Gene-lifestyle interaction on risk of type 2 diabetes

The Glycemic Curve during the Oral Glucose Tolerance Test: Is It Only Indicative of Glycoregulation?

The shape of the glycemic curve during the oral glucose tolerance test (OGTT), interpreted in the correct context, can predict impaired glucose homeostasis. Our aim was to reveal information inherent in the 3 h glycemic trajectory that is of physiological relevance concerning the disruption of glycoregulation and complications beyond, such as components of metabolic syndrome (MS).

METHODS

In 1262 subjects (1035 women, 227 men) with a wide range of glucose tolerance, glycemic curves were categorized into four groups: monophasic, biphasic, triphasic, and multiphasic. The groups were then monitored in terms of anthropometry, biochemistry, and timing of the glycemic peak.

RESULTS

Most curves were monophasic (50%), then triphasic (28%), biphasic (17.5%), and multiphasic (4.5%). Men had more biphasic curves than women (33 vs. 14%, respectively), while women had more triphasic curves than men (30 vs. 19%, respectively) (p < 0.01). Monophasic curves were more frequent in people with impaired glucose regulation and MS compared to bi-, tri-, and multiphasic ones. Peak delay was the most common in monophasic curves, in which it was also most strongly associated with the deterioration of glucose tolerance and other components of MS.

CONCLUSION

The shape of the glycemic curve is gender dependent. A monophasic curve is associated with an unfavorable metabolic profile, especially when combined with a delayed peak.

DNA methylation in peripheral blood leukocytes for the association with glucose metabolism and invasive breast cancer

BACKGROUND

Insulin resistance (IR) is a well-established factor for breast cancer (BC) risk in postmenopausal women, but the interrelated molecular pathways on the methylome are not explicitly described. We conducted a population-level epigenome-wide association (EWA) study for DNA methylation (DNAm) probes that are associated with IR and prospectively correlated with BC development, both overall and in BC subtypes among postmenopausal women.

METHODS

We used data from Women's Health Initiative (WHI) ancillary studies for our EWA analyses and evaluated the associations of site-specific DNAm across the genome with IR phenotypes by multiple regressions adjusting for age and leukocyte heterogeneities. For our analysis of the top 20 IR-CpGs with BC risk, we used the WHI and the Cancer Genomic Atlas (TCGA), using multiple Cox proportional hazards and logit regressions, respectively, accounting for age, diabetes, obesity, leukocyte heterogeneities, and tumor purity (for TCGA). We further conducted a Gene Set Enrichment Analysis.

RESULTS

We detected several EWA-CpGs in TXNIP, CPT1A, PHGDH, and ABCG1. In particular, cg19693031 in TXNIP was replicated in all IR phenotypes, measured by fasting levels of glucose, insulin, and homeostatic model assessment-IR. Of those replicated IR-genes, 3 genes (CPT1A, PHGDH, and ABCG1) were further correlated with BC risk; and 1 individual CpG (cg01676795 in POR) was commonly detected across the 2 cohorts.

CONCLUSIONS

Our study contributes to better understanding of the interconnected molecular pathways on the methylome between IR and BC carcinogenesis and suggests potential use of DNAm markers in the peripheral blood cells as preventive targets to detect an at-risk group for IR and BC in postmenopausal women.

Effect of TCF7L2 on the relationship between lifestyle factors and glycemic parameters: a systematic review

Background

Among candidate genes related to type 2 diabetes (T2DM), one of the strongest genes is Transcription factor 7 like 2 (TCF7L2), regarding the Genome-Wide Association Studies. We aimed to conduct a systematic review of the literature on the modification effect of TCF7L2 on the relation between glycemic parameters and lifestyle factors.

Methods

A systematic literature search was done for relevant publications using electronic databases, including PubMed, EMBASE, Scopus, and Web of Science, from January 1, 2000, to November 2, 2021.

Results

Thirty-eight studies (16 observational studies, six meal test trials, and 16 randomized controlled trials (RCTs)) were included. Most observational studies had been conducted on participants with non-diabetes showing that TCF7L2 modified the association between diet (fatty acids and fiber) and insulin resistance. In addition, findings from meal test trials showed that, compared to non-risk-allele carriers, consumption of meals with different percentages of total dietary fat in healthy risk-allele carriers increased glucose concentrations and impaired insulin sensitivity. However, ten RCTs, with intervention periods of less than ten weeks and more than one year, showed that TCF7L2 did not modify glycemic parameters in response to a dietary intervention involving different macronutrients. However, two weight loss dietary RCTs with more than 1-year duration showed that serum glucose and insulin levels decreased and insulin resistance improved in non-risk allele subjects with overweight/obesity. Regarding artichoke extract supplementation (ALE), two RCTs observed that ALE supplementation significantly decreased insulin concentration and improved insulin resistance in the TT genotype of the rs7903146 variant of TCF7L2. In addition, four studies suggested that physical activity levels and smoking status modified the association between TCF7L2 and glycemic parameters. However, three studies observed no effect of TCF7L2 on glycemic parameters in participants with different levels of physical activity and smoking status.

Conclusion

The modification effects of TCF7L2 on the relation between the lifestyle factors (diet, physical activity, and smoking status) and glycemic parameters were contradictory.

PROSPERO registration number

CRD42020196327

Supplementary Information

The online version contains supplementary material available at 10.1186/s12937-022-00813-w.

Synergistic Effects of Genetic Variants of Glucose Homeostasis and Lifelong Exposures to Cigarette Smoking, Female Hormones, and Dietary Fat Intake on Primary Colorectal Cancer Development in African and Hispanic/Latino American Women

BACKGROUND

Disparities in cancer genomic science exist among racial/ethnic minorities. Particularly, African American (AA) and Hispanic/Latino American (HA) women, the 2 largest minorities, are underrepresented in genetic/genome-wide studies for cancers and their risk factors. We conducted on AA and HA postmenopausal women a genomic study for insulin resistance (IR), the main biologic mechanism underlying colorectal cancer (CRC) carcinogenesis owing to obesity.

