Search

Aims/hypothesis Type 2 diabetes is a complex metabolic disease with increasing prevalence worldwide. Improving the prediction of incident type 2 diabetes using epigenetic markers could help tailor prevention efforts to those at the highest risk. The aim of this study was to identify predictive methylation markers for incident type 2 diabetes by combining epigenome-wide association study (EWAS) results from five prospective European cohorts. Methods We conducted a meta-analysis of EWASs in blood collected 7–10 years prior to type 2 diabetes diagnosis. DNA methylation was measured with Illumina Infinium Methylation arrays. A total of 1250 cases and 1950 controls from five longitudinal cohorts were included: Doetinchem, ESTHER, KORA1, KORA2 and EPIC-Norfolk. Associations between DNA methylation and incident type 2 diabetes were examined using robust linear regression with adjustment for potential confounders. Inverse-variance fixed-effects meta-analysis of cohort-level individual CpG EWAS estimates was performed using METAL. The methylGSA R package was used for gene set enrichment analysis. Confirmation of genome-wide significant CpG sites was performed in a cohort of Indian Asians (LOLIPOP, UK). Results The meta-analysis identified 76 CpG sites that were differentially methylated in individuals with incident type 2 diabetes compared with control individuals (p values −7). Sixty-four out of 76 (84.2%) CpG sites were confirmed by directionally consistent effects and p values 1c) showed no improvement (AUC 0.757 vs 0.753). Gene set enrichment analysis of the full epigenome-wide results clearly showed enrichment of processes linked to insulin signalling, lipid homeostasis and inflammation. Conclusions/interpretation By combining results from five European cohorts, and thus significantly increasing study sample size, we identified 76 CpG sites associated with incident type 2 diabetes. Replication of 64 CpGs in an independent cohort of Indian Asians suggests that the association between DNA methylation levels and incident type 2 diabetes is robust and independent of ethnicity. Our data also indicate that BMI partly explains the association between DNA methylation and incident type 2 diabetes. Further studies are required to elucidate the underlying biological mechanisms and to determine potential causal roles of the differentially methylated CpG sites in type 2 diabetes development. Graphical abstract

Endocrine disrupting chemicals (EDCs) include non-persistent exogenous substances such as parabens, bisphenols and phthalates which have been associated with a range of metabolic disorders and disease. It is unclear if exposure remains consistent over time. We investigated change in indicators of EDC exposure between 2009 and 2016 and assessed its consistency between and within individuals over a median follow-up time of 47 months in a sample of Dutch individuals. Of 500 Dutch individuals, two 24 h urine samples were analysed for 5 parabens, 3 bisphenols and 13 metabolites of in total 8 different phthalates. We calculated per-year differences using metaanalysis and assessed temporal correlations between and within individuals using Spearman correlation coefficients, intra-class correlation coefficients (ICC) and kappa-statistics. We found a secular decrease in concentrations of methyl, ethyl, propyl and n-butyl paraben, bisphenol A, and metabolites of di-ethyl phthalate (DEP), di-butyl phthalate (DBP), di-(2-ethyl-hexyl) phthalate (DEHP), and butylbenzyl phthalate (DBzP) which varied from 8 to 96% (ethyl paraben, propyl paraben) between 2009 and 2016. Within-person temporal correlations were highest for parabens (ICC: 0.34 to 0.40) and poorest for bisphenols (ICC: 0.15 to 0.23). For phthalate metabolites, correlations decreased most between time periods (ICC < 48 months: 0.22 to 0.39; >= 48 months: 0.05 to 0.32). When categorizing EDC concentrations, 33-54% of individuals remained in the lowest or highest category and temporal correlations were similar to continuous measurements. Exposure to most EDCs decreased between 2009 and 2016 in a sample of individuals with impaired fasting glucose from the Dutch population. Temporal consistency was generally poor. The inconsistency in disease associations may be influenced by individual-level or temporal variation exhibited by EDCs. Our findings call for the need for repeated measurements of EDCs in observational studies before and during at-risk temporal windows for the disease.

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.

The link between exposure to endocrine disrupting chemicals (EDCs) and the rapid increase in prevalence of obesity has recently been suggested. However, the magnitude and health impact of EDC exposure in at-risk populations remain largely unclear. In this study, we investigated the effect of a dietary intervention driven reduction in adipose tissue on the magnitude of urinary EDC exposure and mobilization, and whether higher EDC exposure leads to impaired weight loss in obese individuals. In this post-hoc analysis of the Lifestyle, OverWeight, Energy Restriction (LOWER) study from the Netherlands, 218 subjects were included. Five parabens, three bisphenols and thirteen metabolites of eight phthalates were measured in 24-h urine using LC-MS/MS, before and after three-months of a calory-restricted weight reduction intervention program. Associations between adiposity-related traits and EDCs were tested using multivariable linear regression and linear mixed effects models. A multiple testing correction based on the false discovery rate (FDR) was applied. After the 3-month intervention, urinary paraben and bisphenol excretions remained similar. Excretions of mono-butyl phthalates and most high-molecular-weight phthalates decreased, whereas mono-ethyl phthalate increased (all FDR

Glycaemic traits are used to diagnose and monitor type 2 diabetes, and cardiometabolic health. To date, most genetic studies of glycaemic traits have focused on individuals of European ancestry. Here, we aggregated genome-wide association studies in up to 281,416 individuals without diabetes (30% non-European ancestry) with fasting glucose, 2h-glucose post-challenge, glycated haemoglobin, and fasting insulin data. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P-8), 80% with no significant evidence of between-ancestry heterogeneity. Analyses restricted to European ancestry individuals with equivalent sample size would have led to 24 fewer new loci. Compared to single-ancestry, equivalent sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase understanding of diabetes pathophysiology by use of trans-ancestry studies for improved power and resolution.

Background: Exposure to environmental endocrine disrupting chemicals (EDCs) may play an important role in the epidemic of metabolic diseases. Epigenetic alterations may functionally link EDCs with gene expression and metabolic traits. Objectives: We aimed to evaluate metabolic-related effects of the exposure to endocrine disruptors including five parabens, three bisphenols, and 13 metabolites of nine phthalates as measured in 24-hour urine on epigenome-wide DNA methylation. Methods: A blood-based epigenome-wide association study was performed in 622 participants from the Lifelines DEEP cohort using Illumina Infinium HumanMethylation450 methylation data and EDC excretions in 24-hour urine. Out of the 21 EDCs, 13 compounds were detected in >75% of the samples and, together with bisphenol F, were included in these analyses. Furthermore, we explored the putative function of identified methylation markers and their correlations with metabolic traits. Results: We found 20 differentially methylated cytosine-phosphate-guanines (CpGs) associated with 10 EDCs at suggestive p-value < 1 × 10−6, of which four, associated with MEHP and MEHHP, were genome-wide significant (Bonferroni-corrected p-value < 1.19 × 10−7). Nine out of 20 CpGs were significantly associated with at least one of the tested metabolic traits, such as fasting glucose, glycated hemoglobin, blood lipids, and/or blood pressure. 18 out of 20 EDC-associated CpGs were annotated to genes functionally related to metabolic syndrome, hypertension, obesity, type 2 diabetes, insulin resistance and glycemic traits. Conclusions: The identified DNA methylation markers for exposure to the most common EDCs provide suggestive mechanism underlying the contributions of EDCs to metabolic health. Follow-up studies are needed to unravel the causality of EDC-induced methylation changes in metabolic alterations.

