Technological readiness and implementation of genomic-driven precision medicine for complex diseases.

The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data.

Clinical profiles of post-load glucose subgroups and their association with glycaemic traits over time: An IMI-DIRECT study.

AIM: To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration. METHODS: We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months. RESULTS: At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1-4. Participants in Subgroups 2-4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (ß = 0.36, 95% CI 0.13-0.58), Subgroup 3 (ß = 0.30; 95% CI 0.10-0.50) and Subgroup 2 (ß = 0.18; 95% CI 0.04-0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months. CONCLUSIONS: Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.

Birth weight and cardiac function assessed by echocardiography in adolescence: Avon Longitudinal Study of Parents and Children.

OBJECTIVE: Maternal hemodynamics in pregnancy is associated with fetal growth and birth weight, which in turn are associated with offspring cardiovascular disease later in life. The aim of this study was to quantify the extent to which birth weight is associated with cardiac structure and function in adolescence. METHODS: A subset of offspring (n = 1964; 55% female) of the Avon Longitudinal Study of Parents and Children were examined with echocardiography at a mean age of 17.7 (SD, 0.3) years. The associations of birth-weight Z-score for sex and gestational age with cardiac structure (assessed by relative wall thickness, left ventricular mass index (LVMI) and left atrial diameter index), systolic function (assessed by ejection fraction and left ventricular wall velocity) and diastolic function (assessed by early/late mitral inflow velocity (E/A) and early mitral inflow velocity/mitral annular early diastolic velocity (E/e')) were evaluated. Linear regression models were adjusted for several potential confounders, including maternal prepregnancy body mass index, age, level of education and smoking during pregnancy. RESULTS: Higher birth-weight Z-score was associated with lower E/A (mean difference, -0.024; 95% CI, -0.043 to -0.005) and E/e' (mean difference, -0.05; 95% CI, -0.10 to -0.001) and higher LVMI (mean difference, 0.38 g/m2.7 ; 95% CI, 0.09 to 0.67). There was no or inconsistent evidence of associations of birth-weight Z-score with relative wall thickness, left atrial diameter and measurements of systolic function. Further analyses suggested that the association between birth-weight Z-score and LVMI was driven mainly by an association observed in participants born small-for-gestational age and it did not persist when risk factors in adolescence were accounted for. CONCLUSIONS: Higher birth weight adjusted for sex and gestational age was associated with differences in measures of diastolic function in adolescence, but the observed associations were small. It remains to be determined the extent to which these associations translate into increased susceptibility to cardiovascular disease later in life. © 2018 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

Causal inference in obesity research.

Obesity is a risk factor for a plethora of severe morbidities and premature death. Most supporting evidence comes from observational studies that are prone to chance, bias and confounding. Even data on the protective effects of weight loss from randomized controlled trials will be susceptible to confounding and bias if treatment assignment cannot be masked, which is usually the case with lifestyle and surgical interventions. Thus, whilst obesity is widely considered the major modifiable risk factor for many chronic diseases, its causes and consequences are often difficult to determine. Addressing this is important, as the prevention and treatment of any disease requires that interventions focus on causal risk factors. Disease prediction, although not dependent on knowing the causes, is nevertheless enhanced by such knowledge. Here, we provide an overview of some of the barriers to causal inference in obesity research and discuss analytical approaches, such as Mendelian randomization, that can help to overcome these obstacles. In a systematic review of the literature in this field, we found: (i) probable causal relationships between adiposity and bone health/disease, cancers (colorectal, lung and kidney cancers), cardiometabolic traits (blood pressure, fasting insulin, inflammatory markers and lipids), uric acid concentrations, coronary heart disease and venous thrombosis (in the presence of pulmonary embolism), (ii) possible causal relationships between adiposity and gray matter volume, depression and common mental disorders, oesophageal cancer, macroalbuminuria, end-stage renal disease, diabetic kidney disease, nuclear cataract and gall stone disease, and (iii) no evidence for causal relationships between adiposity and Alzheimer's disease, pancreatic cancer, venous thrombosis (in the absence of pulmonary embolism), liver function and periodontitis.

