We identified 37 diseases as associated with obesity and for which MR studies had previously been performed. We next used genetic variants more specific to the adiposity, metabolic and non-metabolic components of higher BMI to help understand the extent to which these factors influence disease. Once we had tested BMI and body fat percentage, we further characterised the likely causal component of higher adiposity as follows (Supplementary 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 p<0.05). We considered the BMI component to diseases as predominantly metabolic when the MR using UFA variants indicated (at p<0.05) a higher risk of disease and the MR using FA genetic variants was directionally consistent with a protective effect but was less conclusive (p>0.05).
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 causal to disease, as well as the MR using the UFA genetic variants. We considered the BMI component to diseases as predominantly non-metabolic when the MR using UFA variants indicated (at p<0.05) a higher risk of disease and the MR using FA genetic variants was directionally consistent with a risk effect but was less conclusive (p>0.05).When the MR using FA genetic variants indicated (at p<0.05) a higher risk of disease and the MR using UFA genetic variants was directionally consistent with a risk effect (p>0.05) we considered these diseases as likely having a non-metabolic component.
We grouped these disease outcomes into eight major organ systems and cancers. Where random-effects meta-analyses were performed, the heterogeneity statistics are given in Supplementary Table 4.
Cardiovascular system
Diseases in this category included coronary artery disease, peripheral artery disease, hypertension, stroke, heart failure, aneurysm, atrial fibrillation, venous thromboembolism, deep vein thrombosis, and pulmonary embolism (Table 1). MR analysis provided evidence for a causal association between higher BMI and higher odds of eight of these diseases, the exceptions being pulmonary embolism and aortic aneurysm. For each of these 8 diseases, our MR analysis using body fat percentage as the exposure indicated that the risk was due to excess adiposity.
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 and stroke. In addition, our results provided evidence that the metabolic effect of higher adiposity is the predominate cause of the link between higher BMI and heart failure and atrial fibrillation. For example, the MR analyses indicated the opposite direction of effects of FA and UFA with stroke; a 1-SD higher genetically-instrumented FA was associated with 0.65 [0.52, 0.83] lower odds of stroke, while a 1-SD higher genetically-instrumented UFA was associated with 1.43 [1.23, 1.67] higher odds of stroke. In contrast, the MR analysis provided evidence that a non-metabolic effect of higher adiposity is causing venous thromboembolism and deep vein thrombosis. For example, the MR analyses indicated the same direction of effects of FA and UFA with venous thromboembolism; a 1-SD higher genetically-instrumented FA was associated with 2.52 [1.82, 3.47] higher odds of venous thromboembolism, and a 1-SD higher genetically-instrumented UFA was associated with 1.63 [1.25, 2.13] higher odds of venous thromboembolism (Figure 1a-b, Table 1). For stroke, our results were consistent when using sub-types of the condition (Supplementary Figure 2a, Supplementary Table 5).
Endocrine system
Diseases in this category included type 2 diabetes and polycystic ovary syndrome. MR analysis provided evidence for a causal association between higher BMI and higher odds of both of these diseases. Our MR analysis using body fat percentage as the exposure, indicated that the risk was due to excess adiposity.
When comparing the MR analyses for FA and UFA our results provided evidence that the metabolic effect of higher adiposity is causing type 2 diabetes and is the predominant cause of the link between BMI and polycystic ovary syndrome. For example, the MR analyses indicated opposing effects of FA and UFA with type 2 diabetes, with a 1-SD genetically-instrumented FA associated with a 0.11 [0.08, 0.16] lower odds of type 2 diabetes, while a 1-SD genetically-instrumented UFA was associated with 5.50 [4.29, 7.05] higher odds of type 2 diabetes (Figure 1c, Table 1).
Renal system
The disease in this category was chronic kidney disease. MR analysis provided evidence for a causal association between higher BMI and higher odds of chronic kidney disease (1.21 [1.08, 1.36]). Our MR analysis using body fat percentage as the exposure indicated that the risk was due to excess adiposity (1.25 [0.98, 1.59]).
When comparing the MR analyses for FA and UFA our results provided evidence of a metabolic effect because FA and UFA had opposing directions of the effects; a 1-SD higher genetically-instrumented FA was associated with 0.64 [0.48, 0.84] lower odds of chronic kidney disease, while there was some evidence of an association between UFA and higher odds of chronic kidney disease (1.19 [0.97, 1.45]) (Figure 1d, Table 1).
Musculoskeletal system
Diseases in this category included gout, osteoarthritis, rheumatoid arthritis and osteoporosis. MR analysis provided evidence for a causal association between higher BMI and higher odds of gout, osteoarthritis and rheumatoid arthritis. Our MR analysis using body fat percentage as the exposure, indicated that for all three diseases the risk was due to excess adiposity.
