Human cardiovascular disease IBC chip-wide association with weight loss and weight regain in the look AHEAD trial

Low-frequency Coding Variants Associated With Body Mass Index Affect the Success of Bariatric Surgery

CONTEXT

A recent study identified 14 low-frequency coding variants associated with body mass index (BMI) in 718 734 individuals predominantly of European ancestry.

OBJECTIVE

We investigated the association of 2 genetic scores (GS) with i) the risk of severe/morbid obesity, ii) BMI variation before weight-loss intervention, iii) BMI change in response to an 18-month lifestyle/behavioral intervention program, and iv) BMI change up to 24 months after bariatric surgery.

METHODS

The 14 low-frequency coding variants were genotyped or sequenced in 342 French adults with severe/morbid obesity and 574 French adult controls from the general population. We built risk and protective GS based on 6 BMI-increasing and 5 BMI-decreasing low-frequency coding variants that were polymorphic in our study.

RESULTS

While the risk GS was not associated with severe/morbid obesity status, BMI-decreasing low-frequency coding variants were significantly less frequent in patients with severe/morbid obesity than in French adults from the general population. Neither the risk nor the protective GS was associated with BMI before intervention in patients with severe/morbid obesity, nor did they affect BMI change in response to a lifestyle/behavioral modification program. The protective GS was associated with a greater BMI decrease following bariatric surgery. The risk and protective GS were associated with a higher and lower risk of BMI regain after bariatric surgery.

CONCLUSION

Our data indicate that in populations of European descent, low-frequency coding variants associated with BMI in the general population also affect the outcomes of bariatric surgery in patients with severe/morbid obesity.

Genetics of Obesity: What We Have Learned Over Decades of Research

There is a genetic component to human obesity that accounts for 40% to 50% of the variability in body weight status but that is lower among normal weight individuals (about 30%) and substantially higher in the subpopulation of individuals with obesity and severe obesity (about 60%-80%). The appreciation that heritability varies across classes of BMI represents an important advance. After controlling for BMI, ectopic fat and fat distribution traits are characterized by heritability levels ranging from 30% to 55%. Defects in at least 15 genes are the cause of monogenic obesity cases, resulting mostly from deficiencies in the leptin-melanocortin signaling pathway. Approximately two-thirds of the BMI heritability can be imputed to common DNA variants, whereas low-frequency and rare variants explain the remaining fraction. Diminishing allele effect size is observed as the number of obesity-associated variants expands, with most BMI-increasing or -decreasing alleles contributing only a few grams or less to body weight. Obesity-promoting alleles exert minimal effects in normal weight individuals but have larger effects in individuals with a proneness to obesity, suggesting a higher penetrance; however, it is not known whether these larger effect sizes precede obesity or are caused by an obese state. The obesity genetic risk is conditioned by thousands of DNA variants that make genetically based obesity prevention and treatment a major challenge.

Strategies to Understand the Weight-Reduced State: Genetics and Brain Imaging

Most individuals with obesity or overweight have difficulty maintaining weight loss. The weight-reduced state induces changes in many physiological processes that appear to drive weight regain. Here, we review the use of cell biology, genetics, and imaging techniques that are being used to begin understanding why weight regain is the normal response to dieting. As with obesity itself, weight regain has both genetic and environmental drivers. Genetic drivers for "thinness" and "obesity" largely overlap, but there is evidence for specific genetic loci that are different for each of these weight states. There is only limited information regarding the genetics of weight regain. Currently, most genetic loci related to weight point to the central nervous system as the organ responsible for determining the weight set point. Neuroimaging tools have proved useful in studying the contribution of the central nervous system to the weight-reduced state in humans. Neuroimaging technologies fall into three broad categories: functional, connectivity, and structural neuroimaging. Connectivity and structural imaging techniques offer unique opportunities for testing mechanistic hypotheses about changes in brain function or tissue structure in the weight-reduced state.

