Gene × environment interactions in type 2 diabetes

Visit to visit transition in TXNIP gene methylation and the risk of type 2 diabetes mellitus: a nested case-control study

Our study aimed to investigate the association between the transition of the TXNIP gene methylation level and the risk of incident type 2 diabetes mellitus (T2DM). This study included 263 incident cases of T2DM and 263 matched non-T2DM participants. According to the methylation levels of five loci (CpG1-5; chr1:145441102-145442001) on the TXNIP gene, the participants were classified into four transition groups: maintained low, low to high, high to low, and maintained high methylation levels. Compared with individuals whose methylation level of CpG2-5 at the TXNIP gene was maintained low, individuals with maintained high methylation levels showed a 61-87% reduction in T2DM risk (66% for CpG2 [OR: 0.34, 95% CI: 0.14, 0.80]; 77% for CpG3 [OR: 0.23, 95% CI: 0.07, 0.78]; 87% for CpG4 [OR: 0.13, 95% CI: 0.03, 0.56]; and 61% for CpG5 [OR: 0.39, 95% CI: 0.16, 0.92]). Maintained high methylation levels of four loci of the TXNIP gene are associated with a reduction of T2DM incident risk in the current study. Our study suggests that preserving hypermethylation levels of the TXNIP gene may hold promise as a potential preventive measure against the onset of T2DM.

The underlying causes, treatment options of gut microbiota and food habits in type 2 diabetes mellitus: a narrative review

Type 2 diabetes mellitus is a long-lasting endocrine disorder characterized by persistent hyperglycaemia, which is often triggered by an entire or relative inadequacy of insulin production or insulin resistance. As a result of resistance to insulin (IR) and an overall lack of insulin in the body, type 2 diabetes mellitus (T2DM) is a metabolic illness that is characterized by hyperglycaemia. Notably, the occurrence of vascular complications of diabetes and the advancement of IR in T2DM are accompanied by dysbiosis of the gut microbiota. Due to the difficulties in managing the disease and the dangers of multiple accompanying complications, diabetes is a chronic, progressive immune-mediated condition that plays a significant clinical and health burden on patients. The frequency and incidence of diabetes among young people have been rising worldwide. The relationship between the gut microbiota composition and the physio-pathological characteristics of T2DM proposes a novel way to monitor the condition and enhance the effectiveness of therapies. Our knowledge of the microbiota of the gut and how it affects health and illness has changed over the last 20 years. Species of the genus Eubacterium, which make up a significant portion of the core animal gut microbiome, are some of the recently discovered 'generation' of possibly helpful bacteria. In this article, we have focused on pathogenesis and therapeutic approaches towards T2DM, with a special reference to gut bacteria from ancient times to the present day.

Partners in diabetes epidemic: A global perspective

There is a recent increase in the worldwide prevalence of both obesity and diabetes. In this review we assessed insulin signaling, genetics, environment, lipid metabolism dysfunction and mitochondria as the major determinants in diabetes and to identify the potential mechanism of gut microbiota in diabetes diseases. We searched relevant articles, which have key information from laboratory experiments, epidemiological evidence, clinical trials, experimental models, meta-analysis and review articles, in PubMed, MEDLINE, EMBASE, Google scholars and Cochrane Controlled Trial Database. We selected 144 full-length articles that met our inclusion and exclusion criteria for complete assessment. We have briefly discussed these associations, challenges, and the need for further research to manage and treat diabetes more efficiently. Diabetes involves the complex network of physiological dysfunction that can be attributed to insulin signaling, genetics, environment, obesity, mitochondria and stress. In recent years, there are intriguing findings regarding gut microbiome as the important regulator of diabetes. Valid approaches are necessary for speeding medical advances but we should find a solution sooner given the burden of the metabolic disorder - What we need is a collaborative venture that may involve laboratories both in academia and industries for the scientific progress and its application for the diabetes control.

Polychlorinated biphenyls, polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans, pesticides, and diabetes in the Anniston Community Health Survey follow-up (ACHS II): single exposure and mixture analysis approaches

Dioxins and dioxin-like compounds measurements were added to polychlorinated biphenyls (PCBs) and organochlorine pesticides to expand the exposure profile in a follow-up to the Anniston Community Health Survey (ACHS II, 2014) and to study diabetes associations. Participants of ACHS I (2005-2007) still living within the study area were eligible to participate in ACHS II. Diabetes status (type-2) was determined by a doctor's diagnosis, fasting glucose ≥125 mg/dL, or being on any glycemic control medication. Incident diabetes cases were identified in ACHS II among those who did not have diabetes in ACHS I, using the same criteria. Thirty-five ortho-substituted PCBs, 6 pesticides, 7 polychlorinated dibenzo-p-dioxins (PCDD), 10 furans (PCDF), and 3 non-ortho PCBs were measured in 338 ACHS II participants. Dioxin toxic equivalents (TEQs) were calculated for all dioxin-like compounds. Main analyses used logistic regression models to calculate odds ratios (OR) and 95 % confidence intervals (CI). In models adjusted for age, race, sex, BMI, total lipids, family history of diabetes, and taking lipid lowering medication, the highest ORs for diabetes were observed for PCDD TEQ: 3.61 (95 % CI: 1.04, 12.46), dichloro-diphenyl dichloroethylene (p,p'-DDE): 2.07 (95 % CI 1.08, 3.97), and trans-Nonachlor: 2.55 (95 % CI 0.93, 7.02). The OR for sum 35 PCBs was 1.22 (95 % CI: 0.58-2.57). To complement the main analyses, we used BKMR and g-computation models to evaluate 12 mixture components including 4 TEQs, 2 PCB subsets and 6 pesticides; suggestive positive associations for the joint effect of the mixture analyses resulted in ORs of 1.40 (95% CI: -1.13, 3.93) for BKMR and 1.32 (95% CI: -1.12, 3.76) for g-computation. The mixture analyses provide further support to previously observed associations of trans-Nonachlor, p,p'- DDE, PCDD TEQ and some PCB groups with diabetes.

