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The associations between ultra-processed food consumption and type 2 diabetes and obesity among young adults

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The associations between ultra-processed food consumption and type 2 diabetes and obesity among young adults

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Content

The Associations between Ultra-Processed Food Consumption and Type 2 Diabetes and Obesity
among Young Adults

By

Yiping Li

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 Yiping Li
ii
TABLE OF CONTENTS
List of Tables ..................................................................................................................................iii
Abstract ..........................................................................................................................................iv
Chapter 1: Introduction ....................................................................................................................1
Chapter 2: Methods .........................................................................................................................2
Chapter 3: Results ............................................................................................................................8
Chapter 4: Discussion ....................................................................................................................11
Chapter 5: Conclusion ...................................................................................................................15
References ...................................................................................................................................16

iii
List of Tables
Table 1: Descriptive statistics for participants’ characteristics at baseline and follow-up
visits. …………………………………………………………………………………….9

Table 2: Descriptive statistics for glucose, insulin, and body composition outcomes at
baseline and follow-up visit. ………………….…………………………….…………..10

Table 3: Odds ratios for the effect of change in UPF consumption and baseline UPF
consumption on prediabetes/type 2 diabetes, impaired fasting glucose, and impaired
glucose tolerance. …………………………….……………………………….………..10

Table 4: Effect estimates for UPF percentage change and baseline UPF consumption on body
composition and glucose, insulin, and body composition measurements. …….………..11

iv
Abstract
Most ultra-processed foods (UPFs) are high in sodium, sugar, and unhealthy fats and
compose more than half of total dietary energy consumption in the U.S. A poor diet composed of
a high amount of UPFs can contribute to obesity, glucose dysregulation and insulin resistance,
which are risk factors for type 2 diabetes (T2D). The goal of this study is to examine associations
between UPF consumption and adiposity, glucose regulation, and prediabetes in youth. Young
adults (n=85) aged 17-22 years old from the Children’s Health Study were enrolled between
2014-2018 and returned for a second visit between 2020-2022. Participants completed two 24-
hour dietary recalls, a body composition assessment, and an oral glucose tolerance test at each
visit. Food items were categorized as either an UPF or non-UPF according to NOVA
classification guidelines. The proportion of the diet composed of UPFs was calculated for each
participant. Regression models were used to assess relationships of UPF consumption with
markers of glucose homeostasis at follow-up. We found that a 10% increase in UPF consumption
between visits was strongly associated with 1.65 times (95% Cl: 1.19, 2.41) increased risk for
prediabetes and 2.29 times (95% CI: 1.39, 4.45) increased risk for impaired glucose tolerance.
UPF consumption may increase the risk for T2D among young adults. Our findings suggest that
limiting UPF consumption can be an important strategy for T2D prevention among young adults.

1
Chapter 1: Introduction
Early onset of Type 2 diabetes (T2D) among young adults has become more concerning
in the recent years
1
. T2D is a significant public health concerns globally because it can affect
individuals’ quality of life, lead to many comorbidities, and increase mortality risk
2,3
. In the
United States (U.S.), an early onset of T2D in young adulthood can lead to more comorbidities
compared to adults
1,4–8
. Obesity greatly increases the risk for T2D and poor diet and unhealthy
lifestyles can be risk factors for both T2D and obesity
3,4
. Since T2D and obesity are closely
related to each other and share some risk factors including diets and physical activity, the
assessment of modifiable risk factors is crucial for prevention and treatment of these conditions.
In the U.S., more than half of total dietary energy consumption is composed of ultra-
processed foods (UPFs)
9,10
. UPFs are food items that go through multiple industrial process,
which cannot be manipulated at home, before people purchase or eat them
11
. Examples of
commonly seen UPFs are soft drinks, packaged snacks, margarine, and sausages
11
. Most UPFs
are calorie-dense and high in sugar, salt, and unhealthy fats, while low in protein, vitamins, and
minerals
9,12–15
. Studies have demonstrated that the increased consumption of UPFs results in a
poor nutritional quality of the overall diet and increased risk for developing chronic diseases,
including T2D and hypertension
12–29
. It is important to limit UPF consumption among children
and adolescents due to the high content of added sugar and lipids in UPFs and their possible
contribution to weight gain and cardiovascular diseases
15–17
.
Most previous studies focusing on UPF consumption and metabolic syndromes were
conducted in Brazil using cross-sectional analysis with only a few longitudinal studies
17,26,27,29–34
.
In addition, many of them were conducted on adults and shown that a higher proportion of UPF
composed diet or the increased consumption of UPFs was associated with a higher risk of T2D
2
among adults
32–40
. Studies also indicated that high UPF consumption was associated with an
increased risk of obesity among adults
26,30,37,41–44
. However, few studies have assessed the
associations between UPFs and T2D and obesity among young adults or children
17,32–34
. Some
studies have shown that limiting the consumption of UPFs can help with reducing T2D and
obesity risks among children and young adults, while two studies did not support that UPF
consumption was associated with obesity and overweight
17,32–34
. The inconsistency findings
make the study of UPF consumption and T2D and obesity rather crucial among young adults.
The aim of this study is to assess the longitudinal associations between UPF consumption
and T2D and obesity in young adults over four years of follow-up. Glucose and insulin
measurements, body composition, and UPF consumption were evaluated at two visits. We
hypothesized that an increased UPF consumption is associated with a higher risk of T2D and
obesity among young adults.

