Shafqat Ahmad

Gene × Physical Activity Interactions in Obesity: Combined Analysis of 111,421 Individuals of European Ancestry

Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age2, sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (Pinteraction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, Pinteraction = 0.014 vs. n = 71,611, Pinteraction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (Pinteraction = 0.003) and the SEC16B rs10913469 (Pinteraction = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal.

The lysine specific demethylase‐1 (LSD1/KDM1A) regulates VEGF‐A expression in prostate cancer

Recurrent prostate cancer remains a major clinical challenge. The lysine specific demethylase‐1 (LSD1/KDM1A), together with the JmjC domain‐containing JMJD2A and JMJD2C proteins, have emerged as critical regulators of histone lysine methylation. The LSD1–JMJD2 complex functions as a transcriptional co‐regulator of hormone activated androgen and estrogen receptors at specific gene promoters. LSD1 also regulates DNA methylation and p53 function. LSD1 is overexpressed in numerous cancers including prostate cancer through an unknown mechanism. We investigated expression of the LSD1 and JMJD2A in malignant human prostate specimens. We correlated LSD1 and JMJD2A expression with known mediators of prostate cancer progression: VEGF‐A and cyclin A1. We show that elevated expression of LSD1, but not JMJD2A, correlates with prostate cancer recurrence and with increased VEGF‐A expression. We show that functional depletion of LSD1 expression using siRNA in prostate cancer cells decreases VEGF‐A and blocks androgen induced VEGF‐A, PSA and Tmprss2 expression. We demonstrate that pharmacological inhibition of LSD1 reduces proliferation of both androgen dependent (LnCaP) and independent cell lines (LnCaP: C42, PC3). We show a direct mechanistic link between LSD1 over‐expression and increased activity of pro‐angiogenic pathways. New therapies targeting LSD1 activity should be useful in the treatment of hormone dependent and independent prostate cancer.

Gene × dietary pattern interactions in obesity: analysis of up to 68 317 adults of European ancestry

Abstract Obesity is highly heritable. Genetic variants showing robust associations with obesity traits have been identified through genome-wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist–hip ratio (WHR)-associated single nucleotide polymorphisms were genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GRS with BMI and BMI-adjusted WHR and (b) diet score modification of genetic associations with BMI and BMI-adjusted WHR. Nominally significant interactions (P = 0.006–0.04) were observed between the diet score and WHR-GRS (but not BMI-GRS), two WHR loci (GRB14 rs10195252; LYPLAL1 rs4846567) and two BMI loci (LRRN6C rs10968576; MTIF3 rs4771122), for the respective BMI-adjusted WHR or BMI outcomes. Although the magnitudes of these select interactions were small, our data indicated that associations between genetic predisposition and obesity traits were stronger with a healthier diet. Our findings generate interesting hypotheses; however, experimental and functional studies are needed to determine their clinical relevance.

Genetic Predisposition to Weight Loss and Regain With Lifestyle Intervention: Analyses From the Diabetes Prevention Program and the Look AHEAD Randomized Controlled Trials

Clinically relevant weight loss is achievable through lifestyle modification, but unintentional weight regain is common. We investigated whether recently discovered genetic variants affect weight loss and/or weight regain during behavioral intervention. Participants at high-risk of type 2 diabetes (Diabetes Prevention Program [DPP]; N = 917/907 intervention/comparison) or with type 2 diabetes (Look AHEAD [Action for Health in Diabetes]; N = 2,014/1,892 intervention/comparison) were from two parallel arm (lifestyle vs. comparison) randomized controlled trials. The associations of 91 established obesity-predisposing loci with weight loss across 4 years and with weight regain across years 2–4 after a minimum of 3% weight loss were tested. Each copy of the minor G allele of MTIF3 rs1885988 was consistently associated with greater weight loss following lifestyle intervention over 4 years across the DPP and Look AHEAD. No such effect was observed across comparison arms, leading to a nominally significant single nucleotide polymorphism×treatment interaction (P = 4.3 × 10−3). However, this effect was not significant at a study-wise significance level (Bonferroni threshold P < 5.8 × 10−4). Most obesity-predisposing gene variants were not associated with weight loss or regain within the DPP and Look AHEAD trials, directly or via interactions with lifestyle.

