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The influence of dietary fructose on genetic effects of GCK and GCKR in Mexican Americans

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The influence of dietary fructose on genetic effects of GCK and GCKR in Mexican Americans

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The Influence of Dietary Fructose on Genetic Effects of GCK and
GCKR in Mexican Americans
by
Leona Ma

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

May 2022

Copyright 2022 Leona Ma
ii

ACKNOWLEDGMENTS
I would like to thank Dr. Richard M. Watanabe for giving me the chance to participate in
this research and mentoring me throughout the process with his patience and support. I would
like to thank Dr. Farzana Choudhury and Dr. Jin Piao, my committee members, for giving me
advice and guidance.
I would like to thank Dr. Trevor Pickering, my advisor, for his instructions on my courses
of obtaining this degree.
Finally, I would like to extend my special thanks to my parents for their support and
understanding in my studies over the years.
This work was supported by National Institutes of Health Grant DK-61628 and an
American Diabetes Association Distinguished Clinical Scientist Award to T.A.B. A portion of
this work was conducted in a facility constructed with support from Research Facilities
Improvement Program Grants C06 (RR10600-01, CA62528-01, and RR14514-01) from the
National Center for Research Resources.

iii
TABLE OF CONTENTS
ACKNOWLEDGMENTS .............................................................................................................. ii
LIST OF TABLES ......................................................................................................................... iv
LIST OF FIGURES ........................................................................................................................ v
ABSTRACT .................................................................................................................................... 6
INTRODUCTION .......................................................................................................................... 7
RESEARCH DESIGN AND METHODS .................................................................................... 11
Subject recruitment ................................................................................................................... 11
Clinical Protocols ...................................................................................................................... 12
Assays ....................................................................................................................................... 13
Phenotype definitions ................................................................................................................ 13
Data analysis ............................................................................................................................. 13
RESULTS ..................................................................................................................................... 15
DISCUSSION ............................................................................................................................... 18
REFERENCES ............................................................................................................................. 21

iv
LIST OF TABLES

Table 1 Subject characteristics ...................................................................................................... 38
Table 2. Association results between GCK rs1799831 and type 2 diabetes-related quantitative
traits. .............................................................................................................................................. 39
Table 3 Association results between GCKR rs780094 and type 2 diabetes-related quantitative
traits. .............................................................................................................................................. 40

