Journal of Clinical Endocrinology & Metabolism
, doi:10.1210/jc.2007-1695
The Journal of Clinical Endocrinology & Metabolism Vol. 92, No. 12 4893-4896
Copyright © 2007 by The Endocrine Society
A Quantitative Trait Locus on Chromosome 13q Affects Fasting Glucose Levels in Hispanic Children
Guowen Cai,
Shelley A. Cole,
Nancy F. Butte,
V. Saroja Voruganti and
Anthony G. Comuzzie
United States Department of Agriculture/Agricultural Research Service Children’s Nutrition Research Center (G.C., N.F.B.), Baylor College of Medicine, Houston, Texas 77030; and Department of Genetics (S.A.C., V.S.V., A.G.C.), Southwest Foundation for Biomedical Research, San Antonio, Texas 78245
Address all correspondence and requests for reprints to: Nancy F. Butte, Ph.D., Baylor College of Medicine, United States Department of Agriculture/Agricultural Research Service Children’s Nutrition Research Center, 1100 Bates Street, Houston, Texas 77030. E-mail: nbutte{at}bcm.tmc.edu.
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Abstract
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Objective: The prevalence of childhood obesity has increased dramatically in the United States. Early presentation of type 2 diabetes has been observed in children and adolescents, especially in the Hispanic population. The genetic contribution of glucose homeostasis related to childhood obesity is poorly understood. The objective of this study was to localize quantitative trait loci influencing fasting serum glucose levels in Hispanic children participating in the Viva La Familia Study.
Design: Subjects were 1030 children ascertained through an overweight child from 319 Hispanic families. Fasting serum glucose levels were measured enzymatically, and genetic linkage analyses were conducted using SOLAR software.
Results: Fasting glucose was heritable, with a heritability of 0.62 ± 0.08 (P < 0.01). Genome-wide scan mapped fasting serum glucose to markers D13S158–D13S173 on chromosome 13q (LOD score of 4.6). A strong positional candidate gene is insulin receptor substrate 2, regulator of glucose homeostasis and a candidate gene for obesity. This region was reported previously to be linked to obesity- and diabetes-related phenotypes.
Conclusions: A quantitative trait locus on chromosome 13q contributes to the variation in fasting serum glucose levels in Hispanic children at high risk for obesity.
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Introduction
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CHILDHOOD OBESITY IN the United States has dramatically increased in recent decades, especially among Mexican-American children. Childhood obesity is associated with several metabolic and endocrine disorders, including hyperinsulinemia and hyperlipidemia, that predispose to early development of type 2 diabetes (T2D), cardiovascular disease, and hypertension. In the 1999–2002 National Health and Nutrition Examination Survey, Hispanic adolescents had the highest values for homeostasis model assessment of insulin resistance (HOMA-IR) (1). By 2031, it is projected that the Hispanic community will have an overwhelming diabetes burden, with more than 20% of adults affected (2).
The pathophysiology leading to diabetes in Hispanic children and adolescents has yet to be completely understood. Genetic linkage and association studies in adults suggest that genetic loci may underlie predisposition to obesity and T2D (3, 4). However, only a small number of linkage studies on obesity in children and none on diabetes risk factors have been published. Therefore, the Viva La Familia Study was designed to map childhood obesity and its comorbidities in the Hispanic population using a genome-wide scan. Compared with fasting glucose, the oral glucose tolerance test is more sensitive in diagnosing diabetes but less convenient to administer in over 1000 children. Here within, we report quantitative trait loci (QTLs) influencing fasting serum glucose in Hispanic children.
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Subjects and Methods
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Study design
The study design of the Viva La Familia Study was described in detail previously (5). Briefly, in-depth phenotyping and genotyping were performed on a total of 1030 children from 319 families recruited through an overweight proband, defined as at least 95th percentile age- and sex-specific percentile for body mass index (BMI) and at least 85th percentile for fat mass (FM). Mean age of the cohort was 11 ± 4 yr (range of 4–19 yr). All subjects gave written informed consent or assent. The protocol was approved by the Institutional Review Boards for Human Subject Research for Baylor College of Medicine and Affiliated Hospitals and for Southwest Foundation for Biomedical Research.
Phenotypes
Phenotyping of the children was performed at the Children’s Nutrition Research Center (Houston, TX). Family pedigree, sociodemographic background, and medical histories were obtained from the parents. Body weight to the nearest 0.1 kg was measured with a digital balance, and height to the nearest 1 mm was measured with a stadiometer. Body composition, partitioned into fat-free mass and FM, was determined by dual-energy x-ray absorptiometry using a Hologic Delphi-A whole-body scanner (Delphi-A; Hologic, Waltham, MA). Blood pressure was measured three times using a DINAMAP Vital Signs Monitor (8100T; Critikon, Tampa, FL). A blood sample was drawn in the morning after a 12-h overnight fast on our metabolic research unit.
