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Adipokines do not account for the association between osteocalcin and insulin sensitivity in Mexican Americans

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Adipokines do not account for the association between osteocalcin and insulin sensitivity in Mexican Americans

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Adipokines Do Not Account for the Association between
Osteocalcin and Insulin Sensitivity in Mexican Americans

by

Yumeng Gao

A Thesis Presented to the
FACULTY OF THE USC KECK SCHOOL OF MEDICINE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(APPLIED BIOSTATISTICS AND EPIDEMIOLOGY)

May 2023

Copyright 2023 Yumeng Gao
ii

Acknowledgements
I would like to express my deepest gratitude to my thesis advisor, Dr. Richard Watanabe, for his
unwavering support, guidance, and patience throughout my thesis research. As my Biostatistics
professor, Dr. Watanabe introduced me to the magic of biostatistics, which sparked my passion
for the field and set me on the path to becoming a biostatistician. Thank you for everything!

I am also grateful to my committee members Dr. Wendy Mack and Dr. Farzana Choudhury. Their
insights and expertise were essential in improving my thesis. Thank you for your valuable feedback
and guidance!

Finally, I extend my thanks to my family and friends who offered me constant love, encouragement,
and understanding. I would like to specially acknowledge my grandparents, Shuqin and Baishan,
who have been so supportive and amazing for the past 24 years. You made me who I am today.

These two years at USC have been incredible because of all of you! Now, as I close this chapter
of my life, I am excited to embark on the next stage of my journey.

iii

TABLE OF CONTENTS

Acknowledgements ........................................................................................................................ ii
List of Tables ................................................................................................................................. iv
List of Figures ................................................................................................................................. v
Abbreviations ................................................................................................................................. vi
Abstract ......................................................................................................................................... vii
Chapter 1: Introduction .................................................................................................................. 1
Chapter 2: Materials and Methods ................................................................................................ 3
Study Population ..................................................................................................... 3
Phenotyping ............................................................................................................ 3
Assays ..................................................................................................................... 4
Statistical Analysis .................................................................................................. 4
Chapter 3: Results ......................................................................................................................... 7
Descriptive Statistics ............................................................................................... 7
Osteocalcin Association Analyses - Minimally Adjusted Models ......................... 8
Adipokine-Adjusted Osteocalcin Association Analyses ......................................... 9
Chapter 4: Discussion .................................................................................................................. 12
Novelty ................................................................................................................. 13
Limitations ............................................................................................................ 13
Conclusion ............................................................................................................ 14
References .................................................................................................................................... 15
Appendices ................................................................................................................................... 18
Appendix A: Supplementary Tables ..................................................................... 18
Appendix B: Supplementary Figures ....................................................................19

iv

List of Tables
Table 1. Participant Characteristics……………………………………………………………….7
Table 2. Minimally Adjusted Associations between Osteocalcin and Phenotypes……………….8
Supplementary Table 1. Associations between Osteocalcin and Phenotypes,
Adjusting for Age, Sex, Bone Mass, and Body Fat Percent...…...………….…...………….…….18
Supplementary Table 2. Multivariate Associations between Glu_OC and SI,
Adjusting for Adipokines……………………………………………………….….…………….18

v

List of Figures
Figure 1. Estimated b±SE for Glu_OC Association with SI……………..…………….………….9
Figure 2. Estimated b±SE for Glu_OC Association with Fasting Insulin……………….…..…..10
Figure 3. Estimated b±SE for Glu_OC Association with 2-Hour Insulin…………………....….10
Supplementary Figure 1. Estimated b±SE for Total_OC Association with SI……………....…19
Supplementary Figure 2. Estimated b±SE for Total_OC Association with Fasting Insulin..….19
Supplementary Figure 3. Estimated b±SE for Total_OC Association with 2-Hour Insulin....…19

vi

Abbreviations
T2D Type 2 Diabetes
OC Osteocalcin
Gla_OC Carboxylated Osteocalcin
Glu_OC Undercarboxylated Osteocalcin
GDM Gestational Diabetes Mellitus
OGTT Oral Glucose Tolerance Test
FSIGT Frequently Sampled Intravenous Glucose Tolerance Test
SI Insulin Sensitivity
SG Glucose Effectiveness
TNF-a Tumor Necrosis Factor Alpha
CRP C-Reactive Protein
AIR Acute Insulin Response
DI Disposition Index

vii

Abstract
Objective: Recent animal studies have suggested a role of adipokines in osteocalcin-related
improvement in insulin sensitivity. Yet knowledge on adipokines’ role in the relationship between
osteocalcin and glucose metabolism in humans is limited. Therefore, we tested whether adipokines
affect the association of osteocalcin with insulin sensitivity and other metabolic phenotypes in
nondiabetic Mexican Americans.
Methods: 867 participants from 342 Mexican American families from the BetaGene study were
used for this analysis. We examined the relationship between inactive, active (Glu_OC), and total
osteocalcin each with diabetes-related phenotypes. We also assessed the potential confounding
effect of leptin, adiponectin, resistin, TNF-a, and C-Reactive Protein on these relationships.
Minimally adjusted and multivariate associations were analyzed by likelihood ratio tests under a
variance components framework.
Results: Glu_OC was negatively associated with insulin sensitivity (p = 0.002). Adjustment for
any of the adipokines did not statistically alter the significance of the association.
Conclusions: Osteocalcin is negatively associated with insulin sensitivity independent of
adipokine levels in Mexican Americans.

Keywords: Osteocalcin, Adipokine, Insulin Sensitivity, Diabetes, Glucose

1

Chapter 1: Introduction
Diabetes mellitus has become a major public health concern. The Centers for Disease Control and
Prevention (CDC) estimates there are 37.3 million people suffering from diabetes in the US,
accounting for 11.3% of the population. Alarmingly, 38% of American adults have prediabetes,
which indicates an increasing trend of diabetes in the future [1]. Diabetes is a risk factor for
cardiovascular disease, chronic kidney disease, and nerve damage, which severely impairs quality
of life as well as life expectancy of patients worldwide [2,3]. Risk factors for type 2 diabetes (T2D),
which accounts for more than 90% of diabetes cases [1], include obesity, lack of physical activity,
and family history [4,5].
Recent studies identified an association between osteocalcin (OC) and glucose metabolism in vivo
[6,7]. OC is derived from osteoblasts in the bone matrix and is found in both inactive or
carboxylated form (Gla_OC) and active or undercarboxylated form (Glu_OC) [8,9]. Specifically,
in mouse models, Glu_OC regulates glucose homeostasis primarily through two adipokines: leptin
and adiponectin [10]. Secreted from adipocytes, adipokines play key roles in inter-organ
communication [11]. Leptin acts on osteoblasts to inhibit insulin receptor phosphorylation,
consequently restricting pancreatic beta-cell insulin signaling in animal models [12]. Glu_OC
stimulates adipocytes to produce more adiponectin, which could suppress lipolysis in muscle and
liver as well as inhibit the formation of triglycerides within the adipocytes, resulting in improved
insulin sensitivity [10]. Meanwhile, Glu_OC modulates the pancreas directly to enhance beta-cell
proliferation and insulin secretion in the pancreas [13].
However, these mechanisms of the effects of OC were mainly generated from mouse models.
Replication of the relationship between osteocalcin and glucose metabolism has been less uniform
in human studies. Some studies indicated that Glu_OC does not have a significant relationship
2

with T2D in humans [14], while others emphasized the statistically positive association between
Glu_OC and diabetes-related traits including insulin sensitivity, insulin secretion, and beta-cell
proliferation [15,16,17]. Variability in results might be attributed to issues of small sample size or
suboptimal measurement methods. Furthermore, to our knowledge, the role of adipokines in the
relationship between osteocalcin and glucose metabolism has not been adequately examined in
humans.
Hence, this research focused on testing whether adipokines affect the association of osteocalcin
(especially Glu_OC) with insulin sensitivity and other metabolic phenotypes in Mexican
Americans with high risk of T2D, to refine the picture of metabolic interactions between organs,
and to provide novel perspectives of the treatment for human diabetes.

3

Chapter 2: Materials and Methods
Study Population
Participants were from the BetaGene study, a family-based study of Mexican Americans in
Southern California[18]. The study population was ascertained on Mexican American probands
with or without a diagnosis of gestational diabetes mellitus (GDM) within the 5 years before the
study and their family members [19]. The GDM probands should be of Mexican ancestry; have
been diagnosed with GDM within the previous five years; have glucose levels indicating poor
beta-cell function and at high risk of diabetes when not pregnant; have no beta-cell autoimmunity.
The BetaGene study also recruited siblings and/or first cousins of these probands and all
participants were diabetes-free. Since GDM history is a strong risk factor, these subjects had higher
risk of T2D than people with a negative family history of GDM [20]. More details regarding
recruitment criteria have been provided in previous publications [21]. This study included 867
participants from 342 families aged 18 years or older who did not have T2D.

Phenotyping
Body composition was assessed by dual-energy x-ray absorptiometry (DXA) scan to measure
percent body fat and bone mass. Oral glucose tolerance tests (OGTT) were performed to assess
glucose tolerance [18]. Frequently-sampled intravenous glucose tolerance tests (FSIGT) were
performed on participants and data were analyzed using the minimal model (MINMOD
Millennium V5.18, Minmod, Los Angeles, CA, USA) [22] to quantify insulin sensitivity (SI),
glucose effectiveness (SG), and acute insulin response (AIR). Disposition index (DI) was
calculated as the product of SI and AIR [18].
4

Assays
Gla_OC and Glu_OC were measured separately using enzyme immunoassay kits (Takara Bio
USA, Inc); the total amount of OC was calculated as the sum of Gla_OC and Glu_OC.
Two Millipore multiplex kits with magnetic bead panels (Millipore, Billerica, MA) were used to
assay fasting leptin, adiponectin, resistin, and tumor necrosis factor-alpha (TNF-a). Fasting C-
reactive protein (CRP) was measured by ELISA (Millipore, Billerica, MA). For all five adipokines,
intra-assay coefficient of variation was <10%, and inter-assay coefficient of variation was <15%.
Detailed information about the assays has been previously discussed [19].

Statistical Analysis
Associations between osteocalcin and diabetes-related phenotypes were assessed using likelihood
ratio tests under a variance components framework implemented in SOLAR Eclipse version 8.1.1
[23].
To account for the strong correlations in family data, a variance components approach was
implemented to model the estimate of the variance of the phenotypic traits. For our family data,
the total variance (σ
2
) of each trait can be broken down into three components:
s
2
= s
2
A + s
2
D + s
2
e
s
2
A is our target to address, the additive genetic variance, which represents the sum effect of all
genes that might affect that trait. Dominance genetic variance (s
2
D) is usually assumed to be zero
in humans since it is difficult to be estimated. s
2
e (random error) reflects all the nongenetic
variance [24].
5

Age, sex, and bone mass were included as covariates. Additional analyses included percent body
fat as an additional covariate to assess the effect of adiposity on the association. Residuals based
on modeling just the covariates were generated and statistically transformed using inverse normal
transformation to reduce the influence of outliers and achieve better normality. The transformed
residuals were used to test for osteocalcin associations.
The initial linear model to obtain residuals was:
Phenotypes = b0 + b1×Age + b2×Sex + b3×BoneMass + e
Previous studies suggested that adipokines, specifically leptin and adiponectin may play important
roles in the association between osteocalcin and insulin sensitivity [10]. We therefore tested the
effect of adipokines on the association between osteocalcin and metabolic phenotypes. Our
analyses started with a base model of the residuals and osteocalcin:
y = b0 + b1×OC
Adipokines were added to the model one by one to test their individual effects on each of the
associations, following this order:
y = b0 + b1×OC + b2×Leptin
y = b0 + b1×OC + b2×Leptin + b3×Adiponectin
y = b0 + b1×OC + b2×Leptin + b3×Adiponectin + b4×Resistin
y = b0 + b1×OC + b2×Leptin+ b3×Adiponectin + b4×Resistin + b5×TNF-a
y = b0 + b1×OC + b2×Leptin+ b3×Adiponectin + b4×Resistin + b5×TNF-a + b6×CRP
Previous studies on rodent models suggested that leptin acts directly on osteoblasts to restrict
insulin signaling, while adiponectin plays a crucial role in improving the insulin sensitivity of
muscle, liver, and fat [10,12]. Their confounding effects have been recurrently emphasized;
6

accordingly, leptin and adiponectin were analyzed first, followed by the other three adipokines to
test their presumed roles in glucose metabolism [6, 11].
All other statistical analyses were performed in R version 4.2.1 [25]. All data are reported as
median and interquartile range unless otherwise indicated. A 2-sided a-level of 0.05 was used for
statistical significance.

7

Chapter 3: Results
Descriptive Statistics
Participant characteristics are summarized in Table 1. The sample consisted of a total of 867
individuals across 342 families. All the metabolic tests were performed on the proband generation,
including the probands, their siblings, and their cousins. AIR and DI were analyzed in a separate
analysis, and they are not presented here.
Table 1. Participant Characteristics
Variable Median (IQR) or Count (Percentage)
Age (year) 34.70 (10.30)
Sex
Female 632 (72.9%)
Male 235 (27.1%)
Participant Type
Proband 259 (29.9%)
Sibling 277 (31.9%)
Cousin 128 (14.8%)
Other 203 (23.4%)
Metabolic Phenotypes
Percent Body Fat (%) 35.80 (11.90)
Bone Mass (grams) 2110.79 (461.27)
BMI (kg/m
2
) 28.70 (7.30)
Fasting Glucose (mmol/L) 5.10 (0.70)
2-Hour Glucose (mmol/L) 7.20 (2.80)
Fasting Insulin (pmol/L) 42.00 (48.00)
2-Hour Insulin (pmol/L) 354.00 (384.00)
Glucose Effectiveness (SG, min
-1
) 0.017 (0.008)
Insulin Sensitivity (SI, x10
-5
min
-1
per pmol/L) 4.49 (3.21)
Osteocalcin
Gla_OC (ng/mL) 7.72 (6.60)
Glu_OC (ng/mL) 2.79 (2.96)
Total_OC (ng/mL) 10.91(7.30)
Adipokines
Leptin (ng/mL) 14.31 (18.59)
Adiponectin (μg/mL) 10.13 (9.30)
Resistin (ng/mL) 17.92 (9.32)
TNF-a (pg/mL) 2.82 (1.83)
C-Reactive Protein (μg/mL) 1.45 (2.51)
8

Among all the participants, there were 632 (72.9%) women and 235 (27.1%) men, and their median
(IQR) age was 34.7 (10.3) years. The median (IQR) percent body fat was 35.8 (11.9) % and BMI
was 28.7 (7.3) kg/m
2
, indicating that a considerable proportion of the participants were overweight
or even obese [26].

Osteocalcin Association Analyses - Minimally Adjusted Models
We first analyzed the associations between three osteocalcin variables and six phenotypes,
adjusting only for age, sex, and bone mass (Table 2).
After adjusting for age, sex, and bone mass, Gla_OC had a significant inverse association with
BMI (p = 0.015). Notably, Glu_OC was significantly positively associated with fasting insulin (p
= 0.003) and negatively associated with insulin sensitivity (p = 0.002). Total OC was not
significantly associated with any phenotype.
Table 2. Minimally Adjusted Associations between Osteocalcin and Phenotypes
Phenotypes
Gla_OC Glu_OC Total OC
b SE p-value* b SE p-value

8
b SE p-value
BMI -0.0193 0.0079 0.015 0.0250 0.0163 0.126 -0.0066 0.0065 0.314
Fasting Glucose 0.0011 0.0087 0.902 0.0000 0.0176 1.000 0.0009 0.0072 0.900
2-Hour Glucose 0.0051 0.0089 0.565 0.0077 0.0186 0.678 0.0057 0.0073 0.437
Fasting Insulin -0.0065 0.0082 0.424 0.0498 0.0168 0.003 0.0044 0.0067 0.518
2-Hour Insulin -0.0009 0.0085 0.919 0.0312 0.0177 0.079 0.0057 0.0070 0.418
SG 0.0076 0.0081 0.349 0.0131 0.0168 0.437 0.0063 0.0066 0.344
SI 0.0100 0.0080 0.213 -0.0510 0.0166 0.002 -0.0027 0.0066 0.682
* Statistically significant p-values are bolded. Listed associations were adjusted for age, sex, and bone mass.

After adding percent body fat as a covariate, the association of Gla_OC and BMI (b = -0.0135,
SE = 0.0079, p = 0.086) and of Glu_OC with fasting insulin (b = 0.0324, SE = 0.0172, p = 0.059)
became statistically non-significant. However, Glu_OC remained significantly negatively
9

associated with insulin sensitivity (b = -0.0419, SE = 0.0169, p = 0.014). Full results are shown in
Supplementary Table 1.

Adipokine-Adjusted Osteocalcin Association Analyses
Participants were measured for five adipokines to test whether osteocalcin’s correlations with
glucose homeostasis were mediated through adipokines. The measurements of osteocalcin consist
of Gla_OC, Glu_OC, and Total_OC. There was no change in the statistical significance of
associations between Gla_OC and each metabolic trait regardless of which adipokine was added
to the model.
Figure 1 shows the negative association between Glu_OC and SI stayed significant across all
covariate combinations. Notably, the b did not change statistically despite adjusting for various
adipokines. Values of the b and corresponding SE are listed in Supplementary Table 2.

Figure 1. Estimated b±SE for Glu_OC Association with SI

10

Figure 2 illustrates that the significance of fasting insulin’s positive association with Glu_OC did
not alter statistically when adding each adipokine into the model, either. Moreover, the b value
remained statistically unchanged for all the covariate combinations.

Figure 2. Estimated b±SE for Glu_OC Association with Fasting Insulin

Figure 3 shows that additionally adjusting for leptin altered the positive association between
Glu_OC and 2-hour insulin (p = 0.079) to be statistically significant (p = 0.012) and following
adiponectin added to the model increased the level of significance (p = 0.003). However, the
quantitative relationship between Glu-OC and 2-hour insulin did not dramatically change.

Figure 3. Estimated b±SE for Glu_OC Association with 2-Hour Insulin
11

Lastly, after adding both leptin and adiponectin into the model, all the nonsignificant
associations of Total_OC with SI, fasting insulin, and 2-hour insulin became statistically
significant (Supplementary Figure 1, 2, 3).

12

Chapter 4: Discussion
The potential endocrine function of bone has gained recent attention in the field of glucose
homeostasis and fat metabolism [12]. The bone-derived hormone, osteocalcin, was hypothesized
to be pharmacologically active at increasing SI via increasing adipokine expression in adipocytes
[27].
As shown in this study, active osteocalcin i.e., Glu_OC was negatively associated with SI, which
was opposite to what previous research found in the mouse model [10]. This finding suggested
that instead of reducing insulin resistance, osteocalcin may have adverse effects on SI in humans.
Consistent with this result, fasting insulin was positively associated with Glu_OC; fasting
hyperinsulinemia is an indicator of insulin resistance.
Adjusting for percent body fat in the linear model did not alter the statistical significance of the
association between Glu_OC and SI (p-value changed from 0.002 to 0.014), indicating that
adipocytes may not engage in the pathway by which Glu_OC regulates SI.
We assessed the potential role of adipokines in regulating the association between osteocalcin and
SI by sequentially adding each adipokine into the linear model and re-assessing the osteocalcin
association. The significance of association between Glu_OC and SI did not change substantially.
Moreover, the slope (b) also did not change with the addition of each adipokine, which suggests
that those adipokines do not play a significant role in the regulation of Glu_OC on SI.
Since inactive osteocalcin’s association with SI, fasting insulin, and 2-hour insulin remained
nonsignificant despite which adipokine was added into the covariate combination, the statistical
change in the significance of total osteocalcin’s association with these three phenotypes could be
mostly attributed to active osteocalcin.
13

In the pathogenic time course of T2D, pancreatic beta-cells secrete increasing insulin to
compensate for progressive insulin resistance. T2D develops when the pancreas is no longer able
to appropriately compensate, resulting in failure of beta-cell function [28, 29].
In another analysis, we observed a significant negative association between Glu_OC and SI; we
also observed a significant positive association between Glu_OC and AIR, consistent with
compensatory insulin secretion. The positive relationship suggests that as active osteocalcin
increases, insulin secretion will increase. Hence, the lack of association between Glu_OC and DI,
our measure of pancreatic beta-cell function, implies that Glu_OC does not appear to have any
direct effect on pancreatic beta-cells and the deduced increasing trend in insulin secretion reflects
the appropriate compensatory response to insulin resistance. We therefore conclude that while
Glu_OC may affect insulin-sensitive tissue, it has no effect on the pancreas.

Novelty
To our knowledge, this study represents the largest human sample (n = 867) to assess the
association between osteocalcin and measures of glucose metabolism. Furthermore, we directly
assessed key metabolic parameters, SI, AIR, and DI, using direct measurement, as opposed to
indirect proxies like HOMA or the Matsuda index [30]. Finally, to our knowledge, this is the first
human study to assess the potential confounding effects of adipokines in glucose metabolism.

Limitations
There were some limitations of this research. First, the underlying mechanism of Glu_OC’s
negative relationship with insulin sensitivity is awaiting explanation. Moreover, since osteocalcin
14

might not regulate insulin sensitivity through adiponectin in humans, further longitudinal research
will be necessary to explore alternative pathways.