v
LIST OF FIGURES

Figure 1 Schematic of our hepatic substrate balance hypothesis. Blue: genes; Green:
environmental factors. See text for details. ................................................................................... 41
Figure 2 Pathways contributing to steatosis. Genetic loci of interest are highlighted in blue and
dietary fructose in green. Regulatory effects are depicted by dashed arrows. GA =
glyceraldehyde; G3P = glycerol 3 phosphate; ACoA = acetylCoA. ............................................ 42
6
ABSTRACT
OBJECTIVES— Genetic variants of glucokinase (GCK), glucokinase regulator (GCKR), patatin-
like phospholipase domain-containing protein 3 (PNPLA3) have been shown to be associated with
type 2 diabetes and diabetes-related traits. According to this, I propose the hepatic substrate
balance hypothesis, which considers that the genetic variation of GCK, GCKR, PNPLA3 results in
an imbalance in substrate flow into and out of the liver. GCK participates in the glucose uptake
process and GCKR regulate the enzyme activity of GCK. As a result, it is possible that genetic
variation affects the separate activities of GCK and GCKR, as well as their interaction, affecting
hepatic glucose absorption and the transformation of glucose to fat. PNPLA3 hydrolyzes fat to
triglycerides, and genetic variations may affect PNPLA3's ability to break down fat in the liver.
The amount of fat in the liver is a result of a balance between these two processes, and an imbalance
leads to the accumulation of liver fat, which leads to hepatic insulin resistance. I further postulate
that dietary fructose, which also add to liver fat, may aggravate the substrate imbalance caused by
genetic variation via regulating GCKR.
RESEARCH DESIGN AND METHODS— In Mexican Americans from the BetaGene Study, I
investigated if these variations were linked to diabetes-related quantitative features. Oral glucose
tolerance test (OGTT), intravenous glucose tolerance test (IVGTT), and dual-energy X-ray
absorptiometry scan were used to obtain phenotypic information on study participants.
RESULTS— In the primary analysis, GCK rs1799831 do not show statistically significant
association with any type 2 diabetes-related quantitative trait when adjusts for age and sex and did
not change after adjusting for percentage of body fat. GCKR rs780094 is statistically significantly
associated with cholesterol level, 30-minute glucose levels (p=0.043), SG (p=0.007), and
7
triglyceride levels (p=0.00001) adjusting for age and sex. The interaction between GCKR and
dietary fructose are found to be significant in some type 2 diabetes-related quantitative traits. In
the test of the GCK associations adjusting for the GCKR´Fructose interaction, statistically
significantly associations also are observed in several type 2 diabetes-related quantitative traits.
CONCLUSIONS— My results show evidence for a statistically significantly interaction between
dietary fructose and GCKR rs780094 on some type 2 diabetes-related quantitative traits to increase
hepatic glucose uptake in Mexican Americans, suggesting a positive contribution to the
accumulation of liver fat. Additionally, after adjusting for GCKR, dietary fructose, and their
interaction term, GCK shows patterns of association that contribute negatively to the accumulation
of liver fat. It is worth mentioning that before the adjustment, univariate analysis of GCK did not
show significant results, indicating the complicated biological relationships among GCK, GCKR,
and dietary fructose.
INTRODUCTION
Diabetes mellitus has quadrupled in global prevalence during the last three decades, and it is now
the ninth leading cause of mortality (1). Approximately one in every eleven persons in the world
now has diabetes, with 90 percent of those suffering from type 2 diabetes (1). In 2020, data showed
that 34.2 million Americans (10.5% of the population) had diabetes, putting a greater financial
strain on the country(2). As a result, effective preventive strategies are required to alleviate this
health and financial burden.
Understanding the physiologic implications of genetic variations that cause variance in
type 2 diabetes related quantitative traits can lead to new biologic insights and better interventions.
Genome-wide association (GWA) studies of type 2 diabetes (3-17) and type 2 diabetes-related
8
quantitative traits (3; 6-8; 11; 13; 18-46) have identified hundreds of loci contributing to type 2
diabetes risk or variation in type 2 diabetes-related quantitative traits. However, it is unclear how
genetic diversity contributes to illness pathogenesis. I chose to investigate the in vivo physiologic
effects of genetic variation in the context of gene × gene (G × G) and gene × environment (G × E)
interactions as a follow-up to GWAS findings.
I start with a trio of loci I hypothesize to be involved in hepatic fat accumulation and
subsequent hepatic insulin resistance. The mechanism(s) underlying hepatic fat accumulation are
unknown, while specific genes such as glucokinase (GCK), glucokinase regulator (GCKR), and
patatin-like phospholipase domain-containing protein 3 (PNPLA3) indicate to metabolic pathways
in the liver that may contribute to hepatic fat accumulation. In human, GCK expresses mainly in
pancreatic β-cells and liver hepatoparenchymal cells. It is an enzyme that catalyzes the first step
in glycolysis, converting glucose to glucose-6-phosphate. GCKR is the earliest discovered, to date