3
Chapter 2: Methods
Cohort
Participants enrolled in the study at the baseline visit between 2014-2018 were young
adults (n=155) aged 17-22 years old, who had originally participated in the Children’s Health
Study
8
. Participants selected at the baseline visit met the inclusion criteria of having overweight
or obesity in early adolescence, were not diagnosed with either type 1 or type 2 diabetes, were
not taking medications that influence glucose metabolism, and had no other medical diagnosis
8
.
Of these, 85 returned for a follow-up visit between 2020-2022
8
. This study was approved by the
Institutional Review Board at the university of Southern California and written informed
consents were obtained from participants
8
.
Dietary Assessment and UPF Classification
Participants completed two non-consecutive 24-hour dietary recalls (24HRs) on one
weekday and one weekend day at each visit
8
. Trained interviewers used the Nutritional Data
System for Research software version 2014 to complete the baseline recalls, while participants
used the Automated Self-Administered 24-hr Dietary Assessment Tool version 2018 to conduct
the recalls at the follow-up visit
8
.
In this study, a total of 1167 food items were reported at the baseline visit and a total of
807 food items were collected the at the follow-up visit. Two reviewers (EC, YL) independently
classified each food at ultra-processed or non-ultra-processed using the NOVA classification
system. UPFs were classified according to the following definitions of unprocessed and
minimally processed foods, processed culinary ingredients, processed foods, and ultra-processed
foods (UPFs)
9,11,45
. Any disagreements on UPF classification were resolved through discussion.
Unprocessed and minimally processed foods in NOVA group 1 are natural foods of
edible parts of plants or animals and foods that have been through basic process such as drying,
4
griding and freezing
11
. Examples are fruits, vegetables, roots, seeds, eggs, milk, meat, seafood,
powdered milk, fresh or pasteurized plain yogurt, corn flour, oats, herbs and spices, tea, coffee,
and water. Processed culinary ingredients in NOVA group 2 mainly composed of vegetable oils,
butter, salt, sugar, and honey for the main purpose of seasoning and cooking
11
. Processed foods
in NOVA group 3 contain food products that are made by adding items in group 2 to group 1 for
the purpose of preservation
11
. Canned vegetables, salted nuts, salted, dried cured, or smoked
meats and fish, fruit in syrup, and freshly made unpackaged breads are examples in group 3.
UPFs in NOVA group 4 are usually made with series of industrial techniques that cannot
be manipulated at home
11
. UPFs can be processed with molding and pre-frying
11
. The addition of
colors and flavors, and ingredients such as sugar, salt, oils, and fats are commonly seen in
UPFs
11
. Most branded foods, packaged foods, pre-prepared ready-to-eat products, instant foods
are in this group
11
. Examples of UPFs are soft drinks, chocolate, candies, cereal, ice-cream,
cookies, pastries, margarines, spreads, milk drinks, fruit yogurts, pizza, nuggets, sausages,
noodles.
UPF classification in this study was made considering the following criteria: the major
ingredient of a food item (for foods composed of multiple ingredients); how the food is typically
prepared in the U.S.; and how most people obtain the food (purchased from a store, restaurant, or
homemade). Items from fast food restaurants were categorized as UPFs, with exceptions such as
whole fruits or vegetables. Mixed dishes that were composed of multiple ingredients made at
restaurants or homemade were categorized as either UPFs or non-UPFs based on the major
ingredients used and where it was obtained. For example, pizza was categorized as an UPF
unless it was homemade; tacos, beef sandwich, pasta, etc. were UPFs because they are usually
obtained from restaurants and their main ingredients are usually purchased by restaurants in bulk
5
from wholesalers. Seafood soup, egg omelets, quesadilla, lasagna, orange chicken, sushi, etc.
with specified ingredients in NOVA groups 1 and 2 were considered as non-UPFs.