The impact of genetic polymorphisms on weight regain after successful weight loss

Obesity is associated with an increased risk of various diseases and mortality. Although nearly 50 % of adults have been reported trying to lose weight, the prevalence of obesity has increased. One factor that hinders weight loss-induced decrease in obesity prevalence is weight regain. Although behavioural, psychological and physiological factors associated with weight regain have been reviewed, the information regarding the relationship between weight regain and genetics has not been previously summarised. In this paper, we comprehensively review the association between genetic polymorphisms and weight regain in adults and children with obesity after weight loss. Based on this information, identification of genetic polymorphism in patients who undergo weight loss intervention might be used to estimate their risks of weight regain. Additionally, the genetic-based risk estimation may be used as a guide for physicians and dietitians to provide each of their patients with the most appropriate strategies for weight loss and weight maintenance.

SGCG rs679482 Associates With Weight Loss Success in Response to an Intensively Supervised Outpatient Program

Weight loss in response to energy restriction is highly variable, and identification of genetic contributors can provide insights into underlying biology. Leveraging 1000 Genomes imputed genotypes, we carried out genome-wide association study (GWAS) analysis in 551 unrelated obese subjects of European ancestry who participated in an intensively supervised weight loss program with replication of promising signals in an independent sample of 1,331 obese subjects who completed the program at a later date. By single nucleotide polymorphism-based and sib-pair analysis, we show that that weight loss is a heritable trait, with estimated heritability (h ² = 0.49) within the range reported for obesity. We find rs679482, intronic to SGCG (sarcoglycan γ), highly expressed in skeletal muscle, to concordantly associate with weight loss in discovery and replication samples reaching GWAS significance in the combined meta-analysis (β = -0.35, P = 1.7 × 10^-12). Located in a region of open chromatin, rs679482 is predicted to bind DMRT2, and allele-specific transcription factor binding analysis indicates preferential binding of DMRT2 to rs679482-A. Concordantly, rs679482-A impairs native repressor activity and increases basal and DMRT2-mediated enhancer activity. These findings confirm that weight loss is a heritable trait and provide evidence by which a novel variant in SGCG, rs679482, leads to impaired diet response.

Benefits and limitations of genome-wide association studies

Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.

Genetic predictors of weight loss in overweight and obese subjects

The aim of our study was to investigate a large cohort of overweight subjects consuming a homogeneous diet to identify the genetic factors associated with weight loss that could be used as predictive markers in weight loss interventions. We retrospectively recruited subjects (N = 788) aged over 18 years with a Body Mass Index (BMI) between 25 and 40 kg/m² who were treated at our lipid unit for at least one year from 2008 to 2016, and we also recruited a control group (168 patients) with normal BMIs. All participants received counselling from a nutritionist that included healthy diet and physical activity recommendations. We genotyped 25 single nucleotide variants (SNVs) in 25 genes that were previously associated with obesity and calculated genetic scores that were derived from 25 SNVs. The risk allele in CADM2 showed a higher frequency in overweight and obese subjects than in controls (p = 0.007). The mean follow-up duration was 5.58 ± 2.68 years. Subjects with lower genetic scores showed greater weight loss during the follow-up period. The genetic score was the variable that best explained the variations in weight from the baseline. The genetic score explained 2.4% of weight change variance at one year and 1.6% of weight change variance at the end of the follow-up period after adjusting for baseline weight, sex, age and years of follow-up.

Genome-wide gene-based analyses of weight loss interventions identify a potential role for NKX6.3 in metabolism

Hundreds of genetic variants have been associated with Body Mass Index (BMI) through genome-wide association studies (GWAS) using observational cohorts. However, the genetic contribution to efficient weight loss in response to dietary intervention remains unknown. We perform a GWAS in two large low-caloric diet intervention cohorts of obese participants. Two loci close to NKX6.3/MIR486 and RBSG4 are identified in the Canadian discovery cohort (n = 1166) and replicated in the DiOGenes cohort (n = 789). Modulation of HGTX (NKX6.3 ortholog) levels in Drosophila melanogaster leads to significantly altered triglyceride levels. Additional tissue-specific experiments demonstrate an action through the oenocytes, fly hepatocyte-like cells that regulate lipid metabolism. Our results identify genetic variants associated with the efficacy of weight loss in obese subjects and identify a role for NKX6.3 in lipid metabolism, and thereby possibly weight control.

