Interpretation and Potential Biases of Mendelian Randomization Estimates With Time-Varying Exposures

Bidirectional causal relational between frailty and mental illness: a two-sample Mendelian randomization study

Background

Frailty has been associated with mental illness (MI) observational studies, but the causal relationship between these factors remains uncertain. We aimed to assess the bidirectional causality between frailty and MI by two-sample Mendelian randomization (MR) analyses.

Methods

To investigate the causal relationship among them, summary statistics of frailty index (FI) and six types of MI: anxiety, depression, affective disorder, mania, schizophrenia, and obsessive-compulsive disorder (OCD) were included in this MR study. This MR analysis was performed using inverse variance weighting (IVW), MR-Egger regression, and weighted median. The stability of the results was evaluated using Cochran's Q test, MR-Egger intercept test, Funnel Plots, and leave-one-out analysis.

Results

Genetic predisposition to FI was significantly associated with increased anxiety (odds ratio [OR] = 1.62, 95% confidence interval [CI] 1.13-2.33, P = 8.18E-03), depression (OR = 1.88, 95% CI 1.30-2.71, P = 8.21E-04), affective disorder (OR = 1.70, 95% CI 1.28-2.27, P = 2.57E-04). However, our study findings do not demonstrate a causal relationship between FI and mania (OR = 1.02, 95% CI 0.99-1.06, P = 2.20E-01), schizophrenia (OR = 1.02, 95% CI 0.07-0.86, P = 9.28E-01). In particular, although the IVW results suggest a potential causal relationship between FI and OCD (OR = 0.64, 95% CI 0.07-0.86, P = 2.85E-02), the directions obtained from the three methods we employed ultimately show inconsistency. Therefore, the result must be interpreted with caution. The results of the reverse MR analysis indicated a statistically significant and causal relationship between anxiety (OR = 1.06, 95% CI 1.01-1.11, P = 2.00E-02), depression (OR = 1.14, 95% CI 1.04-1.26, P = 7.99E-03), affective disorder (OR = 1.15, 95% CI 1.09-1.21, P = 3.39E-07), and schizophrenia (OR = 1.02, 95% CI 1.01-1.04, P = 1.70E-03) with FI. However, our findings do not provide support for a link between mania (OR = 1.46, 95% CI 0.79-2.72, P = 2.27E-01), OCD (OR = 1.01, 95% CI 1.00-1.02, P = 2.11E-01) and an increased risk of FI.

Conclusion

The MR results suggest a potential bidirectional causal relationship between FI and anxiety, depression, and affective disorder. Schizophrenia was found to be associated with a higher risk of FI. The evidence was insufficient to support a causal relationship between Fl and other Ml. These findings offer new insights into the development of effective management strategies for frailty and MI.

A burden of proof study on alcohol consumption and ischemic heart disease

Cohort and case-control data have suggested an association between low to moderate alcohol consumption and decreased risk of ischemic heart disease (IHD), yet results from Mendelian randomization (MR) studies designed to reduce bias have shown either no or a harmful association. Here we conducted an updated systematic review and re-evaluated existing cohort, case-control, and MR data using the burden of proof meta-analytical framework. Cohort and case-control data show low to moderate alcohol consumption is associated with decreased IHD risk - specifically, intake is inversely related to IHD and myocardial infarction morbidity in both sexes and IHD mortality in males - while pooled MR data show no association, confirming that self-reported versus genetically predicted alcohol use data yield conflicting findings about the alcohol-IHD relationship. Our results highlight the need to advance MR methodologies and emulate randomized trials using large observational databases to obtain more definitive answers to this critical public health question.

Association Between Neuroinflammation and Parkinson's Disease: A Comprehensive Mendelian Randomization Study

The objective of the study is to determine the causal relationship and potential mechanisms between Parkinson's disease (PD) and neuroinflammatory and neurotoxic mediators. We conducted two-sample Mendelian randomization (2SMR) study and multivariable Mendelian randomization (MVMR) analysis to investigate the causality between PD and neuroinflammatory and neurotoxic mediators. The mediation analysis with MR was also conducted to determine the potential mediating effect of neuroinflammatory and neurotoxic mediators between asthma and PD. Genetically predicted levels of nine neuroinflammation were associated with changes in PD risk. The associations of PD with CCL24, galectin-3 levels, haptoglobin, and Holo-Transcobalamin-2 remained significant in multivariable analyses. The mediation analysis with MR revealed that asthma affects PD through CCL24 and galectin-3. The results showed neuroinflammation could affect the pathogenesis of PD. In the combined analysis of these nine variables, CCL24, galectin-3 levels, HP, and Holo-Transcobalamin-2 alone were found to be significant. Asthma plays an intermediary role through CCL24 and galectin-3 levels.

Methodological approaches, challenges, and opportunities in the application of Mendelian randomisation to lifecourse epidemiology: A systematic literature review

Diseases diagnosed in adulthood may have antecedents throughout (including prenatal) life. Gaining a better understanding of how exposures at different stages in the lifecourse influence health outcomes is key to elucidating the potential benefits of disease prevention strategies. Mendelian randomisation (MR) is increasingly used to estimate causal effects of exposures across the lifecourse on later life outcomes. This systematic literature review explores MR methods used to perform lifecourse investigations and reviews previous work that has utilised MR to elucidate the effects of factors acting at different stages of the lifecourse. We conducted searches in PubMed, Embase, Medline and MedRXiv databases. Thirteen methodological studies were identified. Four studies focused on the impact of time-varying exposures in the interpretation of "standard" MR techniques, five presented methods for repeat measures of the same exposure, and four described methodological approaches to handling multigenerational exposures. A further 127 studies presented the results of an applied research question. Over half of these estimated effects in a single generation and were largely confined to the exploration of questions regarding body composition. The remaining mostly estimated maternal effects. There is a growing body of research focused on the development and application of MR methods to address lifecourse research questions. The underlying assumptions require careful consideration and the interpretation of results rely on select conditions. Whilst we do not advocate for a particular strategy, we encourage practitioners to make informed decisions on how to approach a research question in this field with a solid understanding of the limitations present and how these may be affected by the research question, modelling approach, instrument selection, and data availability.

Two sample Mendelian Randomisation using an outcome from a multilevel model of disease progression

Identifying factors that are causes of disease progression, especially in neurodegenerative diseases, is of considerable interest. Disease progression can be described as a trajectory of outcome over time-for example, a linear trajectory having both an intercept (severity at time zero) and a slope (rate of change). A technique for identifying causal relationships between one exposure and one outcome in observational data whilst avoiding bias due to confounding is two sample Mendelian Randomisation (2SMR). We consider a multivariate approach to 2SMR using a multilevel model for disease progression to estimate the causal effect an exposure has on the intercept and slope. We carry out a simulation study comparing a naïve univariate 2SMR approach to a multivariate 2SMR approach with one exposure that effects both the intercept and slope of an outcome that changes linearly with time since diagnosis. The simulation study results, across six different scenarios, for both approaches were similar with no evidence against a non-zero bias and appropriate coverage of the 95% confidence intervals (for intercept 93.4-96.2% and the slope 94.5-96.0%). The multivariate approach gives a better joint coverage of both the intercept and slope effects. We also apply our method to two Parkinson's cohorts to examine the effect body mass index has on disease progression. There was no strong evidence that BMI affects disease progression, however the confidence intervals for both intercept and slope were wide.

Use of the instrumental inequalities in simulated mendelian randomization analyses with coarsened exposures

Mendelian randomization (MR) requires strong unverifiable assumptions to estimate causal effects. However, for categorical exposures, the MR assumptions can be falsified using a method known as the instrumental inequalities. To apply the instrumental inequalities to a continuous exposure, investigators must coarsen the exposure, a process which can itself violate the MR conditions. Violations of the instrumental inequalities for an MR model with a coarsened exposure might therefore reflect the effect of coarsening rather than other sources of bias. We aim to evaluate how exposure coarsening affects the ability of the instrumental inequalities to detect bias in MR models with multiple proposed instruments under various causal structures. To do so, we simulated data mirroring existing studies of the effect of alcohol consumption on cardiovascular disease under a variety of exposure-outcome effects in which the MR assumptions were met for a continuous exposure. We categorized the exposure based on subject matter knowledge or the observed data distribution and applied the instrumental inequalities to MR models for the effects of the coarsened exposure. In simulations of multiple binary instruments, the instrumental inequalities did not detect bias under any magnitude of exposure outcome effect when the exposure was coarsened into more than 2 categories. However, in simulations of both single and multiple proposed instruments, the instrumental inequalities were violated in some scenarios when the exposure was dichotomized. The results of these simulations suggest that the instrumental inequalities are largely insensitive to bias due to exposure coarsening with greater than 2 categories, and could be used with coarsened exposures to evaluate the required assumptions in applied MR studies, even when the underlying exposure is truly continuous.

