Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2012 Aug 1.
Published in final edited form as: Obesity (Silver Spring). 2012 Feb 6;20(8):1675–1682. doi: 10.1038/oby.2012.11

Association of Type 2 Diabetes Susceptibility Loci With One-Year Weight Loss in the Look AHEAD Clinical Trial

Inga Peter 1, Jeanne M McCaffery 2, Alyson Kelley-Hedgepeth 3, Hakon Hakonarson 4, Steven Reis 5, Lynne E Wagenknecht 6, Alan S Kopin 3, Gondon S Huggins, for the Genetics Subgroup of the Look AHEAD Study3
PMCID: PMC3406235  NIHMSID: NIHMS367219  PMID: 22307069

Abstract

The importance of lifestyle intervention for the prevention and treatment of type 2 diabetes (T2D) has been underscored by the limited benefit of pharmacologic therapies. We sought to determine whether genetic variants that contribute to T2D risk modify the response of weight and waist circumference to an intensive lifestyle intervention (ILI) in patients with obesity and T2D. Look AHEAD (Action for Health in Diabetes) is a randomized clinical trial comparing an ILI with a control condition on the risk of cardiovascular disease in overweight adults with T2D. We analyzed 28 single-nucleotide polymorphisms (SNPs) at/near 17 T2D-susceptibility genes in 3,903 consented participants. We genetically characterized the cohort by assessing whether T2D-susceptibility loci were overrepresented compared with a nondiabetic community-based cohort (N = 1,016). We evaluated the association of individual variants and a composite genetic risk score (GRS) with anthropometric traits at baseline and after 1-year of intervention. Look AHEAD subjects carried more T2D-susceptibility alleles than the control population. At baseline, TCF7L2 risk alleles and the highest GRS were associated with lower BMI and waist circumference. Nominally significant genotype-by-intervention interactions were detected for 1-year change in waist circumference with JAZF1, MTNR1B, and IRS1, and BMI with JAZF1. Highest GRS was associated with a greater reduction in waist circumference at year 1, although the variance in change attributable to the GRS was small. This study shows that the genetic burden associated with T2D risk does not undermine the effect of lifestyle intervention and suggests the existence of additional genomic regions, distinct from the T2D-susceptibility loci, which may enhance or mitigate weight loss.

INTRODUCTION

Obesity and type 2 diabetes mellitus (T2D) are major public health problems associated with increased risk of morbidity from cardiovascular disease (CVD; refs. 1,2) that result from an interplay of environmental and genetic factors. Weight loss and increased physical activity have been shown to significantly reduce the risk of developing T2D, hypertension, CVD, and other diseases (3,4). Furthermore, weight loss can considerably improve glucose and lipid metabolism in the setting of established T2D (5,6). However, little is known whether genetic variants predisposing to T2D can modify the response to intensive lifestyle intervention (ILI) in the setting of established T2D. Recently, a number of T2D-susceptibility loci have been identified through genome-wide association studies (7–9) and replicated in multiple independent cohorts (10–13), providing new insights into the genetic factors that contribute to the development of T2D. The transcription factor 7-like 2 gene (TCF7L2) was one of the first genes to be identified by this approach, and it has emerged as an important diabetes gene in numerous cohorts (7,8,12,14). Additional genes found to be associated with T2D include a zinc transporter (SLC30A8; ref. 9), a homeobox protein (HHEX; ref. 9), a zinc finger protein (JAZF1; refs. 8,9), insulin-like growth factor mRNA binding protein-2 (IGF2BP2; refs. 9,10), a potassium inwardly rectifying channel (KCNJ11; ref. 8), the cell cycle regulators (CDKAL1; refs. 9,10,15,16) and CDKN2A/2B (refs. 9,10,17), a glucokinase regulator (GCKR; refs. 18,19), and a disintegrin and metalloproteinase with thrombospondin motifs 9 (ADAMTS9; ref. 8), as well as peroxisome proliferator–activated receptor γ(PPARG; refs. 8,9), glucokinase (GCK; ref. 18), neurogenic locus notch homolog protein 2 (NOTCH2; ref. 8), insulin receptor substrate 1 (IRS1; refs. 9,11), member of the potassium voltage-gated channel KQT-like subfamily (KCNQ1; refs. 9,17), melatonin receptor 1B (MTNR1B; refs. 9,18), and wolframin (WFS1; refs. 9,11), a gene causing Wolfram syndrome that gives rise to diabetes insipidus, diabetes mellitus, optic atrophy, and deafness.

The Diabetes Prevention Program (DPP), a randomized clinical trial aimed at discovering whether modest weight loss through dietary changes and increased physical activity or treatment with glucose-lowering medications could prevent or delay the onset of T2D, has conducted a series of studies to determine differences in diabetes and obesity progression with regard to intervention and carriage status for T2D-susceptibility loci. DPP has reported that carriers of the TCF7L2 risk alleles were more likely to transit from impaired glucose tolerance to frank diabetes during a 3-year period of observation (20). By comparison, SLC30A8, CDKN2A/B, IGF2BP2, and HHEX alleles were not associated with differential risk of conversion to diabetes in DPP, while carriers of the protective genotype at CDKN2A/B showed nominal improvement in β-cell function after 1 year of a lifestyle modification (21). KCNJ11 risk variants have been associated with a trend toward a greater likelihood of developing diabetes in the Finnish Diabetes Prevention Study (22), but appeared to be protective against the development of T2D in the DPP study (23). Moreover, while P12/P12 homozygotes at PPARG P12A were more likely to develop diabetes than A12 carriers, no interaction of genotype with intervention has been reported (24). Conversely, a PPARG P12A genotype-by-intervention interaction on 1-year weight change has been reported with a greater weight loss and decrease in subcutaneous adipose tissue in A12 carriers (25). Furthermore, it has been suggested that protective effect of select WFS1 alleles may be magnified by a lifestyle intervention (26). However, no genetic modification of response to lifestyle intervention has been reportedly assessed in individuals with established T2D.