Individuals show large variability in their capacity to lose weight and maintain this weight. Here, the authors perform GWAS in two weight loss intervention cohorts and identify two genetic loci associated with weight loss that are taken forward for Bayesian fine-mapping and functional assessment in flies.

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT): Recommendations from the Biological Domain

BACKGROUND

The responses to behavioral, pharmacological, or surgical obesity treatments are highly individualized. The Accumulating Data to Optimally Predict obesity Treatment (ADOPT) project provides a framework for how obesity researchers, working collectively, can generate the evidence base needed to guide the development of tailored, and potentially more effective, strategies for obesity treatment.

OBJECTIVES

The objective of the ADOPT biological domain subgroup is to create a list of high-priority biological measures for weight-loss studies that will advance the understanding of individual variability in response to adult obesity treatments. This list includes measures of body composition, energy homeostasis (energy intake and output), brain structure and function, and biomarkers, as well as biobanking procedures, which could feasibly be included in most, if not all, studies of obesity treatment. The recommended high-priority measures are selected to balance needs for sensitivity, specificity, and/or comprehensiveness with feasibility to achieve a commonality of usage and increase the breadth and impact of obesity research.

SIGNIFICANCE

The accumulation of data on key biological factors, along with behavioral, psychosocial, and environmental factors, can generate a more precise description of the interplay and synergy among them and their impact on treatment responses, which can ultimately inform the design and delivery of effective, tailored obesity treatments.

The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach

BACKGROUND

Individual variability in response to multiple modalities of obesity treatment is well documented, yet our understanding of why some individuals respond while others do not is limited. The etiology of this variability is multifactorial; however, at present, we lack a comprehensive evidence base to identify which factors or combination of factors influence treatment response.

OBJECTIVES

This paper provides an overview and rationale of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project, which aims to advance the understanding of individual variability in response to adult obesity treatment. This project provides an integrated model for how factors in the behavioral, biological, environmental, and psychosocial domains may influence obesity treatment responses and identify a core set of measures to be used consistently across adult weight-loss trials. This paper provides the foundation for four companion papers that describe the core measures in detail.

SIGNIFICANCE

The accumulation of data on factors across the four ADOPT domains can inform the design and delivery of effective, tailored obesity treatments. ADOPT provides a framework for how obesity researchers can collectively generate this evidence base and is a first step in an ongoing process that can be refined as the science advances.

Genetic modifiers of cardiorespiratory fitness response to lifestyle intervention

PURPOSE

Numerous prospective studies indicate that improved cardiorespiratory fitness reduces type 2 diabetes risk and delays disease progression. We hypothesized that genetic variants modify fitness response to an intensive lifestyle intervention (ILI) in the Action for Health in Diabetes (Look AHEAD) randomized clinical trial, aimed to detect whether ILI will reduce cardiovascular events in overweight/obese subjects with type 2 diabetes compared with a standard of care.

METHODS

Polymorphisms in established fitness genes and in all loci assayed on the Illumina CARe iSelect chip were examined as predictors of change in MET level, estimated using a treadmill test, in response to a 1-yr intervention in 3899 participants.

RESULTS

We identified a significant signal in previously reported fitness-related gene RUNX1 that was associated with 1-yr METs response in ILI (0.19 ± 0.04 MET less improvement per minor allele copy; P = 1.9 × 10(-5)) and genotype-intervention interaction (P = 4.8 × 10(-3)). In the chipwide analysis, FKBP7 rs17225700 showed a significant association with ILI response among subjects not receiving beta-blocker medications (0.47 ± 0.09 METs less improvement; P = 5.3 × 10(-5)) and genotype-treatment interaction (P = 5.3 × 10(-7)). The Gene Relationships Among Implicated Loci pathway-based analysis identified connections between associated genes, including those influencing vascular tone, muscle contraction, cardiac energy substrate dynamics, and muscle protein synthesis.