Fasting serum biochemistries were assayed by enzymatic-colorimetric techniques using the GM7 Analyzer (Analox Instruments, Lundeburg, MA) and Microquant Platereader (Biotek Instruments, Winooski, VT). Glucose was assayed using glucose oxidase. Total triglycerides were determined using lipase, glycerol kinase, glycerol phosphate oxidase, and peroxidase (Thermo Electron Corporation, Louisville, CO). Total cholesterol and high-density lipoprotein cholesterol (HDL) were analyzed using cholesterol esterase, cholesterol oxidase, and peroxidase (Thermo Electron Corporation). Low-density lipoprotein (LDL) cholesterol was calculated as follows: TC – HDL – (TG/5), in which TG is total triglycerides and TC is total cholesterol. Serum insulin and c-peptide were measured using commercial RIA kits (Linco Research, St. Charles, MO).
Genotypes
DNA was extracted from whole blood using the Wizard Genomic DNA Purification Kit (Promega, Madison, WI). Markers with fluorescently labeled PCR primers were obtained from the ABI PRISM Linkage Mapping Set-MD10 version 2.5 (Applied Biosystems, Foster City, CA). PCR amplify dinucleotide single tandem repeats selected from the Genethon human linkage map in Applied Biosystems 9700 thermocyclers. The single tandem repeats were quantified by fluorescent emissions by comparison with the standard, and genotypes were scored using the Genotyper software package (Applied Biosystems). The markers spaced an average of 10 centimorgans (cM) apart on the constructed chromosomal map.
Quantitative genetic analyses
The phenotypic variance in fasting serum glucose was decomposed into additive genetic and environmental components. Heritability (h2) is the proportion of the total phenotypic variance that is attributable to the additive effects of genes (6). Bivariate analysis is an extension of the univariate genetic analysis, in which the phenotypic relationship (
p) between two traits is partitioned into genetic (G) and environmental correlations (E):
Here, h12 and h22 are the heritabilities of the two traits. A null model with the genetic correlation constraint to zero is compared with another model, in which all parameters are estimated by the maximum likelihood method. A significant genetic correlation suggests that a common set of genes influence both traits.
A genome-wide scan was performed to localize QTLs influencing fasting serum glucose. The variance of a phenotype is a function of the identical by descent relationship when a polymorphic marker is linked to the trait. The phenotypic variance is decomposed into a specific QTL effect, other additive genetic effects, and random environmental effects (7). An LOD score of at least 3 represents a statistically significant QTL effect, which corresponds to a genome-wide P value 
0.05. The fasting glucose trait was not normally distributed. Thus, it was log transformed, and outliers were removed before entering the analysis. We adjusted the fasting serum glucose levels with gender, age, age2, and their interactions using a linear regression method. Then the residuals were normalized before the genome-wide scan. Tanner stage was not used in the analysis because its effect was captured by including age, age2, sex, and their interactions into the analyses. These genetic analyses were implemented in SOLAR 2.0 (Southwest Foundation for Biomedical Research, San Antonio, TX). An ascertainment correction conditioning on the trait value of the proband was automatically performed in SOLAR software to obtain unbiased parameter estimates in the variance component model.
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Results
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In this cohort of 1030 children, 51% were classified as overweight. Percentage FM averaged 40% in the overweight children compared with 26% in the non-overweight children. T2D was reported in 11 and 8% of the mothers and fathers, 26 and 24% of maternal grandmothers and grandfathers, and 22 and 18% of paternal grandmothers and grandfathers, respectively.
Fasting serum glucose, insulin, c-peptide, and HOMA-IR were significantly higher in the overweight than the non-overweight children (P < 0.01) (Table 1
). A total of four children had a fasting glucose level above 126 mg/dl (diabetic, not taking any diabetic medications at the time of measurements), and 143 children presented with levels between 100 and 126 mg/dl (impaired fasting glucose). Fasting serum triglycerides and LDL were abnormally elevated in 25 and 20% of the overweight children, respectively. Systolic blood pressure was greater than the 95th percentile for age, sex, and height in 26% of the overweight children.