best characterized GCK interaction partner (47). In liver, GCK and GCKR, regulate hepatic
glucose uptake (substrate in). PNPLA3 is a known hepatic steatosis risk loci (27; 48) that is
involved in the process of fat excretion in the liver (substrate out). I propose the hepatic substrate
balance hypothesis which hypothesizes that the combined effect of genetic variation in this trio of
loci changes the balance between substrate provision to and hydrolysis of hepatic fat (Figure 1). I
further hypothesize that the substrate imbalance caused by genetic differences promotes hepatic
fat accumulation. Increased hepatic fat is also thought to contribute to hepatic insulin resistance
and type 2 diabetes etiology. While it's tempting to think that people who have "risk" alleles at all
three loci acquire the most fat and those who don't accumulate the least, given the complicated
pathways and interconnections, this may not be the case. It's possible that changes in one locus
"balance out" the effects of changes in other loci, resulting in little to no change in a highly
9
regulated phenotype like fasting glucose or insulin. Environmental factors, such as dietary fructose,
may also have an impact on the underlying biology of hepatic steatosis. Thus, I further hypothesize
that dietary fructose can affect substrate balance by directly contributing to hepatic fat or by
interacting with genetic variation in a gene by dietary fructose interaction. Hepatic glucose
metabolism dysregulation is most likely caused by fat buildup in specific metabolic pathways,
although it could also be caused by genetic influences on the expression, activity, or regulation of
important enzymes.
In this thesis, I will focus on the uptake process of glucose (variation in GCK and GCKR)
and its potential effect on liver fat. Dietary fructose appears to contribute to hepatic fat through
providing substrate via the dihydroxyacetone phosphate (DHAP) and glyceraldehyde (GA)
pathways, as well as modulating glycolytic flux through GCKR modulation (Figure2). GCKR
regulates GCK by binding to GCK and sequestering it within the nucleus (49; 50). GCKR
sequestration is inhibited by fructose-1-phosphate (F1P) (49; 51), permitting GCK translocation
to the cytoplasm (51-56). Because hepatic glucose transport is via the high Km isoform glucose
transporter (Solute Carrier Family 2 Member 2), the first opportunity to modulate hepatic glucose
uptake is at the first step of glycolysis (glucose to glucose-6-phosphate).
Fructose-6-phosphate (F6P) promotes GCKR binding to GCK, improving sequestration, and
limiting GCK activity and hepatic glucose uptake through a feedback mechanism(13; 51-53).
Evidence suggests that the antagonistic effects of F1P on GCKR are greater than the agonistic
effects of F6P (53), implying that dietary fructose could influence liver substrate flow by supplying
substrate directly via the DHAP and GA pathways and by generating unconstrained hepatic
glucose uptake by overwhelming the inhibitory effect of F6P on GCKR. GCK variation is linked
to glucose levels (7; 57-59), insulin secretion (37; 57-65), and type 2 diabetes (7; 58; 61). This has
10
led to a lot of attention being paid to GCK activity in pancreatic b-cells and b-cell-related
characteristics. GCK expression, however, is controlled by two promoters: one that is active in
most glucose-sensing endocrine cells (including pancreatic b-cells) and neurons, and the other that
controls hepatic GCK expression. It is believed that the liver contains more than 99 percent of all
GCK moieties(66). Similarly, variation in GCKR has been shown to be linked with fasting glucose
(5; 9; 13; 17; 23), triglyceride (5; 13; 23), steatosis(67), and type 2 diabetes (5; 9; 13; 17). In vitro
studies revealing that GCKR variation resulting in defective GCKR regulation of GCK explained
the pattern of associations: increased triglyceride, lower glucose, and lower type 2 diabetes risk
(68). This defective regulation was accompanied by a decrease in the ability of physiologic levels
of F6P to improve GCKR's activities (68). GCKR variants have been shown to be associated with
liver enzymes and hepatic fat (19; 27; 67; 69), suggesting that GCKR variation may play a role in
hepatic fat accumulation. I provide preliminary evidence GCKR variants may be associated with
other type 2 diabetes-related quantitative traits in an interaction with dietary fructose, emphasizing
the significance of taking dietary fructose into account and reinforcing our focus on G×G and G×E
interactions. Increased glycolytic activity is hypothesized to increase substrate flow via increased
hepatic glucose uptake and increase lipogenesis via substrate flow through malonyl-CoA. In this
thesis, I demonstrate that the combined effects of genetic variations in GCK and GCKR indicates
relationships with hepatic glucose uptake, and the effect of dietary fructose on the associations all
support components of the hepatic substrate balancing hypothesis. Detailed examination of the in
vivo mechanisms underlying these associations will provide insights into accumulation of hepatic
fat, their metabolic consequences, and their role in type 2 diabetes pathogenesis. I tested these
hypotheses using data from the BetaGene study, which sampled from Mexican Americans families
that have a proband with/without gestational diabetes mellitus within the previous 5 years [ref].
11
BetaGene aims to test the association between gene variations and type 2 diabetes and related
quantitative traits (70).