Effect of metformin on the long non-coding RNA expression levels in type 2 diabetes: an in vitro and clinical trial study

BACKGROUND

It has been suggested that the anti-hyperglycemic effect of metformin could be associated with its impact on long non-coding RNA (lncRNA) expression levels. Accordingly, in the current study, we evaluated the effect of metformin on the expression of H19, MEG3, MALAT1, and GAS5 in in vitro and in vivo situations.

METHODS

The effect of hyperglycemia and metformin treatment on the lncRNAs expression level was evaluated in HepG2 cells. A total of 179 age- and sex-matched subjects, including 88 newly diagnosed patients with type 2 diabetes (T2D) and 91 healthy volunteers, were included in the case-control phase of the study. Moreover, 40 newly diagnosed patients participated in the study's open-labeled non-controlled clinical trial phase. The expression levels of lncRNA in HepG2 cells and whole blood samples were determined using QRT-PCR.

RESULTS

In vitro results showed that hyperglycemia induced H19 and MALAT1 and decreased GAS5 expression levels. Moreover, metformin decreased H19 and increased GAS5 expression in high glucose-treated cells. Case-control study findings revealed that the circulating levels of H19, MALAT1, and MEG3 were significantly elevated in T2D patients compared to the control subjects. Finally, results showed that the level of circulating H19 levels decreased while GAS5 increased in T2D patients after taking metformin for 2 months.

CONCLUSION

The results of the current study provided evidence that metformin could exert its effect in the treatment of T2D by altering the expression levels of H19 and GAS5.

Clinical and biological risk factors associated with inflammation in patients with type 2 diabetes mellitus

BACKGROUND

Chronic inflammation has been associated with insulin resistance and related metabolic dysregulation, including type 2 diabetes mellitus (T2DM). Several non modifiable (i.e. genetic predisposition) and modifiable (i.e. sedentary lifestyle, energy-dense food) risk factors were suggested to explain the mechanisms involved in the development of inflammation, but are difficult to assess in clinical routine. The present study aimed to identify easy to asses clinical and biological risk factors associated with inflammation in patients with T2DM.

METHODS

One hundred nine patients (51 men, 58 women), 28-60 years old, from seven primary healthcare centers in Gaza City, Palestine, took part to the cross-sectional study (November 2013-May 2014). Study participants had T2DM with no history of inflammatory diseases, cardiovascular diseases, medication and/or any health condition that might affect the inflammatory markers, interleukin 6 (IL-6) and C-reactive protein (CRP). Inflammation was defined for IL-6 ≥ 2 pg/mL and CRP ≥ 6 mg/L. Multivariable logistic regressions were used to identify the relationship between inflammation and clinical and biological risk factors.

RESULTS

After adjustment for age and gender, inflammation seems to increase with increased body mass index (BMI) (OR: 1.427 [1.055-1.931]), increased fasting blood glucose (OR: 1.029 [1.007-1.052]) and decreased adiponectin values (OR: 0.571 [0.361-0.903]). There were also significant relationships between inflammation and BMI (OR: 1.432 [1.042-1.968]), fasting blood glucose (OR: 1.029 [1.006-1.052]) and adiponectin (OR: 0.569 [0.359-0.902]), after adjustment for smoking habits and physical activity.

CONCLUSION

Managing obesity and associated complications (i.e. hyperglycemia, high adiponectin levels) might help decreasing inflammation in individuals with T2DM.

Burden of non-communicable diseases in Cyprus, 1990-2017: findings from the Global Burden of Disease 2017 study

BACKGROUND

Non-communicable diseases (NCDs) accounted for over 90% of all deaths in the Cypriot population, in 2018. However, a detailed and comprehensive overview of the impact of NCDs on population health of Cyprus over the period of 1990 to 2017, expressed in disability-adjusted life years (DALYs), is currently not available. Knowledge about the drivers of changes in NCD DALYs over time is paramount to identify priorities for the prevention of NCDs in Cyprus and guide evidence-based decision making. The objectives of this paper were to: 1) assess the burden of NCDs in terms of years of life lost (YLLs), years lived with disability (YLDs), and DALYs in Cyprus in 2017, and 2) identify changes in the burden of NCDs in Cyprus over the 28-year period and assess the main drivers of these changes.

METHODS

We performed a secondary database descriptive study using the Global Burden of Disease (GBD) 2017 results on NCDs for Cyprus from 1990 to 2017. We calculated the percentage change of age-standardized DALY rates between 1990 and 2017 and decomposed these time trends to assess the causes of death and disability that were the main drivers of change.

RESULTS

In Cyprus in 2017, 83% (15,129 DALYs per 100,000; 12,809 to 17,707 95%UI) of total DALYs were due to NCDs. The major contributors to NCD DALYs were cardiovascular diseases (16.5%), neoplasms (16.3%), and musculoskeletal disorders (15.6%). Between 1990 and 2017, age-standardized NCD DALY rates decreased by 23%. For both males and females, the largest decreases in DALY rates were observed in ischemic heart disease and stroke. For Cypriot males, the largest increases in DALY rates were observed for pancreatic cancer, drug use disorders, and acne vulgaris, whereas for Cypriot females these were for acne vulgaris, psoriasis and eating disorders.

CONCLUSION

Despite a decrease in the burden of NCDs over the period from 1990 to 2017, NCDs are still a major public health challenge. Implementation of interventions and early detection screening programmes of modifiable NCD risk factors are needed to reduce occurrence and exacerbation of leading causes of NCDs in the Cypriot population.

Associations of Per- and Polyfluoroalkyl Substances (PFAS) With Glucose Tolerance During Pregnancy in Project Viva

CONTEXT

Per- and polyfluoroalkyl substances (PFAS) exposure may alter glucose homeostasis. Research on PFAS exposure and glucose tolerance during pregnancy is limited.

OBJECTIVE

The objective of this work is to estimate associations between first-trimester plasma PFAS concentrations and glucose tolerance assessed in late second pregnancy trimester.

DESIGN, SETTING, PARTICIPANTS, AND MAIN OUTCOME MEASURES

Pregnant women (n = 1540) enrolled in Project Viva in 1999 to 2002 provided first-trimester plasma samples analyzed for 8 PFAS. At approximately 28 weeks' gestation, women completed 1-hour nonfasting, 50-g oral glucose challenge tests (GCTs); if abnormal, women completed subsequent 3-hour oral glucose tolerance tests (OGTTs) to screen for gestational diabetes mellitus (GDM). We assessed both continuous GCT glucose levels and 4 categories of glucose tolerance (normal glycemia [reference], isolated hyperglycemia, impaired glucose tolerance, GDM). We used multinomial logistic regression to estimate associations of PFAS with glucose tolerance categories. We used multivariable linear regression and Bayesian kernel machine regression (BKMR) to assess individual and joint effects of PFAS on continuous GCT glucose levels, respectively. We evaluated effect modification by maternal age and race/ethnicity.

RESULTS

PFAS were not associated with glucose tolerance categories. In BKMR analyses, we observed a positive association between ln-perfluorooctane sulfonate (PFOS) and glucose levels (Δ25th to 75th percentile: 6.2 mg/dL, 95% CI, 1.1-11.3) and an inverse-U shaped association between 2-(N-perfluorooctane sulfonamide) acetate and glucose levels. Individual linear regression results were similar. We found suggestive evidence that associations varied by age and racial/ethnic group.

CONCLUSION

Certain PFAS may alter glucose homeostasis during pregnancy, but may not be associated with overt GDM.

Type 2 diabetes susceptibility genes on mouse chromosome 11 under high sucrose environment

Background

Both genetic and environmental factors contribute to type 2 diabetes development. We used consomic mice established from an animal type 2 diabetes model to identify susceptibility genes that contribute to type 2 diabetes development under specific environments. We previously established consomic strains (C3H-Chr 11^NSY and C3H-Chr 14^NSY) that possess diabetogenic Chr 11 or 14 of the Nagoya-Shibata-Yasuda (NSY) mouse, an animal model of spontaneous type 2 diabetes, in the genetic background of C3H mice. To search genes contribute to type 2 diabetes under specific environment, we first investigated whether sucrose administration deteriorates type 2 diabetes-related traits in the consomic strains. We dissected loci on Chr 11 by establishing congenic strains possessing different segments of NSY-derived Chr 11 under sucrose administration.

Results

In C3H-Chr 11^NSY mice, sucrose administration for 10 weeks deteriorated hyperglycemia, insulin resistance, and impaired insulin secretion, which is comparable to NSY mice with sucrose. In C3H-Chr 14^NSY mice, sucrose administration induced glucose intolerance, but not insulin resistance and impaired insulin secretion. To dissect the gene(s) existing on Chr 11 for sucrose-induced type 2 diabetes, we constructed four novel congenic strains (R1, R2, R3, and R4) with different segments of NSY-derived Chr 11 in C3H mice. R2 mice showed marked glucose intolerance and impaired insulin secretion comparable to C3H-Chr 11^NSY mice. R3 and R4 mice also showed impaired insulin secretion. R4 mice showed significant decreases in white adipose tissue, which is in the opposite direction from parental C3H-Chr 11^NSY and NSY mice. None of the four congenic strains showed insulin resistance.

Conclusions

Genes on mouse Chr 11 could explain glucose intolerance, impaired insulin secretion, insulin resistance in NSY mice under sucrose administration. Congenic mapping with high sucrose environment localized susceptibility genes for type 2 diabetes associated with impaired insulin secretion in the middle segment (26.0–63.4 Mb) of Chr 11. Gene(s) that decrease white adipose tissue were mapped to the distal segment of Chr 11. The identification of diabetogenic gene on Chr 11 in the future study will facilitate precision medicine in type 2 diabetes by controlling specific environments in targeted subjects with susceptible genotypes.

Genetic background and diet affect brown adipose gene coexpression networks associated with metabolic phenotypes

Adipose is a dynamic endocrine organ that is critical for regulating metabolism and is highly responsive to nutritional environment. Brown adipose tissue is an exciting potential therapeutic target; however, there are no systematic studies of gene-by-environment interactions affecting function of this organ. We leveraged a weighted gene coexpression network analysis to identify transcriptional networks in brown adipose tissue from LG/J and SM/J inbred mice fed high- or low-fat diets and correlate these networks with metabolic phenotypes. We identified eight primary gene network modules associated with variation in obesity and diabetes-related traits. Four modules were enriched for metabolically relevant processes such as immune and cytokine response, cell division, peroxisome functions, and organic molecule metabolic processes. The relative expression of genes in these modules is highly dependent on both genetic background and dietary environment. Genes in the immune/cytokine response and cell division modules are particularly highly expressed in high fat-fed SM/J mice, which show unique brown adipose-dependent remission of diabetes. The interconnectivity of genes in these modules is also heavily dependent on diet and strain, with most genes showing both higher expression and coexpression under the same context. We highlight several genes of interest, Col28a1, Cyp26b1, Bmp8b, and Ngef, that have distinct expression patterns among strain-by-diet contexts and fall under metabolic quantitative trait loci previously mapped in an F16 generation of an advanced intercross between LG/J and SM/J. Each of these genes have some connection to obesity and diabetes-related traits, but have not been studied in brown adipose tissue. Our results provide important insights into the relationship between brown adipose and systemic metabolism by being the first gene-by-environment study of brown adipose transcriptional networks.

Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes

Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.

Genomics of Particulate Matter Exposure Associated Cardiopulmonary Disease: A Narrative Review

Particulate matter (PM) exposure is associated with the development of cardiopulmonary disease. Our group has studied the adverse health effects of World Trade Center particulate matter (WTC-PM) exposure on firefighters. To fully understand the complex interplay between exposure, organism, and resultant disease phenotype, it is vital to analyze the underlying role of genomics in mediating this relationship. A PubMed search was performed focused on environmental exposure, genomics, and cardiopulmonary disease. We included original research published within 10 years, on epigenetic modifications and specific genetic or allelic variants. The initial search resulted in 95 studies. We excluded manuscripts that focused on work-related chemicals, heavy metals and tobacco smoke as primary sources of exposure, as well as reviews, prenatal research, and secondary research studies. Seven full-text articles met pre-determined inclusion criteria, and were reviewed. The effects of air pollution were evaluated in terms of methylation (n = 3), oxidative stress (n = 2), and genetic variants (n = 2). There is evidence to suggest that genomics plays a meditating role in the formation of adverse cardiopulmonary symptoms and diseases that surface after exposure events. Genomic modifications and variations affect the association between environmental exposure and cardiopulmonary disease, but additional research is needed to further define this relationship.

Persistent organic pollutants and gestational diabetes: A multi-center prospective cohort study of healthy US women

BACKGROUND

Persistent organic pollutants (POPs) are linked with insulin resistance and type-2 diabetes (T2D) in the general population. However, their associations with gestational diabetes (GDM) are inconsistent.

OBJECTIVE

We prospectively evaluated the associations of POPs measured in early pregnancy with GDM risk. We also assessed whether pre-pregnancy BMI (ppBMI) and family history of T2D modify this risk.

METHODS

In NICHD Fetal Growth Study, Singletons, we measured plasma concentration of 76 POPs, including 11 organochlorine pesticides (OCPs), 9 polybrominated diphenylethers (PBDEs), 44 polychlorinated biphenyls (PCBs), and 11 per-and polyfluoroalkyl substances (PFAS) among 2334 healthy non-obese women at 8-13 weeks of gestation. GDM was diagnosed by Carpenter and Coustan criteria. We constructed chemical networks using a weighted-correlation algorithm and examined the associations of individual chemical and chemical networks with GDM using multivariate Poisson regression with robust variance.

RESULTS

Higher concentrations of PCBs with six or more chlorine atoms were associated with increased risk of GDM in the overall cohort (risk ratios [RRs] range: 1.08-1.13 per 1-standard deviation [SD] increment) and among women with a family history of T2D (RRs range: 1.08-1.48 per 1-SD increment) or normal ppBMI (RRs range: 1.08-1.22 per 1-SD increment). Similar associations were observed for the chemical network comprised of PCBs with ≥6 chlorine atoms and the summary measure of total PCBs and non-dioxin like PCBs (138, 153, 170, 180). Furthermore, four PFAS congeners - perfluorononanoic acid (PFNA), perfluorooctanoic acid (PFOA), perfluoroheptanoic acid (PFHpA), and perfluorododecanoic acid (PFDoDA) - showed significant positive associations with GDM among women with a family history of T2D (RRs range:1.22-3.18 per 1-SD increment), whereas BDE47 and BDE153 showed significant positive associations among women without a family history of T2D.

CONCLUSIONS

Environmentally relevant levels of heavily chlorinated PCBs and some PFAS and PBDEs were positively associated with GDM with suggestive effect modifications by family history of T2D and body adiposity status.

Differential vulnerability to neighbourhood disorder: a gene×environment interaction study

BACKGROUND

Type 2 diabetes (T2D) is preventable, it is increasing in prevalence and it is a major risk factor for morbidity and mortality. Importantly, residents of neighbourhoods with high levels of disorder are more likely to develop T2D than those living in less disordered neighbourhoods and neighbourhood disorder may exacerbate genetic risk for T2D.

METHOD

We use genetic, self-reported neighbourhood, and health data from the Health and Retirement Study. We conducted weighted logistic regression analyses in which neighbourhood disorder, polygenic scores for T2D and their interaction predicted T2D.

RESULTS

Greater perceptions of neighbourhood disorder (OR=1.11, p<0.001) and higher polygenic scores for T2D (OR=1.42, p<0.001) were each significantly and independently associated with an increased risk of T2D. Furthermore, living in a neighbourhood perceived as having high levels of disorder exacerbated genetic risk for T2D (OR=1.10, p=0.001). This significant gene×environment interaction was observed after adjusting for years of schooling, age, gender, levels of physical activity and obesity.

CONCLUSION

Findings in the present study suggested that minimising people's exposure to vandalism, vacant buildings, trash and circumstances viewed by residents as unsafe may reduce the burden of this prevalent chronic health condition, particularly for subgroups of the population who carry genetic liability for T2D.

Blood-Based Biomarkers for Predicting the Risk for Five-Year Incident Coronary Heart Disease in the Framingham Heart Study via Machine Learning

An improved approach for predicting the risk for incident coronary heart disease (CHD) could lead to substantial improvements in cardiovascular health. Previously, we have shown that genetic and epigenetic loci could predict CHD status more sensitively than conventional risk factors. Herein, we examine whether similar machine learning approaches could be used to develop a similar panel for predicting incident CHD. Training and test sets consisted of 1180 and 524 individuals, respectively. Data mining techniques were employed to mine for predictive biosignatures in the training set. An ensemble of Random Forest models consisting of four genetic and four epigenetic loci was trained on the training set and subsequently evaluated on the test set. The test sensitivity and specificity were 0.70 and 0.74, respectively. In contrast, the Framingham risk score and atherosclerotic cardiovascular disease (ASCVD) risk estimator performed with test sensitivities of 0.20 and 0.38, respectively. Notably, the integrated genetic-epigenetic model predicted risk better for both genders and very well in the three-year risk prediction window. We describe a novel DNA-based precision medicine tool capable of capturing the complex genetic and environmental relationships that contribute to the risk of CHD, and being mapped to actionable risk factors that may be leveraged to guide risk modification efforts.

Evolutionarily derived networks to inform disease pathways

Methods to identify genes or pathways associated with complex diseases are often inadequate to elucidate most risk because they make implicit and oversimplified assumptions about underlying models of disease etiology. These can lead to incomplete or inadequate conclusions. To address this, we previously developed human phenotype networks (HPN), linking phenotypes based on shared biology. However, such visualization alone is often uninterpretable, and requires additional filtering. Here, we expand the HPN to include another method, evolutionary triangulation (ET). ET utilizes the hypothesis that alleles affecting disease risk in multiple populations are distributed consistently with differences in disease prevalence and compares allele frequencies among populations and their relationship to phenotype prevalence. We hypothesized that combining these methods will increase our ability to detect genetic patterns of association in complex diseases. We combined HPN and ET to identify network patterns associated with type 2 diabetes mellitus (T2DM), a leading cause of death worldwide. Fasting glucose, a continuous trait, was used as a proxy for T2DM and differs significantly among continental populations. The combined method identified several diabetes-related traits and several phenotypes related to cardiovascular diseases, for which diabetes is a major risk factor. ET-HPN found more phenotypes related to our target and related phenotypes than the application of either method alone. Not only could we detect phenotype connections related to T2DM, but we also identified phenotypes that are distributed in parallel to it, e.g., amyotrophic lateral sclerosis. Our analyses showed that ET-filtered HPN provides information that neither technique can individually.

A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow-up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 × 10-7 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 10-7 ). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases.

Impact of the Polymorphism Near MC4R (rs17782313) on Obesity- and Metabolic-Related Traits in Women Participating in an Aerobic Training Program

The C/T polymorphism (rs17782313) mapped 188 kb downstream of the melanocortin-4 receptor gene (MC4R) shows a strong relationship with an increased body mass index (BMI) and the risk of type 2 diabetes. However, the information on polymorphism’s potential modifying effect on obesity- and metabolic-related traits achieved through training is still unknown. Therefore, we decided to check if selected body measurements observed in physically active participants would be modulated by the genotype. The genotype distribution was examined in a group of 201 Polish women measured for chosen traits before and after the completion of a 12 week moderate-intensive aerobic training program. A statistically significant relationship between the glucose level and the genotype was identified (p = 0.046). Participants with CC and CT genotypes had a higher glucose level during the entire study period compared with the TT genotype. However, our results did not confirm the relationship between the C allele and an increased BMI or other obesity-related traits. Additionally, we did not observe a near MC4R C/T polymorphism x physical activity interaction. However, our results revealed that majority of obesity-related variables changed significantly during the 12 week training program. The effect sizes (d) of these changes ranged from small to medium (d = 0.11-0.80), whereas the largest effect (d = 0.80; i.e. medium) was reported for the fat mass content (FM). We found a relationship between the near MC4R C/T polymorphism and an increased glucose level, and it is thus a candidate to influence type 2 diabetes. Interestingly, after the 12 week training program, participants with the C (risk) allele with fasting hyperglycemia had a normal glucose level. Although, this change was not statistically significant, it shows an important trend which needs further investigation.

Type 2 Diabetes Susceptibility in the Greek-Cypriot Population: Replication of Associations with TCF7L2, FTO, HHEX, SLC30A8 and IGF2BP2 Polymorphisms

Type 2 diabetes (T2D) has been the subject of numerous genetic studies in recent years which revealed associations of the disease with a large number of susceptibility loci. We hereby initiate the evaluation of T2D susceptibility loci in the Greek-Cypriot population by performing a replication case-control study. One thousand and eighteen individuals (528 T2D patients, 490 controls) were genotyped at 21 T2D susceptibility loci, using the allelic discrimination method. Statistically significant associations of T2D with five of the tested single nucleotide polymorphisms (SNPs) (TCF7L2 rs7901695, FTO rs8050136, HHEX rs5015480, SLC30A8 rs13266634 and IGF2BP2 rs4402960) were observed in this study population. Furthermore, 14 of the tested SNPs had odds ratios (ORs) in the same direction as the previously published studies, suggesting that these variants can potentially be used in the Greek-Cypriot population for predictive testing of T2D. In conclusion, our findings expand the genetic assessment of T2D susceptibility loci and reconfirm five of the worldwide established loci in a distinct, relatively small, newly investigated population.

Immunomodulatory roles of CTRP3 in endotoxemia and metabolic stress

C1q/TNF‐related protein 3 (CTRP3) is a secreted hormone that modulates hepatic glucose and lipid metabolism. Its circulating levels are reduced in human and rodent models of obesity, a metabolic state accompanied by chronic low‐grade inflammation. Recent studies have demonstrated an anti‐inflammatory role for recombinant CTRP3 in attenuating LPS‐induced systemic inflammation, and its deficiency markedly exacerbates inflammation in a mouse model of rheumatoid arthritis. We used genetic mouse models to explore the immunomodulatory function of CTRP3 in response to acute (LPS challenge) and chronic (high‐fat diet) inflammatory stimuli. In a sublethal dose of LPS challenge, neither CTRP3 deficiency nor its overexpression in transgenic mice had an impact on IL‐1β, IL‐6, TNF‐α, or MIP‐2 induction at the serum protein or mRNA levels, contrary to previous findings based on recombinant CTRP3 administration. In a metabolic context, we measured 71 serum cytokine levels in wild‐type and CTRP3 transgenic mice fed a high‐fat diet or a matched control low‐fat diet. On a low‐fat diet, CTRP3 transgenic mice had elevated circulating levels of multiple chemokines (CCL11, CXCL9, CXCL10, CCL17, CX3CL1, CCL22 and sCD30). However, when obesity was induced with a high‐fat diet, CTRP3 transgenic mice had lower circulating levels of IL‐5, TNF‐α, sVEGF2, and sVEGFR3, and a higher level of soluble gp130. Contingent upon the metabolic state, CTRP3 overexpression altered chemokine levels in lean mice, and attenuated systemic inflammation in the setting of obesity and insulin resistance. These results highlight a context‐dependent immunomodulatory role for CTRP3.

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.

Genetic variants influencing effectiveness of exercise training programmes in obesity - an overview of human studies

Frequent and regular physical activity has significant benefits for health, including improvement of body composition and help in weight control. Consequently, promoting training programmes, particularly in those who are genetically predisposed, is a significant step towards controlling the presently increasing epidemic of obesity. Although the physiological responses of the human body to exercise are quite well described, the genetic background of these reactions still remains mostly unknown. This review not only summarizes the current evidence, through a literature review and the results of our studies on the influence of gene variants on the characteristics and range of the body's adaptive response to training, but also explores research organization problems, future trends, and possibilities. We describe the most reliable candidate genetic markers that are involved in energy balance pathways and body composition changes in response to training programmes, such as FTO, MC4R, ACE, PPARG, LEP, LEPR, ADRB2, and ADRB3. This knowledge can have an enormous impact not only on individualization of exercise programmes to make them more efficient and safer, but also on improved recovery, traumatology, medical care, diet, supplementation and many other areas. Nevertheless, the current studies still represent only the first steps towards a better understanding of the genetic factors that influence obesity-related traits, as well as gene variant x physical activity interactions, so further research is necessary.

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.

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 × 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.

Personalized medicine in diabetes mellitus: current opportunities and future prospects

Diabetes mellitus affects approximately 382 million individuals worldwide and is a leading cause of morbidity and mortality. Over 40 and nearly 80 genetic loci influencing susceptibility to type 1 and type 2 diabetes, respectively, have been identified. In addition, there is emerging evidence that some genetic variants help to predict response to treatment. Other variants confer apparent protection from diabetes or its complications and may lead to development of novel treatment approaches. Currently, there is clear clinical utility to genetic testing to find the at least 1% of diabetic individuals who have monogenic diabetes (e.g., maturity-onset diabetes of the young and KATP channel neonatal diabetes). Diagnosing many of these currently underdiagnosed types of diabetes enables personalized treatment, resulting in improved and less invasive glucose control, better prediction of prognosis, and enhanced familial risk assessment. Efforts to enhance the rate of detection, diagnosis, and personalized treatment of individuals with monogenic diabetes should set the stage for effective clinical translation of current genetic, pharmacogenetic, and pharmacogenomic research of more complex forms of diabetes.

The role of linkage disequilibrium in case-only studies of gene-environment interactions

Gene-environment (G × E) interactions have been invoked to account, at least in part, for the gap between the known heritability of common human diseases and the phenotypic variation hitherto explained by genetic variants. Noteworthy in this context, a case-only (CO) design has been proposed in the past as a means to detect G × E interactions possibly more efficiently than by using classical case-control and cohort designs. So far, however, most CO studies have followed a candidate (or single) gene approach, and the genome-wide utility of the CO design is still more or less unknown. In particular, the way in which linkage disequilibrium (LD) impacts upon the chance to detect G × E interaction through the analysis of proxy markers has not been studied in much detail before. Therefore, we systematically assessed the power to indirectly detect a given G × E interaction through exploiting LD in a CO design. Our simulations revealed a strong relationship between LD and detection power that was subsequently validated in a real colorectal cancer data set.

Gene-environment interactions in male reproductive health: special reference to the aryl hydrocarbon receptor signaling pathway

Over the last few decades, there have been numerous reports of adverse effects on the reproductive health of wildlife and laboratory animals caused by exposure to endocrine disrupting chemicals (EDCs). The increasing trends in human male reproductive disorders and the mounting evidence for causative environmental factors have therefore sparked growing interest in the health threat posed to humans by EDCs, which are substances in our food, environment and consumer items that interfere with hormone action, biosynthesis or metabolism, resulting in disrupted tissue homeostasis or reproductive function. The mechanisms of EDCs involve a wide array of actions and pathways. Examples include the estrogenic, androgenic, thyroid and retinoid pathways, in which the EDCs may act directly as agonists or antagonists, or indirectly via other nuclear receptors. Dioxins and dioxin-like EDCs exert their biological and toxicological actions through activation of the aryl hydrocarbon-receptor, which besides inducing transcription of detoxifying enzymes also regulates transcriptional activity of other nuclear receptors. There is increasing evidence that genetic predispositions may modify the susceptibility to adverse effects of toxic chemicals. In this review, potential consequences of hereditary predisposition and EDCs are discussed, with a special focus on the currently available publications on interactions between dioxin and androgen signaling.

Jumping on the Train of Personalized Medicine: A Primer for Non-Geneticist Clinicians: Part 2. Fundamental Concepts in Genetic Epidemiology

With the decrease in sequencing costs, personalized genome sequencing will eventually become common in medical practice. We therefore write this series of three reviews to help non-geneticist clinicians to jump into the fast-moving field of personalized medicine. In the first article of this series, we reviewed the fundamental concepts in molecular genetics. In this second article, we cover the key concepts and methods in genetic epidemiology including the classification of genetic disorders, study designs and their implementation, genetic marker selection, genotyping and sequencing technologies, gene identification strategies, data analyses and data interpretation. This review will help the reader critically appraise a genetic association study. In the next article, we will discuss the clinical applications of genetic epidemiology in the personalized medicine area.

BCL11A gene DNA methylation contributes to the risk of type 2 diabetes in males

BCL11A is a critical modulator involved in hemoglobin switching. Recent studies have established an association between BCL11A gene polymorphisms and a risk of type 2 diabetes (T2D). The aim of the present study was to assess the correlation between BCL11A DNA methylation and T2D. A total of 48 T2D cases and 48 age- and gender-matched controls were recruited to evaluate BCL11A methylation using bisulfite pyrosequencing technology. Although no significant association was observed in BCL11A methylation between T2D patients and healthy controls (P=0.322), breakdown analysis by gender identified a significant association between BCL11A methylation and T2D in males (P=0.018). Notably, there was also a significant female-specific association between the mean BCL11A DNA methylation and triglyceride (TG) concentration (r=−0.34; P=0.019). The results indicated that BCL11A methylation contributed to the risk of T2D in males. In addition, BCL11A methylation may have an effect on the development of T2D by influencing TG metabolism. Thus, gender difference may provide new information to aid the understanding of T2D pathogenesis.