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10-8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10-5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|rg| ≈ 0.1-0.3) and positive genetic correlations with physical activity (rg ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (rg ≈-0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction. This research was carried out under the auspices of the Social Science Genetic Association Consortium (SSGAC, https://www.thessgac.org/). The research has also been conducted using the UK Biobank Resource under Application Number 11425. The study was supported by funding from the Ragnar Söderberg Foundation (E9/11 and E42/15), the Swedish Research Council (421-2013-1061), The Jan Wallander and Tom Hedelius Foundation, an ERC Consolidator Grant to Philipp Koellinger (647648 EdGe), the Pershing Square Fund of the Foundations of Human Behavior, The Open Philanthropy Project (2016-152872, 010623-00001), and the NIA/NIH through grants P01-AG005842, P01-AG005842-20S2, P30-AG012810, and T32-AG000186-23 to NBER, and R01-AG042568-02 and R56-AG042568-04 to the University of Southern California. CCC was supported by the Intramural Research Program of the NIH/NIDDK and thanks Kevin Hall for informative discussions. PME was funded by Nestlé Nutrition. We thank the DietGen and CHARGE consortia for sharing diet-composition GWAS summary statistics, and we thank 23andMe, Inc., for sharing physical activity GWAS summary statistics. A full list of acknowledgements is provided in Supplementary Information 13.

Background Severe obesity is a growing, worldwide burden and conventional therapies including radical change of diet and/or increased physical activity have limited results. Bariatric surgery has been proposed as an alternative therapy showing promising results. It leads to substantial weight loss and improvement of comorbidities such as type 2 diabetes. Increased adiposity is associated with changes in epigenetic profile, including DNA methylation. We investigated the effect of bariatric surgery on clinical profile, DNA methylation, and biological age estimated using Horvath’s epigenetic clock. Results To determine the impact of bariatric surgery and subsequent weight loss on clinical traits, a cohort of 40 severely obese individuals (BMI = 30–73 kg/m2) was examined at the time of surgery and at three follow-up visits, i.e., 3, 6, and 12 months after surgery. The majority of the individuals were women (65%) and the mean age at surgery was 45.1 ± 8.1 years. We observed a significant decrease over time in BMI, fasting glucose, HbA1c, HOMA-IR, insulin, total cholesterol, triglycerides, LDL and free fatty acids levels, and a significant small increase in HDL levels (all p values < 0.05). Epigenome-wide association analysis revealed 4857 differentially methylated CpG sites 12 months after surgery (at Bonferroni-corrected p value < 1.09 × 10−7). Including BMI change in the model decreased the number of significantly differentially methylated CpG sites by 51%. Gene set enrichment analysis identified overrepresentation of multiple processes including regulation of transcription, RNA metabolic, and biosynthetic processes in the cell. Bariatric surgery in severely obese patients resulted in a decrease in both biological age and epigenetic age acceleration (EAA) (mean = − 0.92, p value = 0.039). Conclusions Our study shows that bariatric surgery leads to substantial BMI decrease and improvement of clinical outcomes observed 12 months after surgery. These changes explained part of the association between bariatric surgery and DNA methylation. We also observed a small, but significant improvement of biological age. These epigenetic changes may be modifiable by environmental lifestyle factors and could be used as potential biomarkers for obesity and in the future for obesity related comorbidities.

Aims/hypothesis Epigenetic mechanisms may play an important role in the aetiology of type 2 diabetes. Recent epigenome-wide association studies (EWASs) identified several DNA methylation markers associated with type 2 diabetes, fasting glucose and HbA1c levels. Here we present a systematic review of these studies and attempt to replicate the CpG sites (CpGs) with the most significant associations from these EWASs in a case–control sample of the Lifelines study. Methods We performed a systematic literature search in PubMed and EMBASE for EWASs to test the association between DNA methylation and type 2 diabetes and/or glycaemic traits and reviewed the search results. For replication purposes we selected 100 unique CpGs identified in peripheral blood, pancreas, adipose tissue and liver from 15 EWASs, using study-specific Bonferroni-corrected significance thresholds. Methylation data (Illumina 450K array) in whole blood from 100 type 2 diabetic individuals and 100 control individuals from the Lifelines study were available. Multivariate linear models were used to examine the associations of the specific CpGs with type 2 diabetes and glycaemic traits. Results From the 52 CpGs identified in blood and selected for replication, 15 CpGs showed nominally significant associations with type 2 diabetes in the Lifelines sample (p

Obesity is a worldwide epidemic, with major health and economic costs. Here we estimate heritability for body mass index (BMI) in 172,000 sibling pairs and 150,832 unrelated individuals and explore the contribution of genotype-covariate interaction effects at common SNP loci. We find evidence for genotype-age interaction (likelihood ratio test (LRT) = 73.58, degrees of freedom (df) = 1, P = 4.83 × 10-18), which contributed 8.1% (1.4% s.e.) to BMI variation. Across eight self-reported lifestyle factors, including diet and exercise, we find genotype-environment interaction only for smoking behavior (LRT = 19.70, P = 5.03 × 10-5 and LRT = 30.80, P = 1.42 × 10-8), which contributed 4.0% (0.8% s.e.) to BMI variation. Bayesian association analysis suggests that BMI is highly polygenic, with 75% of the SNP heritability attributable to loci that each explain

Endocrine Disrupting Chemicals (EDCs) have been linked to a variety of cardiometabolic diseases. Yet, few studies have investigated the exposure to EDCs and cardiometabolic health taking lifestyle into account. We aimed to assess exposure to five parabens, three bisphenols and thirteen metabolites of in total eight phthalates in a general Dutch population and to investigate their association with cardiometabolic traits. In 662 adult subjects from the population-based Lifelines cohort, 21 EDC analytes were measured in 24-hour urine collected in 2012, using LC-MS/MS. Association analyses between cardiometabolic traits and EDC concentrations were performed using multivariate linear models adjusting for age, sex, education, smoking, diabetes, physical activity and caloric intake. Quartile analyses were performed to assess linearity. Bisphenol A, four parabens and eight phthalate metabolites were detected in 84-100% of the samples. Adjusted associations for MiBP and MBzP and adiposity-related traits were robust for multiple testing (Beta’s, BMI: 1.12, 2.52; waist circumference: 0.64, 1.56, respectively; FDR

People are constantly exposed to a wide variety of chemicals. Some of these compounds, such as parabens, bisphenols and phthalates, are known to have endocrine disrupting potencies. Over the years, these endocrine disrupting chemicals (EDCs) have been a rising cause for concern. In this study, we describe setup and validation of two methods to measure EDCs in human urine, using ultra-performance liquid chromatography tandem mass spectrometry. The phenol method determines methyl-, ethyl-, propyl-, n-butyl- and benzylparaben and bisphenol A, F and S. The phthalate method determines in total 13 metabolites of dimethyl, diethyl, diisobutyl, di-n-butyl, di(2-ethylhexyl), butylbenzyl, diiso-nonyl and diisodecyl phthalate. Runtime was 7 and 8 min per sample for phenols and phthalates, respectively. The methods were validated by the National Institute of Standards & Technology (NIST) for 13 compounds. In addition, EDCs were measured in forty 24-h urine samples, of which 12 EDCs were compared with the same samples measured in an established facility (Rigshospitalet, Copenhagen, Denmark). The intra-assay coefficient of variability (CV) was highest at 10% and inter-assay CV was highest at 12%. Recoveries ranged from 86 to 115%. The limit of detection ranged from 0.06 to 0.43 ng/mL. Of 21 compounds, 10 were detected above limit of detection in ≥93% of the samples. Eight compounds were in accordance to NIST reference concentrations. Differences in intercept were found for two compounds whereas slope differed for six compounds between our method and that used in the Danish facility. In conclusion, we set up and validated two high-throughput methods with very short runtime capable of measuring 5 parabens, 3 bisphenols and 13 different metabolites of 8 phthalates. Sensitivity of the phenol method was increased by using ammonium fluoride in the mobile phase.

There is evidence that lower height is associated with a higher risk of coronary artery disease (CAD) and increased risk of type 2 diabetes (T2D). It is not clear though whether these associations are causal, direct or mediated by other factors. Here we show that one standard deviation higher genetically determined height (~6.5 cm) is causally associated with a 16% decrease in CAD risk (OR = 0.84, 95% CI 0.80–0.87). This causal association remains after performing sensitivity analyses relaxing pleiotropy assumptions. The causal effect of height on CAD risk is reduced by 1–3% after adjustment for potential mediators (lipids, blood pressure, glycaemic traits, body mass index, socio-economic status). In contrast, our data suggest that lung function (measured by forced expiratory volume [FEV1] and forced vital capacity [FVC]) is a mediator of the effect of height on CAD. We observe no direct causal effect of height on the risk of T2D., Eirini Marouli et al. use Mendelian randomisation analyses to investigate the causal relationship between adult height, coronary artery disease (CAD) and type 2 diabetes (T2D) in the UK Biobank. They find that height has a causal effect on CAD, which is mediated by lung function, while there is no direct effect on the risk of T2D.