The Action for Health in Diabetes (Look AHEAD) randomized clinical trial demonstrated that an ILI, involving weight loss and physical activity, produced significantly greater weight loss and improved measures of glucose control in individuals with established T2D after 1 year compared with a control intervention of diabetes support and education (DSE; refs. 27,28). Here, we genetically characterized a subset of the Look AHEAD participants with regard to T2D genetic risk alleles and evaluated the associations of individual variants and a combined genetic score with anthropometric measurements at baseline and with 1 year responses to ILI and DSE. A differential response to intervention-by-genotype would help identify biological pathways involved in weight loss and provide an improved understanding of the pathophysiology and treatment of T2D.

METHODS AND PROCEDURES

Populations

Look AHEAD study

The design and methods of the Look AHEAD trial have been reported elsewhere (29), as have the baseline characteristics of the entire randomized cohort (28). Among the 5,145 ethnically diverse overweight or obese (BMI >25 kg/m2) Look AHEAD subjects with T2D and aged 45–76 years at baseline, 4,041 (2,838 non-Hispanic whites, 618 African Americans, 306 Hispanics, 43 Asians/Pacific Islanders, 20 American Indians/Native Americans, and 78 other or mixed ethnicities) provided consent and DNA for genetic analysis. T2D was determined by self-report with verification (medical records, current treatment, verification from personal health-care provider, or fasting glucose 126 mg/dl, symptoms of hyperglycemia with casual plasma glucose 200 mg/dl or 2-h plasma glucose 200 mg/dl after a 75-g oral glucose load on at least two occasions). Individuals with hemoglobin A1c >11% were excluded, as were individuals who had a clinical history strongly suggestive of type 1 diabetes (29). The baseline values and 1-year changes in BMI and waist circumference in the genetic subcohort were similar to those reported in the entire Look AHEAD cohort (27).

Heart Strategies Concentrating on Risk Evaluation study

Heart Strategies Concentrating on Risk Evaluation (Heart SCORE) is a single-site prospective community-based cohort study investigating the mechanisms accountable for population disparities in CVD (30). Nondiabetic controls, with fasting blood glucose <126 mg/dl and no antidiabetic treatment, consisted of 1,016 individuals (670 of European descent and 346 of African descent).

The Look AHEAD trial and the Heart SCORE study, including their genetic analyses, were approved by local institutional review boards.

Measures

The standardized methods for body height and weight used in the Look AHEAD and Heart SCORE studies have been described in previous publications (28,30). In both studies, BMI was calculated as weight in kilograms divided by height in meters squared. Waist circumference (cm) was measured with subjects in light clothing with a nonmetallic, constant tension tape placed around the body at the midpoint between the highest point of the iliac crest and the lowest part of the costal margin in the mid-axillary line.

Genotyping and single-nucleotide polymorphisms selection

DNA was extracted from peripheral blood leukocytes using standard methods. Genotyping was carried out using the Illumina CARe iSelect (IBC) chip versions 1 and 2, a gene-centric ~50,000 single-nucleotide polymorphism (SNP) array designed to assess potentially relevant loci across a range of cardiovascular, metabolic, and inflammatory syndromes (31).

We selected 28 SNPs at or near 17 genes reported to be associated with T2D by genome-wide association studies and that were present on the IBC assay (31). Disease-associated SNPs not on the IBC chip were replaced by proxies where possible based on haplotype maps of individuals of European ancestry and Yoruba people of Ibadan and their characteristics are provided in Supplementary Table S1 online. After excluding individuals who missed the 1-year visit (N = 25), failed the IBC chip genotyping (N = 88), or had discrepancy between self-reported and X-chromosome-determined gender (N = 25), the Look AHEAD cohort consisted of 3,903 individuals. The mean genotyping success rate for the candidate SNPs was 99.6%.

Statistical analysis

Observed genotype frequencies were compared with those expected under Hardy–Weinberg equilibrium using a χ2 test in each ethnic group separately as were allele frequencies between Look AHEAD and the Heart SCORE study. To control for admixed study population in both cohorts, we conducted a principle component analysis using 40,513 SNP markers and applying the EIGENSTRAT algorithm (32) as implemented in Golden Helix, version 7.1 (Bozeman, MT). Power calculation for the associations was carried out using Quanto, version 1.2.4 (University of Southern California). Multivariable linear regression analyses using a general model of inheritance were carried out to assess the relationship between genotypes and baseline BMI, waist circumference, and their percent changes after 1 year of intervention while adjusting for baseline measurements, age, sex, study site, and the first two principal components that controlled for ethnicity/race. To address the hypothesis that candidate SNP carriage modifies response to weight loss intervention in Look AHEAD, interactions between randomization arm and genotype were assessed in the ethnicity-combined sample by the addition of a multiplicative term to the fully adjusted model.

We constructed a genetic risk score (GRS) by counting the total number of T2D-susceptibility alleles carried by each individual for the 17 candidate genes under study assuming an additive model. For the genes represented by multiple SNPs, genome-wide association studies SNPs, or SNPs significantly associated with at least one phenotype in the multivariate model were preferred over matching proxies or SNPs that did not show statistical significance. We assigned “0” to nonrisk allele homozygote carriers, “1” for heterozygotes, and “2” for risk allele homozygotes over the 17 SNPs resulting in a possible score ranging from 0 to 34. Twenty-four individuals missing one or more SNP genotypes were excluded from the analysis. We evaluated the effect of GRS in fully adjusted regression models at baseline and year 1. The year 1 analysis included a test for intervention-by-GRS interaction.

The nominal threshold for statistical significance of all analyses was set at 0.05. We controlled for multiple hypothesis testing using false discovery rate (FDR; ref. 33). Analyses were performed using SAS/STAT and SAS/Genetics software, version 9.1 (SAS Institute, Cary, NC) at Mount Sinai School of Medicine.

