Invited commentary: Gene X lifestyle interactions and complex disease traits--inferring cause and effect from observational data, sine qua non

The importance of gene-environment interactions in human obesity

The worldwide obesity epidemic has been mainly attributed to lifestyle changes. However, who becomes obese in an obesity-prone environment is largely determined by genetic factors. In the last 20 years, important progress has been made in the elucidation of the genetic architecture of obesity. In parallel with successful gene identifications, the number of gene-environment interaction (GEI) studies has grown rapidly. This paper reviews the growing body of evidence supporting gene-environment interactions in the field of obesity. Heritability, monogenic and polygenic obesity studies provide converging evidence that obesity-predisposing genes interact with a variety of environmental, lifestyle and treatment exposures. However, some skepticism remains regarding the validity of these studies based on several issues, which include statistical modelling, confounding, low replication rate, underpowered analyses, biological assumptions and measurement precision. What follows in this review includes (1) an introduction to the study of GEI, (2) the evidence of GEI in the field of obesity, (3) an outline of the biological mechanisms that may explain these interaction effects, (4) methodological challenges associated with GEI studies and potential solutions, and (5) future directions of GEI research. Thus far, this growing body of evidence has provided a deeper understanding of GEI influencing obesity and may have tremendous applications in the emerging field of personalized medicine and individualized lifestyle recommendations.

Role of Epigenetic Histone Modifications in Diabetic Kidney Disease Involving Renal Fibrosis

One of the commonest causes of end-stage renal disease is diabetic kidney disease (DKD). Renal fibrosis, characterized by the accumulation of extracellular matrix (ECM) proteins in glomerular basement membranes and the tubulointerstitium, is the final manifestation of DKD. The TGF-β pathway triggers epithelial-to-mesenchymal transition (EMT), which plays a key role in the accumulation of ECM proteins in DKD. DCCT/EDIC studies have shown that DKD often persists and progresses despite glycemic control in diabetes once DKD sets in due to prior exposure to hyperglycemia called "metabolic memory." These imply that epigenetic factors modulate kidney gene expression. There is evidence to suggest that in diabetes and hyperglycemia, epigenetic histone modifications have a significant effect in modulating renal fibrotic and ECM gene expression induced by TGF-β1, as well as its downstream profibrotic genes. Histone modifications are also implicated in renal fibrosis through its ability to regulate the EMT process triggered by TGF-β signaling. In view of this, efforts are being made to develop HAT, HDAC, and HMT inhibitors to delay, stop, or even reverse DKD. In this review, we outline the latest advances that are being made to regulate histone modifications involved in DKD.

Putting the Genome in Context: Gene-Environment Interactions in Type 2 Diabetes

The genome is often the conduit through which environmental exposures convey their effects on health and disease. Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined. Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes. It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered. As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases.

Lifestyle may modify the glucose-raising effect of genetic loci. A study in the Greek population

BACKGROUND AND AIMS

Lifestyle habits including dietary intake and physical activity are closely associated with multiple body processes including glucose metabolism and are known to affect human health. Recent genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) associated with glucose levels. The hypothesis tested here is whether a healthy lifestyle assessed via a score is associated with glycaemic traits and whether there is an interaction between the lifestyle and known glucose-raising genetic variants in association with glycaemic traits.

METHODS AND RESULTS

Participants of Greek descent from the THISEAS study were included in this analysis. We developed a glucose preventive score (GPS) including dietary and physical activity characteristics. We also modelled a weighted genetic risk score (wGRS), based on 20 known glucose-raising loci, in order to investigate the impact of lifestyle-gene interaction on glucose levels. The GPS was observed to be significantly associated with lower glucose concentrations (β ± SE: -0.083 ± 0.021 mmol/L, P = 1.6 × 10(-04)) and the wGRS, as expected, with increased glucose levels (β ± SE: 0.020 ± 0.007 mmol/L, P = 8.4 × 10(-3)). The association of the wGRS with glucose levels was attenuated after interaction with the GPS. A higher GPS indicated decreasing glucose levels in the presence of an increasing wGRS (β interaction ± SE: -0.019 ± 0.007 mmol/L, P = 0.014).

