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This research has been conducted using the UK Biobank resource under application number 9072. Copyright © The Author(s) 2021. Higher adiposity is an established risk factor for psychiatric diseases including depression and anxiety. The associations between adiposity and depression may be explained by the metabolic consequences and/or by the psychosocial impact of higher adiposity. We performed one-and two-sample Mendelian randomization (MR) in up to 145 668 European participants from the UK Biobank to test for a causal effect of higher adiposity on 10 well-validated mental health and well-being outcomes derived using the Mental Health Questionnaire (MHQ). We used three sets of adiposity genetic instruments: (a) a set of 72 BMI genetic variants, (b) a set of 36 favourable adiposity variants and (c) a set of 38 unfavourable adiposity variants. We additionally tested causal relationships (1) in men and women separately, (2) in a subset of individuals not taking antidepressants and (3) in non-linear MR models. Two-sample MR provided evidence that a genetically determined one standard deviation (1-SD) higher BMI (4.6 kg/m2) was associated with higher odds of current depression [OR: 1.50, 95%CI: 1.15, 1.95] and lower well-being [ß:-0.15, 95%CI:-0.26,-0.04]. Findings were similar when using the metabolically favourable and unfavourable adiposity variants, with higher adiposity associated with higher odds of depression and lower well-being scores. Our study provides further evidence that higher BMI causes higher odds of depression and lowers well-being. Using genetics to separate out metabolic and psychosocial effects, our study suggests that in the absence of adverse metabolic effects higher adiposity remains causal to depression and lowers well-being. Academy of Medical Sciences (AMS) Springboard award, which is supported by the AMS, the Wellcome Trust, GCRF, the Government Department of Business, Energy and Industrial strategy, the British Heart Foundation and Diabetes UK (SBF004\1079 to J.O., F.C. and J.T.); Wellcome Senior Research Fellowship (WT220390 to R.M.F.). Diabetes UK RD Lawrence fellowship (17/0005594 to H.Y.).

Background & Aims MRI-based corrected T1 (cT1) is a non-invasive method to grade the severity of steatohepatitis and liver fibrosis. We aimed to identify genetic variants influencing liver cT1 and use genetics to understand mechanisms underlying liver fibroinflammatory disease and its link with other metabolic traits and diseases. Methods First, we performed a genome-wide association study (GWAS) in 14,440 Europeans, with liver cT1 measures, from the UK Biobank. Second, we explored the effects of the cT1 variants on liver blood tests, and a range of metabolic traits and diseases. Third, we used Mendelian randomisation to test the causal effects of 24 predominantly metabolic traits on liver cT1 measures. Results We identified 6 independent genetic variants associated with liver cT1 that reached the GWAS significance threshold (p, Graphical abstract, Highlights • Variants in metal ion transporter and NAFLD genes are associated with liver MRI-derived cT1, a steatohepatitis and fibrosis proxy. • cT1 is highly heritable, and is correlated with BMI, NAFLD and VLDL, and inversely correlated with HDL. • Insulin resistance, NAFLD and higher BMI are genetically linked to higher liver cT1, whilst favourable adiposity is linked to lower cT1.

Background Gastro-oesophageal reflux disease (GORD) is associated with multiple risk factors but determining causality is difficult. We used a genetic approach [Mendelian randomization (MR)] to identify potential causal modifiable risk factors for GORD. Methods We used data from 451 097 European participants in the UK Biobank and defined GORD using hospital-defined ICD10 and OPCS4 codes and self-report data (N = 41 024 GORD cases). We tested observational and MR-based associations between GORD and four adiposity measures [body mass index (BMI), waist–hip ratio (WHR), a metabolically favourable higher body-fat percentage and waist circumference], smoking status, smoking frequency and caffeine consumption. Results Observationally, all adiposity measures were associated with higher odds of GORD. Ever and current smoking were associated with higher odds of GORD. Coffee consumption was associated with lower odds of GORD but, among coffee drinkers, more caffeinated-coffee consumption was associated with higher odds of GORD. Using MR, we provide strong evidence that higher WHR and higher WHR adjusted for BMI lead to GORD. There was weak evidence that higher BMI, body-fat percentage, coffee drinking or smoking caused GORD, but only the observational effects for BMI and body-fat percentage could be excluded. This MR estimated effect for WHR equates to a 1.23-fold higher odds of GORD per 5-cm increase in waist circumference. Conclusions These results provide strong evidence that a higher waist–hip ratio leads to GORD. Our study suggests that central fat distribution is crucial in causing GORD rather than overall weight.

Urinary albumin-to-creatinine ratio (ACR) is a marker of diabetic nephropathy and microvascular damage. Metabolic-related traits are observationally associated with ACR, but their causal role is uncertain. Here, we confirmed ACR as a marker of microvascular damage and tested whether metabolic-related traits have causal relationships with ACR. The association between ACR and microvascular function (responses to acetylcholine [ACH] and sodium nitroprusside) was tested in the SUMMIT study. Two-sample Mendelian randomization (MR) was used to infer the causal effects of 11 metabolic risk factors, including glycemic, lipid, and adiposity traits, on ACR. MR was performed in up to 440,000 UK Biobank and 54,451 CKDGen participants. ACR was robustly associated with microvascular function measures in SUMMIT. Using MR, we inferred that higher triglyceride (TG) and LDL cholesterol (LDL-C) levels caused elevated ACR. A 1 SD higher TG and LDL-C level caused a 0.062 (95% CI 0.040, 0.083) and a 0.026 (95% CI 0.008, 0.044) SD higher ACR, respectively. There was evidence that higher body fat and visceral body fat distribution caused elevated ACR, while a metabolically “favorable adiposity” phenotype lowered ACR. ACR is a valid marker for microvascular function. MR suggested that seven traits have causal effects on ACR, highlighting the role of adiposity-related traits in causing lower microvascular function.