RESULTS

Diabetes risk allele frequencies

Phenotype and genotype data were available in 3,903 T2D Look AHEAD and 1,016 nondiabetic Heart SCORE participants (Table 1 and Supplementary Table S2 online). Look AHEAD subjects were evenly distributed at baseline between the ILI and DSE intervention arms with comparable age, gender, ethnicity, and anthropometric traits.

Table 1.

Population characteristics of the Look AHEAD cohort

Intervention assignment
Characteristic ILI DSE
N       1,946       1,957
Women (%)  1,106 (57)  1,092 (56)
Ethnicity
    African American (%)     312 (16.0)     306 (15.6)
    American Indian/Alaskan Native (%)       11 (0.6)         9 (0.5)
    Asian/Pacific Islander (%)       24 (1.2)       19 (1.0)
    Hispanic/Latino (%)     148 (7.6)     158 (8.1)
    Non-Hispanic white (%) 1,415 (72.7) 1,423 (72.7)
    Other (multiple) (%)       36 (1.9)       42 (2.2)
Age (years)    59.0 ± 6.9    59.2 ± 6.8
BMI (kg/m2) at baseline
    Women    36.7 ± 6.3    37.0 ± 6.2
    Men    35.6 ± 5.8    35.1 ± 5.3
BMI (kg/m2) after 1 year
    Women    33.6 ± 6.2    36.5 ± 6.3
    Men    32.1 ± 5.8    34.9 ± 5.4
Waist circumference (cm) at baseline
    Women 111.2 ± 13.8 111.6 ± 13.7
    Men 119.2 ± 13.9 118.7 ± 12.9
Waist circumference (cm) after 1 year
    Women 104.4 ± 13.8 110.5 ± 13.5
    Men 109.9 ± 14.6 118.0 ± 13.1
Open in a new tab

DSE, diabetes support and education; ILI, intensive lifestyle intervention.

All SNPs under study conformed to Hardy–Weinberg equilibrium. We genetically characterized the Look AHEAD cohort with regard to the carriage load of the T2D-susceptibility loci. Several well-established T2D risk alleles were significantly overrepresented in Look AHEAD (Table 2) compared with a nondiabetic cohort from Heart SCORE (see Supplementary Table S3 online). These results confirm that Look AHEAD participants, all of whom are diabetic, carry genetic factors that contributed to the development of T2D.

Table 2.

Risk allele frequencies by self-reported ethnicity in the Look AHEAD cohort

Gene SNP ID Allelesa Black
(N = 615)		 White
(N = 2,820)		 American
Indian/
Alaskan
Native
(N = 20)		 Asian
(N = 41)		 Hispanic
(N = 307)		 Other/
mixed
(N = 78)		 Total
(N = 3,881)b 		Detectable
differences
for 1-year
change in
BMI
(kg/m2)c
NOTCH2 rs1092393d T/G 0.31 0.10 0.18 0.07 0.12 0.16 0.14 0.64
GCKR rs1260326 T/C 0.15 0.40 0.42 0.30 0.32 0.32 0.35 0.46
GCKR rs780094d C/T 0.81 0.60 0.61 0.72 0.68 0.67 0.64 0.46
IRS1 rs2943634d C/A 0.44 0.69 0.34 0.09 0.23 0.39 0.66 0.47
ADAMTS9 rs4607103d C/T 0.70 0.75 0.71 0.66 0.65 0.77 0.73 0.50
IGF2BP2 rs4402960d T/G 0.52 0.33 0.47 0.25 0.28 0.38 0.35 0.46
PPARG rs1801282d C/G 0.98 0.90 0.92 0.93 0.87 0.89 0.91 0.77
WFS1 rs6446482 G/C 0.35 0.39 0.33 0.12 0.29 0.38 0.37 0.46
WFS1 rs734312d A/G 0.12 0.44 0.45 0.20 0.42 0.43 0.49 0.44
CDKAL1 rs7754840d C/G 0.57 0.35 0.45 0.44 0.31 0.39 0.38 0.45
CDKAL1 rs9368222 A/C 0.20 0.29 0.40 0.43 0.24 0.28 0.27 0.49
CDKAL1 rs9465871 C/T 0.53 0.21 0.42 0.51 0.29 0.34 0.27 0.49
GCK rs1799884d T/C 0.17 0.17 0.23 0.16 0.18 0.21 0.17 0.59
GCK rs2908289 A/G 0.26 0.17 0.23 0.16 0.18 0.25 0.19 0.56
GCK rs6975024 G/A 0.08 0.17 0.23 0.16 0.17 0.17 0.16 0.60
JAZF1 rs864745d T/C 0.74 0.52 0.55 0.74 0.61 0.58 0.56 0.44
SLC30A8 rs13266634d C/T 0.91 0.71 0.79 0.60 0.72 0.80 0.75 0.51
CDKN2A/2B rs10811661d T/C 0.92 0.85 0.87 0.66 0.88 0.89 0.86 0.64
HHEX rs1111875d C/T 0.79 0.63 0.55 0.41 0.66 0.68 0.65 0.46
HHEX rs5015480 C/T 0.67 0.62 0.47 0.32 0.49 0.63 0.62 0.45
TCF7L2 rs12243326 G/A 0.31 0.35 0.26 0.07 0.26 0.26 0.33 0.47
TCF7L2 rs7901695 C/T 0.47 0.38 0.37 0.06 0.31 0.36 0.39 0.45
TCF7L2 rs7903146d T/C 0.34 0.37 0.34 0.06 0.29 0.30 0.35 0.46
KCNJ11 rs5219d T/C 0.08 0.37 0.18 0.39 0.38 0.26 0.32 0.47
KCNQ1 rs2283228d A/C 0.11 0.07 0.10 0.18 0.19 0.08 0.09 0.77
KCNQ1 rs231362 A/G 0.21 0.45 0.23 0.18 0.36 0.37 0.40 0.45
MTNR1B rs10830962 C/G 0.24 0.42 0.43 0.43 0.36 0.46 0.39 0.45
MTNR1B rs10830963d G/C 0.09 0.28 0.30 0.41 0.23 0.29 0.25 0.51
Open in a new tab

GRS, genetic risk score; ILI, intensive lifestyle intervention; SNP, single-nucleotide polymorphism.

a

Putative risk allele listed first.

b

Twenty two individuals were missing self-reported ethnicity/race.

c

Detectable differences in the ILI group based on a two-sided test, significance threshold of 0.05, and statistical power of 80% and assuming an additive model.

d

SNPs used to construct a GRS.

