Context Obesity is associated with a high risk of vascular-related dementia with metabolic risk factors as potential mediators, but questions of causality remain unanswered.Objective We aimed to determine whether high body mass index (BMI) is a causal risk factor for vascular-related dementia, and whether any effect is mediated by hypertension, hyperlipidemia, hyperglycemia, and low-grade inflammation.Methods Prospective cohort studies of the general populations from the Copenhagen area and from across the United Kingdom and consortia data were included in the study. Interventions included one-sample mendelian randomization (MR), two-sample MR, and MR in mediation analyses. Both individual-level and summary-level data was used. Main outcome measures included risk of vascular-related dementia, Alzheimer's disease, and ischemic heart disease.Results In a meta-analysis of 2 one-sample MR studies, the odds ratio (OR) for 1-SD higher BMI in predicting vascular-related dementia was 1.63 (95% CI, 1.13-2.35). In a two-sample MR study, the OR for vascular-related dementia per 1-SD higher BMI was 1.54 (1.10-2.16) using the inverse-variance weighted, 1.87 (1.22-2.85) using the weighted median, and 1.98 (1.21-3.22) using the weighted mode methods. Results from MR analyses including extended numbers of genetic variants were directionally consistent. Finally, systolic blood pressure mediated 18% (95% CI, 10%-61%) and diastolic blood pressure mediated 25% (13%-75%) of the genetic effect of BMI on vascular-related dementia.Conclusion Observationally (U-shaped) and genetically (linearly), high BMI is associated with a higher risk of vascular-related dementia, an association partly mediated through high blood pressure. This suggests that high BMI and high blood pressure are important modifiable risk factors for dementia prevention.

Background: Higher Body Mass Index (BMI) is an established risk factor of sleep disturbance. It is not known if the effect is homogeneous across the lifecourse or if there is a particular time point in life that might be best to target. Methods: Two-sample Mendelian randomization (MR) was used to investigated the effect of childhood adiposity (adjusting on adulthood adiposity and obstructive sleep apnea (OSA)) on insomnia, morning chronotype, sleep duration, daytime sleepiness and daytime napping. Similarly, total, and direct effect of adulthood adiposity on these outcomes was explored. We used summary statistics from a genome-wide association study (GWAS) of UK Biobank for childhood and adulthood adiposity (n=453,169) and large-scale consortia of OSA (Million Veteran Program) (n=410,268), insomnia, and chronotype (23andMe) (n=1,978,022 and n=248,1000, respectively). Results: Two-sample univariable MR analysis provided no evidence of an effect of genetically predicted childhood adiposity on later life insomnia (Odds ratio (OR)= 0.94, 95% Confidence interval (CI)= 0.87, 1.03). Whereas, multivariable MR (adjusted for adulthood adiposity) analysis provide strong evidence of direct protective effect of genetically predicted childhood adiposity on later life insomnia (OR= 0.70, CI= 0.64, 0.77). Further, both in univariable and multivariable MR, a strong positive effect of increased childhood body size on morning chronotype was observed (OR= 1.16, CI= 1.01, 1.33 and OR= 1.36, CI= 1.15, 1.62, respectively) after accounting for adulthood body size. In both analysis the estimate did not change considerably after aditionally adjusting for OSA. However, childhood and adulthood adiposity found to be associated with OSA and OSA with insomnia. In both univariable and multivariable analysis, increased body size in adulthood increased the risk of having insomnia and a morning chronotype. Conclusions: The findings suggest that higher body size in childhood is not a risk factor for later life insomnia, whereas higher body size in adulthood was. Further, if healthy body size is maintained in adulthood, high childhood adiposity may decrease the risk of insomnia and increase the risk of being a morning person in later life. Keywords: childhood, adulthood, obesity, insomnia, morning chronotype, medelian randomization ### Competing Interest Statement T.G.R. is a full time employee of GlaxoSmithKline outside of this research. The other authors declare that they have no competing interests. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: GWAS Summary statistic data of childhood and adulthood body size are available through request to UK Biobank. GWAS summary statistic of OSA can be accessed through application at: https://www.ncbi.nlm.nih.gov/gap/ under the MVP accession (phs001672). GWAS summary statistic of Insomnia and Chronotype are requested from 23andME. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes GWAS Summary statistic data of childhood and adulthood body size are available through request to UK Biobank. GWAS summary statistic of OSA can be accessed through application at: https://www.ncbi.nlm.nih.gov/gap/ under the MVP accession (phs001672). GWAS summary statistic of Insomnia and Chronotype are requested from 23andME.