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

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

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

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

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

BACKGROUND: Obesity is a complex disease caused by the interplay of genetic and lifestyle factors, but identification of gene-lifestyle interactions in obesity has remained challenging. Few large-scale studies have reported use of genome-wide approaches to investigate gene-lifestyle interactions in obesity. METHODS: In the Pakistan Risk of Myocardial Infraction Study, a cross-sectional study based in Pakistan, we calculated body mass index (BMI) variance estimates (square of the residual of inverse-normal transformed BMI z-score) in 14 131 participants and conducted genome-wide heterogeneity of variance analyses (GWHVA) for this outcome. All analyses were adjusted for age, age(2), sex and genetic ancestry. RESULTS: The GWHVA analyses identified an intronic variant, rs140133294, in the FLJ33544 gene in association with BMI variance (P-value=3.1 × 10(-8)). In explicit tests of gene × lifestyle interaction, smoking was found to significantly modify the effect of rs140133294 on BMI (Pinteraction=0.0005), whereby the minor allele (T) was associated with lower BMI in current smokers, while positively associated with BMI in never smokers. Analyses of ENCODE data at the FLJ33534 locus revealed features indicative of open chromatin and high confidence DNA-binding motifs for several transcription factors, providing suggestive biological support for a mechanism of interaction. CONCLUSIONS: In summary, we have identified a novel interaction between smoking and variation at the FLJ33534 locus in relation to BMI in people from Pakistan.

Diabetes and onset of natural menopause: results from the European Prospective Investigation into Cancer and Nutrition.

STUDY QUESTION: Do women who have diabetes before menopause have their menopause at an earlier age compared with women without diabetes? SUMMARY ANSWER: Although there was no overall association between diabetes and age at menopause, our study suggests that early-onset diabetes may accelerate menopause. WHAT IS KNOWN ALREADY: Today, more women of childbearing age are being diagnosed with diabetes, but little is known about the impact of diabetes on reproductive health. STUDY DESIGN, SIZE, DURATION: We investigated the impact of diabetes on age at natural menopause (ANM) in 258 898 women from the European Prospective Investigation into Cancer and Nutrition (EPIC), enrolled between 1992 and 2000. PARTICIPANTS/MATERIALS, SETTING, METHODS: Determinant and outcome information was obtained through questionnaires. Time-dependent Cox regression analyses were used to estimate the associations of diabetes and age at diabetes diagnosis with ANM, stratified by center and adjusted for age, smoking, reproductive and diabetes risk factors and with age from birth to menopause or censoring as the underlying time scale. MAIN RESULTS AND THE ROLE OF CHANCE: Overall, no association between diabetes and ANM was found (hazard ratio (HR) = 0.94; 95% confidence interval (CI) 0.89-1.01). However, women with diabetes before the age of 20 years had an earlier menopause (10-20 years: HR = 1.43; 95% CI 1.02-2.01, <10 years: HR = 1.59; 95% CI 1.03-2.43) compared with non-diabetic women, whereas women with diabetes at age 50 years and older had a later menopause (HR = 0.81; 95% CI 0.70-0.95). None of the other age groups were associated with ANM. LIMITATIONS, REASONS FOR CAUTION: Strengths of the study include the large sample size and the broad set of potential confounders measured. However, results may have been underestimated due to survival bias. We cannot be sure about the sequence of the events in women with a late age at diabetes, as both events then occur in a short period. We could not distinguish between type 1 and type 2 diabetes. WIDER IMPLICATIONS OF THE FINDINGS: Based on the literature, an accelerating effect of early-onset diabetes on ANM might be plausible. A delaying effect of late-onset diabetes on ANM has not been reported before, and is not in agreement with recent studies suggesting the opposite association. STUDY FUNDING/COMPETING INTERESTS: The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ) and Federal Ministry of Education and Research (BMMF) (Germany); Ministry of Health and Social Solidarity, Stavros Niarchos Foundation and Hellenic Health Foundation (Greece); Italian Association for Research on Cancer (AIRC) and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); ERC-2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), Regional Governments of Andalucía, Asturias, Basque Country, Murcia (no. 6236) and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Scientific Council and Regional Government of Skåne and Västerbotten (Sweden); Cancer Research UK, Medical Research Council, Stroke Association, British Heart Foundation, Department of Health, Food Standards Agency, and Wellcome Trust (UK). None of the authors reported a conflict of interest.