Gene × Environment Interactions in Obesity: The State of the Evidence

Background/Aims: Obesity is a pervasive and highly prevalent disease that poses substantial health risks to those it affects. The rapid emergence of obesity as a global epidemic and the patterns and distributions of the condition within and between populations suggest that interactions between inherited biological factors (e.g. genes) and relevant environmental factors (e.g. diet and physical activity) may underlie the current obesity epidemic. Methods: We discuss the rationale for the assertion that gene × lifestyle interactions cause obesity, systematically appraise relevant literature, and consider knowledge gaps future studies might seek to bridge. Results: We identified >200 relevant studies, of which most are relatively small scale and few provide replication data. Conclusion: Although studies on gene × lifestyle interactions in obesity point toward the presence of such interactions, improved data standardization, appropriate pooling of data and resources, innovative study designs, and the application of powerful statistical methods will be required if translatable examples of gene × lifestyle interactions in obesity are to be identified. Future studies, of which most will be observational, should ideally be accompanied by appropriate replication data and, where possible, by analogous findings from experimental settings where clinically relevant traits (e.g. weight regain and weight cycling) are outcomes.

Telomere length in blood and skeletal muscle in relation to measures of glycaemia and insulinaemia.

Diabet. Med. 29, e377–e381 (2012)

Inverse relationship between a genetic risk score of 31 BMI loci and weight change before and after reaching middle age

Background/Objective:Genome-wide-association studies have identified numerous body mass index (BMI)-associated variants, but it is unclear how these relate to weight gain in adults at different ages.Methods:We examined the association of a genetic risk score (GRS), consisting of 31 BMI-associated variants, with an annual weight change (AWC) and a substantial weight gain (SWG) of 10% by comparing self-reported weight at 20 years (y) with baseline weight (mean: 58 y; s.d.: 8 y) in 21407 participants from the Malmö Diet and Cancer Study (MDCS), and comparing baseline weight to weight at follow-up (mean: 73 y; s.d.: 6 y) among 2673 participants. Association between GRS and AWG and SWG was replicated in 4327 GLACIER (Gene x Lifestyle interactions And Complex traits Involved in Elevated disease Risk) participants (mean: 45 y; s.d.: 7 y) with 10 y follow-up. Cohort-specific results were pooled by fixed-effect meta-analyses.Results:In MDCS, the GRS was associated with increased AWC (β: 0.003; s.e: 0.01; P: 7 × 10−8) and increased odds for SWG (odds ratio (OR) 1.01 (95% confidence interval (CI): 1.00, 1.02); P: 0.013) per risk-allele from age 20y, but unexpectedly with decreased AWC (β: −0.006; s.e: 0.002; P: 0.009) and decreased odds for SWG OR 0.96 (95% CI: 0.93, 0.98); P: 0.001) between baseline and follow-up. Effect estimates from age 20 y to baseline differed significantly from those from baseline to follow-up (P: 0.0002 for AWC and P: 0.0001 for SWG). Similar to MDCS, the GRS was associated with decreased odds for SWG OR 0.98 (95% CI: 0.96, 1.00); P: 0.029) from baseline to follow-up in GLACIER. In meta-analyses (n=7000), the GRS was associated with decreased AWC (β: −0.005; s.e.m. 0.002; P: 0.002) and decreased odds for SWG OR 0.97 (95% CI: 0.96, 0.99); P: 0.001) per risk-allele.Conclusions:Our results provide convincing evidence for a paradoxical inversed relationship between a high number of BMI-associated risk-alleles and less weight gain during and after middle-age, in contrast to the expected increased weight gain seen in younger age.

Physical activity, smoking, and genetic predisposition to obesity in people from Pakistan: the PROMIS study.