Mendelian randomization (MR) is a technique that uses genetic variation to address causal questions about how modifiable exposures influence health. For some time-varying phenotypes, genetic effects may have differential importance at different periods in the lifecourse. MR studies often employ conventional instrumental variable (IV) methods designed to estimate average lifetime effects. Recently, several extensions of MR have been proposed to investigate time-varying effects, including structural mean models (SMMs). SMMs exploit IVs through g-estimation and circumvent some of the parametric assumptions required by other MR methods. In this study, we applied g-estimation of SMMs within an MR framework to estimate the period effects of adiposity measured at two life stages, childhood and adulthood, on cardiovascular disease (CVD), type 2 diabetes (T2D), and breast cancer. We found persistent period effects of higher adulthood adiposity on increased risk of CVD and T2D. Higher childhood adiposity had a protective period effect on breast cancer risk. We compared this approach with an inverse variance weighted multivariable MR method, which also uses multiple IVs to assess time-varying effects but relies on a different set of assumptions. We highlight the strengths and limitations of each approach and conclude by emphasizing the importance of underlying methodological assumptions in the application of MR to lifecourse research.

Background Higher adiposity in early-life has consistently been associated with a reduced risk of breast cancer in later life, with Mendelian randomization (MR) studies supporting a causal effect. However, concerns have been raised that selection bias, particularly collider stratification due to selective participation or survival, may induce spurious protective MR estimates. Methods We triangulated across empirical analyses and simulations to evaluate whether selection-induced bias could plausibly explain the inverse effect estimate of early-life adiposity on breast cancer risk. First, we analysed proxy-genotype Mendelian randomization (MR) analyses of breast cancer in relatives, in which participant genotype is used as a proxy for relatives’ genotype, and conducted family-based simulations to assess whether attenuation in relative-based estimates could arise without selection bias. Second, we performed multivariable MR analyses of parental survival to evaluate survival-related selection mechanisms. Third, we conducted extensive simulations to quantify the magnitude of bias introduced by selection under a range of plausible and extreme scenarios, including interaction-driven selection. Results The weaker proxy-genotype MR estimates of breast cancer in relatives, compared with MR estimates for an individual's own breast cancer, were reproduced in family-based simulations without selection bias, indicating that this pattern does not provide evidence for selection bias. Multivariable MR analyses of parental survival indicated that survival differences are primarily driven by mid-to-late adulthood, not early-life, adiposity, providing little support for survival-related selection acting through early-life adiposity. In simulation analyses, additive selection produced minimal bias, while interaction-driven selection generated increasing distortion; however, even under extreme scenarios, the magnitude of bias was insufficient to replicate the observed protective effect estimate. In simulations where selection depended on mid-to-late adulthood rather than early-life adiposity, bias was expressed primarily in mid-to-late adulthood MR estimates, with little distortion of early-life MR estimates. Across all simulated scenarios, the combined pattern of empirical univariable and multivariable MR findings was not reproduced by selection alone. Conclusions Although selection bias can influence MR estimates, our findings suggest that plausible selection mechanisms are unlikely to substantively explain the observed inverse effect estimate of early-life adiposity on breast cancer risk. These results support a causal interpretation of the strong protective effect estimate of early-life adiposity on breast cancer risk and highlight the value of triangulating evidence across complementary approaches when evaluating bias in lifecourse MR. Plain Language Summary Previous studies have found that having a larger body size in early life is linked to a lower risk of developing breast cancer later in life. Mendelian randomization studies, which use genetic variation to investigate possible causal effects, have supported this finding. However, it has been suggested that the result could be explained by selection bias arising from who survives or participates in the studies analysed. We used several complementary approaches to evaluate this possibility. Together, the findings provide little evidence that the selection mechanisms examined explain the protective effect estimate of early-life adiposity on breast cancer. This work also provides a practical framework for investigating selection bias in lifecourse Mendelian randomization studies.