Look AHEAD: baseline genetic analysis

At baseline, Look AHEAD participants had a mean BMI of 36 kg/m2 and waist circumference of 111 cm. Carriers of one or two copies of the risk alleles at TCF7L2 rs7901695, rs7903146, and rs12243326 had 1.3–1.4 kg/m2 lower BMI and 3.4–3.9 cm smaller waist circumference than homozygotes for the common allele (FDR-adjusted P ≤ 0.002; data not shown). The remaining genetic markers showed no association with BMI or waist circumference.

Look AHEAD: follow-up quantitative traits and genotype associations

Greater reductions were observed between baseline and year 1 in the ILI compared with the DSE group for BMI (9.1% vs. 1.0%) and waist circumference (8.8% vs. 1.5%) regardless of genotype status (P < 0.0001; Table 1). In the DSE group, homozygote carriers of the PPARG rare nonrisk allele showed a trend toward an increase in waist circumference (nominal P = 0.005, FDR-adjusted P = 0.06), while an opposite, though nonsignificant, trend was detected in the ILI group (see Supplementary Table S4 online) with a borderline nominal genotype-by-intervention interaction (P = 0.058). Carriers of the one or two IRS1 risk alleles assigned to DSE showed the least decrease in waist circumference compared with other IRS1 genotypes (nominal P = 0.009, FDR-adjusted P = 0.06), while carriers of one risk allele assigned to ILI lost almost as much as homozygote nonrisk allele carriers, resulting in nominally significant genotype-by-intervention interaction (P = 0.04). Nominally significant interactions with lifestyle intervention for 1-year changes in waist circumference were also detected at MTNR1B rs10830963 (P = 0.03) and in BMI and waist circumference at JAZF1 rs864745 (P = 0.04 and 0.01, respectively; see Supplementary Table S4 online). However, statistical significance did not remain after FDR adjustment.

GRS analysis

The 17 SNPs used to construct GRS are indicated in Table 2. The median GRS was 18 ranging from 9 to 26. A higher GRS was associated with lower BMI and waist circumference at baseline (P < 0.0001; Table 3). This association remained significant after TCF7L2 rs7903146, which was individually significantly associated with anthropometric measurements at baseline, was excluded from the GRS calculation (P < 0.008, data not shown). Individuals in the upper GRS quartile and randomized to ILI were more likely to reduce their waist circumferences than those in the low GRS quartile (6.2% vs. 7.3%, P = 0.02; Table 3). No intervention-by-GRS interaction was detected.

Table 3.

Association of genetic risk score with BMI and waist circumference at baseline and 1-year change

Quartile of GRS
Q1 Q2 Q3 Q4 P valuea
N 982     1,111     1,057      734      
GRS, median (range) 15 (9–16) 18 (17–18) 19 (19–20) 22 (21–27)
Baseline
    BMI, kg/m2 36.8 ± 0.2 36.0 ± 0.2 36.1 ± 0.2 35.4 ± 0.2 3.5 × 10−6
    Waist circumference, cm 116.7 ± 0.4 115.5 ± 0.4 114.9 ± 0.4 113.8 ± 0.5 5.3 × 10−5
1-year change: ILI
    BMI, % reduction 8.4 ± 0.3 9.2 ± 0.3 8.9 ± 0.3 9.2 ± 0.4 0.16
    Waist circumference, % reduction 6.2 ± 0.3 7.0 ± 0.3 6.6 ± 0.3 7.3 ± 0.4 0.02
1-year change: DSE
    BMI, % reduction 0.90 ± 0.2 0.81 ± 0.2 0.97 ± 0.2 0.53 ± 0.3 0.51
    Waist circumference, % reduction 0.56 ± 0.3 0.83 ± 0.2 0.62 ± 0.2 0.34 ± 0.3 0.99
1-year change: pooled
    BMI, % reduction 4.6 ± 0.2 5.0 ± 0.2 5.0 ± 0.2 4.8 ± 0.2 0.32
    Waist circumference, % reduction 3.3 ± 0.2 3.9 ± 0.2 3.7 ± 0.2 3.8 ± 0.2 0.046
Open in a new tab

Data are least squares means ± SE after adjustment for age, gender, study site, baseline measurement (for 1-year changes) and randomization arm (for pooled analysis).

DSE, diabetes support and education; GRS, genetic risk score; ILI, intensive lifestyle intervention.

a

P value for comparison between the low (Q1) and upper (Q4) quartiles.

DISCUSSION

In this study, we tested the role of T2D-susceptibility loci, individually and in combination, in the response to an ILI and DSE in the Look AHEAD Clinical Trial investigating the long-term health impact of weight loss in an ethnically diverse population of overweight or obese adults with T2D. The Look AHEAD subjects were more likely to carry risk variants in the established T2D-susceptibility loci compared with the nondiabetic Heart SCORE cohort. Although genetic T2D risks did not seem to undermine the success of ILI, we identified select variants that slightly modified the response depending on the intervention assignment, providing potentially important insight into the role of T2D genetic factors in response to a lifestyle intervention.

We detected a nominally significant genotype-by-intervention interaction for JAZF1 and MTNR1B polymorphisms demonstrating greater or lesser response by risk allele. Even though these associations are intriguing, they did not persist with adjustment for multiple testing. Replication of our findings in an independent cohort may help clarify whether these associations are artifactual or too weak to be detected with our sample size. Given the recognized involvement of the selected variants in T2D susceptibility, these associations may help optimize multigenic risk models to predict success of lifestyle intervention.