Genome-wide association and Mendelian randomisation analysis among 30,699 Chinese pregnant women identifies novel genetic and molecular risk factors for gestational diabetes and glycaemic traits

AIMS/HYPOTHESIS

Gestational diabetes mellitus (GDM) is the most common disorder in pregnancy; however, its underlying causes remain obscure. This study aimed to investigate the genetic and molecular risk factors contributing to GDM and glycaemic traits.

METHODS

We collected non-invasive prenatal test (NIPT) sequencing data along with four glycaemic and 55 biochemical measurements from 30,699 pregnant women during a 2 year period at Shenzhen Baoan Women's and Children's Hospital in China. Genome-wide association studies (GWAS) were conducted between genotypes derived from NIPTs and GDM diagnosis, baseline glycaemic levels and glycaemic levels after glucose challenges. In total, 3317 women were diagnosed with GDM, while 19,565 served as control participants. The results were replicated using two independent cohorts. Additionally, we performed one-sample Mendelian randomisation to explore potential causal associations between the 55 biochemical measurements and risk of GDM and glycaemic levels.

RESULTS

We identified four genetic loci significantly associated with GDM susceptibility. Among these, MTNR1B exhibited the highest significance (rs10830963-G, OR [95% CI] 1.57 [1.45, 1.70], p=4.42×10-29), although its effect on type 2 diabetes was modest. Furthermore, we found 31 genetic loci, including 14 novel loci, that were significantly associated with the four glycaemic traits. The replication rates of these associations with GDM, fasting plasma glucose levels and 0 h, 1 h and 2 h OGTT glucose levels were 4 out of 4, 6 out of 9, 10 out of 11, 5 out of 7 and 4 out of 4, respectively. Mendelian randomisation analysis suggested that a genetically regulated higher lymphocytes percentage and lower white blood cell count, neutrophil percentage and absolute neutrophil count were associated with elevated glucose levels and an increased risk of GDM.

CONCLUSIONS/INTERPRETATION

Our findings provide new insights into the genetic basis of GDM and glycaemic traits during pregnancy in an East Asian population and highlight the potential role of inflammatory pathways in the aetiology of GDM and variations in glycaemic levels.

DATA AVAILABILITY

Summary statistics for GDM; fasting plasma glucose; 0 h, 1 h and 2h OGTT; and the 55 biomarkers are available in the GWAS Atlas (study accession no.: GVP000001, https://ngdc.cncb.ac.cn/gwas/browse/GVP000001) .

Relationship between alcohol consumption and dementia with Mendelian randomization approaches among older adults in the United States

INTRODUCTION

In observational studies, the association between alcohol consumption and dementia is mixed.

METHODS

We performed two-sample Mendelian randomization (MR) using summary statistics from genome-wide association studies of weekly alcohol consumption and late-onset Alzheimer's disease and one-sample MR in the Health and Retirement Study (HRS), wave 2012. Inverse variance weighted two-stage regression provided odds ratios of association between alcohol exposure and dementia or cognitively impaired, non-dementia relative to cognitively normal.

RESULTS

Alcohol consumption was not associated with late-onset Alzheimer's disease using two-sample MR (odds ratio [OR] = 1.15, 95% confidence interval [CI]: [0.78, 1.72]). In HRS, doubling weekly alcohol consumption was not associated with dementia (African ancestries, n = 1,322, OR = 1.00, 95% CI [0.45, 2.25]; European ancestries, n = 7,160, OR = 1.37, 95% CI [0.53, 3.51]) or cognitively impaired, non-dementia (African ancestries, n = 1,322, OR = 1.17, 95% CI [0.69, 1.98]; European ancestries, n = 7,160, OR = 0.75, 95% CI [0.47, 1.22]).

DISCUSSION

Alcohol consumption was not associated with cognitively impaired, non-dementia or dementia status.

Highlights

Cross-sectionally in a large, diverse sample, alcohol appears protective for dementia.We apply two- and one-sample Mendelian randomization to test inferred causality.Mendelian randomization approaches show no association with alcohol and dementia.We conclude that alcohol consumption should not be considered protective.

Meta-regression of genome-wide association studies to estimate age-varying genetic effects

Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age (or other characteristics), and not allowing for this in a GWAS might lead to bias. Meta-regression models between study heterogeneity and allows effect modification of the genetic effects to be explored. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both main genetic effects and age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. Our findings challenge the assumption that genetic effects are consistent across all ages and provide a method for exploring this. GWAS consortia should be encouraged to use meta-regression to explore age-varying genetic effects.

Unraveling the link between plasma caffeine concentrations and inflammatory bowel disease risk through Mendelian randomization

BACKGROUND

Caffeine is believed to possess anti-inflammatory properties, yet direct population-based evidence regarding its impact on inflammatory bowel disease (IBD) remains scarce.

OBJECTIVES

In this study, we used 2-sample Mendelian randomization (MR) study to investigate the causal relationship between long-term plasma caffeine concentrations and IBD and its subtypes, ulcerative colitis (UC) and Crohn disease (CD).

METHODS

We used single nucleotide polymorphisms (SNPs) associated with plasma caffeine concentrations at genome-wide significance within a ±100-kb range around the CYP1A2 or AHR genes as instrumental variables. Genome-wide association study (GWAS) data for IBD and its subtypes were obtained from FinnGen and International Inflammatory Bowel Disease Genetics Consortium. We conducted a meta-analysis of MR-related SNPs from both sources and used a multiplicative inverse variance-weighted random effects model to combine the effects of each SNP proxy on exposure to outcomes.

RESULTS

In our study, genetically predicted higher plasma caffeine concentrations were associated with a lower risk of IBD, with an odds ratio (OR) of 0.78 (95% confidence interval [CI]: 0.66, 0.91; PFDR = 0.004). This trend was also observed in UC and CD, with ORs of 0.79 (95% CI: 0.66, 0.94; PFDR = 0.014) and 0.78 (95% CI: 0.62, 0.98; PFDR = 0.032), respectively.

CONCLUSION

Our study indicates a potential causal link between genetically predicted higher plasma caffeine concentrations and a reduced risk of IBD, including its subtypes UC and CD.

Assessment of Bidirectional Relationships between Frailty and Mental Disorders: A Bidirectional Mendelian Randomization Study

OBJECTIVES

Although observational studies have reported the association between frailty and mental disorders, the causality remains unclear. We aimed to evaluate the bidirectional causal association between frailty levels and mental disorders using a 2-sample Mendelian randomization (MR) analysis.

DESIGN

A bidirectional, 2-sample Mendelian randomization (MR) analysis.

SETTING AND PARTICIPANTS

Instrumental variables were obtained from large-scale genome-wide association study (GWAS) of a European-descent population for frailty index (FI, n = 175,226), Fried Frailty Score (FFS, n = 386,565), major depressive disorder (MDD, n = 674,452), bipolar disorder (n = 353,899), anxiety and stress-related disorder (ASRD, n = 31,880), and schizophrenia (n = 127,906).

METHODS

Two-sample MR analyses were conducted using inverse variance-weighted method, with sensitivity analyses using MR-Egger, weighted median, and simple median methods.

RESULTS

Per SD increase in genetically predicted FI and FFS increased the risk of MDD [odds ratio (OR) 1.56, 95% CI 1.27-1.94, P = 3.65 × 10-5, and OR 1.67, 95% CI 1.26-2.20, P = 3.02 × 10-4, respectively]. Per-SD increase in genetically predicted FI also increased the risk of ASRD (OR 2.76, 95% CI 1.36-5.60, P = .005). No significant effect was observed for frailty levels on the risk of bipolar disorder and schizophrenia. In the reverse direction, genetically predicted MDD was associated with higher FI (β 0.182, 95% CI 0.087-0.277, P = 1.79 × 10-4) and FFS (β 0.121, 95% CI 0.087-0.155, P = 4.43 × 10-12). No reliable evidence supported the effects of genetically predicted bipolar disorder, ASRD, or schizophrenia on frailty levels.

CONCLUSIONS AND IMPLICATIONS

A bidirectionally causal association exists between frailty levels and MDD, and higher FI is associated with a higher risk of ASRD. No reliable evidence suggested the causal associations of other mental disorders with frailty. Our findings provided evidence for introduction of psychological-related strategies in management of frailty.