Long-term changes in dietary and food intake behaviour in the Diabetes Prevention Program Outcomes Study.

AIMS: To compare change in dietary intake, with an emphasis on food groups and food intake behaviour, over time across treatment arms in a diabetes prevention trial and to assess the differences in dietary intake among demographic groups within treatment arms. METHODS: Data are from the Diabetes Prevention Program and Diabetes Prevention Program Outcomes Study. Participants were randomized to a lifestyle intervention (n = 1079), metformin (n = 1073) or placebo (n = 1082) for an average of 3 years, after which the initial results regarding the benefits of the lifestyle intervention were released and all participants were offered a modified lifestyle intervention. Dietary intake was assessed using a food frequency questionnaire at baseline and at 1, 5, 6 and 9 years after randomization. RESULTS: Compared with the metformin and placebo arms, participants in the lifestyle arm maintained a lower total fat and saturated fat and a higher fibre intake up to 9 years after randomization and lower intakes of red meat and sweets were maintained for up to 5 years. Younger participants had higher intakes of poultry and lower intakes of fruits compared with their older counterparts, particularly in the lifestyle arm. Black participants tended to have lower dairy and higher poultry intakes compared with white and Hispanic participants. In the lifestyle arm, men tended to have higher grain, fruit and fish intakes than women. CONCLUSIONS: Changes in nutrient intake among participants in the lifestyle intervention were maintained for up to 9 years. Younger participants reported more unhealthy diets over time and thus may benefit from additional support to achieve and maintain dietary goals.

Consumption of sweet beverages and type 2 diabetes incidence in European adults: results from EPIC-InterAct.

AIMS/HYPOTHESIS: Consumption of sugar-sweetened beverages has been shown, largely in American populations, to increase type 2 diabetes incidence. We aimed to evaluate the association of consumption of sweet beverages (juices and nectars, sugar-sweetened soft drinks and artificially sweetened soft drinks) with type 2 diabetes incidence in European adults. METHODS: We established a case-cohort study including 12,403 incident type 2 diabetes cases and a stratified subcohort of 16,154 participants selected from eight European cohorts participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. After exclusions, the final sample size included 11,684 incident cases and a subcohort of 15,374 participants. Cox proportional hazards regression models (modified for the case-cohort design) and random-effects meta-analyses were used to estimate the association between sweet beverage consumption (obtained from validated dietary questionnaires) and type 2 diabetes incidence. RESULTS: In adjusted models, one 336 g (12 oz) daily increment in sugar-sweetened and artificially sweetened soft drink consumption was associated with HRs for type 2 diabetes of 1.22 (95% CI 1.09, 1.38) and 1.52 (95% CI 1.26, 1.83), respectively. After further adjustment for energy intake and BMI, the association of sugar-sweetened soft drinks with type 2 diabetes persisted (HR 1.18, 95% CI 1.06, 1.32), but the association of artificially sweetened soft drinks became statistically not significant (HR 1.11, 95% CI 0.95, 1.31). Juice and nectar consumption was not associated with type 2 diabetes incidence. CONCLUSIONS/INTERPRETATION: This study corroborates the association between increased incidence of type 2 diabetes and high consumption of sugar-sweetened soft drinks in European adults.

Association between dietary meat consumption and incident type 2 diabetes: the EPIC-InterAct study.

AIMS/HYPOTHESIS: A diet rich in meat has been reported to contribute to the risk of type 2 diabetes. The present study aims to investigate the association between meat consumption and incident type 2 diabetes in the EPIC-InterAct study, a large prospective case-cohort study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. METHODS: During 11.7 years of follow-up, 12,403 incident cases of type 2 diabetes were identified among 340,234 adults from eight European countries. A centre-stratified random subsample of 16,835 individuals was selected in order to perform a case-cohort design. Prentice-weighted Cox regression analyses were used to estimate HR and 95% CI for incident diabetes according to meat consumption. RESULTS: Overall, multivariate analyses showed significant positive associations with incident type 2 diabetes for increasing consumption of total meat (50 g increments: HR 1.08; 95% CI 1.05, 1.12), red meat (HR 1.08; 95% CI 1.03, 1.13) and processed meat (HR 1.12; 95% CI 1.05, 1.19), and a borderline positive association with meat iron intake. Effect modifications by sex and class of BMI were observed. In men, the results of the overall analyses were confirmed. In women, the association with total and red meat persisted, although attenuated, while an association with poultry consumption also emerged (HR 1.20; 95% CI 1.07, 1.34). These associations were not evident among obese participants. CONCLUSIONS/INTERPRETATION: This prospective study confirms a positive association between high consumption of total and red meat and incident type 2 diabetes in a large cohort of European adults.

The link between family history and risk of type 2 diabetes is not explained by anthropometric, lifestyle or genetic risk factors: the EPIC-InterAct study.

AIMS/HYPOTHESIS: Although a family history of type 2 diabetes is a strong risk factor for the disease, the factors mediating this excess risk are poorly understood. In the InterAct case-cohort study, we investigated the association between a family history of diabetes among different family members and the incidence of type 2 diabetes, as well as the extent to which genetic, anthropometric and lifestyle risk factors mediated this association. METHODS: A total of 13,869 individuals (including 6,168 incident cases of type 2 diabetes) had family history data available, and 6,887 individuals had complete data on all mediators. Country-specific Prentice-weighted Cox models were fitted within country, and HRs were combined using random effects meta-analysis. Lifestyle and anthropometric measurements were performed at baseline, and a genetic risk score comprising 35 polymorphisms associated with type 2 diabetes was created. RESULTS: A family history of type 2 diabetes was associated with a higher incidence of the condition (HR 2.72, 95% CI 2.48, 2.99). Adjustment for established risk factors including BMI and waist circumference only modestly attenuated this association (HR 2.44, 95% CI 2.03, 2.95); the genetic score alone explained only 2% of the family history-associated risk of type 2 diabetes. The greatest risk of type 2 diabetes was observed in those with a biparental history of type 2 diabetes (HR 5.14, 95% CI 3.74, 7.07) and those whose parents had been diagnosed with diabetes at a younger age (<50 years; HR 4.69, 95% CI 3.35, 6.58), an effect largely confined to a maternal family history. CONCLUSIONS/INTERPRETATION: Prominent lifestyle, anthropometric and genetic risk factors explained only a marginal proportion of the excess risk associated with family history, highlighting the fact that family history remains a strong, independent and easily assessed risk factor for type 2 diabetes. Discovering factors that will explain the association of family history with type 2 diabetes risk will provide important insight into the aetiology of type 2 diabetes.

Genetic risk scores ascertained in early adulthood and the prediction of type 2 diabetes later in life.

It is hoped that information garnered from studies on population genetics will one day be translated into a form in which it meaningfully improves the prediction, prevention or treatment of type 2 diabetes. Type 2 diabetes genetics researchers have made extraordinary progress in identifying common genetic variants that are associated with type 2 diabetes, which has shed light on the biological pathways in which molecular defects that cause the disease likely reside. However, the expectation that genetic discoveries will aid the prevention or treatment of type 2 diabetes has not, so far, been fulfilled. In a paper published in this edition of the journal, Vassy and colleagues (DOI: 10.1007/s00125-012-2637-7) test the hypothesis that the predictive accuracy of established genetic risk markers for type 2 diabetes varies by age, with the predictive accuracy being greatest in younger cohorts. The authors found no substantive support for this hypothesis. However, a number of interesting questions are raised by their study concerning why risk alleles for a given genotype may differ in younger and older cohorts and why prospective cohort studies may yield results that are inconsistent with those derived from cross-sectional studies; this commentary discusses these points.

Physical activity reduces the risk of incident type 2 diabetes in general and in abdominally lean and obese men and women: the EPIC-InterAct Study.

AIMS/HYPOTHESIS: We examined the independent and combined associations of physical activity and obesity with incident type 2 diabetes in men and women. METHODS: The InterAct case-cohort study consists of 12,403 incident type 2 diabetes cases and a randomly selected subcohort of 16,154 individuals, drawn from a total cohort of 340,234 participants with 3.99 million person-years of follow-up. Physical activity was assessed by a four-category index. Obesity was measured by BMI and waist circumference (WC). Associations between physical activity, obesity and case-ascertained incident type 2 diabetes were analysed by Cox regression after adjusting for educational level, smoking status, alcohol consumption and energy intake. In combined analyses, individuals were stratified according to physical activity level, BMI and WC. RESULTS: A one-category difference in physical activity (equivalent to approximately 460 and 365 kJ/day in men and women, respectively) was independently associated with a 13% (HR 0.87, 95% CI 0.80, 0.94) and 7% (HR 0.93, 95% CI 0.89, 0.98) relative reduction in the risk of type 2 diabetes in men and women, respectively. Lower levels of physical activity were associated with an increased risk of diabetes across all strata of BMI. Comparing inactive with active individuals, the HRs were 1.44 (95% CI 1.11, 1.87) and 1.38 (95% CI 1.17, 1.62) in abdominally lean and obese inactive men, respectively, and 1.57 (95% CI 1.19, 2.07) and 1.19 (95% CI 1.01, 1.39) in abdominally lean and obese inactive women, respectively. CONCLUSIONS/INTERPRETATION: Physical activity is associated with a reduction in the risk of developing type 2 diabetes across BMI categories in men and women, as well as in abdominally lean and obese men and women.

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

AIMS: Skeletal muscle is a major metabolic organ and plays important roles in glucose metabolism, insulin sensitivity and insulin action. Muscle telomere length reflects the myocyte's exposure to harmful environmental factors. Leukocyte telomere length is considered a marker of muscle telomere length and is used in epidemiologic studies to assess associations with ageing-related diseases where muscle physiology is important. However, the extent to which leucocyte and muscle telomere length are correlated is unknown, as are their relative correlations with glucose and insulin concentrations. The purpose of this study was to determine the extent of these relationships. METHODS: Leucocyte and muscle telomere length were measured by quantitative real-time polymerase chain reaction in participants from the Malmö Exercise Intervention (n = 27) and the Prevalence, Prediction and Prevention of Diabetes-Botnia studies (n = 31). Participants in both studies were free from Type 2 diabetes. We assessed the association between leucocyte telomere length, muscle telomere length and metabolic traits using Spearmen correlations and multivariate linear regression. Bland-Altman analysis was used to assess agreement between leucocyte and muscle telomere length. RESULTS: In age-, study-, diabetes family history- and sex-adjusted models, leucocyte and muscle telomere length were positively correlated (r = 0.39, 95% CI 0.15-0.59). Leucocyte telomere length was inversely associated with 2-h glucose concentrations (r = -0.58, 95% CI -1.0 to -0.16), but there was no correlation between muscle telomere length and 2-h glucose concentrations (r = 0.05, 95% CI -0.35 to 0.46) or between leucocyte or muscle telomere length with other metabolic traits. CONCLUSIONS: In summary, the current study supports the use of leucocyte telomere length as a proxy for muscle telomere length in epidemiological studies of Type 2 diabetes aetiology.

Common variants in genes encoding adiponectin (ADIPOQ) and its receptors (ADIPOR1/2), adiponectin concentrations, and diabetes incidence in the Diabetes Prevention Program.

AIMS: Baseline adiponectin concentrations predict incident Type 2 diabetes mellitus in the Diabetes Prevention Program. We tested the hypothesis that common variants in the genes encoding adiponectin (ADIPOQ) and its receptors (ADIPOR1, ADIPOR2) would associate with circulating adiponectin concentrations and/or with diabetes incidence in the Diabetes Prevention Program population. METHODS: Seventy-seven tagging single-nucleotide polymorphisms (SNPs) in ADIPOQ (24), ADIPOR1 (22) and ADIPOR2 (31) were genotyped. Associations of SNPs with baseline adiponectin concentrations were evaluated using linear modelling. Associations of SNPs with diabetes incidence were evaluated using Cox proportional hazards modelling. RESULTS: Thirteen of 24 ADIPOQ SNPs were significantly associated with baseline adiponectin concentrations. Multivariable analysis including these 13 SNPs revealed strong independent contributions of rs17366568, rs1648707, rs17373414 and rs1403696 with adiponectin concentrations. However, no ADIPOQ SNPs were directly associated with diabetes incidence. Two ADIPOR1 SNPs (rs1342387 and rs12733285) were associated with ∼18% increased diabetes incidence for carriers of the minor allele without differences across treatment groups, and without any relationship with adiponectin concentrations. CONCLUSIONS: ADIPOQ SNPs are significantly associated with adiponectin concentrations in the Diabetes Prevention Program cohort. This observation extends prior observations from unselected populations of European descent into a broader multi-ethnic population, and confirms the relevance of these variants in an obese/dysglycaemic population. Despite the robust relationship between adiponectin concentrations and diabetes risk in this cohort, variants in ADIPOQ that relate to adiponectin concentrations do not relate to diabetes risk in this population. ADIPOR1 variants exerted significant effects on diabetes risk distinct from any effect of adiponectin concentrations.

Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study.

AIMS/HYPOTHESIS: Studying gene-lifestyle interaction may help to identify lifestyle factors that modify genetic susceptibility and uncover genetic loci exerting important subgroup effects. Adequately powered studies with prospective, unbiased, standardised assessment of key behavioural factors for gene-lifestyle studies are lacking. This case-cohort study aims to investigate how genetic and potentially modifiable lifestyle and behavioural factors, particularly diet and physical activity, interact in their influence on the risk of developing type 2 diabetes. METHODS: Incident cases of type 2 diabetes occurring in European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts between 1991 and 2007 from eight of the ten EPIC countries were ascertained and verified. Prentice-weighted Cox regression and random-effects meta-analyses were used to investigate differences in diabetes incidence by age and sex. RESULTS: A total of 12,403 verified incident cases of type 2 diabetes occurred during 3.99 million person-years of follow-up of 340,234 EPIC participants eligible for InterAct. We defined a centre-stratified subcohort of 16,154 individuals for comparative analyses. Individuals with incident diabetes who were randomly selected into the subcohort (n = 778) were included as cases in the analyses. All prevalent diabetes cases were excluded from the study. InterAct cases were followed-up for an average of 6.9 years; 49.7% were men. Mean baseline age and age at diagnosis were 55.6 and 62.5 years, mean BMI and waist circumference values were 29.4 kg/m(2) and 102.7 cm in men, and 30.1 kg/m(2) and 92.8 cm in women, respectively. Risk of type 2 diabetes increased linearly with age, with an overall HR of 1.56 (95% CI 1.48-1.64) for a 10 year age difference, adjusted for sex. A male excess in the risk of incident diabetes was consistently observed across all countries, with a pooled HR of 1.51 (95% CI 1.39-1.64), adjusted for age. CONCLUSIONS/INTERPRETATION: InterAct is a large, well-powered, prospective study that will inform our understanding of the interplay between genes and lifestyle factors on the risk of type 2 diabetes development.

Abdominal and gynoid adiposity and the risk of stroke.

BACKGROUND: Previous studies have indicated that fat distribution is important in the development of cardiovascular disease (CVD). We investigated the association between fat distribution, as measured by dual energy X-ray absorptiometry (DXA), and the incidence of stroke. METHODS: A cohort of 2751 men and women aged ≥40 years was recruited. Baseline levels of abdominal, gynoid and total body fat were measured by DXA. Body mass index (BMI, kg m(-2)) was calculated. Stroke incidence was recorded using the regional stroke registry until subjects reached 75 years of age. RESULTS: During a mean follow-up time of 8 years and 9 months, 91 strokes occurred. Of the adiposity indices accessed abdominal fat mass was the best predictor of stroke in women (hazard ratio (HR)=1.66, 95% confidence interval (CI)=1.23-2.24 per standard deviation increase), whereas the ratio of gynoid fat to total fat mass was associated with a decreased risk of stroke (HR=0.72, 95% CI=0.54-0.96). Abdominal fat mass was the only of the adiposity indices assessed that was found to be a significant predictor of stroke in men (HR=1.49, 95% CI=1.06-2.09). The associations between abdominal fat mass and stroke remained significant in both women and men after adjustment for BMI (HR=1.80, 95% CI=1.06-3.07; HR=1.71, 95% CI=1.13-2.59, respectively). However, in a subgroup analyses abdominal fat was not a significant predictor after further adjustment for diabetes, smoking and hypertension. CONCLUSION: Abdominal fat mass is a risk factor for stroke independent of BMI, but not independent of diabetes, smoking and hypertension. This indicates that the excess in stroke risk associated with abdominal fat mass is at least partially mediated through traditional stroke risk factors.

Evaluating the discriminative power of multi-trait genetic risk scores for type 2 diabetes in a northern Swedish population.

AIMS/HYPOTHESIS: We determined whether single nucleotide polymorphisms (SNPs) previously associated with diabetogenic traits improve the discriminative power of a type 2 diabetes genetic risk score. METHODS: Participants (n = 2,751) were genotyped for 73 SNPs previously associated with type 2 diabetes, fasting glucose/insulin concentrations, obesity or lipid levels, from which five genetic risk scores (one for each of the four traits and one combining all SNPs) were computed. Type 2 diabetes patients and non-diabetic controls (n = 1,327/1,424) were identified using medical records in addition to an independent oral glucose tolerance test. RESULTS: Model 1, including only SNPs associated with type 2 diabetes, had a discriminative power of 0.591 (p < 1.00 x 10(-20) vs null model) as estimated by the area under the receiver operator characteristic curve (ROC AUC). Model 2, including only fasting glucose/insulin SNPs, had a significantly higher discriminative power than the null model (ROC AUC 0.543; p = 9.38 x 10(-6) vs null model), but lower discriminative power than model 1 (p = 5.92 x 10(-5)). Model 3, with only lipid-associated SNPs, had significantly higher discriminative power than the null model (ROC AUC 0.565; p = 1.44 x 10(-9)) and was not statistically different from model 1 (p = 0.083). The ROC AUC of model 4, which included only obesity SNPs, was 0.557 (p = 2.30 x 10(-7) vs null model) and smaller than model 1 (p = 0.025). Finally, the model including all SNPs yielded a significant improvement in discriminative power compared with the null model (p < 1.0 x 10(-20)) and model 1 (p = 1.32 x 10(-5)); its ROC AUC was 0.626. CONCLUSIONS/INTERPRETATION: Adding SNPs previously associated with fasting glucose, insulin, lipids or obesity to a genetic risk score for type 2 diabetes significantly increases the power to discriminate between people with and without clinically manifest type 2 diabetes compared with a model including only conventional type 2 diabetes loci.

Abdominal and gynoid adipose distribution and incident myocardial infarction in women and men.

OBJECTIVE: The relationships between objectively measured abdominal and gynoid adipose mass with the prospective risk of myocardial infarction (MI) has been scarcely investigated. We aimed to investigate the associations between fat distribution and the risk of MI. SUBJECTS: Total and regional fat mass was measured using dual-energy X-ray absorptiometry (DEXA) in 2336 women and 922 men, of whom 104 subsequently experienced an MI during a mean follow-up time of 7.8 years. RESULTS: In women, the strongest independent predictor of MI was the ratio of abdominal to gynoid adipose mass (hazard ratio (HR)=2.44, 95% confidence interval (CI) 1.79-3.32 per s.d. increase in adipose mass), after adjustment for age and smoking. This ratio also showed a strong association with hypertension, impaired glucose tolerance and hypertriglyceridemia (P<0.01 for all). In contrast, the ratio of gynoid to total adipose mass was associated with a reduced risk of MI (HR= 0.57, 95% CI 0.43-0.77), and reduced risk of hypertension, impaired glucose tolerance and hypertriglyceridemia (P<0.001 for all). In men, gynoid fat mass was associated with a decreased risk of MI (HR=0.69, 95% CI 0.48-0.98), and abdominal fat mass was associated with hypertriglyceridemia (P for trend 0.02). CONCLUSION: In summary, fat distribution was a strong predictor of the risk of MI in women, but not in men. These different results may be explained by the associations found between fat distribution and hypertension, impaired glucose tolerance and hypertriglyceridemia.