We also found a nominally borderline-significant PPARG P12A genotype-by-intervention interaction on 1-year change in waist circumference (P = 0.058). In the DSE group, homozygote carriers of the rare nonrisk PPARG A12 allele had an increase in waist circumference after 1 year compared with other genotypes, whereas in the ILI group, PPARG A12 carriers showed a nonsignificant trend toward the greatest reduction. DPP has reported a significant genotype-by-intervention interaction with regard to weight loss with A12 allele carriers also losing more weight in the lifestyle intervention arm compared with other genotypes, but gaining weight in the placebo group, which is opposite to what we observed (25). Although DPP studied individuals at risk of developing diabetes, Look AHEAD enrolled subjects with established T2D. Differences in the diabetes status may, at least in part, explain the difference in our results.

TCF7L2 alleles, in high linkage disequilibrium and reproducibly related to T2D in a number of studies (7–9,14,16,20), showed the strongest association with BMI and waist circumference at baseline in Look AHEAD. Carriers of two TCF7L2 risk alleles for T2D were found to have a 1.3–1.4 kg/ m2 lower BMI and 3.4–3.9 cm smaller waist circumference than noncarriers. This finding, though counterintuitive, is similar to the results from five studies totaling >3,000 T2D patients who have shown that a haplotype containing the TCF7L2 rs7903146 T risk allele is associated with decreased BMI (−1.3% per copy, P = 0.0016; ref. 34). DPP also found that the carriers of risk alleles at TCF7L2 rs7903146 and rs12255372 had a lower mean BMI and waist circumference at baseline (20). The inverse association of the three TCF7L2 variants with BMI and waist circumference in Look AHEAD remained significant at 1 year following randomization regardless of intervention assignment (data not shown).

A higher GRS based on 17 T2D-susceptibility loci in this study was associated with 0.16 kg/m2 and 0.39 cm lower BMI and waist circumference per risk allele copy, respectively (P < 0.0001). This association remained after the TCF7L2 variant was excluded from the score calculation. A similar correlation between a higher weighted GRS based upon 34 T2D-susceptibility loci and smaller waist circumference has been reported by DPP (35). As most of the T2D-susceptibility loci included in GRS are related to β-cell function, it is possible that individuals with a greatest genetic burden (higher GRS) develop diabetes with a lower level of central adiposity. It would be important to determine whether individuals without impaired glucose metabolism also show a similar association of a T2D GRS with waist circumference.

We observed that Look AHEAD participants with the highest GRS had a slightly greater reduction in waist circumference after 1 year of ILI (0.12% difference per risk allele copy), but no difference in BMI change, even after the adjustment for baseline measurements (Table 3). Future studies are required to clarify whether this result is due to ascertainment bias or indicate a genetic modification of weight reduction in a setting of T2D.

We acknowledge several limitations of this study. Although we did not exclude Heart SCORE participants who were prediabetic, the mean Heart SCORE fasting blood glucose level was <100 mg/dl, and, therefore, we consider the majority of Heart SCORE participants to be nondiabetic. Although restricting our analysis to 1-year follow-up may mitigate detectable genetic effects associated with the ability to lose weight, we note that the largest weight loss in the Look AHEAD cohort occurred during the first year of the ILI (36). Our findings, based on individuals selected for this randomized clinical trial, may not be generalizable. Similar to other genetic association studies, the size of the cohort may have prevented us from detecting more modest effects. Moreover, the significant associations observed for several variants in/near the same loci are likely to be a consequence of high linkage disequilibrium between the variants. However, due to the differences in linkage disequilibrium architecture between various ethnic and racial groups, it is possible that different variants would represent the same signal based on the ancestry. Therefore, inclusion of several SNPs from the same locus was aimed to better capture genetic associations in an ethnically diverse cohort like the Look AHEAD.

In summary, our findings show that individuals assigned to ILI lost more weight than those in the DSE group independent of genetic burden. Although we present suggestive evidence for the involvement of select risk alleles in response to an ILI, the extent of the effects should inspire additional studies to identify novel genomic regions that may be distinct from the diabetes-susceptibility loci but that enhance or mitigate weight loss during caloric restriction in a setting of T2D.