CONCLUSION

Our results indicate that lower glucose levels underlie a healthier lifestyle and also support an interaction between the wGRS for known glycaemic loci and GPS associated with lower glucose levels. These scores could be useful tools for monitoring glucose metabolism.

Genome-wide variant by serum urate interaction in Parkinson's disease

OBJECTIVE

Serum urate levels have been associated with risk for and progression of Parkinson's disease (PD). Urate-related compounds are therapeutic candidates in neuroprotective efforts to slow PD progression. A urate-elevating agent is currently under investigation as a potential disease-modifying strategy in people with PD. However, PD is a heterogeneous disorder, and genetic variation may explain divergence in disease severity and progression.

METHODS

We conducted a genome-wide association study to identify gene variant × serum urate interaction effects on the striatal (123) I-ioflupane (DaTscan) binding ratio measured using single photon emission computed tomography in patients with possible PD from the Parkinson's Progression Markers Initiative (PPMI, n = 360). Follow-up analyses were conducted to assess gene variant × serum urate interaction effects on magnetic resonance imaging-derived regional brain volumes and clinical status. We then attempted to replicate our primary analysis in patients who entered the Parkinson Research Examination of CEP-1347 Trial (PRECEPT) with a clinical diagnosis of PD (n = 349).

RESULTS

Rs1109303 (T>G) variant within the INPP5K gene on chromosome 17p13.3 demonstrated a genome-wide significant interaction with serum urate level to predict striatal dopamine transporter density among all PPMI participants (n = 359) with possible PD (p = 2.01 × 10(-8) ; after excluding participants with SWEDD [scan without evidence of dopaminergic deficit]: p = 1.12 × 10(-9) ; n = 316). Independent of striatal dopamine transporter density, similar effects on brain atrophy, bradykinesia, anxiety, and depression were observed. No effect was present in the PRECEPT sample at baseline; however, in non-SWEDD PD participants in PRECEPT (n = 309), we observed a significant longitudinal genotype × serum urate interaction effect, consistent in direction with the PPMI sample, on progression of striatal dopamine transporter density over the 22-month follow-up.

INTERPRETATION

Genetic profile combined with serum urate level can be used to predict disease severity and potential disease progression in patients with PD. These results may be relevant to therapeutic efforts targeting the urate pathway.

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

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.

Gene-environment interactions and obesity: recent developments and future directions

Obesity, a major public health concern, is a multifactorial disease caused by both environmental and genetic factors. Although recent genome-wide association studies have identified many loci related to obesity or body mass index, the identified variants explain only a small proportion of the heritability of obesity. Better understanding of the interplay between genetic and environmental factors is the basis for developing effective personalized obesity prevention and management strategies. This article reviews recent advances in identifying gene-environment interactions related to obesity and describes epidemiological designs and newly developed statistical approaches to characterizing and discovering gene-environment interactions on obesity risk.

Epigenetics of progression of chronic kidney disease: fact or fantasy?

Epigenetic modifications are important in the normal functioning of the cell, from regulating dynamic expression of essential genes and associated proteins to repressing those that are unneeded. Epigenetic changes are essential for development and functioning of the kidney, and aberrant methylation, histone modifications, and expression of microRNA could lead to chronic kidney disease (CKD). Here, epigenetic modifications modulate transforming growth factor β signaling, inflammation, profibrotic genes, and the epithelial-to-mesenchymal transition, promoting renal fibrosis and progression of CKD. Identification of these epigenetic changes is important because they are potentially reversible and may serve as therapeutic targets in the future to prevent subsequent renal fibrosis and CKD. In this review we discuss the different types of epigenetic control, methods to study epigenetic modifications, and how epigenetics promotes progression of CKD.

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.

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 (P_interaction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, P_interaction = 0.014 vs. n = 71,611, P_interaction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (P_interaction = 0.003) and the SEC16B rs10913469 (P_interaction = 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.

We undertook analyses in 111,421 adults of European descent to examine whether physical activity diminishes the genetic risk of obesity predisposed by 12 single nucleotide polymorphisms, as previously reported in a study of 20,000 UK adults (Li et al, PLoS Med. 2010). Although the study by Li et al is widely cited, the original report has not been replicated to our knowledge. Therefore, we sought to confirm or refute the original study's findings in a combined analysis of 111,421 adults. Our analyses yielded a statistically significant interaction effect (P_interaction = 0.015), confirming the original study's results; we also identified an interaction between the FTO locus and physical activity (P_interaction = 0.003), verifying previous analyses (Kilpelainen et al, PLoS Med., 2010), and we detected a novel interaction between the SEC16B locus and physical activity (P_interaction = 0.025). We also examined the power constraints of interaction analyses, thereby demonstrating that sources of within- and between-study heterogeneity and the manner in which data are treated can inhibit the detection of interaction effects in meta-analyses that combine many cohorts with varying characteristics. This suggests that combining many small studies that have measured environmental exposures differently may be relatively inefficient for the detection of gene × environment interactions.

Meta-analysis investigating associations between healthy diet and fasting glucose and insulin levels and modification by loci associated with glucose homeostasis in data from 15 cohorts

Whether loci that influence fasting glucose (FG) and fasting insulin (FI) levels, as identified by genome-wide association studies, modify associations of diet with FG or FI is unknown. We utilized data from 15 U.S. and European cohort studies comprising 51,289 persons without diabetes to test whether genotype and diet interact to influence FG or FI concentration. We constructed a diet score using study-specific quartile rankings for intakes of whole grains, fish, fruits, vegetables, and nuts/seeds (favorable) and red/processed meats, sweets, sugared beverages, and fried potatoes (unfavorable). We used linear regression within studies, followed by inverse-variance-weighted meta-analysis, to quantify 1) associations of diet score with FG and FI levels and 2) interactions of diet score with 16 FG-associated loci and 2 FI-associated loci. Diet score (per unit increase) was inversely associated with FG (β = -0.004 mmol/L, 95% confidence interval: -0.005, -0.003) and FI (β = -0.008 ln-pmol/L, 95% confidence interval: -0.009, -0.007) levels after adjustment for demographic factors, lifestyle, and body mass index. Genotype variation at the studied loci did not modify these associations. Healthier diets were associated with lower FG and FI concentrations regardless of genotype at previously replicated FG- and FI-associated loci. Studies focusing on genomic regions that do not yield highly statistically significant associations from main-effect genome-wide association studies may be more fruitful in identifying diet-gene interactions.

Type 2 Diabetes Genetics: Beyond GWAS

The global epidemic of type 2 diabetes mellitus (T2D) is one of the most challenging problems of the 21(st) century leading cause of and the fifth death worldwide. Substantial evidence suggests that T2D is a multifactorial disease with a strong genetic component. Recent genome-wide association studies (GWAS) have successfully identified and replicated nearly 75 susceptibility loci associated with T2D and related metabolic traits, mostly in Europeans, and some in African, and South Asian populations. The GWAS serve as a starting point for future genetic and functional studies since the mechanisms of action by which these associated loci influence disease is still unclear and it is difficult to predict potential implication of these findings in clinical settings. Despite extensive replication, no study has unequivocally demonstrated their clinical role in the disease management beyond progression to T2D from impaired glucose tolerance. However, these studies are revealing new molecular pathways underlying diabetes etiology, gene-environment interactions, epigenetic modifications, and gene function. This review highlights evolving progress made in the rapidly moving field of T2D genetics that is starting to unravel the pathophysiology of a complex phenotype and has potential to show clinical relevance in the near future.

Gene × environment interactions in type 2 diabetes

People vary genetically in their susceptibility to the effects of environmental risk factors for many diseases. Genetic variation also underlies the extent to which people respond appropriately to clinical therapies. Defining the basis to the interactions between the genome and the environment may help elucidate the biologic basis to diseases such as type 2 diabetes, as well as help target preventive therapies and treatments. This review examines 1) some of the most current evidence on gene × environment interactions in relation to type 2 diabetes; 2) outlines how the availability of information on gene × environment interactions might help improve the prevention and treatment of type 2 diabetes; and 3) discusses existing and emerging strategies that might enhance our ability to detect and exploit gene × environment interactions in complex disease traits.