Genetic Predisposition to Weight Loss and Regain With Lifestyle Intervention

Analyses From the Diabetes Prevention Program and the Look AHEAD Randomized Controlled Trials

George D. Papandonatos; Qing Pan; Nicholas M. Pajewski; Linda M. Delahanty; Inga Peter; Bahar Erar; Shafqat Ahmad; Maegan Harden; Ling Chen; Pierre Fontanillas; GIANT Consortium, Lynne E. Wagenknecht; Steven E. Kahn; Rena R. Wing; Kathleen A. Jablonski; Gordon S. Huggins; William C. Knowler; Jose C. Florez; Jeanne M. McCaffery; Paul W. Franks

Disclosures

Diabetes. 2015;64(12):4312-4321. 

In This Article

Discussion

Many loci have been robustly associated with anthropometric indices of obesity through genome-wide association studies (GWAS) meta-analyses, revealing multiple biologic pathways.[18,19] However, as these studies focused exclusively on cross-sectional epidemiological data, little is known of whether these variants influence weight change or modify the response to weight-loss interventions. Here, we sought to address these two clinically relevant questions by examining the effects of these variants on weight loss and weight regain in two large RCTs of lifestyle modification, one in people with prediabetes (DPP) and the other in patients diagnosed with type 2 diabetes prior to randomization (Look AHEAD). We found that of nearly 100 loci examined, variant rs1885988 at MTIF3, a close proxy for GIANT variant rs12016871, appeared to modify the effects of the lifestyle interventions on weight loss, reaching study-wide statistical significance within the lifestyle intervention arms in year 3 (P = 2 × 10−4) and demonstrating consistently beneficial effects across all 4 years of follow-up (P = 2.4 × 10−3). As no similar benefit was observed within the comparison arms, this led to a nominally significant pooled SNP×treatment interaction across all 4 years of follow-up (P = 4.3 × 10−3). For weight regain, no SNPs modified treatment responses at a level reaching study-wide statistical significance. The strongest effect was observed for an SNP near FUBP1-DNAJB4 (rs12401738), which appeared to modify the effects of the lifestyle interventions in both studies (P interaction = 0.014) by slowing weight regain within the comparison but not the lifestyle interventions.

MTIF3 encodes a 29-kDa nuclear-encoded protein that promotes the formation of the initiation complex on the mitochondrial 55S ribosome.[20,21] The mitochondrial ribosome is responsible for the synthesis of 13 of the inner mitochondrial membrane proteins and its regulation is essential for ATP synthesis, energy balance, and modulation of reactive oxygen species production in the mitochondria by the electron transport chain.[21] Using the HaploReg interface (http://www.broadinstitute.org/mammals/haploreg/haploreg.php) to access the ENCODE database, we looked up the functional properties of our lead SNP (rs1885988). Although this is an intronic variant, it is 411 bp from a triallelic missense SNP with a deoxyribonuclease (DNAse) peak, with which it is in perfect linkage disequilibrium (r2 = 1; D' = 1) in the 1000 Genomes database. Thus, the rs1885988 variant tags a chromatin site that is sensitive to transcription factor binding and hence likely regulates gene expression. We further queried the transcriptional properties of this variant in RegulomeDB (http://regulome.stanford.edu/index), which characterized the variant as a DNAse peak site in immune-regulating T cells (score 5).

A recent publication in Look AHEAD[22] examined the relationships between obesity gene variants, including MTIF3-rs12016871 and diet preference, but no other published studies have examined variation at this locus and lifestyle. Here, the minor G allele was associated with lifestyle-elicited weight loss in both trials and with weight regain in the DPP but not Look AHEAD. The minor G allele has previously been associated with higher BMI.[18,19] Thus, carriers of the MTIF3 obesity-inducing allele appear to benefit more from intensive lifestyle interventions than noncarriers whether they have prediabetes or overt type 2 diabetes. In a recently published, cross-sectional meta-analysis of 32 gene×dietary pattern interactions for BMI (N >65,000),[23] the locus with the strongest signal of an interaction with a healthy diet was a close proxy of our MTIF3 variant. The obvious differences in study design make extrapolation of those cross-sectional data to the clinical trial context difficult. However, the fact that the MTIF3 locus was top ranked in both studies strengthens the credibility of our findings and reinforces MTIF3 as a plausible candidate locus for gene×lifestyle interactions in obesity.