Fasting serum glucose level was significantly heritable, with a heritability of 0.62 ± 0.08. Suggestive linkage for fasting serum glucose was found on chromosomes 7 near D7S657 (LOD score of 2.2), 10 between D10S597 and D10S1693 (LOD score of 2.6), and 19 between D19S884 and D19S221 (LOD score of 2.7), and highly significant linkage on chromosome 13 (LOD score of 4.6) (Fig. 1). The highly significant QTL for fasting serum glucose mapped to markers D13S158–D13S173 (85–99 cM, according to Marshfield Genetic Maps) on chromosome 13q (Fig. 2). Bivariate analyses revealed significant genetic correlations between fasting serum glucose and c-peptide (G = 0.26; P = 0.03) and insulin (G = 0.27; P = 0.02) but not with body weight, BMI, fat-free mass, FM, and percentage FM, or other fasting biochemistries (HDL, LDL, and triglycerides) or blood pressure. The linkage analyses of insulin and c-peptide mapped to chromosome 1 and were reported previously (8). The bivariate linkage analyses of glucose and c-peptide (or insulin) did not improve the glucose linkage signals.
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Discussion
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Fasting serum glucose in Hispanic children enrolled in the Viva La Familia Study was shown to be heritable, with a moderate heritability of 0.62. Significant evidence of pleiotropy was found between fasting serum glucose and insulin and c-peptide, as might be expected given the pivotal role of insulin in the regulation of serum glucose. However, we did not detect any significant genetic correlations between fasting glucose and other metabolic risk factors such as FM, lipids, or blood pressure, possibly because of the compensatory insulin secretion necessary to tightly control glucose levels, consistent with previous adult studies.
Fasting serum glucose mapped to a region between markers D13S158 and D13S173 (85–99 cM, according to Marshfield Genetic Maps) on chromosome 13q (LOD score of 4.6). Our QTL replicates previously reported linkage results for obesity- and diabetes-related phenotypes. Linkage was documented nearby marker D13S779 (83 cM, according to Marshfield Genetic Maps) associated with BMI (9, 10), central obesity (11), and waist circumference (12). Linkage nearby marker D13S285 (111 cM, according to Marshfield Genetic Maps) was related to obesity before age 35 yr (12), abdominal sc fat (13), and HOMA-IR index (14).
Our suggestive signal on 7q replicated the results of a linkage study on BMI and waist circumference in 440 adults (15). The 10q region duplicated a study on obesity (BMI > 27 kg/m2) in French Caucasians (16) and waist-to-hip ratio in whites and African-Americans (17). A childhood obesity linkage study in French families indicated that the age-related adiposity rebound was linked to the same area on chromosome 19 in our study (18). The insulin receptor gene INSR resides 1 cM to the D19S884 marker.
Insulin receptor substrate 2 (IRS2) is a strong candidate gene in the chromosome 13 area. Animal models substantiate that dysfunction of IRS2 may contribute to the pathophysiology of T2D and obesity. In obese Canadian women, IRS2 gene polymorphisms were influential in severe obesity and glucose intolerance (19). A strong association was found in Italian patients between T2D and the G1057D common genetic variant of IRS2, which appears to be protective against T2D in a codominant manner (20).
In addition to IRS2, this QTL encompasses only 26 other genes, half of which are hypothetical proteins of unknown function. Given the advances in sequencing technology, it is now feasible to sequence this entire region directly to identify the causal genetic variants contributing to the variation in fasting glucose levels in these Hispanic children.
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Acknowledgments
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We thank the families who participated in this study and acknowledge the contributions of Mercedes Alejandro and Marilyn Navarrete for study coordination, Sopar Seributra for nursing, and Theresa Wilson, Tina Ziba, Maurice Puyau, Firoz Vohra, Anne Adolph, Roman Shypailo, JoAnn Pratt, and Maryse Laurent for technical assistance. This work is a publication of the United States Department of Agriculture/Agricultural Research Service Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine and Texas Children’s Hospital, Houston, Texas. This project was funded with federal funds from the National Institutes of Health Grant R01 DK59264 and from USDA/ARS under Cooperative Agreement 58-6250-51000-037.
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Footnotes
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Disclosure Information: All authors have nothing to declare.
First Published Online October 9, 2007
Abbreviations: BMI, Body mass index; cM, centimorgans; FM, fat mass; HDL, high-density lipoprotein; HOMA-IR, homeostasis model assessment of insulin resistance; IRS2, insulin receptor substrate 2; LDL, low-density lipoprotein; QTL, quantitative trait locus; T2D, type 2 diabetes.
Received July 30, 2007.
Accepted September 27, 2007.
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Abstract
Introduction