RESEARCH DESIGN AND METHODS
Subject recruitment. Participation in BetaGene was restricted to Mexican Americans from families
of a proband with or without gestational diabetes mellitus diagnosed within the previous 5 years.
Patients at the Los Angeles County/USC Medical Center, OB/GYN clinics at local hospitals, and
the Kaiser Permanente health plan membership in Southern California were used to identify
probands. Probands were eligible if they (a) were of Mexican ancestry (defined as both parents
and ³3/4 of grandparents Mexican or of Mexican descent), (b) had a confirmed diagnosis of
gestational diabetes mellitus within the previous 5 years, (c) had glucose levels associated with
poor pancreatic b-cell function and a high risk of diabetes when not pregnant (71), and (d) had no
evidence of b-cell autoimmunity by GAD-65 antibody testing. Recruitment concentrated on two
general family structures using siblings and/or first cousins of GDM probands, all with fasting
glucose levels <126 mg/dl (7 mM): (a) at least 2 siblings and 3 first cousins from a single nuclear
family or (b) at least five siblings available for study. Using information from the probands to
determine preliminary eligibility, siblings and first cousins were invited to participate in screening
and, if eligible, detailed phenotyping and collection of DNA. Available parents and connecting
uncles and aunts were invited to give DNA and undergo a fasting glucose determination. In
addition, women of Mexican ancestry who completed pregnancy without gestational diabetes
mellitus, as evidenced by a plasma or serum glucose level <120 mg/dl after a 50 g oral glucose
screen for gestational diabetes mellitus, were also collected. Control probands were recruited
12
using the similar criteria as gestational diabetic mellitus probands, but additionally chosen to have
age, body mass index, and parity matched to the gestational diabetes mellitus probands.
All protocols for BetaGene were approved by the Institutional Review Boards of the
participating institutions.
Clinical Protocols. All participants required two visits to the University of Southern California
General Clinical Research Center for phenotyping. The first visit consisted of an oral glucose
tolerance test (OGTT) to identify women who match the eligibility requirements for the study,
assess glucose tolerance, and obtain other phenotypes such the insulinogenic index (72). A fasting
blood sample was collected via an indwelling IV catheter after an overnight fast, and the individual
consumed 75 g of dextrose. After dextrose intake, further samples were taken at 5, 30, 60, 90, and
120 minutes. Plasma was isolated, stored at -80 °C, and glucose and insulin levels were measured.
Glucose tolerance and fasting glucose level were interpretated based on American Diabetes
Association criteria (73). This visit also included a basic physical examination and DNA collection.
Probands and their siblings and cousins whose fasting glucose were <126 mg/ml in OGTT were
invited to return for second visit which included an intravenous glucose tolerance test (IVGTT) to
obtain quantitative measures of whole-body insulin sensitivity (SI), acute insulin response (AIR),
glucose effectiveness (SG) and the product of AIR and SI, which is known as disposition index.
(DI). Following an overnight fast and a fasting blood draw, dextrose (300mg/kg) was injected into
an antecubital vein followed by a 5-min infusion of insulin (0.3 U/kg) at 20 minutes post-injection.
A total of 21 arterialized venous blood samples were taken using the heated hand method. Plasma
was isolated immediately, stored at -80°C, and measured for glucose and insulin levels.
Additionally, body composition, including percentage of body fat, was measured by Dual Energy
X-ray Absorptiometry (DXA; Hologic QDR 4500W) at this visit.
13
Assays. DNA was isolated from 20 ml of blood using the Puregene DNA Purification Kit (Gentra
Systems). Yield was typically 450-600 µg and was quantified by spectrophotometry. Plasma
glucose was measured by glucose oxidase and lactate oxidase on an autoanalyzer (YSI 2300,
Yellow Springs, OH). Plasma insulin was measured by two-site immunoenzymometric assay
(Tosoh) with <0.1% cross-reactivity with proinsulin and split products. Genotyping was performed
using the ABI TaqMan system (Foster City, CA) (74; 75). Genotyping assays was selected through
ABI’s “Assays on Demand” database or custom designed using ABI’s “Assay by Design” service.
Phenotype definitions. The difference between the 30 minute and fasting plasma insulin
concentration from OGTT (30-minute Δinsulin) and the incremental area under the insulin curve
for during the first 10 min of the IVGTT (AIR) were used to quantify insulin response to glucose.
The insulinogenic index was derived by dividing the 30-minute Dinsulin by the 30-minute change
in glucose (30-minute glucose – fasting glucose). IVGTT glucose and insulin data were analyzed
using the minimal model (MINMOD Millennium V5.18, Minmod, Los Angeles, CA, USA) to
obtain estimates of SG and SI. The DI was calculated as the product of SI and early insulin response
(DI = SI × AIR from the IVGTT), which was a measure of beta cell compensation for insulin
resistance. An analogous index of beta cell compensation from the OGTT (DI30) using the 30-
minute Δinsulin (DI30 = SI ×30 minutes Δinsulin) from the OGTT was also calculated. (70; 76)
Data analysis. Genotype data were tested for deviation from the Hardy-Weinberg equilibrium and
for non-Mendelian inheritance using PEDSTATS version 0.6.4 (77) and allele frequencies were
estimated using SOLAR version 2.1.4 (78).
To assess that association between SNPs (single nucleotide polymorphisms) and
continuous phenotypes, the measured genotypes approach was utilized in a likelihood ratio
framework with variance components implemented in SOLAR. Covariate-adjusted residuals for
14
quantitative traits of interest were generated by linear modelling using SAS (SAS OnDemand for
Academics: https://www.sas.com/en_us/software/on-demand-for-academics.html). An additive
genetic model was assumed using the minor alleles the reference. The additive model was used
only if the minor allele frequency (MAF) ³ 0.25, which ensures n=15 for the rare homozygous
genotype. Thus, a=0.05 significance level is appropriate. The type 2 diabetes-related quantitative
traits I was interested in included acute insulin response, alanine transaminase (AST), aspartate
aminotransferase (AST), body mass index (BMI), cholesterol level, disposition index (DI), 30-
minute disposition index, fasting glucose, 30-minute glucose, 2-hour glucose, high-density
lipoprotein cholesterol, fasting insulin, 30-minute insulin, 2-hour insulin, insulinogenic index,
percentage body fat, glucose effectiveness (SG), whole-body insulin sensitivity (SI), triglycerides
level. Since I do not have measures of hepatic fat, liver enzyme levels (AST, ALT) were used as
proxies, and we additionally adjusted for alcohol intake for these QTs. Quantitative trait data were
statistically transformed to approximate univariate normality using inverse normal transformation.
Initially, I performed univariate analyses to test the association between GCK and GCKR
and the type 2 diabetes-related quantitative traits mentioned above. In this analysis, my linear
model included age, sex, and, where appropriate, percentage body fat as covariates. Covariate-
adjusted residuals generated from this model were used to test the association between genetic
variants and type 2 diabetes-related quantitative traits using likelihood ratio test under a variance
components framework as implemented in SOLAR.
For G × E interaction analyses, given the known biological interaction between GCKR and
dietary fructose, I performed a 1-df (degree of freedom) likelihood ratio test on the interaction term
between dietary fructose and GCKR to assess the trend for an interaction using a p<0.1 cutoff. If
the interaction term was significant at this cutoff, I subsequently performed a joint 2-df likelihood
15
ratio tests on the main effect and interaction term to assess the overall genetic association. By
stratifying the quantitative traits on different genotype combinations, the nature of any significant
interactions could be investigated. The covariates in linear model contained age, sex, and, where
appropriate, percentage body fat.
Since the hypothesized fructose effect on GCKR may extend to GCK, I performed an
additional analysis in which I added covariates GCKR, dietary fructose, and their interaction to
get a refined estimation of association between GCK and type 2 diabetes-related traits.
Unfortunately, due to the low minor allele frequency for GCK, I did not have sufficient
statistical power to perform the interaction analysis with GCK.