BackgroundMultiple genetic variants have been reliably associated with obesity-related traits in Europeans, but little is known about their associations and interactions with lifestyle factors in South Asians.MethodsIn 16,157 Pakistani adults (8232 controls; 7925 diagnosed with myocardial infarction [MI]) enrolled in the PROMIS Study, we tested whether: a) BMI-associated loci, individually or in aggregate (as a genetic risk score - GRS), are associated with BMI; b) physical activity and smoking modify the association of these loci with BMI. Analyses were adjusted for age, age2, sex, MI (yes/no), and population substructure.ResultsOf 95 SNPs studied here, 73 showed directionally consistent effects on BMI as reported in Europeans. Each additional BMI-raising allele of the GRS was associated with 0.04 (SE = 0.01) kg/m2 higher BMI (P = 4.5 × 10−14). We observed nominal evidence of interactions of CLIP1 rs11583200 (Pinteraction = 0.014), CADM2 rs13078960 (Pinteraction = 0.037) and GALNT10 rs7715256 (Pinteraction = 0.048) with physical activity, and PTBP2 rs11165643 (Pinteraction = 0.045), HIP1 rs1167827 (Pinteraction = 0.015), C6orf106 rs205262 (Pinteraction = 0.032) and GRID1 rs7899106 (Pinteraction = 0.043) with smoking on BMI.ConclusionsMost BMI-associated loci have directionally consistent effects on BMI in Pakistanis and Europeans. There were suggestive interactions of established BMI-related SNPs with smoking or physical activity.

Established BMI-associated genetic variants and their prospective associations with BMI and other cardiometabolic traits: the GLACIER Study

Background:Recent cross-sectional genome-wide scans have reported associations of 97 independent loci with body mass index (BMI). In 3541 middle-aged adult participants from the GLACIER Study, we tested whether these loci are associated with 10-year changes in BMI and other cardiometabolic traits (fasting and 2-h glucose, triglycerides, total cholesterol, and systolic and diastolic blood pressures).Methods:A BMI-specific genetic risk score (GRS) was calculated by summing the BMI-associated effect alleles at each locus. Trait-specific cardiometabolic GRSs comprised only the loci that show nominal association (P⩽0.10) with the respective trait in the original cross-sectional study. In longitudinal genetic association analyses, the second visit trait measure (assessed ~10 years after baseline) was used as the dependent variable and the models were adjusted for the baseline measure of the outcome trait, age, age2, fasting time (for glucose and lipid traits), sex, follow-up time and population substructure.Results:The BMI-specific GRS was associated with increased BMI at follow-up (β=0.014 kg m−2 per allele per 10-year follow-up, s.e.=0.006, P=0.019) as were three loci (PARK2 rs13191362, P=0.005; C6orf106 rs205262, P=0.043; and C9orf93 rs4740619, P=0.01). Although not withstanding Bonferroni correction, a handful of single-nucleotide polymorphisms was nominally associated with changes in blood pressure, glucose and lipid levels.Conclusions:Collectively, established BMI-associated loci convey modest but statistically significant time-dependent associations with long-term changes in BMI, suggesting a role for effect modification by factors that change with time in this population.

A novel interaction between the FLJ33534 locus and smoking in obesity: a genome-wide study of 14 131 Pakistani adults.

Background:Obesity is a complex disease caused by the interplay of genetic and lifestyle factors, but identification of gene–lifestyle interactions in obesity has remained challenging. Few large-scale studies have reported use of genome-wide approaches to investigate gene–lifestyle interactions in obesity.Methods:In the Pakistan Risk of Myocardial Infraction Study, a cross-sectional study based in Pakistan, we calculated body mass index (BMI) variance estimates (square of the residual of inverse-normal transformed BMI z-score) in 14 131 participants and conducted genome-wide heterogeneity of variance analyses (GWHVA) for this outcome. All analyses were adjusted for age, age2, sex and genetic ancestry.Results:The GWHVA analyses identified an intronic variant, rs140133294, in the FLJ33544 gene in association with BMI variance (P-value=3.1 × 10−8). In explicit tests of gene × lifestyle interaction, smoking was found to significantly modify the effect of rs140133294 on BMI (Pinteraction=0.0005), whereby the minor allele (T) was associated with lower BMI in current smokers, while positively associated with BMI in never smokers. Analyses of ENCODE data at the FLJ33534 locus revealed features indicative of open chromatin and high confidence DNA-binding motifs for several transcription factors, providing suggestive biological support for a mechanism of interaction.Conclusions:In summary, we have identified a novel interaction between smoking and variation at the FLJ33534 locus in relation to BMI in people from Pakistan.