Supplementary Material

Supplemental Material

ACKNOWLEDGMENTS

We acknowledge the Look AHEAD sites that participated in this ancillary study. Frederick L. Brancati1, Jeff Honas2, Lawrence Cheskin3, Jeanne M. Clark3, Kerry Stewart3, Richard Rubin3, Jeanne Charleston, and Kathy Horak from the Johns Hopkins Medical Institutions. George A. Bray1, Kristi Rau2, Allison Strate2, Brandi Armand2, Frank L. Greenway3, Donna H. Ryan3, Donald Williamson3, Amy Bachand, Michelle Begnaud, Betsy Berhard, Elizabeth Caderette, Barbara Cerniauskas, David Creel, Diane Crow, Helen Guay, Nancy Kora, Kelly LaFleur, Kim Landry, Missy Lingle, Jennifer Perault, Mandy Shipp, Marisa Smith, and Elizabeth Tucker from the Pennington Biomedical Research Center. Cora E. Lewis1, Sheikilya Thomas2, Monika Safford3, Vicki DiLillo, Charlotte Bragg, Amy Dobelstein, Stacey Gilbert, Stephen Glasser, Sara Hannum, Anne Hubbell, Jennifer Jones, DeLavallade Lee, Ruth Luketic, Karen Marshall, L. Christie Oden, Janet Raines, Cathy Roche, Janet Truman, Nita Webb, and Audrey Wrenn from the University of Alabama at Birmingham. David M. Nathan1, Heather Turgeon2, Kristina Schumann2, Enrico Cagliero3, Linda Delahanty3, Kathryn Hayward3, Ellen Anderson3, Laurie Bissett, Richard Ginsburg, Valerie Goldman, Virginia Harlan, Charles McKitrick, Alan McNamara, Theresa Michel, Alexi Poulos, Barbara Steiner, and Joclyn Tosch from the Massachusetts General Hospital, Harvard Center. Edward S. Horton1, Sharon D. Jackson2, Osama Hamdy3, A. Enrique Caballero3, Sarah Bain, Elizabeth Bovaird, Ann Goebel-Fabbri, Lori Lambert, Sarah Ledbury, Maureen Malloy, and Kerry Ovalle from the Joslin Diabetes Center, Harvard Center. George Blackburn1, Christos Mantzoros3, Kristinia Day, and Ann McNamara from the Beth Israel Deaconess Medical Center, Harvard Center. James O. Hill1, Marsha Miller2, JoAnn Phillipp2, Robert Schwartz3, Brent Van Dorsten3, Judith Regensteiner3, Salma Benchekroun, Ligia Coelho, Paulette Cohrs, Elizabeth Daeninck, Amy Fields, Susan Green, April Hamilton, Jere Hamilton, Eugene Leshchinskiy, Michael McDermott, Lindsey Munkwitz, Loretta Rome, Kristin Wallace, and Terra Worley from the University of Colorado Health Sciences Center. John P. Foreyt1, Rebecca S. Reeves2, Henry Pownall3, Ashok Balasubramanyam3, Peter Jones3, Michele Burrington, Chu-Huang Chen, Allyson Clark, Molly Gee, Sharon Griggs, Michelle Hamilton, Veronica Holley, Jayne Joseph, Patricia Pace, Julieta Palencia, Olga Satterwhite, Jennifer Schmidt, Devin Volding, and Carolyn White from the Baylor College of Medicine. Karen C. Johnson1, Carolyn Gresham2, Stephanie Connelly3, Amy Brewer, Mace Coday, Lisa Jones, Lynne Lichtermann, Shirley Vosburg, and J. Lee Taylor from the University of Tennessee East, University of Tennessee Health Science Center. Abbas E. Kitabchi1, Helen Lambeth2, Debra Clark, Andrea Crisler, Gracie Cunningham, Donna Green, Debra Force, Robert Kores, Renate Rosenthal, Elizabeth Smith, Maria Sun, and Judith Soberman3 from the University of Tennessee Downtown, University of Tennessee Health Science Center. Robert W. Jeffery1, Carolyn Thorson2, John P. Bantle3, J. Bruce Redmon3, Richard S. Crow3, Scott Crow3, Susan K Raatz3, Kerrin Brelje, and Carolyne Campbell, Jeanne Carls, Tara Carmean-Mihm, Emily Finch, Anna Fox, Elizabeth Hoelscher, La Donna James, Vicki A. Maddy, Therese Ockenden, Birgitta I. Rice, Tricia Skarphol, Ann D. Tucker, Mary Susan Voeller, and Cara Walcheck from the University of Minnesota. Xavier Pi-Sunyer1, Jennifer Patricio2, Stanley Heshka3, Carmen Pal3, Lynn Allen, Diane Hirsch, and Mary Anne Holowatyfrom the St. Luke’s Roosevelt Hospital Center. Thomas A. Wadden1, Barbara J. Maschak-Carey2, Stanley Schwartz3, Gary D. Foster3, Robert I. Berkowitz3, Henry Glick3, Shiriki K. Kumanyika3, Johanna Brock, Helen Chomentowski, Vicki Clark, Canice Crerand, Renee Davenport, Andrea Diamond, Anthony Fabricatore, Louise Hesson, Stephanie Krauthamer-Ewing, Robert Kuehnel, Patricia Lipschutz, Monica Mullen, Leslie Womble, and Nayyar Iqbal from the University of Pennsylvania. David E. Kelley1, Jacqueline Wesche-Thobaben2, Lewis Kuller3, Andrea Kriska3, Janet Bonk, Rebecca Danchenko, Daniel Edmundowicz3, Mary L. Klem3, Monica E. Yamamoto3, Barb Elnyczky, George A. Grove, Pat Harper, Janet Krulia, Juliet Mancino, Anne Mathews, Tracey Y. Murray, Joan R. Ritchea, Jennifer Rush, Karen Vujevich, and Donna Wolf from the University of Pittsburgh. Rena R. Wing1, Renee Bright2, Vincent Pera3, John Jakicic3, Deborah Tate3, Amy Gorin3, Kara Gallagher3, Amy Bach, Barbara Bancroft, Anna Bertorelli, Richard Carey, Tatum Charron, Heather Chenot, Kimberley Chula-Maguire, Pamela Coward, Lisa Cronkite, Julie Currin, Maureen Daly, Caitlin Egan, Erica Ferguson, Linda Foss, Jennifer Gauvin, Don Kieffer, Lauren Lessard, Deborah Maier, J.P. Massaro, Tammy Monk, Rob Nicholson, Erin Patterson, Suzanne Phelan, Hollie Raynor, Douglas Raynor, Natalie Robinson, Deborah Robles, and Jane Tavares from the Miriam Hospital/Brown Medical School. Steven M. Haffner1, Maria G. Montez2, and Carlos Lorenzo3 from the University of Texas Health Science Center at San Antonio. Steven Kahn1, Brenda Montgomery2, Robert Knopp3, Edward Lipkin3, Matthew L. Maciejewski3, Dace Trence3, Terry Barrett, Joli Bartell, Diane Greenberg, Anne Murillo, Betty Ann Richmond, and April Thomas from the University of Washington/VA Puget Sound Health Care System. Mark A. Espeland1, Judy L. Bahnson2, Lynne Wagenknecht3, David Reboussin3, W. Jack Rejeski3, Alain Bertoni3, Wei Lang3, Gary Miller3, David Lefkowitz3, Patrick S. Reynolds3, Paul Ribisl3, Mara Vitolins3, Michael Booth2, Kathy M. Dotson2, Amelia Hodges2, Carrie C. Williams2, Jerry M. Barnes, Patricia A. Feeney, Jason Griffin, Lea Harvin, William Herman, Patricia Hogan, Sarah Jaramillo, Mark King, Kathy Lane, Rebecca Neiberg, Andrea Ruggiero, Christian Speas, Michael P. Walkup, Karen Wall, Michelle Ward, Delia S. West, and Terri Windham from the Coordinating Center, Wake Forest University. Santica M. Marcovina1 and Greg Strylewicz from the Central Laboratory, Northwest Lipid Research Laboratories.

