Search

Background: Observational studies have linked vitamin K and cancer, but the causality of this association remains unknown. This Mendelian randomization (MR) study aims to investigate the association between circulating phylloquinone (vitamin K1) levels and four female-specific cancers. Methods: We used four single-nucleotide polymorphisms (SNPs) to instrument phylloquinone, with the reported F-statistic 16.00–28.44 for all variants. SNP–outcome associations were obtained from consortia meta-analyses, UK Biobank, and the FinnGen database (up to 145,257/419,675, 27,446/362,324, 15,181/591,477, and 2211/320,454 cases/controls for breast, ovarian, endometrial, and cervical cancer, respectively). Analyses were conducted using five complementary MR methods including pleiotropy robust approaches. The MR Egger intercept test, MR PRESSO global test and leave-one-out analyses were used to test for and identify pleiotropic variants. Results: The relevance of the instrument was validated by positive control analyses on coagulation factor IX (p = 0.01). However, the main MR analysis and all sensitivity analyses were consistently supportive of a null association between phylloquinone and all four cancers (p > 0.05 for all analyses, across all methods). MR-PRESSO did not detect outlying variants, and there was no evidence of horizontal pleiotropy relating to any cancer outcome (pintercept > 0.26 for all). Conclusions: We found no evidence for an association between genetically predicted circulating phylloquinone levels and the risk of four female-specific cancers. [ABSTRACT FROM AUTHOR]

Background Many observational studies support light-to-moderate alcohol intake as potentially protective against premature death. We used a genetic approach to evaluate the linear and nonlinear relationships between alcohol consumption and mortality from different underlying causes. Methods We used data from 278 093 white-British UK Biobank participants, aged 37–73 years at recruitment and with data on alcohol intake, genetic variants, and mortality. Habitual alcohol consumption was instrumented by 94 variants. Linear Mendelian randomization (MR) analyses were conducted using five complementary approaches, and nonlinear MR analyses by the doubly-ranked method. Results There were 20 834 deaths during the follow-up (median 12.6 years). In conventional analysis, the association between alcohol consumption and mortality outcomes was 'J-shaped'. In contrast, MR analyses supported a positive linear association with premature mortality, with no evidence for curvature (P nonlinearity ≥ 0.21 for all outcomes). The odds ratio [OR] for each standard unit increase in alcohol intake was 1.27 (95% confidence interval [CI] 1.16–1.39) for all-cause mortality, 1.30 (95% CI 1.10–1.53) for cardiovascular disease, 1.20 (95% CI 1.08–1.33) for cancer, and 2.06 (95% CI 1.36–3.12) for digestive disease mortality. These results were consistent across pleiotropy-robust methods. There was no clear evidence for an association between alcohol consumption and mortality from respiratory diseases or COVID-19 (1.32, 95% CI 0.96–1.83 and 1.46, 95% CI 0.99–2.16, respectively; P nonlinearity ≥ 0.21). Conclusion Higher levels of genetically predicted alcohol consumption had a strong linear association with an increased risk of premature mortality with no evidence for any protective benefit at modest intake levels. [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]

Coffee is a highly popular beverage worldwide, containing caffeine which is a central nervous system stimulant. We examined whether habitual coffee consumption is associated with differences in brain volumes or the odds of dementia or stroke. We conducted prospective analyses of habitual coffee consumption on 398,646 UK Biobank participants (age 37–73 years), including 17,702 participants with MRI information. We examined the associations with brain volume using covariate adjusted linear regression, and with odds of dementia (4,333 incident cases) and stroke (6,181 incident cases) using logistic regression. There were inverse linear associations between habitual coffee consumption and total brain (fully adjusted β per cup −1.42, 95% CI −1.89, −0.94), grey matter (β −0.91, 95% CI −1.20, −0.62), white matter (β −0.51, 95% CI −0.83, −0.19) and hippocampal volumes (β −0.01, 95% CI −0.02, −0.003), but no evidence to support an association with white matter hyperintensity (WMH) volume (β −0.01, 95% CI −0.07, 0.05). The association between coffee consumption and dementia was non-linear (Pnon-linearity = 0.0001), with evidence for higher odds for non-coffee and decaffeinated coffee drinkers and those drinking >6 cups/day, compared to light coffee drinkers. After full covariate adjustment, consumption of >6 cups/day was associated with 53% higher odds of dementia compared to consumption of 1–2 cups/day (fully adjusted OR 1.53, 95% CI 1.28, 1.83), with less evidence for an association with stroke (OR 1.17, 95% CI 1.00, 1.37, p= 0.055). High coffee consumption was associated with smaller total brain volumes and increased odds of dementia. [ABSTRACT FROM AUTHOR]

Background: Depression is more common in obese than non-obese individuals, especially in women, but the causal relationship between obesity and depression is complex and uncertain. Previous studies have used genetic variants associated with BMI to provide evidence that higher body mass index (BMI) causes depression, but have not tested whether this relationship is driven by the metabolic consequences of BMI nor for differences between men and women.Methods: We performed a Mendelian randomization study using 48 791 individuals with depression and 291 995 controls in the UK Biobank, to test for causal effects of higher BMI on depression (defined using self-report and Hospital Episode data). We used two genetic instruments, both representing higher BMI, but one with and one without its adverse metabolic consequences, in an attempt to 'uncouple' the psychological component of obesity from the metabolic consequences. We further tested causal relationships in men and women separately, and using subsets of BMI variants from known physiological pathways.Results: Higher BMI was strongly associated with higher odds of depression, especially in women. Mendelian randomization provided evidence that higher BMI partly causes depression. Using a 73-variant BMI genetic risk score, a genetically determined one standard deviation (1 SD) higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals [odds ratio (OR): 1.18, 95% confidence interval (CI): 1.09, 1.28, P = 0.00007) and women only (OR: 1.24, 95% CI: 1.11, 1.39, P = 0.0001). Meta-analysis with 45 591 depression cases and 97 647 controls from the Psychiatric Genomics Consortium (PGC) strengthened the statistical confidence of the findings in all individuals. Similar effect size estimates were obtained using different Mendelian randomization methods, although not all reached P < 0.05. Using a metabolically favourable adiposity genetic risk score, and meta-analysing data from the UK biobank and PGC, a genetically determined 1 SD higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals (OR: 1.26, 95% CI: 1.06, 1.50], P = 0.010), but with weaker statistical confidence.Conclusions: Higher BMI, with and without its adverse metabolic consequences, is likely to have a causal role in determining the likelihood of an individual developing depression. [ABSTRACT FROM AUTHOR]