Estimating the health impact of nicotine exposure by dissecting the effects of nicotine versus non-nicotine constituents of tobacco smoke: A multivariable Mendelian randomisation study

The detrimental health effects of smoking are well-known, but the impact of regular nicotine use without exposure to the other constituents of tobacco is less clear. Given the increasing daily use of alternative nicotine delivery systems, such as e-cigarettes, it is increasingly important to understand and separate the effects of nicotine use from the impact of tobacco smoke exposure. Using a multivariable Mendelian randomisation framework, we explored the direct effects of nicotine compared with the non-nicotine constituents of tobacco smoke on health outcomes (lung cancer, chronic obstructive pulmonary disease [COPD], forced expiratory volume in one second [FEV-1], forced vital capacity [FVC], coronary heart disease [CHD], and heart rate [HR]). We used Genome-Wide Association Study (GWAS) summary statistics from Buchwald and colleagues, the GWAS and Sequencing Consortium of Alcohol and Nicotine, the International Lung Cancer Consortium, and UK Biobank. Increased nicotine metabolism increased the risk of COPD, lung cancer, and lung function in the univariable analysis. However, when accounting for smoking heaviness in the multivariable analysis, we found that increased nicotine metabolite ratio (indicative of decreased nicotine exposure per cigarette smoked) decreases heart rate (b = -0.30, 95% CI -0.50 to -0.10) and lung function (b = -33.33, 95% CI -41.76 to -24.90). There was no clear evidence of an effect on the remaining outcomes. The results suggest that these smoking-related outcomes are not due to nicotine exposure but are caused by the other components of tobacco smoke; however, there are multiple potential sources of bias, and the results should be triangulated using evidence from a range of methodologies.

Childhood Non-HDL Cholesterol and LDL Cholesterol and Adult Atherosclerotic Cardiovascular Events

BACKGROUND

Although low-density lipoprotein cholesterol (LDL-C) remains the primary cholesterol target in clinical practice in children and adults, non-high-density lipoprotein cholesterol (non-HDL-C) has been suggested as a more accurate measure of atherosclerotic cardiovascular disease (ASCVD) risk. We examined the associations of childhood non-HDL-C and LDL-C levels with adult ASCVD events and determined whether non-HDL-C has better utility than LDL-C in predicting adult ASCVD events.

METHODS

This prospective cohort study included 21 126 participants from the i3C Consortium (International Childhood Cardiovascular Cohorts). Proportional hazards regressions were used to estimate the risk for incident fatal and fatal/nonfatal ASCVD events associated with childhood non-HDL-C and LDL-C levels (age- and sex-specific z scores; concordant/discordant categories defined by guideline-recommended cutoffs), adjusted for sex, Black race, cohort, age at and calendar year of child measurement, body mass index, and systolic blood pressure. Predictive utility was determined by the C index.

RESULTS

After an average follow-up of 35 years, 153 fatal ASCVD events occurred in 21 126 participants (mean age at childhood visits, 11.9 years), and 352 fatal/nonfatal ASCVD events occurred in a subset of 11 296 participants who could be evaluated for this outcome. Childhood non-HDL-C and LDL-C levels were each associated with higher risk of fatal and fatal/nonfatal ASCVD events (hazard ratio ranged from 1.27 [95% CI, 1.14-1.41] to 1.35 [95% CI, 1.13-1.60] per unit increase in the risk factor z score). Non-HDL-C had better discriminative utility than LDL-C (difference in C index, 0.0054 [95% CI, 0.0006-0.0102] and 0.0038 [95% CI, 0.0008-0.0068] for fatal and fatal/nonfatal events, respectively). The discordant group with elevated non-HDL-C and normal LDL-C had a higher risk of ASCVD events compared with the concordant group with normal non-HDL-C and LDL-C (fatal events: hazard ratio, 1.90 [95% CI, 0.98-3.70]; fatal/nonfatal events: hazard ratio, 1.94 [95% CI, 1.23-3.06]).

CONCLUSIONS

Childhood non-HDL-C and LDL-C levels are associated with ASCVD events in midlife. Non-HDL-C is better than LDL-C in predicting adult ASCVD events, particularly among individuals who had normal LDL-C but elevated non-HDL-C. These findings suggest that both non-HDL-C and LDL-C are useful in identifying children at higher risk of ASCVD events, but non-HDL-C may provide added prognostic information when it is discordantly higher than the corresponding LDL-C and has the practical advantage of being determined without a fasting sample.

Causal relationship between particulate matter 2.5 and infectious diseases: A two-sample Mendelian randomization study

Background

Previous observational studies suggested a correlation between particulate matter 2.5 (PM2.5) and infectious diseases, but causality remained uncertain. This study utilized Mendelian randomization (MR) analysis to investigate causal relationships between PM2.5 concentrations and various infectious diseases (COVID-19 infection, hospitalized COVID-19, very severe COVID-19, urinary tract infection, bacterial pneumonia, and intestinal infection).

Methods

Inverse variance weighted (IVW) was the primary method for evaluating causal associations. For significant causal estimates, multiple sensitivity tests were further performed: (i) three additional MR methods (MR-Egger, weighted median, and maximum likelihood method) for supplementing IVW; (ii) Cochrane's Q test for assessing heterogeneity; (iii) MR-Egger intercept test and MR-PRESSO global test for evaluating horizontal pleiotropy; (iv) leave-one-out sensitivity test for determining the stability.

Results

PM2.5 concentration significantly increased the risk of hospitalized COVID-19 (OR = 1.91, 95 % CI: 1.06-3.45, P = 0.032) and very severe COVID-19 (OR = 3.29, 95 % CI: 1.48-7.35, P = 3.62E-03). However, no causal effect was identified for PM2.5 concentration on other infectious diseases (P > 0.05). Furthermore, various sensitivity tests demonstrated the reliability of significant causal relationships.

Conclusions

Overall, lifetime elevated PM2.5 concentration increases the risk of hospitalized COVID-19 and very severe COVID-19. Therefore, controlling air pollution may help mitigate COVID-19 progression.

The causal effects of education on adult health, mortality and income: evidence from Mendelian randomization and the raising of the school leaving age

BACKGROUND

On average, educated people are healthier, wealthier and have higher life expectancy than those with less education. Numerous studies have attempted to determine whether education causes differences in later health outcomes or whether another factor ultimately causes differences in education and subsequent outcomes. Previous studies have used a range of natural experiments to provide causal evidence. Here we compare two natural experiments: a policy reform, raising the school leaving age in the UK in 1972; and Mendelian randomization.

METHODS

We used data from 334 974 participants of the UK Biobank, sampled between 2006 and 2010. We estimated the effect of an additional year of education on 25 outcomes, including mortality, measures of morbidity and health, ageing and income, using multivariable adjustment, the policy reform and Mendelian randomization. We used a range of sensitivity analyses and specification tests to assess the plausibility of each method's assumptions.

RESULTS

The three different estimates of the effects of educational attainment were largely consistent in direction for diabetes, stroke and heart attack, mortality, smoking, income, grip strength, height, body mass index (BMI), intelligence, alcohol consumption and sedentary behaviour. However, there was evidence that education reduced rates of moderate exercise and increased alcohol consumption. Our sensitivity analyses suggest that confounding by genotypic or phenotypic confounders or specific forms of pleiotropy are unlikely to explain our results.

CONCLUSIONS

Previous studies have suggested that the differences in outcomes associated with education may be due to confounding. However, the two independent sources of exogenous variation we exploit largely imply consistent causal effects of education on outcomes later in life.

Relationship between alcohol consumption and dementia with Mendelian randomization approaches among older adults in the United States

Background

In observational studies, the association between alcohol consumption and dementia is mixed.

Methods

We performed two-sample Mendelian randomization (MR) using summary statistics from genome-wide association studies of weekly alcohol consumption and late-onset Alzheimer's disease and one-sample MR in the Health and Retirement Study (HRS), wave 2012. Inverse variance weighted two-stage regression provided odds ratios of association between alcohol exposure and dementia or cognitively impaired, non-dementia relative to cognitively normal.

Results

Alcohol consumption was not associated with late-onset Alzheimer's disease using two-sample MR (OR=1.15, 95% confidence interval (CI):[0.78, 1.72]). In HRS, doubling weekly alcohol consumption was not associated with dementia (African ancestries, n=1,322, OR=1.00, 95% CI [0.45, 2.25]; European ancestries, n=7,160, OR=1.37, 95% CI [0.53, 3.51]) or cognitively impaired, non-dementia (African ancestries, n=1,322, OR=1.17, 95% CI [0.69, 1.98]; European ancestries, n=7,160, OR=0.75, 95% CI [0.47, 1.22]).

Conclusion

Alcohol consumption was not associated with cognitively impaired, non-dementia or dementia status.

A drug target for erectile dysfunction to help improve fertility, sexual activity, and wellbeing: mendelian randomisation study

OBJECTIVE

To investigate the association of genetically proxied (using a surrogate biomarker) inhibition of phosphodiesterase 5 (PDE5), an established drug target for erectile dysfunction, with fertility, sexual behaviour, and subjective wellbeing.

DESIGN

Two sample cis-mendelian randomisation study.