The Federal sponsors were the National Institute of Diabetes and Digestive and Kidney Diseases: Barbara Harrison, Van S. Hubbard, and Susan Z.Yanovski. National Heart, Lung, and Blood Institute: Lawton S. Cooper, Jeffrey Cutler, and Eva Obarzanek. Centers for Disease Control and Prevention: Edward W. Gregg, David F. Williamson, and Ping Zhang.

This study was supported by the Department of Health and Human Services through the following cooperative agreements from the National Institutes of Health: DK57136, DK57149, DK56990, DK57177, DK57171, DK57151, DK57182, DK57131, DK57002, DK57078, DK57154, DK57178, DK57219, DK57008, DK57135, and DK56992. The following federal agencies have contributed support: National Institute of Diabetes and Digestive and Kidney Diseases, National Heart, Lung, and Blood Institute, National Institute of Nursing Research, National Center on Minority Health and Health Disparities, Office of Research on Women’s Health, and the Centers for Disease Control and Prevention.

Additional support was received from The Johns Hopkins Medical Institutions Bayview General Clinical Research Center (M01RR02719), the Massachusetts General Hospital Mallinckrodt General Clinical Research Center (M01RR01066), the University of Colorado Health Sciences Center General Clinical Research Center (M01RR00051) and Clinical Nutrition Research Unit (P30 DK48520), the University of Tennessee at Memphis General Clinical Research Center (M01RR0021140), the University of Pittsburgh General Clinical Research Center (M01RR000056 44) and NIH grants (DK 046204 and DK072497), and the University of Washington/VA Puget Sound Health Care System Medical Research Service, Department of Veterans Affairs, Frederic C. Bartter General Clinical Research Center (M01RR01346). A.K.H. was supported by the Training Program in Cardiovascular Research (NIH, 5T32HL069770).

The following organizations have committed to make major contributions to Look AHEAD: Federal Express, Health Management Resources, Johnson & Johnson, LifeScan Inc., Optifast-Novartis Nutrition, Roche Pharmaceuticals, Ross Product Division of Abbott Laboratories, Slim-Fast Foods Company, and Unilever.

The superscript numbers 1, 2, and 3 indicate the Principal Investigator, Program Coordinator, and the Co-Investigator, respectively. All other Look AHEAD staffs are listed alphabetically by site.

Footnotes

SUPPLEMENTARY MATERIAL

Supplementary material is linked to the online version of the paper at http://www.nature.com/oby

Disclosure

The authors declared no conflict of interest.