METHODS

With 780 genome-wide IR-specific single-nucleotide polymorphisms (SNPs) among 4,692 AA and 1,986 HA women, we constructed a CRC-risk prediction model. Along with these SNPs, we incorporated CRC-associated lifestyles in the model of each group and detected the topmost influential genetic and lifestyle factors. Further, we estimated the attributable risk of the topmost risk factors shared by the groups to explore potential factors that differentiate CRC risk between these groups.

RESULTS

In both groups, we detected IR-SNPs in PCSK1 (in AA) and IFT172, GCKR, and NRBP1 (in HA) and risk lifestyles, including long lifetime exposures to cigarette smoking and endogenous female hormones and daily intake of polyunsaturated fatty acids (PFA), as the topmost predictive variables for CRC risk. Combinations of those top genetic- and lifestyle-markers synergistically increased CRC risk. Of those risk factors, dietary PFA intake and long lifetime exposure to female hormones may play a key role in mediating racial disparity of CRC incidence between AA and HA women.

CONCLUSIONS

Our results may improve CRC risk prediction performance in those medically/scientifically underrepresented groups and lead to the development of genetically informed interventions for cancer prevention and therapeutic effort, thus contributing to reduced cancer disparities in those minority subpopulations.

Genetic Signatures of Glucose Homeostasis: Synergistic Interplay With Long-Term Exposure to Cigarette Smoking in Development of Primary Colorectal Cancer Among African American Women

INTRODUCTION

Insulin resistance (IR)/glucose intolerance is a critical biologic mechanism for the development of colorectal cancer (CRC) in postmenopausal women. Whereas IR and excessive adiposity are more prevalent in African American (AA) women than in White women, AA women are underrepresented in genome-wide studies for systemic regulation of IR and the association with CRC risk.

METHODS

With 780 genome-wide IR single-nucleotide polymorphisms (SNPs) among 4,692 AA women, we tested for a causal inference between genetically elevated IR and CRC risk. Furthermore, by incorporating CRC-associated lifestyle factors, we established a prediction model on the basis of gene-environment interactions to generate risk profiles for CRC with the most influential genetic and lifestyle factors.

RESUTLS

In the pooled Mendelian randomization analysis, the genetically elevated IR was associated with 9 times increased risk of CRC, but with lack of analytic power. By addressing the variation of individual SNPs in CRC in the prediction model, we detected 4 fasting glucose-specific SNPs in GCK, PCSK1, and MTNR1B and 4 lifestyles, including smoking, aging, prolonged lifetime exposure to endogenous estrogen, and high fat intake, as the most predictive markers of CRC risk. Our joint test for those risk genotypes and lifestyles with smoking revealed the synergistically increased CRC risk, more substantially in women with longer-term exposure to cigarette smoking.

DISCUSSION

Our findings may improve CRC prediction ability among medically underrepresented AA women and highlight genetically informed preventive interventions (e.g., smoking cessation; CRC screening to longer-term smokers) for those women at high risk with risk genotypes and behavioral patterns.

Multi-Omics Data Analysis Uncovers Molecular Networks and Gene Regulators for Metabolic Biomarkers

The insulin-like growth factors (IGFs)/insulin resistance (IR) axis is the major metabolic hormonal pathway mediating the biologic mechanism of several complex human diseases, including type 2 diabetes (T2DM) and cancers. The genomewide association study (GWAS)-based approach has neither fully characterized the phenotype variation nor provided a comprehensive understanding of the regulatory biologic mechanisms. We applied systematic genomics to integrate our previous GWAS data for IGF-I and IR with multi-omics datasets, e.g., whole-blood expression quantitative loci, molecular pathways, and gene network, to capture the full range of genetic functionalities associated with IGF-I/IR and key drivers (KDs) in gene-regulatory networks. We identified both shared (e.g., T2DM, lipid metabolism, and estimated glomerular filtration signaling) and IR-specific (e.g., mechanistic target of rapamycin, phosphoinositide 3-kinases, and erb-b2 receptor tyrosine kinase 4 signaling) molecular biologic processes of IGF-I/IR axis regulation. Next, by using tissue-specific gene–gene interaction networks, we identified both well-established (e.g., IRS1 and IGF1R) and novel (e.g., AKT1, HRAS, and JAK1) KDs in the IGF-I/IR-associated subnetworks. Our results, if validated in additional genomic studies, may provide robust, comprehensive insights into the mechanisms of IGF-I/IR regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for the associated diseases, e.g., T2DM and cancers.

Gene-lifestyle interaction on risk of type 2 diabetes: A systematic review

The pathophysiological influence of gene-lifestyle interactions on the risk to develop type 2 diabetes (T2D) is currently under intensive research. This systematic review summarizes the evidence for gene-lifestyle interactions regarding T2D incidence. MEDLINE, EMBASE, and Web of Science were systematically searched until 31 January 2019 to identify publication with (a) prospective study design; (b) T2D incidence; (c) gene-diet, gene-physical activity, and gene-weight loss intervention interaction; and (d) population who are healthy or prediabetic. Of 66 eligible publications, 28 reported significant interactions. A variety of different genetic variants and dietary factors were studied. Variants at TCF7L2 were most frequently investigated and showed interactions with fiber and whole grain on T2D incidence. Further gene-diet interactions were reported for, eg, a western dietary pattern with a T2D-GRS, fat and carbohydrate with IRS1 rs2943641, and heme iron with variants of HFE. Physical activity showed interaction with HNF1B, IRS1, PPARγ, ADRA2B, SLC2A2, and ABCC8 variants and weight loss interventions with ENPP1, PPARγ, ADIPOR2, ADRA2B, TNFα, and LIPC variants. However, most findings represent single study findings obtained in European ethnicities. Although some interactions have been reported, their conclusiveness is still low, as most findings were not yet replicated across multiple study populations.

Breast Cancer Risk and Insulin Resistance: Post Genome-Wide Gene-Environment Interaction Study Using a Random Survival Forest

Obesity-insulin connections have been considered potential risk factors for postmenopausal breast cancer, and the association between insulin resistance (IR) genotypes and phenotypes can be modified by obesity-lifestyle factors, affecting breast cancer risk. In this study, we explored the role of IR in those pathways at the genome-wide level. We identified IR-genetic factors and selected lifestyles to generate risk profiles for postmenopausal breast cancer. Using large-scale cohort data from postmenopausal women in the Women's Health Initiative Database for Genotypes and Phenotypes Study, our previous genome-wide association gene-behavior interaction study identified 58 loci for associations with IR phenotypes (homeostatic model assessment-IR, hyperglycemia, and hyperinsulinemia). We evaluated those single-nucleotide polymorphisms (SNP) and additional 31 lifestyles in relation to breast cancer risk by conducting a two-stage multimodal random survival forest analysis. We identified the most predictive genetic and lifestyle variables in overall and subgroup analyses [stratified by body mass index (BMI), exercise, and dietary fat intake]. Two SNPs (LINC00460 rs17254590 and MKLN1 rs117911989), exogenous factors related to lifetime cumulative exposure to estrogen, BMI, and dietary alcohol consumption were the most common influential factors across the analyses. Individual SNPs did not have significant associations with breast cancer, but SNPs and lifestyles combined synergistically increased the risk of breast cancer in a gene-behavior, dose-dependent manner. These findings may contribute to more accurate predictions of breast cancer and suggest potential intervention strategies for women with specific genetic and lifestyle factors to reduce their breast cancer risk. SIGNIFICANCE: These findings identify insulin resistance SNPs in combination with lifestyle as synergistic factors for breast cancer risk, suggesting lifestyle changes can prevent breast cancer in women who carry the risk genotypes.

Genome-Wide Meta-analysis of Gene-Environmental Interaction for Insulin Resistance Phenotypes and Breast Cancer Risk in Postmenopausal Women

Insulin resistance (IR)-related genetic variants are possibly associated with breast cancer, and the gene-phenotype-cancer association could be modified by lifestyle factors including obesity, physical inactivity, and high-fat diet. Using data from postmenopausal women, a population highly susceptible to obesity, IR, and increased risk of breast cancer, we implemented a genome-wide association study (GWAS) in two steps: (1) GWAS meta-analysis of gene-environmental (i.e., behavioral) interaction (G*E) for IR phenotypes (hyperglycemia, hyperinsulinemia, and homeostatic model assessment-insulin resistance) and (2) after the G*E GWAS meta-analysis, the identified SNPs were tested for their associations with breast cancer risk in overall or subgroup population, where the SNPs were identified at genome-wide significance. We found 58 loci (55 novel SNPs; 5 index SNPs and 6 SNPs, independent of each other) that are associated with IR phenotypes in women overall or women stratified by obesity, physical activity, and high-fat diet; among those 58 loci, 29 (26 new loci; 2 index SNPs and 2 SNPs, independently) were associated with postmenopausal breast cancer. Our study suggests that a number of newly identified SNPs may have their effects on glucose intolerance by interplaying with obesity and other lifestyle factors, and a substantial proportion of these SNPs' susceptibility can also interact with the lifestyle factors to ultimately influence breast cancer risk. These findings may contribute to improved prediction accuracy for cancer and suggest potential intervention strategies for those women carrying genetic risk that will reduce their breast cancer risk.

Precision nutrition for prevention and management of type 2 diabetes

Precision nutrition aims to prevent and manage chronic diseases by tailoring dietary interventions or recommendations to one or a combination of an individual's genetic background, metabolic profile, and environmental exposures. Recent advances in genomics, metabolomics, and gut microbiome technologies have offered opportunities as well as challenges in the use of precision nutrition to prevent and manage type 2 diabetes. Nutrigenomics studies have identified genetic variants that influence intake and metabolism of specific nutrients and predict individuals' variability in response to dietary interventions. Metabolomics has revealed metabolomic fingerprints of food and nutrient consumption and uncovered new metabolic pathways that are potentially modified by diet. Dietary interventions have been successful in altering abundance, composition, and activity of gut microbiota that are relevant for food metabolism and glycaemic control. In addition, mobile apps and wearable devices facilitate real-time assessment of dietary intake and provide feedback which can improve glycaemic control and diabetes management. By integrating these technologies with big data analytics, precision nutrition has the potential to provide personalised nutrition guidance for more effective prevention and management of type 2 diabetes. Despite these technological advances, much research is needed before precision nutrition can be widely used in clinical and public health settings. Currently, the field of precision nutrition faces challenges including a lack of robust and reproducible results, the high cost of omics technologies, and methodological issues in study design as well as high-dimensional data analyses and interpretation. Evidence is needed to support the efficacy, cost-effectiveness, and additional benefits of precision nutrition beyond traditional nutrition intervention approaches. Therefore, we should manage unrealistically high expectations and balance the emerging field of precision nutrition with public health nutrition strategies to improve diet quality and prevent type 2 diabetes and its complications.

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.

Interaction between genes and macronutrient intake on the risk of developing type 2 diabetes: systematic review and findings from European Prospective Investigation into Cancer (EPIC)-InterAct

Background: Gene-diet interactions have been reported to contribute to the development of type 2 diabetes (T2D). However, to our knowledge, few examples have been consistently replicated to date.Objective: We aimed to identify existing evidence for gene-macronutrient interactions and T2D and to examine the reported interactions in a large-scale study.Design: We systematically reviewed studies reporting gene-macronutrient interactions and T2D. We searched the MEDLINE, Human Genome Epidemiology Network, and WHO International Clinical Trials Registry Platform electronic databases to identify studies published up to October 2015. Eligibility criteria included assessment of macronutrient quantity (e.g., total carbohydrate) or indicators of quality (e.g., dietary fiber) by use of self-report or objective biomarkers of intake. Interactions identified in the review were subsequently examined in the EPIC (European Prospective Investigation into Cancer)-InterAct case-cohort study (n = 21,148, with 9403 T2D cases; 8 European countries). Prentice-weighted Cox regression was used to estimate country-specific HRs, 95% CIs, and P-interaction values, which were then pooled by random-effects meta-analysis. A primary model was fitted by using the same covariates as reported in the published studies, and a second model adjusted for additional covariates and estimated the effects of isocaloric macronutrient substitution.Results: Thirteen observational studies met the eligibility criteria (n < 1700 cases). Eight unique interactions were reported to be significant between macronutrients [carbohydrate, fat, saturated fat, dietary fiber, and glycemic load derived from self-report of dietary intake and circulating n-3 (ω-3) polyunsaturated fatty acids] and genetic variants in or near transcription factor 7-like 2 (TCF7L2), gastric inhibitory polypeptide receptor (GIPR), caveolin 2 (CAV2), and peptidase D (PEPD) (P-interaction < 0.05). We found no evidence of interaction when we tried to replicate previously reported interactions. In addition, no interactions were detected in models with additional covariates.Conclusions: Eight gene-macronutrient interactions were identified for the risk of T2D from the literature. These interactions were not replicated in the EPIC-InterAct study, which mirrored the analyses undertaken in the original reports. Our findings highlight the importance of independent replication of reported interactions.

Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions

Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (G×E) or gene-gene interactions. We modeled variance heterogeneity for blood lipids and BMI in up to 44,211 participants and investigated relationships between variance effects (P_v), G×E interaction effects (with smoking and physical activity), and marginal genetic effects (P_m). Correlations between P_v and P_m were stronger for SNPs with established marginal effects (Spearman’s ρ = 0.401 for triglycerides, and ρ = 0.236 for BMI) compared to all SNPs. When P_v and P_m were compared for all pruned SNPs, only BMI was statistically significant (Spearman’s ρ = 0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the P_v distribution (P_binomial <0.05). SNPs from the top 1% of the P_m distribution for BMI had more significant P_v values (P_{Mann–Whitney}= 1.46×10⁻⁵), and the odds ratio of SNPs with nominally significant (<0.05) P_m and P_v was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant G×E interaction P-values (P_int<0.05) were enriched with nominally significant P_v values (P_binomial = 8.63×10⁻⁹ and 8.52×10⁻⁷ for SNP × smoking and SNP × physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for G×E, and variance-based prioritization can be used to identify them.

Most contemporary studies of gene-environment interactions focus on gene variants that are known to bear strong and reliable associations with the traits of interest. The strategy is intuitive because it helps limit the number of tests performed by focusing on a relatively small number of gene variants. However, this approach is predicated on an implicit assumption that these loci are strong candidates for interactions owing to their established relationships with the index traits. The counter-argument is that, because these loci have highly consistent signals within and between populations that vary by environmental characteristics, the probability that these variants interact with other factors is low. The current analysis tests whether variants with strong marginal effects signals (i.e., those prioritized through conventional genome-wide association analyses) are strong or weak candidates for gene-environment interactions. Here we describe analyses focused on lipids and BMI that test this hypothesis by comparing marginal effect signals with variance effect signals and those derived from explicit genome-wide, gene-environment interaction analyses. We conclude that for BMI, there are features of the top-ranking marginal effect loci that render them stronger candidates for interactions than is true of variants with weaker marginal effects signals. These findings are likely to help optimize the efficiency of future gene-environment interaction analyses by providing evidence-based rankings for strong candidate loci.

Obesity genetics in mouse and human: back and forth, and back again

Obesity is a major public health concern. This condition results from a constant and complex interplay between predisposing genes and environmental stimuli. Current attempts to manage obesity have been moderately effective and a better understanding of the etiology of obesity is required for the development of more successful and personalized prevention and treatment options. To that effect, mouse models have been an essential tool in expanding our understanding of obesity, due to the availability of their complete genome sequence, genetically identified and defined strains, various tools for genetic manipulation and the accessibility of target tissues for obesity that are not easily attainable from humans. Our knowledge of monogenic obesity in humans greatly benefited from the mouse obesity genetics field. Genes underlying highly penetrant forms of monogenic obesity are part of the leptin-melanocortin pathway in the hypothalamus. Recently, hypothesis-generating genome-wide association studies for polygenic obesity traits in humans have led to the identification of 119 common gene variants with modest effect, most of them having an unknown function. These discoveries have led to novel animal models and have illuminated new biologic pathways. Integrated mouse-human genetic approaches have firmly established new obesity candidate genes. Innovative strategies recently developed by scientists are described in this review to accelerate the identification of causal genes and deepen our understanding of obesity etiology. An exhaustive dissection of the molecular roots of obesity may ultimately help to tackle the growing obesity epidemic worldwide.

Evidence for novel genetic loci associated with metabolic traits in Yup'ik people

OBJECTIVES

To identify genomic regions associated with fasting plasma lipid profiles, insulin, glucose, and glycosylated hemoglobin in a Yup'ik study population, and to evaluate whether the observed associations between genetic factors and metabolic traits were modified by dietary intake of marine derived omega-3 polyunsaturated acids (n-3 PUFA).

METHODS

A genome-wide linkage scan was conducted among 982 participants of the Center for Alaska Native Health Research study. n-3 PUFA intake was estimated using the nitrogen stable isotope ratio (δ(15) N) of erythrocytes. All genotyped SNPs located within genomic regions with LOD scores > 2 were subsequently tested for individual SNP associations with metabolic traits using linear models that account for familial correlation as well as age, sex, community group, and n-3 PUFA intake. Separate linear models were fit to evaluate interactions between the genotype of interest and n-3 PUFA intake.

RESULTS

We identified several chromosomal regions linked to serum apolipoprotein A2, high density lipoprotein-, low density lipoprotein-, and total cholesterol, insulin, and glycosylated hemoglobin. Genetic variants found to be associated with total cholesterol mapped to a region containing previously validated lipid loci on chromosome 19, and additional novel peaks of biological interest were identified at 11q12.2-11q13.2. We did not observe any significant interactions between n-3 PUFA intake, genotypes, and metabolic traits.

CONCLUSIONS

We have completed a whole genome linkage scan for metabolic traits in Native Alaskans, confirming previously identified loci, and offering preliminary evidence of novel loci implicated in chronic disease pathogenesis in this population.

The complex interplay of genetic and lifestyle risk factors in type 2 diabetes: an overview

Type 2 diabetes (T2D) is one of the scourges of modern times, with many millions of people affected by the disease. Diabetes occurs most frequently in those who are overweight or obese. However, not all overweight and obese persons develop diabetes, and there are those who develop the disease who are lean and physically active. Certain ethnicities, especially indigenous populations, are at considerably higher risk of obesity and diabetes than those of white European ancestry. The patterns and distributions of diabetes have led some to speculate that the disease is caused by interactions between genetic and obesogenic lifestyle factors. Whilst to many this is a plausible explanation, remarkably little reliable evidence exists to support it. In this review, an overview of published literature relating to genetic and lifestyle risk factors for T2D is provided. The review also describes the concepts and rationale that have motivated the view that gene-lifestyle interactions cause diabetes and overviews the empirical evidence published to date to support this hypothesis.

No interactions between previously associated 2-hour glucose gene variants and physical activity or BMI on 2-hour glucose levels

Gene-lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13-0.31], P = 1.63 × 10(-6)). All SNPs were associated with 2-h glucose (β = 0.06-0.12 mmol/allele, P ≤ 1.53 × 10(-7)), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene-lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.

Gene-environment interactions in the development of type 2 diabetes: recent progress and continuing challenges

Type 2 diabetes (T2D) is thought to arise from the complex interplay of both genetic and environmental factors. Since the advent of genome-wide association studies (GWAS), we have seen considerable progress in our understanding of the role that genetics and gene-environment interactions play in the development of T2D. Recent work suggests that the adverse effect of several T2D loci may be abolished or at least attenuated by higher physical activity levels or healthy lifestyle, whereas low physical activity and dietary factors characterizing a Western dietary pattern may augment it. However, there still remain inconsistencies warranting further investigation. Lack of statistical power and measurement errors for the environmental factors continue to challenge our efforts for characterizing interactions. Although our recent focus on established T2D loci is reasonable, we may be overlooking many other potential loci not captured by recent T2D GWAS. Agnostic approaches to the discovery of gene and environment interactions may address this possibility, but their application to the field is currently limited and still faces conceptual challenges. Nonetheless, continued investment in gene-environment interaction studies through large collaborative efforts holds promise in furthering our understanding of the interplay between genetic and environmental factors.

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.

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

Observational epidemiology has made outstanding contributions to the discovery and elucidation of relations between lifestyle factors and common complex diseases such as type 2 diabetes. Recent major advances in the understanding of the human genetics of this disease have inspired studies that seek to determine whether the risk conveyed by bona fide risk loci might be modified by lifestyle factors such as diet composition and physical activity levels. A major challenge is to determine which of the reported findings are likely to represent causal interactions and which might be explained by other factors. The authors of this commentary use the Bradford-Hill criteria, a set of tried-and-tested guidelines for causal inference, to evaluate the findings of a recent study on interaction between variation at the cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) locus and total energy intake with respect to prevalent metabolic syndrome and hemoglobin A₁(c) levels in a cohort of 313 Japanese men. The current authors conclude that the study, while useful for hypothesis generation, does not provide overwhelming evidence of causal interactions. They overview ways in which future studies of gene × lifestyle interactions might overcome the limitations that motivated this conclusion.

Challenges in the use of literature-based meta-analysis to examine gene-environment interactions

Statistical interactions between genes and environmental exposures with respect to disease outcomes may help to identify biologic mechanisms and pathways and inform behavioral interventions. The number of persons required for a single study to have sufficient statistical power to detect such interactions may be considered prohibitively large, making a meta-analysis of published literature an apparently attractive alternative. However, meta-analysis of gene-environment interactions using published literature is challenging, with the conclusions being likely to suffer from bias and lack of generalizability. The authors highlight these challenges and biases using an illustrative example: meta-analysis of interactions between the Pro12Ala variant of the peroxisome proliferator-activated receptor gamma (PPARgamma) gene and various diet and lifestyle factors in the risk of diabetes. The authors conclude that literature-based meta-analysis conducted to examine gene-environment interactions is unlikely to provide a meaningful quantitative conclusion. Alternative strategies are required, including analyses in scientific consortia established to assess main genetic effects, where individual participant data can be shared, allowing both greater power and consistency of analysis methods. However, these consortia are likely to be limited by lack of standardization of the measures of environmental factors. This issue may ultimately only be resolvable by the de novo establishment of large single or multicenter cohorts using comparable methods.

Improvements in glucose homeostasis in response to regular exercise are influenced by the PPARG Pro12Ala variant: results from the HERITAGE Family Study

AIMS/HYPOTHESIS

Exercise training improves glucose homeostasis, but large inter-individual differences are reported, suggesting a role of genetic factors. We investigated whether variants either confirmed or newly identified as diabetes susceptibility variants through genome-wide association studies (GWAS) modulate changes in phenotypes derived from an IVGTT in response to an endurance training programme.

METHODS

We analysed eight polymorphisms in seven type 2 diabetes genes (CDKAL1 rs7756992; CDKN2A and CDKN2B rs10811661 and rs564398; HHEX rs7923837; IGF2BP2 rs4402960; KCNJ11 rs5215; PPARG rs1801282; and TCF7L2 rs7903146) in a maximum of 481 sedentary, non-diabetic white individuals, who participated in a 20-week endurance training programme. Associations were tested between the variants and changes in IVGTT-derived phenotypes.

RESULTS

The only evidence of association with training response was found with PPARG rs1801282 (Pro12Ala). We observed that Ala carriers experienced greater increase in overall glucose tolerance (Deltaglucose disappearance index Ala/Ala 0.22 +/- 0.22, Pro/Ala 0.14 +/- 0.06, Pro/Pro 0.004 +/- 0.03; p = 0.0008), glucose effectiveness (Ala/Ala 0.28 +/- 0.41, Pro/Ala 0.44 +/- 0.14, Pro/Pro 0.09 +/- 0.06; p = 0.004), acute insulin response to glucose (Ala/Ala 64.21 +/- 37.73, Pro/Ala -11.92 +/- 40.30, Pro/Pro -46.30 +/- 14.70; p = 0.03) and disposition index (Ala/Ala 551.8 +/- 448.5, Pro/Ala 534.6 +/- 218.3, Pro/Pro -7.44 +/- 88.18; p = 0.003).

CONCLUSIONS/INTERPRETATION

Compared with Pro/Pro individuals, PPARG Ala carriers experienced greater improvements in glucose and insulin metabolism in response to regular endurance training. However, we did not find evidence of association between type 2 diabetes susceptibility variants recently identified through GWAS and glucose homeostasis response to exercise. Our results extend those of previous studies showing that Ala carriers appear to be more responsive to beneficial health effects of lifestyle interventions.

Effects of maternal dietary manipulation during different periods of pregnancy on hepatic glucogenic capacity in fetal and pregnant rats near term

BACKGROUND AND AIM

Low birth weight is associated with an increased incidence of adult glucose intolerance, type 2 diabetes and cardiovascular disease in humans. In pregnant rats, dietary calorie or protein deprivation results in growth retarded pups, which become glucose intolerant adults with abnormal hepatic glucose metabolism and gluconeogenic enzyme activities. However, whether these abnormalities are present before birth remain unknown.

METHODS AND RESULTS

This study examined the effects of manipulating dietary protein and carbohydrate intake during rat pregnancy on the fetal and maternal hepatic activities of the gluconeogenic enzymes, glucose-6-phosphatase (G6Pase) and phosphoenolpyruvate carboxykinase (PEPCK). Wistar rats were fed ad libitum with either standard chow throughout pregnancy (25% protein, 57% carbohydrate, n=6) or an isocaloric, low protein, high carbohydrate diet (LPHC, 8% protein, 81% carbohydrate) for different periods of pregnancy (early, 0-10 days, n=6; late, 10-20 days, n=7; throughout, 0-20 days, n=6) before tissue collection at day 20. The LPHC diet had no effect on fetal or placental weights, or on fetal hepatic activities of G6Pase and PEPCK in the early LPHC group. In contrast, fetuses of dams fed the LPHC diet in late or throughout pregnancy had lower body and placental weights, and higher hepatic G6Pase and PEPCK activities than controls. Maternal hepatic G6Pase activity was elevated in all LPHC groups, while maternal PEPCK activity was only increased significantly in the late LPHC group.

CONCLUSIONS

Feeding a LPHC diet, particularly during late pregnancy, therefore, up-regulates fetal and maternal hepatic glucogenic capacity.

Previously associated type 2 diabetes variants may interact with physical activity to modify the risk of impaired glucose regulation and type 2 diabetes: a study of 16,003 Swedish adults

OBJECTIVE

Recent advances in type 2 diabetes genetics have culminated in the discovery and confirmation of multiple risk variants. Two important and largely unanswered questions are whether this information can be used to identify individuals most susceptible to the adverse consequences of sedentary behavior and to predict their response to lifestyle intervention; such evidence would be mechanistically informative and provide a rationale for targeting genetically susceptible subgroups of the population.

RESEARCH DESIGN AND METHODS

Gene x physical activity interactions were assessed for 17 polymorphisms in a prospective population-based cohort of initially nondiabetic middle-aged adults. Outcomes were 1) impaired glucose regulation (IGR) versus normal glucose regulation determined with either fasting or 2-h plasma glucose concentrations (n = 16,003), 2) glucose intolerance (in mmol/l, n = 8,860), or 3) incident type 2 diabetes (n = 2,063 events).

RESULTS

Tests of gene x physical activity interactions on IGR risk for 3 of the 17 polymorphisms were nominally statistically significant:CDKN2A/B rs10811661 (P(interaction) = 0.015), HNF1B rs4430796 (P(interaction) = 0.026), and PPARG rs1801282 (P(interaction) = 0.04). Consistent interactions were observed for the CDKN2A/B (P(interaction) = 0.013) and HNF1B (P(interaction) = 0.0009) variants on 2-h glucose concentrations. Where type 2 diabetes was the outcome, only one statistically significant interaction effect was observed, and this was for the HNF1B rs4430796 variant (P(interaction) = 0.0004). The interaction effects for HNF1B on IGR risk and incident diabetes remained significant after correction for multiple testing (P(interaction) = 0.015 and 0.0068, respectively).

CONCLUSIONS

Our observations suggest that the genetic predisposition to hyperglycemia is partially dependent on a person's lifestyle.

Interaction between HNF4A polymorphisms and physical activity in relation to type 2 diabetes-related traits: results from the Quebec Family Study

AIMS

To test for associations between type 2 diabetes mellitus (T2DM)-related traits and polymorphisms (SNPs) in the hepatocyte nuclear factor 4-alpha gene (HNF4A) in the Quebec Family Study cohort, and determine whether these associations are modulated by physical activity (PA).

METHODS

Two HNF4A SNPs (rs1885088 G>A; rs745975 C>T), previously reported to be associated with T2DM, were studied in 528 non-diabetic subjects who underwent a 75g oral glucose tolerance test (OGTT). Glucose, insulin and C-peptide plasma levels, measured in the fasting state and during the OGTT, were used in the analysis. The amount (hours per week) of PA was assessed by questionnaire.

RESULTS

The HNF4A rs1885088 SNP was not independently associated with T2DM-related traits, whereas the rs745975 was associated with fasting insulin, HOMA-IR and 2-h glucose levels (p<0.05 for all). Genotype by PA interactions were found for glucose homeostasis (p<0.0001) and insulin secretion (p<or=0.03). When subjects were stratified by PA level (according to the median value), we found that high level of PA (>2h/week) was associated with smaller glucose area under the curve (AUC) and 2-h glucose levels in rs1885088 A/A homozygotes and with lower fasting C-peptide and insulin AUC in rs745975 T/T homozygotes.

CONCLUSION

These results indicate that the associations of HNF4A rs1885088 with glucose tolerance and rs745975 with insulin secretion are modulated by PA. Our finding therefore suggests that the effect of HNF4A polymorphisms on the risk of T2DM is influenced by PA.

Preventive advice given by patients with type 2 diabetes to their offspring

BACKGROUND

Patients' advice-giving behaviour could be a useful preventive strategy for type 2 diabetes.

AIM

To investigate the conditions under which patients offer advice to their offspring and to assess the factors that facilitate advice giving.

DESIGN OF STUDY

Cross-sectional observational study.

SETTING

A general hospital with a diabetes clinic in a metropolitan suburb in Japan.

METHOD

Parents with type 2 diabetes (n = 221) who had offspring aged 20-49 years inclusive without diabetes completed a self-administered questionnaire containing items relating to advice-giving behaviour, demographic characteristics, risk perception, and their disease status.

RESULTS

A total of 184 (83.3%) patients responded that parental advice-giving behaviour is needed for their offspring, while 138 (62.4%) actually advised their offspring. Multiple logistic regression analysis showed that patients who were female (odds ratio [OR] = 1.94, 95% confidence interval [CI] = 1.03 to 3.65, P = 0.041), living with their offspring (OR =1.92, 95% CI = 1.04 to 3.57, P = 0.038), had complications (OR = 2.74, 95% CI = 1.25 to 6.00, P = 0.029), or perceived that their offspring had a high risk of developing diabetes (OR =1.45, 95% CI = 1.09 to 1.93, P = 0.011) were most likely to advise their offspring.

CONCLUSION

Patients with type 2 diabetes recognised the need to give advice about preventive behaviour to their offspring but were not necessarily engaging in advice-giving behaviour. Advice-giving behaviour was affected by the parents' own disease status, their perception of their offspring's risk of developing diabetes, and the relationship between the patients and their offspring.

The association between low birth weight and type 2 diabetes: contribution of genetic factors

BACKGROUND

Low birth weight has been associated with an increased risk of type 2 diabetes in adulthood. Poor fetal nutrition has been suggested to explain this association. Our objective was to determine whether genetic factors contribute to the association between low birth weight and subsequent risk of type 2 diabetes.

METHODS

We retrieved information from original birth records on same-sex Swedish twins with known zygosity, born from 1926 to 1958. We used regression models to investigate whether birth weight was associated with risk of type 2 diabetes in the cohort of twins overall, and in case-control analyses within disease-discordant dizygotic and monozygotic twin pairs.

RESULTS

Of 18,230 twins, 592 (3.2%) had type 2 diabetes. The rate of type 2 diabetes consistently increased with decreasing birth weight, from 2.4% among twins with birth weights of 3500 g or more to 5.3% among those with birth weights less than 2000 g. In the cohort analysis, in which twins are analyzed as independent individuals, the adjusted odds ratio (95% confidence interval) of type 2 diabetes per 500-g decrease in birth weight was 1.44 (1.28-1.63). When we compared the diseased twin with the healthy cotwin, the corresponding odds ratios were 1.38 (1.02-1.85), among dizygotic twins, and 1.02 (0.63-1.64), among monozygotic twins.

CONCLUSIONS

Low birth weight is associated with type 2 diabetes in adulthood. The difference in this association between monozygotic and dizygotic twin pairs suggests that genetic mechanisms play an important role in this association.

Defining the regulated secreted proteome of rodent adipocytes upon the induction of insulin resistance

Insulin resistance defines the metabolic syndrome and precedes, as well is the hallmark of, type II diabetes. Adipocytes, besides being a major site for energy storage, are endocrine in nature and secrete a variety of proteins, adipocytokines (adipokines), that can modulate insulin sensitivity, inflammation, obesity, hypertension, food intake (anorexigenic and orexigenic), and general energy homeostasis. Recent data demonstrates that increased intracellular glycosylation of proteins via O-GlcNAc can induce insulin resistance and that a rodent model with genetically elevated O-GlcNAc levels in muscle and fat displays hyperleptinemia. The link between O-GlcNAc levels, insulin resistance, and adipocytokine secretion is further explored here. First, with the use of immortalized and primary rodent adipocytes, the secreted proteome of differentiated adipocytes is more fully elucidated by the identification of 97 and 203 secreted proteins, respectively. Mapping of more than 80 N-linked glycosylation sites on adipocytokines from the cell lines further defines this proteome. Importantly, adipocytokines that are modulated when cells are shifted from insulin responsive to insulin resistant conditions are determined. By the use of two protocols for inducing insulin resistance, classical hyperglycemia with chronic insulin exposure and pharmacological elevation of O-GlcNAc levels, several proteins are identified that are regulated in a similar fashion under both conditions including HCNP, Quiescin Q6, Angiotensin, lipoprotein lipase, matrix metalloproteinase 2, and slit homologue 3. Detection of these potential prognostic/diagnostic biomarkers for metabolic syndrome, type II diabetes, and the resulting complications of both diseases further establishes the central role of the O-GlcNAc modification of intracellular proteins in the pathophysiology of these conditions.

The Pro12Ala variant at the peroxisome proliferator-activated receptor gamma gene and change in obesity-related traits in the Diabetes Prevention Program

AIMS/HYPOTHESIS

Peroxisome proliferator-activated receptor gamma (PPARgamma), encoded by the PPARG gene, regulates insulin sensitivity and adipogenesis, and may bind polyunsaturated fatty acids (PUFA) and thiazolidinediones in a ligand-dependent manner. The PPARG proline for alanine substitution at position 12 (Pro12Ala polymorphism) has been related with obesity directly and via interaction with PUFA.

METHODS

We tested the effect-modifying role of Pro12Ala on the 1 year change in obesity-related traits in a randomised clinical trial of treatment with metformin (n = 989), troglitazone (n = 363) or lifestyle modification (n = 1,004) vs placebo (n = 1,000) for diabetes prevention in high-risk individuals.

RESULTS

At baseline, Ala12 carriers had larger waists (p < 0.001) and, in a subset, more subcutaneous adipose tissue (SAT; lumbar 2/3; p = 0.04) than Pro12 homozygotes. There was a genotype-by-intervention interaction on 1-year weight change (p = 0.01); in the placebo arm, Pro12 homozygotes gained weight and Ala12 carriers lost weight (p = 0.001). In the metformin and lifestyle arms, weight loss occurred across genotypes, but was greatest in Ala12 carriers (p < 0.05). Troglitazone treatment induced weight gain, which tended to be greater in Ala12 carriers (p = 0.08). In the placebo group, SAT (lumbar 2/3, lumbar 4/5) decreased in Ala12 allele carriers, but was unchanged in Pro12 homozygotes (p < or = 0.005). With metformin treatment, SAT decreased independently of genotype. In the lifestyle arm, SAT (lumbar 2/3) reductions occurred across genotypes, but were greater in Ala12 carriers (p = 0.03). A genotype-by-PUFA intake interaction on reduction in visceral fat (lumbar 4/5; p = 0.04) was also observed, which was most evident with metformin treatment (p < 0.001).

CONCLUSIONS/INTERPRETATION

Within the Diabetes Prevention Program, the Ala12 allele influences central obesity, an effect which may differ by treatment group and dietary PUFA intake (ClinicalTrials.gov ID no: NCT00004992).

Gene-environment interactions in the pathogenesis of type 2 diabetes and metabolism

PURPOSE OF REVIEW

Gene-environment interaction can be viewed as a departure from an otherwise expected additivity of genetic and environmental factors on a given outcome measure. Important genetic and environmental factors contribute to the pathogenesis of type 2 diabetes and intermediary traits, probably modulated by their complex interaction. This paper provides an update on the current literature investigating gene-environment interactions of type 2 diabetes and metabolic phenotypes, and discusses the future perspectives of this research.

RECENT FINDINGS

Recent advances in gene-environment interaction studies of metabolism have involved LIPC, APOA5 and PPARG variation, and nutrition and physical activity, of which the most consistently replicated observations have been obtained for APOA5. Also, intervention studies of the promising TCF7L2 type 2 diabetes gene and possible future strategies are discussed.

SUMMARY

Possibly as a result of the complexity of these multifactorial diseases, recent years have seen only limited success in unravelling significant gene-environment interactions, but important insights have been gained and they hold promise for implementation in lifestyle intervention strategies. We need to evolve to more complex, but realistic, scenarios involving several genes and environmental factors. Recent progress in statistical methods allowing for higher-order interactions may make this possible.

Peroxisome proliferator-activated receptors as molecular targets in relation to obesity and Type 2 diabetes

The three isotypes of peroxisome proliferator-activated receptors (PPARs) are currently perceived as major regulatory nodes (or hubs) of metabolic pathway networks, linking most prevalent diseases including Type 2 diabetes, obesity, dyslipidemia and atherosclerosis. The integrative functions of PPARs are also reflected in their ecogenetic profile, when the variants underlying pharmacogenetic interactions were also shown to modulate the effect of lifestyle factors. Despite their extensive clinical use, there are many outstanding issues, especially concerning their safety. Critical pharmacogenomic assessment is warranted for the new potent ligands of multiple PPAR isoforms as many have displayed serious side-effects in a limited number of treated subjects. Nevertheless, the advent of genomic, transcriptomic and system biology-level approaches, integrating knowledge from model systems and human biology, should greatly facilitate the transition to individualized PPAR-based therapies.