FUBP1 encodes a single-stranded DNA and RNA binding protein that regulates gene transcription, stability, and splicing,[24,25] particularly for the C-MYC oncogene.[26] Mutations at FUPB1 are especially common in oligodendroglioma,[27] a rare form of brain cancer. DNAJB4 is preferentially expressed in heart, skeletal muscle, and pancreas and encodes a 337 amino acid heat-inducible protein.[28] Expression of DNAJB4 is associated with non–small-cell lung cancer survival.[29] No publication to our knowledge has reported on the variation at FUBP1 or DNAJB4 and lifestyle factors in obesity or metabolic disease. In the current analyses, the minor A allele at the FUBP1-DNAJB4 rs12401738 variant appeared to slow weight regain within the comparison arms in both trials, but not within the lifestyle arms. In the GIANT meta-analysis, the minor A allele was associated with higher BMI.

The FTO locus (proximal to rs9960939), which has been shown previously to interact with lifestyle factors, was not associated with weight loss directly or via treatment interactions in either the DPP[30] or Look AHEAD.[10] Previously in the DPP, the FTO rs9939609 variant predicted a greater increase in subcutaneous adipose tissue in the placebo group compared with lifestyle intervention at year 1, but no significant genotype×treatment interaction was observed for weight loss. In Look AHEAD, several SNPs in high linkage disequilibrium showed a nominal association with weight regain at year 4 among those who had lost 3% or more of their baseline weight at year 1, but there was no effect on weight loss. The previously reported additive effect on weight change of FTO rs1421085 was weakened to nonsignificance in this report (from year 1–4 in the comparison arm, P = 0.10; treatment arm interaction, P = 0.10), likely due to a different subsample of Look AHEAD, including a greater representation of Native and Hispanic Americans with available MetaboChip genotyping (vs. IBC chip genotyping presented in the prior article), and to differences in analytic methods, predominantly the derivation and adjustment for new principal components from MetaboChip data to statistically adjust for ancestry. We note, however, that the effect of the lead SNP on weight regain from the prior article, FTO rs3751812, was maintained in this more diverse group (from year 1–4 in the comparison arm, P = 0.02; treatment arm interaction, P = 0.03). Thus, it appears that any impact of the obesity-associated region of FTO on weight loss following clinical intervention is weak and that larger studies designed to examine effects within racial/ethnic groups or with more detailed measurements of body composition will be needed for the effects reported here to be confirmed.

Although a handful of potential gene×treatment interaction effects are evident in these two large RCTs, one of the key findings is that the vast majority of GWAS-derived obesity-associated loci do not appear to convey clinically meaningful effects on weight loss or weight regain. This is key because many anticipate that modern population genetics research will help prevent or treat diabetes and obesity.[31] Although discoveries made through GWAS meta-analyses help elucidate biological pathways, the use of individual obesity-associated SNPs derived through GWAS is unlikely to help clinicians optimize the delivery of weight-loss interventions through targeted intervention. One possible reason that GWAS-derived loci do not serve this role well is because GWAS meta-analyses are conventionally performed without accounting for interactions with lifestyle factors and by ranking loci based solely on marginal effect P values, which may bias against the discovery of loci that modify the effects of lifestyle interventions.[32]

The DPP and Look AHEAD are the largest existing RCTs of lifestyle intervention in people who are either at high-risk of developing or who have already developed type 2 diabetes, respectively; nevertheless, even with ~6,000 participants this analysis is likely underpowered to detect effects as small as those observed in large observational meta-analyses. However, it is unlikely these effects are clinically relevant and failing to detect them here is thus of little consequence in the context of our clinically oriented objectives. It is also important to bear in mind some key differences between the DPP and Look AHEAD trials, not least that the former focuses on people with prediabetes and the latter on people who have already developed the disease, some who are on pharmacotherapy known to influence weight. Differences in intervention protocols also exist. These factors may inhibit the detection of gene×treatment interactions. Moreover, it is likely that gene×diet interactions are nutrient specific; as such, it may be that dietary regimes that focus on different elements of the diet from the DPP and Look AHEAD interventions, such as carbohydrate or salt intake, might yield interaction effects with the variants studied here. Last, although the test of heterogeneity between the DPP and Look AHEAD suggests that there were no statistically significant differences in interaction effects between studies, it is likely that with only two studies included in this analysis the heterogeneity test was underpowered, and, as such, the absence of a significant heterogeneity test statistic should be interpreted with caution.

In conclusion, we assessed the effects on weight change of 91 established BMI-associated loci in two large RCTs of intensive lifestyle modification. The strongest association with weight loss across studies was MTIF3-rs1885988. Although studying BMI-associated variants derived from cross-sectional observational studies has, in this instance, provided few new insights into the genetics of behavioral weight loss and weight regain, future studies focused on genome-wide hypothesis-free discovery efforts may yield more promising results.

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