Data availability: Our study uses two-sample MR. We used both published summary results (i.e. taking results from published research papers and websites) and individual participant cohort data: journal-published and website summary data were used for sample one of the two-sample MR (published GWAS of BMI and body fat percentage). The references to the published data sources are provided in the main paper. The data for the GWAS of BMI are available at https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files. The data for the GWAS of body fat percentage are available at https://walker05.u.hpc.mssm.edu. We used individual participant data for the second MR sample and for undertaking sensitivity analyses from the UKB, ALSPAC, BiB, EFSOCH and HAPO cohorts. The data in UKB, ALSPAC and BiB are fully available, via managed systems, to any researchers. The managed system for both studies is a requirement of the study funders but access is not restricted on the basis of overlap with other applications to use the data or on the basis of peer review of the proposed science. Researchers have to pay for a dataset to be prepared for them. Full information on how to access UKB data can be found at www.ukbiobank.ac.uk/using-the-resource/. The ALSPAC data management plan (www.bristol.ac.uk/alspac/researchers/data-access/documents/alspac-data-management-plan.pdf) describes, in detail, the policy regarding data sharing, which is through a system of managed open access. The following steps highlight how to apply for access to the data included in this paper and all other ALSPAC data: (1) please read the ALSPAC access policy (PDF, 627 kB), which describes the process of accessing the data and samples in detail, and outlines the costs associated with doing so; (2) you may also find it useful to browse the fully searchable ALSPAC research proposals database, which lists all research projects that have been approved since April 2011; (3) please submit your research proposal for consideration by the ALSPAC Executive Committee and you will receive a response within 10 working days to advise you whether your proposal has been approved. If you have any questions about accessing data, please e-mail alspac-data@bristol.ac.uk. Full information on how to access BiB data can be found at https://borninbradford.nhs.uk/research/how-to-access-data/. Requests for access to the original EFSOCH dataset should be made in writing in the first instance to the EFSOCH data team via the Exeter Clinical Research Facility (crf@exeter.ac.uk). Copyright © The Author(s) 2022. Aims/hypothesis: Higher maternal BMI during pregnancy is associated with higher offspring birthweight, but it is not known whether this is solely the result of adverse metabolic consequences of higher maternal adiposity, such as maternal insulin resistance and fetal exposure to higher glucose levels, or whether there is any effect of raised adiposity through non-metabolic (e.g. mechanical) factors. We aimed to use genetic variants known to predispose to higher adiposity, coupled with a favourable metabolic profile, in a Mendelian randomisation (MR) study comparing the effect of maternal ‘metabolically favourable adiposity’ on offspring birthweight with the effect of maternal general adiposity (as indexed by BMI). Methods: To test the causal effects of maternal metabolically favourable adiposity or general adiposity on offspring birthweight, we performed two-sample MR. We used variants identified in large, published genetic-association studies as being associated with either higher adiposity and a favourable metabolic profile, or higher BMI (n = 442,278 and n = 322,154 for metabolically favourable adiposity and BMI, respectively). We then extracted data on the metabolically favourable adiposity and BMI variants from a large, published genetic-association study of maternal genotype and offspring birthweight controlling for fetal genetic effects (n = 406,063 with maternal and/or fetal genotype effect estimates). We used several sensitivity analyses to test the reliability of the results. As secondary analyses, we used data from four cohorts (total n = 9323 mother–child pairs) to test the effects of maternal metabolically favourable adiposity or BMI on maternal gestational glucose, anthropometric components of birthweight and cord-blood biomarkers. Results: Higher maternal adiposity with a favourable metabolic profile was associated with lower offspring birthweight (−94 [95% CI −150, −38] g per 1 SD [6.5%] higher maternal metabolically favourable adiposity, p = 0.001). By contrast, higher maternal BMI was associated with higher offspring birthweight (35 [95% CI 16, 53] g per 1 SD [4 kg/m2] higher maternal BMI, p = 0.0002). Sensitivity analyses were broadly consistent with the main results. There was evidence of outlier SNPs for both exposures; their removal slightly strengthened the metabolically favourable adiposity estimate and made no difference to the BMI estimate. Our secondary analyses found evidence to suggest that a higher maternal metabolically favourable adiposity decreases pregnancy fasting glucose levels while a higher maternal BMI increases them. The effects on neonatal anthropometric traits were consistent with the overall effect on birthweight but the smaller sample sizes for these analyses meant that the effects were imprecisely estimated. We also found evidence to suggest that higher maternal metabolically favourable adiposity decreases cord-blood leptin while higher maternal BMI increases it. Conclusions/interpretation: Our results show that higher adiposity in mothers does not necessarily lead to higher offspring birthweight. Higher maternal adiposity can lead to lower offspring birthweight if accompanied by a favourable metabolic profile. US National Institute of Health (R01 DK10324); European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no 669545; British Heart Foundation (CS/16/4/32482 and AA/18/7/34219); NIHR Biomedical Centre at the University Hospitals Bristol NHS Foundation Trust; University of Bristol.

Type 2 diabetes is linked to higher risk of many non-metabolic conditions (e.g. cancers, neuropsychiatric and musculoskeletal disease) which all share a common risk factor: obesity. The causal pathways from excess weight to disease are unknown. We aimed to use specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects. Using metabolic biomarkers from 442,278 UK Biobank individuals, we identified two clusters of body fat % increasing alleles: a) 36 “favourable adiposity” (FA) alleles associated with a favourable metabolic profile (higher HDL and SHBG and lower triglyceride and liver enzymes); b) 38 “unfavourable adiposity” (UFA) alleles associated with an unfavourable profile (lower HDL and SHBG and higher triglyceride and liver enzymes). We used two-sample Mendelian randomization with 43 diseases from non-UK Biobank GWASs. We identified two sets of diseases. 1) Those where the metabolic effect of higher adiposity is the likely primary cause of the disease. Here the FA and UFA clusters had an opposing effect on the risk of disease: type 2 diabetes (FA: 0.11 OR per 1 standard deviation higher body fat % [95%CI: 0.08, 0.16] vs. UFA: 5.50 [4.29, 7.05]), stroke (0.65 [0.52, 0.83] vs. 1.43 [1.23, 1.67]), gout (0.44 [0.29, 0.68] vs. 2.49 [1.88, 3.29]), breast cancer (1.12 [0.64, 1.94] vs. 0.52 [0.39, 0.68]) and colorectal cancer (0.67 [0.52, 0.85] vs. 1.20 [1, 1.50]). 2) Those where the mechanical excess weight (or other non-metabolic effects) is likely a cause of the disease. Here the FA cluster, despite having lower metabolic risk, and the UFA cluster were both associated with higher disease risk: venous thromboembolism (2.52 [1.82, 3.47] vs. 1.63 [1.25, 2.13]), osteoarthritis (1.45 [1.19, 1.76] vs. 2.20 [1.64, 2.95]), depression (2.85 [1.38, 5.89] vs. 2.59 [1.60, 4.21]). Our results assist in understanding the consequences of higher adiposity uncoupled from its adverse metabolic effects in non-metabolic diseases pathophysiological mechanisms. Disclosure S. Martin: None. J. D. Bell: None. T. M. Frayling: None. H. Yaghootkar: None. J. Tyrrell: None. E. L. Thomas: None. M. Bown: None. J. T. Elder: None. C. Kabrhel: Research Support; Self; Diagnostica Stago, Grifols. N. Papadimitriou: None. M. Gunter: None. E. E. Vincent: None. Funding Diabetes UK (17/0005594)

The CREBRF missense variant (p.Arg457Gln) is paradoxically associated with lower risk of type 2 diabetes, yet higher body mass index (BMI). Here we sought to determine whether this CREBRF variant might be associated with adult height. Linear regression was used to analyse the association of the CREBRF minor (A) allele with height in 2286 Māori and Pacific adults living in Aotearoa/New Zealand. A potential type 2 diabetes index event was corrected to account for a bias that may be the cause of paradoxical association between the CREBRF diabetes-protective allele and higher BMI and height. The CREBRF protective allele was associated with increased adult height (s = 1.25 cm, P = 3.9 × 10−6), with the effect being more pronounced in males. The lower odds of diabetes remained similar when analyses were adjusted for height (OR = 0.67–0.65). We found no evidence of a diabetes index event bias to explain the paradoxical effect of CREBRF with either BMI or height and diabetes. The orthologous CREBRF p.Arg457Gln variant was created in knock-in mice to independently assess the effect of the variant, and length was found to be greater in male mice at 8 weeks of age. These data taken together indicate that CREBRF p.Arg457Gln is associated with taller stature in Māori and Pacific adults.

Aim To examine the extent to which discriminatory testing using antibodies and Type 1 diabetes genetic risk score, validated in European populations, is applicable in a non‐European population. Methods We recruited 127 unrelated children with diabetes diagnosed between 9 months and 5 years from two centres in Iran. All children underwent targeted next‐generation sequencing of 35 monogenic diabetes genes. We measured three islet autoantibodies (islet antigen 2, glutamic acid decarboxylase and zinc transporter 8) and generated a Type 1 diabetes genetic risk score in all children. Results We identified six children with monogenic diabetes, including four novel mutations: homozygous mutations in WFS1 (n=3), SLC19A2 and SLC29A3, and a heterozygous mutation in GCK. All clinical features were similar in children with monogenic diabetes (n=6) and in the rest of the cohort (n=121). The Type 1 diabetes genetic risk score discriminated children with monogenic from Type 1 diabetes [area under the receiver‐operating characteristic curve 0.90 (95% CI 0.83–0.97)]. All children with monogenic diabetes were autoantibody‐negative. In children with no mutation, 59 were positive to glutamic acid decarboxylase, 39 to islet antigen 2 and 31 to zinc transporter 8. Measuring zinc transporter 8 increased the number of autoantibody‐positive individuals by eight. Conclusions The present study provides the first evidence that Type 1 diabetes genetic risk score can be used to distinguish monogenic from Type 1 diabetes in an Iranian population with a large number of consanguineous unions. This test can be used to identify children with a higher probability of having monogenic diabetes who could then undergo genetic testing. Identification of these individuals would reduce the cost of treatment and improve the management of their clinical course., What's new? Studies in white European populations have recently shown that a genetic risk score for Type 1 diabetes has a high ability to discriminate between Type 1 diabetes and monogenic diabetes.The diagnostic utility of this genetic risk score in non‐European populations is unknown.This study provides the first evidence that the Type 1 diabetes genetic risk score discriminates children with monogenic diabetes from those with Type 1 diabetes in the Iranian population with a large number of consanguineous unions.The Type 1 diabetes genetic risk score can be used to improve the selection of non‐European children for monogenic diabetes testing, resulting in the correct diagnosis, improving their clinical management and providing families with recurrence risk information.

Background Wolcott-Rallison syndrome is a rare autosomal recessive disorder characterized by neonatal/early-onset non-autoimmune insulin-dependent diabetes, multiple epiphyseal dysphasia and growth retardation. It is caused by mutations in the gene encoding eukaryotic translation initiation factor 2α kinase 3 (EIF2AK3). We aimed to study the clinical characteristics and frequency of the disease in the Iranian population. Methods We recruited 42 patients who referred to the endocrine and metabolism clinic at Mashhad Imam Reza Hospital with neonatal diabetes. Molecular screening of KCNJ11, INS, ABCC8 and EIF2AK3 was performed at the Exeter Molecular Genetics Laboratory, UK. We calculated the frequency of the disease in 124 patients referred from Iran to the Exeter Molecular Genetics Laboratory for genetic screening and compared it to other countries worldwide. Results We identified seven patients as having Wolcott-Rallison syndrome. Genetic testing confirmed the clinical diagnosis and indicated five novel mutations. Only two patients developed clinical features of the syndrome by 6 months of age. Of all 124 cases of Iranian neonatal diabetes referred to the Exeter Molecular Genetics Laboratory for genetic screening, 28 patients (22.58%) had a recessive mutation in EIF2AK3. Conclusions The results of this study raises awareness of the condition and provides further accurate data on the genetic and clinical presentation of Wolcott-Rallison syndrome in the Iranian population. Our study highlights the importance of genetic testing in patients from consanguineous families with diabetes diagnosed within the first 6 months of life.

To understand the consequences of higher adiposity uncoupled from its adverse metabolic effects, we selected 37 diseases associated with obesity and genetic variants associated with different aspects of excess weight including metabolically “favourable adiposity” (FA) and “unfavourable adiposity” (UFA). Mendelian randomisation (MR) identified two sets of diseases. First, 12 conditions where the metabolic effect of higher adiposity is the likely primary cause of the disease. Here MR with the FA and UFA genetics showed opposing effects on the risk of disease, including colorectal and ovarian cancer, and gout. Second, 7 conditions where the non-metabolic effects of excess weight (e.g. mechanical effect) is likely a cause. Here MR with the FA genetics, despite leading to lower metabolic risk, and MR with the UFA genetics, were both associated with higher disease risk, including osteoarthritis and venous thromboembolism. Individuals with high BMI are at higher risk of some diseases despite being relatively metabolically healthy.

To understand the causal role of adiposity and ectopic fat in type 2 diabetes and cardiometabolic diseases, we aimed to identify two clusters of adiposity genetic variants: one with “adverse” metabolic effects (UFA) and the other with, paradoxically, “favorable” metabolic effects (FA). We performed a multivariate genome-wide association study using body fat percentage and metabolic biomarkers from UK Biobank and identified 38 UFA and 36 FA variants. Adiposity-increasing alleles were associated with an adverse metabolic profile, higher risk of disease, higher CRP, and higher fat in subcutaneous and visceral adipose tissue, liver, and pancreas for UFA and a favorable metabolic profile, lower risk of disease, higher CRP and higher subcutaneous adipose tissue but lower liver fat for FA. We detected no sexual dimorphism. The Mendelian randomization studies provided evidence for a risk-increasing effect of UFA and protective effect of FA for type 2 diabetes, heart disease, hypertension, stroke, nonalcoholic fatty liver disease, and polycystic ovary syndrome. FA is distinct from UFA by its association with lower liver fat and protection from cardiometabolic diseases; it was not associated with visceral or pancreatic fat. Understanding the difference in FA and UFA may lead to new insights in preventing, predicting, and treating cardiometabolic diseases.

Data availability statement: We used patient-level data which was fully anonymised prior to analysis. Any requests for access to the Salford data should be made to Dr Adrian Heald. Copyright © 2021 The Authors. Aims Change in weight, HbA1c, lipids, blood pressure and cardiometabolic events over time is variable in individuals with type 2 diabetes. We hypothesised that people with a genetic predisposition to a more favourable adiposity distribution could have a less severe clinical course/progression. Methods We involved people with type 2 diabetes from two UK-based cohorts: 11,914 individuals with GP follow-up data from the UK Biobank and 723 from Salford. We generated a ‘favourable adiposity’ genetic score and conducted cross-sectional and longitudinal studies to test its association with weight, BMI, lipids, blood pressure, medication use and risk of myocardial infarction and stroke using 15 follow-up time points with 1-year intervals. Results The ‘favourable adiposity’ genetic score was cross-sectionally associated with higher weight (effect size per 1 standard deviation higher genetic score: 0.91 kg [0.59,1.23]) and BMI (0.30 kg/m2 [0.19,0.40]), but higher high-density lipoprotein (0.02 mmol/L [0.01,0.02]) and lower triglycerides (−0.04 mmol/L [−0.07, −0.02]) in the UK Biobank at baseline, and this pattern of association was consistent across follow-up. There was a trend for participants with higher ‘favourable adiposity’ genetic score to have lower risk of myocardial infarction and/or stroke (odds ratio 0.79 [0.62, 1.00]) compared to those with lower score. A one standard deviation higher score was associated with lower odds of using lipid-lowering (0.91 [0.86, 0.97]) and anti-hypertensive medication (0.95 [0.91, 0.99]). Conclusions In individuals with type 2 diabetes, having more ‘favourable adiposity’ alleles is associated with a marginally better lipid profile long-term and having lower odds of requiring lipid-lowering or anti-hypertensive medication in spite of relatively higher adiposity. Diabetes UK. Grant Number: 17/0005594; European Research Council. Grant Number: 323195.

Leptin influences food intake by informing the brain about the status of body fat stores. Rare LEP mutations associated with congenital leptin deficiency cause severe early-onset obesity that can be mitigated by administering leptin. However, the role of genetic regulation of leptin in polygenic obesity remains poorly understood. We performed an exome-based analysis in up to 57,232 individuals of diverse ancestries to identify genetic variants that influence adiposity-adjusted leptin concentrations. We identify five novel variants, including four missense variants, in LEP, ZNF800, KLHL31, and ACTL9, and one intergenic variant near KLF14. The missense variant Val94Met (rs17151919) in LEP was common in individuals of African ancestry only and its association with lower leptin concentrations was specific to this ancestry (P=2x10-16, n=3,901). Using in vitro analyses, we show that the Met94 allele decreases leptin secretion. We also show that the Met94 allele is associated with higher BMI in young African-ancestry children but not in adults, suggesting leptin regulates early adiposity.

Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between mean adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive mean adipocyte area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler: R2visceral = 0.94, P < 2.2 × 10−16, R2subcutaneous = 0.91, P < 2.2 × 10−16), and faster run times (10,000 images: 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that mean adipocyte area positively correlated with body mass index (BMI) (Psubq = 8.13 × 10−69, βsubq = 0.45; Pvisc = 2.5 × 10−55, βvisc = 0.49; average R2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts (Pmeta = 9.8 × 10−7). Lastly, we performed the largest GWAS and subsequent meta-analysis of mean adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations., Author summary Fundamental aspects of biology such as how the size or number of adipocytes relates to obesity and cardiometabolic health are still unanswered. To answer such questions, fast, accurate and automated measurements need to be acquired, free from human biases. Glastonbury et al., 2020 describe a novel machine learning method to perform rapid acquisition of adipocyte area estimates from histological imaging data. Using these imaging derived phenotypes, Glastonbury et al., 2020 assess the relationship between adipocyte size and a range of cardio-metabolic comorbidities, demonstrating that adipocyte size can vary depending on where adipose is stored throughout the body. By tying genetics with imaging data, Glastonbury et al., 2020 were able to demonstrate that previous findings associating adipocyte size with Type 2 Diabetes variants, are likely to be false positives. This study provides a means of being able to scale up GWAS type analyses to imaging derived phenotypes.

Objective Differences in the content and distribution of body fat and ectopic lipids may be responsible for ethnic variations in metabolic disease susceptibility. The aim of this study was to examine the ethnic distribution of body fat in two separate UK-based populations. Methods Anthropometry and body composition were assessed in two separate UK cohorts: the Hammersmith cohort and the UK Biobank, both comprising individuals of South Asian descent (SA), individuals of Afro-Caribbean descent (AC), and individuals of European descent (EUR). Regional adipose tissue stores and liver fat were measured by magnetic resonance techniques. Results The Hammersmith cohort (n = 747) had a mean (SD) age of 41.1 (14.5) years (EUR: 374 men, 240 women; SA: 68 men, 22 women; AC: 14 men, 29 women), and the UK Biobank (n = 9,533) had a mean (SD) age of 55.5 (7.5) years (EUR: 4,483 men, 4,873 women; SA: 80 men, 43 women, AC: 31 men, 25 women). Following adjustment for age and BMI, no significant differences in visceral adipose tissue or liver fat were observed between SA and EUR individuals in the either cohort. Conclusions Our data, consistent across two independent UK-based cohorts, present a limited number of ethnic differences in the distribution of body fat depots associated with metabolic disease. These results suggest that the ethnic variation in susceptibility to features of the metabolic syndrome may not arise from differences in body fat.

Aims/HypothesisHigher maternal BMI during pregnancy results in higher offspring birth weight, but it is not known whether this is solely the result of adverse metabolic consequences of higher maternal adiposity, such as maternal insulin resistance and fetal exposure to higher glucose levels, or whether there is any effect of raised adiposity through non-metabolic (e.g. mechanical) factors. We aimed to use genetic variants known to predispose to higher adiposity coupled with a favourable metabolic profile, in a Mendelian Randomisation (MR) study comparing the effect of maternal “metabolically favourable adiposity” on offspring birth weight with the effect of maternal general adiposity (as indexed by BMI).MethodsTo test the causal effects of maternal metabolically favourable adiposity or general adiposity on offspring birth weight, we performed two sample MR. We used variants identified in large genetic association studies as associated with either higher adiposity and a favourable metabolic profile, or higher BMI (N = 442,278 and N = 322,154 for metabolically favourable adiposity and BMI, respectively). We then used data from the same variants in a large genetic study of maternal genotype and offspring birth weight independent of fetal genetic effects (N = 406,063 with maternal and/or fetal genotype effect estimates). We used several sensitivity analyses to test the reliability of the results. As secondary analyses, we used data from four cohorts (total N = 9,323 mother-child pairs) to test the effects of maternal metabolically favourable adiposity or BMI on maternal gestational glucose, anthropometric components of birth weight and cord-blood biomarkers.ResultsHigher maternal adiposity with a favourable metabolic profile was associated with lower offspring birth weight (−94 (95% CI: −150 to −38) grams per 1 SD (6.5%) higher maternal metabolically favourable adiposity). By contrast, higher maternal BMI was associated with higher offspring birth weight (35 (95% CI: 16 to 53) grams per 1 SD (4 kg/m2) higher maternal BMI). Sensitivity analyses were broadly consistent with the main results. There was evidence of outlier SNPs for both exposures and their removal slightly strengthened the metabolically favourable adiposity estimate and made no difference to the BMI estimate. Our secondary analyses found evidence to suggest that maternal metabolically favourable adiposity decreases pregnancy fasting glucose levels whilst maternal BMI increases them.The effects on neonatal anthropometric traits were consistent with the overall effect on birth weight, but the smaller sample sizes for these analyses meant the effects were imprecisely estimated. We also found evidence to suggest that maternal metabolically favourable adiposity decreases cord-blood leptin whilst maternal BMI increases it.Conclusions/InterpretationOur results show that higher adiposity in mothers does not necessarily lead to higher offspring birth weight. Higher maternal adiposity can lead to lower offspring birth weight if accompanied by a favourable metabolic profile.Research in ContextWhat is already known about this subject?Studies in non-pregnant people with obesity suggest many people can have a “metabolically healthy” form of obesity, but effects in pregnancy and on offspring are not known.Multiple lines of evidence show that higher maternal BMI is causally associated with higher offspring birth weight, and that this may be mediated by the fetal insulin response to higher maternal gestational glucose.Recently, genetic variants have been identified, where one allele is associated with higher adiposity but lower risk of type II diabetes and favourable metabolic profile, including lower insulin and glucose levels, so called “metabolically favourable adiposity”; the mechanism is thought to be due to greater subcutaneous adipose storage capacity that leads to higher insulin sensitivity.What is the key question?What is the effect of maternal metabolically favourable adiposity on birth weight, and how does it compare with the effect of maternal BMI on birth weight?What are the new findings?Higher maternal adiposity can lead to lower, not higher birth weight, if it is also associated with a metabolically favourable profile; this contrasts with the effect of higher maternal general adiposity (BMI), on higher birth weight.Higher maternal metabolically favourable adiposity causes lower maternal fasting plasma glucose levels, most likely due to higher insulin sensitivity; in contrast higher maternal general adiposity leads to higher maternal fasting plasma glucose levels, most likely due to lower insulin sensitivity.How might this impact on clinical practice in the foreseeable future?Identifying ways of stratifying overweight and obese pregnant women into those with and without metabolically favourable adiposity could allow for targeted management to obtain healthy fetal growth and birth weight.

Recent developments in the field of genetics have accelerated our understanding of the aetiology of complex diseases. Type 2 diabetes mellitus and cancer are no exception, with large-scale genome-wide association studies (GWAS) facilitating exploration of the underlying pathology. Here, we discuss how genetics studies can be used to investigate the relationship between these complex diseases. Observational epidemiological studies consistently report that people with type 2 diabetes have a higher risk of several types of cancer. Indeed, type 2 diabetes and cancer share many common risk factors, such as obesity, ageing, poor diet and low levels of physical activity. However, questions remain regarding the biological mechanisms that link these two diseases. Large-scale GWAS of type 2 diabetes and cancer allow us to consider the evidence for shared genetic architecture. Several shared susceptibility genes have been identified, yet tissue specificity and direction of effect must be taken into account when considering common genetic aetiology. We also consider how GWAS, and associated techniques such as Mendelian randomisation, allow us to dissect the link between the two diseases and address questions such as ‘Does type 2 diabetes cause cancer or is the increased risk observed driven by higher adiposity or another associated metabolic feature?’ Graphical abstract Electronic supplementary material The online version of this article (10.1007/s00125-020-05228-y) contains a slideset of the figures for download, which is available to authorised users.

More than one in three adults worldwide is either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, are more informative for predicting risk of obesity sequelae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio (WHR) adjusted for body mass index (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.

Background & AimsA common genetic variant near MBOAT7 (rs641738C>T) has been previously associated with hepatic fat and advanced histology in non-alcoholic fatty liver disease (NAFLD), however, these findings have not been consistently replicated in the literature. We aimed to establish whether rs641738C>T is a risk factor across the spectrum of NAFLD and characterize its role in the regulation of related metabolic phenotypes through meta-analysis.MethodsWe performed meta-analysis of studies with data on the association between rs641738C>T genotype and: liver fat, NAFLD histology, and serum ALT, lipids, or insulin. These included directly genotyped studies and population-level data from genome-wide association studies (GWAS). We performed random effects meta-analysis using recessive, additive, and dominant genetic models.ResultsData from 1,047,265 participants (8,303 with liver biopsies) across 42 studies was included in the meta-analysis. rs641738C>T was associated with higher liver fat on CT/MRI (+0.03 standard deviations [95% CI: 0.02 - 0.05]) and diagnosis of NAFLD (OR 1.22 [95% CI 1.08 - 1.39]) in Caucasian adults. The variant was also positively associated with presence of severe steatosis, NASH, and advanced fibrosis (OR: 1.32 [95% CI: 1.06 - 1.63]) in Caucasian adults using a recessive model of inheritance (CC+CT vs. TT). Meta-analysis of data from previous GWAS found the variant to be associated with higher ALT (Pz=0.002) and lower serum triglycerides (Pz=1.5×10−4). rs641738C>T was not associated with fasting insulin and no effect was observed in children with NAFLD.ConclusionOur study validates rs641738C>T near MBOAT7 as a risk factor for the presence and severity of NAFLD in individuals of European descent.

MRI-based corrected T1 (cT1) is a non-invasive method to grade the severity of steatohepatitis and liver fibrosis. We aimed to identify genetic variants influencing liver cT1 and use genetics to understand mechanisms underlying liver fibroinflammatory disease and its link with other metabolic traits and diseases.First, we performed a genome-wide association study (GWAS) in 14,440 Europeans, with liver cT1 measures, from the UK Biobank. Second, we explored the effects of the cT1 variants on liver blood tests, and a range of metabolic traits and diseases. Third, we used Mendelian randomisation to test the causal effects of 24 predominantly metabolic traits on liver cT1 measures.We identified 6 independent genetic variants associated with liver cT1 that reached the GWAS significance threshold (p 5×10The association between 2 metal ion transporters and cT1 indicates an important new mechanism in steatohepatitis. Future studies are needed to determine whether interventions targeting the identified transporters might prevent liver disease in at-risk individuals.We estimated levels of liver inflammation and scarring based on magnetic resonance imaging of 14,440 UK Biobank participants. We performed a genetic study and identified variations in 6 genes associated with levels of liver inflammation and scarring. Participants with variations in 4 of these genes also had higher levels of markers of liver cell injury in blood samples, further validating their role in liver health. Two identified genes are involved in the transport of metal ions in our body. Further investigation of these variations may lead to better detection, assessment, and/or treatment of liver inflammation and scarring.

Background and aims Excess liver fat affects up to 1 in 4 adults globally1 and has been implicated in the pathogenesis of liver disease including cirrhosis and hepatocellular carcinoma, as well as extrahepatic diseases such as type 2 diabetes and cardiovascular diseases2. We aimed to find genetic variants influencing liver fat content. Methods Data was acquired from UK Biobank (application 9914). Liver phenotypes were calculated from MRI data by trained analysts using LiverMultiScanTM. We used GEMMA to perform a genome-wide association study (GWAS) of MRI scan measures of liver fat using 8,289 individuals of European ancestry from UK Biobank. We adjusted our analysis for age, sex, BMI, genotyping array, and population structure. Results We identified two loci in/near PNPLA3 (rs738409, p = 2.1 x 10−41) and TM6SF2 (rs58542926, p = 4.3 x 10−40) that reached genome-wide significance. We further identified four suggestive loci previously associated with circulating lipid levels, type 2 diabetes and obesity (APOE, GPAM, TRIB1, GCKR). Phenome-wide association analysis (PheWAS) of rs58542926 in TM6SF2 demonstrated positive associations with diabetes, rosacea and liver cirrhosis, and inverse associations with cholesterol, peripheral vascular disease, gout, pulmonary embolism and gallstones. Phenome-wide association analysis of rs738409 in PNPLA3 demonstrated positive associations with liver disease, type 2 diabetes and hypertension, and inverse associations with height, hip circumference, leg and arm fat free mass, cholesterol, and ischaemic heart diseases. Conclusion This is the first GWAS using MRI determined liver fat content to date. Mechanisms underlying elevated liver fat content contribute to hypertension and type 2 diabetes risk, but may also confer health benefits. The identification of loci previously associated with non-alcoholic fatty liver disease provide genetic validation of the utility of MRI for a fast and non-invasive assessment of liver fat content. References Younossi, Z et al. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nat. Rev. Gastroenterol. Hepatol.15, 11–20 (2018). Adams, L A, Anstee, Q M, Tilg, H. & Targher, G. Non-alcoholic fatty liver disease and its relationship with cardiovascular disease and other extrahepatic diseases. Gut (2017). doi:10.1136/gutjnl-2017–313884

Introduction Gastro-oesophageal Reflux Disease (GORD) is a common condition in which acid from the stomach leaks to the oesophagus causing heartburn and unpleasant taste in mouth, potentially leading to Barrett’s Oesophagus and Oesophageal Adenocarcinoma. Observationally, it has been reported that low body mass index (BMI), high BMI, past smoking, alcohol and caffeine consumption increase risk of GORD. Recently, it was reported that waist hip ratio (WHR) was a better measure of obesity than BMI for predicting GORD. However, observational relationships may be affected by bias and confounding. Methods We used data from 379,713 unrelated European participants in the UK Biobank, including 23,123 GORD cases to firstly explore the observational associations between GORD and adiposity measures, smoking status and smoking frequency, alcohol and caffeine consumption. We then used one- and two-sample Mendelian Randomisation (MR) techniques to test the causal relationship between the predictors and GORD. MR is a genetic technique which uses genetic variation to examine the causal effect of a risk factor on an outcome through the use of a genetic instrument for the risk factor. Results Observationally male gender, age, past smoking, current smoking, higher BMI, higher WHR, higher body fat percentage and waist circumference associated with higher odds of GORD, whilst higher caffeinated coffee consumption was associated with lower risk of GORD. There was some tentative evidence of a J-shaped relationship between BMI and GORD. Alcohol consumption was not associated with GORD. MR provided strong evidence of a causal role for WHR, with a one-SD higher WHR causing a 1.34 higher odds of GORD (95%CI: 1.16–1.55, p=5e-5). Similarly, a one-SD higher WHR adjusted for BMI was associated with 1.21 higher odds of GORD (95%CI: 1.14–1.29, p=2e-9). However, there was no evidence of a causal relationship for BMI (OR: 1.00 (95%CI: 0.09–1.12), p=0.97). There was no evidence of a causal role for body fat percentage, smoking and caffeine. All results were robust to 2 sample MR approaches. Conclusions The results show robust causal evidence for the link between obesity and reflux being primarily down to WHR, not BMI. These results emphasise the importance of measuring WHR when studying gastrointestinal disorders, and the importance of weight loss in reducing the risk of reflux.

Background and aims Excess liver iron content is common, affecting approximately 10% of the population, and observationally associates with hepatic and extrahepatic diseases such as liver fibrosis, hepatocellular carcinoma and metabolic syndrome1. Genome wide association studies (GWAS) on MRI determined liver iron content, compared to circulating iron traits, permit detection of liver-specific susceptibility loci. Here we aim to find genetic variants influencing liver iron content, using data from UK Biobank. Methods Data was acquired from UK Biobank (application 9914). Liver phenotypes were calculated from MRI data by trained analysts using LiverMultiScanTM. We used GEMMA to perform a GWAS of MRI scan measures of liver iron in 8,289 individuals of European ancestry. We adjusted for age, sex, BMI, genotyping array, and population structure. Results We identified three independent genetic variants (rs1800562 & rs1799945 in HFE, rs855791 in TMPRSS6) associated with liver iron content at genome-wide significance (p Conclusion Our study provides genetic evidence that mechanisms underlying liver iron content are mostly systemic and not organ specific. The identification of loci which affect circulating iron traits provide genetic validation of the utility of MRI for a fast and non-invasive assessment of liver iron content. Reference Deugnier, Y., Bardou-Jacquet, E. & Laine, F. Dysmetabolic iron overload syndrome (DIOS). Presse Med.46, e306–e311 (2017).

Birth weight (BW) variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. These associations have been proposed to reflect the lifelong consequences of an adverse intrauterine environment. In earlier work, we demonstrated that much of the negative correlation between BW and adult cardio-metabolic traits could instead be attributable to shared genetic effects. However, that work and other previous studies did not systematically distinguish the direct effects of an individual’s own genotype on BW and subsequent disease risk from indirect effects of their mother’s correlated genotype, mediated by the intrauterine environment. Here, we describe expanded genome-wide association analyses of own BW (n=321,223) and offspring BW (n=230,069 mothers), which identified 278 independent association signals influencing BW (214 novel). We used structural equation modelling to decompose the contributions of direct fetal and indirect maternal genetic influences on BW, implicating fetal- and maternal-specific mechanisms. We used Mendelian randomization to explore the causal relationships between factors influencing BW through fetal or maternal routes, for example, glycemic traits and blood pressure. Direct fetal genotype effects dominate the shared genetic contribution to the association between lower BW and higher type 2 diabetes risk, whereas the relationship between lower BW and higher later blood pressure (BP) is driven by a combination of indirect maternal and direct fetal genetic effects: indirect effects of maternal BP-raising genotypes act to reduce offspring BW, but only direct fetal genotype effects (once inherited) increase the offspring’s later BP. Instrumental variable analysis using maternal BW-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring BP. In successfully separating fetal from maternal genetic effects, this work represents an important advance in genetic studies of perinatal outcomes, and shows that the association between lower BW and higher adult BP is attributable to genetic effects, and not to intrauterine programming.

Circulating levels of adiponectin, an adipocyte-secreted protein associated with cardiovascular and metabolic risk, are highly heritable. To gain insights into the biology that regulates adiponectin levels, we performed an exome array meta-analysis of 265,780 genetic variants in 67,739 individuals of European, Hispanic, African American, and East Asian ancestry. We identified 20 loci associated with adiponectin, including 11 that had been reported previously (p -7). Comparison of exome array variants to regional linkage disequilibrium (LD) patterns and prior genome-wide association study (GWAS) results detected candidate variants (r2 > .60) spanning as much as 900 kb. To identify potential genes and mechanisms through which the previously unreported association signals act to affect adiponectin levels, we assessed cross-trait associations, expression quantitative trait loci in subcutaneous adipose, and biological pathways of nearby genes. Eight of the nine loci were also associated (p -4) with at least one obesity or lipid trait. Candidate genes include PRKAR2A, PTH1R, and HDAC9, which have been suggested to play roles in adipocyte differentiation or bone marrow adipose tissue. Taken together, these findings provide further insights into the processes that influence circulating adiponectin levels.

Leptin influences food intake by informing the brain about the status of body fat stores. Rare LEP mutations associated with congenital leptin deficiency cause severe early-onset obesity that can be mitigated by administering leptin. However, the role of genetic regulation of leptin concentrations in polygenic obesity remains poorly understood. We performed an exome-based analysis in up to 57,232 individuals of diverse ancestries to identify genetic variants that influence adiposity-adjusted leptin concentrations. We confirmed five previously established and identified five novel variants, including four missense variants, in LEP, ZNF800, KLHL31, and ACTL9, and one intergenic variant near KLF14. The novel missense Val94Met (rs17151919) variant in LEP was common in individuals with African ancestry ( minor allele frequency ( MAF) AFR=8%; MAFEUR=0.02%) and was associated with 0.34 standard deviations lower leptin concentrations per Met94 allele in adults (P=2x10-16, n=3,901). Using in vitro analyses, we showed that the Met94 allele decreases leptin secretion (P=0.025). The Met94 allele was associated with higher BMI in young African-ancestry children (P=0.002, n=2,030) but not in adults, suggesting leptin regulates early adiposity.

Genetic studies have identified “favourable adiposity” variants - where the allele associated with higher adiposity is associated with lower risk of type 2 diabetes. We took a novel approach to find more of these alleles and find the underlying mechanisms. We first performed a genome wide association study (GWAS) of body fat percentage using 451000 individuals from UK Biobank. Second, we used published genetic data in a multivariate test to find alleles associated with higher adiposity but a “favourable” metabolic phenotype: higher HDL-C, adiponectin, sex hormone binding globulin, but lower triglycerides, fasting insulin and alanine transaminase. Third, we used abdominal imaging data from 4 studies to define the adiposity phenotype in more detail. We identified 620 variants associated with body fat percentage (p “Favourable adiposity” alleles associated with higher BMI but lower risk of type 2 diabetes are characterized by higher subcutaneous but lower liver fat. Disclosure H. Yaghootkar: None. Y. Ji: None. A.M. Yiorkas: None. F. Frau: None. D. Mook-Kanamori: None. R. de Mutsert: None. J. Tyrrell: None. S.E. Jones: None. R. Beaumont: None. A.R. Wood: None. L. Thomas: None. K.V. Allebrandt: None. N. Stefan: Consultant; Self; Merck Sharp & Dohme Corp.. Speaker's Bureau; Self; Novo Nordisk A/S. Research Support; Self; AstraZeneca. Speaker's Bureau; Self; AstraZeneca, OmniaMed Ltd.. H. Staiger: None. E. Pearson: Speaker's Bureau; Self; Eli Lilly and Company. A.I. Blakemore: None. J.D. Bell: None. T.M. Frayling: None.

Gene-lifestyle interactions of relevance to type 2 diabetes have been difficult to identify but may help stratify groups of people at especially high risk. In large genetic studies, measures of diabetes risk factors such as diet and activity are usually limited to self-reported data. We used genetic and accelerometer data from 97,8individuals to test for genetic interactions with physical activity and sleep lifestyles. We tested the hypothesis that lower levels of physical activity and abnormal sleep patterns accentuate genetic susceptibility to obesity. We used 82,322 individuals from the UK Biobank, 10,885 from the Women’s Genome Health Study (WGHS) and 4,599 individuals from the Avon Longitudinal Study of Parents and Children (ALSPAC). We derived measures for physical activity and sleep, including total physical activity, time spent undertaking sedentary, light, moderate and vigorous activity, and sleep duration and sleep efficiency. We used BMI as the outcome and tested for interactions between a BMI genetic risk score (GRS) and the accelerometer derived measures of activity and sleep. We also performed several negative control experiments to test for residual confounding. We found evidence of gene-activity interactions. For example, in the UK Biobank, when analysing non-sedentary physical activity, the 10% of individuals carrying the most BMI-raising alleles were 5.9kg heavier if they were inactive compared to active (for height 1.73m). In contrast, the 10% of individuals carrying the fewest BMI-raising alleles were 4.1kg heavier if they were inactive compared to active (Pinteraction=4×10-4). This observation was consistent in the WGHS and ALSPAC data although not all tests reached P Disclosure A.R. Wood: None. S.E. Jones: None. R. Richmond: None. S. Ahmad: None. H. Yaghootkar: None. R. Beaumont: None. K.S. Ruth: None. M. Tuke: None. A. Murray: None. R.M. Freathy: None. G. Davey Smith: None. I. Lee: None. N. Timpson: None. D. Chasman: None. Z. Kutalik: None. M.N. Weedon: None. J. Tyrrell: None. T.M. Frayling: None.

Recent genetic studies have identified alleles associated with opposite effects on adiposity and risk of type 2 diabetes. We aimed to identify more of these variants and test the hypothesis that such “favourable adiposity” alleles are associated with higher subcutaneous fat and lower ectopic fat. We combined magnetic resonance imaging (MRI) data with genome-wide association studies (GWAS) of body fat % and metabolic traits. We report 14 alleles, including 7 newly characterized alleles, associated with higher adiposity, but a favourable metabolic profile. Consistent with previous studies, individuals carrying more “favourable adiposity” alleles had higher body fat % and higher BMI, but lower risk of type 2 diabetes, heart disease and hypertension. These individuals also had higher subcutaneous fat, but lower liver fat and lower visceral-to-subcutaneous adipose tissue ratio. Individual alleles associated with higher body fat % but lower liver fat and lower risk of type 2 diabetes included those in PPARG, GRB14 and IRS1, whilst the allele in ANKRD55 was paradoxically associated with higher visceral fat but lower risk of type 2 diabetes. Most identified “favourable adiposity” alleles are associated with higher subcutaneous and lower liver fat, a mechanism consistent with the beneficial effects of storing excess triglyceride in metabolically low risk depots. Diabetes UK RD Lawrence fellowship, European Research Council, Wellcome Trust and Royal Society grant, European Regional Development Fund, Medical Research Council, German Federal Ministry of Education and Research, German Research Foundation, Innovative Medicines Initiative Joint Undertaking, European Union's Seventh Framework Programme, Dutch Science Organisation, Scottish Government Health Directorates, Scottish Funding Council and Medical Research Council UK and the Wellcome Trust.

One in four adults worldwide are either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, is most informative for predicting risk of obesity sequellae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio adjusted for BMI (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

SummaryFibroblast Growth Factor 21 (FGF21) is a hormone that induces weight loss in model organisms. These findings have led to trials in humans of FGF21 analogues with some showing weight loss and lipid lowering effects. Recent genetic studies have shown that a common allele in the FGF21 gene alters the balance of macronutrients consumed but there was little evidence of an effect on metabolic traits. We studied a common FGF21 allele (A:rs838133) in 451,099 people from the UK Biobank study. We replicated the association between the A allele and higher percentage carbohydrate intake. We then showed that this allele is more strongly associated with body fat distribution, with less fat in the lower body, and higher blood pressure, than it is with BMI, where there is only nominal evidence of an effect. These human phenotypes of naturally occurring variation in the FGF21 gene will inform decisions about FGF21’s therapeutic potential.

Women with X chromosome aneuploidy such as 45,X (Turner syndrome) or 47,XXX (Triple X syndrome) present with characteristics including differences in stature, increased cardiovascular disease risk and primary ovarian insufficiency. Many women with X chromosome aneuploidy undergo lifetime clinical monitoring for possible complications. However, ascertainment of cases in the clinic may mean that the phenotypic penetrance is overestimated. Studies of prenatally ascertained X chromosome aneuploidy cases have limited follow-up data and so the long-term consequences into adulthood are often not reported. We aimed to characterise the prevalence and phenotypic consequences of X chromosome aneuploidy in a large population of women over 40 years of age. We detected 30 women with 45,X, 186 with mosaic 45,X/46,XX and 110 with 47,XXX among 244,848 UK Biobank women, using SNP array data. The prevalence of non-mosaic 45,X (1/8,162) and 47,XXX (1/2,226) was lower than expected, but was higher for mosaic 45,X/46,XX (1/1,316). The characteristics of women with 45,X were consistent with the characteristics of a clinically recognised Turner syndrome phenotype, including a 17.2cm shorter stature (SD = 5.72cm; P = 1.5 × 10−53) and 16/30 did not report an age at menarche. The phenotype of women with 47,XXX included taller stature (5.3cm; SD = 5.52cm; P = 5.8 × 10−20), earlier menopause age (5.12 years; SD = 5.1 years; P = 1.2 x 10−14) and a lower fluid intelligence score (24%; SD = 29.7%; P = 3.7 × 10−8). In contrast, the characteristics of women with mosaic 45,X/46,XX were much less pronounced than expected. Women with mosaic 45,X/46,XX were less short, had a normal reproductive lifespan and birth rate, and no reported cardiovascular complications. In conclusion, the availability of data from 244,848 women allowed us to assess the phenotypic penetrance of traits associated with X chromosome aneuploidy in an adult population setting. Our results suggest that the clinical management of women with 45,X/46,XX mosaicism should be minimal, particularly those identified incidentally.FundingNone

Variability in red blood cell volumes (distribution width, RDW) increases with age and is strongly predictive of mortality, incident coronary heart disease and cancer. We investigated inherited genetic variation associated with RDW in 116,666 UK Biobank human volunteers.A large proportion RDW is explained by genetic variants (29%), especially in the older group (60+ year olds, 33.8%, GAS6; the SNP is also an eQTL for this gene in whole blood. RDW-associated exonic genetic signals included a missense variant in PNPLA3, which codes for a triacylglycerol lipase, and a rare (1% frequency) deletion in SMIM1, involved in red blood cell formation.Although increased RDW is predictive of cardiovascular outcomes, this was not explained by known CVD or related lipid genetic risks. The predictive value of RDW for a range of negative health outcomes may in part be due to variants influencing fundamental pathways of aging.

We screened DNA sequence variants on an exome-focused genotyping array in >300,000 participants with replication in >280,000 participants and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice revealed lipid changes consistent with the human data. We utilized mapped variants to address four clinically relevant questions and found the following: (1) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease; (2) outside of the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (3) only some mechanisms of lowering LDL-C seemed to increase risk for type 2 diabetes; and (4) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (e.g., TM6SF2, PNPLA3) tracked with higher liver fat, higher risk for type 2 diabetes, and lower risk for coronary artery disease whereas TG-lowering alleles involved in peripheral lipolysis (e.g., LPL, ANGPTL4) had no effect on liver fat but lowered risks for both type 2 diabetes and coronary artery disease.

H syndrome is a hereditary disease transmitted in an autosomal recessive pattern.It consists of two major clusters of cutaneous and systemic manifestations [1,2].The symptoms include short stature, hyperpigmentation of skin, hypertrichosis, deafness, hypogonadism, anomalies of heart, hyperglycemia(insulin dependent diabetes mellitus) and hepatosplenomegaly [1]. H syndrome is the result of mutations in theSLC29A3 gene that can be homozygous or compound heterozygous. The SLC29A3 gene is located on chromosome 10q22 and encodes a transporter protein that causes passive transportation of nucleosides which is critical for several functions such as DNA and RNA repair [3–5]. Several different mutations withinSLC29A3 can cause H syndrome. In this report, we present a 4.5 year old girl diagnosed with H syndrome based on genetic investigation.

Birth weight (BW) has been shown to be influenced by both fetal and maternal factors and in observational studies is reproducibly associated with future risk of adult metabolic diseases including type 2 diabetes (T2D) and cardiovascular disease. These life-course associations have often been attributed to the impact of an adverse early life environment. Here, we performed a multi-ancestry genome-wide association study (GWAS) meta-analysis of BW in 153,781 individuals, identifying 60 loci where fetal genotype was associated with BW (P&thinsp

Susceptibility to obesity in today’s environment has a strong genetic component. Lower socioeconomic position (SEP) is associated with a higher risk of obesity but it is not known if it accentuates genetic susceptibility to obesity. We aimed to use up to 120,000 individuals from the UK Biobank study to test the hypothesis that measures of socioeconomic position accentuate genetic susceptibility to obesity. We used the Townsend deprivation index (TDI) as the main measure of socioeconomic position, and a 69-variant genetic risk score (GRS) as a measure of genetic susceptibility to obesity. We also tested the hypothesis that interactions between BMI genetics and socioeconomic position would result in evidence of interaction with individual measures of the obesogenic environment and behaviours that correlate strongly with socioeconomic position, even if they have no obesogenic role. These measures included self-reported TV watching, diet and physical activity, and an objective measure of activity derived from accelerometers. We performed several negative control tests, including a simulated environment correlated with BMI but not TDI, and sun protection use. We found evidence of gene-environment interactions with TDI (Pinteraction=3×10−10) such that, within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. We also observed evidence of interaction between sun protection use and BMI genetics, suggesting that residual confounding may result in evidence of non-causal interactions. Our findings provide evidence that relative social deprivation best captures aspects of the obesogenic environment that accentuate the genetic predisposition to obesity in the UK.

The Wellcome Trust provides support for Wellcome Trust United Kingdom Type 2 Diabetes Case Control Collection (GoDARTS) and informatics support is provided by the Chief Scientist Office. The Atherosclerosis Risk in Communities Study (ARIC) is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. H.Y., A.R.W. and T.M.F. are supported by the European Research Council grant: 323195; SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. S.E.J. is funded by the Medical Research Council (grant: MR/M005070/1). M.A.T., M.N.W. and A.M. are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). R.M.F. is a Sir Henry Dale Fellow (Wellcome Trust and Royal Society grant: 104150/Z/14/Z). R.B. is funded by the Wellcome Trust and Royal Society grant: 104150/Z/14/Z. J.T. is funded by a Diabetes Research and Wellness Foundation Fellowship. Z.K. received financial support from the Leenaards Foundation, the Swiss Institute of Bioinformatics and the Swiss National Science Foundation (31003A-143914) and SystemsX.ch ((40)). The work of M.P.B was supported by the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award Number T32HL007779. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. E.R.P. holds a WT New investigator award 102820/Z/13/Z.

Adiponectin is strongly inversely associated with insulin resistance and type 2 diabetes, but its causal role remains controversial. We used a Mendelian randomization approach to test the hypothesis that adiponectin causally influences insulin resistance and type 2 diabetes. We used genetic variants at the ADIPOQ gene as instruments to calculate a regression slope between adiponectin levels and metabolic traits (up to 31,000 individuals) and a combination of instrumental variables and summary statistics–based genetic risk scores to test the associations with gold-standard measures of insulin sensitivity (2,969 individuals) and type 2 diabetes (15,960 case subjects and 64,731 control subjects). In conventional regression analyses, a 1-SD decrease in adiponectin levels was correlated with a 0.31-SD (95% CI 0.26–0.35) increase in fasting insulin, a 0.34-SD (0.30–0.38) decrease in insulin sensitivity, and a type 2 diabetes odds ratio (OR) of 1.75 (1.47–2.13). The instrumental variable analysis revealed no evidence of a causal association between genetically lower circulating adiponectin and higher fasting insulin (0.02 SD; 95% CI −0.07 to 0.11; N = 29,771), nominal evidence of a causal relationship with lower insulin sensitivity (−0.20 SD; 95% CI −0.38 to −0.02; N = 1,860), and no evidence of a relationship with type 2 diabetes (OR 0.94; 95% CI 0.75–1.19; N = 2,777 case subjects and 13,011 control subjects). Using the ADIPOQ summary statistics genetic risk scores, we found no evidence of an association between adiponectin-lowering alleles and insulin sensitivity (effect per weighted adiponectin-lowering allele: −0.03 SD; 95% CI −0.07 to 0.01; N = 2,969) or type 2 diabetes (OR per weighted adiponectin-lowering allele: 0.99; 95% CI 0.95–1.04; 15,960 case subjects vs. 64,731 control subjects). These results do not provide any consistent evidence that interventions aimed at increasing adiponectin levels will improve insulin sensitivity or risk of type 2 diabetes.

We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterize their association with intermediate phenotypes, and to investigate their role in type 2 diabetes (T2D) risk among normal-weight, overweight, and obese individuals. We investigated the association of genetic scores with euglycemic-hyperinsulinemic clamp– and oral glucose tolerance test–based measures of insulin resistance and secretion and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight, and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (β in SDs per allele [95% CI], −0.03 [−0.04, −0.01]; P = 0.004). This score was associated with lower BMI (−0.01 [−0.01, −0.0]; P = 0.02) and gluteofemoral fat mass (−0.03 [−0.05, −0.02; P = 1.4 × 10−6) and with higher alanine transaminase (0.02 [0.01, 0.03]; P = 0.002) and γ-glutamyl transferase (0.02 [0.01, 0.03]; P = 0.001). While the secretion score had a stronger association with T2D in leaner individuals (Pinteraction = 0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI or waist strata (Pinteraction > 0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.