When comparing the MR analyses for FA and UFA our results provided evidence that the metabolic effect of higher adiposity is causing gout. For example, the MR analyses indicated opposing effects of FA and UFA with gout, with a 1-SD genetically-instrumented FA associated with 0.44 [0.29, 0.68] lower odds of gout, while a 1-SD genetically-instrumented UFA was associated with 2.49 [1.88, 3.29] higher odds of gout. In contrast, the MR analysis provided evidence that there is a non-metabolic effect of higher adiposity causing osteoarthritis and rheumatoid arthritis. For example, the MR analyses indicated the same direction of the effects of FA and UFA with osteoarthritis; a 1-SD higher genetically-instrumented FA was associated with 1.45 [1.19, 1.76] higher odds of osteoarthritis, and a 1-SD higher genetically-instrumented UFA was associated with 2.20 [1.64, 2.95] higher odds of osteoarthritis (Figure 1e, Table 1). For osteoarthritis, our results were consistent when using sub-types of the condition (Supplementary Figure 2b, Supplementary Table 5).
Gastrointestinal system
Diseases in this category included gastro-oesophageal reflux disease and cholelithiasis (gallstones). MR analysis provided evidence for a causal association between higher BMI and higher odds of both diseases. For both diseases, our MR analysis using body fat percentage as the exposure indicated that the risk was due to excess adiposity.
When comparing the MR analyses for FA and UFA our results suggested that the non-metabolic effect of higher adiposity is the predominant cause of the link between higher BMI and gallstones. For example, a 1-SD higher genetically-instrumented UFA was associated with 2.55 [1.88, 3.45] higher odds of gallstones and higher genetically-instrumented FA was associated with higher risk (1.37 [0.86, 2.19]) but with less conclusive evidence (p>0.05) (Figure 1f, Table 1). For gastro-oesophageal reflux disease, the results were less conclusive but the same direction and similar effect sizes suggested a non-metabolic effect (Figure 1g, Table 1).
Nervous system
Diseases in this category included depression, Parkinson's disease, multiple sclerosis and Alzheimer's disease. MR analysis provided no evidence (at p<0.05) for a causal association between BMI and any of these diseases. For depression, our MR analysis indicated that excess adiposity was a risk factor (1.19 [1.11, 1.28]).
When comparing the MR analyses for FA and UFA there was no conclusive evidence (at p<0.05) of a non-metabolic or metabolic effect on depression, with both MR of UFA and FA directionally consistent with higher risk, but results were consistent with the null: higher FA (1.20 [0.98, 1.48] and higher UFA (1.02 [0.81, 1.30]) (Figure 1g, Table 1).
Integumentary System
Diseases in this category included psoriasis. MR analysis provided evidence for a causal association between higher BMI and higher odds of psoriasis (1.62 [1.20, 2.19]). Our MR analysis using body fat percentage as the exposure indicated that the risk was due to excess adiposity (1.78 [1.54, 2.05]).
When comparing the MR analyses for FA and UFA, our results suggested that the non-metabolic effect of higher adiposity is the predominant cause of the link between higher BMI and psoriasis. A 1-SD genetically-instrumented UFA was associated with a 2.11 [1.49, 2.99] higher odds of psoriasis and higher genetically-instrumented FA was associated with higher odds (1.20 [0.70, 2.06]) but this result was consistent with the null (p>0.05) (Figure 1h, Table 1).
Respiratory system
The diseases in this category included adult-onset asthma. MR analysis provided evidence for a causal association between higher BMI and higher odds of adult-onset asthma 1.25 [1.03, 1.52]. Our MR analysis using body fat percentage as the exposure indicated that the risk was due to excess adiposity (1.43 [1.25, 1.63]).
When comparing the MR analyses for FA and UFA, our results did not provide conclusive evidence for either a non-metabolic or metabolic effect. Whilst the MR analyses indicated the same direction of the effects of FA (1.14 [0.88,1.49]) and UFA (1.34 [0.97, 1.87]) with higher odds of adult-onset asthma, these results included the null (p<0.05) (Figure 1i, Table 1). Our results when using child-onset asthma are given in Supplementary Figure 2c and Supplementary Table 5.
Cancer
Diseases in this category included breast cancer, endometrial cancer, renal cancer, meningioma, prostate cancer, myeloma, pancreatic cancer, colorectal cancer, lung cancer, Barrett's oesophagus, ovarian cancer, and thyroid cancer. MR analysis provided evidence for a causal association between higher BMI and higher odds of endometrial (1.82 [1.26, 2.63]) and renal cancer (1.47 [1.12, 1.92]), and higher BMI and lower odds of breast cancer (0.60 [0.51, 0.70]). The MR evidence that excess adiposity was the predominant cause of the link between higher BMI and these three cancers was less clear than for other types of disease (Figure 1j-l, Table 1).
When comparing the MR analyses for FA and UFA our results did not provide consistent evidence for either a non-metabolic or a metabolic effect. 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 (Supplementary Figure 2d-f, Supplementary Table 5).
Sensitivity analyses
Out of a possible 82 total study-specific traits, weighted median MR results were directionally consistent with IVW analysis for 75 traits 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 traits was 87, and 76 were directionally consistent for both exposures, with 22 and 39 having p<0.05 respectively. Meanwhile, MR-Egger results were directionally consistent with IVW for 71 traits 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 traits, with 6 and 15 having p<0.05 respectively (Supplementary Table 5). Of the 37 identified diseases, 31 were available in the UK Biobank, and the IVW analysis of these were 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 Table 6). Of these, 18, 21, 9 and 16 had p<0.05 respectively.