Mendelian randomisation (MR) is an approach to causal inference that uses genetic variants to infer whether or not a causal effect exists, unbiased by unobserved confounding. MR estimation usually considers the effect of a single exposure on an outcome; it has recently been extended to explore potential effects of multiple exposures using multivariable MR (MVMR). Existing MVMR models are restricted to a few exposure traits in a single estimation, particularly if those traits are highly correlated. However, for many relationships of interest there are many highly correlated exposures which may have a causal effect on the outcome. MVMR Bayesian model averaging (MVMR-BMA) provides a hypothesis-free exposure selection approach with many correlated exposures. Although potentially very powerful, BMA approaches to estimation are not commonly applied in epidemiological studies. Here we describe the application of MVMR-BMA to the selection of maternal metabolites that are causal for offspring birthweight. We describe the inputs and outputs of the model in detail and discuss the appropriate sensitivity analyses, illustrating these with our application. Through this, we hope to provide a guide to help other researchers, who are potentially unfamiliar with the terminology of Bayesian analysis, but would like to apply the method to their data.

Abstract Medication use is common in large-scale population cohorts, and can modify phenotypic traits of interest. This can potentially bias effect estimates in genome-wide association studies (GWAS) and impact downstream analyses such as mendelian randomization (MR). The best approach to account for medication use in GWAS is unclear. In this study, we compared seven different methods of adjusting for antihypertensive use in a systolic blood pressure (SBP) GWAS of 407,960 White British individuals in the UK Biobank. We found that direct adjustments to measured SBP (adding constants, class-specific constants, censored normal regression) in general yielded a greater number of genome-wide significant variant associations and unmasked stronger GWAS effect estimates than unadjusted measures of SBP. Adjustment for class-specific constants showed the greatest difference relative to unadjusted GWAS. Restriction methods which limit the sample to either untreated individuals or age ranges with low levels of antihypertensive use had less power, due to reduced sample sizes. Effect estimates of treated individuals were deflated relative to untreated individuals, demonstrating the importance of medication adjustment. In MR analyses, we found no substantial differences in inverse-variance weighted (IVW) estimates when using differing exposure GWAS methods in estimation of the effect of SBP on coronary artery disease. Larger variations in IVW estimates were observed for the causal effect of body mass index on SBP across adjustment approaches. This suggests that bias may arise in MR analyses when the exposure included in the estimation affects the probability of treatment. Finally, we demonstrate that medication adjustment can reveal potentially novel genetic loci, offering additional insight into the biology of a trait.

Genome-wide association studies (GWAS) are conventionally conducted in cohorts spanning a wide age-range. These studies typically assume that genetic associations are constant across different ages. Some traits, however, may have age-varying genetic associations. This has implications for the interpretation of genetic effects derived in downstream applications, such as Mendelian randomization (MR) analyses. In this study we conducted a series of age-stratified GWAS on individuals aged 40-69 years in the UK Biobank, for body-mass index (BMI) and three blood pressure traits (systolic, diastolic and pulsatile pressure (PP)) in 2-year age strata (N up to 26,330). We used a meta-regression approach to systematically identify single nucleotide polymorphisms (SNPs) with evidence for age interaction effects among trait-associated GWAS signals and additional loci genome-wide. Within an MR framework, we examine the relationship between BMI and blood pressure traits on cardiovascular and cardiometabolic outcomes (type-2 diabetes (T2D), stroke, peripheral artery disease (PAD), heart failure, coronary heart disease and atrial fibrillation). Next, we describe the effect of the SNP*Age interaction on those relationships in a modified inverse-variance weighted (ivw) analysis. We identified differential enrichment of age-interaction effects, which was trait dependent. For example, 10.3% of BMI discovery SNPs had evidence for an age-interaction in our data compared to 44.7% for PP (at P < 0.05). Our downstream MR and modified ivw analyses highlight the influence of age on the genetically predicted relationship between PP and adverse cardiovascular outcomes. For example, our results indicated that an increased rate of change in genetically predicted PP across the age period is associated with higher susceptibility to PAD (interaction odds ratio = 2.71; P = 1.82x10-13; 95%-CI: 2.08-3.53). The data generated in this project provides a valuable resource for further exploration of mechanisms relevant to the genetic architecture of complex traits and all summary data has been made accessible to the research community.

