Evaluating the relationship between alcohol consumption, tobacco use, and cardiovascular disease: A multivariable Mendelian randomization study

BackgroundAlcohol consumption and smoking, 2 major risk factors for cardiovascular disease (CVD), often occur together. The objective of this study is to use a wide range of CVD risk factors and outcomes to evaluate potential total and direct causal roles of alcohol and tobacco use on CVD risk factors and events.Methods and findingsUsing large publicly available genome-wide association studies (GWASs) (results from more than 1.2 million combined study participants) of predominantly European ancestry, we conducted 2-sample single-variable Mendelian randomization (SVMR) and multivariable Mendelian randomization (MVMR) to simultaneously assess the independent impact of alcohol consumption and smoking on a wide range of CVD risk factors and outcomes. Multiple sensitivity analyses, including complementary Mendelian randomization (MR) methods, and secondary alcohol consumption and smoking datasets were used. SVMR showed genetic predisposition for alcohol consumption to be associated with CVD risk factors, including high-density lipoprotein cholesterol (HDL-C) (beta 0.40, 95% confidence interval (CI), 0.04-0.47, P value = 1.72 × 10-28), triglycerides (TRG) (beta -0.23, 95% CI, -0.30, -0.15, P value = 4.69 × 10-10), automated systolic blood pressure (BP) measurement (beta 0.11, 95% CI, 0.03-0.18, P value = 4.72 × 10-3), and automated diastolic BP measurement (beta 0.09, 95% CI, 0.03-0.16, P value = 5.24 × 10-3). Conversely, genetically predicted smoking was associated with increased TRG (beta 0.097, 95% CI, 0.014-0.027, P value = 6.59 × 10-12). Alcohol consumption was also associated with increased myocardial infarction (MI) and coronary heart disease (CHD) risks (MI odds ratio (OR) = 1.24, 95% CI, 1.03-1.50, P value = 0.02; CHD OR = 1.21, 95% CI, 1.01-1.45, P value = 0.04); however, its impact was attenuated in MVMR adjusting for smoking. Conversely, alcohol maintained an association with coronary atherosclerosis (OR 1.02, 95% CI, 1.01-1.03, P value = 5.56 × 10-4). In comparison, after adjusting for alcohol consumption, smoking retained its association with several CVD outcomes including MI (OR = 1.84, 95% CI, 1.43, 2.37, P value = 2.0 × 10-6), CHD (OR = 1.64, 95% CI, 1.28-2.09, P value = 8.07 × 10-5), heart failure (HF) (OR = 1.61, 95% CI, 1.32-1.95, P value = 1.9 × 10-6), and large artery atherosclerosis (OR = 2.4, 95% CI, 1.41-4.07, P value = 0.003). Notably, using the FinnGen cohort data, we were able to replicate the association between smoking and several CVD outcomes including MI (OR = 1.77, 95% CI, 1.10-2.84, P value = 0.02), HF (OR = 1.67, 95% CI, 1.14-2.46, P value = 0.008), and peripheral artery disease (PAD) (OR = 2.35, 95% CI, 1.38-4.01, P value = 0.002). The main limitations of this study include possible bias from unmeasured confounders, inability of summary-level MR to investigate a potentially nonlinear relationship between alcohol consumption and CVD risk, and the generalizability of the UK Biobank (UKB) to other populations.ConclusionsEvaluating the widest range of CVD risk factors and outcomes of any alcohol consumption or smoking MR study to date, we failed to find a cardioprotective impact of genetically predicted alcohol consumption on CVD outcomes. However, alcohol was associated with and increased HDL-C, decreased TRG, and increased BP, which may indicate pathways through impact CVD risk, warranting further study. We found smoking to be a risk factor for many CVDs even after adjusting for alcohol. While future studies incorporating alcohol consumption patterns are necessary, our data suggest causal inference between alcohol, smoking, and CVD risk, further supporting that lifestyle modifications might be able to reduce overall CVD risk

Threshold response to extreme drought shifts inter-tree growth dominance in Pinus sylvestris

Many studies quantify short-term drought impact on tree growth relative to pre-drought growth averages. However, fewer studies examine the extent to which droughts of differing severity differentially impact tree growth or shape stand dynamics. Focusing on three droughts in high and low density stands of Pinus sylvestris in Scotland, we calculated pre-drought growth averages using climatically standardised antecedent growth years to assess tree level drought and post-drought growth performance as percentage growth change (PGC). We then used mixed-effects models to understand how droughts of differing severity impact tree growth and calculated indices of growth dominance (Gd), size inequality (Si) and size asymmetry (Sa) to detect changes in stand structure. Mixed-effects model results indicate that the magnitude and duration of the growth reduction during and following the more extreme drought was significantly larger compared to less severe droughts, for which we found limited evidence of drought impact. While no changes in Si or Sa were noted following any drought, we found evidence of a difference in Gd after the most extreme drought in both stand densities indicative of a threshold response, with smaller trees contributing proportionally more to stand growth relative to their size. Under less severe droughts, inter-tree variability may have partially buffered against stand-level growth change, however a small increase in drought severity was associated with a significant reduction in average tree growth, an increase in the number of trees growing at > 2SD below pre-drought levels and a shift in Gd towards smaller trees, indicating that a drought severity threshold in P. sylvestris may have been exceeded

A validation of the diathesis-stress model for depression in Generation Scotland

Abstract Depression has well-established influences from genetic and environmental risk factors. This has led to the diathesis-stress theory, which assumes a multiplicative gene-by-environment interaction (GxE) effect on risk. Recently, Colodro-Conde et al. empirically tested this theory, using the polygenic risk score for major depressive disorder (PRS, genes) and stressful life events (SLE, environment) effects on depressive symptoms, identifying significant GxE effects with an additive contribution to liability. We have tested the diathesis-stress theory on an independent sample of 4919 individuals. We identified nominally significant positive GxE effects in the full cohort (R 2 = 0.08%, p = 0.049) and in women (R 2 = 0.19%, p = 0.017), but not in men (R 2 = 0.15%, p = 0.07). GxE effects were nominally significant, but only in women, when SLE were split into those in which the respondent plays an active or passive role (R 2 = 0.15%, p = 0.038; R 2 = 0.16%, p = 0.033, respectively). High PRS increased the risk of depression in participants reporting high numbers of SLE (p = 2.86 × 10−4). However, in those participants who reported no recent SLE, a higher PRS appeared to increase the risk of depressive symptoms in men (β = 0.082, p = 0.016) but had a protective effect in women (β = −0.061, p = 0.037). This difference was nominally significant (p  = 0.017). Our study reinforces the evidence of additional risk in the aetiology of depression due to GxE effects. However, larger sample sizes are required to robustly validate these findings

Genetic stratification of depression in UK Biobank

Depression is a common and clinically heterogeneous mental health disorder that is frequently comorbid with other diseases and conditions. Stratification of depression may align sub-diagnoses more closely with their underling aetiology and provide more tractable targets for research and effective treatment. In the current study, we investigated whether genetic data could be used to identify subgroups within people with depression using the UK Biobank. Examination of cross-locus correlations were used to test for evidence of subgroups using genetic data from seven other complex traits and disorders that were genetically correlated with depression and had sufficient power (>0.6) for detection. We found no evidence for subgroups within depression for schizophrenia, bipolar disorder, attention deficit/hyperactivity disorder, autism spectrum disorder, anorexia nervosa, inflammatory bowel disease or obesity. This suggests that for these traits, genetic correlations with depression were driven by pleiotropic genetic variants carried by everyone rather than by a specific subgroup