Article Figures and data Abstract Editor's evaluation Introduction Methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Some individuals living with obesity may be relatively metabolically healthy, whilst others suffer from multiple conditions that may be linked to adverse metabolic effects or other factors. The extent to which the adverse metabolic component of obesity contributes to disease compared to the non-metabolic components is often uncertain. We aimed to use Mendelian randomisation (MR) and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases. Methods: We selected 37 chronic diseases associated with obesity and genetic variants associated with different aspects of excess weight. These genetic variants included those associated with metabolically 'favourable adiposity' (FA) and 'unfavourable adiposity' (UFA) that are both associated with higher adiposity but with opposite effects on metabolic risk. We used these variants and two sample MR to test the effects on the chronic diseases. Results: MR identified two sets of diseases. First, 11 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 risk of disease: coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, polycystic ovary syndrome, heart failure, atrial fibrillation, chronic kidney disease, renal cancer, and gout. Second, 9 conditions where the non-metabolic effects of excess weight (e.g. mechanical effect) are likely a cause. Here, MR with the FA genetics, despite leading to lower metabolic risk, and MR with the UFA genetics, both indicated higher disease risk: osteoarthritis, rheumatoid arthritis, osteoporosis, gastro-oesophageal reflux disease, gallstones, adult-onset asthma, psoriasis, deep vein thrombosis, and venous thromboembolism. Conclusions: Our results assist in understanding the consequences of higher adiposity uncoupled from its adverse metabolic effects, including the risks to individuals with high body mass index who may be relatively metabolically healthy. Funding: Diabetes UK, UK Medical Research Council, World Cancer Research Fund, National Cancer Institute. Editor's evaluation The authors have conducted a robust and very comprehensive study using Mendelian randomisation to disentangle metabolic and non-metabolic effects of overweight on a long list of disease outcomes. They have tested if effects of overweight work through either or both effects for a particular condition. This is an important topic and can help us better understand how overweight influences risk of several important outcomes. https://doi.org/10.7554/eLife.72452.sa0 Decision letter eLife's review process Introduction Obesity is associated with a higher risk of many diseases, notably metabolic conditions such as type 2 diabetes, but many individuals are often relatively metabolically healthy compared to others of similar body mass index (BMI). Whilst these metabolically healthier individuals may be at lower risk of some obesity-related conditions, they may be at risk of conditions that are linked to other aspects of obesity, such as the load-bearing effects. The burden of obesity on individuals and health-care systems is very large, and in the absence of a widely applicable, sustainable treatment or effective public health measures, it is important to understand the disease consequences of obesity, and how they may be best alleviated, in more detail. To better understand the disease consequences of obesity, many previous studies have used the approach of Mendelian randomisation (MR) (Smith and Ebrahim, 2004). These studies used common genetic variants robustly associated with BMI as proxies for obesity to assess the causal effects of higher BMI on many diseases. MR studies have provided strong evidence that higher BMI leads to osteoarthritis (Tachmazidou et al., 2019), colorectal cancer (Thrift et al., 2015; Suzuki et al., 2021; Bull et al., 2020), and psoriasis (Budu-Aggrey et al., 2019), as well as metabolic conditions such as type 2 diabetes, cardiovascular disease (Hägg et al., 2015), and heart failure (Cheng et al., 2019; Corbin et al., 2016; Fall et al., 2013). Other MR studies indicate that higher BMI may lead to lower risk of some diseases, including postmenopausal breast cancer (Guo et al., 2016) and Parkinson's disease (Noyce et al., 2017). Obesity is heterogeneous – for example, for a given BMI, people vary widely in their amount of fat versus fat free mass, predominantly muscle, and their distribution of fat, predominantly subcutaneous versus ectopic and upper versus lower body fat. Even when there is strong evidence of causality, obesity may lead to disease through a variety of mechanisms. Despite many MR studies testing the role of higher BMI in disease, few have attempted to separate and test the different mechanisms that could lead from obesity to disease. Some MR studies have investigated the effects of fat distribution using genetic variants associated with waist-hip ratio (WHR) adjusted for BMI and shown that adverse fat distribution (more upper body, less lower body) leads to higher risk of metabolic disease (Emdin et al., 2017), some cancers (Cornish et al., 2020), and gastro-oesophageal reflux disease (Green et al., 2020). Previous studies have identified genetic variants associated with more specific