CONCLUSIONS

This is the first study to identify genetic variants associated with fitness responses to a randomized lifestyle intervention in overweight/obese diabetic individuals. RUNX1 and FKBP7, involved in erythropoesis and muscle protein synthesis, respectively, are related to change in cardiorespiratory fitness in response to exercise.

Insight into rheumatological cause and effect through the use of Mendelian randomization

Establishing causality of risk factors is important to determine the pathogenetic mechanisms underlying rheumatic diseases, and can facilitate the design of interventions to improve care for affected patients. The presence of unmeasured confounders, as well as reverse causation, is a challenge to the assignment of causality in observational studies. Alleles for genetic variants are randomly inherited at meiosis. Mendelian randomization analysis uses these genetic variants to test whether a particular risk factor is causal for a disease outcome. In this Review of the Mendelian randomization technique, we discuss published results and potential applications in rheumatology, as well as the general clinical utility and limitations of the approach.

NIH working group report-using genomic information to guide weight management: From universal to precision treatment

OBJECTIVE

Precision medicine utilizes genomic and other data to optimize and personalize treatment. Although more than 2,500 genetic tests are currently available, largely for extreme and/or rare phenotypes, the question remains whether this approach can be used for the treatment of common, complex conditions like obesity, inflammation, and insulin resistance, which underlie a host of metabolic diseases.

METHODS

This review, developed from a Trans-NIH Conference titled "Genes, Behaviors, and Response to Weight Loss Interventions," provides an overview of the state of genetic and genomic research in the area of weight change and identifies key areas for future research.

RESULTS

Although many loci have been identified that are associated with cross-sectional measures of obesity/body size, relatively little is known regarding the genes/loci that influence dynamic measures of weight change over time. Although successful short-term weight loss has been achieved using many different strategies, sustainable weight loss has proven elusive for many, and there are important gaps in our understanding of energy balance regulation.

CONCLUSIONS

Elucidating the molecular basis of variability in weight change has the potential to improve treatment outcomes and inform innovative approaches that can simultaneously take into account information from genomic and other sources in devising individualized treatment plans.

USAT: A Unified Score-Based Association Test for Multiple Phenotype-Genotype Analysis

Genome-wide association studies (GWASs) for complex diseases often collect data on multiple correlated endo-phenotypes. Multivariate analysis of these correlated phenotypes can improve the power to detect genetic variants. Multivariate analysis of variance (MANOVA) can perform such association analysis at a GWAS level, but the behavior of MANOVA under different trait models has not been carefully investigated. In this paper, we show that MANOVA is generally very powerful for detecting association but there are situations, such as when a genetic variant is associated with all the traits, where MANOVA may not have any detection power. In these situations, marginal model based methods, however, perform much better than multivariate methods. We investigate the behavior of MANOVA, both theoretically and using simulations, and derive the conditions where MANOVA loses power. Based on our findings, we propose a unified score-based test statistic USAT that can perform better than MANOVA in such situations and nearly as well as MANOVA elsewhere. Our proposed test reports an approximate asymptotic P-value for association and is computationally very efficient to implement at a GWAS level. We have studied through extensive simulations the performance of USAT, MANOVA, and other existing approaches and demonstrated the advantage of using the USAT approach to detect association between a genetic variant and multivariate phenotypes. We applied USAT to data from three correlated traits collected on 5, 816 Caucasian individuals from the Atherosclerosis Risk in Communities (ARIC, The ARIC Investigators []) Study and detected some interesting associations.

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.

Prospective association of a genetic risk score and lifestyle intervention with cardiovascular morbidity and mortality among individuals with type 2 diabetes: the Look AHEAD randomised controlled trial

AIMS/HYPOTHESIS

Both obesity and genetics contribute to cardiovascular disease (CVD). We examined whether a genetic risk score (GRS) prospectively predicted cardiovascular morbidity and mortality among overweight/obese individuals with type 2 diabetes and whether behavioural weight loss could diminish this association.

METHODS

Look AHEAD (Action for Health in Diabetes) is a randomised controlled trial to determine the effects of intensive lifestyle intervention (ILI), including weight loss and physical activity, relative to diabetes support and education, on cardiovascular outcomes among overweight/obese individuals with type 2 diabetes. Of the participants, 4,016 provided consent for genetic analyses and had DNA samples passing quality control procedures. These secondary data analyses focused on whether a GRS derived from 153 single nucleotide polymorphisms (SNPs) associated with coronary artery disease in the most recent genome-wide association study predicted cardiovascular morbidity and mortality over a median of 9.6 years of follow-up, and whether ILI would diminish this association.

RESULTS

The GRS significantly predicted the primary composite endpoint of death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke, or hospitalisation for angina in the full sample (HR, 95% CI per 1 SD increase in GRS: 1.19 [1.10, 1.28]) and among individuals with no known history of CVD at baseline (HR 1.18 [95% CI 1.07, 1.30]). In no case did ILI significantly alter this association.

CONCLUSIONS/INTERPRETATION

A GRS comprised of SNPs significantly predicts cardiovascular morbidity and mortality over 9.6 years of follow-up in Look AHEAD. Lifestyle intervention did not alter the genetic association.

CLINICAL TRIAL REGISTRATION

NCT00017953; NCT01270763.

Effectiveness of lifestyle-based weight loss interventions for adults with type 2 diabetes: a systematic review and meta-analysis

AIMS

To provide a systematic review and meta-analysis of recent evidence on the effectiveness of lifestyle-based weight loss interventions for adults with type 2 diabetes.

METHODS

A search of the literature from January 2003 to July 2013 was conducted (PubMed, Embase, CINAHL and Web of Science). The studies considered eligible were randomized controlled trials evaluating weight loss interventions (diet and physical activity, with or without behavioural strategies) of ≥12 weeks duration, compared with usual care or another comparison intervention. Ten studies were included for review. Some heterogeneity was present in the sample, therefore, random-effects models were used to calculate pooled effects.

RESULTS

Intervention duration ranged from 16 weeks to 9 years, with all but one delivered via individual or group face-to-face sessions. From six studies comparing lifestyle intervention with usual care the pooled effect on weight (n = 5795) was -3.33 kg [95% confidence interval (CI) -5.06, -1.60 kg], and on glycated haemoglobin (HbA1c; n = 5784) was -0.29% (95% CI -0.61, 0.03%), with both attenuated in sensitivity analyses. The pooled within-group effect on weight (n = 3063) from all 10 lifestyle intervention groups was -5.33 kg (95% CI -7.33, -3.34 kg), also attenuated in sensitivity analyses. None of the participant or intervention characteristics examined explained the heterogeneity. Only one study assessed whether intervention effects were maintained after the end of the intervention.

CONCLUSIONS

Lifestyle-based weight loss intervention trials in type 2 diabetes achieve, on average, modest reductions in weight and HbA1c levels, but results were heavily influenced by one trial. Evidence-based approaches for improving the effectiveness of lifestyle-based interventions in type 2 diabetes are needed, along with future studies reporting on maintenance and cost-effectiveness.

Look inside Look AHEAD: why the glass is more than half-full

Look AHEAD is the only long-term study in a large cohort of subjects with type 2 diabetes that assessed the effect of intensive lifestyle, predominantly diet and exercise, on a number of outcomes. While Look AHEAD was not able to detect a significant effect of intensive lifestyle modification on cardiovascular outcomes, it clearly demonstrated numerous beneficial and sustained effects on health outcomes that are relevant to this population. Without the exceptional retention of study participants, it would have been difficult to detect these benefits. Our review provides a perspective on aspects related to exercise, diet, and weight loss in relation to cardiovascular outcomes and potential future research.