Conclusion
This study tested the relationship between osteocalcin and diabetes-related phenotypes. We
explored the confounding effect of adipokines on the association between osteocalcin and insulin
sensitivity. Our findings suggested that osteocalcin may suppress insulin sensitivity and their
association is independent of adipokines’ effect in adult Mexican Americans with high risk of T2D.

15

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Diabetologia. 2014;57(2):334-338. doi:10.1007/s00125-013-3121-8

18

Appendices
Appendix A: Supplementary Tables
Supplementary Table 1. Associations between Osteocalcin and Phenotypes,
Adjusting for Age, Sex, Bone Mass, and Body Fat Percent
Phenotypes
Gla_OC Glu_OC Total OC
b SE p-value* b SE
p-value

8
b SE p-value
BMI -0.0135 0.0079 0.086 0.0134 0.0161 0.406 -0.0076 0.0065 0.241
Fasting Glucose 0.0046 0.0087 0.598 -0.0042 0.0176 0.812 0.0021 0.0071 0.767
2-Hour Glucose 0.0081 0.0089 0.362 0.0023 0.0186 0.903 0.0062 0.0073 0.395
Fasting Insulin 0.0032 0.0084 0.705 0.0324 0.0172 0.059 0.0061 0.0069 0.379
2-Hour Insulin 0.0050 0.0086 0.560 0.0115 0.0179 0.520 0.0054 0.0071 0.447
SG 0.0040 0.0081 0.624 0.0164 0.0167 0.327 0.0048 0.0066 0.467
SI 0.0029 0.0082 0.721 -0.0419 0.0169 0.014 -0.0049 0.0067 0.461
* Statistically significant p-values are bolded.

Supplementary Table 2. Multivariate Associations between Glu_OC and SI,
Adjusting for Adipokines
Covariate Combinations b SE p-value
Age, Sex, and Bone Mass -0.0510 0.0166 0.002
+ Leptin -0.0637 0.0154 3.95×10
-5

+ Adiponectin -0.0696 0.0150 3.98×10
-6

+ Resistin -0.0670 0.0151 1.03×10
-5

+ TNF-a -0.0644 0.0152 2.43×10
-5

+ C-Reactive Protein -0.0649 0.0152 2.14×10
-5

19

Appendix B: Supplementary Figures

Supplementary Figure 1. Estimated b±SE for Total_OC Association with SI

Supplementary Figure 2. Estimated b±SE for Total_OC Association with Fasting Insulin

Supplementary Figure 3. Estimated b±SE for Total_OC Association with 2-Hour Insulin

Abstract (if available)

Abstract Objective: Recent animal studies have suggested a role of adipokines in osteocalcin-related improvement in insulin sensitivity. Yet knowledge on adipokines’ role in the relationship between osteocalcin and glucose metabolism in humans is limited. Therefore, we tested whether adipokines affect the association of osteocalcin with insulin sensitivity and other metabolic phenotypes in nondiabetic Mexican Americans.
Methods: 867 participants from 342 Mexican American families from the BetaGene study were used for this analysis. We examined the relationship between inactive, active (Glu_OC), and total osteocalcin each with diabetes-related phenotypes. We also assessed the potential confounding effect of leptin, adiponectin, resistin, TNF-alpha, and C-Reactive Protein on these relationships. Minimally adjusted and multivariate associations were analyzed by likelihood ratio tests under a variance components framework.
Results: Glu_OC was negatively associated with insulin sensitivity (p = 0.002). Adjustment for any of the adipokines did not statistically alter the significance of the association.
Conclusions: Osteocalcin is negatively associated with insulin sensitivity independent of adipokine levels in Mexican Americans.

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Creator Gao, Yumeng (author)

Core Title Adipokines do not account for the association between osteocalcin and insulin sensitivity in Mexican Americans

School Keck School of Medicine

Degree Master of Science

Degree Program Applied Biostatistics and Epidemiology

Degree Conferral Date 2023-05

Publication Date 04/14/2023

Defense Date 04/14/2023

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

Tag adipokine,Diabetes,glucose,insulin sensitivity,OAI-PMH Harvest,osteocalcin

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Language English

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Advisor Watanabe, Richard (committee chair), Mack, Wendy (committee member), Choudhury, Farzana (committee member)

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