measures of adiposity. For example, several studies have characterised variants associated with 'favourable adiposity' (FA) or reduced adipose storage capacity using a variety of approaches (Ji et al., 2019; Lotta et al., 2017; Kilpeläinen et al., 2011; Huang et al., 2021). We recently identified 36 FA alleles which are collectively associated with a favourable metabolic profile, higher subcutaneous fat but lower ectopic liver fat (Ji et al., 2019; Martin et al., 2021), resembling a polygenic phenotype opposite to lipodystrophy (Semple et al., 2011). We also identified 38 unfavourable adiposity (UFA) alleles which are associated with higher fat in subcutaneous and visceral adipose tissue, and higher ectopic liver and pancreatic fat (Ji et al., 2019; Martin et al., 2021), resembling monogenic obesity (Supplementary file 1a). We performed MR studies and showed that FA and UFA have opposite causal effects on six metabolic conditions (Martin et al., 2021). While both FA and UFA were associated with higher adiposity, FA was causally associated with lower risk of type 2 diabetes, heart disease, hypertension, stroke, polycystic ovary syndrome, and non-alcoholic fatty liver disease. In contrast, as expected, UFA was associated with higher risk of these conditions. These results confirmed the ability of the two sets of adiposity variants to partially separate out the metabolic from the non-metabolic effects of higher adiposity. In this study, we aimed to investigate the effects of separate components to higher adiposity on risk of additional metabolic diseases and many non-metabolic diseases. We used genetic variants associated with BMI, body fat percentage, FA, and UFA to understand the components of higher adiposity that are the predominant causes of disease risk. Our findings may give guidance on some obesity-related risks which are not dependent on metabolic consequences, thereby guiding appropriate medical care. Methods Study design An overview of our approach is shown in Figure 1. First, we identified diseases by performing a literature search of studies that had used MR to assess the consequences of BMI on outcome phenotypes. We used the search terms 'BMI and Mendelian randomisation' and 'BMI and Mendelian randomization'. We identified 37 diseases associated with BMI and for which MR studies had previously been performed (Supplementary file 1b). We included all diseases regardless of the MR result in the published study. Second, we reperformed MR studies using BMI as an exposure. Third, for those diseases where MR indicated higher BMI was causal, we tested the effects of body fat percentage to confirm that the causal effect was due to fat mass rather than fat-free mass. Fourth, for diseases where MR suggested the BMI effect was due to excess adiposity, we used genetic variants more specific to the metabolic and non-metabolic components of higher adiposity to help understand the extent to which these factors influence disease. Figure 1 Download asset Open asset Study design. Data sources We used three data sources for disease outcomes: (i) published genome-wide association studies (GWAS; Okada et al., 2014; Nikpay et al., 2015; Jones et al., 2017; Michailidou et al., 2017; Phelan et al., 2017; Scelo et al., 2017; Tsoi et al., 2017; Day et al., 2018; Mahajan et al., 2018; Malik et al., 2018; O'Mara et al., 2018; Roselli et al., 2018; Schumacher et al., 2018; Wray et al., 2018; An et al., 2019; Ferreira et al., 2019; Huyghe et al., 2019; Jansen et al., 2019; Kunkle et al., 2019; Law et al., 2019; Lindström et al., 2019; Morris et al., 2019; Nalls et al., 2019; Shah et al., 2019; Tachmazidou et al., 2019; Tin et al., 2019; Wuttke et al., 2019; Huyghe et al., 2021) and (ii) FinnGen (FinnGen, 2021) as our main results, and (iii) UK Biobank (RRID:SCR_012815; Collins, 2012) as additional validation. FinnGen is a cohort of 176,899 individuals with linked medical records. UK Biobank is a population cohort of >500,000 individuals aged 37–73 years recruited between 2006 and 2010 from across the UK. For the 37 identified diseases, 25 had summary GWAS data available from both a published GWAS consortium and FinnGen, and 12 diseases had GWAS summary data available in FinnGen only. In addition, data from 31 of the 37 diseases were available in the UK Biobank. No GWAS data were available for Barrett's oesophagus, but we included gastro-oesophageal reflux. The characteristics of the studies and measures, disease outcomes, and the definition of cases and controls are described in Supplementary file 1ci–iii. GWAS of UK Biobank participants For the GWAS of 31 diseases available in UK Biobank, we used a linear mixed model implemented in BOLT-LMM to account for population structure and relatedness (Loh et al., 2015). We used age, sex, genotyping platform, study centre, and the first five principal components as covariates in the model. Genetic variants We used four sets of genetic variants as proxies of four exposures (Supplementary file 1d). Body mass index In the broadest category, we used a set of 73 variants independently associated with BMI at genome-wide significance (p<5 × 10–8). These variants were identified in the GIANT consortium of up to 339,224 individuals of European ancestry (Locke et al., 2015). Body fat percentage We used 696 variants from a GWAS in the UK Biobank (Martin et al., 2021). We used bio-impedance measures of body fat % taken by the Tanita BC-418MA body composition analyser in 442,278 individuals of European ancestry. The BMI and body fat percentage variants were partially overlapping (n = 5 variants), but we used exposure-trait-specific weights for each variant. FA variants There are 36 FA variants (Martin et al., 2021). These variants were identified in two steps. First, they were associated (at p<5 × 10–8) with body fat percentage and a composite metabolic phenotype consisting of body fat percentage, HDL-cholesterol, triglycerides, SHBG, alanine transaminase, and aspartate transaminase. Second, in a k-means clustering approach (a hard clustering approach) (Martin et al., 2021), they formed a cluster of variants that were collectively associated with higher HDL-cholesterol, higher SHBG, and lower triglycerides and liver enzymes – resembling a phenotype opposite to lipodystrophy. UFA variants There are 38 UFA variants (Martin et al., 2021). These variants were identified in two steps. First, they were associated (at p<5 × 10–8) with body fat percentage and a composite metabolic phenotype as detailed above. Second, in a k-means clustering approach (Martin et al., 2021), they formed a cluster of variants that were collectively associated with lower HDL-cholesterol, lower SHBG, and higher triglycerides and liver enzymes - resembling monogenic obesity. Mendelian randomisation We investigated the causal associations between the four exposures (BMI, body fat percentage, FA, and UFA) and 37 disease outcomes by performing two-sample MR analysis (Pierce and Burgess, 2013). We used the inverse-variance weighted (IVW) approach as our main analysis, and MR-Egger and weighted median as sensitivity analyses in order to detect and partially account for unidentified pleiotropy of our genetic instruments. For BMI, we used effect size estimates from the GWAS of BMI (Locke et al., 2015), and for body fat percentage, FA, and UFA, we used effect size estimates from the GWAS of body fat percentage (442,278 European ancestry individuals from the UK Biobank study) (Ji et al., 2019). To estimate the effects of variants on our disease outcomes, we used two main sources of data: FinnGen GWAS summary results and published GWAS of the same diseases (Supplementary file 1ci–ii). We performed MR within each data source and then meta-analysed the results across the two datasets using a random-effects model with the R package metafor (RRID:SCR_003450; Viechtbauer, 2010), where the data was available in both. For one published GWAS (the GECCO consortium), we only had information for FA and UFA variants. To provide further MR evidence, we used a third source of disease data – disease status in the UK Biobank (Supplementary file 1ciii). We ran the same models but did not meta-analyse with published GWAS and FinnGen because most of the body fat percentage, FA, and UFA variants were identified in the UK Biobank. We obtained heterogeneity Q statistics for each IVW MR and MR-Egger, and I2 statistics for each MR-Egger analysis using the MendelianRandomization R package (Yavorska and Burgess, 2017). All statistical analyses were conducted using R software (R Development Core Team, 2020). Given the number of tests performed, we used a Benjamini–Hochberg false discovery rate (FDR) procedure and an FDR of 0.1 to define meaningful results for each of the four exposures (Benjamini and Hochberg, 1995). Results We identified 37 diseases as associated with obesity and for which MR studies had previously been performed. Of these 37, 5 metabolic conditions were part of our previous study that validated the use of FA and UFA genetic variants as a way of partially separating the metabolic from non-metabolic components of higher adiposity (Martin et al., 2021). Once we had tested BMI and body fat percentage, we further characterised the likely causal component of higher adiposity using FA and UFA variants as follows (Figure 1, step 5): (i) diseases with evidence that the metabolic effect of higher adiposity is causal. Here, MR using the UFA genetic variants indicated that higher adiposity with its adverse metabolic consequences was causal to disease, whilst MR using the FA genetic variants indicated that higher adiposity with favourable metabolic effects was protective (at FDR 0.1). (ii) Diseases with evidence that there is a non-metabolic causal effect (e.g. mechanical effect, psychological/adverse social effect). Here, MR using the FA genetic variants indicated that higher adiposity without its adverse metabolic consequences was likely contributing to the disease, as well as the MR using the UFA genetic variants. (iii) Diseases with evidence that there is a combination of causal effects but with a predominantly metabolic component. Here, MR using the UFA genetic variants indicated that higher adiposity with its adverse metabolic consequences was causal to disease, and MR using the FA genetic variants was directionally consistent with higher adiposity with favourable metabolic effects being protective but FDR > 0.1. (iv) Diseases with evidence that there is a combination of causal effects but with a predominantly non-metabolic component. Here, MR using the UFA genetic variants indicated that higher adiposity without its adverse metabolic consequences was likely contributing to the disease, and MR of the FA genetic variants was directionally consistent with this but FDR > 0.1. We grouped these disease outcomes into seven major categories – cardiovascular and metabolic conditions, musculoskeletal, gastrointestinal, nervous, integumentary and respiratory systems, and cancer. MR analysis of five conditions (coronary artery disease, hypertension, stroke, type 2 diabetes, and polycystic ovary syndrome) was part of our previous study (Martin et al., 2021). We focused on the MR of body fat percentage if a causal effect of BMI was indicated, and the MR of FA and UFA if a causal effect of BMI and body fat percentage was indicated, but have presented all results in Supplementary file 1e for completeness. Where random-effects meta-analyses were performed, the heterogeneity statistics are given in Supplementary file 1f. (i) Diseases with evidence that the metabolic effect of higher adiposity is causal When comparing the MR analyses for FA and UFA, our results provided evidence that the metabolic effect of higher adiposity is contributing causally to coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, and gout (Figures 2—12, Supplementary file 1e). For stroke, our results were consistent when using sub-types of the condition (Figure 3—figure supplement 1, Supplementary file 1g). Our results also indicated that the metabolic effect of higher adiposity is causal to chronic kidney disease, although the results from BMI and body fat percentage were less conclusive (Figure 3). Figure 2 Download asset Open asset The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), "favourable adiposity" (FA) and "unfavourable adiposity" (UFA) on type 2 diabetes, hypertension, polycystic ovary syndrome and coronary artery disease. The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results. Figure 3 with 1 supplement see all Download asset Open asset The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), "favourable adiposity" (FA) and "unfavourable adiposity" (UFA) on stroke, peripheral artery disease, heart failure, atrial fibrillation and chronic kidney disease. The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results. Figure 4 Download asset Open asset The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), "favourable adiposity" (FA) and "unfavourable adiposity" (UFA) on venous thromboembolism, deep vein thrombosis, pulmonary embolism and abdominal aneurysm. The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results. Figure 5 with 1 supplement see all Download asset Open asset The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), "favourable adiposity" (FA) and "unfavourable adiposity" (UFA) on gout, osteoarthritis, osteoporosis and rheumatoid arthritis. The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results. Figure 6 Download asset Open asset The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), "favourable adiposity" (FA) and "unfavourable adiposity" (UFA) on gallstones and gastro-oesophageal reflux disease. The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results. Figure 7 Download asset Open asset The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), "favourable adiposity" (FA) and "unfavourable adiposity" (UFA) on Alzheimer's disease, depression, multiple sclerosis and Parkinson's disease. The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results. Figure 8 Download asset Open asset The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), "favourable adiposity" (FA) and "unfavourable adiposity" (UFA) on psoriasis. The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results. Figure 9 with 1 supplement see all Download asset Open asset The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), "favourable adiposity" (FA) and "unfavourable adiposity" (UFA) on adult-onset asthma. The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results. Figure 10 with 1 supplement see all Download asset Open asset The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), "favourable adiposity" (FA) and "unfavourable adiposity" (UFA) on Barrett's oesophagus, breast cancer, cancer myeloma and colorectal cancer. The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results. Figure 11 with 2 supplements see all Download asset Open asset The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), "favourable adiposity" (FA) and "unfavourable adiposity" (UFA) on endometrial and lung cancer, meningioma and ovarian cancer. The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results. Figure 12 Download asset Open asset The inverse-variance weighted (IVW) two-sample MR analysis/meta-analysis of the effects of body mass index (BMI), body fat percentage (BFP), "favourable adiposity" (FA) and "unfavourable adiposity" (UFA) on pancreatic, prostate, renal and thyroid cancer. The error bars represent the 95% confidence intervals of the IVW estimates in odds ratio per standard deviation change in genetically determined BMI, body fat percentage, FA and UFA. Italics give our best interpretation of the data using the FDR 0.1 results. (ii) Diseases with evidence that there is a non-metabolic causal effect When comparing the MR analyses for FA and UFA, our results provided evidence that some non-metabolic effect of higher adiposity is contributing causally to venous thromboembolism, deep vein thrombosis, osteoarthritis, and rheumatoid arthritis (Figures 2—12, Supplementary file 1e). For osteoarthritis, our results were consistent when using sub-types of the condition (Figure 5—figure supplement 1, Supplementary file 1g). (iii) Diseases with evidence that there is a combination of causal effects but with a predominantly metabolic component When comparing the MR analyses for FA and UFA, our results provided evidence that the metabolic effect of higher adiposity is the predominate cause of the link between higher BMI and polycystic ovary syndrome, heart failure, and atrial fibrillation. Our results also provided evidence that the metabolic effect of higher adiposity is the predominate cause of the link between higher BMI and a reduced risk of breast cancer and higher risk of renal cancer, although the results from body fat percentage were less conclusive (Figures 2—12, Supplementary file 1e). (iv) Diseases with evidence that there is a combination of causal effects but with a predominantly non-metabolic component When comparing the MR analyses for FA and UFA, our results suggested that some non-metabolic effect of higher adiposity is the predominant cause of the link between higher BMI and gallstones, gastro-oesophageal reflux disease, adult-onset asthma, and psoriasis (Figures 2—12, Supplementary file 1e). Our results also indicated that some non-metabolic effect of higher adiposity is causal to osteoporosis, although the results from BMI were less conclusive (Figure 5). Our results found no evidence (at p<0.05) of an effect of BMI or adiposity on child-onset asthma (Figure 9—figure supplement 1, Supplementary file 1g). All other disease outcomes Fifteen disease outcomes did not fit the criteria for definitions i–iv. For five of these conditions, our MR results indicated a causal effect of higher BMI or adiposity, but results from FA and UFA were inconclusive: pulmonary embolism, depression, endometrial cancer, lung cancer, and prostate cancer (Figures 2—12, Supplementary file 1e). Additionally, we identified some evidence of a metabolic effect of higher adiposity with colorectal and ovarian cancer, with the MR of FA indicating lower odds of colorectal (0.67 [0.52, 0.85]) and ovarian (0.35 [0.18, 0.70]) cancers, but MR of UFA was consistent with the null (p>0.05). For colorectal and ovarian cancer, our results were consistent when using sub-types of the conditions (Figure 10—figure supplement 1, Figure 11—figure supplements 1 and 2, Supplementary file 1g). Sensitivity analyses Out of 82 disease outcomes (including subtypes), weighted median MR results were directionally consistent with IVW analysis for 75 diseases for BMI and 73 for body fat percentage, with 33 and 47 of these having p<0.05, respectively. For FA and UFA, where sub-type colorectal cancer data was available, the total number of diseases was 87, and 76 were directionally consistent for both exposures, with 22 and 39 having p<0.05, respectively. MR-Egger results were broadly consistent with the primary IVW MR results, indicating that pleiotropy (variants acting on the outcomes through more than one mechanism) appears to have had limited effect on our results. MR-Egger results were directionally consistent with IVW for 71 diseases for BMI and 70 for body fat percentage, with 25 and 38 of these having p<0.05, respectively. For FA and UFA, MR-Egger was directionally consistent for 60 and 67 diseases, with 6 and 15 having p<0.05, respectively (Supplementary file 1g). Of the 31 diseases available in the UK Biobank, the IVW analysis of these was directionally consistent with the FinnGen and/or published GWAS analysis for 28, 27, 24, and 27 traits for BMI, body fat percentage, FA, and UFA, respectively (Supplementary file 1h). Of these, 18, 21, 9, and 16 had p<0.05, respectively. Discussion We used a genetic approach to understand the role of higher adiposity uncoupled from its adverse metabolic effects in mechanisms linking obesity to higher risk of disease. We first used MR to provide evidence that higher BMI was causally associated with 21 diseases, broadly consistent with those from previous studies. For the majority (17) of these diseases, our results indicated that the BMI effect was predominantly due to excess adiposity rather than a non-fat mass component to BMI. We then used a more specific approach to test the separate roles of higher adiposity with and without its adverse metabolic effects. We provided genetic evidence that the adverse metabolic consequences of higher BMI lead to coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, polycystic ovary syndrome, heart failure, atrial fibrillation, chronic kidney disease, renal cancer, and gout, and the adverse non-metabolic consequences of higher BMI likely contribute to osteoarthritis, rheumatoid arthritis, osteoporosis, gastro-oesophageal reflux disease, gallstones, adult-onset asthma, psoriasis, deep vein thrombosis, and venous thromboembolism. Understanding the reasons why obesity leads to disease is important in order to