Genome-wide DNA methylation analysis of human pancreatic islets from type 2 diabetic and non-diabetic donors identifies candidate genes that influence insulin secretion

Impaired insulin secretion is a hallmark of type 2 diabetes (T2D). Epigenetics may affect disease susceptibility. To describe the human methylome in pancreatic islets and determine the epigenetic basis of T2D, we analyzed DNA methylation of 479,927 CpG sites and the transcriptome in pancreatic islets from T2D and non-diabetic donors. We provide a detailed map of the global DNA methylation pattern in human islets, β- and α-cells. Genomic regions close to the transcription start site showed low degrees of methylation and regions further away from the transcription start site such as the gene body, 3'UTR and intergenic regions showed a higher degree of methylation. While CpG islands were hypomethylated, the surrounding 2 kb shores showed an intermediate degree of methylation, whereas regions further away (shelves and open sea) were hypermethylated in human islets, β- and α-cells. We identified 1,649 CpG sites and 853 genes, including TCF7L2, FTO and KCNQ1, with differential DNA methylation in T2D islets after correction for multiple testing. The majority of the differentially methylated CpG sites had an intermediate degree of methylation and were underrepresented in CpG islands (∼ 7%) and overrepresented in the open sea (∼ 60%). 102 of the differentially methylated genes, including CDKN1A, PDE7B, SEPT9 and EXOC3L2, were differentially expressed in T2D islets. Methylation of CDKN1A and PDE7B promoters in vitro suppressed their transcriptional activity. Functional analyses demonstrated that identified candidate genes affect pancreatic β- and α-cells as Exoc3l silencing reduced exocytosis and overexpression of Cdkn1a, Pde7b and Sept9 perturbed insulin and glucagon secretion in clonal β- and α-cells, respectively. Together, our data can serve as a reference methylome in human islets. We provide new target genes with altered DNA methylation and expression in human T2D islets that contribute to perturbed insulin and glucagon secretion. These results highlight the importance of epigenetics in the pathogenesis of T2D.

Evolutionary perspectives on the obesity epidemic: adaptive, maladaptive, and neutral viewpoints

The prevalence of obesity in modern societies has two major contributory factors-an environmental change that has happened in historical times and a genetic predisposition that has its origins in our evolutionary history. Understanding both aspects is complex. From an evolutionary perspective, three different types of explanation have been proposed. The first is that obesity was once adaptive and enabled us to survive (or sustain fecundity) through periods of famine. People carrying so-called thrifty genes that enabled the efficient storage of energy as fat between famines would be at a selective advantage. In the modern world, however, people who have inherited these genes deposit fat in preparation for a famine that never comes, and the result is widespread obesity. The key problem with this, and any other adaptive scenario, is to understand why, if obesity was historically so advantageous, many people did not inherit these thrifty genes and in modern society are able to remain slim, despite the environmental change favoring fat storage. The second type of explanation is that obesity is not adaptive and may never even have existed in our evolutionary past, but it is favored today as a maladaptive by-product of positive selection on some other trait. An example of this type of explanation is the suggestion that obesity results from variation in brown adipose tissue thermogenesis. Finally, a third class of explanation is that most mutations in the genes that predispose us to obesity are neutral and have been drifting over evolutionary time--so-called drifty genes, leading some individuals to be obesity prone and others obesity resistant. In this article, I review the current evidence for and against these three different scenarios and conclude that the thrifty gene hypothesis is untenable but the other two ideas may provide a cogent explanation of the modern obesity phenomenon.

Untangling the interplay of genetic and metabolic influences on beta-cell function: Examples of potential therapeutic implications involving TCF7L2 and FFAR1

Deteriorating beta-cell function is a common feature of type 2 diabetes. In this review, we briefly address the regulation of beta-cell function, and discuss some of the main determinants of beta-cell failure. We will focus on the role of interactions between the genetic background and metabolic environment (insulin resistance, fuel supply and flux as well as metabolic signaling). We present data on the function of the strongest common diabetes risk variant, the single nucleotide polymorphism (SNP) rs7903146 in TCF7L2. As also mirrored by its interaction with glycemia on insulin secretion, this SNP in large part confers resistance against the incretin effect. Genetic influence on insulin secretion also interacts with free fatty acids, as evidenced by data on rs1573611 in FFAR1. Several medications marketed by now or currently under development for diabetes treatment engage these pathways, and therapeutic implications from these findings are soon to be expected.

Common polymorphism in the cannabinoid type 1 receptor gene (CNR1) is associated with microvascular complications in type 2 diabetes

Endocannabinoids exert their biological effects via interaction with G-protein coupled cannabinoid receptors CB1 and CB2. Polymorphisms in the CNR1 gene (encoding CB1 receptor) were previously found to be associated with dyslipidemia and cardiovascular diseases. We investigated a role of the polymorphism in CNR1 gene in type 2 diabetes and its complications. The study involved 667 T2DM patients and 450 healthy individuals. All subjects were genotyped for G1359A polymorphism by PCR-RFLP procedure. Genotype frequencies did not differ significantly between patients and controls. The statistically significant differences were seen between T2DM patients with diabetic nephropathy (DN) and those without it (OR for risk allele 2.84, 95% CI 2.04-3.94, p<0.0001). There were also differences between patients with diabetic retinopathy (DR) and those without DR (OR for risk allele 1.81, 95% CI 1.30-2.53, p=0.0005). No differences were observed in diabetic neuropathy. The A allele was more frequent in patients with coexisting cardiovascular disease (CVD) compared to patients without CVD (p=0.0044). The novel finding of our study is the association of the G1359A polymorphism with diabetic nephropathy and diabetic retinopathy in patients with T2DM. This polymorphism was also associated with cardiovascular disease in the patient group.

Dietary magnesium and genetic interactions in diabetes and related risk factors: a brief overview of current knowledge

Nutritional genomics has exploded in the last decade, yielding insights—both nutrigenomic and nutrigenetic—into the physiology of dietary interactions and our genes. Among these are insights into the regulation of magnesium transport and homeostasis and mechanisms underlying magnesium’s role in insulin and glucose handling. Recent observational evidence has attempted to examine some promising research avenues on interaction between genetics and dietary magnesium in relation to diabetes and diabetes risk factors. This brief review summarizes the recent evidence on dietary magnesium’s role in diabetes and related traits in the presence of underlying genetic risk, and discusses future potential research directions.

New opportunities: harnessing induced pluripotency for discovery in diabetes and metabolism

The landmark discovery of induced pluripotent stem cells (iPSCs) by Shinya Yamanaka has transformed regenerative biology. Previously, insights into the pathogenesis of chronic human diseases have been hindered by the inaccessibility of patient samples. However, scientists are now able to convert patient fibroblasts into iPSCs and differentiate them into disease-relevant cell types. This ability opens new avenues for investigating disease pathogenesis and designing novel treatments. In this review, we highlight the uses of human iPSCs to uncover the underlying causes and pathological consequences of diabetes and metabolic syndromes, multifactorial diseases whose etiologies have been difficult to unravel using traditional methodologies.

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.

Genetics of type 2 diabetes

Type 2 diabetes (T2D) is the result of interaction between environmental factors and a strong hereditary component. We review the heritability of T2D as well as the history of genetic and genomic research in this area. Very few T2D risk genes were identified using candidate gene and linkage-based studies, but the advent of genome-wide association studies has led to the identification of multiple genes, including several that were not previously known to play any role in T2D. Highly replicated genes, for example TCF7L2, KCNQ1 and KCNJ11, are discussed in greater detail. Taken together, the genetic loci discovered to date explain only a small proportion of the observed heritability. We discuss possible explanations for this “missing heritability”, including the role of rare variants, gene-environment interactions and epigenetics. The clinical utility of current findings and avenues of future research are also discussed.

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.

Higher magnesium intake is associated with lower fasting glucose and insulin, with no evidence of interaction with select genetic loci, in a meta-analysis of 15 CHARGE Consortium Studies

Favorable associations between magnesium intake and glycemic traits, such as fasting glucose and insulin, are observed in observational and clinical studies, but whether genetic variation affects these associations is largely unknown. We hypothesized that single nucleotide polymorphisms (SNPs) associated with either glycemic traits or magnesium metabolism affect the association between magnesium intake and fasting glucose and insulin. Fifteen studies from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium provided data from up to 52,684 participants of European descent without known diabetes. In fixed-effects meta-analyses, we quantified 1) cross-sectional associations of dietary magnesium intake with fasting glucose (mmol/L) and insulin (ln-pmol/L) and 2) interactions between magnesium intake and SNPs related to fasting glucose (16 SNPs), insulin (2 SNPs), or magnesium (8 SNPs) on fasting glucose and insulin. After adjustment for age, sex, energy intake, BMI, and behavioral risk factors, magnesium (per 50-mg/d increment) was inversely associated with fasting glucose [β = -0.009 mmol/L (95% CI: -0.013, -0.005), P < 0.0001] and insulin [-0.020 ln-pmol/L (95% CI: -0.024, -0.017), P < 0.0001]. No magnesium-related SNP or interaction between any SNP and magnesium reached significance after correction for multiple testing. However, rs2274924 in magnesium transporter-encoding TRPM6 showed a nominal association (uncorrected P = 0.03) with glucose, and rs11558471 in SLC30A8 and rs3740393 near CNNM2 showed a nominal interaction (uncorrected, both P = 0.02) with magnesium on glucose. Consistent with other studies, a higher magnesium intake was associated with lower fasting glucose and insulin. Nominal evidence of TRPM6 influence and magnesium interaction with select loci suggests that further investigation is warranted.

Genetic predisposition to higher body mass index or type 2 diabetes and leukocyte telomere length in the Nurses' Health Study

BACKGROUND

Although cross-sectional studies have linked higher body mass index (BMI) and type 2 diabetes (T2D) to shortened telomeres, whether these metabolic conditions play a causal role in telomere biology is unknown. We therefore examined whether genetic predisposition to higher BMI or T2D was associated with shortened leukocyte telomere length (LTL).

METHODOLOGY

We conducted an analysis of 3,968 women of European ancestry aged 43-70 years from the Nurses' Health Study, who were selected as cases or controls in genome-wide association studies and studies of telomeres and disease. Pre-diagnostic relative telomere length in peripheral blood leukocytes, collected in 1989-1990, was measured by quantitative PCR. We combined information from multiple risk variants by calculating genetic risk scores based on 32 polymorphisms near 32 loci for BMI, and 36 polymorphisms near 35 loci for T2D.

FINDINGS

After adjustment for age and case-control status, there was no association between the BMI genetic risk score and LTL (β per standard deviation increase: -0.01; SE: 0.02; P = 0.52). Similarly, the T2D genetic score was not associated with LTL (β per standard deviation increase: -0.006; SE: 0.02; P = 0.69).

CONCLUSIONS

In this population of middle-aged and older women of European ancestry, those genetically predisposed to higher BMI or T2D did not possess shortened telomeres. Although we cannot exclude weak or modest effects, our findings do not support a causal relation of strong magnitude between these metabolic conditions and telomere dynamics.

Genetic predisposition to higher body mass index or type 2 diabetes and leukocyte telomere length in the Nurses' Health Study

BACKGROUND

Although cross-sectional studies have linked higher body mass index (BMI) and type 2 diabetes (T2D) to shortened telomeres, whether these metabolic conditions play a causal role in telomere biology is unknown. We therefore examined whether genetic predisposition to higher BMI or T2D was associated with shortened leukocyte telomere length (LTL).

METHODOLOGY

We conducted an analysis of 3,968 women of European ancestry aged 43-70 years from the Nurses' Health Study, who were selected as cases or controls in genome-wide association studies and studies of telomeres and disease. Pre-diagnostic relative telomere length in peripheral blood leukocytes, collected in 1989-1990, was measured by quantitative PCR. We combined information from multiple risk variants by calculating genetic risk scores based on 32 polymorphisms near 32 loci for BMI, and 36 polymorphisms near 35 loci for T2D.

FINDINGS

After adjustment for age and case-control status, there was no association between the BMI genetic risk score and LTL (β per standard deviation increase: -0.01; SE: 0.02; P = 0.52). Similarly, the T2D genetic score was not associated with LTL (β per standard deviation increase: -0.006; SE: 0.02; P = 0.69).

CONCLUSIONS

In this population of middle-aged and older women of European ancestry, those genetically predisposed to higher BMI or T2D did not possess shortened telomeres. Although we cannot exclude weak or modest effects, our findings do not support a causal relation of strong magnitude between these metabolic conditions and telomere dynamics.

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.