Aims/hypothesis Earlier studies have shown that skin autofluorescence measured with an AGE reader estimates the accumulation of AGEs in the skin, which increases with ageing and is associated with the metabolic syndrome and type 2 diabetes. In the present study, we examined whether the measurement of skin autofluorescence can predict 4 year risk of incident type 2 diabetes, cardiovascular disease (CVD) and mortality in the general population. Methods For this prospective analysis, we included 72,880 participants of the Dutch Lifelines Cohort Study, who underwent baseline investigations between 2007 and 2013, had validated baseline skin autofluorescence values available and were not known to have diabetes or CVD. Individuals were diagnosed with incident type 2 diabetes by self-report or by a fasting blood glucose ≥7.0 mmol/l or HbA1c ≥48 mmol/mol (≥6.5%) at follow-up. Participants were diagnosed as having incident CVD (myocardial infarction, coronary interventions, cerebrovascular accident, transient ischaemic attack, intermittent claudication or vascular surgery) by self-report. Mortality was ascertained using the Municipal Personal Records Database. Results After a median follow-up of 4 years (range 0.5–10 years), 1056 participants (1.4%) had developed type 2 diabetes, 1258 individuals (1.7%) were diagnosed with CVD, while 928 (1.3%) had died. Baseline skin autofluorescence was elevated in participants with incident type 2 diabetes and/or CVD and in those who had died (all p

BACKGROUND: Skin autofluorescence (SAF) is a non-invasive marker of advanced glycation end products (AGEs). In diabetes, higher SAF levels has been positively associated with long-term complications, cardiovascular morbidity and mortality. Because little is known about the factors that influence SAF in non-diabetic individuals, we assessed the association of clinical and lifestyle parameters with SAF as well as their interactions in a large-scale, non-diabetic population and performed the same analyses in a type 2 diabetic subgroup.METHODS: In a cross-sectional study in participants from the LifeLines Cohort Study, extensive clinical and biochemical phenotyping, including SAF measurement, was assessed in 9009 subjects of whom 314 (3.5%) subjects with type 2 diabetes.RESULTS: Mean SAF was 2.04 ± 0.44 arbitrary units (AU) in non-diabetic individuals and 2.44 ± 0.55 AU in type 2 diabetic subjects (pCONCLUSIONS: In addition to the established literature on type 2 diabetes, we have demonstrated that SAF levels are associated with several clinical and lifestyle factors in the non-diabetic population. These parameters should be taken into consideration when using SAF as a screening or prediction tool for populations at risk for cardiovascular disease and diabetes. This article is protected by copyright. All rights reserved.

Publisher's version (útgefin grein), Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels., The present work was largely supported by a grant from the US National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (R01HL118305). The full list of acknowledgments appears in the Supplementary Notes 3 and 4.

Introduction Environmental chemicals acting as metabolic disruptors have been implicated with diabetogenesis, but evidence is weak among short-lived chemicals, such as disinfection byproducts (trihalomethanes, THM composed of chloroform, TCM and brominated trihalomethanes, BrTHM).Objectives We assessed whether THM were associated with type 2 diabetes (T2D) and we explored alterations in metabolic profiles due to THM exposures or T2D status.Methods A prospective 1:1 matched case-control study (n = 430) and a cross-sectional 1:1 matched case-control study (n = 362) nested within the HUNT cohort (Norway) and the Lifelines cohort (Netherlands), respectively, were set up. Urinary biomarkers of THM exposure and mass spectrometry-based serum metabolomics were measured. Associations between THM, clinical markers, metabolites and disease status were evaluated using logistic regressions with Least Absolute Shrinkage and Selection Operator procedure.Results Low median THM exposures (ng/g, IQR) were measured in both cohorts (cases and controls of HUNT and Lifelines, respectively, 193 (76, 470), 208 (77, 502) and 292 (162, 595), 342 (180, 602). Neither BrTHM (OR = 0.87; 95% CI: 0.67, 1.11 vertical bar OR = 1.09; 95% CI: 0.73, 1.61), nor TCM (OR = 1.03; 95% CI: 0.88, 1.2 vertical bar OR = 1.03; 95% CI: 0.79, 1.35) were associated with incident or prevalent T2D, respectively. Metabolomics showed 48 metabolites associated with incident T2D after adjusting for sex, age and BMI, whereas a total of 244 metabolites were associated with prevalent T2D. A total of 34 metabolites were associated with the progression of T2D. In data driven logistic regression, novel biomarkers, such as cinnamoylglycine or 1-methylurate, being protective of T2D were identified. The incident T2D risk prediction model (HUNT) predicted well incident Lifelines cases (AUC = 0.845; 95% CI: 0.72, 0.97).Conclusion Such exposome-based approaches in cohort-nested studies are warranted to better understand the environmental origins of diabetogenesis.

We conducted genome-wide association study (GWAS) meta-analyses of relative caloric intake from fat, protein, carbohydrates and sugar in over 235,000 individuals. We identified 21 approximately independent lead SNPs. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15 – 0.5). Relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood poverty (|rg| ≈ 0.1 – 0.3). Overall, our results show that the relative intake of each macronutrient has a distinct genetic architecture and pattern of genetic correlations suggestive of health implications beyond caloric content.

Evidence of bisphenols’ obesogenic effects on humans is mixed and inconsistent. We aimed to explore the presence of bisphenol A (BPA), bisphenol F (BPF) and chlorinated BPA (ClBPA), collectively called the bisphenols, in different brain regions and their association with obesity using post-mortem hypothalamic and white matter brain material from twelve pairs of obese (body mass index (BMI) >30 kg/m2) and normal-weight individuals (BMI 2). Mean ratios of hypothalamus:white matter for BPA, BPF and ClBPA were 1.5, 0.92, 0.95, respectively, suggesting no preferential accumulation of the bisphenols in the grey matter (hypothalamic) or white matter-enriched brain areas. We observed differences in hypothalamic concentrations among the bisphenols, with highest median level detected for ClBPA (median: 2.4 ng/g), followed by BPF (2.2 ng/g) and BPA (1.2 ng/g); similar ranking was observed for the white matter samples (median for: ClBPA-2.5 ng/g, BPF-2.3 ng/g, and BPA-1.0 ng/g). Furthermore, all bisphenol concentrations, except for white-matter BPF were associated with obesity (p

Background Diversity in the reported prevalence of metabolically healthy obesity (MHO), suggests that modifiable factors may be at play. We evaluated differences in dietary patterns and physical activity between MHO and metabolically unhealthy obesity (MUO). Methods Cross-sectional data of 9270 obese individuals (30–69 years) of the Lifelines Cohort Study was used. MHO was defined as obesity and no metabolic syndrome risk factors and no cardiovascular disease history. MUO was defined as obesity and ≥2 metabolic syndrome risk factors. Sex-specific associations of dietary patterns (identified by principal component analysis) and physical activity with MHO were assessed by multivariable logistic regression (reference group: MUO). Analyses were adjusted for multiple covariates. Results Among 3442 men and 5828 women, 10.2% and 24.4% had MHO and 56.9% and 35.3% MUO, respectively. We generated four obesity-specific dietary patterns. Two were related to MHO, and in women only. In the highest quartile (Q) of ‘bread, potatoes and sweet snacks’ pattern, odds ratio (OR) (95% CI) for MHO was 0.52 (0.39–0.70). For the healthier pattern ‘fruit, vegetables and fish’, an OR of 1.36 (1.09–1.71) in Q3 and 1.55 (1.21–1.97) in Q4 was found for MHO. For physical activity, there was a positive association between moderate physical activity and vigorous physical activity in the highest tertile and MHO in women and men, respectively (OR 1.19 (1.01–1.41) and OR 2.02 (1.50–2.71)). Conclusion The healthier diet -characterized by ‘fruit, vegetables and fish’- and moderate physical activity in women, and vigorous physical activity in men may be related to MHO. The (refined) carbohydrate-rich ‘bread, potatoes and sweet snacks’ dietary pattern was found to counteract MHO in women. Electronic supplementary material The online version of this article (10.1186/s12937-018-0319-0) contains supplementary material, which is available to authorized users.