RESULTS
I report results from 2,579 BetaGene individuals in 545 families with available phenotype and
genotype data (Table 1). Characteristics are similar in male and female, except that female tend to
have higher percentage of body fat. Two GCK SNPs, rs1799884 and rs4607517, failed the test of
Hardy-Weinberg equilibrium and were not further analyzed. All analyses are performed using
GCK rs1799831 and GCKR rs780094. GCK rs1799831 (G>A) has minor allele frequency of
14.9%. GCKR rs780094 (G>A) has minor allele frequency of 32.1%.
In the primary analysis, GCK rs1799831 did not show statistically significant association
with any type 2 diabetes-related quantitative trait when adjusts for age and sex (Table 2) and did
the results did not change after adjusting for percentage of body fat. GCKR rs780094 is statistically
significantly associated with cholesterol level (b=0.12± 0.04 per A allele, p= 0.001), 30-minute
glucose levels (b=-0.08± 0.04 per A allele, p= 0.043), SG (b=0.12± 0.04 per A allele, p= 0.007),
and triglycerides level (b=0.16± 0.04 per A allele, p= 0.000012) adjusting for age and sex. Results
16
are similar when percentage of body fat is included in the model, with the additional statistically
significant association with fasting insulin level (b=-0.08± 0.04 per A allele, p= 0.014) (Table 3).
The interaction between GCKR and dietary fructose are found to be significantly associated
with some type 2 diabetes-related quantitative traits (Table 3). Even though the ALT level does
not show to be significantly associated the interaction term in the 1-df likelihood ratio test, GCKR
effect and dietary fructose effect in 2-df test are statistically significantly associated with ALT
(b=-0.002± 0.0007, p=0.0475 and b=-0.002± 0.0007, p=0.0025, respectively), but the GCKR
effect in the 2-df test becomes not statistically significant after additional adjustment for
percentage body fat. Interaction between GCKR and dietary fructose and BMI is statistically
significant in both 1-df likelihood ratio test and two 2-df tests, but the interaction does not remain
in these tests after adding percentage of body fat as covariate. GCKR and dietary fructose have a
significant interaction in 30-minute disposition index (b=-0.002± 0.001, p=0.0119), but the GCKR
effect is not statistically significantly associated with 30-minute disposition index in the 2-df
likelihood ratio test. Additional adjustment for percentage of body fat strengthens the interaction
between GCKR and dietary fructose for 30-minute disposition index (b=-0.002± 0.001, p=0.0002),
and the association stays significant with GCKR effect in the 2-df test (b=-0.002± 0.001, p=0.0017).
The interaction between GCKR and dietary fructose is not statistically significantly associated with
2-hour insulin before adjusting for percentage of body fat, but after the adjustment, marginally
significant interaction appears (b=0.003±0.002, p=0.0949). The interaction between GCKR and
dietary fructose is statistically significantly associated with SG (b=-0.004± 0.002, p=0.0235).
Nevertheless, the GCKR effect in 2-df likelihood ratio test has opposite association with SG
(b=0.143± 0.045, p=0.0009 and b=0.005± 0.002, p=0.0360, respectively). Additional adjustment
of percentage of body fat enhances the interaction between GCKR and dietary fructose and SG
17
slightly (b=-0.005± 0.002, p=0.0072 and b=0.134± 0.045, p=0.0005 and b=0.006± 0.002,
p=0.0162, respectively). The association between the interaction between GCKR and dietary
fructose and SI is not statistically significant before the adjustment of percentage of body fat,
although it is marginally significant in the 1-df test after the adjustment (b=-0.003± 0.002,
p=0.0794).
I did not observe any significant univariate results for GCK, but given the GCKR´Fructose
interaction, I decide to test the GCK associations adjusting for the GCKR´Fructose interaction,
which are summarized in Table 2. Statistically significantly associations are observed in ALT level
(b=0.123± 0.037 per A allele, p= 0.0034), 30-minute insulin level (b=-0.090± 0.037 per A allele,
p= 0.0235), and percentage of body fat (b=-0.089± 0.037 per A allele, p= 0.0186) when adjusting
for the effects of GCKR and fructose. The additional adjustment for body fat percentage reveals
statistically significant relationships between GCK variation and fasting glucose (b=0.098± 0.037
per A allele, p= 0.0116) and SG (b=-0.083± 0.037 per A allele, p= 0.0293). The GCK variation is
statistically significantly associated with AIR (b=-0.121± 0.037 per A allele, p= 0.0019), 2-hour
insulin level (b=-0.116± 0.037 per A allele, p= 0.0028), insulinogenic index (b=-0.092± 0.037 per
A allele, p= 0.01023), SI (b=-0.116± 0.037 per A allele, p= 0.0018), and triglyceride level (b=-
0.137± 0.037 per A allele, p= 0.0009), and additional correction for percentage of body fat
moderately reduces these associations (b=-0.104± 0.037 per A allele, p= 0.0073 and b=-0.098±
0.037 per A allele, p= 0.0126 and b=-0.076± 0.037 per A allele, p= 0.0314 and b=0.091± 0.037
per A allele, p= 0.0186 b=-0.132± 0.037 per A allele, p= 0.0018, respectively).

