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Background: Ovarian cancer (OC) is commonly diagnosed among older women who have comorbidities. This hypothesis‐free phenome‐wide association study (PheWAS) aimed to identify comorbidities associated with OC, as well as traits that share a genetic architecture with OC. Methods: We used data from 181,203 white British female UK Biobank participants and analysed OC and OC subtype‐specific genetic risk scores (OC‐GRS) for an association with 889 diseases and 43 other traits. We conducted PheWAS and colocalization analyses for individual variants to identify evidence for shared genetic architecture. Results: The OC‐GRS was associated with 10 diseases, and the clear cell OC‐GRS was associated with five diseases at the FDR threshold (p = 5.6 × 10−4). Mendelian randomizaiton analysis (MR) provided robust evidence for the association of OC with higher risk of "secondary malignant neoplasm of digestive systems" (OR 1.64, 95% CI 1.33, 2.02), "ascites" (1.48, 95% CI 1.17, 1.86), "chronic airway obstruction" (1.17, 95% CI 1.07, 1.29), and "abnormal findings on examination of the lung" (1.51, 95% CI 1.22, 1.87). Analyses of lung spirometry measures provided further support for compromised respiratory function. PheWAS on individual OC variants identified five genetic variants associated with other diseases, and seven variants associated with biomarkers (all, p ≤ 4.5 × 10−8). Colocalization analysis identified rs4449583 (from TERT locus) as the shared causal variant for OC and seborrheic keratosis. Conclusions: OC is associated with digestive and respiratory comorbidities. Several variants affecting OC risk were associated with other diseases and biomarkers, with this study identifying a novel genetic locus shared between OC and skin conditions. [ABSTRACT FROM AUTHOR]

Aims: Lipid‐lowering medications are widely used to control blood cholesterol levels and manage a range of cardiovascular and lipid disorders. We aimed to explore the possible associations between LDL lowering and multiple disease outcomes or biomarkers. Methods: We performed a Mendelian randomization phenome‐wide association study (MR‐PheWAS) in 337 475 UK Biobank participants to test for associations between four proposed LDL‐C‐lowering genetic risk scores (PCSK9, HMGCR, NPC1L1 and LDLR) and 1135 disease outcomes, with follow‐up MR analyses in 52 serum, urine, imaging and clinical biomarkers. We used inverse‐variance weighted MR in the main analyses and complementary MR methods (weighted median, weighted mode, MR‐Egger and MR‐PRESSO) as sensitivity analyses. We accounted for multiple testing with false discovery rate correction (P < 2.0 × 10−4 for phecodes, P < 1.3 × 10−2 for biomarkers). Results: We found evidence for an association between genetically instrumented LDL lowering and 10 distinct disease outcomes, suggesting potential causality. All genetic instruments were associated with hyperlipidaemias and cardiovascular diseases in the expected directions. Biomarker analyses supported an effect of LDL‐C lowering through PCSK9 on lung function (FEV [beta per 1 mg/dL lower LDL‐C −1.49, 95% CI −2.21, −0.78]; FVC [−1.42, 95% CI −2.29, −0.54]) and through HMGCR on hippocampal volume (beta per 1 mg/dL lower LDL‐C 6.09, 95% CI 1.74, 10.44). Conclusions: We found genetic evidence to support both positive and negative effects of LDL‐C lowering through all four LDL‐C‐lowering pathways. Future studies should further explore the effects of LDL‐C lowering on lung function and changes in brain volume. [ABSTRACT FROM AUTHOR]

Background: Cancer is a leading cause of morbidity and mortality worldwide, and better understanding of the risk factors could enhance prevention. Methods: We conducted a hypothesis‐free analysis combining machine learning and statistical approaches to identify cancer risk factors from 2828 potential predictors captured at baseline. There were 459,169 UK Biobank participants free from cancer at baseline and 48,671 new cancer cases during the 10‐year follow‐up. Logistic regression models adjusted for age, sex, ethnicity, education, material deprivation, smoking, alcohol intake, body mass index and skin colour (as a proxy for sun sensitivity) were used for obtaining adjusted odds ratios, with continuous predictors presented using quintiles (Q). Results: In addition to smoking, older age and male sex, positively associating features included several anthropometric characteristics, whole body water mass, pulse, hypertension and biomarkers such as urinary microalbumin (Q5 vs. Q1 OR 1.16, 95% CI = 1.13–1.19), C‐reactive protein (Q5 vs. Q1 OR 1.20, 95% CI = 1.16–1.24) and red blood cell distribution width (Q5 vs. Q1 OR 1.18, 95% CI = 1.14–1.21), among others. High‐density lipoprotein cholesterol (Q5 vs. Q1 OR 0.84, 95% CI = 0.81–0.87) and albumin (Q5 vs. Q1 OR 0.84, 95% CI = 0.81–0.87) were inversely associated with cancer. In sex‐stratified analyses, higher testosterone increased the risk in females but not in males (Q5 vs. Q1 ORfemales 1.23, 95% CI = 1.17–1.30). Phosphate was associated with a lower risk in females but a higher risk in males (Q5 vs. Q1 ORfemales 0.94, 95% CI = 0.90–0.99 vs. ORmales 1.09, 95% CI 1.04–1.15). Conclusions: This hypothesis‐free analysis suggests personal characteristics, metabolic biomarkers, physical measures and smoking as important predictors of cancer risk, with further studies needed to confirm causality and clinical relevance. [ABSTRACT FROM AUTHOR]

Objective: In this Mendelian randomization (MR) study, the objective was to investigate the causal effect of metabolically different adiposity subtypes on osteoarthritis. Methods: We performed 2‐sample MR using summary‐level data for osteoarthritis (10,083 cases and 40,425 controls) from a genome‐wide association using the UK Biobank, and for site‐specific osteoarthritis from the Arthritis Research UK Osteoarthritis Genetics consortium. We used 3 classes of genetic instruments, which all increase body mass index but are associated with different metabolic profiles (unfavorable, neutral, and favorable). Primary analysis was performed using inverse variance weight (IVW), with additional sensitivity analysis from different MR methods. We further applied a nonlinear MR using UK Biobank data to understand the nature of the adiposity–osteoarthritis relationship. Results: Greater metabolically unfavorable and metabolically neutral adiposity were associated with higher odds of osteoarthritis (IVW odds ratio [OR] 1.56 [95% confidence interval (95% CI) 1.31, 1.85] and OR 1.60 [95% CI 1.15, 2.23], respectively). The estimate for the association between metabolically favorable adiposity and osteoarthritis was similar, although with notable imprecision (OR 1.55 [95% CI 0.70, 3.41]). Using site‐specific osteoarthritis, metabolically unfavorable, neutral, and favorable adiposity were all associated with higher odds of knee osteoarthritis (OR 1.44 [95% CI 1.04, 1.98], OR 2.28 [95% CI 1.04, 4.99], and OR 6.80 [95% CI 2.08, 22.19], respectively). We found generally consistent estimates with a wider confidence interval crossing the null from other MR methods. The nonlinear MR analyses suggested a nonlinear relationship between metabolically unfavorable adiposity and osteoarthritis (Pnonlinear = 0.003). Conclusion: Metabolic abnormalities did not explain the association between greater adiposity and the risk of osteoarthritis, which might suggest that the association is largely due to a mechanical effect on the joints. [ABSTRACT FROM AUTHOR]

Aims: To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia. Materials and methods: Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self‐organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition. Results: In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high‐density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV (βstandardized −0.20, 95% confidence interval [CI] −0.24 to −0.16), HV (βstandardized −0.09, 95% CI −0.13 to −0.04), WMH volume (βstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (βstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C‐reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition (βstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (βstandardized −0.15, 95% CI −0.16 to −0.14) and HV (βstandardized −0.11, 95% CI −0.12 to −0.10), and between BP and WMH volume (βstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP). Conclusions: Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations. [ABSTRACT FROM AUTHOR]

Background: This study aimed to explore the association between depression and body mass index (BMI), and to investigate whether genetic susceptibility to high BMI is different among individuals with or without depression.Methods: We used data on 251,125 individuals of white British ancestry from the UK Biobank. We conducted Mendelian randomization (MR) analysis to test for a causal association between depression and BMI using a major depressive disorder (MDD)-related genetic risk score (GRSMDD ) as an instrument for depression. We also examined whether depression modifies genetic susceptibility to high BMI, by investigating the interaction between depression and the BMI-related GRSBMI .Results: We found observational and genetic evidence for an association between depression and BMI (MR beta: 0.09, 95% confidence interval [CI] 0.04-0.13). Further, the contribution of genetic risk to high BMI was higher among individuals with depression compared to controls. Carrying 10 additional BMI increasing alleles was associated with 0.24 standard deviation (SD; 95%CI 0.23-0.25) higher BMI among depressed individuals compared to 0.20 SD (95%CI 0.19-0.21) higher in controls, which corresponds to 3.4 kg and 2.8 kg extra weight for an individual of average height. Amongst the individual loci, the evidence for interaction was most notable for a variant near MC4R, a gene known to affect both appetite regulation and the hypothalamic-pituitary adrenal axis (pinteraction  = 5.7 × 10-5 ).Conclusion: Genetic predisposition to high BMI was higher among depressed than to nondepressed individuals. This study provides support for a possible role of MC4R in the link between depression and obesity. [ABSTRACT FROM AUTHOR]

Background: An unfavourable lipid profile has been associated with the increased risk of dementia or Alzheimer's disease in both observational and genetic studies1,2. However, it is challenging to investigate each serum lipid measure individually due to the high correlation between the lipid traits. We aimed to discover causal associations between genetically instrumented serum lipid measures (low‐density lipoprotein cholesterol (LDL‐C), high‐density lipoprotein cholesterol (HDL‐C), triglycerides, apolipoprotein‐A1 (apoA) and apolipoprotein‐B (apoB)) and the risk of dementia. Method: We conducted multivariable and univariable Mendelian randomisation (MR) analyses on 329,896 UK Biobank participants (age 37‐73 years) to examine the associations between each serum lipid measure and the risk of dementia. The multivariable approach allows us to assess the association of each lipid measure with the outcome, in cases where the genetic variants used to estimate the exposures are mediated by one another. Result: In the univariable MR analyses, we found that genetically determined higher LDL‐C and apoB were both associated with increased risk of dementia, while higher HDL‐C and apoA were associated with decreased risk of dementia. Meanwhile, using multivariable MR analyses, in a model containing LDL‐C, triglycerides and apoB, only apoB remained significantly associated with increased risk of dementia. Conclusion: Our findings suggests that when considering the correlation between lipid measures, apoB plays an important role in the previously reported association between serum lipids and increased risk of dementia. References: 1Iwagami, M, Qizilbash, N, Gregson, J, Douglas, I, Johnson, M, Pearce, N, Evans, S & Pocock, S 2021, 'Blood cholesterol and risk of dementia in more than 1·8 million people over two decades: a retrospective cohort study', The Lancet Healthy Longevity, vol. 2, no. 8, pp. e498‐e506. 2Zhang, X, Tian, Q, Liu, D, Geng, T, Ge, S, Zheng, D, Wu, L, Song, M, Hou, H, Wang, W & Wang, Y 2020, 'Causal association of circulating cholesterol levels with dementia: a mendelian randomization meta‐analysis', Transl Psychiatry, vol. 10, no. 145, https://doi.org/10.1038/s41398‐020‐0822‐x. [ABSTRACT FROM AUTHOR]