REFERENCES

  • 1.Kannel WB, McGee DL. Diabetes and glucose tolerance as risk factors for cardiovascular disease: the Framingham study. Diabetes Care. 1979;2:120–126. doi: 10.2337/diacare.2.2.120. [DOI] [PubMed] [Google Scholar]
  • 2.Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation. 1983;67:968–977. doi: 10.1161/01.cir.67.5.968. [DOI] [PubMed] [Google Scholar]
  • 3.Knowler WC, Barrett-Connor E, Fowler SE, et al. Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346:393–403. doi: 10.1056/NEJMoa012512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Maruthur NM, Wang NY, Appel LJ. Lifestyle interventions reduce coronary heart disease risk: results from the PREMIER Trial. Circulation. 2009;119:2026–2031. doi: 10.1161/CIRCULATIONAHA.108.809491. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Wing RR, Koeske R, Epstein LH, et al. Long-term effects of modest weight loss in type II diabetic patients. Arch Intern Med. 1987;147:1749–1753. [PubMed] [Google Scholar]
  • 6.Alexandrides TK, Skroubis G, Kalfarentzos F. Resolution of diabetes mellitus and metabolic syndrome following Roux-en-Y gastric bypass and a variant of biliopancreatic diversion in patients with morbid obesity. Obes Surg. 2007;17:176–184. doi: 10.1007/s11695-007-9044-z. [DOI] [PubMed] [Google Scholar]
  • 7.Sladek R, Rocheleau G, Rung J, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007;445:881–885. doi: 10.1038/nature05616. [DOI] [PubMed] [Google Scholar]
  • 8.Zeggini E, Scott LJ, Saxena R, et al. Wellcome Trust Case Control Consortium. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40:638–645. doi: 10.1038/ng.120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Voight BF, Scott LJ, Steinthorsdottir V, et al. MAGIC investigators; GIANT Consortium. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet. 2010;42:579–589. doi: 10.1038/ng.609. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Takeuchi F, Serizawa M, Yamamoto K, et al. Confirmation of multiple risk Loci and genetic impacts by a genome-wide association study of type 2 diabetes in the Japanese population. Diabetes. 2009;58:1690–1699. doi: 10.2337/db08-1494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Rung J, Cauchi S, Albrechtsen A, et al. Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat Genet. 2009;41:1110–1115. doi: 10.1038/ng.443. [DOI] [PubMed] [Google Scholar]
  • 12.Zeggini E, Weedon MN, Lindgren CM, et al. Wellcome Trust Case Control Consortium (WTCCC) Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007;316:1336–1341. doi: 10.1126/science.1142364. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Tan JT, Ng DP, Nurbaya S, et al. Polymorphisms identified through genome-wide association studies and their associations with type 2 diabetes in Chinese, Malays, and Asian-Indians in Singapore. J Clin Endocrinol Metab. 2010;95:390–397. doi: 10.1210/jc.2009-0688. [DOI] [PubMed] [Google Scholar]
  • 14.Scott LJ, Mohlke KL, Bonnycastle LL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316:1341–1345. doi: 10.1126/science.1142382. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Sim X, Ong RT, Suo C, et al. Transferability of type 2 diabetes implicated loci in multi-ethnic cohorts from Southeast Asia. PLoS Genet. 2011;7 doi: 10.1371/journal.pgen.1001363. e1001363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Steinthorsdottir V, Thorleifsson G, Reynisdottir I, et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet. 2007;39:770–775. doi: 10.1038/ng2043. [DOI] [PubMed] [Google Scholar]
  • 17.Parra EJ, Below JE, Krithika S, et al. Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium. Genome-wide association study of type 2 diabetes in a sample from Mexico City and a meta-analysis of a Mexican-American sample from Starr County, Texas. Diabetologia. 2011;54:2038–2046. doi: 10.1007/s00125-011-2172-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Dupuis J, Langenberg C, Prokopenko I, et al. DIAGRAM Consortium; GIANT Consortium; Global BPgen Consortium; Anders Hamsten on behalf of Procardis Consortium; MAGIC investigators. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42:105–116. doi: 10.1038/ng.520. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Saxena R, Hivert MF, Langenberg C, et al. GIANT Consortium; MAGIC investigators. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet. 2010;42:142–148. doi: 10.1038/ng.521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Florez JC, Jablonski KA, Bayley N, et al. Diabetes Prevention Program Research Group. TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. N Engl J Med. 2006;355:241–250. doi: 10.1056/NEJMoa062418. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Moore AF, Jablonski KA, McAteer JB, et al. Diabetes Prevention Program Research Group. Extension of type 2 diabetes genome-wide association scan results in the Diabetes Prevention Program. Diabetes. 2008;57:2503–2510. doi: 10.2337/db08-0284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Laukkanen O, Pihlajamäki J, Lindström J, et al. Finnish Diabetes Prevention Study Group. Polymorphisms of the SUR1 (ABCC8) and Kir6.2 (KCNJ11) genes predict the conversion from impaired glucose tolerance to type 2 diabetes. The Finnish Diabetes Prevention Study. J Clin Endocrinol Metab. 2004;89:6286–6290. doi: 10.1210/jc.2004-1204. [DOI] [PubMed] [Google Scholar]
  • 23.Florez JC, Jablonski KA, Kahn SE, et al. Type 2 diabetes-associated missense polymorphisms KCNJ11 E23K and ABCC8 A1369S influence progression to diabetes and response to interventions in the Diabetes Prevention Program. Diabetes. 2007;56:531–536. doi: 10.2337/db06-0966. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Florez JC, Jablonski KA, Sun MW, et al. Diabetes Prevention Program Research Group. Effects of the type 2 diabetes-associated PPARG P12A polymorphism on progression to diabetes and response to troglitazone. J Clin Endocrinol Metab. 2007;92:1502–1509. doi: 10.1210/jc.2006-2275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Franks PW, Jablonski KA, Delahanty L, et al. Diabetes Prevention Program Research Group. The Pro12Ala variant at the peroxisome proliferator-activated receptor γ gene and change in obesity-related traits in the Diabetes Prevention Program. Diabetologia. 2007;50:2451–2460. doi: 10.1007/s00125-007-0826-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Florez JC, Jablonski KA, McAteer J, et al. Diabetes Prevention Program Research Group. Testing of diabetes-associated WFS1 polymorphisms in the Diabetes Prevention Program. Diabetologia. 2008;51:451–457. doi: 10.1007/s00125-007-0891-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Pi-Sunyer X, Blackburn G, Brancati FL, et al. Look AHEAD Research Group. Reduction in weight and cardiovascular disease risk factors in individuals with type 2 diabetes: one-year results of the look AHEAD trial. Diabetes Care. 2007;30:1374–1383. doi: 10.2337/dc07-0048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Bray G, Gregg E, Haffner S, et al. Look AHEAD Research Group. Baseline characteristics of the randomised cohort from the Look AHEAD (Action for Health in Diabetes) study. Diab Vasc Dis Res. 2006;3:202–215. doi: 10.3132/dvdr.2006.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Ryan DH, Espeland MA, Foster GD, et al. Look AHEAD Research Group. Look AHEAD (Action for Health in Diabetes): design and methods for a clinical trial of weight loss for the prevention of cardiovascular disease in type-2 diabetes. Control Clin Trials. 2003;24:610–628. doi: 10.1016/s0197-2456(03)00064-3. [DOI] [PubMed] [Google Scholar]
  • 30.Aiyer AN, Kip KE, Marroquin OC, et al. Racial differences in coronary artery calcification are not attributed to differences in lipoprotein particle sizes: the Heart Strategies Concentrating on Risk Evaluation (Heart SCORE) Study. Am Heart J. 2007;153:328–334. doi: 10.1016/j.ahj.2006.11.002. [DOI] [PubMed] [Google Scholar]
  • 31.Keating BJ, Tischfield S, Murray SS, et al. Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies. PLoS ONE. 2008;3:e3583. doi: 10.1371/journal.pone.0003583. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Price AL, Patterson NJ, Plenge RM, et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–909. doi: 10.1038/ng1847. [DOI] [PubMed] [Google Scholar]
  • 33.Benjamini Y, Hochberg A. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc. 1995;57:289–300. [Google Scholar]
  • 34.Helgason A, Pálsson S, Thorleifsson G, et al. Refining the impact of TCF7L2 gene variants on type 2 diabetes and adaptive evolution. Nat Genet. 2007;39:218–225. doi: 10.1038/ng1960. [DOI] [PubMed] [Google Scholar]
  • 35.Hivert MF, Jablonski KA, Perreault L, et al. DIAGRAM Consortium; Diabetes Prevention Program Research Group. Updated genetic score based on 34 confirmed type 2 diabetes Loci is associated with diabetes incidence and regression to normoglycemia in the Diabetes Prevention Program. Diabetes. 2011;60:1340–1348. doi: 10.2337/db10-1119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Wing RR Look AHEAD Research Group. Long-term effects of a lifestyle intervention on weight and cardiovascular risk factors in individuals with type-2 diabetes mellitus: four-year results of the Look AHEAD trial. Arch Intern Med. 2010;170:1566–1575. doi: 10.1001/archinternmed.2010.334. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Material
NIHMS367219-supplement-Supplemental_Material.doc (478.5KB, doc)

RESOURCES