18
DISCUSSION
In our analysis, adjustments with or without percentage of body fat were similar, but because the
results did not substantially change after additional adjustment of percentage body fat, the
discussion of results will focus on the analyses without additional adjustment of percentage of
body fat. There are two novel findings from this study. First, in my interaction analysis, dietary
fructose has a statistically significant interaction with rs780094 in GCKR on several type 2
diabetes-related quantitative traits. This is first demonstration of the interaction between GCKR
and dietary fructose in vivo that confirms in vitro observations. Vaxillaire et al. (13) showed that
GCKR variation is linked to higher triglycerides levels in a general French population. In vitro
experiment, data obtained using comparable regulatory protein activities revealed the expected
reduction in GKRP-mediated inhibition of GCK activity (68). Our data show that the combined
effect of GCKR rs780094 and interaction term between dietary fructose and GCKR is related to
higher level of triglycerides in Mexican Americans. However, it is worth noting that the beta
direction of ALT and AST, which are liver enzyme shown to be correlated with liver fat levels, is
opposite to that of triglycerides levels. I have not yet come up with a reasonable explanation for
this result, which will require further analysis. Even after adjusting for alcohol consumption, it is
plausible that AST and ALT are easily affected by other factors, such as medication use. In the 2-
df likelihood ratio tests, SG presents to have a positive association with GCKR and dietary fructose.
SG represents glucose disappearance at fasting insulin levels, and it has been shown that
approximately 30% of this parameter is due to hepatic glucose uptake (79). The effect of the
interaction between GCKR and fructose to increase SG provides the first in vivo evidence consistent
with the previous in vitro studies. This is also consistent with our hypothesis that GCKR variants
and elevated dietary fructose stimulate the formation of hepatic fat substrates by increasing hepatic
19
glucose uptake. These findings are comparable to the univariate analysis of GCKR in the primary
analysis.
Second, in the univariate analysis of GCK rs1799831 with type 2 diabetes-related
quantitative traits, no statistically significant relationship is found. Considering of the potential
biological regulation among GCKR, GCK and dietary fructose, I adjust for those components in
our third analysis. Subsequently, the statistically significant relationships between type 2 diabetes-
related quantitative traits and GCK can be found. The results show that the A-allele of GCK
rs1799831 is associated with higher fasting glucose level and lower 2-hour insulin level from the
OGTT, which means that the A-allele of GCK leads to reduced hepatic glucose uptake. This is
also in line with decreased triglycerides levels. Similar in the results appears in the second analysis,
the beta direction of ALT and AST is also in the opposite way of triglycerides levels.
It is noteworthy that the betas of fasting glucose, SG, ALT, and triglycerides in our second
and third analysis are all in completely inverse directions. I can say that the minor alleles in GCK
rs1799831 and GCKR rs780094 have opposite effects on hepatic glucose uptake. This probably
explains that why none of the univariate analysis between GCK and type 2 diabetes-related
quantitative traits are statistically significant. As a result, I believe that further analysis is necessary
to figure out the additive effects of GCK rs1799831, GCKR rs780094, and dietary fructose on the
type 2 diabetes-related quantitative traits, and their relationship with liver fat accumulation.
In conclusion, I observe evidence for a statistically significantly interaction between
dietary fructose and GCKR rs780094 on some type 2 diabetes-related quantitative traits to increase
hepatic glucose uptake in Mexican Americans, suggesting a positive contribution to the
accumulation of liver fat. Additionally, after adjusting for GCKR, dietary fructose, and their
interaction term, GCK shows patterns of association that contribute negatively to the accumulation
20
of liver fat. It is worth mentioning that before the adjustment, univariate analysis of GCK did not
show significant results, indicating the complicated biological relationships among GCK, GCKR,
and dietary fructose. My results are consistent with the substrate balance hypothesis, providing
evidence that these genetic variants may contribute to accumulation of hepatic fat and the
development of subsequent hepatic insulin resistance. Further analysis and exploration are still
needed to identify the combined effect of them on liver fat accumulation.