UPF Percentage Calculation
The contribution of UPFs to the diet was calculated by weight rather than calories, to
account for some foods that provide no contribution to energy intake, such as diet soda. The
percent of diet that was ultra-processed was calculated using the total amount of UPFs in grams
and the total amount of foods and beverages consumed in grams. UPF percentages (UPF%s) at
the two non-consecutive recalls were averaged and used for data analysis.
Study Outcomes
Diabetes status was assessed using glucose and insulin related measures. A 2-hour oral
glucose tolerance test (OGTT) was conducted and glucose and insulin were measured in plasma
while fasting and at 30, 60-, 90- and 120 minutes after glucose administration
8,46
. Hemoglobin
A1c (HbA1c) was measured in fasting whole blood samples
8
. The glucose and insulin area under
the curves (AUCs) were calculated by using the trapezoidal method with the 5 time points from
the OGTT
8,46
.
Prediabetes or T2D was categorized according to the American Diabetes Association
criteria. Participants were considered to have prediabetes if they had the HbA1c of 5.7% to 6.4%,
if their fasting glucose was 100 mg/dL to 125 mg/dL, or if their glucose at 2-hour was 140
mg/dL to 199 mg/dL, and T2D if their HbA1c was greater than 6.5%, if their fasting glucose was
greater than 125 mg/dL, or if their glucose at 2-hour was greater than 200 mg/dL
46,47
. Impaired
fasting glucose (IFG) was defined as having a fasting glucose value greater than 100 mg/dL and
6
impaired glucose tolerance (IGT) was defined as having a glucose at two-hour glucose value
greater than 140 mg/dL
48
.
To assess insulin resistance and beta-cell function, the homeostatic model assessment of
insulin resistance (HOMA-IR) and homeostatic model assessment of β-cell function (HOMA- β)
were calculated from fasting glucose and fasting insulin values
46
. The Matsuda Index was used
to estimate the insulin sensitivity of the entire body using the 5 time points from the OGTT
46
.
Body composition was assessed using body mass index (BMI, kg/m
2
) and the dual-
energy X-ray absorptiometry (DEXA)
8
. DEXA measures included body fat percentage, android
to gynoid ratio, fat mass to height ratio (kg/m
2
), and visceral adipose tissue (VAT) mass (g)
8
.
BMI was categorized into three groups: normal weight (<25 kg/m
2
), overweight (25-29.9 kg/m
2
),
and obesity (>29.9 kg/m
2
).

Covariates
Demographic information including age, sex, race and ethnicity, and physical activity
were collected though questionnaires
8
. Ethnicity was categorized into White, Hispanic/Latino,
and Other. Exercise level at the follow-up visit was categorized into High, Medium, and Low by
using the International Physical Activity Questionnaire Form and the scoring guidelines to
calculate metabolic equivalent of task (MET) minutes
8,49
. Participants were considered to have a
“high” amount of exercise based on the criteria: reported vigorous physical activity (VPA) at
least 3 days per week plus more than 1500 Met minutes per week; or reported any combination
of VPA or moderate physical activity (MPA) 7 days per week plus more than 3000 MET minutes
of walking
8
. Participants were considered as having a “moderate” amount of exercise based on
the criteria: reported more than 30 minutes of VPA for at least 3 days per week; or reported some
7
combinations of VPA, MPA, and more than 600 MET minutes of walking for more than 5 days
per week; or reported MPA or walking 5 days per week, which activity lasted at least 30
minutes
8
. Participants were considered as having a “low” amount of exercise if none of the above
criteria was met
8
.

Statistical Analysis
Descriptive statistics for %UPF consumption and all outcomes at both visits were
calculated. Differences between categorical variables were assessed using McNamar’s test and
differences between continuous variables were assessed using paired t-tests. Few participants
were found to have T2D, so prediabetes and T2D were combined into one group
(Prediabetes/T2D) for analysis.
Linear and logistic regressions were used to evaluate the effects of UPF consumption on
each outcome, measured at the follow-up visit. Each model contained the UPF% consumption at
baseline, the change in UPF% consumption between the baseline and follow-up visits (UPF%∆),
and adjusted for covariates as follows: 𝑌 𝑜𝑢𝑡𝑐𝑜𝑚𝑒 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 = 𝛽 0
+ 𝛽 𝑈𝑃𝐹 % ∆
𝑋 𝑈𝑃𝐹 %∆
+
𝛽 𝑈𝑃𝐹 % 𝑎 𝑡 𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒 𝑣𝑖𝑠𝑖𝑡 𝑋 𝑈𝑃𝐹 % 𝑎𝑡 𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒 𝑣𝑖𝑠𝑖𝑡 + 𝑐𝑜𝑣𝑎𝑟𝑖𝑎𝑡𝑒𝑠 . All longitudinal models were adjusted
for covariates including age, sex, ethnicity, and exercise at the follow-up visit. Beta estimates
and odds ratios were scaled by 10. All analyses were performed using R (version 2022.02.3+492;
R Core Development Team).

8
Chapter 3: Results
Descriptive Statistics
Descriptive statistics for participants’ characteristics at the baseline and follow-up visits
are presented in Table 1. BMI, T2D, and IGT were not significantly different between visits,
while IFG was significantly different between visits (Table 1). Fasting glucose significantly
increased from the baseline to the follow-up visit; HbA1c, two-hour glucose, and glucose AUC
increased from the baseline to the follow-up visit but not statistically significant (Table 2).
Fasting insulin, two-hour insulin, HOMA- 𝛽 , and HOMA-IR significantly increased between the
two visits, while Matsuda index significantly decreased (Table 2). All body composition
measurements significantly increased from the baseline to the follow-up visit, except the
android/gynoid ratio (Table 2).

Prediabetes/T2D and Insulin Resistance
A 10-unit increase in UPF%∆ was significantly associated with 1.65 times higher risk of
having prediabetes/T2D (95% CI: 1.19, 2.41) (Table 3). A 10-unit increase in UPF%∆ was
significantly associated with 2.29 times higher risk of having IGT (95% CI: 1.39, 4.45) (Table
3). There were no significant associations between the continuous glucose measurement and
either UPF%∆ or baseline UPF consumption (Table 4). Increases in UPF consumption from the
baseline to follow-up visit were significantly associated with higher fasting insulin, insulin after
2-hour, and insulin AUC, and a lower Matsuda index (Table 4).

9
Table 1. Descriptive statistics for participants’ characteristics at baseline and follow-up visits.
Baseline Follow-Up
p-value
1
Age (years), mean
(SD)
19.97 (1.20) 24.07 (0.75) -
Sex, n (%)
Female
Male

43 (50.59)
42 (49.41)

43 (50.59)
32 (49.41)

-
Ethnicity
White
Hispanic/Latino
Other

30 (35.29)
49 (57.65)
6 (7.06)

30 (35.29)
49 (57.65)
6 (7.06)

-
BMI Category
Normal weight
Overweight
Obesity

13 (15.29)
35 (41.18)
37 (43.53)

11 (12.94)
32 (37.65)
42 (49.41)

0.47
Physical Activity
Low
Medium
High
N/A

-

16 (18.82)
21 (24.71)
47 (55.29)
1 (1.18)

-
Type 2 Diabetes
No Diabetes
Prediabetes/T2D

61 (71.76)
24 (28.24)

53 (62.35)
32 (37.65)

0.17
IFG

Normal
Abnormal
Missing

80 (94.12)
5 (5.88)
0

68 (80.00)
16 (18.82)
1 (1.18)