SETTING

Summary data on genetic associations obtained from the International Consortium for Blood Pressure and UK Biobank.

PARTICIPANTS

Individuals of European ancestry from the International Consortium for Blood Pressure (n=757 601) for estimating PDE5 inhibition (using the surrogate biomarker of diastolic blood pressure reduction), and UK Biobank (n=211 840) for estimating the fertility, sexual behaviour, and subjective wellbeing outcomes in male participants.

INTERVENTION

Genetically proxied PDE5 inhibition.

MAIN OUTCOME MEASURES

Number of children fathered, number of sexual partners, probability of never having had sexual intercourse, and subjective wellbeing.

RESULTS

Genetically proxied PDE5 inhibition was associated with male participants having 0.28 (95% confidence interval 0.16 to 0.39) more children (false discovery rate corrected P<0.001). This association was not identified in female participants. No evidence was found of an association between genetically proxied PDE5 inhibition and number of sexual partners, probability of never having had sexual intercourse, or self-reported wellbeing in male participants.

CONCLUSIONS

The findings of this study provide genetic support for PDE5 inhibition potentially increasing the number of children fathered by male individuals. Absence of this association in female participants supports increased propensity for sustained and robust penile erections as a potential underlying mechanism. Further studies are required to confirm this, however, and these findings should not promote indiscriminate use of PDE5 inhibitors, which can also have harmful adverse effects.

Genetically Predicted Vegetable Intake and Cardiovascular Diseases and Risk Factors: An Investigation with Mendelian Randomization

BACKGROUND

The associations between vegetable intake and cardiovascular diseases have been demonstrated in observational studies, but less sufficiently in randomized trials. Mendelian randomization has been considered a promising alternative in causal inference. The separate effects of cooked and raw vegetable intake remain unclear. This study aimed to investigate the associations between cooked and raw vegetable intake with cardiovascular outcomes using MR.

METHODS

We identified 15 and 28 genetic variants statistically and biologically associated with cooked and raw vegetable intake, respectively, from previous genome-wide association studies, which were used as instrumental variables to estimate associations with coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF). The independent effects of genetically predicted cooked and raw vegetable intake were examined using multivariable MR analysis. We performed one-sample and two-sample MR analyses and combined their results using meta-analysis. Bonferroni correction was applied for multiple comparisons. We performed two-sample MR analysis for cardiometabolic risk factors (serum lipids, blood pressure, body mass index, and glycemic traits) to explore the potential mechanisms.

RESULTS

In the MR meta-analysis of 1.2 million participants, we found null evidence for associations between genetically predicted cooked and raw vegetable intake with CHD, HF, or AF. Raw vegetable intake was nominally associated with stroke (odds ratio [95% confidence interval] 0.82 [0.69-0.98] per 1 daily serving increase, p = 0.03), but this association did not pass the corrected significance level. We found consistently null evidence for associations with serum lipids, blood pressure, body mass index, or glycemic traits.

CONCLUSIONS

We found null evidence for associations between genetically predicted vegetable intake with CHD, AF, HF, or cardiometabolic risk factors in this MR study. Raw vegetable intake may reduce risk of stroke, but this warrants more research. True associations between vegetable intake and CVDs cannot be completely ruled out, and future investigations are required for causal inference in nutritional research.

Appraising the causal relationship between plasma caffeine levels and neuropsychiatric disorders through Mendelian randomization

BACKGROUND

Caffeine exposure modifies the turnover of monoamine neurotransmitters, which play a role in several neuropsychiatric disorders. We conducted a Mendelian randomization study to investigate whether higher plasma caffeine levels are causally associated with the risk of anorexia nervosa, bipolar disorder, major depressive disorder (MDD), and schizophrenia.

METHODS

Summary-level data on the neuropsychiatric disorders were obtained from large-scale genome-wide association studies (GWASs) of European ancestry participants (n = 72,517 to 807,553) and meta-analyzed with the corresponding data from the FinnGen study (n = 356,077). Summary-level data on plasma caffeine were extracted from a GWAS meta-analysis of 9876 European ancestry individuals. The Mendelian randomization analyses estimated the Wald ratio for each genetic variant and meta-analyzed the variant-specific estimates using multiplicative random effects meta-analysis.

RESULTS

After correcting for multiple testing, genetically predicted higher plasma caffeine levels were associated with higher odds of anorexia nervosa (odds ratio [OR] = 1.124; 95% confidence interval [CI] = 1.024-1.238, pFDR = 0.039) and a lower odds of bipolar disorder (OR = 0.905, 95% CI = 0.827-0.929, pFDR = 0.041) and MDD (OR = 0.965, 95% CI = 0.937-0.995, pFDR = 0.039). Instrumented plasma caffeine levels were not associated with schizophrenia (OR = 0.986, 95% CI = 0.929-1.047, pFDR = 0.646).

CONCLUSIONS

These Mendelian randomization findings indicate that long-term higher plasma caffeine levels may lower the risk of bipolar disorder and MDD but increase the risk of anorexia nervosa. These results warrant further research to explore whether caffeine consumption, supplementation, or abstinence could render clinically relevant therapeutic or preventative psychiatric effects.

Genetically predicted circulating levels of cytokines and the risk of depression: a bidirectional Mendelian-randomization study

Objective: Inflammatory cytokines disturbance is the main result of immune dysregulation, which is widely described in major depressive disorder (MDD). However, the potential causal relationship between these two factors has not been discovered. Therefore, the purpose of this study was to investigate the causal relationship between inflammatory cytokines and MDD risk by using the two-sample Mendelian randomization (MR) analysis. Method: Two genetic instruments obtained from publicly available gene profile data were utilized for the analysis. We obtained the genetic variation data of 41 inflammatory cytokines from genome-wide association studies (GWAS) meta-analysis of 8293 individuals of Finnish descent. The MDD data, including 135,458 MDD cases and 344,901 controls, were obtained from the Psychiatric Genomics Consortium Database. For the Mendelian randomization (MR) estimation, several methods were employed, namely, MR-Egger regression, inverse-variance weighted (IVW), weighted median, and MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) methods. Result: A causal relationship was identified between the genetically proxied levels of Interleukin (IL) -18, IL-1β, and Regulated upon activation normal T cell expressed and secreted (RANTES) and the risk of MDD (OR = 0.968, 95%CI = 0.938, 0.998, p = 0.036; OR = 0.875, 95%CI = 0.787, 0.971, p = 0.012; OR = 0.947, 95%CI = 0.902, 0.995, p = 0.03; respectively). However, our Mendelian randomization (MR) estimates provided no causality of MDD on inflammatory cytokines. Conclusion: Our study elucidates the connection between inflammatory cytokines and MDD by using MR analysis, thereby enhancing our comprehension of the potential mechanisms. By identifying these associations, our findings hold substantial implications for the development of more effective treatments aimed at improving patient outcomes. However, further investigation is required to fully comprehend the exact biological mechanisms involved.

Guidelines for performing Mendelian randomization investigations: update for summer 2023

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.

Testing the causal relationships of physical activity and sedentary behaviour with mental health and substance use disorders: a Mendelian randomisation study

Observational studies suggest that physical activity can reduce the risk of mental health and substance use disorders. However, it is unclear whether this relationship is causal or explained by confounding bias (e.g., common underlying causes or reverse causality). We investigated the bidirectional causal relationship of physical activity (PA) and sedentary behaviour (SB) with ten mental health and substance use disorders, applying two-sample Mendelian Randomisation (MR). Genetic instruments for the exposures and outcomes were derived from the largest available, non-overlapping genome-wide association studies (GWAS). Summary-level data for objectively assessed PA (accelerometer-based average activity, moderate activity, and walking) and SB and self-reported moderate-to-vigorous PA were obtained from the UK Biobank. Data for mental health/substance use disorders were obtained from the Psychiatric Genomics Consortium and the GWAS and Sequencing Consortium of Alcohol and Nicotine Use. MR estimates were combined using inverse variance weighted meta-analysis (IVW). Sensitivity analyses were conducted to assess the robustness of the results. Accelerometer-based average PA was associated with a lower risk of depression (b = -0.043, 95% CI: -0.071 to -0.016, effect size[OR] = 0.957) and cigarette smoking (b = -0.026; 95% CI: -0.035 to -0.017, effect size[β] = -0.022). Accelerometer-based SB decreased the risk of anorexia (b = -0.341, 95% CI: -0.530 to -0.152, effect size[OR] = 0.711) and schizophrenia (b = -0.230; 95% CI: -0.285 to -0.175, effect size[OR] = 0.795). However, we found evidence of reverse causality in the relationship between SB and schizophrenia. Further, PTSD, bipolar disorder, anorexia, and ADHD were all associated with increased PA. This study provides evidence consistent with a causal protective effect of objectively assessed but not self-reported PA on reduced depression and cigarette smoking. Objectively assessed SB had a protective relationship with anorexia. Enhancing PA may be an effective intervention strategy to reduce depressive symptoms and addictive behaviours, while promoting sedentary or light physical activities may help to reduce the risk of anorexia in at-risk individuals.

Relationship between genetically proxied vitamin D and psoriasis risk: a Mendelian randomization study

BACKGROUND

Observational research suggests that vitamin D levels affect psoriasis. However, observational studies are prone to potential confounding or reverse causation, which complicates interpreting the data and drawing causal conclusions.

AIM

To apply Mendelian randomization (MR) methods to comprehensively assess a potential association between vitamin D and psoriasis.

METHODS

Genetic variants strongly associated with 25-hydroxyvitamin D (25OHD) in genome-wide association study (GWAS) data from 417 580 and 79 366 individuals from two independent studies served as instrumental variables (used as the discovery and replication datasets, respectively). As the outcome variable, we used GWAS data of psoriasis (13 229 people in the case group, 21 543 in the control group). We used (i) biologically validated genetic instruments, and (ii) polygenic genetic instruments to assess the relationship between genetically proxied vitamin D and psoriasis. We carried out inverse-variance weighted (IVW) MR analyses for the primary analysis. In sensitivity analyses, we used robust MR approaches.

RESULTS

MR analyses of both the discovery and replication datasets did not show an effect of 25OHD on psoriasis. Neither the IVW MR analysis of the biologically validated instruments [discovery dataset: odds ratio (OR) 0.99; 95% confidence interval (CI) 0.88-1.12, P = 0.873; replication dataset: OR 0.98, 95% CI 0.66-1.46, P = 0.930] nor that of the polygenic genetic instruments (discovery dataset: OR 1.00, 95% CI 0.81-1.22, P = 0.973; replication dataset: OR 0.94, 95% CI 0.64-1.38, P = 0.737) revealed an impact of 25OHD on psoriasis.

CONCLUSION

The present MR study did not support the hypothesis that vitamin D levels, measured by 25OHD, affect psoriasis. This study was conducted on Europeans, so the conclusions may not be applicable to all ethnicities.

Bounding the average causal effect in Mendelian randomisation studies with multiple proposed instruments: An application to prenatal alcohol exposure and attention deficit hyperactivity disorder

BACKGROUND

As large-scale observational data become more available, caution regarding causal assumptions remains critically important. This may be especially true for Mendelian randomisation (MR), an increasingly popular approach. Point estimation in MR usually requires strong, often implausible homogeneity assumptions beyond the core instrumental conditions. Bounding, which does not require homogeneity assumptions, is infrequently applied in MR.

OBJECTIVES

We aimed to demonstrate computing nonparametric bounds for the causal risk difference derived from multiple proposed instruments in an MR study where effect heterogeneity is expected.

METHODS

Using data from the Norwegian Mother, Father and Child Cohort Study (n = 2056) and Avon Longitudinal Study of Parents and Children (n = 6216) to study the average causal effect of maternal pregnancy alcohol use on offspring attention deficit hyperactivity disorder symptoms, we proposed 11 maternal SNPs as instruments. We computed bounds assuming subsets of SNPs were jointly valid instruments, for all combinations of SNPs where the MR model was not falsified.

RESULTS

The MR assumptions were violated for all sets with more than 4 SNPs in one cohort and for all sets with more than 2 SNPs in the other. Bounds assuming one SNP was an individually valid instrument barely improved on assumption-free bounds. Bounds tightened as more SNPs were assumed to be jointly valid instruments, and occasionally identified directions of effect, though bounds from different sets varied.

CONCLUSIONS

Our results suggest that, when proposing multiple instruments, bounds can contextualise plausible magnitudes and directions of effects. Computing bounds over multiple assumption sets, particularly in large, high-dimensional data, offers a means of triangulating results across different potential sources of bias within a study and may help researchers to better evaluate and emphasise which estimates are compatible with the most plausible assumptions for their specific setting.

Comparison of intergenerational instrumental variable analyses of body mass index and mortality in UK Biobank

BACKGROUND

An increasing proportion of people have a body mass index (BMI) classified as overweight or obese and published studies disagree whether this will be beneficial or detrimental to health. We applied and evaluated two intergenerational instrumental variable methods to estimate the average causal effect of BMI on mortality in a cohort with many deaths: the parents of UK Biobank participants.

METHODS

In Cox regression models, parental BMI was instrumented by offspring BMI using an 'offspring as instrument' (OAI) estimation and by offspring BMI-related genetic variants in a 'proxy-genotype Mendelian randomization' (PGMR) estimation.

RESULTS

Complete-case analyses were performed in parents of 233 361 UK Biobank participants with full phenotypic, genotypic and covariate data. The PGMR method suggested that higher BMI increased mortality with hazard ratios per kg/m2 of 1.02 (95% CI: 1.01, 1.04) for mothers and 1.04 (95% CI: 1.02, 1.05) for fathers. The OAI method gave considerably higher estimates, which varied according to the parent-offspring pairing between 1.08 (95% CI: 1.06, 1.10; mother-son) and 1.23 (95% CI: 1.16, 1.29; father-daughter).

CONCLUSION

Both methods supported a causal role of higher BMI increasing mortality, although caution is required regarding the immediate causal interpretation of these exact values. Evidence of instrument invalidity from measured covariates was limited for the OAI method and minimal for the PGMR method. The methods are complementary for interrogating the average putative causal effects because the biases are expected to differ between them.

Exploring the Lifetime Effect of Children on Wellbeing Using Two-Sample Mendelian Randomisation

BACKGROUND

Observational research implies a negative effect of having children on wellbeing.

OBJECTIVES

To provide Mendelian randomisation evidence of the effect of having children on parental wellbeing.

DESIGN

Two-sample Mendelian randomisation.

SETTING

Non-clinical European ancestry participants.

PARTICIPANTS

We used the UK Biobank (460,654 male and female European ancestry participants) as a source of genotype-exposure associations, the Social Science Genetics Consortia (SSGAC) (298,420 male and female European ancestry participants), and the Within-Family Consortia (effective sample of 22,656 male and female European ancestry participants) as sources of genotype-outcome associations.

INTERVENTIONS

The lifetime effect of an increase in the genetic liability to having children.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary analysis was an inverse variance weighed analysis of subjective wellbeing measured in the 2016 SSGAC Genome Wide Association Study (GWAS). Secondary outcomes included pleiotropy robust estimators applied in the SSGAC and an analysis using the Within-Family consortia GWAS.

RESULTS

We did not find strong evidence of a negative (standard deviation) change in wellbeing (β = 0.153 (95% CI: -0.210 to 0.516) per child parented. Secondary outcomes were generally slightly deflated (e.g., -0.049 [95% CI: -0.533 to 0.435] for the Within-Family Consortia and 0.090 [95% CI: -0.167 to 0.347] for weighted median), implying the presence of some residual confounding and pleiotropy.

CONCLUSIONS

Contrary to the existing literature, our results are not compatible with a measurable negative effect of number of children on the average wellbeing of a parent over their life course. However, we were unable to explore non-linearities, interactions, or time-varying effects.

Obesity and risk of gestational diabetes mellitus: A two-sample Mendelian randomization study

AIMS

To estimate genetically predicted causal associations of general and central obesity with GDM, and to determine the mediating role of circulating lipids.

METHODS

Summary-level data was obtained from the largest available genome-wide association studies of five obesity traits, five lipid traits and GDM. Two-sample univariate Mendelian randomization (MR), multivariate MR, and MR-based mediation analysis was applied to determine the total effect, direct effect and the mediating effect, respectively.

RESULTS

Univariate MR showed that the odds of GDM increased per 1-SD increase in body mass index (BMI) (OR = 1.64, P = 5.05 × 10^-17), waist-to-hip ratio (WHR) (OR = 1.57, P = 2.27 × 10^-14) and WHR adjusted for BMI (OR = 1.42, P = 6.11 × 10^-15). The heterogeneous associations of waist circumference (OR = 1.64, P = 5.57 × 10^-14) and hip circumference (OR = 1.20, P = 0.002) on GDM further reflected that body fat distribution could influence GDM risk. Mediation analysis suggested that triglycerides, high-density lipoprotein-cholesterol and apolipoprotein A-I each mediated between 5% and 10% of the association between obesity and GDM.

CONCLUSION

Our findings supported a deleterious causal effect of obesity on GDM risk, where lipid metabolism acted as potential drivers of the relationships between both general and central obesity and GDM.

Association of Hypertensive Disorders of Pregnancy With Future Cardiovascular Disease

CONCLUSIONS AND RELEVANCE

The findings of this study provide genetic evidence supporting an association between HDPs and higher risk of coronary artery disease and stroke, which is only partially mediated by cardiometabolic factors. This supports classification of HDPs as risk factors for cardiovascular disease.

DESIGN, SETTING, AND PARTICIPANTS

A genome-wide genetic association study using mendelian randomization (MR) was performed from February 16 to March 4, 2022. Primary analysis was conducted using inverse-variance-weighted MR. Mediation analyses were performed using a multivariable MR framework. All studies included patients predominantly of European ancestry. Female-specific summary-level data from FinnGen (sixth release).

EXPOSURES

Uncorrelated (r2<0.001) single-nucleotide variants (SNVs) were selected as instrumental variants from the FinnGen consortium summary statistics for exposures of any HDP, gestational hypertension, and preeclampsia or eclampsia.

IMPORTANCE

Hypertensive disorders in pregnancy (HDPs) are major causes of maternal and fetal morbidity and are observationally associated with future maternal risk of cardiovascular disease. However, observational results may be subject to residual confounding and bias.

MAIN OUTCOMES AND MEASURES

Genetic association estimates for outcomes were extracted from genome-wide association studies of 122 733 cases for coronary artery disease, 34 217 cases for ischemic stroke, 47 309 cases for heart failure, and 60 620 cases for atrial fibrillation.

OBJECTIVE

To investigate the association of HDPs with multiple cardiovascular diseases.

RESULTS

Genetically predicted HDPs were associated with a higher risk of coronary artery disease (odds ratio [OR], 1.24; 95% CI, 1.08-1.43; P = .002); this association was evident for both gestational hypertension (OR, 1.08; 95% CI, 1.00-1.17; P = .04) and preeclampsia/eclampsia (OR, 1.06; 95% CI, 1.01-1.12; P = .03). Genetically predicted HDPs were also associated with a higher risk of ischemic stroke (OR, 1.27; 95% CI, 1.12-1.44; P = 2.87 × 10-4). Mediation analysis revealed a partial attenuation of the effect of HDPs on coronary artery disease after adjustment for systolic blood pressure (total effect OR, 1.24; direct effect OR, 1.10; 95% CI, 1.02-1.08; P = .02) and type 2 diabetes (total effect OR, 1.24; direct effect OR, 1.16; 95% CI, 1.04-1.29; P = .008). No associations were noted between genetically predicted HDPs and heart failure (OR, 0.97; 95% CI, 0.76-1.23; P = .79) or atrial fibrillation (OR, 1.11; 95% CI, 0.65-1.88; P = .71).

Estimation of time-varying causal effects with multivariable Mendelian randomization: some cautionary notes

INTRODUCTION

For many exposures present across the life course, the effect of the exposure may vary over time. Multivariable Mendelian randomization (MVMR) is an approach that can assess the effects of related risk factors using genetic variants as instrumental variables. Recently, MVMR has been used to estimate the effects of an exposure during distinct time periods.

METHODS

We investigated the behaviour of estimates from MVMR in a simulation study for different time-varying causal scenarios. We also performed an applied analysis to consider how MVMR estimates of body mass index on systolic blood pressure vary depending on the time periods considered.

RESULTS

Estimates from MVMR in the simulation study were close to the true values when the outcome model was correctly specified: i.e. when the outcome was a discrete function of the exposure at the precise time points at which the exposure was measured. However, in more realistic cases, MVMR estimates were misleading. For example, in one scenario, MVMR estimates for early life were clearly negative despite the true causal effect being constant and positive. In the applied example, estimates were highly variable depending on the time period in which genetic associations with the exposure were estimated.

CONCLUSIONS

The poor performance of MVMR to study time-varying causal effects can be attributed to model misspecification and violation of the exclusion restriction assumption. We would urge caution about quantitative conclusions from such analyses and even qualitative interpretations about the direction, or presence or absence, of a causal effect during a given time period.

Body Mass Index, Abdominal Adiposity, and Incidence of Parkinson Disease in French Women From the E3N Cohort Study

BACKGROUND AND OBJECTIVES

Previous studies on the relationship between body mass index (BMI) and Parkinson disease (PD) provided inconsistent results, likely due to reverse causation explained by weight loss during the prodromal phase. We examined the association of BMI and abdominal adiposity with PD incidence using lagged analyses to address the potential for reverse causation and compared BMI trajectories in patients before diagnosis and matched controls.

METHODS

We used data from the E3N cohort study of French women with a 29-year follow-up (1990-2018). BMI (kg/m²) was computed based on self-reported weight and height up to 11 times; up to 6 waist circumference (WC) and hip circumference measures were available. PD diagnoses were validated based on medical records and drug claim databases. Multivariable time-varying Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs according to BMI categories (underweight <18.5 kg/m²; normal = [18.5-25.0[ kg/m²; overweight = [25.0-30.0[ kg/m²; obese ≥30.0 kg/m²). Exposures were lagged by 5 years in main analyses; we used longer lags (10 and 20 years) in sensitivity analyses. We examined trajectories of BMI categories within a nested case-control study using multivariable generalized estimating equations multinomial logistic models.

RESULTS

Of 96,702 women (baseline age = 40-65 years), 1,164 developed PD. PD incidence was lower (HR = 0.76, 95% CI = 0.59-0.98, p = 0.032) among women with obesity compared with those with normal BMI. There was a similar association in analyses using longer lag times (20 years, 598 cases, HR = 0.52, 95% CI = 0.30-0.88, p = 0.016). A similar pattern was seen for WC and waist-height ratio but not waist-hip ratio. Trajectories of BMI categories (1,196 patients and 23,876 controls) showed that obesity was less frequent in patients with PD before diagnosis than in controls, with a statistically significant difference 29 years before. In addition, the frequency of obesity decreased 5-10 years before diagnosis in patients.

DISCUSSION

In this large cohort of women with a long follow-up, obesity was associated with a lower hazard of PD, even when measured 20 years before diagnosis, in agreement with Mendelian randomization studies. Our analyses underscore the importance of lagged analyses to account for reverse causation. These findings warrant further investigations to understand the mechanisms underlying this inverse association.

Leveraging family data to design Mendelian Randomization that is provably robust to population stratification

Mendelian Randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases due to weak instruments as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We demonstrate in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, while standard MR methods yield inflated false positive rates. We applied MR-Twin to 121 trait pairs in the UK Biobank dataset and found that MR-Twin identifies likely causal trait pairs and does not identify trait pairs that are unlikely to be causal. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, while MR-Twin is immune to this type of confounding.

The association of obesity-related traits on COVID-19 severity and hospitalization is affected by socio-economic status: a multivariable Mendelian randomization study

BACKGROUND

Due to its large impact on human health, socio-economic status (SES) could at least partially influence the established association between obesity and coronavirus disease 2019 (COVID-19) severity. To estimate the independent effect of body size and SES on the clinical manifestations of COVID-19, we conducted a Mendelian randomization (MR) study.

METHODS

Applying two-sample MR approaches, we evaluated the effects of body mass index (BMI, n = 322 154), waist circumference (WC, n = 234 069), hip circumference (n = 213 019) and waist-hip ratio (n = 210 088) with respect to three COVID-19 outcomes: severe respiratory COVID-19 (cases = 8779, controls = 1 000 875), hospitalized COVID-19 (cases = 17 992, controls = 1 810 493) and COVID-19 infection (cases = 87 870, controls = 2 210 804). Applying a multivariable MR (MVMR) approach, we estimated the effect of these anthropometric traits on COVID-19 outcomes accounting for the effect of SES assessed as household income (n = 286 301).

RESULTS

BMI and WC were associated with severe respiratory COVID-19 [BMI: odds ratio (OR) = 1.51, CI = 1.24-1.84, P = 3.01e-05; WC: OR = 1.48, 95% CI = 1.15-1.91, P = 0.0019] and hospitalized COVID-19 (BMI: OR = 1.50, 95% CI = 1.32-1.72, P = 8.83e-10; WC: OR = 1.41, 95% CI = 1.20-1.67, P = 3.72e-05). Conversely, income was associated with lower odds of severe respiratory (OR = 0.70, 95% CI = 0.53-0.93, P = 0.015) and hospitalized COVID-19 (OR = 0.78, 95% CI = 0.66-0.92, P = 0.003). MVMR analyses showed that the effect of these obesity-related traits on increasing the odds of COVID-19 negative outcomes becomes null when accounting for income. Conversely, the association of income with lower odds of COVID-19 negative outcomes is not affected when including the anthropometric traits in the multivariable model.

CONCLUSION

Our findings indicate that SES contributes to the effect of obesity-related traits on COVID-19 severity and hospitalization.

Circulating free testosterone and risk of aggressive prostate cancer: Prospective and Mendelian randomisation analyses in international consortia

Previous studies had limited power to assess the associations of testosterone with aggressive disease as a primary endpoint. Further, the association of genetically predicted testosterone with aggressive disease is not known. We investigated the associations of calculated free and measured total testosterone and sex hormone-binding globulin (SHBG) with aggressive, overall and early-onset prostate cancer. In blood-based analyses, odds ratios (OR) and 95% confidence intervals (CI) for prostate cancer were estimated using conditional logistic regression from prospective analysis of biomarker concentrations in the Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group (up to 25 studies, 14 944 cases and 36 752 controls, including 1870 aggressive prostate cancers). In Mendelian randomisation (MR) analyses, using instruments identified using UK Biobank (up to 194 453 men) and outcome data from PRACTICAL (up to 79 148 cases and 61 106 controls, including 15 167 aggressive cancers), ORs were estimated using the inverse-variance weighted method. Free testosterone was associated with aggressive disease in MR analyses (OR per 1 SD = 1.23, 95% CI = 1.08-1.40). In blood-based analyses there was no association with aggressive disease overall, but there was heterogeneity by age at blood collection (OR for men aged <60 years 1.14, CI = 1.02-1.28; Phet  = .0003: inverse association for older ages). Associations for free testosterone were positive for overall prostate cancer (MR: 1.20, 1.08-1.34; blood-based: 1.03, 1.01-1.05) and early-onset prostate cancer (MR: 1.37, 1.09-1.73; blood-based: 1.08, 0.98-1.19). SHBG and total testosterone were inversely associated with overall prostate cancer in blood-based analyses, with null associations in MR analysis. Our results support free testosterone, rather than total testosterone, in the development of prostate cancer, including aggressive subgroups.

Iron status and the risk of sepsis and severe COVID-19: a two-sample Mendelian randomization study

Observational studies have indicated an association between iron status and risk of sepsis and COVID-19. We estimated the effect of genetically-predicted iron biomarkers on risk of sepsis and risk of being hospitalized with COVID-19, performing a two-sample Mendelian randomization study. For risk of sepsis, one standard deviation increase in genetically-predicted serum iron was associated with odds ratio (OR) of 1.14 (95% confidence interval [CI] 1.01-1.29, P = 0.031). The findings were supported in the analyses for transferrin saturation and total iron binding capacity, while the estimate for ferritin was inconclusive. We found a tendency of higher risk of hospitalization with COVID-19 for serum iron; OR 1.29 (CI 0.97-1.72, P = 0.08), whereas sex-stratified analyses showed OR 1.63 (CI 0.94-2.86, P = 0.09) for women and OR 1.21 (CI 0.92-1.62, P = 0.17) for men. Sensitivity analyses supported the main findings and did not suggest bias due to pleiotropy. Our findings suggest a causal effect of genetically-predicted higher iron status and risk of hospitalization due to sepsis and indications of an increased risk of being hospitalized with COVID-19. These findings warrant further studies to assess iron status in relation to severe infections, including the potential of improved management.

Interpretation of Mendelian randomization using a single measure of an exposure that varies over time

BACKGROUND

Mendelian randomization (MR) is a powerful tool through which the causal effects of modifiable exposures on outcomes can be estimated from observational data. Most exposures vary throughout the life course, but MR is commonly applied to one measurement of an exposure (e.g. weight measured once between ages 40 and 60 years). It has been argued that MR provides biased causal effect estimates when applied to one measure of an exposure that varies over time.

METHODS

We propose an approach that emphasizes the liability that causes the entire exposure trajectory. We demonstrate this approach using simulations and an applied example.

RESULTS

We show that rather than estimating the direct or total causal effect of changing the exposure value at a given time, MR estimates the causal effect of changing the underlying liability for the exposure, scaled to the effect of the liability on the exposure at that time. As such, results from MR conducted at different time points are expected to differ (unless the effect of the liability on exposure is constant over time), as we illustrate by estimating the effect of body mass index measured at different ages on systolic blood pressure.

CONCLUSION

Univariable MR results should not be interpreted as time-point-specific direct or total causal effects, but as the effect of changing the liability for the exposure. Estimates of how the effects of a genetic variant on an exposure vary over time, together with biological knowledge that provides evidence regarding likely effective exposure periods, are required to interpret time-point-specific causal effects.

Assessment of the Causal Effects of IgG N-Glycosylation Level on Risk of Dementia: A 2-Sample Mendelian Randomization Study

Background: Previous prospective studies highlighted aberrant immunoglobulin G (IgG) N-glycosylation as a risk factor for dementia [such as Alzheimer’s disease (AD) and vascular dementia (VaD)]. It is unclear whether this association is causal or explained by confounding or reverse causation. Objective: The aim is to examine the association of genetically predicted IgG N-glycosylation with dementia using 2-sample Mendelian randomization (MR). Methods: Independent genetic variants for IgG N-glycosylation traits were selected as instrument variables from published genome-wide association studies (GWAS) among individuals of European ancestry. We extracted their corresponding summary statistics from large-scale clinically diagnosed AD GWAS dataset and FinnGen biobank VaD GWAS dataset. The inverse variance weighted (IVW) was performed to calculate the effect estimates. Meanwhile, multiple sensitivity analyses were used to assess horizontal pleiotropy and outliers. Results: There were no associations of genetically predicted IgG N-glycosylation traits with the risk of AD and VaD using the IVW method (all p >  0.0013). These estimates of four additional sensitivity analyses methods were consistent with the IVW estimates in terms of direction and magnitude. Additionally, the MR-PRESSO global test and the intercept of MR-Egger regression indicated no evidence of horizontal pleiotropy. Meanwhile, the heterogeneity test showed no significant heterogeneity using the Cochran Q statistic. The leave-one-out sensitivity analyses also did not detect any significant change. Conclusion: Our MR study did not support evidence for the hypothesis that IgG N-glycosylation level may be causally associated with the risk of dementia.

Investigating the causal nature of the relationship of subcortical brain volume with smoking and alcohol use

BACKGROUND

Structural variation in subcortical brain regions has been linked to substance use, including the most commonly used substances nicotine and alcohol. Pre-existing differences in subcortical brain volume may affect smoking and alcohol use, but there is also evidence that smoking and alcohol use can lead to structural changes.

AIMS

We assess the causal nature of the complex relationship of subcortical brain volume with smoking and alcohol use, using bi-directional Mendelian randomisation.

METHOD

Mendelian randomisation uses genetic variants predictive of a certain 'exposure' as instrumental variables to test causal effects on an 'outcome'. Because of random assortment at meiosis, genetic variants should not be associated with confounders, allowing less biased causal inference. We used summary-level data of genome-wide association studies of subcortical brain volumes (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen and thalamus; n = 50 290) and smoking and alcohol use (smoking initiation, n = 848 460; cigarettes per day, n = 216 590; smoking cessation, n = 378 249; alcoholic drinks per week, n = 630 154; alcohol dependence, n = 46 568). The main analysis, inverse-variance weighted regression, was verified by a wide range of sensitivity methods.

RESULTS

There was strong evidence that liability to alcohol dependence decreased amygdala and hippocampal volume, and smoking more cigarettes per day decreased hippocampal volume. From subcortical brain volumes to substance use, there was no or weak evidence for causal effects.

CONCLUSIONS

Our findings suggest that heavy alcohol use and smoking can causally reduce subcortical brain volume. This adds to accumulating evidence that alcohol and smoking affect the brain, and likely mental health, warranting more recognition in public health efforts.

Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization

Mendelian Randomisation (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants as instrumental variables (IVs) for the exposure. The effect estimates obtained from MR studies are often interpreted as the lifetime effect of the exposure in question. However, the causal effects of some exposures are thought to vary throughout an individual's lifetime with periods during which an exposure has a greater effect on a particular outcome. Multivariable MR (MVMR) is an extension of MR that allows for multiple, potentially highly related, exposures to be included in an MR estimation. MVMR estimates the direct effect of each exposure on the outcome conditional on all the other exposures included in the estimation. We explore the use of MVMR to estimate the direct effect of a single exposure at different time points in an individual's lifetime on an outcome. We use simulations to illustrate the interpretation of the results from such analyses and the key assumptions required. We show that causal effects at different time periods can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure measured at those time periods varies. However, this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome. Prior knowledge regarding the biological basis of exposure trajectories can help interpretation. We illustrate the method through estimation of the causal effects of childhood and adult BMI on C-Reactive protein and smoking behaviour.

Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization

With the increasing size and number of genome-wide association studies, individual single nucleotide polymorphisms are increasingly found to associate with multiple traits. Many different mechanisms could result in proposed genetic IVs for an exposure of interest being associated with multiple non-exposure traits, some of which could bias MR results. We describe and illustrate, through causal diagrams, a range of scenarios that could result in proposed IVs being related to non-exposure traits in MR studies. These associations could occur due to five scenarios: (i) confounding, (ii) vertical pleiotropy, (iii) horizontal pleiotropy, (iv) reverse causation and (v) selection bias. For each of these scenarios we outline steps that could be taken to explore the underlying mechanism and mitigate any resulting bias in the MR estimation. We recommend MR studies explore possible IV-non-exposure associations across a wider range of traits than is usually the case. We highlight the pros and cons of relying on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic or other biasing paths via known traits. We apply our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in UK Biobank.

A Mendelian randomization study investigating the causal role of inflammation on Parkinson’s disease

There is increasing evidence for inflammation as a determinant in the pathogenesis of Parkinson’s disease, but its role in parkinsonian neurodegeneration remains elusive. It is not clear whether inflammatory cascades are causes or consequences of dopamine neuron death. In the present study, we aim to perform an in-depth statistical investigation of the causal relationship between inflammation and Parkinson’s disease using a two-sample Mendelian randomization design.

Genetic instruments were selected using summary-level data from the largest genome-wide association studies to date (sample size ranging from 13 955 to 204 402 individuals) conducted on a European population for the following inflammation biomarkers: C-reactive protein, interleukin-6, interleukin 1 receptor antagonist and tumour necrosis factor α. Genetic association data on Parkinson’s disease (56 306 cases and 1 417 791 controls) and age at onset of Parkinson’s disease (28 568 cases) were obtained from the International Parkinson’s Disease Genomics Consortium. On primary analysis, causal associations were estimated on sets of strong (P-value < 5 × 10⁻⁸; F-statistic > 10) and independent (linkage disequilibrium r² < 0.001) genetic instruments using the inverse-variance weighted method. In sensitivity analysis, we estimated causal effects using robust Mendelian randomization methods and after removing pleiotropic genetic variants. Reverse causation was also explored. We repeated the analysis on different data sources for inflammatory biomarkers to check the consistency of the findings.

In all the three data sources selected for interleukin-6, we found statistical evidence for an earlier age at onset of Parkinson’s disease associated with increased interleukin-6 concentration [years difference per 1 log-unit increase = −2.364, 95% confidence interval (CI) = −4.789–0.060; years difference per 1 log-unit increase = −2.011, 95% CI = −3.706 to −0.317; years difference per 1 log-unit increase = −1.569, 95% CI = −2.891 to −0.247]. We did not observe any statistical evidence for causal effects of C-reactive protein, interleukin 1 receptor antagonist and tumour necrosis factor α on both Parkinson’s disease and its age at onset. Results after excluding possible pleiotropic genetic variants were consistent with findings from primary analyses. When investigating reverse causation, we did not find evidence for a causal effect of Parkinson’s disease or age at onset on any biomarkers of inflammation.

We found evidence for a causal association between the onset of Parkinson’s disease and interleukin-6. The findings of this study suggest that the pro-inflammatory activity of the interleukin-6 cytokine could be a determinant of prodromal Parkinson’s disease.

Effects of depression on employment and social outcomes: a Mendelian randomisation study

BACKGROUND

Depression is associated with socioeconomic disadvantage. However, whether and how depression exerts a causal effect on employment remains unclear. We used Mendelian randomisation (MR) to investigate whether depression affects employment and related outcomes in the UK Biobank dataset.

METHODS

We selected 227 242 working-age participants (40-64 in men, 40-59 years for women) of white British ethnicity/ancestry with suitable genetic data in the UK Biobank study. We used 30 independent genetic variants associated with depression as instruments. We conducted observational and two-sample MR analyses. Outcomes were employment status (employed vs not, and employed vs sickness/disability, unemployment, retirement or caring for home/family); weekly hours worked (among employed); Townsend Deprivation Index; highest educational attainment; and household income.

RESULTS

People who had experienced depression had higher odds of non-employment, sickness/disability, unemployment, caring for home/family and early retirement. Depression was associated with reduced weekly hours worked, lower household income and lower educational attainment, and increased deprivation. MR analyses suggested depression liability caused increased non-employment (OR 1.16, 95% CI 1.06 to 1.26) and sickness/disability (OR 1.56, 95% CI 1.34 to 1.82), but was not causal for caring for home/family, early retirement or unemployment. There was little evidence from MR that depression affected weekly hours worked, educational attainment, household income or deprivation.

CONCLUSIONS

Depression liability appears to cause increased non-employment, particularly by increasing disability. There was little evidence of depression affecting early retirement, hours worked or household income, but power was low. Effective treatment of depression might have important economic benefits to individuals and society.

Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner. Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

A review of Mendelian randomization in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a relatively common and rapidly progressive neurodegenerative disease that, in the majority of cases, is thought to be determined by a complex gene-environment interaction. Exponential growth in the number of performed genome-wide association studies combined with the advent of Mendelian randomization is opening significant new opportunities to identify environmental exposures that increase or decrease the risk of amyotrophic lateral sclerosis. Each of these discoveries has the potential to shape new therapeutic interventions. However, to do so, rigorous methodological standards must be applied in the performance of Mendelian randomization. We have reviewed Mendelian randomization studies performed in amyotrophic lateral sclerosis to date. We identified 20 Mendelian randomization studies, including evaluation of physical exercise, adiposity, cognitive performance, immune function, blood lipids, sleep behaviours, educational attainment, alcohol consumption, smoking and type 2 diabetes mellitus. We have evaluated each study using gold standard methodology supported by the Mendelian randomization literature and the STROBE-Mendelian randomization checklist. Where discrepancies exist between Mendelian randomization studies, we suggest the underlying reasons. A number of studies conclude that there is a causal link between blood lipids and risk of amyotrophic lateral sclerosis; replication across different datasets and even different populations adds confidence. For other putative risk factors, such as smoking and immune function, Mendelian randomization studies have provided cause for doubt. We highlight the use of positive control analyses in choosing exposure single nucleotide polymorphisms (SNPs) to make up the Mendelian randomization instrument, use of SNP clumping to avoid false positive results due to SNPs in linkage and the importance of multiple testing correction. We discuss the implications of survival bias for study of late age of onset diseases such as amyotrophic lateral sclerosis and make recommendations to mitigate this potentially important confounder. For Mendelian randomization to be useful to the amyotrophic lateral sclerosis field, high methodological standards must be applied to ensure reproducibility. Mendelian randomization is already an impactful tool, but poor-quality studies will lead to incorrect interpretations by a field that includes non-statisticians, wasted resources and missed opportunities.

Genetic estimates of correlation and causality between blood-based biomarkers and psychiatric disorders

There is a long-standing interest in exploring the relationship between blood-based biomarkers and psychiatric disorders, despite their causal role being difficult to resolve in observational studies. In this study, we leverage genome-wide association study data for a large panel of heritable serum biochemical traits to refine our understanding of causal effect in biochemical-psychiatric trait pairings. We observed widespread positive and negative genetic correlation between psychiatric disorders and biochemical traits. Causal inference was then implemented to distinguish causation from correlation, with strong evidence that C-reactive protein (CRP) exerts a causal effect on psychiatric disorders. Notably, CRP demonstrated both protective and risk-increasing effects on different disorders. Multivariable models that conditioned CRP effects on interleukin-6 signaling and body mass index supported that the CRP-schizophrenia relationship was not driven by these factors. Collectively, these data suggest that there are shared pathways that influence both biochemical traits and psychiatric illness.

Genetically predicted telomere length and multiple sclerosis

BACKGROUND

Previous epidemiological studies have indicated a role for telomere length in multiple sclerosis (MS) severity and phenotype. However, these studies failed to establish the causality between telomere length and MS susceptibility. Hence, we performed two-sample Mendelian randomization (MR) analysis to explore the causal relationship between telomere length and MS susceptibility.

METHODS

We used data of genetic variants associated with leukocyte telomere length as instrumental variables (IVs), which was identified from the largest and latest genome-wide association study (GWAS) from UK Biobank (UKB) with 472,174 participants. Summary data of MS was obtained from the International Multiple Sclerosis Genetics Consortium. We performed two-sample MR analyses using the inverse-variance weighted method as the primary approach. Other MR approaches, including the MR-Egger, the inverse variance weighted (multiplicative random effects), weighted median, simple median, weighted mode-based methods, and Causal Analysis Using Summary Effect estimates (CAUSE), were also conducted to detect the result robustness.

RESULTS

The genetic liability to longer telomere length was associated with a higher risk of MS susceptibility (odds ratio [OR] per one-SD telomere length, 1.91; 95% confidence interval [CI], 1.48-2.47; P = 8.04 × 10^-7). The results remained consistent across multiple sensitivity analyses.

CONCLUSIONS

Our study supports the causal relationship between longer telomere length and increased risk of MS susceptibility.