21
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38
Table 1 Subject characteristics
*

Male Female Male/female
†

Age (years) 32.90 (17.30) 34.40 (13.9) 1058/1521
BMI (kg/m
2
) 27.60 (6.80) 28.15 (8.70) 1031/1490
Body fat (%) 25.2 (6.70) 38.80 (7.80) 799/1235
Fasting glucose (mM) 5.10 (0.80) 4.90 (0.80) 884/1344
30' glucose (mM) 8.40 (2.10) 7.80 (2.00) 690/1073
2-h glucose (mM) 6.80 (2.40) 7.30 (2.70) 687/1072
Fasting insulin (pM) 42 (47) 42 (42) 884/1343
30' insulin (pM) 396 (366) 360 (312) 690/1071
2-h insulin (pM) 312 (360) 378 (378) 687/1071
Insulinogenic Index 19.15 (17.4) 19.0 (16.8) 690/1070
30' Disposition index 1485 (1271) 1421 (1170.50) 322/840
Cholesterol (mmol/L) 4.76 (1.26) 4.42 (1.16) 790/1242
HDL cholesterol (mmol/L) 1.06 (0.31) 1.24 (0.36) 791/1242
Triglycerides (mmol/L) 1.32 (1.06) 1.04 (0.81) 789/1242
ALT (U/L) 41 (24) 29 (19) 783/1223
AST (U/L) 24 (11) 20 (8) 781/1227
SG (×10
-2
min
-1
) 1.74 (0.85) 1.66 (0.81) 347/903
SI (×10
-3
min
-1
per pM) 4.45 (3.40) 4.63 (3.16) 347/903
AIR (pM × 10
-3
min) 2782 (3360) 2510 (2760) 347/903
Disposition index 12563 (12501) 11710 (11428) 347/903

*
Data are unadjusted median (interquartile range) unless otherwise indicated.
†
Sample size of male/female
39
Table 2. Association results between GCK rs1799831 and type 2 diabetes-related quantitative
traits.

Additional adjustment for
GCRK rs780094, fructose and
their interaction term
p value
0.807
0.019
0.105
0.215
0.817
0.059
0.023
2.83× 10
-3

0.01
0.655
1.87 × 10
-3

0.723
9.46× 10
-4

3.35 × 10
-3

0.72
0.138
1.80× 10
-3

0.349
0.236
SE
0.0374
0.0373
0.0374
0.0372
0.0372
0.0374
0.0372
0.0372
0.0372
0.0341
0.0374
0.0374
0.0374
0.0373
0.0373
0.0374
0.0374
0.0377
0.0375
b
0.0158
-0.0809
0.0653
0.0431
-0.012
-0.0687
-0.0904
-0.1157
-0.0919
0.0123
-0.1211
0.0197
-0.1372
0.1234
0.0078
-0.0582
0.1161
-0.0349
-0.0458

Adjusted for age and sex
p value
0.724
0.073
0.357
0.606
0.628
0.204
0.209
0.247
0.143
0.913
0.169
0.455
0.766
0.758
0.714
0.637
0.271
0.654
0.484
SE
0.0537
0.0533
0.0511
0.0559
0.0553
0.0509
0.0557
0.0554
0.0556
0.0346
0.0648
0.054
0.0535
0.0254
0.0254
0.0648
0.0647
0.0503
0.065
b
0.0406
-0.0614
0.0525
0.0298
0.0309
-0.0559
-0.087
-0.0812
-0.0902
0.0003
-0.1133
-0.0181
-0.0257
-0.0022
0.0026
-0.0459
0.0859
-0.0194
-0.047

Traits
BMI (kg/m
2
)
Body fat (%)
Fasting glucose (mM)
30' glucose (mM)
2-h glucose (mM)
Fasting insulin (pM)
30' insulin (pM)
2-h insulin (pM)
Insulinogenic Index
30' Disposition index
Cholesterol (mmol/L)
HDL cholesterol (mmol/L)
Triglycerides (mmol/L)
ALT (U/L)
AST (U/L)
SG (×10
-2
min
-1
)
SI (×10
-3
min
-1
per pM)
AIR (pM × 10
-3
min)
Disposition index

40
Table 3 Association results between GCKR rs780094 and type 2 diabetes-related quantitative
traits.
2df GCKR rs780094
p value
0.024
0.289
0.707
0.691
0.533
0.564
0.923
0.214
0.396
0.296
0.434
0.61
0.284
2.46 × 10
-3

0.092
0.036
0.849
0.58
0.324
SE
0.0019
0.0021
0.0019
0.0019
0.0019
0.0019
0.0019
0.0019
0.0019
0.0006
0.0018
0.0019
0.0018
0.0007
0.0007
0.002
0.002
0.002
0.002
b
0.0026
1.62 × 10-6
-3.15× 10-5
-0.0013
-0.0018
2.98 × 10
-6

0.0004
-0.0036
0.0016
0.001
-0.001
4.40 × 10-5
-0.0013
-0.0022
-0.0015
0.0047
0.0013
0.0025
0.0038

1df interaction
p
value
0.007
0.158
0.41
0.923
0.922
0.328
0.768
0.287
0.973
0.012
0.51
0.427
0.582
0.316
0.295
0.024
0.782
0.342
0.31
SE
0.0017
0.0019
0.0018
0.0017
0.0018
0.0017
0.0017
0.0018
0.0018
0.0006
0.0017
0.0018
0.0016
0.0007
0.0007
0.0018
0.0018
0.0018
0.0018
b
-0.0061
-0.0031
-0.0005
0.0002
0.0002
-0.0023
-0.0008
0.0017
-0.0007
-0.0016
-0.0005
0.0004
0.0001
-0.0006
-0.0007
-0.0042
0.0001
-0.0023
-0.0021

univariate
p value
0.791
0.839
0.113
0.043
0.977
0.058
0.68
0.104
0.572
0.996
0.001
0.644
1.20 × 10
-5

0.827
0.326
0.007
0.149
0.699
0.169
SE
0.0339
0.0363
0.0337
0.037
0.0369
0.0342
0.037
0.0371
0.037
0.023
0.036
0.0362
0.0358
0.0127
0.0127
0.0441
0.0438
0.0438
0.0439
b
-0.0271
-0.0244
-0.0526
-0.0826
-0.0198
-0.0777
-0.0207
-0.0781
0.0281
5.42 × 10
-5

0.1183
0.0042
0.1622
-0.0026
-0.0123
0.1245
0.063
0.0132
0.0573

Traits
BMI (kg/m
2
)
Body fat (%)
Fasting glucose (mM)
30' glucose (mM)
2-h glucose (mM)
Fasting insulin (pM)
30' insulin (pM)
2-h insulin (pM)
Insulinogenic Index
30' Disposition index
Cholesterol (mmol/L)
HDL cholesterol
(mmol/L)
Triglycerides (mmol/L)
ALT (U/L)
AST (U/L)
SG (×10
-2
min
-1
)
SI (×10
-3
min
-1
per pM)
AIR (pM × 10
-3
min)
Disposition index
41

Figure 1 Schematic of our hepatic substrate balance hypothesis. Blue: genes; Green:
environmental factors. See text for details.

42

Figure 2 Pathways contributing to steatosis. Genetic loci of interest are highlighted in blue and
dietary fructose in green. Regulatory effects are depicted by dashed arrows. GA =
glyceraldehyde; G3P = glycerol 3 phosphate; ACoA = acetylCoA.

Abstract (if available)

Abstract OBJECTIVES— Genetic variants of glucokinase (GCK), glucokinase regulator (GCKR), patatin- like phospholipase domain-containing protein 3 (PNPLA3) have been shown to be associated with type 2 diabetes and diabetes-related traits. According to this, I propose the hepatic substrate balance hypothesis, which considers that the genetic variation of GCK, GCKR, PNPLA3 results in an imbalance in substrate flow into and out of the liver. GCK participates in the glucose uptake process and GCKR regulate the enzyme activity of GCK. As a result, it is possible that genetic variation affects the separate activities of GCK and GCKR, as well as their interaction, affecting hepatic glucose absorption and the transformation of glucose to fat. PNPLA3 hydrolyzes fat to triglycerides, and genetic variations may affect PNPLA3's ability to break down fat in the liver. The amount of fat in the liver is a result of a balance between these two processes, and an imbalance leads to the accumulation of liver fat, which leads to hepatic insulin resistance. I further postulate that dietary fructose, which also add to liver fat, may aggravate the substrate imbalance caused by genetic variation via regulating GCKR.
RESEARCH DESIGN AND METHODS— In Mexican Americans from the BetaGene Study, I investigated if these variations were linked to diabetes-related quantitative features. Oral glucose tolerance test (OGTT), intravenous glucose tolerance test (IVGTT), and dual-energy X-ray absorptiometry scan were used to obtain phenotypic information on study participants.
RESULTS— In the primary analysis, GCK rs1799831 do not show statistically significant association with any type 2 diabetes-related quantitative trait when adjusts for age and sex and did not change after adjusting for percentage of body fat. GCKR rs780094 is statistically significantly associated with cholesterol level, 30-minute glucose levels (p=0.043), SG (p=0.007), and triglyceride levels (p=0.00001) adjusting for age and sex. The interaction between GCKR and dietary fructose are found to be significant in some type 2 diabetes-related quantitative traits. In the test of the GCK associations adjusting for the GCKR ́Fructose interaction, statistically significantly associations also are observed in several type 2 diabetes-related quantitative traits.
CONCLUSIONS— My results show evidence for a statistically significantly interaction between dietary fructose and GCKR rs780094 on some type 2 diabetes-related quantitative traits to increase hepatic glucose uptake in Mexican Americans, suggesting a positive contribution to the accumulation of liver fat. Additionally, after adjusting for GCKR, dietary fructose, and their interaction term, GCK shows patterns of association that contribute negatively to the accumulation of liver fat. It is worth mentioning that before the adjustment, univariate analysis of GCK did not show significant results, indicating the complicated biological relationships among GCK, GCKR, and dietary fructose.

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Asset Metadata

Creator Ma, Leona (author)

Core Title The influence of dietary fructose on genetic effects of GCK and GCKR in Mexican Americans

School Keck School of Medicine

Degree Master of Science

Degree Program Biostatistics

Degree Conferral Date 2022-05

Publication Date 04/11/2022

Defense Date 04/09/2022

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

Tag Diabetes,dietary fructose,GCK,GCKR,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Choudhury, Farzana (committee chair), Watanabe, Richard (committee chair), Piao, Jin (committee member)

Creator Email leona.ma.0812@qq.com,yunyunma@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC110920247

Unique identifier UC110920247

Document Type Thesis

Format application/pdf (imt)

Rights Ma, Leona

Type texts

Source 20220411-usctheses-batch-921 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu