a lower burden of infection, lower levels of CRP ( ≥
3.0 mg/dl) are considered indicators of cardiovascular risk (Danesh et al., 1998; Ridker et al.,
2009). Tsimane reaching their 43 year life expectancy have experienced twice the average

64
number of years cumulatively lived with high CRP ( ≥ 3.0mg.dl) above the U.S. (Gurven et
al., 2008).
Blood lipids are important mediators of host defense during the acute phase of innate
immunity. Infection and inflammation typically lower blood total cholesterol (total-C) and
high-density lipoprotein cholesterol (HDL-C), but increase triglycerides (Esteve, Ricart, and
Fernández-Real, 2005; Finch, 2007; Jahangiri et al., 2009; Khovidhunkit et al., 2004;
McGillicuddy et al., 2009). Several types of infections— viral, bacterial, and parasitic—
have been linked to blood lipid levels. Viral infection, as with HIV infections, are associated
with lower blood levels of total-C and HDL-C (Anastos et al., 2007; Riddler et al., 2007;
Rose et al., 2006), with a greater degree of dyslipidemia associated with greater immune
suppression (Constans et al., 1994; Grunfeld et al., 1992; Zangerle et al., 1994). Among
those infected with HIV and taking anti-viral therapy, the total-C, and in some cases, the
HDL-C, was increased (Rimland et al., 2006). Experimental inflammation from bacterial
endotoxin (LPS) induces similar dyslipdemias (McGillicuddy et al., 2009). The
hypocholesterolemia and remodeling of lipoproteins during acute phase responses of innate
immunity increases clearance of LPS, particularly through increased binding of LPS to HDL
particles (Kitchens and Thompson, 2003; Levels et al., 2007), is one example.
Specific parasitic infections also cause dyslipidemias. A study of the Shipibo,
another indigenous Amazonian group, showed an inverse correlation of HDL-C with the
density of infection by three of five parasitic worm species (N=32) (Wiedermann et al.,
1991). Similar sized samples from a city hospital in Chandrigarh, India showed lower HDL-
C for patients with entamoebic and giardia parasites (Bansal et al., 2005). A novel

65
hypothesis linking parasitic infection to CVD risk is that parasitic worms (helminths) may
attenuate atherosclerosis through interactions with host defense systems (Magen et al., 2005).
This relationship may involve several mechanisms. First, helminths can suppress the host
immune response through production of anti-inflammatory molecules, thereby reducing the
risk of CVD. Second, parasitic worms may lower LDL levels both directly and indirectly:
via regulating innate antibodies to cholesterol and interfering with host nutrition
(respectively). About 1/3 of LDL turnover is attributed to the effects of these naturally
occurring antibodies to cholesterol (Alving and Wassef, 1999; Caspar-Bauguil et al., 1999;
Folcik, Aamir, and Cathcart, 1997). Moreover, infections potentially elicited by parasites
may also regulate host lipid metabolism by stimulating a decrease in total cholesterol levels
(Doenhoff et al., 2002).
Nutrition has long been associated with blood-C levels (Clarke et al., 1997).
Generally, greater intake of saturated fatty acids has been associated with higher levels of
serum cholesterol (C) (Mattson et al., 1972). In subsistence populations, where food
containing high saturated fats is less available than in modern societies, C levels are lower.
For instance, blood-C was below U.S. norms in several indigenous African populations and
Trobiand Islanders (Expert Panel on Detection Evaluation, and Treatment of High Blood
Cholesterol in Adults, 2001; Lindeberg et al., 2003; Pavan et al., 1997; Pauletto et al., 1996)
and total-C levels of hunter-gatherers were about 125 mg/dl (Eaton, Konner, and Shostak,
1988).
Another reason for suspecting that inflammatory markers and parasite burden may be
associated with lower C among the Tsimane is that in populations with low energy balance

66
and fat reserves, the burden of disease may reduce available energy further, thus affecting
circulating C. Considered together, we predict that in high infection environments there
would be inverse relationships between markers of infection and inflammation and blood
lipid levels.
This study extends previous studies on the separate effects of viral, bacterial, and
parasitic infections. We considered that analyzing a combination of these types of infections
could further clarify the links to blood lipids. We examined the relationships of blood lipid
levels to markers of (1) inflammation (CRP and interleukin-6 [IL-6]), (2) the general burden
of infection (erythrocyte sedimentation rate [ESR] and white blood cell count [WBC] and
distribution), and (3) specific infections (parasite prevalence and indicators of parasite
prevalence, including WBC subtype eosinphil count and immunoglobulin IgE). We also
examine body mass index [BMI], stunting, and location of village in relation to cultural
influences from the nearest town, San Borja.
This paper tests the hypothesis that the Tsimane, with a high infectious load, will
exhibit relatively low lipid levels; thereby indicating an inverse relationship between
infection and cholesterol. We hypothesize that lower levels of blood lipids will be associated
with lower levels of past nutrition (i.e., lower BMI) and that blood-C levels will be higher in
the more acculturated village regions located near San Borja.

67
Methods
Study Sample
This study sample was drawn from the Tsimane Life History and Health Project
(Gurven et al., 2008), which has been examining health across the life course since 2002.
Interviews, medical examinations, and blood were collected from 17 communities across
the traditional Tsimane territories. Blood and feces were sampled in 2004. This forager-
farmer population, of about 7,000, has low caloric intake relative to energy expenditure,
and consequently low BMI. The Tsimane provides a model for preindustrial human
populations with limited food supply, high-energy expenditure, no sanitation systems, no
water treatment, and limited medical intervention (Byron, 2003; Reyes-Garcia et al.,
2008; Vadez et al., 2004).
Table 3.1 summarizes the Tsimane adult sample (N=418) for this analysis. The
present study was restricted to individuals age 20+ (range: 20-84) with blood and fecal
samples taken in 2004. Because Tsimane villages have different access to markets and
medical care, which may affect C and infections, we categorized the Tsimane
communities into three geographic regions (Appendix A) (Gurven et al., 2007): more
acculturated villages near the town of San Borja; villages in the interior forest; and,
remote villages along the upper Maniqui River. These categorizations also reflect
differences in diet; for example, the Maniqui River villagers obtain more food from fish,
whereas those in the forests obtain more from hunting. The Tsimane do not have regular
exposure to modern medicines; however, those living closer to San Borja have more
access to health care and richer diets, which are indicated by differences in stunting

68
(Table 3.1). We include region as a covariate in the analysis to adjust for differences in
access to modern medicine and diet.
Cholesterol Measures
Blood serum was analyzed for total-C, HDL-C, and low-density lipoprotein
cholesterol (LDL-C). Persons were asked to fast before coming for the medical testing,
but fasting was not verified. Cholesterol was stratified into high and low levels
associated with adverse health outcomes in the U.S. (Table 3.2): high-risk cutoffs of
HDL-C <40 mg/dl, LDL-C >160 mg/dl, and total-C >240 mg/dl. In ordinary least
squares (OLS) regressions (models described in further detail below), total-C, HDL-C,
and LDL-C were used as continuous variables.
Measures of Infection and Inflammation

Blood samples were analyzed for CRP (high sensitivity (hs)-CRP), interleukin-6 (IL-
6), erythrocyte sedimentation rate (ESR), white blood cell (WBC) count and distribution
(neutrophils, eosinophils, basophils, monocytes, and lymphocytes), and immunoglobulins
(Ig) A, E, G, and M. CRP is an innate immune system response to acute and chronic
infections. In the U.S. and other populations with low levels of infection, CRP may also be
an indicator of general systemic inflammatory responses due to chronic diseases including
atherosclerosis and diabetes. IL-6 is a cytokine with broad cellular roles in health and
disease. Serum hs-CRP and IL-6 were determined from samples collected and frozen in the
field, and assayed at 0.1-150.0 mg/L and 2.0-1000.0 pg/mL (respectively) at the Tricore
Reference Laboratories in Albuquerque, New Mexico using Immulite 2000 kits. The mean
replicate interassay coefficient variation was 5.6% for hs-CRP and 5.8% for IL-6

69
(Diagnostics Products Corporation, Siemens, Deerfield, IL). Clinically normal ranges and
mean values defined for U.S. populations are shown in Table 3.3. Using cutpoints employed
in large-scale epidemiological studies to define low and high levels, (Ferucci et al. 2005;
Seeman et al., 2004), we created an inflammatory score based on levels of both markers CRP
and IL-6 (range, 0-2: 0=high on neither, high on 1 only, high on both). An alternative score
based on the highest Tsimane tertile for CRP ( ≥5.23 mg/l) and IL-6 ( ≥3.02 pg/ml) yielded
similar results in relation to the blood-C measures; thus the CRP-IL6 score is based on
published cutpoints.
The ESR in mm/hr gives a non-specific measure of inflammation (Ingelsson et al.,
2005; Sox and Liang, 1986). For a point of reference, we note that the mean ESR in the
U.S. is 15 mm/hr (Gillum et al., 1994; Smith and Samadian, 1994) (Table 3.3). In our
OLS regressions, ESR is included as sex-specific quintiles, as follows: Males – 1: <10, 2:
10-18, 3: 19-24, 4: 25-41, 5: >42; Females – 2: <23, 2: 23-33, 3: 34-42, 4: 43-61, 5: >62.
Different levels are used for males and females due to gender variations in ESR (Piva et
al., 2001).
The WBC (leukocyte) total count includes five types: eosinophils, neutrophils,
leukocytes, basophils, and monocytes, which can indicate the type of infection. The
complete blood cell counts were analyzed in the field using fresh samples. Because
eosinophils are elevated in some parasitic infections, the level of eosinophils was
included as a categorical variable by distributing the percentages into quartiles (Q) in
OLS regression.

70
Four different immunoglobulins were measured. IgE levels may be associated
with parasitic infection and allergic reactions. IgM is the body’s primary response to
infection, whereas IgG is the secondary response. IgA antibodies protect body surfaces
exposed to external foreign substances (e.g., ears, eyes, and nose). IgE was included in
OLS regressions because of its relationship to parasite prevalence. Because of the
varying sensitivity to high levels of the assays used in this project, IgE was categorized
into <2000 and ≥2000 IU/ml (Barbee et al., 1981) and mean values are not reported (in
Table 3.3) due to the differences in assay sensitivity.
Using one fresh fecal sample for each person, experienced medical technologists
detected the presence of 17 species of parasites (Table 3.4): nine species of protozoans
(Balantidium coli, Bastocystis hominis, Chilomastix mesnili, Entamoeba coli, Entamoeba
hartmanni, Entamoeba histolytica, Giardia lamblia, Iodamoeba butschilii, and
Trichomonas hominis), and seven worm species (roundworms Ascaris lumbricoides,
Strongyloide stercoralis; whipworm Trichuris trichiura; tapeworms Hymenolepis
diminuta and Taenia solium [presumably]; pinworm, Enterobus vermicularis; hookworm
Uncinaria necator or ancylostoma [presumably]). Among these, six parasites can alter
blood-C: Ascaris, Trichuris, Giardia, Unicinara, Strongyloides, and Entamoeba
histolytica (Bansal, Bhatti, and Seghal, 2005; Wiedermann et al., 1991). Dummy
variables were constructed for the presence of each of the six parasites; the total number
of these six C-related parasites was also examined.

71
Covariates
Links between blood-C levels and age, sex, and hemoglobin levels are also
investigated (Au and Schilling, 1986; Crimmins et al., 2008a, b; Inouye et al., 1999; Mjos
et al., 1977; Oguntibeju, 2003; Wilson et al., 1994). Because childhood infections can
stunt growth by reallocating resources for development to combat infection, we include
stunted height as an indicator of past exposure to infection (Crimmins and Finch, 2006a,
b; Finch, 2007; Finch and Crimmins, 2004; Godoy et al., 2009; McDade et al., 2007,
2008). Stunting is defined using CDC guidelines ( ≤155 cm for males, ≤140 cm for
females; Center for Disease Control and Prevention, 1998). Current BMI (kg/m
2
) is an
indicator of both past and present diet and health. In the OLS regressions, BMI is
included as population-derived sex-specific Qs. Hemoglobin, which is related to
inflammation and infection as well as with lower lipid levels, was included as a
categorical variable based on the sex-specific population Qs. Variable-specific Q ranges
are listed at the bottom of Table 3.3.
Statistical Analyses

OLS regression was used to determine the associations between blood-C and
infection and inflammation. To predict blood-C levels, two separate models were run for
total-C, HDL-C, and LDL-C. The first models include age, sex, region, stunting, sex-
specific Qs of BMI, sex-specific Qs of hemoglobin, CRP-IL6 score, sex-specific quintiles
of ESR, IgE, eosinophil Qs, and six parasites associated with blood-C. The second
models include all the covariates from Model I, except a variable indicating the total

72
number of C-related parasites was included instead of the indicators of the six individual,
C-related parasites. Analyses used SAS 9.1 (SAS Institute, Inc., Cary, NC).

Results
The sample (Table 3.1) was restricted to individuals age 20 and older; average age
was 40 years (range, 20-84). About half resided in the river region (52%); 19% lived in
the forest villages; and 29% lived near San Borja. About a fifth (21%) was overweight,
but few were underweight or obese (both 3%) or stunted (4%).
Cholesterol

The distribution of total-C, HDL-C, and LDL-C in the Tsimane, compared to the
U.S., overlaps very little, with the Tsimane exhibiting lower levels for all blood-C levels
(Figures 3.1-3.3, respectively). We used data from persons of the same age in the U.S.
National Health and Nutrition Examination Survey 2001-2006, a nationally
representative study of U.S. residents, to compare the distribution of blood lipids to the
Tsimane in a modern and a traditional society. Compared to levels of blood-C in the U.S.,
the mean total-C, HDL-C, and LDL-C levels among Tsimane adults are lower (Table 3.2).
Only one individual, a 35-year-old female, had high-risk level of total-C (>240
mg/dl). None had elevated LDL-C estimated as >160 mg/dl. Nonetheless, about two-
thirds had lower HDL-C in the range representing risk for CVD (<40 mg/dl).
Mean hemoglobin levels (Hb) were normal. Tsimane males and females had an
average Hb of 13 and 12 g/dl (respectively), as compared to U.S. male and female
averages of 15 and 13 g/dl (respectively) (Table 3.2). Half of the Tsimane women and

73
37% men were anemic, compared to 7% of U.S. women and 3% of U.S. men. Fifty two
percent of Tsimane men and 61% of women with Ascaris were anemic. All men infected
with Trichuris had anemia.
Infection and Inflammation
Table 3.3 lists the means and range for various markers of infection and
inflammation in the Tsimane and in the U.S. For many of these indicators, the Tsimane
mean levels exceed the U.S. clinical norms. About 48% had elevated levels of CRP ( ≥3
mg/l), 33% had high IL-6 ( ≥2.68 pg/ml), and nearly 25% had elevations of both CRP and
IL-6. The total WBC averaged 10,442 cells/mm
3
, in the upper range of the clinical norm.
In contrast, mean WBC for individuals in the U.S. without coronary heart disease is 7500
cells/ mm
3
(Friedman, Klatsky, and Siegelaub, 1974). Neutrophils, lymphocytes, and
eosinophils in the Tsimane constituted 51%, 28%, and 20% of WBC (respectively)
(Table 3.3); mean Tsimane lymphocyte percentage (28%) is above the U.S. mean (19%)
(Horne et al., 2005). Relative to U.S. norms, eosinophils were elevated in 97% of
Tsimane, with mean values about 4-fold above the U.S. mean (International Still’s
Disease Foundation, 2008). ESR was elevated in about one quarter (>50 mm/hr), with
Tsimane mean ESR values (35.8 mm/hr) two-fold above the U.S. mean (15 mm/hr)
(Gillum et al., 1994; Smith and Samadian, 1994). Means of all immunoglobulins
exceeded the U.S. range, with a substantial proportion in the high range for IgA (23.2%),
IgE (94.2%), IgG (69.1%), and IgM (14.9%).
The majority (60%) of fecal samples had at least one of the 6 C-related parasites;
two or more parasites were carried by 40% and the average number of parasite species

74
per person was 1.3 (Table 3.4). The prevalence by species was: Uncinaria (46.2%),
Ascaris lumbricoides (17.0%), Entamoeba histolytica (7.3%), Strongyloides (7.1%),
Trichuris (2.9%), and Giardia lamblia (1.6%). Worms are apparently more prevalent
infections than protozoans. These numbers may underestimate the actual parasite
prevalence. Using Percoll gradients to concentrate parasites in a small sample of
Tsimane samples, we observed about one additional parasite species per person above the
fecal smears. Nonetheless, these prevalence rates approximated those of other
indigenous populations in South America (Barruzzi, 1970; Benefice and Barral, 1991;
Chernela and Thatcher, 1989; Kaplan et al., 1980; Miranda, Xavier, and Menezes, 1998;
Santos et al., 1995).
Associations of Cholesterol with Parasitic Infection, Other Markers of Infection and
Blood Inflammatory Markers
Most blood chemical markers vary inversely with levels of infection (Table 3.5).
Moreover, those in the highest ESR quintile had lower total-C levels than the lowest
quintile; relative to no parasites, those with one or more parasites had lower total-C levels.
However, there was no significant inverse relationship of total-C to eosinophil level (%).
The associations of HDL-C with parasitic infection and other markers of infection
and blood inflammatory markers resemble those of total-C (Table 3.5): lower HDL-C
levels were associated with elevated CRP, IL-6, the composite of high CRP and IL-6, and
ESR quintiles 4 and 5 (compared to quintiles 1 and 2). Additionally, lower HDL-C was
associated with the prevalence of Entamoeba histolytica and Strongyloides, and having at
least one of the six C-related parasites.

75
For LDL-C, there were no sigificant relationships between indicators of infection
and inflammatory markers (Table 3.5). Only elevated levels of IL-6 were significantly
associated with lower LDL-C levels.
Multivariate Analysis

Markers of infection and blood inflammatory markers were generally associated
with lower total-C, HDL-C, and LDL-C (Table 3.6). Models I and II showed an inverse
relationship of total-C to elevated levels of both CRP and IL-6 (p=0.02, for both Models),
having high IgE (p<0.01, for both Models), being in the highest Q of eosinophils (p=0.09
and p=0.05, for Model I and II respectively), and being in the lowest Q of hemoglobin
(p<.01, for both Models). Age and sex were associated with total-C (p=0.02 for both
Models), such that increasing age was related to higher total-C levels (p=0.03 and p=0.02,
for Model I and II, respectively) and females had higher total-C than males (p=0.02 and
p=0.01, respectively).
Relative to Tsimane near San Borja, residents in the more remote forest region
had higher total-C (p=0.16 and p=0.13, Model I and II, respectively). Compared to
individuals with BMI in Q 2 and Q3, those in Q4 had higher total-C (p<.01, for both
Models). These multivariate models could underestimate the total effect of parasite
prevalence, which presumably affects IgE levels and eosinophils.
Models I and II also showed an inverse relationship between HDL-C and high
levels of both CRP and IL-6 (p<.01, for both Models) and being in the lowest
hemoglobin Q (p=0.03 and p=0.05, for Model I and II respectively) (Table 3.6). Akin to
total-C, there were no significant relationships of total-C to parasite prevalence (Model I).

76
Also similar to the total-C findings, forest residents had higher HDL-C levels compared
to those living near San Borja (p<.01, for both). In comparison to individuals with BMI
Q2 and Q3, those in Q1 and Q4 had higher HDL-C levels, although this relationship was
significant only for Q1 (p=0.02, for both Models).
Similar to total-C and HDL-C, no relationships of LDL-C to parasite prevalence
were significant (Table 3.6; Model I). Compared to individuals with BMI in Q2 and Q3,
those in Q4 had significantly higher LDL-C (p=0.01; Model II); in contrast to females,
males had lower LDL-C levels as well (p<.01). Those residing in the forest area also had
higher LDL-C than those living in San Borja (p<0.01; Model II).
Overall, our regression analyses show that after adjusting for various covariates,
there is a significant inverse relationship between blood lipid levels and indicators of
infection and inflammation. Of note, however, is the difference in the relationships
between control variables and the different lipids. For instance, sex and BMI are
significantly associated with total-C and LDL-C, but not to HDL-C. The village region is
only associated with HDL-C and not to total-C and LDL-C.

Discussion

This study documents the high pathogen load and low blood lipids of the Tsimane
and is the first to investigate multiple indicators of infection and inflammation to blood-C
levels in a highly infected population. The high levels of pathogens are consistent with
earlier reports on the Tsimane, in this locale (McDade et al., 2005; Gurven et al., 2007;
Tanner, 2005), and their low blood lipid levels. We found that higher levels of infection

77
and inflammation were associated with lower levels of total-C, HDL-C, and LDL-C.
These relationships remained after adjusting for other variables related to blood-C,
including age, sex, nutrition (as indicated by village region and BMI), past infection
(stunting), and hemoglobin.
The lower total-C, HDL-C, and LDL-C in the Tsimane are suggestive of
remodeling of the HDL-C particle during infections. In acute phase responses of innate
immunity, HDL is altered (“acute phase HDL”), including a reduction of HDL-C,
decreased anti-oxidant activity, and other structural-compositional changes and
interactions with inflammatory proteins (Khovidhunkit et al., 2001, 2004).
Experimentally-induced inflammation by endotoxin (LPS) impairs multiple aspects of
reverse C transport that are anti-atherogenic, including efflux of blood-C from
macrophages to HDL-C (McGillicuddy et al., 2009). Future studies may characterize the
subclasses of HDL-C particles, particularly the remodeled particles associated with serum
amyloid (SAA) that arise during acute phase responses (McGillicuddy et al., 2009). The
low-fat diet of the Tsimane (Byron, 2003; Reyes-García et al., 2008) may also be a factor,
because blood-C can be lowered by low caloric diets or fasting; we adjusted for this
variability in caloric intake by including village region and BMI in the multivariate
analysis. Given the interrelatedness of infection and caloric intake in determining energy
balance, it is often difficult to parcel out their independent effects. The lack of fat
reserves may also interact with infectious load in producing low levels of blood-C. An
adaptive response to high infectious load may be to divert energy to combating infections.

78
These results may provide insight into the endogenous adaptive process of energy
regulation. Where the infectious burden is high, the body allocates more energy to immune
responses, both invoking innate and acquired immunity, and thereby reducing available
energy for other activities. The results on HDL-C are particularly interesting in this regard,
as is the relationship of hemoglobin to total-C, hence suggesting that energy limitation is
probably critical here. Some parasitic infections have been shown to cause anemia (e.g.,
Ascaris lumbricoides, Trichuris trichiura, Entamoeba histolytica) (Oguntibeju, 2003; Walter
et al., 1997). This may indicate that infection reduces oxygen transport to muscles, possibly
through an adaptive reallocation of energy to immune function. Similarly, the presence of
Ascaris and Trichuris in our Tsimane sample was associated with anemia, whereas neither
the presence of Entabmoeba histolytica nor the total number of prevalent parasites was not.
In addition to considering indicators of infection and inflammatory markers, none of
the six C auxotrophic parasites that has been previously associated with lower blood-C was
associated with individual differences in blood-C in our study. These results indirectly
suggests that other types of infection, aside from parasitic infection, are important to the
relationship between blood lipid levels and infection. Alternatively, the presence or absence
of a specific parasite might be too crude for detecting an effect on blood-C. If most
individuals have low-level parasitic infection (as shown in Table 4), egg or worm burden
information might more reliably predict blood-C levels. We found that the number of
parasites was a better predictor of blood-C, perhaps because individuals carrying a greater
number of parasites (or polyparasitism) also may have greater intensity infections, which
may explain the differences in effect between our measure of the total number of parasites

79
and blood-C compared to the relationship between the presence of each individual parasite
and blood-C levels. Moreover, IgE and eosinophil percentage may be a better indicator of
parasitism compared to the presence/absence of parasite measure employed here; this may be
especially true if parasitism is more chronic. Our study, however, does find a significant
inverse relationship between high IgE levels and total-C.
Gender differences in blood-C among the Tsimane are similar to those of other
populations, with males exhibiting lower total-C and LDL-C compared to females
(Assman and Schulte, 1987; Kastarinen et al. 1997; Mazzarolo-Cruz et al. 1995; O’Meara
et al., 2004; Stern et al. 2000). Proposed reasons that account for these sex differences
include intrinsic differences in biological risk levels and acquired risks due to differences
in work, lifestyle, and health aspects (Waldron, 1983). Increasing blood-C levels with
age, as found for total-C in the Tsimane, have also been reported in studies of populations
with low-fat diets (e.g., Tarahumara and Guatamalan Indians) (Conner et al., 1978;
Mendez, Tejada, and Flores, 1962; Werner and Sareen, 1978), as well as higher fat diets
(e.g., the U.S.) (Jacobs et al., 1980; Keys et al., 1952). Proposed determinants of this
increase with age in total-C include increases in body fatness with age (Berns, de Vries
and Katan, 1989).
In the present study, modern medications are unlikely to be an important factor
because, until recently, these Tsimane populations have had almost no access to modern
medicine. Though still very limited, the availability of medicine has increased within the
past 10 years. Ethnobotanic knowledge may also be an important variable in these local
differences; McDade et al. (2007) showed that the level of maternal ethnobotanic

80
knowledge correlated with Tsimane child growth and health, and could have been
similarly employed more by those living in the more remote villages of the present study,
as suggested by a moderate correlation between mother’s ethnobotanic knowledge and
village distance to nearest commercial center (0.49, p<.001).
This current study has several strengths, particularly the availability of multiple
markers of immune activation and inflammation and parasite prevalence, in addition to
evaluations on blood-C levels, in a unique population. This study sample gave a unique
opportunity to investigate the relationship between living in a high infection environment
and blood-C levels within an indigenous population. While several studies have
examined the relationship of markers of infection and immune activation to blood-C or
the relationship between parasites and blood-C, none has examined relationships among
all of these indicators in a well-defined indigenous population.
We also note some limitations. Without a longitudinal component, these cross-
sectional observations do not allow us to evaluate causal effects. We do not know whether
specific infections result in the activation of the inflammatory cascade, which in turn may
affect blood-C. Another unknown variable is the certainty of fasting in the blood samples.
The observed low LDL-C might be even lower if fasting were complete. When we
categorize individuals into those with blood samples drawn in the morning (before 12pm –
samples likely reflecting fasting conditions) and in the afternoon (at or after 12pm – samples
less likely to reflect fasting conditions), we find no significant differences in total-C, HDL-C,
nor LDL-C levels (p=0.16, 0.69, and 0.13, respectively). Lastly, our use of fecal smears to
determine parasite prevalence may result in an underestimate of the actual parasite load.

81
Depending on the location of the stool from which the slide smear sample was taken, traces
of a prevalent parasite may not have been detected. This would alter the observed
relationships. Another limitation of our measure of parasite prevalence is the single fecal
sample, which may give an inaccurate parasite prevalence. For a subsample (N=56) with
two fecal samples taken on different dates in 2004, the majority (70%) showed no difference
in the total number of C-associated parasites.
Variations in blood-C show about 35% heritability in North America, Europe, and
Japan (Dahlen et al., 1983; Hegele et al., 1997; Heller et al, 1993; Rao et al., 1982). Genetic
heritabilities for Amerindians enrolled in the Strong Heart Study (North et al., 2003) and
among Mexican Americans (Braxton et al., 1996) were also found for HDL-C. Among
Yucatan Mayans, polymorphisms in the apolipoprotein AI/CIII/AIV gene cluster were
associated with a lowering of total-C (Ahn et al., 1991). Furthermore, Mexican Americans
of the San Antonio Family Heart Study, the additive effects of both shared genes and shared
environments contributed to the inverse relationship between HDL-C and triglycerides
(Mahaney et al., 1995), suggesting that gene/environment interactions underscore a
substantial amount of the variation in blood lipid levels. Genetic analysis for the Tsimane is
planned, and based on previous studies, we expect that accounting for the effects of genetic
differences, the variance in blood-C levels due to some factors (e.g., BMI) will be slightly
reduced.
In summary, we conclude that the highly infected environment of the Tsimane is
related to low levels of blood total-C, HDL-C, and LDL-C. Our recent study examines other
vascular risk factors and potentially related health outcomes, which have provided more

82
insight as to the immediate and long-term consequences of living under such highly infected
environmental conditions (Gurven et al., 2009). Decreases in ankle brachial index, a
measure for peripheral arterial disease (PAD) diagnosis, was associated with higher ESR and
diastolic blood pressure, suggesting a relationship between cardiovascular risk factors, PAD,
and infection. Moreover, higher ESR also associated with lower SBP and DBP in the
Tsimane. This study indicates that arterial disease is largely absent in the Tsimane, and our
present study indicates that one mechanism promoting this would be low levels of blood-C
and disease-mediated reductions in blood-C. Moreover, the Tsimane could be an important
population for evaluating the hypothesis that parasitic helminths attenuate atherosclerosis
and CVD risk through interactions with host defense systems (Magen et al., 2005). The
present samples allow further analysis of a population with limited access to antibiotics that
can alter inflammatory processes of atherogenesis. The Tsimane thus represents a unique
and fleeting opportunity to study relationships of infection, inflammation, and aging-related
conditions under pre-industrial conditions similar to those of our ancestral past in the
absence of modern medicine.

83
Table 3.1 Characteristics of Tsimane sample, 20 years of age and
older
Region
N
Mean (SD)
or % Range Forest River
San
Borja
Age 418 39.7 (14.6) 20-84
40.4
(13.4) 38.9 (14.8)
40.6
(15.1)
Males (%) 418 46.7 47.4 47.7 44.3
Regions (%) 418
Interior Forest 18.7
Upper Maniqui
River 52.2
Near San Borja 29.2
Anthropometric
measures
Height (cm) 403 155.8 (7.7)
139.6-
177.8
Males 195 162.2 (5.3) 145-177.8
161.6
(5.6) 162.4 (5.6)
162.2
(4.4)
Females 223 150.5 (4.7)
139.6-
170.6
149.7
(5.3) 150.9 (4.8)
150.5
(4.2)
Stunted* 16 4.0 6.5 4.3 1.7
Body mass
index (BMI,
kg/m2) 403 23.2 (2.9) 15.3-39.1
Underweight
(BMI <18.5) 11 2.7 5.2 2.9 0.9
Overweight
(BMI ≥ 25) 85 21.1 16.9 19.1 27.4
Obese (BMI ≥
30) 11 2.7 2.6 1.9 4.3
*males ≤155cm, females ≤140cm (criteria of Centers for Disease Control and
Prevention, 1998)

84
Table 3.2 Blood serum lipids and hemoglobin for
Tsimane adults

Tsimane U.S.

Mean (SD)
or % Range N
Mean (SD) or
(SE)* N
Clinically
normal
range Age Reference
Lipoproteins
Total cholesterol 138.0 (29.2) 69-258 415 203.0 (0.8)* 8809 <240 20+ Carroll et al., 2005;
% High (>240 mg/dl ) 0.2 16.4† 1090 Expert Panel, 2001
HDL 36.8 (8.9) 4-71 356 51.3 (0.4)* 8808 >40 20+ Carroll et al., 2005;
% High (<40 mg/dl ) 64.0 16.2† Expert Panel, 2001
LDL 70.6 (21.9)
18.4-
158.8 231 123.0 (1.0)* 3867 <160 20+ Carroll et al., 2005;
% High (>160 mg/dl) 0 11.1† Expert Panel, 2001;
Total/HDL cholesterol 3.9 (1.6)
2.2-
29.8 353 4.3 (1.4) 3014 <5.92
20-
74 Kannel et al., 2008;
% High (>5.92) 2.6 9.9† Seeman et al., 2004
Hemoglobin (g/dl) 12.5 (1.7)
4.3-
16.6 414 14.1 (0.03)* 15419 20+ Astor et al., 2002;
Males
Mean 13.2 (1.7) 14.9 (1.3) 8506 14-18 18+
Hsu et al., 2002; MedicineNet
website
% Anemic (<13) 35.8 3.3†
% Anemic with Ascaris 51.7
% Anemic without
Ascaris 32.2
p=0.04

% Anemic with Trichuris 100.0
% Anemic without
Trichuris 34.3
p=0.02

Females
Mean 11.9 (1.4) 13.1 (1.2) 7465 12-16 18+
Hsu et al., 2002; MedicineNet
website

85

Table 3.2, Continued

% Anemic (<12) 47.1 7.2†
% Anemic with Ascaris 61.1
% Anemic without
Ascaris 41.1
p=0.03

% Anemic with Trichuris 62.5
% Anemic without
Trichuris 42.9
p=0.30

HDL = High-density lipoprotein
LDL = Low-density lipoprotein
†
Unpublished analysis using the U.S. NHANES 2003-2006

86
Tsimane U.S.

Mean (SD)
or % Range N
Mean # cells
(% of WBC)
Clinically
normal
range
N Age
Reference
C-reactive protein (CRP, mg/l) 9.2 (19.6) 0.19-150 417 4.1 3873 18+ Malik et al., 2005
<3 (%) 52.5 62.8 <3 Alley et al., 2006; Ridker, 2003
3.0-9.99 (%) 28.1 27.2
≥10.00 (%) 19.4 10.0

Interleukin-6 (IL6, pg/ml) 5.2 (9.0) 2-105 394 Men: 1.6; <4.64 586 65+ Ferrucci et al., 2005; Seeman et al., 2004
<2.68 (%) 67.0 Women: 1.5 741 65+
≥2.68 (%) 33.0 (Italy)
CRP-IL6 score* 418
High on neither 44.0
High on 1 only 61.6
High on both 24.4
White blood cells (WBC)
WBC count 10442 (2960)
2850-
19500 408 7820
3800-
10800 3227
63
(mean) Horne et al., 2005; International Still's Disease Foundation
% Neutrophils 52.0 (11.5) 0.84
65.90% 48-73% 3227
63
(mean)
Horne et al., 2005; International Still's Disease Foundation
% Lymphocytes 27.9 (7.9) 0-53 19.90% 18-48% 3227
63
(mean) Horne et al., 2005; International Still's Disease Foundation
% Eosinophils
†
20.2 (10.3) 0-49 <5% International Still's Disease Foundation
% Basophils 0.1 (2.0) 0-33 0-2% International Still's Disease Foundation
% Monocytes 0.1 (0.9) 0-18 0.70% 0-9% 3227
63
(mean) Horne et al., 2005; International Still's Disease Foundation
ESR (mm/hr) 35.8 (24.0) 3-130 413 15 <50 25-74 Gillum et al., 1994
High ESR (>50) (%) 25.7 Smith and Samadian, 1994
Immunoglobulins (Ig)
IgA (mg/dl) 322.7 (146.3) 125-2050 410 80-350 Lymphomation website
High (>385) (%) 23.2
Low (<85) (%) 0
IgE (IU/ml)
†

223-
40000 398 32.1 IU/ml <150 IU/ml; 2743 6+ Barbee et al., 1981; DiaMed, 2007; Lymphomation website
High ( ≥2000) (%) 94.2
Low (<2000) (%) 5.8
IgG (mg/dl) 1993.9 (468.9) 210-6110 411 620-1400 Lymphomation website
High (>1765) (%) 69.1
Low (<565) (%) 0.5
IgM (mg/dl) 268.0 (262.2) 14-2460 410 45-250 Lymphomation website
High (>375) (%) 14.9
Low (<55) (%) 0.2
ESR = Erythrocyte sedimentation rate
†
Related to parasitic infection
*CRP-IL6 index score (where high CRP is ≥3 mg/l; high IL6 is ≥2.68 pg/ml)

Table 3.3 Measures of infection and inflammation in the Tsimane and U.S.

87
Table 3.4 Parasite prevalence within Bolivia sample (N=383)

% with each parasite
Region
All regions Forest River San Borja
Ascaris lumbricoides (roundworm)* 17.0 2.6 26.0 10.9
Balantidium coli (protozoa) 1.3 1.3 2.0 0.0
Blastocystis hominis (protozoa) 2.6 2.6 3.1 1.8
Chilomastix mesnili (protozoa) 3.4 2.6 3.6 3.6
Entamoeba coli (protozoa) 21.9 27.3 19.4 22.7
Entamoeba hartmanni (protozoa) 0.3 0.0 0.5 0.0
Entamoeba histolytica (protozoa)* 7.3 1.3 10.7 5.5
Enterobus vermicularis (pinworm) 0.0 0.0 0.0 0.0
Giardia lamblia (protozoa)* 1.6 2.6 1.5 0.9
Hymenollepis diminuta (tapeworm) 0.0 0.0 0.0 0.0
Hymenolepis nana (tapeworm) 0.0 0.0 0.0 0.0
Iodamoeba butschilii (protozoa) 17.0 23.4 15.8 14.6
Strongyloides (roundworm) 7.1 3.9 7.7 8.2
Trichomonas hominis (protozoa) 3.9 0.0 5.1 4.6
Trichuris trichiura (roundworm) 2.9 1.3 5.1 0
Uncinaria (roundworm) 46.2 44.2 55.1 31.8
Taenia solium (tapeworm) 0.0 0.0 0.0 0.0
Average number of 17 parasites 1.3 (1.1)
0 (%) 26.5 28.6 20.0 35.5
1 33.2 39.0 32.0 31.8
2 26.4 23.4 28.0 26.4
3 or more 13.9 9.1 20.0 6.3
Average number of 6 cholesterol-associated
parasites
†
0.8 (0.8)
0 (%) 40.2 50.7 27.0 56.4
1 41.0 44.2 45.0 31.8
2 15.9 3.9 24.0 10.0
3 or more 2.9 1.3 4.0 1.8
*Cholesterol-requiring
†Entamoeba histolytica, Giardia lamblia, Ascaris lumbricoides, Trichuris trichiura, Uncinaria, Strongiloides

88

Table 3.5 Presence of six cholesterol-related parasites, indicators of infection, inflammation, and lipid levels

Total cholesterol (mg/dl) HDL cholesterol (mg/dl) LDL cholesterol (mg/dl)

Mean
(SD) p-value N
Mean
(SD) p-value N
Mean
(SD) p-value N
C-reactive protein (CRP, mg/L)
<3
a

140.5
(26.2) 216 38.1 (8.1) 185
68.1
(20.1) 184
3.0-9.99
b

140.7
(33.1) 0.99
a
117 36.4 (9.4) 0.27
a
102
71.9
(23.8) 0.41
a
81
≥10.00
127.4
(28.6) <.01
ab
81 33.8 (9.6) <.01
a
, 0.16
b
69
65.8
(20.1) 0.76
a
, 0.24
b
60
Interleukin-6 (IL6, pg/ml)
<2.68 (%)
140.8
(28.5) <.01 277 38.2 (8.4) <.01 230
71.6
(21.5) <.01 199
≥2.68 (%)
132.3
(29.7) 138 34.2 (9.3) 126
64.0
(19.8) 126
CRP-IL6 score
High on neither
a

141.2
(26.0) 181 38.7 (8.1) 151
69.9
(20.0) 141
High on 1 only
b

139.9
(31.2) 0.85
a
132 36.8 (8.8) 0.20
a
113
70.1
(23.3) 0.99
a
101
High on both
130.6
(30.5) 0.01
a
, 0.08
b
102 33.6 (9.7) <.01
a
, 0.04
b
92 64.8 (83) 0.22
a
, 0.23
b
83

Erythrocyte sedimentation rate
Q1a
137.8
(24.9) 113 38.2 (7.7) 97
72.9
(16.6) 63
Q2b
137.8
(27.8) 1.00
a
115 37.7 (9.6) 0.99
a
96
68.4
(22.9) 0.84
a
61
Q3c
133.2
(23.2) 0.81
a
, 0.81
b
113 34.6 (8.5) 0.08
a
, 0.19
b
94
71.0
(17.5) 0.99a, 0.98
b
47
Q4d
133.3
(28.7) 0.80
a
, 0.81
b
, 1.00
c
135 34.0 (7.0) 0.01
a
, 0.04
b
, 0.99
c
118
67.8
(23.5) 0.73a, 0.99
b
, 0.95
c
77

89
Table 3.5, Continued
Q5
126.6
(31.9)
0.04
a
, 0.04
b
, 0.50
c
,
0.42
d
127 32.6 (10.0)
<.01
a
, <.01
b
, 0.61
c
,
0.85
d
110
65.1
(20.0)
0.32a, 0.93
b
, 0.68
c
,
0.96
d
76
Immunoglobulin E
<2000
146.7
(28.2) <.01 34 36.1 (10.8) 0.63 27
73.8
(20.7) 0.29 18
≥2000
133.8
(27.8) 541 35.2 (8.7) 460
68.3
(21.2) 305
Eosinophils
Q1
a

134.9
(33.6) 137 36.1 (9.8) 103
66.7
(23.4) 68
Q2
b

134.0
(28.3) 1.00
a
141 35.3 (8.5) 0.94
a
110
72.1
(18.2) 0.51
a
74
Q3
c

133.4
(26.3) 0.98
a
, 1.00
b
166 35.1 (9.3) 0.84
a
, 1.00
b
152
67.9
(18.9) 0.99
a
, 0.66
b
90
Q4
131.6
(23.7) 0.79
a
, 0.91
b
, 0.95
c
168 34.8 (7.9) 0.72
a
, 0.97
b
, 0.99
c
155
68.0
(23.5)
0.99
a
, 0.67
b
,
1.00
c
93

Table 3.5 continued

Parasite
Giardia lamblia
Present
123.8
(44.1) 0.26 5
40.0
(15.1) 0.61 6
66.5
(17.6) 0.74 8
Not present
138.6
(29.0) 376 36.6 (8.7) 332
69.0
(21.2) 310
Entamoeba histolytica
Present
128.1
(29.5) 0.05 28
32.4
(6.9) 0.02 22
66.7
(16.8) 0.57 26
Not present
139.2
(29.1) 353 37.0 (8.9) 316
69.1
(21.5) 292
Ascaris lumbricoides
Present
139.0
(36.2) 0.88 65 36.8 (9.7) 0.87 61
71.4
(25.1) 0.33 70

90
Table 3.5, Continued
Not present
138.4
(27.7) 316 36.6 (8.7) 277
68.2
(19.8) 248
Strongyloides
Present
128.1
(25.5) 0.06 27
32.3
(7.6) 0.02 20
63.2
(15.2) 0.26 16
Not present
139.2
(29.4) 354 36.9 (8.8) 318
69.2
(21.3) 302
Trichiura trichuris
Present
137.8
(36.9) 0.94 11 36.4 (8.3)
0.92
10
66.4
(25.1) 0.65 14
Not present
138.4
(29.1) 370 36.7 (8.9) 328
69.0
(20.9) 304
Uncinaria
Present
137.9
(27.5) 0.75 176 36.3 (8.8) 0.47 162
67.5
(19.6) 0.27 147
Not present
138.9
(30.7) 205
138.9
(30.7) 205
70.1
(22.3) 171
Number of cholesterol-associated parasites
0
137.7
(27.7) 200 36.7 (7.9) 173
70.1
(21.7) 117
1a
133.1
(28.2) 0.40
a
242 35.0 (9.2) 0.33
a
217
68.3
(20.6) 0.92
a
133
2
b

128.0
(26.1) 0.05
a
, 0.51
b
65 33.1 (8.4) 0.02
a
, 0.38
b
85
68.6
(21.1) 0.98
a
, 0.99
b
58
3 or more
c

130.2
(29.0) 0.67
a
, 0.97
b
, 0.99
c
24 32.2 (8.2) 0.20
a
, 0.58
b
, 0.98
c
19
65.1
(22.8)
0.92
a
, 0.98
b
,
0.97
c
10
Bolded indicates significant difference (p<.05) between means of a given indicator of infection or
inflammation
a b c d
refer to p-values that have been compared to the given level denoted with
the corresponding symbol

91

Table 3.6 Regression model predicting total and high-density lipoprotein cholesterol levels from markers of infection, inflammation, and
parasite burden

Total-C (N=345) HDL-C (N=318) LDL-C (N=218)
Model I Model II Model I Model II Model I Model II
Beta
p-
value Beta
p-
value Beta
p-
value Beta
p-
value Beta
p-
value Beta
p-
value
Age 0.24 0.03 0.25 0.02 0.03 0.36 0.03 0.45 0.21 0.09 0.23 0.05
Males vs. Females -7.32 0.02 -8.02 0.01 0.01 0.99 -0.28 0.78
-
10.37 <.01
-
11.22 <.01
Region
San Borja Reference Reference Reference Reference Reference Reference
Forest 6.08 0.16 6.5 0.13 4.11 <.01 4.15 <.01 1.60 0.71 1.00 0.81
River 0.64 0.86 1.52 0.68 0.32 0.80 0.24 0.85 -1.18 0.77 -0.35 0.93
Stunting 0.69 0.92 1.55 0.83 1.65 0.49 1.69 0.48 -2.42 0.71 -1.83 0.78
Body mass index (BMI), quartiles
(Q1-4)*
Q1 -0.39 0.92 -0.01 0.99 2.68 0.02 2.71 0.02 0.01 0.99 0.82 0.82
Q2

Q3
Reference

Reference

Reference

Reference

Reference

Reference
Q4 13.13 <.01 12.43 <.01 0.31 0.80 0.41 0.75 10.02 <.01 9.93 0.01
Hemoglobin, quartiles
(Q1-4)**
Q1
-
12.66 <.01
-
12.67 <.01 -2.83 0.03 -2.56 0.05 -2.11 0.60 -1.66 0.68
Q2

Q3

Q4
Reference

Reference

Reference

Reference

Reference

Reference
CRP-IL6 score
High on neither Reference Reference Reference Reference Reference Reference
High on 1 only -3.81 0.28 -3.14 0.37 -1.96 0.09 -1.82 0.11 0.61 0.86 0.65 0.85
High on both -9.53 0.02 -0.90 0.02 -4.27 <.01 -4.54 <.01 -3.81 0.38 -4.91 0.26

92
Table 3.6, Continued
ESR , quintiles (Q1-5)
†

Q1

Q2

Q3

Q4
Reference

Reference

Reference

Reference

Reference

Reference
Table 3.6 continued

Q5 1.79 0.66 1.94 0.63
-
1.04 0.91 -0.02 0.99 -4.41 0.26 -4.44 0.26
Immunoglobulin E
(IgE)
-
18.63 <.01
-
18.11 <.01
-
2.82 0.25 -2.72 0.27 0.97 0.90 1.18 0.88
Eosinophils,
quartiles (Q1-4)
‡

Q1
Q2
Q3
Reference

Reference

Reference

Reference

Reference

Reference
Q4 -5.73 0.09 -6.60 0.05
-
0.85 0.43 -1.11 0.31 0.25 0.94 -1.38 0.68
Giardia lamblia -2.51 0.86 7.65 0.05 -3.96 0.76
Entamoeba
histolytica -5.97 0.34
-
2.41 0.27 -4.14 0.52
Ascaris lumbricoides 5.04 0.22 0.57 0.66 6.74 0.10
Strongyloides -6.09 0.33
-
2.71 0.22
-
11.36 0.12
Trichiura trichuris 12.12 0.21 5.76 0.06 7.93 0.36
Unicinara 2.86 0.36 0.16 0.87 0.55 0.86
Total number of 6 cholesterol-related
parasites
0 Reference Reference Reference
1 1.33 0.70 0.34 0.77 1.96 0.57
2 3.70 0.43 -0.25 0.87 2.57 0.58
3+ 9.33 0.31 3.75 0.25 2.13 0.83

93
Table 3.6, Continued
Total-C = Total cholesterol; HDL-C = High-density
lipoprotein cholesterol
CRP = C-reactive protein; IL6 = Interleukin-6; ESR = Erythrocyte
sedimentation rate
Model I: adjusted for age, sex, BMI*, hemoglobin**, CRP-IL6 index score (where high
CRP is >3; high IL6 is >2.68),
ESR
†
, IgE [dummy var: 0 <2000; 1 ≥2000], eosinophils
‡
, stunting, 6
cholesterol-related parasites
Model II: adjusted for Model I covariates and total number of 6
cholesterol-related parasites
Bold indicates significant effect at
p<.05 level
*Body mass index
quartiles:
Males - Q1: <21.9; Q2: 21.9-23.3; Q3: 23.3-24.5;
Q4: ≥24.5
Females - Q1: <20.8, Q2: 20.8-22.3, Q3: 22.3-
24.6, Q4: ≥24.6
**Hemoglobin
quartiles:
Males - Q1: <12.4; Q2: 12.4-13.3; Q3:
13.4-14.1; ≥14.2
Females - Q1: <11.3; Q2: 11.3-12.0; Q3: 12.1-
12.8; Q4: >12.9
†
ESR quintiles:
Males - Q1: <10; Q2: 10-18; Q3: 19-24; Q4: 25-41; Q5: >42
Females - Q1: <23; Q2: 23-33; Q3: 34-42; Q4: 43-61; Q5: >62
‡
Eosinophil quartiles:
Q1: <12, Q2: 12-19, Q3: 19-26, Q4: >27

94
Figure 3.1 Distribution of total cholesterol (total-C) in U.S. and Tsimane adults
Total-C distributions
0
1
2
3
4
5
6
7
8
9
69 87 105 121 137 153 169 185 201 220 260 340
Total-C (mg/dl)
Frequency (%)
US
Tsimane

95

Figure 3.2 Distribution of high-density lipoprotein cholesterol (HDL-C) in U.S. and
Tsimane adults

HDL-C Distributions
0
1
2
3
4
5
6
7
8
9
4.4 24 32 40 48 56 65.2 78 98 118 138 158 178
HDL-C (mg/dl)
Frequency (%)
US
Tsimane

96
Figure 3.3 Distribution of low-density lipoprotein cholesterol (LDL-C) in U.S. and
Tsimane adults

LDLC-C Distributions
0
2
4
6
8
10
12
14
16
20
35
50
65
80
95
110
125
140
155
170
185
200
215
230
245
Frequency (%)
LDL-C (mg/dl)
US
Tsimane

97
CHAPTER 4: INFLAMMATORY GENE VARIANTS IN THE TSIMANE, AN
INDIGENOUS BOLIVIAN POPULATION WITH A HIGH INFLAMMATORY LOAD

Introduction

Variation in the distribution of genetic markers both within and between
populations has been associated with exposure to different environmental conditions,
including responses to pathogen burden (Pennington et al., 2009). The classic example is
natural selection in populations where malaria is present, where individuals who are
heterozygous for the β-globin sickle-cell allele are more resistant to malaria and thus
have a survival advantage. In such populations, the presence of a single β-globin sickle-
cell allele has been favored and has risen to a higher frequency than in environments
without malaria (Flint et al., 1998). Chronic infection may also influence the frequencies
of genetic variants at different ages within a population (Drenos Westendorp, and
Kirkwood, 2006; Harpending and Cochran, 2006), which also represent selective
advantages from innate immune responses (Kuningas et al., 2009). Genes that increase
the likelihood of survival from infection through the reproductive years may also promote
aging processes later in life and would illustrate the principle of antagonistic pleiotropy in
the evolution of senescence (Finch and Crimmins, 2004; Finch and Stanford 2004;
Williams and Day, 1957).

98
Background
Genetic Markers Associated with Infection and Inflammation
This study analyzed the distribution of allele frequencies of selected immune
response genes associated with inflammation and infection. Prior studies of the Tsimane
of Bolivia, a population exposed to very high infectious burden, show elevations of C-
reactive protein (CRP), parasite infection, and white blood cell count (Gurven et al., 2007,
2008; McDade et al., 2005; Vasunilashorn et al., in press). In this study we examine
variants of genes encoding apolipoprotein E (apoE), C-reactive protein (CRP), and
interleukin-6 (IL-6). Differences in allele frequencies among populations may provide
some insight as to the interaction between genes and environment, and the relevance of
natural selection in shaping variation in immune-related genes within and among
populations. We also examined genotype frequencies across various age ranges to
evaluate possible genetically related survival advantages in a highly infectious
environment. Some of the genes that enhance survival from infection by increased
inflammatory responses may also promote aging among survivors later in life through
their effects on inflammatory mechanisms in the chronic conditions of aging (Franceschi
et al., 2005; Finch, 2007). Finally, we investigate the relationship between serum
markers of infection and inflammation and proinflammatory genotypes among the
Tsimane. We hypothesize that there will be a positive association between
proinflammatory genotypes and blood markers of inflammation.

99
C-Reactive Protein
CRP, an acute-phase response protein, is a marker of systemic inflammation and
its expression is influenced by genetic factors, infection, trauma, and the presence of
chronic conditions (Ridker et al., 2008, 2009). Heritability estimates for plasma CRP
levels in various populations range from 27-40% (Dupuis et al., 2005; Pankow et al.,
2001), suggesting the influence of genetic factors. In agreement with this notion, several
SNPs and haplotypes at the CRP locus have been associated with plasma CRP levels
(Crawford et al., 2006; Eirksdottir et al. 2009; Kathiresan et al., 2006; Ridker et al. 2008;
Szalai et al., 2005; Teng et al., 2009), including those located in introns (Szalai et al.,
2002), exons (Zee and Ridker, 2002), the upstream promoter (Brull et al., 2003; Kovacs
et al., 2005), and the 3’-untranslated regions of the mRNA (Brull et al., 2003). Genetic
markers related to CRP production have also been associated with survival from infection
as well as plasma levels of other inflammatory markers (Balding et al., 2003; Slattery et
al., 2007; Walston et al., 2005).
Interleukin-6
IIL-6 is another protein involved in the acute phase inflammatory response that
has broad metabolic roles in normal metabolism and is controlled by genetic variation.
For example, a G>C substitution (rs1800795) at position -174 of the IL-6 promoter has
been associated with resistance to infection and enhanced survival during bacterial
meningitis (Balding et al., 2003). Importantly, this variant has also been associated with
plasma IL-6 levels (de Craen et al., 2005; Slattery et al., 2007; Walston et al., 2005).

100
Apolipoprotein E
ApoE has three common alleles with the order of prevalence being apoE3 >
apoE4 > apoE2 in most populations. The apoE4 allele is a common genetic risk factor
for cardiovascular disease and Alzheimer disease (Corder et al., 1996; Eichner et al.,
2002; Lahoz et al., 2001; Rosvall et al., 2009), and in some populations, shortens lifespan
by about five years (Ewbank, 2004, 2007; Schachter et al., 1994; Smith, 2002). By
contrast, apoE4 has also been associated with protection to infectious conditions, such as
hepatitis C virus, and lowers progression to liver fibrosis (Fabris et al., 2005; Wozniak et
al., 2002). In addition, children in a Brazilian favela with an apoE4 allele had less
Giardia and fewer bouts of diarrhea than those with other apoE alleles (Oria et al., 2005).
The apoE4 allele influences inflammatory responses and, relative to apoE3, is
proinflammatory in some conditions. Among humans after surgery, apoE4 carriers
exhibited higher levels of tumor necrosis factor (TNF)- α compared to apoE3/E3 patients
(Drabe et al., 2001; Grunenfelder et al., 2004; Olgiati et al., 2010). A mouse model
supports these allele effects. For example, brain monocytes (microglia) from transgenic
mice expressing apoE4 secreted more TNF- α, IL-6, and nitric oxide in those mice with
the apoE3 allele (Vitek et al., 2009).
The apoE allele frequencies vary widely among human populations of Europe,
Africa, Australia, and North America (Corbo and Scacchi 1999; Finch, 2007; Singh et al,
2006). ApoE2 is the least prevalent, with complex variations in phenotype among
individuals carrying this allele (Sakuma et al., 1995), whereas apoE3 is the most
prevalent, with >50% frequency in all human populations studied. ApoE4 shows more

101
variation with prevalence, varying from 49% among the Huli of New Guinea to being
absent among the Ache in Paraguay (Demarchi et al., 2005). Geographic distributions
indicate that Northern Europe has greater apoE4 prevalence than Mediterranean regions
(Corbo and Scacchi, 1999; Demarchi et al., 2005; Gerdes et al., 1992; Panza et al., 2003).
In less developed societies, there is also wide variation amongst these apoE alleles:
apoE2 is absent in East Greenland Inuits (Gerdes et al., 1996), Mayans of Mexico
(Kamboh et al., 1991b), Yanomamo of Brazil (Crews et a l., 1993), Australian aborigines
(Kamboh et al., 1991a), and Amerindians from nine different tribes in Central and South
America (Asakawa et al., 1985). Conversely, apoE4 was relatively frequent in the East
Greenland Inuit (Gerdes et al., 1996), Saami of Finland (Lehtinen et al., 1994), Nigerians
(Kamboh et al., 1989; Sandholzer et al., 1995; Sepehrnia et al., 1988), and aborigines
from Australia (Kamboh et al., 1991a), Papua New Guinea (Kamboh et al. 1990), and
Malaysia (Gajra et al., 1994b). These wide distributions in allele frequencies suggest the
influence of founder effects and/or potential selective influences on survival via
proinflammatory responses in environments with variable pathogenicity.
Mortality and Infection in the Tsimane
Our analysis focuses on the Tsimane, who live in a highly infectious environment
in lowland Bolivia. The Tsimane are an indigenous forager-farmer population living
under conditions similar to pre-industrial European populations with high infectious
morbidity, limited diets, and short life expectancy (Gurven et al., 2007, 2008; McDade et
al., 2005). Extensive demographic data documents the level of mortality in two periods:
1950-1989 and 1990-2000 (Gurven et al., 2007). Life tables demonstrate the shorter life

102
expectancy at birth by the standards of contemporary developed countries: 44.2 years for
males and 42.8 years for females before 1990, and 54.3 for males and 54.0 for females
during the 1990-2000 period. Survival curves from life tables for these two periods
(Figure 4.1) show marked recent improvements in survival. For example, between 1950-
1989, 20% of the Tsimane (21% of females and 19% of males) died before their 5
th

birthday; this high early mortality decreased by about 25% during the next decade, 1990-
2000. By age 35 in the earlier period (the midpoint of the 10-59 year range), the
proportion dying was about 37% (39% for women and 34% for men). From 1990-2000,
this proportion decreased to 25%. By age 60, 60% of the cohort had died (61% for
females; 59% for males), which was reduced to 40% at the later date. The major cause of
death at all ages is infectious diseases. Among children, respiratory and gastrointestinal
conditions are the primary cause of death, but among people over 60, deaths are due to
respiratory conditions, including tuberculosis (Gurven et al., 2007).
The Tsimane are exposed to high levels of inflammation, infection and parasitic
load (Gurven et al., 2008; McDade et al., 2007; Tanner et al., 2009; Vasunilashorn et al.,
2010). About 40% had elevated levels of CRP ( ≥3 mg/l) and 60% had high IL-6 ( ≥2.68
pg/ml). The total white blood cell count averaged 10,344 cells/mm
3
, which is in the
upper normal range of clinical samples from the U.S. (Table 4.1). In contrast, mean
WBC for individuals in the US without coronary heart disease is 7500 cells/ mm
3

(Friedman et al., 1974). At any given time, the majority (nearly 75%) of subjects had at
least one parasite; two or more parasites were carried by 40% of individuals sampled in
2004, and the average number of parasite species per person was 1.3 (Vasunilashorn et al.,

103
2010). During medical exams, over 60% had symptoms of respiratory or gastrointestinal
illness (Gurven et al., 2008). Malaria and HIV have not been reported among the
Tsimane through 2010.
To investigate the relationship between living in a highly infectious environment
and allele and genotype frequencies associated with inflammation, we used blood
samples collected from the Tsimane. Since blood was not collected from children under
the age of five, the present study examined the distribution of the genetic markers in three
age groups 5-9, 10-59, and 60+. We assume that the children currently between the ages
of 5-9 live under the more recent mortality level and that about 90% of the original birth
cohort has survived. At the older ages, the proportion of subjects alive most likely
reflects a combination of the mortality schedules from the earlier and the later periods.
We assume that about 30% of the original cohort, which would now be 35 year-olds, has
died, and that about 55% of those who were in the original cohort of those 60 years of
age or older is deceased.
In the present study, we first compare the allelic frequencies of loci associated
with infection in the Tsimane to other populations in order to provide insight as to the
relationship between genetic variation and environment. Second, we examine genotype
frequencies with age to determine whether living in a highly infectious environment with
certain genetic predispositions toward combating infection confers a survival advantage.
Lastly, we investigate the relationship between genotype and plasma levels of an
inflammatory marker.

104
Methods
Clinical Measurements
Blood was separated and spun in the field and frozen in liquid nitrogen for
transport to New Mexico, where it was assayed for serum CRP levels. Frozen
lymphocytes were sent to the University of Southern California for DNA extraction and
genotyping. Markers of infection based on blood samples were assayed in the field.
Serum high sensitivity-CRP and IL-6 were measured using assays with a detectable range
of 0.1-150.0 mg/L and 2.0-1000.0 pg/mL (respectively) at the Tricore Reference
Laboratories in Albuquerque using Immulite 2000 kits. The mean replicate interassay
coefficient variation was 5.6% for hs-CRP and 5.8% for IL-6 (Diagnostics Products
Corporation, Siemens, Deerfield, IL). White blood cell count was analyzed in the field
using fresh samples.
Molecular Analysis
SNP genotyping was performed using the Applied Biosystems, Inc. (ABI)
TaqMan system (Livak, 2003). Briefly, for each SNP, a PCR reaction containing 2 ng of
genomic DNA, amplification primers, and two 20-30 bp oligonucleotides encompassing
the polymorphic site was carried out according to manufacturer's protocols. Genotyping
assays were selected through ABI's "Assays on Demand" database
(http://myscience.appliedbiosystems.com/%20navigation/
http://myscience.appliedbiosystems.com/navigation/mysciapplications.jsp) or custom-
designed using ABI's "Assays by Design" service. Primer and probe sequences can be
provided upon request.

105
Genetic loci examined in this study were chosen based on previously reported
associations with plasma levels of CRP or IL-6 (Table 4.2). These included 8 SNPs in
the CRP gene and a promoter SNP in IL-6. Determination of the apoE2/E3/E4 alleles
used genotypes derived from two SNPs, rs429358 and rs7412, as reported previously
(Nyholt et al., 2009).
To investigate how genotypes vary with age, we examined genotypes for specific
SNPs associated with the highest blood CRP and IL-6 based on published studies. These
genotypes are listed as “proinflammatory genotypes” in Table 4.3. For genetic variants
associated with apoE, the proinflammatory genotype was designated by the genotype
associated with the greatest risk for adverse conditions or outcomes in old age. The pro-
inflammatory genotypes for the SNPs examined in the current study are: apoE4/E4 and
rs405509_GG for apoE (Mahley, 1988; Mahley and Rall, 2000; Schachter et al., 1994;
Song et al., 2004); rs1205_GG, rs1417938_TT, rs1800947_GG, rs3093061_AA,
rs3093062_GG, rs2808630_AA, rs3091244_AA for CRP (Crawford et al., 2006;
Eiriksdottir et al., 2009; Kathiresan et al., 2006; Ridker et al., 2008; Szalai et al., 2005;
Teng et al., 2009); and rs1800795_GG for IL-6 (Bamouldi et al., 2006; Walston et al.,
2005).
All protocols were approved by the Internal Review Boards at the University of
New Mexico, University of Southern California, and the University of California, Santa
Barbara.

106
Comparison Populations
We also compared allele frequencies in the Tsimane to various other populations,
including Africans, Asians, Caucasians, and Hispanics using the available data in from
the HapMap project.
Statistical Analyses
Age-associated frequencies and chi-squared tests were used to determine
genotype prevalence and frequency differences with age. Ordinary Least Squares
regression was used to determine the associations between proinflammatory genotypes
and CRP levels. Model I was adjusted for age and sex. Model II included covariates
from Model I plus BMI, since adiposity can influence CRP levels. Given the non-linear
distribution of CRP in the population, CRP was log-transformed in multivariate analyses.
All analyses were carried out with SAS 9.1 (SAS Institute, Inc., Cary, NC), and p-values
less than 0.05 were considered significant.

Results
Allele Frequencies of CRP SNPs in the Tsimane and Other Populations
The allele frequencies of the selected SNPs in the CRP gene for the Tsimane and
other populations are summarized in Table 4.3. While the frequencies of SNPs
rs1800947, rs3093061, rs3093062, rs1205 were comparable between the Tsimane and
other populations, there were also some distinct differences as well. For example, the A
allele of rs1417938 was present at 62% and 47% in this sample of Tsimane subjects and
Caucasians, respectively, but less than 5% in Asians and Africans. In addition, the A

107
allele of the tri-allelic SNP rs3091244 was twice as frequent in the Tsimane, as compared
to Caucasians and completely absent in Africans whereas rs2808630 was least frequent in
the Tsimane (Table 4.3).
Proinflammatory Genotypes
Of three SNPs in the CRP gene and one in the IL-6 gene, only the genotypes
considered to be pro-inflammatory were present in the Tsimane (rs1800947_GG,
rs3093062_GG, and rs3093061_AA, and rs1800795_GG). In addition, the AA genotype
of the tri-allelic SNP rs3091244 was present at 38%.
For apoE, the apoE4/E4 proinflammatory genotype was present in 2.9% of the
Tsimane sample while the at-risk genotype (GG) of SNP in the promoter region
(rs405509), which has also been associated with Alzheimer’s Disease (Bizzarro et al.
2009) was found in 6.4% of the Tsimane (Table 4.3).
Distributions of Genotypes by Age
Based on the allele frequency variations observed in the Tsimane at the selected
loci, we examined the age distribution pro-inflammatory genotypes. These included the
apoE4 allele, apoE rs405509, and CRP SNPs rs1417938_TT, rs3091244_AA,
rs1205_GG, and rs2808630_AA, which have been associated with plasma CRP levels.
ApoE allele frequencies by age group are shown in Table 4.4. ApoE2 was absent from
the sample, and E3/E3 was the most prevalent for all age groups (range: 68-76%),
followed by E3/E4 (range: 21-27%); E4/E4 was the least prevalent (range: 0-5%) and
there was a slightly higher proportion of E4/E4 at ages 60+ compared to earlier ages.
There was a non-significant trend between age and the frequency of E4/E4 (r=0.05,

108
p=0.13). CRP genotypes showed divergent trends with age (Table 4.4) with
rs3091244_AA exhibiting the strongest association with age (42.0% among age 5-9 and
30.3% among adults age 60+; P<0.03). These preliminary findings do not support the
hypothesis that proinflammatory genotype CRP- would favor survival to later ages.
Blood CRP and Genotypes
We next determined whether plasma CRP levels differed as a function of
genotype. Subjects homozygous for the apoE3 allele had significantly higher CRP levels
compared to apoE3/E4 and apoE4/E4 subjects (Figure 4.2; Table 4.5). By comparison,
there was no relationship between proinflammatory genotypes of apoE SNP rs405509
and CRP SNPs rs1417938, rs3091244, and rs2808630 and CRP levels (p=0.81, p=0.48,
p=0.21, and p=0.44, respectively) (Table 4.5). Adjusting for BMI did not attenuate the
relationship between the apoE4 allele and blood CRP levels or alter the results with the
other four SNPs. The proinflammatory genotype of CRP rs1205 was associated with
higher levels of blood CRP (p=0.04, Model I), but was no longer significant after
adjusting for BMI.

Discussion
This preliminary study of Tsimane suggests some notable difference in allele
frequencies relative to other populations. The apoE2 allele is absent in this relatively
large sample of Tsimane subjects, which is consistent with studies of five Brazilian
Amazonian tribes (Yanomami, Wayan-Apalai, Wayampi, Arara, Kayapo) (Marin et al.,
1997). In addition, we did not find support for our hypothesis that pro-inflammatory

109
alleles of apoE and CRP would favor survival to later ages, although the apoE4 allele was
observed at slightly, but not significantly higher frequency in older Tsimane groups. This
trend, if validated with larger samples, may indicate that, under the environmental
conditions in which the Tsimane live, subjects carrying apoE4 have greater survival to
older age (Finch 2007; Finch and Morgan, 2007). Additional Tsimane samples will be
required to further evaluate this possibility, which would be opposite to trends for lower
survival of E4 carriers at older ages in U.S. and European populations, which have low
levels of infections (Gerdes et al., 2000; Ewbank, 2004; Rontu et al., 2006). However, we
did observe an association of the apoE4 allele with CRP in the Tsimane, where apoE3/E4
and apoE4/E4 carriers had lower levels compared to apoE3/E3 subjects (Figure 4.2), even
after adjustment for BMI. Similar associations with apoE4 have been reported in Finnish
nonagenarians (Rontu et al., 2006), Latinos, and Japanese Americans (Aiello et al, 2008;
Austin et al., 2004). These associations could arise from various mechanisms, including
differential binding of apoE4 to VLDL (Austin et al., 2004) and hepatic clearance of CRP
with involvement of the mevalonate pathway (Rontu et al., 2006).
We note some limitations of our study. For example, babies and children under
age five were not included in our analysis because obtaining blood samples was not
feasible in this young group. In addition, using CRP levels based on a single sampling
may not accurately represent relationships between genetic variants and inflammatory
phenotypes, since CRP levels can vary substantially during infection and are influenced
by other genes, such as IL-1, and IL-6. We also acknowledge that the allelic associations
(or lack thereof) we observe need to be validated with additional samples in order to draw

110
any firm conclusions. Moreover, examining individual SNPs and their effects on a given
phenotype must be interpreted cautiously due to linkage disequilibrium with other
variants, which can vary across genomic regions and have not been determined in an
isolated and unique population such as the Tsimane. Thus, carrying out dense SNP
genotyping across loci of interest, such as IL-6 or IL-1, in conjunction with quantitating
additional biomarkers/cytokines may help to provide a better understanding of the
complex relationship between genetic variation and inflammation within the context of
Tsimane life history.
We also acknowledge that the OLS regressions testing for the allelic associations
must be validated with additional samples in order to examine this question more fully.
Moreover, plasma CRP levels are also influenced by IL-1, IL-6, and apoE alleles (Austin
et al., 2004; Ferrari et al., 2003; Latkovskis et al., 2004; Rontu et al., 2006). Examination
of additional loci on the IL-6 and IL-1 gene will further resolve the complex relationship
between blood CRP and genetic variants and the Tsimane life history under high
infection loads. The advent of high throughput DNA technology, along with the growing
characterization of genes, has made it possible to determine genetic dispositions to
disease (Collins, Guyer, and Charkravarti, 1997; Hardy and Singleton, 2009) and the
associations between genetic variation and human traits (Goldstein, 2009).
In conclusion, the current study examined genetic information and an indicator of
inflammation (CRP) in an indigenous population, which provided an opportunity to
examine the relationship between genes and living in a highly infectious environment in a
population that models pre-industrial societies. Although such studies have been carried

111
out before (Huang et al., 2007; Khor et al., 2010; Walston et al., 2005), few have
examined this relationship in an indigenous population (Kuningas et al., 2009), and
among those, even fewer have examined the distribution of genotypes with age.
Compared to other populations, the indigenous peoples of the Amazonian basin may also
harbor unique SNPs and/or lack additional variants as a result of selective pressures
present in their environment over time. Future studies of this distinct population utilizing
novel genetic/genomics methods, such as genome-wide association studies (Collins,
Guyer, and Charkravarti, 1997; Goldstein, 2009; Hardy and Singleton, 2009), may help
to expand our understanding of the links between environmental factors, genes, selection,
and traits relevant to human disease.

112
Table 4.1 Clinical characteristics of the Tsimane Population.

N
Mean (SD)
or % Range
Age 917 35.8 (19.6) 5-90
Males (%) 917 49.2
Anthropometric measures
Height (cm) 854 149.9 (28.7) 81.3-178.2
Males 152.6 (17.9)
Females 147.3 (35.8)
Body mass index (BMI, kg/m2) 852 22.2 (4.0) 0.7-38.1
Underweight (BMI <18.5) 19.8
Overweight (BMI ≥ 25) 21.1
Obese (BMI ≥ 30) 3.1
Markers of inflammation and infection
C-reactive protein (CRP, mg/l) 635 7.4 (16.2) 0.15-150
<3 (%) 58.9
3.0-9.99 (%) 26.6
≥10.00 (%) 14.5
Interleukin-6 (IL6, pg/ml) 292 5.3 (7.9) 2-57.4
<2.68 (%) 40.4
≥2.68 (%) 59.6
White blood cells count 829 10344 4400-21500
Erythrocyte sedimentation rate 831 34.7 (19.9) 2-115
*Note: The majority of the %underweight is children age ≤11

113

Table 4.2 Proinflammatory genotypes of apoE, CRP, and IL-6 based on previous studies

dbSNP Number
Associated
with
Proinflammatory or at-
risk genotype
(reference)
Proinflammatory
genotype in Tsimane?
rs429358/rs7412 apoE E4/E4 Yes, E3/E4 alleles present
rs405509 apoE GG (Bizarro et al., 2009) Yes, G/T alleles present
rs1205 CRP
GG (Crawford et al., 2006;
Eiriksdottir et al 2009;
Teng et al., 2009) Yes, G/A alleles present
rs1417938 CRP TT (Crawford et al., 2006) Yes, A/T alleles present
rs1800947 CRP
GG (Crawford et al., 2006;
Teng et al. 2009) Yes, only G allele present
rs3093061 CRP AA (Crawford et al., 2006) Yes, only A allele present
rs3093062 CRP GG (Szalai et al., 2005) Yes, only G allele present
rs2808630 CRP AA (Crawford et al 2006) Yes A/G alleles present
rs3091244 CRP
AA (Kathiresan et al 2006;
Ridker et al., 2008; Teng
et al., 2009) Yes, A/C/T alleles present
rs1800795 IL-6
GG (Bamouldi et al., 2006;
Walston et al., 2005) Yes, only G allele present

114
Table 4.3 Population-based allele frequencies at C-reactive protein (CRP)-associated SNPs: rs1417938, rs1800947,
rs2093061, rs3093062, rs3091244, rs1205, and rs2808630; apolipoprotein E (apoE)-associated SNP: rs405509;
interleukin-6 (IL-6)-associated SNP: rs1800795
Populations
African Asian Caucasian
Tsimane African Yoruba Asian
Han
Chinese Japanese Caucasian European Hispanic
CRP
rs1417938
A 62 na 4 na 3 5 47 na na
T 38 na 96 na 97 95 53 na na
rs1800947
C 0 na 0 na 7 3 9 na na
G 100 na 100 na 93 97 91 na na
rs3093061
A 100 100 na 94 na na na 78 na
G 0 0 na 6 na na na 22 na
rs3093062
A 0 6 na na 0 0 0 na 6
G 100 94 na na 100 100 100 na 94
rs3091244
A 62 na 0 na na na 33 na na
C 37 na 78 na na na 61 na na
T 1 na 22 na na na 35 na na
rs1205
A 32 14 15 55 56 73 31 25 33
G 68 86 85 45 44 27 69 75 66
rs2808630
A 95 97 82 81 76 89 70 64 76
G 5 6 18 19 24 11 30 36 24
apoE
rs405509

115
Table 4.3, Continued
G 24 na 76 na 34 27 na 50 na
T 76 na 24 na 66 73 na 50 na
IL-6
rs1800795
Table 4.3
continued

C 0 7 0 0 0 0 50 52 20
G 100 96 100 100 100 100 50 48 80
CEU = Utah residents of Northern and Western European decent
Yoruba =Yoruba people of Nigeria

116
Table 4.4 Frequency (%) of apolipoprotein E (apoE), CRP, and IL-6 polymorphisms by
age group

Age
5-9 10-59 60+ X
2
p-value
Apolipoprotein E
apoE
E3/E3 74.0 76.0 68.1 6.13 0.19
E3/E4 26.0 21.3 26.9
E4/E4* 0.0 2.7 5.0
rs405509
GG* 6.1 6.4 5.9 3.6 0.46
GT 22.4 35.0 36.4
TT 71.4 58.6 57.6
C-reactive protein
rs1417938
AA 42.0 42.6 30.3 6.62 0.16
AT 42.0 40.4 48.7
TT* 16.0 17.0 21.0
rs3091244
AA* 42.0 42.8 30.3 16.81 0.03
AC 42.0 39.6 46.2
AT 0.0 0.8 2.5
CC 16.0 16.7 19.3
CT 0.0 0.1 1.7
rs1205
GG* 42.9 50.0 43.3 4.39 0.36
AG 49.0 37.5 42.5
AA 8.1 12.5 14.2
rs2808630
GG 0.0 0.3 0.0 5.14 0.27
AG 6.0 9.4 15.1
AA* 94.0 90.3 84.9
*Proinflammatory allele in some populations (U.S. National Health and Nutrition
Examination Survey, U.S. Framingham Heart Study, and a Taiwanese population)

117
Table 4.5 Regression models predicting levels of log transformed C-reactive protein
(CRP) from genotypes of apoE and CRP.

Gene
log CRP

Model I (N=624) Model II (N=578)

b p-value b p-value
apoE
Age 0.004 <.01 0.003 <.01

Male -0.042 0.32 -0.010 0.77

Body mass index 0.010 0.11

apoE4 presence -0.153 <.01 -0.154 <.01

apoE
Age 0.004 <.01 0.003 <.01

Male -0.049 0.26 -0.024 0.60

Body mass index 0.011 0.11

rs405509_GG 0.0218 0.82 -0.005 0.96

CRP
Age 0.004 <.01 0.003 0.01

Male -0.045 0.30 -0.019 0.68

Body mass index 0.011 0.09

rs1417938_TT -0.040 0.48 -0.023 0.70

CRP
Age 0.004 <.01 0.003 <.01

Male -0.044 0.31 -0.019 0.68

Body mass index 0.011 0.10

rs3091244_AA 0.056 0.21 0.039 0.40

CRP
Age 0.004 <.01 0.003 0.01

Male -0.050 0.25 -0.023 0.61

Body mass index 0.012 0.07

rs2808630_AA -0.055 0.44 -0.073 0.33

CRP
Age 0.004 <.01 0.003 0.01

Male -0.044 0.31 -0.022 0.64

Body mass index 0.012 0.08

rs1205_GG 0.091 0.04 0.074 0.11

118
Figure 4.1 Probability of survival (lx) by age in 1950-1989 and 1990-2000 among the
Tsimane
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
<1 5 1015 202530 3540 45 505560 65 7075 75+
Age
lx
Female: 1950-1989 Male: 1950-1989
Female: 1990-2000 Male: 1990-2000

119
Figure 4.2 C-reactive protein (CRP) in blood plasma in association with alleles of
apolipoprotein E (apoE).

*p<0.05

Legend: Blood CRP (mg/dl plasma) from all ages in association with apoE alleles. No
apoE2 was detected. Further statistical analysis is in Table 5.

120
CHAPTER 5: CONCLUSION

Summary

The three papers in this dissertation have made important contributions to our
understanding of aging in a highly infectious environment, like that of the Tsimane of
Bolivia. The first paper finds that levels of biomarkers differ in the Tsimane and the U.S.,
with the Tsimane exhibiting lower levels of cardiovascular and metabolic risk but higher
levels of markers of infection and inflammation compared to the U.S. The second paper
demonstrates that elevated levels of inflammation and indicators of infection are
associated with lower blood lipid levels. Lastly, the third paper indicates that high
exposure to infection may influence the force of selection of some genetic variants in the
Tsimane.

Findings and Implications
These papers have several important findings and highlight future directions for
further research. First, they demonstrate the different biomarker profiles exhibited in the
Tsimane compared to the industrialized U.S. Not only do these profiles differ between
the two societies at all ages, but the age-associated trajectories of the biomarkers vary as
well. This suggests that living under a high infection environment results in a different
cardiovascular, metabolic, and inflammatory and infection profile than living under post-
industrialized conditions. Moreover, the findings also suggest that constant exposure to
infection results in a different aging profile from that of westernized populations – one
that seems to be less driven by cardiovascular risks and more by risks due to high

121
infection. This may suggest that aging profiles under conditions of high exposure to
infection are very different from that of aging under industrialized conditions with
relatively little exposure to infection.
Second, to my knowledge, the slight increase in apoE4 prevalence with increasing
age in the Tsimane has not been reported in other populations. In studies of older
populations, the E4 prevalence decreases with age. If this age-associated increase in
apoE4 among the Tsimane is a valid finding, this would suggest that selective pressures
of continued exposure to infectious agents predisposes individuals to a genetic profile
with a survival advantage. It is possible that in highly infectious environments, young
children (and possibly infants) with an apoE4 have selective pressures that place them at
a greater survival advantage; at older ages with chronic conditions, the E4 allele is no
longer protective, but rather has adverse consequences for survival. This would indicate
the possibility that in the populations with constant exposure to infection, like the
Tsimane, genetic variantion with age may differ from that of modernized societies. This
speculation requires additional studies with genetic information from individuals at all
ages, including infants, from other indigenous populations living under high infection
circumstances.
A third and final observation encompasses the findings from each of the three
research papers, which demonstrates the important role of infection on various levels of
biomarkers with age and potentially the distribution of genetic variants with age. In
particular, this dissertation focused on the relationship of infection to cardiovascular and
metabolic markers, with some examination of indicators of the functioning of certain

122
organ systems (e.g., respiratory functioning). It is probable that the relationship of
infection to cardiovascular and metabolic systems extends to other systems, including
renal and respiratory function. Future studies that address the potential of this
hypothesized relationship would expand our understanding of the potentially central role
that inflammation plays in the aging process.

Limitations
Though this dissertation has several strengths, it also has some important
limitations. First, the gradual improvements in some access to modern medicine likely
influence the relationships examined. With the introduction of the Tsimane Health and
Life History research team, ethical obligations to providing basic health care to the
Tsimane participants could not be ignored. This involvement has impacted the health of
the Tsimane, though the extent of this impact is not known. As such, we draw our
conclusions with caution and do not assert any definitive conclusions.
A second limitation is our cross-sectional sample and sample size. To examine
the relationships of various blood indicators and age, many of the available sample sizes
were small (<500). This size limits both the power in determining true associations and
my ability to determine whether some relationships were significant or not. Also without
a longitudinal component, the cross-sectional observations prohibited me from
determining any causal effects and fully testing the energy allocation model proposed in
the Introduction. It is hoped that future studies will include longitudinal data and
methods to more accurately investigate the directionality of these relationships.

123
Third, the Tsimane of Bolivia represents one indigenous population with a
specific ancestral background, who live under a certain environmental condition. It is
difficult to generalize our findings from the Tsimane to other populations who may be
similar in having both limited access to modern medicine and high infectious morbidity,
but who likely differ on other characteristics that influence aging. Additional studies that
similarly examine levels of biomarkers and genetic distributions in other indigenous
populations will allow for more generalizable conclusions on the impact of living under
high infection environments on health and aging.

Future Research
Future research should also address the relationship of early life conditions to
better understanding the relationship between early life and later life health. Specifically,
investigations of infection and Tsimane childhood characteristics could improve our
knowledge of the impact of infection during the earlier years of life. The balance of
energy expenditure between growth and maintenance is greatly affected by pathogen
burden. In response to a high pathogen load, the largest reallocation of energy resources
during inflammation occurs at the expense of growth and in favor of maintenance and
homeostasis (Finch, 2007: 5; McDade, 2005). A high infectious load in early life results
in reduced growth during childhood, and possibly stunting. Fogel and Costa´s
technophysio evolution hypothesis (Fogel, 2004; Fogel and Costa, 1997) drew similar
conclusions to the Crimmins and Finch (2006) hypothesis that reductions in infections,
along with improvements in nutrition, enhanced childhood growth. The significant

124
relationship between nutrition and population growth has been highlighted in studying
the Industrial Revolution, when improvements in food and its distribution aligned with
marked population growth (Fogel and Costa, 1997; McKeown, 1976). Infection also
plays an indirect role in affecting growth, such that infections can result in malnutrition
through impairments in food consumption and digestion, as well as by reallocating
energy toward host defense and ultimately retarding growth (Finch, 2007: 5). Future
research should address this relationship between changes in childhood height and
infection to better understand the influence of environment on early life conditions and
how such conditions likely influence health in later years of life.
This dissertation has demonstrated several new findings and made some
potentially valuable contributions to the literature. The three research papers generally
suggest that aging in a high infection environment, like that of the Tsimane, is different
from aging profiles in environments where exposure to infection is less common. Studies
of industrialized nations often link aging with an increase in cardiovascular risk and
conditions. This dissertation suggests that this age profile may not characterize aging in
all environments and that under environments where exposure to infection is high, the
aging profile may be less characterized by such cardiovascular risks. While this
dissertation leaves much room for future studies, it has attempted to address some general
questions about aging under a high infection condition and marks the beginning of a
longer path of additional research questions.

125
REFERENCES

Aceves D, Ruiz B, Nuño P, Roman S, Zepeda E, Panduro A. 2006. Heterogenity of
Apolipoprotein E Polymorphism in different Mexican Populations. Hum Biol
78:65-75.

Ahn YI, Valdez R, Reddy AP, Cole SA, Weiss KM, Ferrell RE. 1991. DNA
polymorphisms of the apolipoprotein AI/CIII/AIV gene cluster influence plasma
cholesterol and triglyceride levels in the Mayans of the Yucatan Peninsula,
Mexico. Hum Hered 41:281-289.

Aiello AE, Nguyen H-O, Haan MN. 2008. C-reactive protein mediates the effect of
apolipoprotein E on cytomegalorvirus infection. J Infect Dis 197:34-41.

Alley DE, Crimmins E, Bandeen-Roche K, Guralnik J, Ferrucci L. 2007. Three-year
change in inflammatory markers in elderly people and mortality: the Invecchiare
InCHIANTI Study. J Am Geriatr Soc 55:1801-1807.

Alley DE, Seeman TE, Kim JK, Karlamangla A, Hu P, Crimmins EM. 2006.
Socioeconomic status and C-reactive protein levels in the U.S. population:
NHANES IV. Brain Behav Immun 20, 495-504.

Alving CR, Wassef NM. 1999. Naturally occurring antibodies to cholesterol: a new
theory of LDL cholesterol metabolism. Immunol Today 20:362-366.

Anastos K, Lu D, Shi Q, Tien PC, Kaplan RC, Hessol NA, Cole S, Vigen C, Cohen
M, Young M, Justman J. 2007. Association of serum lipid levels with HIV
serostatus, specific antiretroviral agents, and treatment regimens. J Acquir
Immune Defic Syndr 45:34-42.

Annesi I, Oryszczyn M-P, Frette C, Neukirch F, Orvoen-Frija E, Kauffman F. 1992.
Total circulating IgE and FEV1 in adult men. An epidemiologic longitudinal
study. Chest 101:642-648.

Arntzenius AC, Kromhout D, Barth JD, Reiber JH, Bruschke AV, Buis B, van Gent CM,
Kampen-Voogd N, Strikwerda S, van der Velde EA. 1985. Diet, lipoproteins, and
the progression of coronary atherosclerosis. The Leiden Intervention Trial. N Engl
J Med 312:805811.

Asakawa J, Takahashi N, Rosenblum BB, Neel JV. 1985. Two-dimensional gel studies of
genetic variation in the plasma proteins of Amerindians and Japanese. Hum Genet
70:222-230.

126
Assman G, Schulte H. 1987. The Prospective Cardiovascular Munster Study
prevalence and prognostic significance of hyperlipidemia in men with
systemic hypertension. Am J Cardiol 59:9-17.

Astor BC, Muntner P, Levin A, Eustace JA, Coresh J. 2002. Association of kidney
function with anemia. Arch Intern Med 162:1401-1408.

Au YPT, Schilling RF. 1986. Relationship between anemia and cholesterol
metabolism in ‘sex-linked anemic’ (gene symbol, sla) mouse. Biochimica et
Biophysica Acta 883:242-246.

Austin MA, Zhang C, Humphries SE, Chadler WL, Talmud PJ, Edwards KL, Leonetti
DL, McNeely MJ, Fujimoto WY. 2004. Heritability of C-reactive protein and
association with apolipoprotein E genotypes in Japanese Americans. Ann Human
Genet 68:179-188.

Aviv A. Pulse pressure and human longevity. 2001. Hypertension 37:1060-1066.

Balding J, Healy CM, Livingstone WJ, White B, Mynett-Johnson L, Cafferkey M, Simth
OP. 2003. Genomic polymorphic profiles in an Irish population with
meningococaemia: Is it possible to predict severity and outcome of disease?
Genes and Immunity 4:533-540.

Bamouldi J, Courivaud C, Deschamps M, Mercier P, Ferrand C, Penfornis A, Tiberghien
P, Chaolpin JM, Saas P, Ducloux D. 2006. IL-6 promoter polymorphism -174 is
associated with new-onset of diabetes after transplantation. J Am Soc Nephrol 17:
2333-2340.

Banatvala N, Mayo K, Megraud F, Jennings R, Deeks JJ, Feldman RA. 1993. The cohort
effect and helicobacter pylori. J Infect Dis168:219-221.

Bande J, Clément J, Van de Woestinjne KP. 1980. The influence of smoking habits and
body weight on vital capcity and FEV1 in male Air Force personnel: a
longitudinal and cross-sectional analysis. Am Rev Respir Dis 122:781-790.

Bang HO, Dyerber J, Nielsen AB. 1971. Plasma lipid and lipoprotein pattern in
Greenlandic west-coast Eskimos. Lancet 192:1-6.

Barker DJP. 2004. The developmental origins of well-being. Philos Trans R Soc Lond B
Biol Sci 359:1359-1366.

127
Bansal D, Bhatti HS, Seghal R. 2005. Altered lipid parameters in patients infected
with Entamoeba histolytica, Entamoeba dispar and Giardia lamblia. Br J
Biomed Sci 62:63-65.

Barbee RA, Halonen M, Lebowitz M, Burrows B. 1981. Distribution of IgE in a
community population sample: correlations with age, sex, and allergen skin
test reactivity. J Allergy Clin Immunol 68:106-111.

Baruzzi RG. 1970. Contribution to the study of toxoplasmosis epidemiology.
Serologic survey among the Indians of the Upper Xingu River, central Brazil.
Revist Do Institudo de Medicine Tropical de Sao Paulo 12:93-104.

Benefice E, Barral H. 1991. Differences in life style and nutritional status between
settler and Siona-Secoya Indians living in the same Amazonian Milieu. Ecol Food
Nutr 25:1-16.

Benetos A, Safar M, Rudnichi A, Smulyan H, Richard J-L, Ducimetiere P, Guize L. 1997.
Pulse pressure: A predictor of long-term cardiovascular mortality in a Frenchmale
population. Hypertension 30:1410-1415.

Bermudez EA, Rifai N, Buring J, Manson JE, Ridker PM. 2002. Interrelationships among
circulating interleukin-6, CRP, and traditional cardiovascular risk factors in
women. Arterioscl Thromb Vas Biol 22:1668-1673.

Bibile SW, Cullumbine H, Kurtsinghe C, Watson RS, Wikamanyake T. 1949. The
variation with age and sex of blood pressure and pulse rate for Ceylonese
subjects. Ceylon J Med Sc 6, 79.

Blacher J, Staessen JA, Girerd X, Gasowski J, Thijs L, Liu L, Wang JG, Fagard RH,
Safar ME. 2000. Pulse pressure not mean pressure determines cardiovascular risk
in older hypertensive patients. Arch Intern Med 160:1085-1089.

Blurton Jones NB, Hawkes K, O’Connell J. 2002. Differences between Hadza and !Kung
children’s work: Original affluence or practical reason? In ES Burch (Eds.), Key
Issues in Hunter-Gatherer Research, pp. 189-215. Oxford: Berg.

Boe J, Humerfelt S, Wedervang F. 1957. The blood pressure in a population. Blood
pressure readings and height and weight determinations in the adult population
of the city of Bergen. Acta Med Scandinav Suppl 321, 1-336.

128
Bonafe M, Olivieri F, Cavallone L, Giovagnetti S, Mayegiani F, Cardelli M, Pieri C,
Marra M, Antonicelli R, Lisa R, Rizzo MR, Paolisso G, Monti D, Franceschi C.
2001. A gender-depedent genetic predisposition to produce high levels of IL-6 is
detrimental for longevity. Eur J Immunol 31:2357-2361.

Böttiger LE, Svedberg CA. 1967. Normal erythrocyte sedimentation rate and age. Br
Med J 2:85-87.

Braunwald E, Isselbacher KJ, Petersdorf RG, Wilson JD, Marti JB, Fauci AS. 2001.
Harrison’s Principles of Internal Medicine. McGraw-Hill.

Braxton M, Kammerer CM, Blangero J, Mahaney M, Rainwater DL, Dyke B, Hixson
J, Henkel R, Sharp M, Comuzzie A et al. 1996. Prevention of cardiovascular
disease: Genetic and environmental contributions to cardiovascular risk factors
in Mexican Americans: The San Antonio Family Heart Study. Circulation
94:2159-2170.

Brenner AV, Butler MA, Wang SS, Ruder AM, Rothman N, Schulte PA, Chanock SJ,
Fine HA, Linet MS, Inskip PD. 2007. Single-nucleotide polymorphisms in
selected cytokine genes and risk of adult glioma. Carcinogenesis 28:2543-2547.

Brull D, Serrano N, Zito F, Jones L, Montgomery HE, Rumley A, Sharma P, Lowe GD,
World MJ, Humphries SE, Hingorani AD. 2003. Human CRP gene polymorphism
influences CRP levels: implications for the prediction and pathogenesis of
coronary heart disease. Arterioscler Thromb Vasc Biol 23:2063-2069.

Byron EM. 2003. Market integration and health: The impact of markets and
acculturation on the self-perceived morbidity, diet, and nutritional status of the
Tsimane’ Amerindians of lowland Bolivia. University of Florida Dissertaion.

Cardon LR, Abecasis GR. 2003. Using haplotype blocks to map human complex trait loci.
Trends in Genetics 19:135-140.

Carlson CS, Aldred SF, Lee PK, Tracy RP, Schwartz SM, Rieder M, Liu K, Williams D,
Iribarren C, Lewis EC, Fornage M, Boerwinkle E, Gross M, Jawuish C,
Nickerson DA, Myers RM, Siscovick DS, Reiner AP. 2005. Polymorphisms with
the C-reactive protein promoter region are associated with plasma CRP levels.
Am J Hum Genet 77:64-77.

Carroll MD, Lacher DA, Sorlie PD, Cleeman JI, Gordon DJ, Wolz M, Grundy SM,
Johnson CL. 2005. Trends in serum lipids and lipoproteins of adults, 1960-
2002. JAMA 294:1773-1781.

129
Carvalho JJ, Baruzzi RG, Howard PF, Poulter N, Alpers MP, Franco LJ, Marcopito LF,
Spooner VJ, Dyer AR, Elliott P. 1989. Blood pressure in four remote populations
in the INTERSALT Study. Hypertension 14:238-246.

Carvalho JJ, Lima JAC, Carvalho JV, Souza CAM. 1985. Blood pressure is directly
related to the degree of acculturation among primitive Yanomamo Indians.
Circulation 72:296.

Casiglia E, Spolaore P, Mormino P, Maschio O, Colangeli G, Celegon L, Maggiolo G,
Tittoto L, Pasinato A, Ambrosio GB. 1991. The CASTEL project (Cardiovascular
Study in the Elderly): protocol, study design, and preliminary results of the initial
survey. Cardiologica 36:569-576.

Caspar-Bauguil S, Tcaczuk J, Haure MJ, Durand M, Alcouffe J, Thomsen M,
Salvayre R, Benosit H. 1999. Mildly oxidized low density lipoproteins
decrease early production of interleukin-2 and nuclear factor kB binding to
DNA in activated T-lymphocytes. Biochem J 337:269-274.

Cassel J. 1975. Studies of hypertension in migrants. In The Epidemiology and Control of
Hypertension (Edited by Paul O.), pp. 41-61. Stratton Intercontinental Medical
Book Corp., New York.

Centers for Disease Control and Prevention. 1998. Pediatric nutrition surveillance,
1997 full report. Atlanta: U.S. Department of Health and Human Services,
Centers for Disease Control and Prevention.

Centers for Disease Control. 2004. NHANES III and IV Documentation. Available at:
http://www.cdc.gov/nchs/nhanes.htm. Accesssed on April 25, 2007.

Charlesworth B. 1994. Evolution in Age-Structured Populations. Cambridge: Cambridge
University Press.

Charlesworth B. 1996. Evolution of senescence: Alzheimer’s disease and evolution. Curr
Biol 6:20-22.

Chen S, Averett NT, Manelli A, Ladu MJ, May W, Ard MD. 2005. Isoform-specific
effects of apolipoprotein E on secretion of inflammatory mediatros in adult rat
microglia. J Alzheimers Dis 7:25-35.

Chernela JM, Thatcher WE. 1989. Comparison of parasite burdens in two native
Amazonian populations. Med Anthropol 10:279-285.

130
Chiappelli M, Tampieri C, Tumini E, Porcellini E, Caldarera CM, Nanni S, Branzi A, Lio
D, Caruso M, Hoffmann E, Caruso C, Licastro F. 2005. Interleukin-6 gene
polymorphism is an age-dependent risk factor for myocardial infarction in men. In
J Immunogenet 32:349-353.

Cirillo M, Laurenzi M, Trevisan M, Stamler J. 1992. Hematocrit, blood pressure, and
hypertension. The Gubbio Population Study. Hypertension 20:319-326.

Clarke R, Frost C, Collins R, Appleby R, Peto R. 1997. Dietary lipids and blood
cholesterol: quanititative meta-analysis of metabolic ward studies. BMJ
11:112-117.

Collins FS, Guyer MS, Charkravarti A. 1997. Variations on a theme: cataloging human
DNA sequence variation. Science 278:1580-1581.

Colton CA, Needham LK,W, Brown C, Cook D, Rasheed K, Burke JR, Strittmatter WJ,
Schmechel DE, Vitek MP. 2004. APOE genotype-specific differences in human
and mouse macrophage nitric oxide production. J Neuroimmunol 147:62-67.

Cookson JB, Blake GTW, Faranisic C. 1976. Normal values for ventillatory function in
Rhodesian Africans. Br J Dis Chest 70:107-111.

Connor WE, Cerqeuira MT, Connor RW, Wallace RB, Malinow R, Casdorph HR. 1978.
The plasma lipids, lipoproteins, and diet of the Tarahumara Indians of Mexico.
Am J Clin Nutr 31:1131-1142.

Constans J, Pellegrin JL, Peuchant E, Dumon MF, Pellegrin I, Sergeant C, Simonoff
M, Brossard G, Barbeau P, Fleury H, et al. 1994. Eur J Clin Invest 24:416- 420.

Cooper R, Rotimi C, Ataman S, McGee D, Osotimehin B, Kadiri S, et al. 1997. The
prevalence of hypertension in seven populations of West African origin. Am J
Pub Health 87:160-168.

Corbo RM, Scacchi R. 1999. Apolipoprotein E (APOE) allele distribution in the world. Is
APOE4 a ‘thrifty’ allele? Ann Hum Genet 63:301-310.

Corder EH, Lannfelt L, Viitanen M, Corder LS, Manton KG, Winblad B, Basun H. 1996.
Apolipoprotein E genotype determines survival in the oldest old (85 years or
older) who have good cognition. Arch Neurol 53:418-422.

Cotes JE, Russiter CE, Higgins ITT, Gilson JC. 1966. Average normal values for the
forced expiratory volume in white Caucasian males. Br Med J 1:1016-1019.

131
Crawford DC, Sanders CL, Qin X, Smith JD, Shephard C, Wong M, Witrak L, Rieder MJ,
Nickerson DA. 2006. Genetic variation is associated with C-reactive protein
levels in the Third National Health and Nutrition Examination Survey. Circulation
114:2458-2465.

Crimmins EM, Finch CE. 2005. Infection, inflammation, height, and longevity.
Proceedings of the National Academy of Sciences 103:498-503.

Crimmins EM, Vasunilashorn S, Kim J, Alley DE. 2008a. Biomarkers of aging in human
populations. Advances in Clinical Chemistry 46:161-216.

Crimmins EM, Vasunilashorn S, Kim JK, Hagedorn A, Saito Y. 2008b. A comparison
of biological risk factors in two populations: The United States and Japan. Popul
Dev Rev 34:457-482.

Crimmins EM, Finch CE. 2006a. Commentary: do older men and women gain equally
from improving childhood conditions? Int J Epidemiol 35:1270-1271.

Crimmins EM, Finch CE. 2006b. Infection, inflammation, height, and longevity. Proc
Natl Acad Sci U S A 103:498-495.

Cruickshank JK, Beevers DG. 1985. Ethnic and geographic differences in blood pressure.
Handbook of Hypertension, Vol. 6: Epidemiology of Hypertension. Elsevier
Science. Pp.70-88.

Dahlen G, Ericson C, de Faire U, Iselius L, Lundman T. 1983. Genetic and
environmental determinants of cholesterol and HDL-cholesterol concentrations
in blood. Int J Epidemiol 12:32-5.

Das M, Pal S, Ghosh A. 2008. Apolipoprotein E gene polymorphism and dyslipidaemia
in adult Asian Indians: A population based study from Calcutta, India. Indian J
Hum Genet 14:87-91.

de Carli CMD, Miller BLMD, Swan GEP, Reed TP, Wolf PAMD, et al. 2001.
Cerebrovascular and brain morphologic correlates of mild cognitive impairment
in the National Heart, Lung, and Blood Institute Twin Study. Archives of
Neurology 58:643-647.

de Craen AJM, Posthuma D, Remarque EJ, van den Biggelaar AHJ, Westendorp RGJ,
Boomsma DI. 2005. Heritability estimates of innate immunity: an extended twin
study. Genes & Immuinty 6:167-170.

132
Demarchi DA, Salzano FM, Altuna ME, Fiegenbaum M, Hill K, Hurtado AM, Tsunetto
LT, Petzl-Erler ML, Hutz MH. 2005. APOE polymorphism distribution among
Native Americans and related populations. Ann Hum Biol 32:351-365.

DiaMed EuroGen. 2007. IgE Microtiterstrip ELISA kit. Turnhour, Belgium.

DiGiovanna AG. 2000. Human Aging: Biological Perspectives. Columbus, OH:
McGraw-Hill.

Doenhoff MJ, Stanley RG, Griffiths K, Jackson CL. 2002. An anti-atherogenic effect
of Schistoma mansoni infectios in mice associated with a parasite-induced
lowering of blood total cholesterol. Parasitology 125:415-421.

Donnison CP. 1929. Blood pressure in the African native. Lancet 1:6-7.

Drabe N, Zund G, Grunenfelder J, Sprenger M, Hoerstrup SP, Bestmann I, Maly FE,
Turina M. 2001.Genetic predisposition in patients undergoing cardiopulmonary
bypass surgery is associated with an increase of inflammatory cytokines. Eur J
Cardio-Thoracic Surgery 20:609-613.

Drenos F, Kirkwod TBL. 2010. Selection on alleles affecting human longevity and late-
life disease: The example of Apolipoprotein E. PLoS One 5:10022.

Drenos F, Westendorp RGJ, Kirkwood TBL. 2006 Trade-off mediated effects on the
genetics of human survival caused by increasingly benign living conditions
Biogeront 7:287-295.

Dressler WW. 1999. Modernization, stress, and blood pressure: New directions in
research. Hum Biol 71:583-605.

Drizd T, Dannenberg AL, Engel A. 1986. Blood pressure levels in persons 18-74 years of
age in 1976-80, and trends in blood pressure from 1960 to 1980 in the United
States. Vital Health Stats 234:1-68.

Dufetel P, Pigearias B, Lonsdorfer J, Derossi G, Diaine C, Paltot PJ. 1989. Spirometric
reference values in Senegalese black adults. Eu Respir J 2:352-358.

Dupuis J, Larson MG, Vasan RS, Massaro JM, Wilson PW, Lipinska I, Corey D, Vita JA,
Keaney JF Jr, Benjamin EJ. 2005. Genome scan of systemic biomarkers of
vascular inflammation in the Framingham Heart Study: evidence for susceptibility
loci on 1q. Atherosclerosis. 182:307-314.

133
Eaton SB, Konner M, Shostak M. 1988. Stone agers in the fast lane: Chronic
degenerative diseases in evoluntionary perspective. Am J Med 84:739-749.

Edberg JC, Wu J, Langefeld CD, Brown EE, Marion MC, McGwin G Jr, Petri M,
Ramsey-Goldman R, Reveille JF, Frank SG, Kaufman KM, Harley JB, Alarcon
GS, Kimberly RP. 2008. Genetic variation in the CRP promoter: Association with
systemic lupus erythematosus. Hum Mol Genet 17:1147-1155.

Epstein FH, Eckoff RD. 1967. The epidemiology of high blood pressure: geographic
distribution and etiologic factors. In The Epidemiology of Hypertension (Edited
by Stamler J. et al.). Grune & Stratton, New York.

Eichner JE, Dunn ST, Perveen G, Thompson DM, Stewart KE, Stroehla BC. 2002.
Apolipoprotein E polymorphism and cardiovascular disease: A HuGE Review.
Am J Epidemiol 155:487-495.

Eiriksdottir G, Smith AV, Aspelund T, Hafsteinsdottir SH, Olafsdottir E, Launer LJ,
Harris TB, Gudnason V. 2009. The interaction of adiposity with the CRP gene
affects CRP levels: Age, Gene/Environment Susceptibility-Reykjavik Study. Int J
Obesity 33:267-272.

Epstein M. 1996. Aging and the kidney. J Am Soc Nephrol 7:1106-1122.

Erickson RV, Hoang GN. 1980. Health problems among Indochinese refugees. Am J
Public Health 70:1003-1006.

Erkeller-Yusell FM, Deneys V, Yuksel B, Hannet I, Hulstaert F, Hamilton C, Mackinnon
H, Stokes LT, Munhyeshuli V, Vanlangendonck F, De Bruyère M, Bach BA,
Lydyard PM. 1992. Age-related changes in human blood lymphocyte
subpopulations. J Pediatr 120:216-222.

Erlinger TP, Platz EA, Rifai N, Helzlsouer KJ. 2004. C-reactive protein and the risk of
incident colorectal cancer. JAMA 291:585-590.

Esteve E, Ricart W, Fernández-Real JM. 2005. Dyslipidemia and inflammation: an
evolutionary conserved mechanism. Clin Nutr 24:16-31.

Ewbank DC. 2004. The APOE gene and differences in life expectancy in Europe. J
Gerontol A Biol Sci Med Sci 59:16-20.

Ewbank DC. 2007. Differences in the association between apolipoprotein E genotype and
mortality across populations. J Gerontol Biol Sc Med Sci 62:899-907.

134
Expert Panel on Detection Evaluation, and Treatment of High Blood Cholesterol in
Adults. 2001. Executive summary of the Third Report of The National
Cholesterol Education Program (NCEP) Expert Panel on Detection,
Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult
Treatment Panel III). JAMA 285:2486-2497.

Fabris C. Toniutto P, Bitetto D, Minisini R, Smirne C, Caldato M, Pirisi M. 2005. Low
fibrosis progression of recurrent hepatitis C in apolipoprotein E epsilon4 carriers:
Relationship with the blood lipid profile. Liver Int 25:1128-1135.

Ferrari SL, Anh-Luoang L, Garnero P, Humphries SE, Greenspan SL. 2003. Two
promoter polymorphisms regulating interleukin-6 gene expression are associated
with circulating levels of C-reactive protein and markers of bone resorption in
postmenopausal women. J Clin Endocrinol Metab 88:255-259.

Ferrari AU, Radaelli A, Centola M. 2003. Invited Review: Aging and the cardiovascular
system. J Appl Physiol 95:2591-2597.

Ferrucci L, Coris A, Lauretani F, Bandinelli S, Bartali B, Taub DD, Guralnik JM, Longo
DL. 2005. The origins of age-related proinflammatory state. Blood 105:2294-
2299.

Finch CE. 1996. Biological bases for plasticity during aging of individual life histories. In
D Magnusson (Eds.), The Lifespan Development of Individuals: Biological and
Psychosocial Perspectives, a Synthesis (pp. 488-512). Cambridge: Cambridge
University Press.

Finch CE. 2007. The Biology of Human Longevity. Burlington, MA: Elsevier.

Finch CE. 2010. Inflammation in aging processes: an integrative and ecological
perspective. In: Handbook of Biology of Aging (eds.), E. Masoro & S. Austad.
San Diego: Academic Press.

Finch CE, Crimmins. 2004. Inflammatory exposure and historical changes in human life-
spans. Science 305:1736-1739.

Finch CE, Morgan TE. 2007. Systemic inflammation, infection, apoE alleles, and
Alzheimer Disease: A position paper. Curr Alz Res 4:185-189.

Finch CE, Pike MC, Whitten M. 1990. Slow mortality rate accelerations during aging in
some animals approximate that of humans. Science 249:902-905.

135
Finch CE, Rose MR. 1995. Hormones and the physiological architecture of life history
evolution. Q Rev Biol 70:1-52.

Finch CE, Stanford CB. 2004. Meat-adaptive genes and the evolution of slower aging in
humans. Q Rev Biol 79:3-50.

Fleming-Moran M, Coimbra CEA Jr. 1990. Blood pressure studies among Amazonian
native populations: A review from an epidemiological perspective. Soc Sci Med
31:593-601.

Flint J, Harding RM, Boyce AJ, Clegg JB. 1998. The population genetics of the
haemoglobinopathies. Baillieres Clin Haematol 11:1-51.

Fogel RW. 2005. Changes in the Physiology of Aging During the Twentieth
Century. NBER Working Paper No. W11233.

Fogel RW, Costa D. 1997. A theory of technophysio evolution,with some implications
for forecasting population, health care costs, and pension costs. Demography
34:49-66.

Folcik VA, Aamir R, Cathcart MK. 1997. Cytokine modulation of LDL oxidation by
activated human monocytes. Arterioscl Thromb Vasc Biol 17:1954-1961.

Franceschi C, Bonafe M, Valensin S, Olivieri F, De Luca M, Ottaviani E, De Benedictis
G. 2000. Inflamm-aging. An evolutionary perspective on immunosenescence.
Annals NY Acad Sci 908:244-254.

Francheschi C, Olivieri F, Marchegiani F, Cardelli M, Cavallone L, Capri M, Salvioli S,
Valensin S, De Bendicitis G, Di Iorio A, Caruso C, Paolisso G, Monti D. 2005.
Genes involved in immune response/inflammation, IG1/insulin pathway and
response to oxidative stress play a major role in the genetics of human longevity:
the lesson of centenarians. Mech Ageing Develop 126:351-361.

Franco LJ. 1981. Aspectos Metabólicos da População Indígena do Alto Xingú (Brasil
Central). Doctoral dissertation. Escola Paulista de Medicina, São Paulo.

Franklin SS, Gustin W 4th, Wong ND, Larson MG, Weber MA, Kannel WB, Levy D.
1997. Hemodynamic patterns of age-related changes in blood pressure: the
Framingham Heart Study. Circulation 96:308-315.

Fredrickson DS, Levy RI. 1972. Familial hyperlipoporteinemia. In: The Metabolic Basis
of Inherited Disease (3
rd
ed.), edited by J.B. Standbury, JB Wyngaarden, DS
Fredrickson. New York: McGraw-Hill Book Co. pp. 545-614.

136
Freis ED. 1976. Salt, volume and prevention of hypertension. Circulation 53:589-595.

Friedman GD, Klatsky AL, Siegelaub AB. 1974. The leukocyte count as a predictor
of myocardial infarction. N Engl J Med 290:1275-1278.

Gahutu JB, Wane J. A Rwandan spirometry and resting ventilation study. African J
Biomed Res 2006;9:137-140.

Gajra B, Candlish JK, Sha N, Mak JW, Tay JSH. 1994. Effect of apolipoprotein E
variants on plasma lipids and apolipoproteins in the Orang Asli (‘Aborigines’) of
Malaysia. Hum Hered 44:209-213.

Galbraith D. 1967. Studies of aborigines at Aurukun and Weipa missions. Clinical
findings on children and adolescents, erythrocyte sedimentation rates and
antistretolysin titres. J Paediatr Child Health 3:206-212.

Gauderman WJ, Witte JS, Thomas DC. 1999. 1999. Family-based association studies. J
Nat Cancer Institution Monogram 26:31-37.

Gerdes LU, Klausen IC, Sihm I, Faergeman O. 1992. Apolipoprotein E polymorphism in
a Danish population compared to findings in 45 other study populations around
the world. Genet Epidemiol 9:155-167.

Gerdes LU, Gerdes C, Hansen PS, Klausen IC, Faergaeman O, Dyerberg J. 1996. The
apolipoprotein E polymorphism in Greenland Inuit in its global perspective. Hum
Genet 98:546-550.

Gerdes LU, Jeune B, Ranberg KA, Nybo H, Vaupel JW. 2000. Estimation of
apolipoprotein E genotype-specific relative mortality risks from the distribution of
genotypes in centenarians and middle-aged men: Apolipoprotein E genes is a
“frailty gene,” not a “longevity gene,” Genetic Epidemiology 19:202-210.

Gillum RF. 1992. Epidemiology of resting pulse rate of persons age 25-74 – data from
NHANES 1974-1974. Pub Health Rep107:193-201.

Gillum RF, Mussolino ME, Makuc DM. 1994. Erythrocyte sedimentation rate and
coronary heart disease: The NHANES I epidemiologic follow-up study. J Clin
Epidemiol 48:353-361.

Godoy R, Nyberg C, Eisenberg DT, Magvanjav O, Shinnar E, Leonard WR, Gravlee
C, Reyes-García V, McDade TW, Huanca T, Tanner S, Bolivian TAPS Study
Team. 2009. Short but catching up: Statural growth among native Amazonian
Bolivian children. Am J Hum Biol 22:336-347.

137
Goldstein DB. 2009. Common genetic variation and human traits. N Engl J Med
360:1696-1698.

Grunenfelder J, Umbehr M, Plass A, Bestmann L, Maly FE, Zèund G, Turina M. 2004.
Genetic polymorphisms of apolipoprotein E4 and tumor necrosis factor beta as
predisposing factors for increased inflammatory cytokines after cardiopulmonary
bypass. J Thorac Cardiovasc Surgery 128:92-97.

Grunfeld C, Pang M, Doerrler W, Sigenaga, JK, Jensen P, Feingold KR. 1992. Lipids,
lipoproteins, triglyceride clearance, and cytokines in Human
Immunodeficiency Virus infection and the Acquired Immunodeficiency
Syndrome. J Clin Endocrinol Metab 74:1045-1052.

Guo L, LaDu MJ, Van Eldik LJ. 2004. A dual role for apolipoprotein E in
neuroinflammation: Anti- and pro-inflammatory activity. J Mol Neurosci 23:205-
212.

Gurven M, Kaplan H, Supa AZ. 2007. Mortality experience of Tsimane Amerindians
of Bolivia: Regional variation and temporal trends. Am J Hum Biol 19:376-
398.

Gurven M, Kaplan H, Winking J, Finch C, Crimmins EM. 2008. Aging and
inflammation in two epidemiological worlds. Journal of Gerontology: Medical
Sciences 63:196-199.

Gurven M, Kaplan H, Winking J, Eid Rodriguez, Vasunilashorn S, Kim JK, Finch C,
Crimmins E. 2009. Inflammation and infection do not promote arterial aging and
cardiovascular disease risk factors among lean horticulturalists. PLos One
11:6590.

Halldórsson BV, Istrail S, De La Vega FM. 2004. Optimal selection of SNP markers for
disease association studies. Hum Hered 58:190-202.

Hamilton M, Pickering GW, Roberts JAF, Sowry GSC. 1954. The aetiology of
essential hypertension. I. The arterial pressure in the general population. Clin
Sc 13, 11-35.

Hamilton WD. 1966. The moudling of senescence by natural selection. Journal of
Theoretical Biology 12:12-45.

Hankinson JL, Odencrantz JR, Fedan KB. 1999. Spirometric reference values from a
sample of the general U.S. population. Am J Respir Crit Care Med 1999;159:179-
187.

138
HapMap website. Accessed on April 15, 2010:
http://hapmap.ncbi.nlm.nih.gov/cgi-perl/gbrowse/hapmap27_B36/#search

Hardy J, Singleton A. 2009. Genomewide association studies and human disease. N Engl
J Med 360:1759-1768.

Harpending H, Cochran G. 2006. Genetic diversity and genetic burden in humans.
Infection, Genetics, and Evolution 6:154-162.

Harris T, Ferrucci L, Tracy RP, Corti MC, Wacholder S, Ettinger WH Jr, et al. 1999.
Associations of elevated interleukin-6 and C-reactive protein levels with mortality
in the elderly. Am J Med 106:506-512.

Hegele RA, Connelly PW, Hanley AJF, Sun F, Harris SB, Zinman B. 1997. Common
genomic variants associated with variation in plasma lipoprotein in aboriginal
Canadians. Arterioscler Thromb Vasc Biol 17:1060-1066.

Heller DA, de Faire, Pedersen NL, Dahlen G, McClearn GE. 1993. Genetic and
environmental influences on serum lipid levels in twins. N Engl J Med 328:
1150-1156.

Henry JP, Cassel JC. 1969. Psychosocial factors in essential hypertension—Recent
epidemiologic and animal experimental evidence. Am J Epid 90:171-2000.

Hindorff LA, Rice KM, Lange LA, Diehr P, Halder I, Walston J, Kwok P, Ziv E,
Nievergelt C, Cummings SR, Newman AB, Tracy RP, Psaty BM, Reiner AP.
2008. Common variants in the CRP gene in relation to longevity and cause-
specific mortality in older adults: the Cardiovascular Health Study.
Atherosclerosis 197:922-930.

Horne BD, Anderson JL, John JM, Weaver A, Bair TL, Jensen KR, Renlund DG,
Muhlestein JB, Intermountain heart Collaborative (IHC) Study Group. 2005.
Which white blood cell subtypes predict increased cardiovascular risk? J Am Coll
Cardiol 45:1638-1643.

Hsu C-Y, McCulloch CE, Curhan GC. 2002. Epedemiology of anemia associated
with chronic renal insufficiency among adults in the United States: Results
from the Third National Health and Nutrition Examination Survey. J Am Soc
Nephrol 13:504-510.

Huang H-Y, Thuita L, Strickland P, Hoffman SC, Comstock GW, Helzlsouer K. 2007.
Frequencies of single nucleotide polymorphisms in genes regulating inflammatory
responses in a community-based population. BMC Genetics 8:7.

139
Huang YD. 2006. Apolipoprotein E and Alzheimer Disease. Neurology 66:79-85.

Ingelsson E, Ärnlöv J, Sundström J, Lind L. 2005. Inflammation, as measured by
erythrocyte sedimentation rate, is an independent predictor for the development
of heart failure. J Am Coll Cardiol 45:1802-1806.

Inouye M, Mio T, Sumino K. 1999. Glycated hemoglobin and lipid peroxidation in
erythrocytes of diabetic patients. Metabolism 48:205-209.

International Still’s Disease Foundation website. Accessed on January 21, 2008:
http://www.stillsdisease.org/lab_tests/cbc

Izzo JL Jr, Levy D, Black HR. 2000. Clinical advisory statement: importance of systolic
blood pressure in older Americans. Hypertension 35:1021-1024.

Jacobs DR, Jr, Hunninghake DB, Dempsy ME, Taylor HL, Kuba K, Luepker RV,
Dawson EA, Frantz ID, Jr, Hannan P. 1980. Blood lipids and lipoproteins in a
Minnesota urban population. J Chron Dis 33:395-406.

Jacobs DR, Jr, Nelson ET, Donats AS, Keller J, Slattery ML, Higgins M. 1992. Are race
and sex differences in lung function explained by frame? The CARDIA Study.
Am Rev Respir Dis 146:644-649.

Jahangiri A, de Beer MC, Noffsinger V, Tannock LR, Ramaiah C, Webb NR, van der
Westhuyzen DR, de Beer FC. 2009. HDL remodeling during the acute phase
response. Arterioscler Throm Vasc Biol 29:261-267.

James GD, Baker PT. 1995. Human population biology and blood pressure:
evolutionary and ecological considerations and interpretations of population
studies. In JH Laragh, BM Brenner (Eds.) Hypertension: Pathyophysiology,
Diagnostics and Management, pp. 115-126. New York: Raven Press.

Johannesen ZM, Erasmus LD. Spirometry in normal Bantu. 1968. Am Rev Respir Dis
97:585-597.

Jones CA, McQuillan GM, Kusek JW, Eberhardt MS, Herman WH, Coresh J, Salive M,
Jones CP, Agodoa LY. 1998. Serum creatinine levels in the U.S. population:
Third National Health and Nutrition Examination Survey. Am J Kid Dis 32:992-
999.

140
Kamboh MI, Aston CE, Ferrell RE, Hamman RF. 1993. Impact of apolipoprotein E
polymorphism in determining interindividual variation in total cholesterol and
low density lipoprotein cholesterol in Hispancis and non-Hispanic whites.
Atherosclerosis 98:201-211.

Kamboh MI, Sepehrnia B, Ferrell RE. 1989. Genetic studies of human apolipoproteins.
VI. Common polymorphism of apolipoprotein E in blacks. Dis Markers 7:49-55.

Kamboh MI, Bhatia KK, Ferrell RE. 1990. Genetic of human apolipoproteins: XII.
Population genetics of apolipoproteins in Papua New Guinea. Am J Hum Bio
2:17-23.

Kamboh MI, Serjeantson SW, Ferrell RE. 1991a. Genetic studies of human
apolipoproteins. XVIII. Apolipoprotein polymorphisms in Australian Aborgines.
Hum Biol 63:179-186.

Kamboh MI, Weiss KM, Ferrell RE. 1991b. Genetic studies of human apolipoproteins.
XVI. APOE polymorphism and cholesterol levels in the Mayans of the Ycatan
peninsula, Mexico. Clin Genet 39:26-32.

Kaminer B, Lutz NPW. 1960. Blood pressure in Bushmen of the Kalahari Dessert.
Circulation 22, 289-295.

Kampmann J, Siersbaek-Nielsen K, Kristensen M, Mølholm Hansen J. 1974. Rapid
evaluation of creatinine clearance. Acta Medica Scandinavica 196:517-520.

Kannel WB, Vasan RS, Keyes MJ, Sullivan LM, Robins SJ. 2008. Usefulness of
triglyceride-high-density lipoprotein versus the cholesterol-high-density
lipoprotein ratio for predicting insulin resistance and cariometabolic risk (from
the Framingham Offspring Cohort). Am J Cardiol 101:497-501.

Kaplan JE, Larrick JW, Yost J, Farrell L, Greenberg HB, Herrmann KL, Sulzer AJ,
Walls KW, Pederson L. 1980. Infectious disease patterns in the Waorani, an
isolated Amerindian population. Am J Trop Med Hyg 29:298-312.

Kaplan NB. 1982. Clinical hypertension, 3
rd
ed. Williams & Wilkins, Baltimore, MD.

Kastarinen MJ, Nissinen AM, Vartiainen EA, Jousilahti PJ, Korhonen HJ, Puska PM.
Tuomilehto JO. 2000. Blood pressure levels and obesity trends in hypertensive
and normotensive Finnish population from 1982 to 1997. J Hypertens 18:255-
262.

141
Kathiresan S, Larson MG, Vasan RS, Guo C-Y, Gona P, Keaney JF, Jr, Wilson PWF,
Newton-Cheh C, Musone SL, Camargo AL, Drake JA, Levy D, O’Donnell CJ,
Hirschhorn JN, Benjamin EJ. 2006. Contribution of clinical correlates and 13 C-
reactive protein gene polymorphisms to interindividual variability in serum C-
reactive protein level. Circulation 116:1415-1423.

Kean BH. 1944. The blood pressure of Cuna Indians. Am J Trop Med 24, 341-343.

Kerstjens HAM, Rijcken B, Schouten JP, Postma DS. 1997. Decline of FEV1 by age and
smoking status: facts, figures, and fallacies. Thorax 52:820-827.

Keys A, Fidanza F, Scardi V, Bergami G. 1952. The trend of serum-cholesterol levels
with age. Lancet 2:209-210.

Keys A, Mickelsen O, Miller EO, Hayes ER, Todd RL. 1950. The concentration of
cholesterol in the blood serum of normal man and its relation to age. J Clin
Investigation 29:1347-1353.

Khor CC, Vannberg FO, Chapman SJ, Guo H, Wong SH, Walley AJ, et al. 2010. CISH
and susceptibility to infectious diseases. N Engl J Med in press.

Khovidhunkit W, Kim M.-S., Mmon RA, Shigenaga JK, Moser AH, Feingold KR,
Grunfeld C. 2004. Effects of infection and inflammation on lipid and
lipoprotein metabolism: mechanisms and consequences to the host. J Lipid
Res 45:1169-1196.

Khovidhunkit W, Shigenaga JK, Moser AH, Feingold KR, Grunfeld C. 2001.
Cholesterol efflux by acute-phase high density lipoprotein: role of
lecithin:cholesterol acyltransferase. J Lipid Res 42:967-975.

Kikuya M, Hozawa A, Ohokubo T, Tsuji I, Michimata M, Matsubara M, Ota M, Nagai K,
Araki T, Satoh H, Ito S, Hisamichi S, Imai Y. 2000. Prognostic significance of
blood pressure and heart rate variabilities: The Ohasama Study. Hypertension
36:901-906.

Kitchens RL, Thompson PA. 2003. Impact of sepsis-induced changes in plasma on
LPS interactions with monocytes and plasma lipoproteins: roles of coluble
CD14, LBP, and acute phase lipoproteins. J Endotoxin Res 9:113-118.

Knowler WC, Bennett PH, Hamman RF, Miller M. 1978. Diabetes incidence and
prevalence in Pima Indians: a 19-fold greater incidence than in Rochester,
Minnesota. Am J Epidemiol 108:497-505.

142
Kovacs A, Green F, Hansson LO, Lundman P, Samnegard A, Boquist S, Ericsson CG,
Watkins H, Hamsten A, Tornvall P. 2005. A novel common single nucleotide
polymorphism in the promoter region of the C-reactive protein gene associated
with the plasma concentration of C-reactive protein. Atherosclerosis 178:193-198.

Kuningas M, May L, Tamm R, van Bodegom D, van den Biggelaar AHJ, Meij JJ, Frölich
M, Ziem JB, Suchiman HED, Metspalu A, Slagboom PE, Westendorp RGJ.
2009. Selection for genetic variation inducing pro-inflammatory response under
adverse environmental conditions in a Ghanaian population. PLoS One 4:7795.

Kurylowicz W, Kopczynski J. 1986. Diseases of civilization, today and tomorrow.
MIRCEN J 2, 253-265.

Lahoz C, Schaefer EJ, Cupples LA, Wilson PWF, Levy D, Osgood D, Parpos S, Pedro-
Botet J, Daly JA, Ordovas JM. 2001. Apolipoprotein E genotype and
cardiovascular disease in the Framingham Heart Study. Atherosclerosis 154:529-
537.

Lakatta EG, Levy D. 2003. Arterial and cardiac aging: Major shareholders in
cardiovascular disease enterprises: Part I: Aging arterieies: A “set up” for vascular
disease. Ciruculation 107:139-146.

Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al. 2001.
International Human Genome Sequencing Consortium. Initial sequencing and
analysis of the human genome. Nature 409:448-450.

Lange LA, Carlson CS, Hindorff LA, Lange EM, Walston J, Durda JP, Cushman M, Bis
JC, Zeng D, Lin D, Kuller LH, Nickerson DA, Psaty BM, Tracy RP, Reiner AP.
2006. Association of polymorphisms in the CRP gene with circulating C-reactive
protein levels and cardiovascular events. JAMA 296:2703-2711.

Langholz B, Thomas DC, Witte J, Peters RK. 1995. Re: A population-based case-cohort
evaluation of the efficacy of mammographic screening for breast cancer. Am J
Epidemiol 143:448-450.

Latkovskis G, Licis N, Kalnis U. 2004. C-reactive protein levels and common
polymorphisms of the interleukin-1 gene cluster and interleukin-6 gene in patients
with coronary heart disease. Eur J Immunogenet 31:207-213.

Lehtinen S, Luoma P, Lehtimäki T, Näyhä S, Hassi J, Nikkari T. 1994. Differences in
genetic variation of apolipoprotein E in Lapps and Finns (abstract).
Atherosclerosis 109:263.

143
Lengani A, Laville M, Serme D, Fauvel JP, Ouandaogo BJ. Zech P. 1994. Insuffisance
rénale au cours de l’hypertension artérielle en Afrique Noire. Press Med 23:788-
792.

Levels JH, Pajkrt D, Schultz M, et al. 2007. Alterations in lipoprotein homeostasis
during human experimental endotoxemia and clinical sepsis. Biochim Biophys
Acta 1771:1429-1438.

Lindeberg S, Ahren B, Nilsson A, Cordain L, Nilsson-Ehle P, Vessby B. 2003.
Determinants of serum triglycerides and high-density lipoprotein cholesterol
in traditional Trobriand Islanders: the Kitava Study. Scand J Clin Lab Invest
63:175-180.

Lindsay KM, Miller A, Aiello MO, Leonard WR, McDade TW. 2003. Variation in
hemoglobin levels and rates of anemia among the Tsimane’ of lowland Bolivia.
Am J Hum Biol 15:271.

Livak KJ. 2003. SNP genotyping by the 5 –nuclease reaction. Methods Mol Biol
212:129-147.

Lowenstein FW. 1961. Blood pressure in relation to age and sex in the tropics and
subtropics. Lancet 1:389-392.

Lymphomation website. Accessed on January 21, 2008:
http://www.lymphomation.org/tests-immunoglobulins.htm#IgA

Lynch JR, Tang W, Wang H, Vitek MP, Bennett ER, Sullivan PM, Warner DS,
Laskowitz DT. 2003.APOE genotype and an ApoE-mimetic peptide modify the
systemic and central nervous system inflammatory response. J Biol Chem
278:48529-48533.

Maddock I, Rovin L. 2005. A New Guinea population in which blood pressure appears to
fall as age advances. Papua New Guinea Medical Journal 48:122-126.

Maddocks I, Rovin L. 1965. A New Guinea population in which blood pressure appears
to fall as age advances. P N G Med J 8:17-21.

Magen E, Borkow G, Bentwich Z, Mishal J, Scharf S. 2005. Can worms defend our
hearts? Chronic helminthic infections may attenuate the development
ofcardiovascular diseases. Med Hypoth 64:904-909.

144
Mahaney MC, Blangero J, Comuzzie AG, VandeBerg JL, Stern MP, MacCluer JW.
1995. Plasma HDL cholesterol, triglycerides, and adiposity: A quantitative
genetic test of the Conjoint Trait Hypothesis in the San Antonio Family Heart
Study. Circulation 92:3240-3248.

Mahley RW. 1988. Apolipoprotein E: Cholesterol transport protein with expanding role
in cell biology. Science 240:622-630.

Mahley RW, Rall SC, Jr. 2000. Apolipoprotein E: Far more than a lipid transport protein.
Annu Rev Egnomics Hum Genet 1: 507-537.

Malik S, Wong ND, Franklin S, Pio J, Fairchild C, Chen R. 2005. Cardiovascular
disease in U.S. patients with metabolic syndrome, diabetes, and elevated C-
reactive protein. Diabetes Care 28:690-693.

Marin GB, Tavella MH, Guerreiro JF, Santos SEB, Zago MA. 1997. Absence of the E2
allele of apolipoprotein in Amerindians. Brazilian J Genetics 20:741-743.

Marmot MG. 1980. Affluence, urbanization and coronary heart disease. In Disease and
Urbanization, by Clegg EJ and Garlick JP, pp. 127-144. London: Taylor & France.

Martin G. 1999. APOE alleles and lipophylic pathogens. Neurobiol Aging 20:441-443.

Massberg S, Brand K, Grüner, Page S, Müller E, Müller I, Bergeier W, Richter T,
Lorenz M, Konrad I, Nieswandt B, Gawaz M. 2002. A critical role of platelet
adhesion in the initiation of atherosclerotic lesion formation. J Exp Med 196:887-
896.

Master AM, Garfield CI, Waters MB. 1952. Normal blood pressure and hypertension.
Philadelphia: Lea and Febiger.

Mattson FH, Erikson BA, Kligman AM. 1972. Effect of dietary cholesterol on serum
cholesterol in man. Am J Clin Nutr 25:589-594.

May L, van den Biggelaar AHJ, van Bodegom D, Meij HJ, de Craen AJM, Amankwa J,
Frölich M, Kuningas M, Westendorp RGJ. 2009. Adverse environmental
conditions influence age-related innate immune responsiveness. Immun Aging 6:7.

Mazzarolo-Cruz HM, Cruz J, Milon D Jr. 1995. Dyslipidaemias in white
normotensive subjects and in white patients treated for essential hypertension.
Rev Hosp Clin Fac Med Sao Paulo 50:326-329.

145
McClearn GE. Johansson B, Berg S, Pedersen NL, Ahern F, Petrill SA, Plomin R. 1997.
Substantial genetic influence on cognitive abilities in twins 80 or more years old.
Science 276:1560-1563.

McDade TW, Leonard WR, Burhop J, Reyes-Garciá, Vadez V, Huanca T, Godoy RA.
2005. Predictors of C-reactive protein in Tsimane’ 2 to 15 year-olds in
lowland Bolivia. Am J Phys Anthr 128:906-913.

McDade TW, Reyes-Garcia V, Blackinton P, Tanner S, Huanca T, Leonard WR. 2007.
Maternal ethnobotanical knowledge is associated with multiple measures of
child health in the Bolivian Amazon. Proc Natl Acad Sci U S A 104:6134- 6139.

McDade TW, Reyes-García V, Tanner S, Huanca T, Leonard WR. 2008. Maintenance
versus growth: investigating the costs of immune activation among children in
lowland Bolivia. Am J Phys Anthropol 136:478-484.

McDonald S, Maguire G, Duarte N, Wang XL, Hoy W. 2004. C-reactive protein,
cardiovascular risk, and renal disease in a remote Australian aboriginal
community. Clin Sci 106:121-128.

McGarvey ST, Baker PT. 1979. The effects of modernization and migration on Samoan
blood pressure. Hum Biol 51:467-479.

McGullicuddy FC, de la Llera Moy M, Hinkle CC, Joshi MR, Chinquoine EH,
Billheimer JT, Rothblat GH, Reilly MP. 2009. Inflammation impairs reverse
cholesterol transport in vivo. Circulation 119:1135-1145.

McKeown T. 1976. The modern rise of population. New York: Academy Press.

MedicineNet website. Accessed on January 21, 2008:
http://www.medterms.com/script/main/art.asp?articlekey=15737

Mendez J, Tejada C, Flores M. 1962. Serum lipid levels among rural Guatemalan Indians.
Am J Clin Nutr 10:403-409.

Mengesha YA, Mekonnem Y. 1985. Spirometric lung function tests in normal non-
smoking Ethiopian men and women. Thorax 40:465-468.

Meyer KC, Rosenthal NS, Soergel P, Peterson K. 1998. Neutrophils and low-grade
inflammation in seemingly normal aging human lung. Mech Age Develop
104:169-181.

146
Miranda RA, Xavier FB, Menezes RC. 1998. Parasitismo intestinal em uma aldeia
indígena Parakanã, sudeste do Estado do Pará, Brasil (Intestinal parasitism in a
Parakanã indigenous community in southwestern Pará state, Brasil). Cadernos
de Saúde Pública 14:507-511.

Mitchell HM, Li YY, Hu PJ, Liu Q, Chen M, Du GG, Wang ZJ, Lee A, Hazell SL. 1992.
Epidemiology of Helicobacter pylori in Southern China. Identification of early
childhood as the critical period for acquisition. J Infect Diseases 166:149-153.

Mjos OD, Thelle DS, Forde OH, Vik-Mo H. 1977. Family study of high density
lipoprotein cholesterol and the relation to age and sex. The Tromso Heart Study.
Acta Med Scand 201:323-329.

Morris JF, Koski A, Johnson LC. 1971. Spirometric standards for health non-smoking
adults. Am Rev Respir Dis 70:107-111.

Murril RJ. 1949. A blood pressure study of the natives of Ponape Island, Eastern
Carolines. Hum Biol 21, 47-59.

Mustafa KY.1977. Spirometric lung function tests in normal men and African ethnic
origin. Am Rev Respir Dis 116:209-213.

Neel JV, Salazano FM, Junqueira PC, Keiter F, Maybury-Lewis D. 1964. Studies of the
Xavante Indians of the Brazilian Mato Grosso. Am J Hum Genet 16:52-140.

Nelson RG, Bennett RH, Beck GJ, Tan M, Knowler WC, Mitch WE, Hirschman GH,
Myers BD, for the Diabetes Renal Disease Study Group. 1996. N Engl J Med
335:1636-1642.

Nichols WW, O’Rourke MF. 1998. McDonald’s Blood Flow in Arteries: Theoretical,
Experimental and Clinical Principles, 4
th
ed, pp. 347-376.
London/Sydney/Aukland: Arnold.

North KE, Howard BV, Welty TK, Best LG, Lee ET, Yeh JL, Fabsitz RR, Roman
MJ, MacCluer JW. 2003. Genetic and environmental contributions to
cardiovascular disease risk in American Indians: the strong heart family study.
Am J Epidemiol 157:303-314.

Nyholt DR, Yu C-E, Visscher PM. 2009. On Jim Watson’s APOE status: genetic
information is hard to hide. Eur J Hum Genetics 17:147-150.

147
O’Dea K, Patel M, Kubisch D, Hopper J, Traianedes K. 1993. Obesity, diabetes, and
hyperlipidemia in a central Australian aboriginal community with a long history
of acculturation. Diabetes Care 16:1004-1010.

O’Meara JG, Kardia SL, Armom JJ, Brown CA, Boerwinkle E, Turner ST. 2004. Ethnic
and sex differences in the prevalence, treatment, and control of dyslipidemia
among hypertensive adults in the GENOA Study. Arch Int Med 164:1313-1318.

O’Rouke M, Frohlich ED. 1999. Pulse pressure: is this a clinically useful risk factor?
Hypertension 34:372-374.

Oguntibeju OO. 2003. Parasitic infestation and anaemia: the prevalence in a rural hospital
setting. J Indian Acad Clin Med 4:210-212.

Oligati P, Politis A, Malitas P, Albani D, Dusi S, Polito L, De Mauro S, Zisaki A, Piperi
C, Samouli E, Mallis A, Batelli S, Forloni G, De Ronchi D, Kalofoutis A, Liappas
I, Serretti A. 2010. APOE epsilon-4 allele and cytokine production in Alzheimer’s
disease. Int J Geriatr Psychiatry 25:338-344.

Oliver WJ, Cohen EL, Neel JV. 1975. Blood pressure, sodium intake and sodium related
hormones in the Yanomamo Indians, a “no-salt” culture. Circulation 52:146-151.

Oliver WJ, Neel JV, Grekin RJ, Cohen EL. 1981. Hormonal adaptation to the stresses
imposed upon sodium balance by pregnancy and lactation in the Yanomamo
Indians, a culture without salt. Circulation 63:110-116.

Oria RB, Patrick PD, Zhang H, Lorntz B, de Castro Costa CM, Brito GA, Barrett LJ,
Lima AA, Guerrant RL. 2005. APOE4 protects the cognitive development in
children with heavy diarrhea burdens in Northeast Brazil. Pediatr Res 57:310-
316.

Ostfeld AM, D’Atri DA. 1977. Rapid sociocultural change and high blood pressure. Adv
Psychosom Med 9:20.

Page L. 1974. Hypertension and atherosclerosis in primitive and acculturating s
ocieties. In JC Hung (Ed.) International Symposium on Hypertension, pp. 1-
12. New York: Health Learning Systems.

Page L. 1976. Epidemiologic evidence on the etiology of human hypertension and its
possible prevention. Am Heart J 91:527-534.

Palanti P, Julius S. 1999. Relevance of heart rate as a risk factor in hypertension. Curr
Hypertens Rep 3:219-224.

148
Palomino-Morales R, Vasquez-Rodriguez TR, Miranda-Filloy JA, Martin J, Gonazlez-
Gay MA. 2009. C-reactive protein gene polymorphisms in biopsy-proven giant
cell arteritis from northwestern Spain. J Rhematol 36:341-346.

Pankow JS, Folsom AR, Cushman M, Borecki IB, Hopkins PN, Eckfeldt JH, Tracy RP.
2001. Familial and genetic determinants of systemic markers of inflammation: the
NHLBI family heart study. Atherosclerosis 154:681-689.

Panza F, Solfrizzi V, Colacicco A, Basile A, D’Introno A, Capurso C, Sabba M, Capurso
S, Capurso A. 2003. Apolipoprotein E (APOE) polymorphism influences serum
APOE levels in Alzheimer’s disease patients and centenarians. Neuroreport
14:605-608.

Papanicolaou DA, Wilder RL, Manolagas SC, Chrousos GP. 1998. The pathophysiologic
roles of interleukin-6 in human disease. Ann Intern Med 128:127-137.

Paper S. The effects of age in reducing renal function. Geriatrics 1973;28:83-97.

Patrick RC, Prior IM, Simth JC, et al. 1983. The relationship between blood pressure and
modernity among Panopeans. Int J Epidemiol 12:36-44.

Pauletto P, Puato M, Caroli MG, Casiglia E, Munhambo AE, Cazzolato G, Bon GB,
Angeli MT, Galli C, Pessina AC. 1996. Blood pressure and atherogenic
lipoprotein profiles of fish-diet and vegetarian villagers in Tanzania: the
Lugalawa study. Lancet 348:784-788.

Pavan L, Casiglia E, Pauletto P, Batista SL, Ginocchio G, Kwankam MM, Biasin R,
Mazza A, Puato M, Russo E, Pessina AC. 1997. Blood pressure, serum
cholesterol and nutritional state in Tanzania and in the Amazon: comparison with
an Italian population. J Hypertension 15:1083-1090.

Piva E, Sanzari MC, Servidio G, Plebani M. 2001. Length of sedimentation reaction
in undiluted blood (erythrocyte sedimentation rate): Variations with sex and
age and reference limits. Clin Chem Lab Med 39:451-454.

Poulter N, Khaw KT, Hopwood BEC, Mugambi M, Peart WS, Rose G, Sever PS. 1984.
Blood pressure and its correlates in an African tribe in urban and rural
environments. J Epidemiol Community Health 38:181-186.

Poulter NR, Sever PS. 1994. Blood pressure in other populations. In Textbook of
Hypertension, by Swales JD, pp. 22-45. Oxford: Blackwell Science Ltd.

149
Prior IAM, Evans JG, Harvey HPB, Davidson F, Lindsey M. 1968. Sodium intake and
blood pressure in two Polynesian populations. N Engl J Med 279:515-520.

Prior IAM, Stanhope JM. 1980. Blood pressure patterns, salt use, and migration in the
Pacific. In The Epidemiology of Arterial Blood Pressure. Developments in
Cardiovascular Medicine, Vol. 8, by H Kesteloot & JV Joosen, pp. 243-262.
Martinus Nijhoff: The Hague.

Rao DC, Laskarzewski PM, Morrison JA, Khoury P, Kelly K, Wette R, Russell J, Glueck
CJ. 1982. The Cincinnati Lipid Research Clinic Family Study; Cutural and
biological determinants of lipids and lipoprotein concentration. Am J Hum Genet
34:888-903.

Reuben DB, Cheh AI, Harris TB, Ferrucci L, Rowe JW, Tracy RP, et al. 2002. Peripheral
blood markers of inflammation predict mortality and functional decline in high-
functioning community-dwelling older persons. J Am Geriatr Soc 50:638-644.

Reyes-García V, Vadez V, Godoy R, Huanaca T, Leoanrd W, McDade T, Tanner S.
2008. Non-market returns to traditional and modern human capital: nutritional
status in a native Amazonian society. J Dev Stud 44:206-221.

Rich MW. 1997. Epidemiology, pathogphysiology, and etiology of congestive heart
failure in older adults. J Am Geriatr Soc 45:968-874.

Riddler SA, Li X, Chu H, Kingsley LA, Dobs A, Evans R, Palella F, Visscher B,
Chmiel JS, Sharrett AR. 2007. Longitudinal changes in serum lipids among
HIV-infected men on highly active antiretroviral therapy. HIV Med 8:280- 287.

Ridker PM. 2003. C-reactive protein: A simple test to help predict risk of heart attack
and stroke. Circulation 108:81-85.

Ridker PM, Cushman M, Stampfer MJ, Tracy RP, Hennekens CH. 1997. Inflammation,
aspirin, and the risk of cardiovascular disease in apparently healthy men. N Engl J
Med 336:973-979.

Ridker PM, Danielson E, Fonseca FAH, Genest J, Gotto AM, Jr, Kastelein JJP, Koenig
W, Libby P, Lorenzatti AJ, MacFadyen JG, Nordestgaard B, Shephard J,
Willerson JT, Glynn RJ, for the JUPITER Study Group. 2008. Rosuvastatin to
prevent vascular events in men and women with elevated C-reactive protein.
NEJM 359:2195-2207.

150
Ridker PM, Danielson E, Fonseca FA, Genest J, Gotto AM, Kastelein JJP, Koenig W,
Libby P, Lorenzatti AJ, Macfadyen JG, Nordestgaard BG, Shephard J,
Willerson JT, Glynn RJ, JUPITER Trial Study Group. 2009. Reduction in C-
reactive protein and LDL cholesterol and cardiovascular event rates after
initiation of rosuvastatin: a prospective study of the JUPITER trial. Lancet
4:1175-1182.

Ridker PM, Hennekens CH, Buring JE, Rifai N. 2000. C-reactive protein and other
markers of inflammation in the prediction of cardiovascular disease in women. N
Engl J Med 342:836-843.

Rimland D, Guest JL, Hernandez-Ramos I, et al. 2006. Antiretroviral therapy in HIV-
positive women is associated with increased apolipoproteins and total
cholesterol. J Acquir Immune Defic Syndr 42:307-313.

Roe C, Kinney J. 1965. The caloric equivalent of fever. Influence of major trauma.
Annals Surgery 161:140-148.

Rontu R, Ojala P, Hervonen A, Goebeler S, Karhunen PJ, Nikkilèa M, Kunnas T, Jylhä
M, Eklund C, Hurme M, Lehtimèaki T. 2006. Apoplipoprotein E genotype is
related to plasma levels of C-reactive protein and lipids and to longevity in
nonagenarians. Clin Endocrinol 64:265-270.

Rose H, Woolley I, Hoy J, Dart A, Bryant B, Mijch A, Sviridov D. 2006. HIV
infection and high-density lipoprotein: the effect of the disease vs the effect of
treatment. Metabolism 55:90-95.

Ross GW, Petrovitch H, White LR, Masaki KH, Li CY, Curb JD, Yano K, Rodriguez BL,
Foley DJ, Blanchette PL, Havlik R. 1999. Characterization of risk factors for
vascular dementia: The Honolulu-Asia Aging Study. Neurology 53:337-343.

Rosvall L, Rizzuto D, Wang H-X, Winblad B, Graff C, Fratiglioni L.2009. APOE-related
mortality: Effect of dementia, cardiovascular disease and gender. Neurobiol
Aging 20:1545-1551.

Rubenstein AH, Seftel HC, Miller K, Bersohn I, Wright AD. 1969. Metabolic response to
oral glucose in health South African White, Indian, and African subjects. Brit
Med J 1:748-751.

Ruggiero C, Metter EJ, Cherubini A, Maggio M, Sen R, Najjar SS, Windham GB, Ble A,
Senin U, Ferrucci L. 2007. White blood cell count and mortality in the Baltimore
Longitundinal Study of Aging 49:1841-1850.

151
Russell AI, Graham DSC, Shepherd C, Robertson CA, Whittaker J, Meeks J, Powell RJ,
Isenberg DA, Walport MJ, Vyse TJ. 2004. Polymorphism at the C-reactive
protein locus influences gene expression and predisposes to systemic lupus
erythematosus. Hum Mol Genetics 13:137-147.

Sakuma N, Iwata S, Ikeuchi R, Ichikawa T, Hibino T, Kamiya Y, Ohte N, Kawaguchi M,
Kunimatsu M, Kawahara H, et al. 1995. Coexisting type III hyperlipoproteinemia
and familial hypercholesterolemia: a case report. Metabolism 44:460-465.

Salive ME, Cornoni-Huntley J, Guralnik JM, Phillips CL, Wallace RB, Ostfeld AM,
Cohen HJ. 1992. Amemia and hemoglobin levels in older persons: relationship
with age, gender, and health status. J Am Geriatr Soc 40:489-496.

Santos RV, Coimbra CEA, Flowers NM, Silva JP. 1995. Intestinal parasitism in the
Xavánte Indians, Central Brazil. Rev Inst Med Trop São Paulo 37:145-148.

Sandholzer C, Delport R, Vermaak H, Utermann G. 1995. High frequency of the apo
epsilon 4 allele in Khoi San from South Africa. Hum Genet 95:46-48.

Schachter F, Faure-DiDelanef L, Guenot F, Rouger H, Froguel P, Lesueur-Ginot L,
Cohen D. 1994. Genetic associations with human longevity at the APOE and
ACE loci. Nat Genet 6:29-32.

Scholz W. 1996. Interleukin 6 in diseases: Cause or cure? Immunopharmacology 31:131-
150.

Sepehrnia B, Kamboh MI, Adams Campbell LL, Nwankwo M, Ferrell RE. 1988. Genetic
studies of human apolipoproteins. VII. Population distribution of polymorphisms
of apolipoproteins A-I, A-II, A-IV, C-II, E, and H in Nigeria. Am J Hum Genet
43:847-853.

Seeman T, Glei D, Goldman N, Weinstein M, Singer B, Lin YH. Social relationships
and allostatic load in Taiwanese elderly and near elderly. 2004. Soc Sci Med
59:2245-2257.

Seidell JC, Visscher TLS. 2000. Body weight and weight change and their health
implications for the elderly. Eur J Clin Nutri 54:33-39.

Schoenberg JB, Beck GJ, Bouhuys A. 1978. Growth and decay of pulmonary function in
health blacks and whites. Resp Physiol 33:367-393.

Shaper AG, Williams AW, Spencer P. 1961. Blood pressure and body build in an
African tribe living on a diet of milk and blood. E Afr Med J 38, 569-580.

152
Sing CF, Davignon J. 1985. Role of apolipoprotein E polymorphism in determining
normal plasma lipid and lipoprotein variation. Am J Hum Genet 37:268-285.

Singh PP, Singh M, Mastana SS. 2006. APOE distribution in world populations with new
data from India and the UK. Ann Hum Biol 33:279-308.

Skurnick JH, Aladjem M, Aviv A. 2010. Sex differences in pulse pressure trends with
age are cross-cultural. Hypertension 55:40-47.

Slattery ML, Wolff RK, Herrick JS, Caan BJ, Potter JD. 2007. IL6 genotypes and colon
and rectal cancer. Cancer Causes Control 18:1095-1105.

Smith EM, Samadian S. 1994. Use of the erythrocyte sedimentation rate in the elderly. Br
J Hosp Med 51:394-397.

Smith JD. 2002. Apolipoproteins and aging: Emerging mechanisms. Ageing Res 1:345-
365.

Snodgrass JJ, Leonard WR, Tarskaia LA, McDade TW, Sorensen MV, Alekseev VP,
Krivoshapkin VG. 2007. Anthropometric correlates of C-reactive protein among
indigenous Siberians. J Phys Anthropol 26:241-246.

Sohal R, Weindruch R. 1996. Oxidative stress, caloric restriction and aging. Science 273:
59-63.

Song YQ, Stampfer MJ, Liu SM. 2004. Meta-analysis: Apolipoprotein E genotypes and
risk for coronary heart disease. Annals of Internal Med 141:137-147.

Sorlie PD, Garcia-Palmieri MR, Costas R, Havlik RJ. 1981. Hematocrit and risk of
coronary heart disease: the Puerto Rico Heart Health Program. Am Heart J
101:456-461.

Sowers M, Jannausch M, Stein E, Jamadar D, Hochberg M, Lacance L. 2002. C-reactive
protein as a biomarker of emergent osteoarthritis. Osteoarthritis Cartilage 10:595-
601.

Sox HC Jr, Liang MH. 1986. The erythrocyte sedimentation rate-guidelines for
rational use. Ann Intern Med 104:515-523.

Stern N, Grosskopf I, Shapira I, Kisch E, Isaacov A, Limor R, Baz M, Leshem Y, Flatau
E, Miller A, Greenman Y. 2000. Risk factor clustering in hypertensive patients:
impact of the reports of NCEP-II and second joint task force on coronary
prevention on JNC-VI guidelines. J Int Med 248:203-210.

153
Stevenson DR. 1999. Blood pressure and age in cross-cultural perspective. Hum Biol
71:529-551.

Stevenson JC, Crook D, Godsland IF. 1993. Influence of age and menopause on serum
lipids and lipoproteins in healthy women. Atherosclerosis 98:83-90.

Suerbaum S, Michetti P. 2002. Helicobacter pylori infection. N Engl J Med 347:1175-
1186.

Szalai AJ. 2002. The biological functions of C-reactive protein. Vascul Pharmacol
39:105-107.

Szalai AJ, Wu J, Lange EM, McCrory MA, Langefeld CD, Williams A, Zakharkin SO,
George V, Allison DB, Cooper GS, Xie F, Fan Z, Edberg JC, Kimberly RP. 2005.
Single-nucleotide polymorphisms in the C-reactive protein (CRP) gene promoter
that affect transcription factor binding, alter transcriptional activity, and associate
with difference in baseline serum CRP level. J Mol Med 83:440-447.

Tanner SN. 2005. A population in transition: Health, culture change, and intestinal
parasitism among the Tsimane’ of lowland Bolivia. University of Michigan
Dissertation.

Tanner S, Leonard WR, McDade TW, Reyes-Garcia V, Godoy R, Huanca T. 2009.
Influence of helminth infections on childhood nutritional status in lowland
Bolivia. Am J Hum Biol 21:651-656.

Taranto NJ, Cajal SP, De Marzi MC, Gernandez MM, Frank FM, Bru AM, Minvielle MC,
Basualdo JA, Malchiodi EL. 2003. Clinical status and parasitic infection in a
Wichí Aboriginal community in Salt, Argentina. Trans Royal Soc Tropical Med
Hygeine 97:554-558.

Tejada C, Gore I. 1957. Comparison of athersclerosis in Guatemala City and New
Orleans. Am Path 33:887-894.

Tejada C, Gore I, Strong JP, McGill HC. 1958. Comparative severity of atherosclerosis in
Costa Rica, Guatemala and New Orleans. Circulation 18:92-97.

Teng M-S, Hsu L-A, Wu S, Chang H-H, Chou H-H, Ko Y-L. 2009. Association between
C-reactive protein gene haplotypes and C-reactive protein gene haplotypes and C-
reactive protein levels in Taiwanese: Interaction with obesity. Atherosclerosis
204: 64-69.

154
Thomas DC, Witte J. 2002. Point: Population stratification: a problem for case’control
studies of candidate-gene associations? Cancer Epidemiol Biomarkers Prevention
11:505-512.

Truswell AS, Kennelly BM, Hansen JDL, Lee RB. 1972. Blood pressure of !Kung
bushmen in Northern Botswana. Am Heart J 84:5-12.

U.S. Census Bureau. 2001. Profiles of general demographic characteristics: 2000 Census
of Population and Housing, United States. Washington, DC: U.S. Government
Printing Office.

U.S. National Center for Health Statistics. 2008. National Vital Statistics Reports 56, no.
10 (April).

Vadez V, Reyes-García V, Godoy RA, Apaza VL, Byron E, Huanca T, Leonard WR,
Perez E, Wilkie D. 2004. Does integration to the market threaten agricultural
diversity? Panel and cross-sectional data from a horticultural-foraging society
in the Bolivian Amazon. Hum Ecol 32, 635-646.

van Bodegom D, May L, Meij HJ, Westendorp RGJ. 2007. Regulation of human life
histories. The role of the inflammatory host response. Ann NY Acad Sci 1100:84-
97.

Vasunilashorn S, Crimmins EM, Kim JK, Winking J, Gurven M, Kaplan H, Finch CE.
2010. Blood lipids, infection and inflammatory markers in the Tsimane of Bolivia.
Am J Hum Biol in press.

Vasunilashorn S, Finch CE, Kim JK, Winking J, Gurven M, Kaplan H, Crimmins EM.
2010. Biomarkers of aging in two societies: The U.S. and the Tsimane of Bolivia.
Forthcoming.

Vaughan JP. 1978. A review of cardiovascular disease in developing countries. A Trop
Med Parasit 72:101-109.

Vaughan JP. 1979. Blood pressure and heart murmurs in a rural population in the United
Republic of Tanzania. Bull World Health Organiz 57:89-97.

Vieira-Filho JPB. 1979. O diabetes mellitus entre os índios dos Estados Unidos do Brasil.
Revta Antrop 21:53-60.

Vieira-Filho JPB. 1981. Problemas da aculturação alimentar dos Xavante e Bororo. Revta
Antrop 24:37-40.

155
Vitek MP, Brown CM, Colton CA. 2009. APOE genotype-specific differences in the
innate immune response. Neurobiol Aging 30:1350-1360.

Waldron I. 1983. Sex differences in human mortality: The role of genetic factors. Soc Sci
Med 17:321-333.

Waldron I, Nowotarski M, Ferimer M, Henry JP, Post N, Witten C. 1982. Cross-cultural
variation in blood pressure: a quantitative analysis of the relationships of blood
pressure to cultural characteristics, salt consumption, and body weight. Soc Sci
Med 16:419-430.

Walker RS, Gurven M, Burger O, Hamilton MJ. 2008. The trade-off between number
and size of offspring in humans and other primates. Proc R Soc B 275:827-
833.

Walter T, Olivares M, Pizzaro F, Muño C. 1997. Iron, anemia, and infection. Nutr Rev
55:111-124.

Walston J, Arking DE, Fallin D, Li T, Beamer B, Xue Q, Ferrucci L, Fried LP,
Chakravarti A. 2005. IL-6 gene variation is not associated with increased serum
levels of IL-6 muscle, weakness, or frailty in older women. Exp Gerontol 40:344-
352.

Waterlow JC. 1984. Protein turnover with special reference to man. Q J Exp Physiol
69:40-438.

Werner GT, Sareen DK. 1978. Serum chlesterol levels in the population of Punjab in
north west India. Am J Clin Nutr 31:1479-1483.

Westendorp RGJ. 2004. Are we becoming less disposable? EMBO 5:2-6.

Welton PK, He J, Klag MJ. 1994. Blood pressure in westernized populations. In Swales
JD, ed. Textbook of Hypertension, pp. 11-21. London, UK: Blackwell Scientific
Publications.

Whelton PK. Blood pressure in adults and the elderly. 1985. Handbook of Hypertension,
Vol. 6: Epidemiology of Hypertension. Elsevier Science. Pp.51-69.

Whyte HM. 1958. Body fat and blood pressure of natives in New Guinea: Refections on
essential hypertension. Australasian Annals of Medicine 7:36-46.

156
Wiedermann U, Stemberger H, Unfried E, Widhalm K, Kundi M, Altenriederer M,
Savedra M, Wiedermann G. 1991. Intestinal worm burden and serum
cholesterol or lipid concentration in a Shipibo population (Peru). Zentralbl
Bakteriol 275:279-286.

Williams PD, Day T. 2003. Antagonistic Pleiotropy, mortality source interactions, and
the Evolutionary Theory of Senescence. Evolution 57:1478-1488.

Wilson PW, Anderson KM, Harris T, Kannel WB, Casteli WP. 1994. Determinants of
change in total cholesterol and HDL-C with age: the Framingham Study. J
Gerontol A Biol Sc Med Sci 49:252-257.

Wilson PWF, Schaefer EJ, Larson MG, Ordovas JM. 1996. Apolipoprotein E alleles and
risk of coronary disease – A meta-analysis. Arteriosclerosis Thrombroosis &
Vascular Biol 16:1250-1255.

Woo KS, Chook P, Raitakari OT, McQuillan B, Feng JZ, Celermajer DS. 1998.
Prevalence of atherosclerosis risk factors in adolescent school children. Indian
Heart J 19:511-515.

Wozniak MA, Itzhaki RF, Faragher EB, Jame MW, Ryder SD, Irving WL. 2002.
Apolipoprotein E-epsilon 4 protects against severe liver disease caused by
hepatitisC virus. Hepatology 36:456-463.

Xu CF, Talmud PJ, Angelico F, Del Ben M, Savill J, Humphries SE. 1991.
Apolipoprotein E polymorphism and plasma lipid, lipoprotein, and apolipoprotein
levels in Italian children. Genet Epidemiol 8:389-398.

Yudkin JS, Yajnik CS, Mohamed-Ali V, Bulmer K. 1999. High levels of circulating pro-
inflammatory cytokines and leptin in urban, but not rural, Asian Indians. A
potential explanation for increased risk of diabetes and coronary heart disease.
Diabetes Care 22:363-364.

Zangerle R, Sarcletti M, Gallati H, Reibnegger G, Wachter H, Fuchs D. 1994.
Decreased plasma concentrations of HDL cholesterol in HIV-infected
individuals are associated with immune activation. J Acquir Immune Defic
Syndr Hum Retrovirol 7:1149-1156.

Zee RY, Ridker PM. 2002. Polymorphism in the human C-reactive protein (CRP) gene,
plasma concentrations of CRP, and the risk of future arterial thrombosis.
Atherosclerosis 162:217-219.

157

APPENDIX: MAP OF TSIMANE COMMUNITIES

Abstract (if available)

Abstract Increasing evidence suggests that aging is accelerated by exposure to infections and inflammation. Previous studies suggest that in environments where infection is high, the processes associated with aging will accelerate and indicators of the aging process will be more apparent at earlier ages than in environments where exposure to infection is relatively low. To further our understanding of aging and how biological and genetic indicators associated with aging vary in a high infection environment, the Tsimane of Bolivia are studied. The Tsimane are an indigenous population of forager-farmers with little access to modern medicine, high infectious morbidity, and high mortality.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Statistical algorithms for examining gene and environmental influences on human aging

PDF

Predicting cognitive decline and dementia in elderly twins from indicators of early life oral health

PDF

Biomarkers of age-related health changes: associations with health outcomes and disparities

PDF

International sex and age differences in physical function and disability

PDF

Longitudinal change in active life expectancy: the longitudinal studies of aging 1984-2000

PDF

The effect of body weight on mortality: different countries and age groups

PDF

Social determinants of physiological health and mortality in China

PDF

Longitudinal assessment of neural stem-cell aging

PDF

Self-perceptions of Aging in the Context of Neighborhood and Their Interplay in Late-life Cognitive Health

PDF

Air pollution neurotoxicity throughout the lifespan: studies on the mechanism of toxicity and interactions with effects of sex and genetic background

PDF

Translating research into practice: a community-based medication management intervention

PDF

Health differences between the elderly in Japan and the United States

PDF

Childlessness and psychological well-being across life course as manifested in significant life events

PDF

Identifying and measuring cell-intrinsic and cell-extrinsic factors influencing aging

PDF

The immediate and long term legacy of relationships with grandparents for the well-being of grandchildren

PDF

A biodemographic approach to understanding sociodemographic disparities in kidney functioning on three dimensions: individual, population, and cross-national

PDF

Marital quality, gender, and biomarkers of disease risk in the MIDUS cohort

PDF

Computational approaches to identify genetic regulators of aging and late-life mortality

PDF

Internet communication use, psychological functioning and social connectedness at older ages

PDF

Transgenic biomarkers of aging in drosophila melanogaster

Asset Metadata

Creator Vasunilashorn, Sarinnapha (author)

Core Title Aging in a high infection society

School Leonard Davis School of Gerontology

Degree Doctor of Philosophy

Degree Program Gerontology

Publication Date 09/27/2012

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag aging,biomarkers,gene variants,health,infection,Inflammation,OAI-PMH Harvest

Place Name Bolivia (countries), USA (countries)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Crimmins, Eileen M. (committee chair), Finch, Caleb E. (committee chair), Sattler, Fred R. (committee member)

Creator Email fahvasu@gmail.com,vasunila@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m3480

Unique identifier UC1468214

Identifier etd-Vasunilashorn-4043 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-412549 (legacy record id),usctheses-m3480 (legacy record id)

Legacy Identifier etd-Vasunilashorn-4043.pdf

Dmrecord 412549

Document Type Dissertation

Rights Vasunilashorn, Sarinnapha

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu