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Higher adiposity and mental health: causal inference using Mendelian randomization.

ABSTRACT: Higher adiposity is an established risk factor for psychiatric diseases including depression and anxiety. The associations between adiposity and depression may be explained by the metabolic consequences and/or by the psychosocial impact of higher adiposity. We performed one- and two- sample Mendelian randomization (MR) in up to 145 668 European participants from the UK Biobank to test for a causal effect of higher adiposity on 10 well-validated mental health and well-being outcomes derived using the Mental Health Questionnaire (MHQ). We used three sets of adiposity genetic instruments: (a) a set of 72 BMI genetic variants, (b) a set of 36 favourable adiposity variants and (c) a set of 38 unfavourable adiposity variants. We additionally tested causal relationships (1) in men and women separately, (2) in a subset of individuals not taking antidepressants and (3) in non-linear MR models. Two-sample MR provided evidence that a genetically determined one standard deviation (1-SD) higher BMI (4.6 kg/m2) was associated with higher odds of current depression [OR: 1.50, 95%CI: 1.15, 1.95] and lower well-being [ß: -0.15, 95%CI: -0.26, -0.04]. Findings were similar when using the metabolically favourable and unfavourable adiposity variants, with higher adiposity associated with higher odds of depression and lower well-being scores. Our study provides further evidence that higher BMI causes higher odds of depression and lowers well-being. Using genetics to separate out metabolic and psychosocial effects, our study suggests that in the absence of adverse metabolic effects higher adiposity remains causal to depression and lowers well-being.

SUBMITTER: Casanova F

PROVIDER: S-EPMC8643500 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Establishing causal relationships between sleep and adiposity traits using Mendelian randomization.

Project description:ObjectiveThe aim of this study was to systematically evaluate the direction of any potential causal effect between sleep and adiposity traits.MethodsTwo-sample Mendelian randomization was used to assess the association of genetically predicted sleep traits with adiposity and vice versa. Using data from UK Biobank and 23andMe, the sleep traits explored were morning preference (chronotype; N = 697,828), insomnia (N = 1,331,010), sleep duration (N = 446,118), napping (N = 452,633), and daytime sleepiness (N = 452,071). Using data from the Genetic Investigation of ANthropometric Traits (GIANT) and Early Growth Genetics (EGG) consortia, the adiposity traits explored were adult BMI, hip circumference (HC), waist circumference (WC), waist-hip ratio (WHR; N = 322,154), and childhood BMI (N = 35,668).ResultsThis study found evidence that insomnia symptoms increased mean WC, BMI, and WHR (difference in means, WC = 0.39 SD [95% CI: 0.13-0.64], BMI = 0.47 SD [95% CI: 0.22-0.73], and WHR = 0.34 SD [95% CI: 0.16-0.52]). Napping increased mean WHR (0.23 SD [95% CI: 0.08-0.39]). Higher HC, WC, and adult BMI increased odds of daytime sleepiness (HC = 0.02 SD [95% CI: 0.01-0.04], WC = 0.04 SD [95% CI: 0.01-0.06], and BMI 0.02 SD [95% CI: 0.00-0.04]). This study also found that higher mean childhood BMI resulted in lower odds of napping (-0.01 SD [95% CI: 0.02-0.00]).ConclusionsThe effects of insomnia on adiposity and of adiposity on daytime sleepiness suggest that poor sleep and weight gain may contribute to a feedback loop that could be detrimental to overall health.

| S-EPMC10946465 | biostudies-literature

Causal association between adiposity and hemorrhoids: a Mendelian randomization study.

Project description:BackgroundHemorrhoids are a very common anorectal disorder affecting a large number of individuals throughout the world. This study aimed to evaluate the causal effects of four adiposity traits including body mass index (BMI), body fat percentage, waist circumference, and waist-to-hip ratio on hemorrhoids by Mendelian randomization (MR).MethodsWe used summary statistics of BMI (N = 461,460), body fat percentage (N = 454,633), waist circumference (N = 462,166), waist-to-hip ratio (N = 212,244), and hemorrhoids (N = 337,199) from large-scale genome wide association studies of European ancestry. Univariable and multivariable MR were carried out to infer causality. The MR Steiger directionality test was used to test the causal direction.ResultsThe primary MR analysis using the inverse variance weighted (IVW) method showed that there were positive effects of genetically determined BMI [odds ratio (OR) = 1.005, 95% confidence interval (CI): 1.003-1.008, per standard deviation (SD), p = 7.801 × 10-5], body fat percentage (OR = 1.005, 95% CI: 1.001-1.008, per SD, p = 0.008), waist circumference (OR = 1.008, 95% CI: 1.005-1.011, per SD, p = 1.051 × 10-6), and waist-to-hip ratio (OR = 1.010, 95% CI: 1.003-1.017, per SD, p = 0.003) on hemorrhoids. These findings were robust in multivariable MR adjusting for physical activity. The Steiger directionality test showed evidence against reverse causation.ConclusionOur MR study supports a causal role of adiposity in the development of hemorrhoids. Adiposity prevention may be an important strategy for reducing hemorrhoids risk.

| S-EPMC10587414 | biostudies-literature

Mendelian Randomization analysis of the causal effect of adiposity on hospital costs.

Project description:Estimates of the marginal effect of measures of adiposity such as body mass index (BMI) on healthcare costs are important for the formulation and evaluation of policies targeting adverse weight profiles. Most estimates of this association are affected by endogeneity bias. We use a novel identification strategy exploiting Mendelian Randomization - random germline genetic variation modelled using instrumental variables - to identify the causal effect of BMI on inpatient hospital costs. Using data on over 300,000 individuals, the effect size per person per marginal unit of BMI per year varied according to specification, including £21.22 (95% confidence interval (CI): £14.35-£28.07) for conventional inverse variance weighted models to £18.85 (95% CI: £9.05-£28.65) for penalized weighted median models. Effect sizes from Mendelian Randomization models were larger in most cases than non-instrumental variable multivariable adjusted estimates (£13.47, 95% CI: £12.51-£14.43). There was little evidence of non-linearity. Within-family estimates, intended to address dynastic biases, were imprecise.

| S-EPMC7188219 | biostudies-literature

Mendelian randomization suggests a bidirectional, causal relationship between physical inactivity and adiposity.

Project description:Physical inactivity and increased sedentary time are associated with excess weight gain in observational studies. However, some longitudinal studies indicate reverse causality where weight gain leads to physical inactivity and increased sedentary time. As observational studies suffer from reverse causality, it is challenging to assess the true causal directions. Here, we assess the bidirectional causality between physical inactivity, sedentary time, and adiposity by bidirectional Mendelian randomization analysis. We used results from genome-wide association studies for accelerometer-based physical activity and sedentary time in 91,105 individuals and for body mass index (BMI) in 806,834 individuals. We implemented Mendelian randomization using CAUSE method that accounts for pleiotropy and sample overlap using full genome-wide data. We also applied inverse variance-weighted, MR-Egger, weighted median, and weighted mode methods using genome-wide significant variants only. We found evidence of bidirectional causality between sedentary time and BMI: longer sedentary time was causal for higher BMI [beta (95% CI) from CAUSE method: 0.11 (0.02, 0.2), p = 0.02], and higher BMI was causal for longer sedentary time (0.13 (0.08, 0.17), p = 6.3 x 10-4). Our analyses suggest that higher moderate and vigorous physical activity are causal for lower BMI (moderate: -0.18 (-0.3,-0.05), p = 0.006; vigorous: -0.16 (-0.24,-0.08), p = 3.8 × 10-4), but indicate that the association between higher BMI and lower levels of physical activity is due to horizontal pleiotropy. The bidirectional, causal relationship between sedentary time and BMI suggests that decreasing sedentary time is beneficial for weight management, but also that targeting adiposity may lead to additional health benefits by reducing sedentary time.

| S-EPMC8975550 | biostudies-literature

Inference of a causal relation between low-density lipoprotein cholesterol and hypertension using mendelian randomization analysis.

Project description:BackgroundIt is known in some studies that higher the LDL-C, the greater the risk of developing cardiovascular disease. However, studies of the causal effects between LDL-C and hypertension are limited by their observational study design, and genetic epidemiology studies of associations between LDL-C and hypertension are lacking, as are studies using data for Koreans. In this study, we confirmed the causal effect of LDL-C on hypertension using Korean chip data.MethodThe epidemiology and genotype data were collected from the Korean Genome and Epidemiology Study conducted by the Korea National Institute of Health and covered 20,701 subjects. Single-nucleotide polymorphisms associated with LDL-C were selected (p-value < 5 × 10- 8) from the Global Lipids Genetics Consortium database, and Mendelian randomization analysis (MRA) was performed with counted genetic risk scores and weighted genetic risk scores (WGRSs) for 24 single-nucleotide polymorphisms.ResultThe assumptions for MRA were statistically confirmed, and WGRSs showed a strong association with LDL-C. Interestingly, while the relationship between LDL-C and hypertension was not statistically significant in the observational study, MRA study demonstrated that the risk of hypertension increased as LDL-C increased in both men and women.ConclusionsThe results of this study confirmed that the relationship between LDL-C and hypertension is greatly influenced by genetic information.

| S-EPMC7913402 | biostudies-literature

Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization?

Project description:Observational epidemiologic studies are prone to confounding, measurement error, and reverse causation, undermining robust causal inference. Mendelian randomization (MR) uses genetic variants to proxy modifiable exposures to generate more reliable estimates of the causal effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within cardio-metabolic epidemiology, but also holds much promise for identifying possible interventions for cancer prevention and treatment. However, some methodologic challenges in the implementation of MR are particularly pertinent when applying this method to cancer etiology and prognosis, including reverse causation arising from disease latency and selection bias in studies of cancer progression. These issues must be carefully considered to ensure appropriate design, analysis, and interpretation of such studies. In this review, we provide an overview of the key principles and assumptions of MR, focusing on applications of this method to the study of cancer etiology and prognosis. We summarize recent studies in the cancer literature that have adopted a MR framework to highlight strengths of this approach compared with conventional epidemiological studies. Finally, limitations of MR and recent methodologic developments to address them are discussed, along with the translational opportunities they present to inform public health and clinical interventions in cancer. Cancer Epidemiol Biomarkers Prev; 27(9); 995-1010. ©2018 AACR.

| S-EPMC6522350 | biostudies-literature

Characterizing the Causal Pathway From Childhood Adiposity to Right Heart Physiology and Pulmonary Circulation Using Lifecourse Mendelian Randomization.

Project description:BackgroundObservational epidemiological studies have reported an association between childhood adiposity and altered cardiac morphology and function in later life. However, whether this is due to a direct consequence of being overweight during childhood has been difficult to establish, particularly as accounting for other measures of body composition throughout the lifecourse can be exceptionally challenging.Methods and resultsIn this study, we used human genetics to investigate this using a causal inference technique known as lifecourse Mendelian randomization. This approach allowed us to evaluate the effect of childhood body size on 11 measures of right heart and pulmonary circulation independent of other anthropometric traits at various stages in the lifecourse. We found strong evidence that childhood body size has a direct effect on an enlarged right heart structure in later life (eg, right ventricular end-diastolic volume: β=0.24 [95% CI, 0.15-0.33]; P=3×10-7) independent of adulthood body size. In contrast, childhood body size effects on maximum ascending aorta diameter attenuated upon accounting for body size in adulthood, suggesting that this effect is likely attributed to individuals remaining overweight into later life. Effects of childhood body size on pulmonary artery traits and measures of right atrial function became weaker upon accounting for adulthood fat-free mass and childhood height, respectively.ConclusionsOur findings suggest that, although childhood body size has a long-term influence on an enlarged heart structure in adulthood, associations with the other structural components of the cardiovascular system and their function may be largely attributed to body composition at other stages in the lifecourse.

| S-EPMC11010002 | biostudies-literature

Causal associations between severe mental illness and sepsis: a Mendelian randomization study.

Project description:ObjectiveSMI (severe mental illness) has been identified as a risk factor for sepsis in observational studies; however, the causal association between them has yet to be firmly established. We conducted MR (mendelian randomization) to unveil the causal relationship between SMI and sepsis as well as sepsis mortality.MethodsGWAS (Genome-wide association) data for major depression and schizophrenia were selected as exposure. GWAS data for sepsis and sepsis mortality were selected as outcome. Genetic variants significantly associated with the exposure (P value<1x10-6) were selected as instruments. We primarily employed the IVW (inverse-variance weighted) method for analysis. Furthermore, we employed Cochrane's Q test to assess heterogeneity and the MR-Egger intercept test to identify horizontal pleiotropy.ResultsWe selected 108 SNPs (single nucleotide polymorphism) used to predict major depression and 260 SNPs that predicted schizophrenia. Genetically predicted major depression was suggestively linked to a higher sepsis risk (OR=1.13, 95%CI 1.02-1.26, P=0.023). In contrast, MR analysis did not find an association between schizophrenia and sepsis risk (OR=1.00, 95%CI 0.97-1.04, P=0.811). Furthermore, no significant causal evidence was found for genetically predicted SMI in sepsis mortality. Moreover, no heterogeneity and horizontal pleiotropy were detected.ConclusionOur research revealed a suggestive association between genetically predicted major depression and an elevated risk of sepsis in individuals of European ancestry. This finding can serve as a reminder for clinicians to consider the possibility of subsequent infection and sepsis in depressive patients, which may help reduce the incidence of sepsis in individuals with depression.

| S-EPMC10964346 | biostudies-literature

Mendel's laws, Mendelian randomization and causal inference in observational data: substantive and nomenclatural issues.

Project description:We respond to criticisms of Mendelian randomization (MR) by Mukamal, Stampfer and Rimm (MSR). MSR consider that MR is receiving too much attention and should be renamed. We explain how MR links to Mendel's laws, the origin of the name and our lack of concern regarding nomenclature. We address MSR's substantive points regarding MR of alcohol and cardiovascular disease, an issue on which they dispute the MR findings. We demonstrate that their strictures with respect to population stratification, confounding, weak instrument bias, pleiotropy and confounding have been addressed, and summarise how the field has advanced in relation to the issues they raise. We agree with MSR that "the hard problem of conducting high-quality, reproducible epidemiology" should be addressed by epidemiologists. However we see more evidence of confrontation of this issue within MR, as opposed to conventional observational epidemiology, within which the same methods that have demonstrably failed in the past are simply rolled out into new areas, leaving their previous failures unexamined.

| S-EPMC7125255 | biostudies-literature

Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data.

Project description:Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer's disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app (https://zhaotongl.shinyapps.io/cMLgraph/) for users to explore any subset of the 17 traits of interest.

| S-EPMC10231771 | biostudies-literature

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