The high reactivity of bisphenol A (BPA) with disinfectant chlorine is evident in the instantaneous formation of chlorinated BPA derivatives (ClxBPA) in various environmental media that show increased estrogen-activity when compared with that of BPA. The documented health risks associated with BPA exposures have led to the gradual market entry of BPA structural analogs, such as bisphenol S (BPS), bisphenol F (BPF), bisphenol B (BPB), etc. A suite of exposure sources to ClxBPA and BPA analogs in the domestic environment is anticipated to drive the nature and range of halogenated BPA derivatives that can form when residual BPA comes in contact with disinfectant in tap water and/or consumer products. The primary objective of this review was to survey all available studies reporting biomonitoring protocols of ClxBPA and structural BPA analogs (BPS, BPF, BPB, etc.) in human matrices. Focus was paid on describing the analytical methodologies practiced for the analysis of ClxBPA and BPA analogs using hyphenated chromatography and mass spectrometry techniques, because current methodologies for human matrices are complex. During the last decade, an increasing number of ecotoxicological, cell-culture and animal-based and human studies dealing with ClxBPA exposure sources and routes of exposure, metabolism and toxicity have been published. Up to date findings indicated the association of ClxBPA with metabolic conditions, such as obesity, lipid accumulation, and type 2 diabetes mellitus, particularly in in-vitro and in-vivo studies. We critically discuss the limitations, research needs and future opportunities linked with the inclusion of ClxBPA and BPA analogs into exposure assessment protocols of relevant epidemiological studies. Keywords: Biomonitoring, Bisphenol A, BPA analogs, BPA free, analogs, Chlorinated derivatives, Disinfection, Emerging contaminants, Human exposure, Mass spectrometry, Metabolites

Background The metabolic syndrome (MetS) is a combination of unfavourable health factors which includes abdominal obesity, dyslipidaemia, elevated blood pressure and impaired fasting glucose. Earlier studies have reported a relationship between thyroid function and some MetS components or suggested that serum free thyroxine (FT4) or free triiodothyronine (FT3) levels within the normal range were independently associated with insulin resistance. We assessed how thyroid function relates to MetS prevalence in a large population-based study. Methods Data of 26,719 people of western European descent, aged 18–80 years from the Dutch LifeLines Cohort study, all with normal thyroid stimulating hormone (TSH), FT4 and FT3 levels (electrochemiluminescent immunoassay, Roche Modular E170 Analyzer), were available. MetS was defined with the revised National Cholesterol Education Programs Adults Treatment Panel III (NCEP ATP III) criteria. We calculated prevalence of all MetS components according to TSH, FT4 and FT3 quartiles. Results At similar TSH levels and age (mean 45 yrs), men had significantly higher levels of FT4, FT3, blood pressure (BP), heart rate, total and LDL-cholesterol, triglycerides (TG), and creatinine, but lower HDL-cholesterol compared to women (all p

Introduction To evaluate the prevalence of metabolic syndrome (MetS) and its individual components within sex-, body mass index (BMI)- and age combined clusters. In addition, we used the age-adjusted blood pressure thresholds to demonstrate the effect on the prevalence of MetS and elevated blood pressure. Subjects and methods Cross-sectional data from 74,531 Western European participants, aged 18–79 years, were used from the Dutch Lifelines Cohort Study. MetS was defined according to the revised NCEP-ATPIII. Age-adjusted blood pressure thresholds were defined as recommended by the eight reports of the Joint National Committee (≥140/90 mmHg for those aged Results 19.2% men and 12.1% women had MetS. MetS prevalence increased with BMI and age. Independent of BMI, abdominal obesity dominated MetS prevalence especially in women, while elevated blood pressure was already highly prevalent among young men. Applying age-adjusted blood pressure thresholds resulted in a 0.2–11.9% prevalence drop in MetS and 6.0–36.3% prevalence drop in elevated blood pressure, within the combined sex, BMI and age clusters. Conclusions We observed a gender disparity with age and BMI for the prevalence of MetS and, especially, abdominal obesity and elevated blood pressure. The strict threshold level for elevated blood pressure in the revised NCEP-ATPIII, results in an overestimation of MetS prevalence.

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery., The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. Funding for this study was provided by the Aase and Ejner Danielsens Foundation; Academy of Finland (102318; 104781, 120315, 123885, 129619, 286284, 134309, 126925, 121584, 124282, 129378, 117787, 250207, 258753, 41071, 77299, 124243, 1114194, 24300796); Accare Center for Child and Adolescent Psychiatry; Action on Hearing Loss (G51); Agence Nationale de la Recherche; Agency for Health Care Policy Research (HS06516); Age UK Research into Ageing Fund; Åke Wiberg Foundation; ALF/LUA Research Grant in Gothenburg; ALFEDIAM; ALK-Abello´ A/S (Hørsholm, Denmark); American Heart Association (13POST16500011, 10SDG269004); Ardix Medical; Arthritis Research UK; Association Diabète Risque Vasculaire; AstraZeneca; Australian Associated Brewers; Australian National Health and Medical Research Council (241944, 339462, 389927, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485, 552498); Avera Research Institute; Bayer Diagnostics; Becton Dickinson; Biobanking and Biomolecular Resources Research Infrastructure (BBMRI –NL, 184.021.007); Biocentrum Helsinki; Boston Obesity Nutrition Research Center (DK46200); British Heart Foundation (RG/10/12/28456, SP/04/002); Canada Foundation for Innovation; Canadian Institutes of Health Research (FRN-CCT-83028); Cancer Research UK; Cardionics; Center for Medical Systems Biology; Center of Excellence in Complex Disease Genetics and SALVECenter of Excellence in Genomics (EXCEGEN); Chief Scientist Office of the Scottish Government; City of Kuopio; Cohortes Santé TGIR; Contrat de Projets État-Région; Croatian Science Foundation (8875); Danish Agency for Science, Technology and Innovation; Danish Council for Independent Research (DFF–1333-00124, DFF–1331-007308); Danish Diabetes Academy; Danish Medical Research Council; Department of Psychology and Education of the VU University Amsterdam; Diabetes Hilfs- und Forschungsfonds Deutschland; Dutch Brain Foundation; Dutch Ministry of Justice; Emil Aaltonen Foundation; Erasmus Medical Center; Erasmus University; Estonian Government (IUT20-60, IUT24-6); Estonian Ministry of Education and Research (3.2.0304.11-0312); European Commission (230374, 284167, 323195, 692145, FP7 EurHEALTHAgeing-277849, FP7 BBMRI-LPC 313010, nr 602633, HEALTH-F2-2008-201865-GEFOS, HEALTH-F4-2007-201413, FP6 LSHM-CT-2004-005272, FP5 QLG2-CT-2002-01254, FP6 LSHG-CT-2006-01947, FP7 HEALTH-F4-2007-201413, FP7 279143, FP7 201668, FP7 305739, FP6 LSHG-CT-2006-018947, HEALTH-F4-2007-201413, QLG1-CT-2001-01252); European Regional Development Fund; European Science Foundation (EuroSTRESS project FP-006, ESF, EU/QLRT-2001-01254); Faculty of Biology and Medicine of Lausanne; Federal Ministry of Education and Research (01ZZ9603, 01ZZ0103, 01ZZ0403, 03ZIK012, 03IS2061A); Federal State of Mecklenburg - West Pomerania; Fédération Française de Cardiologie; Finnish Cultural Foundation; Finnish Diabetes Association; Finnish Foundation of Cardiovascular Research; Finnish Heart Association; Food Standards Agency; Fondation de France; Fonds Santé; Genetic Association Information Network of the Foundation for the National Institutes of Health; German Diabetes Association; German Federal Ministry of Education and Research (BMBF, 01ER1206, 01ER1507); German Research Council (SFB-1052, SPP 1629 TO 718/2-1); GlaxoSmithKline; Göran Gustafssons Foundation; Göteborg Medical Society; Health and Safety Executive; Heart Foundation of Northern Sweden; Icelandic Heart Association; Icelandic Parliament; Imperial College Healthcare NHS Trust; INSERM, Réseaux en Santé Publique, Interactions entre les déterminants de la santé; Interreg IV Oberrhein Program (A28); Italian Ministry of Economy and Finance; Italian Ministry of Health (ICS110.1/RF97.71); John D and Catherine T MacArthur Foundation; Juho Vainio Foundation; King's College London; Kjell och Märta Beijers Foundation; Kuopio University Hospital; Kuopio, Tampere and Turku University Hospital Medical Funds (X51001); Leiden University Medical Center; Lilly; LMUinnovativ; Lundbeck Foundation; Lundberg Foundation; Medical Research Council of Canada; MEKOS Laboratories (Denmark); Merck Santé; Mid-Atlantic Nutrition Obesity Research Center (P30 DK72488); Ministère de l’Économie, de l’Innovation et des Exportations; Ministry for Health, Welfare and Sports of the Netherlands; Ministry of Cultural Affairs of the Federal State of Mecklenburg-West Pomerania; Ministry of Education and Culture of Finland (627;2004-2011); Ministry of Education, Culture and Science of the Netherlands; MRC Human Genetics Unit; MRC-GlaxoSmithKline Pilot Programme Grant (G0701863); Municipality of Rotterdam; Netherlands Bioinformatics Centre (2008.024); Netherlands Consortium for Healthy Aging (050-060-810); Netherlands Genomics Initiative; Netherlands Organisation for Health Research and Development (904-61-090, 985-10-002, 904-61-193, 480-04-004, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192); Netherlands Organisation for Health Research and Development (2010/31471/ZONMW); Netherlands Organisation for Scientific Research (10-000-1002, GB-MW 940-38-011, 100-001-004, 60-60600-97-118, 261-98-710, GB-MaGW 480-01-006, GB-MaGW 480-07-001, GB-MaGW 452-04-314, GB-MaGW 452-06-004, 175.010.2003.005, 175.010.2005.011, 481-08-013, 480-05-003, 911-03-012); Neuroscience Campus Amsterdam; NHS Foundation Trust; Novartis Pharmaceuticals; Novo Nordisk; Office National Interprofessionel des Vins; Paavo Nurmi Foundation; Påhlssons Foundation; Päivikki and Sakari Sohlberg Foundation; Pierre Fabre; Republic of Croatia Ministry of Science, Education and Sport (108-1080315-0302); Research Centre for Prevention and Health, the Capital Region for Denmark; Research Institute for Diseases in the Elderly (014-93-015, RIDE2); Roche; Russian Foundation for Basic Research (NWO-RFBR 047.017.043); Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06); Sanofi-Aventis; Scottish Executive Health Department (CZD/16/6); Siemens Healthcare; Social Insurance Institution of Finland (4/26/2010); Social Ministry of the Federal State of Mecklenburg-West Pomerania; Société Francophone du Diabète; State of Bavaria; Stroke Association; Swedish Diabetes Association; Swedish Foundation for Strategic Research; Swedish Heart-Lung Foundation (20140543); Swedish Research Council (2015-03657); Swedish Medical Research Council (K2007-66X-20270-01-3, 2011-2354); Swedish Society for Medical Research; Swiss National Science Foundation (33CSCO-122661, 33CS30-139468, 33CS30-148401); Tampere Tuberculosis Foundation; The Marcus Borgström Foundation; The Royal Society; The Wellcome Trust (084723/Z/08/Z, 088869/B/09/Z); Timber Merchant Vilhelm Bangs Foundation; Topcon; Torsten and Ragnar Söderberg's Foundation; UK Department of Health; UK Diabetes Association; UK Medical Research Council (MC_U106179471, G0500539, G0600705, G0601966, G0700931, G1002319, K013351, MC_UU_12019/1); UK National Institute for Health Research BioResource Clinical Research Facility and Biomedical Research Centre; UK National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre; UK National Institute for Health Research (RP-PG-0407-10371); Umeå University Career Development Award; United States – Israel Binational Science Foundation Grant (2011036); University Hospital Oulu (75617); University Medical Center Groningen; University of Tartu (SP1GVARENG); National Institutes of Health (AG13196, CA047988, HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, HHSC271201100004C, HHSN268200900041C, HHSN268201300025C, HHSN268201300026C, HHSN268201300027C, HHSN268201300028C, HHSN268201300029C, HHSN268201500001I, HL36310, HG002651, HL034594, HL054457, HL054481, HL071981, HL084729, HL119443, HL126024, N01-AG12100, N01-AG12109, N01-HC25195, N01-HC55015, N01-HC55016, N01-HC55018, N01-HC55019, N01-HC55020, N01-HC55021, N01-HC55022, N01-HD95159, N01-HD95160, N01-HD95161, N01-HD95162, N01-HD95163, N01-HD95164, N01-HD95165, N01-HD95166, N01-HD95167, N01-HD95168, N01-HD95169, N01-HG65403, N02-HL64278, R01-HD057194, R01-HL087641, R01-HL59367, R01HL-086694, R01-HL088451, R24-HD050924, U01-HG-004402, HHSN268200625226C, UL1-RR025005, UL1-RR025005, UL1-TR-001079, UL1-TR-00040, AA07535, AA10248, AA11998, AA13320, AA13321, AA13326, AA14041, AA17688, DA12854, MH081802, MH66206, R01-D004215701A, R01-DK075787, R01-DK089256, R01-DK8925601, R01-HL088451, R01-HL117078, R01-DK062370, R01-DK072193, DK091718, DK100383, DK078616, 1Z01-HG000024, HL087660, HL100245, R01DK089256, 2T32HL007055-36, U01-HL072515-06, U01-HL84756, NIA-U01AG009740, RC2-AG036495, RC4-AG039029, R03 AG046389, 263-MA-410953, 263-MD-9164, 263-MD-821336, U01-HG004802, R37CA54281, R01CA63, P01CA33619, U01-CA136792, U01-CA98758, RC2-MH089951, MH085520, R01-D0042157-01A, MH081802, 1RC2-MH089951, 1RC2-MH089995, 1RL1MH08326801, U01-HG007376, 5R01-HL08767902, 5R01MH63706:02, HG004790, N01-WH22110, U01-HG007033, UM1CA182913, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, 44221); USDA National Institute of Food and Agriculture (2007-35205-17883); Västra Götaland Foundation; Velux Foundation; Veterans Affairs (1 IK2 BX001823); Vleugels Foundation; VU University’s Institute for Health and Care Research (EMGO+, HEALTH-F4-2007-201413) and Neuroscience Campus Amsterdam; Wellcome Trust (090532, 091551, 098051, 098381); Wissenschaftsoffensive TMO; and Yrjö Jahnsson Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Background: The metabolic syndrome (MetS) comprises several cardiometabolic risk factors associated with increased risk for both type 2 diabetes and cardiovascular disease. Skin autofluorescence (SAF), a non-invasive bio-marker of advanced glycation end products accumulation, is associated with cardiovascular complications in subjects with diabetes. The aim of the present study was to examine the association between SAF and the presence of MetS as well as its individual components in a general population.Methods: For this cross-sectional analysis, we included 78,671 non-diabetic subjects between 18 and 80 years of age who participated in the LifeLines Cohort Study and had SAF measurement obtained non-invasively using the AGE Reader. MetS was defined according to the revised NCEP ATP III criteria. Students unpaired t test was used to test differences between groups. Both logistic and linear regression analyses were performed in order to test associations between the individual MetS components and SAF.Results: Subjects with MetS had higher SAF (2.07 +/- 0.45 arbitrary units, AU) compared to individuals without MetS (1.89 +/- 0.42 AU) (p Conclusion: Skin autofluorescence was associated with the presence of MetS and some of its individual components. In addition, increasing SAF Z-scores were observed with a higher number of MetS components. Prospective studies are needed to establish whether SAF can be used as an (additional) screening tool to predict both cardiovascular disease and type 2 diabetes in high-risk populations.

In human populations, assortative mating is almost univer-sally positive, with similarities between partners for quantit-ative phenotypes 1-6, common disease risk 1,3,7-10, beha-vi-our 6,11, social factors 12-14 and personality 4,5,11. The causes and genetic consequences of assortative mating remain un-re-solved because partner similarity can arise from different mechanisms: Phenotypic assortment based on mate choice 15,16, partner interaction and convergence in phenotype over time 14,17, or social homogamy where individuals pair according to social or environmental background. Here, we present theory and an analytical approach to test for genetic evidence of assortative mating and find a correlation in genetic value among partners for a range of phenotypes. Across three independent samples of 24,662 spousal pairs in total, we infer a correlation at trait-associated loci between partners for height (0.200, 0.004 standard error, SE) that matched the phenotypic correlation (0.201, 0.004 SE), and a correlation at trait-associated loci for BMI (0.143, 0.007 SE) that was significantly lower than the phenotypic value (0.228, 0.004 SE). We extend our analysis to the UK Biobank study (7,780 pairs), finding evidence of a correlation at trait-associated loci for waist-to-hip ratio (0.101, 0.041 SE), systolic blood pressure (0.138, 0.064 SE) and educational attainment (0.654, 0.014 SE). Our results imply that mate choice, combined with widespread pleiotropy among traits, affects the genomic architecture of traits in humans.

Non-persistent endocrine disrupting chemicals (npEDCs) can affect multiple organs and systems in the body. Whether npEDCs can accumulate in the human brain is largely unknown. The major aim of this pilot study was to examine the presence of environmental phenols and parabens in two distinct brain regions: the hypothalamus and white-matter tissue. In addition, a potential association between these npEDCs concentrations and obesity was investigated. Post-mortem brain material was obtained from 24 individuals, made up of 12 obese and 12 normal-weight subjects (defined as body mass index (BMI) > 30 and BMI < 25 kg/m2, respectively). Nine phenols and seven parabens were measured by isotope dilution TurboFlow-LC-MS/MS. In the hypothalamus, seven suspect npEDCs (bisphenol A, triclosan, triclocarban and methyl-, ethyl-, n-propyl-, and benzyl paraben) were detected, while five npEDCs (bisphenol A, benzophenone-3, triclocarban, methyl-, and n-propyl paraben) were found in the white-matter brain tissue. We observed higher levels of methylparaben (MeP) in the hypothalamic tissue of obese subjects as compared to controls (p = 0.008). Our findings indicate that some suspected npEDCs are able to cross the blood–brain barrier. Whether the presence of npEDCs can adversely affect brain function and to which extent the detected concentrations are physiologically relevant needs to be further investigated., Jana V. van Vliet-Ostaptchouk is supported by a Diabetes Funds Junior Fellowship from the Dutch Diabetes Research Foundation (project no. 2013.81.1673). This work was supported by the National Consortium for Healthy Ageing (NCHA) (NCHA NGI Grant 050-060-810), and the European Union’s Seventh Framework program (FP7/2007-2013) through the BioSHaRE-EU (Biobank Standardization and Harmonization for Research Excellence in the European Union) project, grant agreement 261433, and by the Danish Center on Endocrine Disrupters and the International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC).

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution., A full list of acknowledgments appears in the Supplementary Note 4. Co-author A.J.M.d.C. recently passed away while this work was in process. This work was performed under the auspices of the Genetic Investigation of ANthropometric Traits (GIANT) consortium. We acknowledge the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium for encouraging CHARGE studies to participate in this effort and for the contributions of CHARGE members to the analyses conducted for this research. Funding for this study was provided by the Aase and Ejner Danielsens Foundation; Academy of Finland (41071, 77299, 102318, 110413, 117787, 121584, 123885, 124243, 124282, 126925, 129378, 134309, 286284); Accare Center for Child and Adolescent Psychiatry; Action on Hearing Loss (G51); Agence Nationale de la 359 Recherche; Agency for Health Care Policy Research (HS06516); ALF/LUA research grant in Gothenburg; ALFEDIAM; ALK-Abelló A/S; Althingi; American Heart Association (13POST16500011); Amgen; Andrea and Charles Bronfman Philanthropies; Ardix Medical; Arthritis Research UK; Association Diabète Risque Vasculaire; Australian National Health and Medical Research Council (241944, 339462, 389875, 389891, 389892, 389927, 389938, 442915, 442981, 496739, 552485, 552498); Avera Institute; Bayer Diagnostics; Becton Dickinson; BHF (RG/14/5/30893); Boston Obesity Nutrition Research Center (DK46200), Bristol-Myers Squibb; British Heart Foundation (RG/10/12/28456, RG2008/08, RG2008/014, SP/04/002); Medical Research Council of Canada; Canadian Institutes for Health Research (FRCN-CCT-83028); Cancer Research UK; Cardionics; Cavadis B.V., Center for Medical Systems Biology; Center of Excellence in Genomics; CFI; CIHR; City of Kuopio; CNAMTS; Cohortes Santé TGIR; Contrat de Projets État-Région; Croatian Science Foundation (8875); Danish Agency for Science, Technology and Innovation; Danish Council for Independent Research (DFF-1333-00124, DFF-1331-00730B); County Council of Dalarna; Dalarna University; Danish Council for Strategic Research; Danish Diabetes Academy; Danish Medical Research Council; Department of Health, UK; Development Fund from the University of Tartu (SP1GVARENG); Diabetes Hilfs- und Forschungsfonds Deutschland; Diabetes UK; Diabetes Research and Wellness Foundation Fellowship; Donald W. Reynolds Foundation; Dr Robert Pfleger-Stiftung; Dutch Brain Foundation; Dutch Diabetes Research Foundation; Dutch Inter University Cardiology Institute; Dutch Kidney Foundation (E033); Dutch Ministry of Justice; the DynaHEALTH action No. 633595, Economic Structure Enhancing Fund of the Dutch Government; Else Kröner-Fresenius-Stiftung (2012_A147, P48/08//A11/08); Emil Aaltonen Foundation; Erasmus University Medical Center Rotterdam; Erasmus MC and Erasmus University Rotterdam; the Municipality of Rotterdam; Estonian Government (IUT20-60, IUT24-6); Estonian Research Roadmap through the Estonian Ministry of Education and Research (3.2.0304.11-0312); European Research Council (ERC Starting Grant and 323195:SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC); European Regional Development Fund; European Science Foundation (EU/QLRT-2001-01254); European Commission (018947, 018996, 201668, 223004, 230374, 279143, 284167, 305739, BBMRI-LPC-313010, HEALTH-2011.2.4.2-2-EU-MASCARA, HEALTH-2011-278913, HEALTH-2011-294713-EPLORE, HEALTH-F2-2008-201865-GEFOS, HEALTH-F2-2013-601456, HEALTH-F4-2007-201413, HEALTH-F4-2007-201550-HYPERGENES, HEALTH-F7-305507 HOMAGE, IMI/115006, LSHG-CT-2006-018947, LSHG-CT-2006-01947, LSHM-CT-2004-005272, LSHM-CT-2006-037697, LSHM-CT-2007-037273, QLG1-CT-2002-00896, QLG2-CT-2002-01254); Faculty of Biology and Medicine of Lausanne; Federal Ministry of Education and Research (01ZZ0103, 01ZZ0403, 01ZZ9603, 03IS2061A, 03ZIK012); Federal State of Mecklenburg-West Pomerania; Fédération Française de Cardiologie; Finnish Cultural Foundation; Finnish Diabetes Association; Finnish Foundation of Cardiovascular Research; Finnish Heart Association; Fondation Leducq; Food Standards Agency; Foundation for Strategic Research; French Ministry of Research; FRSQ; Genetic Association Information Network (GAIN) of the Foundation for the NIH; German Federal Ministry of Education and Research (BMBF, 01ER1206, 01ER1507); GlaxoSmithKline; Greek General Secretary of Research and Technology; Göteborg Medical Society; Health and Safety Executive; Healthcare NHS Trust; Healthway; Western Australia; Heart Foundation of Northern Sweden; Helmholtz Zentrum München—German Research Center for Environmental Health; Hjartavernd; Ingrid Thurings Foundation; INSERM; InterOmics (PB05 MIUR-CNR); INTERREG IV Oberrhein Program (A28); Interuniversity Cardiology Institute of the Netherlands (ICIN, 09.001); Italian Ministry of Health (ICS110.1/RF97.71); Italian Ministry of Economy and Finance (FaReBio di Qualità); Marianne and Marcus Wallenberg Foundation; the Ministry of Health, Welfare and Sports, the Netherlands; J.D.E. and Catherine T, MacArthur Foundation Research Networks on Successful Midlife Development and Socioeconomic Status and Health; Juho Vainio Foundation; Juvenile Diabetes Research Foundation International; KfH Stiftung Präventivmedizin e.V.; King's College London; Knut and Alice Wallenberg Foundation; Kuopio University Hospital; Kuopio, Tampere and Turku University Hospital Medical Funds (X51001); La Fondation de France; Leenaards Foundation; Lilly; LMUinnovativ; Lundberg Foundation; Magnus Bergvall Foundation; MDEIE; Medical Research Council UK (G0000934, G0601966, G0700931, MC_U106179471, MC_UU_12019/1); MEKOS Laboratories; Merck Santé; Ministry for Health, Welfare and Sports, The Netherlands; Ministry of Cultural Affairs of Mecklenburg-West Pomerania; Ministry of Economic Affairs, The Netherlands; Ministry of Education and Culture of Finland (627;2004-2011); Ministry of Education, Culture and Science, The Netherlands; Ministry of Science, Education and Sport in the Republic of Croatia (108-1080315-0302); MRC centre for Causal Analyses in Translational Epidemiology; MRC Human Genetics Unit; MRC-GlaxoSmithKline pilot programme (G0701863); MSD Stipend Diabetes; National Institute for Health Research; Netherlands Brain Foundation (F2013(1)-28); Netherlands CardioVascular Research Initiative (CVON2011-19); Netherlands Genomics Initiative (050-060-810); Netherlands Heart Foundation (2001 D 032, NHS2010B280); Netherlands Organization for Scientific Research (NWO) and Netherlands Organisation for Health Research and Development (ZonMW) (56-464-14192, 60-60600-97-118, 100-001-004, 261-98-710, 400-05-717, 480-04-004, 480-05-003, 481-08-013, 904-61-090, 904-61-193, 911-11-025, 985-10-002, Addiction-31160008, BBMRI–NL 184.021.007, GB-MaGW 452-04-314, GB-MaGW 452-06-004, GB-MaGW 480-01-006, GB-MaGW 480-07-001, GB-MW 940-38-011, Middelgroot-911-09-032, NBIC/BioAssist/RK 2008.024, Spinozapremie 175.010.2003.005, 175.010.2007.006); Neuroscience Campus Amsterdam; NHS Foundation Trust; National Institutes of Health (1RC2MH089951, 1Z01HG000024, 24152, 263MD9164, 263MD821336, 2R01LM010098, 32100-2, 32122, 32108, 5K99HL130580-02, AA07535, AA10248, AA11998, AA13320, AA13321, AA13326, AA14041, AA17688, AG13196, CA047988, DA12854, DK56350, DK063491, DK078150, DK091718, DK100383, DK078616, ES10126, HG004790, HHSN268200625226C, HHSN268200800007C, HHSN268201200036C, HHSN268201500001I, HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, HHSN271201100004C, HL043851, HL45670, HL080467, HL085144, HL087660, HL054457, HL119443, HL118305, HL071981, HL034594, HL126024, HL130114, KL2TR001109, MH66206, MH081802, N01AG12100, N01HC55015, N01HC55016, N01C55018, N01HC55019, N01HC55020, N01HC55021, N01HC55022, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC95159, N01HC95160, N01HC95161, N01HC95162, N01HC95163, N01HC95164, N01HC95165, N01HC95166, N01HC95167, N01HC95168, N01HC95169, N01HG65403, N01WH22110, N02HL6‐4278, N01-HC-25195, P01CA33619, R01HD057194, R01HD057194, R01AG023629, R01CA63, R01D004215701A, R01DK075787, R01DK062370, R01DK072193, R01DK075787, R01DK089256, R01HL53353, R01HL59367, R01HL086694, R01HL087641, R01HL087652, R01HL103612, R01HL105756, R01HL117078, R01HL120393, R03 AG046389, R37CA54281, RC2AG036495, RC4AG039029, RPPG040710371, RR20649, TW008288, TW05596, U01AG009740, U01CA98758, U01CA136792, U01DK062418, U01HG004402, U01HG004802, U01HG007376, U01HL080295, UL1RR025005, UL1TR000040, UL1TR000124, UL1TR001079, 2T32HL007055-36, T32GM074905, HG002651, HL084729, N01-HC-25195, UM1CA182913); NIH, National Institute on Aging (Intramural funding, NO1-AG-1-2109); Northern Netherlands Collaboration of Provinces; Novartis Pharma; Novo Nordisk; Novo Nordisk Foundation; Nutricia Research Foundation (2016-T1); ONIVINS; Parnassia Bavo group; Pierre Fabre; Province of Groningen; Päivikki and Sakari Sohlberg Foundation; Påhlssons Foundation; Paavo Nurmi Foundation; Radboud Medical Center Nijmegen; Research Centre for Prevention and Health, the Capital Region of Denmark; the Research Institute for Diseases in the Elderly; Research into Ageing; Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center; Roche; Royal Society; Russian Foundation for Basic Research (NWO-RFBR 047.017.043); Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06); Sanofi-Aventis; Scottish Government Health Directorates, Chief Scientist Office (CZD/16/6); Siemens Healthcare; Social Insurance Institution of Finland (4/26/2010); Social Ministry of the Federal State of Mecklenburg-West Pomerania; Société Francophone du 358 Diabète; State of Bavaria; Stiftelsen för Gamla Tjänarinnor; Stockholm County Council (560183, 592229); Strategic Cardiovascular and Diabetes Programmes of Karolinska Institutet and Stockholm County Council; Stroke Association; Swedish Diabetes Association; Swedish Diabetes Foundation (2013-024); Swedish Foundation for Strategic Research; Swedish Heart-Lung Foundation (20120197, 20150711); Swedish Research Council (0593, 8691, 2012-1397, 2012-1727, and 2012-2215); Swedish Society for Medical Research; Swiss Institute of Bioinformatics; Swiss National Science Foundation (3100AO-116323/1, 31003A-143914, 33CSCO-122661, 33CS30-139468, 33CS30-148401, 51RTP0_151019); Tampere Tuberculosis Foundation; Technology Foundation STW (11679); The Fonds voor Wetenschappelijk Onderzoek Vlaanderen, Ministry of the Flemish Community (G.0880.13, G.0881.13); The Great Wine Estates of the Margaret River Region of Western Australia; Timber Merchant Vilhelm Bangs Foundation; Topcon; Tore Nilsson Foundation; Torsten and Ragnar Söderberg's Foundation; United States – Israel Binational Science Foundation (Grant 2011036), Umeå University; University Hospital of Regensburg; University of Groningen; University Medical Center Groningen; University of Michigan; University of Utrecht; Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) (b2011036); Velux Foundation; VU University’s Institute for Health and Care Research; Västra Götaland Foundation; Wellcome Trust (068545, 076113, 079895, 084723, 088869, WT064890, WT086596, WT098017, WT090532, WT098051, 098381); Wissenschaftsoffensive TMO; Yrjö Jahnsson Foundation; and Åke Wiberg Foundation. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute (NHLBI); the National Institutes of Health (NIH); or the U.S. Department of Health and Human Services.

Background: In the present study, we examined the effect of sample storage on the reproducibility of several inflammatory biomarkers, including high-sensitivity C-reactive protein (hsCRP), high-sensitivity interleukin-6 (hsIL6), and high-sensitivity tumor necrosis factor alpha (hsTNFα). In addition, we assessed inter- and intra-assay variability between collaborating biobanks.Methods: In total, 240 fasting plasma samples were obtained from the LifeLines biobank. Samples had been stored for less than 2 or more than 4 years at −80°C. Measurements were performed at three different laboratories. hsCRP was measured by immunonephelometry and ELISA, hsIL6, and hsTNFα samples were measured with ELISAs from two different manufacturers. For confirmation, similar analyses were performed on samples obtained from a subpopulation of 80 obese individuals. Passing–Bablok regression analysis and Bland–Altman plots were used to compare the results.Results: We observed good stability of samples stored at −80°C. hsCRP measured on the day of blood draw was similar to levels measured after more than 4 years of storage. There were small interlaboratory differences with the R&D ELISAs for hsIL6 and hsTNFα. We found a linear correlation between the Bender Medsystems ELISA and the R&D ELISA for hsIL6, with significantly higher levels measured with the R&D ELISA. Over 90% of hsTNFα samples measured with the IBL ELISA were below the detection limit of 0.13 ng/L, rendering this assay unsuitable for large-scale analysis. Similar results were found in the confirmation study.Conclusion: In summary, plasma hsCRP showed good stability in samples stored for either less than 2 years or more than 4 years at −80°C. Both the R&D and Bender Medsystems for hsIL6 measurement yielded similar results. The IBL hsTNFα assay is not suited for use in biobanking samples. Assays for the measurement of inflammatory biomarker assays should be rigorously tested before large sample sets are measured. This is a submitted manuscript of an article published by Mary Ann Liebert in Biopreservation and Biobanking, 1 Dec 2017. Locked until 1.12.2018 due to copyright restrictions.

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways., published version, peerReviewed

Introduction: The presence of the Thr92Ala polymorphism of deiodinase-2 (D2) has been thought to have several effects. It may influence its enzymatic function, is associated with increased expression of genes involved in oxidative stress in brain tissue, and may predict favorable response to combination levothyroxine (LT4) plus triiodothyronine (T3) therapy. It was hypothesized that homozygous carriers of the D2-92Ala allele have different thyroid hormone parameters, and reduced health-related quality of life (HRQoL) and cognitive functioning.Methods: In 12,625 participants from the LifeLines cohort study with genome-wide genetic data available, the effects of the Thr92Ala polymorphism (rs225014) were evaluated in the general population and in 364 people treated with thyroid hormone replacement therapy, the latter mainly because of primary hypothyroidism. In addition to evaluating anthropometric data, medication use, and existence of metabolic syndrome, HRQoL was assessed with the RAND 36-Item Health Survey, and the Ruff Figural Fluency Test was used as a sensitive test for executive functioning. Data on thyrotropin, free thyroxine (fT4), and free T3 (fT3) levels were available in a subset of 4479 participants.Results: The mean age (-standard deviation) was 53 +/- 12 years and the body mass index was 27.0 +/- 4.5 kg/m(2) in the LT4 users compared with 48 +/- 11 years and 26.2 +/- 4.1 kg/m(2) in participants from the general population. The Ala/Ala genotype of the D2-Thr92Ala polymorphism was present in 11.3% of LT4 users and in 10.7% of the general population. In total, 3742/4479 subjects with thyroid hormone data available had normal TSH (0.4-4.0 mIU/L), and 88% of LT4 users were females. LT4 users had higher fT4, lower fT3, and a lower fT3/fT4 ratio, and female patients had lower scores on the HRQoL domains of physical functioning, vitality, mental health, social functioning, bodily pain, and general health compared with those not using LT4 (p Conclusion: The Thr92Ala polymorphism of D2 was not associated with thyroid parameters, HRQoL, and cognitive functioning in the general population and in participants on thyroid hormone replacement therapy.

There is evidence across several species for genetic control of phenotypic variation of complex traits, such that the variance among phenotypes is genotype dependent. Understanding genetic control of variability is important in evolutionary biology, agricultural selection programmes and human medicine, yet for complex traits, no individual genetic variants associated with variance, as opposed to the mean, have been identified. Here we perform a meta-analysis of genome-wide association studies of phenotypic variation using ∼170,000 samples on height and body mass index (BMI) in human populations. We report evidence that the single nucleotide polymorphism (SNP) rs7202116 at the FTO gene locus, which is known to be associated with obesity (as measured by mean BMI for each rs7202116 genotype), is also associated with phenotypic variability. We show that the results are not due to scale effects or other artefacts, and find no other experiment-wise significant evidence for effects on variability, either at loci other than FTO for BMI or at any locus for height. The difference in variance for BMI among individuals with opposite homozygous genotypes at the FTO locus is approximately 7%, corresponding to a difference of ∼0.5 kilograms in the standard deviation of weight. Our results indicate that genetic variants can be discovered that are associated with variability, and that between-person variability in obesity can partly be explained by the genotype at the FTO locus. The results are consistent with reported FTO by environment interactions for BMI, possibly mediated by DNA methylation. Our BMI results for other SNPs and our height results for all SNPs suggest that most genetic variants, including those that influence mean height or mean BMI, are not associated with phenotypic variance, or that their effects on variability are too small to detect even with samples sizes greater than 100,000. ispartof: Nature vol:490 issue:7419 pages:267-272 ispartof: location:England status: published

Genome-wide association studies (GWASs) have identified many SNPs underlying variations in plasma-lipid levels. We explore whether additional loci associated with plasma-lipid phenotypes, such as high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TGs), can be identified by a dense gene-centric approach. Our meta-analysis of 32 studies in 66,240 individuals of European ancestry was based on the custom ∼50,000 SNP genotyping array (the ITMAT-Broad-CARe array) covering ∼2,000 candidate genes. SNP-lipid associations were replicated either in a cohort comprising an additional 24,736 samples or within the Global Lipid Genetic Consortium. We identified four, six, ten, and four unreported SNPs in established lipid genes for HDL-C, LDL-C, TC, and TGs, respectively. We also identified several lipid-related SNPs in previously unreported genes: DGAT2, HCAR2, GPIHBP1, PPARG, and FTO for HDL-C; SOCS3, APOH, SPTY2D1, BRCA2, and VLDLR for LDL-C; SOCS3, UGT1A1, BRCA2, UBE3B, FCGR2A, CHUK, and INSIG2 for TC; and SERPINF2, C4B, GCK, GATA4, INSR, and LPAL2 for TGs. The proportion of explained phenotypic variance in the subset of studies providing individual-level data was 9.9% for HDL-C, 9.5% for LDL-C, 10.3% for TC, and 8.0% for TGs. This large meta-analysis of lipid phenotypes with the use of a dense gene-centric approach identified multiple SNPs not previously described in established lipid genes and several previously unknown loci. The explained phenotypic variance from this approach was comparable to that from a meta-analysis of GWAS data, suggesting that a focused genotyping approach can further increase the understanding of heritability of plasma lipids. © 2012 The American Society of Human Genetics.

Obesity is a complex multifaceted disease resulting from interactions between genetics and lifestyle. The proportion of phenotypic variance ascribed to genetic variance is 0.4 to 0.7 for obesity and recent years have seen considerable success in identifying disease-susceptibility variants. Although with the advent of genome-wide association studies the list of genetic variants predisposing to obesity has significantly increased the identified variants only explain a fraction of disease heritability. Studies of gene–environment interactions can provide more insight into the biological mechanisms involved in obesity despite the challenges associated with such designs. Epigenetic changes that affect gene function without DNA sequence modifications may be a key factor explaining interindividual differences in obesity, with both genetic and environmental factors influencing the epigenome. Disentangling the relative contributions of genetic, environmental and epigenetic marks to the establishment of obesity is a major challenge given the complex interplay between these determinants.