0.003
IGT
Normal
Abnormal
Missing

69 (81.18)
16 (18.82)
0

66 (77.64)
15 (17.65)
4 (4.71)

0.82
1
p-values were calculated using McNamar’s test.
*Abbreviations: BMI: body mass index; T2D: type 2 diabetes; IFG: impaired fasting glucose; IGT: impaired glucose
tolerance; SD: standard deviation

Body Composition
There were no statistically significant associations between UPF%∆ or baseline UPF
consumption and body composition (Table 4). However, we observed positive but non-
statistically significant associations between baseline UPF consumption and all body
composition measurements (Table 4).
10
Table 2. Descriptive statistics for glucose, insulin, and body composition outcomes at baseline
and follow-up visits.
Mean (SD) p-value
1

Baseline Follow-Up
Glucose Measurements
HbA1c 5.22 (0.28) 5.26 (0.52) 0.35
Fasting Glucose
(mg/dL)
90.41 (7.55) 95.32 (16.61) 0.003
Two-Hour Glucose
(mg/dL)
119 (26.43) 121.3 (35.04) 0.39
Glucose AUC 263.1 (44.98) 270.5 (45.11) 0.02
Insulin Measurements
Fasting Insulin
(μIU/mL)
7.44 (4.92) 13.27 (11.18) <0.001
Two-Hour Insulin
(μIU/mL)
57.19 (49.56) 88.33 (128.47) 0.03
Insulin AUC 152.74 (104.16) 186.5 (176.91) 0.20
HOMA- 𝜷 100.01 (63.22) 151.43 (120.26) 0.004
HOMA-IR 1.68 (1.18) 3.36 (3.73) 0.001
Matsuda Index 5.32 (3.85) 4.32 (2.83) 0.03
Body Composition
BMI (kg/m
2
) 30.09 (4.95) 31.85 (7.03) <0.001
Body Fat (%) 35.24 (8.13) 38.3 (8.37) <0.001
Android/Gynoid
Ratio
0.99 (0.14) 1.01 (0.15) 0.29
Fat Mass/Height
2
10.62 (3.66) 12.21 (4.79) <0.001
VAT mass (g) 506.4 (197.78) 594.3 (301.43) <0.001
1
p-values were calculated using paired t-tests.
*Abbreviations: SD: standard deviation; BMI: body mass index; VAT: visceral adipose tissue; HbA1c: Hemoglobin
A1c; AUC: Area Under the Curve; HOMA- 𝛽 : homeostatic model assessment of 𝛽 -cell function; HOMA-IR:
homeostatic model assessment for insulin resistance.

Table 3. Odds ratios for the effect of change in UPF consumption and baseline UPF consumption
on prediabetes/type 2 diabetes, impaired fasting glucose, and impaired glucose tolerance.
OR (95% CI)
UPF Percentage Change UPF Percentage at
Baseline
Prediabetes/T2D

1.65 (1.19, 2.41) 1.34 (0.87, 2.12)
IFG 1.19 (0.85, 1.69) 1.17 (0.69, 1.96)
IGT 2.29 (1.39, 4.45) 1.22 (0.62, 2.41)
*All outcomes were measured at the follow-up visit. The models were adjusted for age, sex, ethnicity, exercise, and
either UPF percentage at baseline or UPF percentage change. Effects are scaled by 10-unit.
**Abbreviations: T2D: type 2 diabetes; IFG: impaired fasting glucose; IGT: impaired glucose tolerance.

11
Table 4. Effect estimates for UPF percentage change and baseline UPF consumption on body
composition and glucose, insulin, and body composition measurements.
β (95% CI)
UPF Percentage Change UPF Percentage at
Baseline
Glucose Measurements
HbA1c 0.02 (-0.05, 0.09) -0.01 (-0.11, 0.09)
Fasting Glucose 0.73 (-1.58, 3.04) 0.29 (-2.95, 3.53)
Glucose After 120 minutes 4.46 (-0.23, 9.15) 0.53 (-6.11, 7.17)
Glucose AUC 3.56 (-2.45, 9.57) 3.51 (-5.00, 12.03)
Insulin Measurements
Fasting Insulin -0.15 (-1.64, 1.34) 2.15 (0.07, 4.23)
Insulin After 120 mins -1.16 (-17.43, 15.11) 46.10 (23.16, 69.04)
Insulin AUC -7.39 (-28.59, 13.82) 66.17 (36.27, 96.07)
HOMA- 𝜷 -4.60 (-19.94, 10.73) 21.38 (-14.63, 42.91)
HOMA-IR -0.02 (-0.53, 0.49) 0.44 (-0.27, 1.16)
Matsuda Index -0.26 (-0.62, 0.11) -0.64 (-1.13, -0.15)
Body Composition
BMI -0.03 (-1.01, 0.94) 0.79 (-0.57, 2.15)
Body Fat Percentage 0.29 (-0.54, 1.11) 1.06 (-0.08, 2.19)
Android/Gynoid Ratio -0.002 (-0.02, 0.02) 0.006 (-0.02, 0.03)
Fat Mass/Height
2
-0.08 (-0.70, 0.54) 0.45 (-0.40, 1.31)
VAT Mass -11.06 (-53.67, 31.54) 23.48 (-35.31, 82.26)
*All outcomes were measured at the follow-up visit. The models were adjusted for age, sex, ethnicity, exercise, and
either UPF percentage at baseline or UPF percentage change. Effects are scaled by 10-unit.
**Abbreviations: BMI: body mass index; VAT: visceral adipose tissue; HbA1c: Hemoglobin A1c; AUC: Area
Under the Curve; HOMA- 𝛽 : homeostatic model assessment of 𝛽 -cell function; HOMA-IR: homeostatic model
assessment for insulin resistance.

12
Chapter 4: Discussion
This longitudinal analysis showed that there is a positive association between the increase
in UPF consumption and the risk of developing T2D among young adults. The finding also
applied to young adults who had IGT compared to who did not have IGT. We found that higher
UPF consumption was associated with significant increases in some markers of insulin
sensitivity including fasting insulin, two-hour insulin, and insulin AUC. In addition, we also
found that higher UPF consumption was associated with a significant decrease of Matsuda index.
Both statistically significant findings from insulin measurements suggest a positive association
between UPF consumption and insulin resistance. The secondary study of interest, obesity, has
no statistically significant relationship with UPF consumption in the longitudinal study. These
results emphasize the adverse contribution of UPF consumption to the development of T2D
among young adults.
As more of the total dietary energy consumption is composed of UPFs, which are usually
high in unhealthy nutrients, a poor diet and subsequent chronic diseases are becoming concerns
11,
14-31
. Most existing studies focusing on UPF consumption have focused on adults, though early
onsets of T2D among the youth is increasing; an issue which needs more attention from the
public
1
. Studies in adults have showed that higher UPF consumption is associated with an
increased risk of T2D, which is consistent with the primary finding in our study
29–31,35–42
.
Findings from existing studies showed different relationships between UPF consumption and
obesity among populations with different ages. Some studies suggested that UPF consumption is
positively associated with overweight and obesity among adults
25,26,29,30,37,41–44,50
. Other studies
indicated that UPF consumption is not associated with overweight or obesity among children
33,34
.
Our study only found a positive but non-significant relationship between UPF consumption and
13
obesity-related body compositive measurements, which is inconsistent previous studies. The
inconsistency might be the different age groups and demographic information of studies.
Since diet is a risk factor to obesity and T2D to the youth, the adverse contribution of
high contents of salt, fat, and sugar in UPFs to public health is noticeable
23,51,52
. Some articles
indicated that high salt intake may be a contributor to obesity and diabetes
53–55
. Dietary fat,
especially trans fatty acids and saturated fats, is positively associated with T2D and obesity risks
due to its effects on insulin sensitivity
55–58
. Consumption of soft drinks and foods that contain
high amounts of added sugar were found to have positive associations with T2D and obesity, and
limiting added sugar intake and UPFs may prevent children and adolescents from developing
chronic diseases
15,58,59
. Based on the above findings, although salt, fat, and sugar are not able to
increase the risk of T2D, they are all likely to contribute to obesity. As obesity is the major risk
factor for T2D by causing insulin resistance, high fasting glucose, IGT, etc., it indicates that
those high contents of salt, fat, and sugar in UPFs can possibly contribute to T2D
development
4,55,60
. Our results correspond with the mechanism of how UPF consumption can
possibly lead to T2D, but no strong association between UPF consumption and obesity.
This longitudinal study has many strengths. Firstly, recruited participants were from the
Southern California Children’s Health Study and many T2D- and obesity-related measurements
were able to be obtained using OGTT and DEXA, which gave us very detailed information
8
.
Secondly, the age group of young adults of the study is unique because most previous studies
were focusing on adults and a few studies focused on children. People in this age group just
become physically mature and how their body functions towards food intake and chronic
diseases is worth to studying. Thirdly, this study has two time points to access the exposure and
outcomes of interest, so the exposure and outcomes at baseline and follow-up visits can be easily
14
compared. More importantly, the nature of a longitudinal study allows us to see the changes of
UPF consumption and outcomes of interest in a time of period rather than at a specific time
point. This study also has some limitations. The sample size at the baseline was small, and
participants who completed both visits were included, making the sample size even smaller
(n=85). In addition, the 24HR can be subjective and not representative for a eating habit, which
can introduce bias to the study
8
. Although a reproducible UPF-classification system was
generated by using the NOVA classification, it was independently grouped by two researchers
with some subjective judgement. This could lead to some statistically non- and significant
results. Based on the strengths and limitations of this study, further epidemiological research
studies with larger sample sizes and other dietary assessment tools on the association between
UPFs and metabolic syndromes among young adults are worth to be conducted.
Findings from this study suggested that reducing UPF consumption may reduce the risk
for prediabetes and T2D in youth. Young adults may also benefit from limiting foods that
contain high amounts of salt, fat, and sugar as they all potentially contribute to obesity, which
greatly increases the risk for T2D
3,4
. This study is meaningful to the field of public health
especially because the UPFs composed more than half of the total daily energy consumption int
the U.S.
9,10
. Metabolic syndromes such as T2D and obesity are top public health problems and
are becoming more prevalent among young adults
1,61
. The study of both UPFs and metabolic
syndromes among the youth can help with improving health status of young adults and adults as
they become older. In addition, future studies with such focuses can be useful to help with
metabolic syndromes intervention and prevention. Both public health settings clinical settings
might use prospective findings to improve public policy implementation and clinical treatments.

15
Chapter 5: Conclusions
This prospective study found that UPF consumption is positively associated with the risk
of T2D among young adults. This study stands out from previous studies because the unique
population involved and future studies on this particular population are encouraged. The findings
indicated that limiting the consumption of UPFs may be an important strategy for T2D
prevention among young adults.

16
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Abstract (if available)

Abstract Most ultra-processed foods (UPFs) are high in sodium, sugar, and unhealthy fats and compose more than half of total dietary energy consumption in the U.S. A poor diet composed of a high amount of UPFs can contribute to obesity, glucose dysregulation and insulin resistance, which are risk factors for type 2 diabetes (T2D). The goal of this study is to examine associations between UPF consumption and adiposity, glucose regulation, and prediabetes in youth. Young adults (n=85) aged 17-22 years old from the Children’s Health Study were enrolled between 2014-2018 and returned for a second visit between 2020-2022. Participants completed two 24-hour dietary recalls, a body composition assessment, and an oral glucose tolerance test at each visit. Food items were categorized as either an UPF or non-UPF according to NOVA classification guidelines. The proportion of the diet composed of UPFs was calculated for each participant. Regression models were used to assess relationships of UPF consumption with markers of glucose homeostasis at follow-up. We found that a 10% increase in UPF consumption between visits was strongly associated with 1.65 times (95% Cl: 1.19, 2.41) increased risk for prediabetes and 2.29 times (95% CI: 1.39, 4.45) increased risk for impaired glucose tolerance. UPF consumption may increase the risk for T2D among young adults. Our findings suggest that limiting UPF consumption can be an important strategy for T2D prevention among young adults.

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Creator Li, Yiping (author)

Core Title The associations between ultra-processed food consumption and type 2 diabetes and obesity among young adults

School Keck School of Medicine

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Publication Date 03/24/2023

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