BACKGROUND & AIMS:While cirrhosis is a primary risk factor for hepatocellular carcinoma (HCC), a significant proportion of HCC cases attributed to metabolic dysfunction-associated steatotic liver disease (MASLD) develop in the absence of cirrhosis. MASLD is strongly linked to obesity, a known risk factor for multiple cancers. Whether the effect of obesity on HCC is mediated via cirrhosis or other factors is unknown. METHODS:We used univariable Mendelian randomization (MR) to test the total effect of a higher body mass index (BMI), a proxy for obesity, on HCC, and multivariable MR to test the direct effect. RESULTS:We estimated that the effect of BMI was a 1.65-fold higher risk of HCC per standard deviation increase (95% confidence interval (CI): 1.28-2.12, p-value = 1.0 × 10-5). The BMI effect became indistinguishable from zero when taking liability to cirrhosis into account with multivariable MR (odds ratio = 1.12, 95% CI: 0.84-1.50, p-value = 0.44). We investigated additional potential pathways linking BMI to HCC-such as inflammation and type 2 diabetes-and explored the direct effect of childhood obesity on the risk of HCC. We found no direct effect of inflammation or type 2 diabetes (p-values > 0.05). Childhood body size increased the risk of HCC (odds ratio = 1.78, 95% CI: 1.27-2.49, p-value = 8 × 10-4), but the effect disappeared when we took adult body size into account using multivariable MR. CONCLUSIONS:Cirrhosis liability is the primary mediator of the causal effect of obesity on HCC.

Previous evidence suggests that higher prepubertal adiposity protects against breast cancer risk. Whether this protection extends into early adulthood remains uncertain. We conducted genome-wide association studies on body mass index (BMI) in nulliparous women from menarche to <40 years across five cohorts, with additional analyses in three subintervals of this life stage. Results were meta-analyzed, and two-sample univariable and multivariable Mendelian randomization was applied within a lifecourse framework to assess the effect of BMI on breast cancer risk. Between menarche and <40 years, we observed heterogeneity in genetic effects. Genome-wide correlations further suggest that BMI during this early adult period may be partly influenced by distinct genetic factors compared with adiposity at other life stages. Higher genetically proxied BMI between menarche and 40 years reduced breast cancer risk. This protective effect attenuated after adjusting for prepubertal adiposity. These findings refine our understanding of adiposity's role in breast cancer and highlight earlier life stages as critical windows for risk modulation.

Observational studies link high blood pressure in pregnancy to numerous adverse pregnancy and perinatal outcomes; however, findings may be affected by residual confounding or reverse causation. This study aimed to assess the causal effect of blood pressure during pregnancy on a range of pregnancy and perinatal outcomes. We performed two-sample Mendelian randomization (MR) to assess the effect of systolic and diastolic blood pressure (SBP/DBP) during pregnancy on 16 primary and eight secondary adverse pregnancy and perinatal outcomes. We obtained genetic association data from large-scale meta-analyses of genome-wide association studies involving predominantly European ancestry individuals for SBP/DBP (N = 1,028,980), and pregnancy and perinatal outcomes (N = 74,368–714,899). We used inverse-variance weighted (IVW) MR for main analyses and MR-Egger, weighted median, weighted mode, multivariable MR, and IVW adjusted for fetal genetic effects for sensitivity analyses. A 10 mmHg higher genetically predicted maternal SBP increased the odds of gestational diabetes, induction of labour, low birth weight (LBW), small-for-gestational age (SGA), preterm birth (PTB), and neonatal intensive care unit (NICU) admission (OR ranging from 1.11 [95

Abstract Background Loneliness is a psychosocial stressor associated with elevated risk of severe mental illness (SMI), including major depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BD). Loneliness is theorized to become biologically embedded via inflammation-related mechanisms, yet its causal relationship with SMI and the role of inflammatory signaling remain unclear. Aims To investigate whether loneliness causally influences SMI risk and whether inflammatory cytokines mediate this relationship. Method We applied univariable Mendelian randomization (MR) to estimate the causal effect of loneliness on SMI and multivariable MR (MVMR) to assess mediation by inflammatory signaling. We integrated genome-wide association study (GWAS) summary statistics for loneliness and SMI with genetic instruments for inflammatory cytokines. MVMR models estimated the direct effect of loneliness after accounting for inflammatory signaling using eQTL and pQTLs for interleukin-1 receptor antagonist (IL-1RA), interleukin-6 (IL-6), IL-6 receptor (IL-6R), tumor necrosis factor alpha (TNF-α), and TNF receptors (TNF-R1/2). Bidirectional MR examined potential reverse causal pathways between inflammation, SMI, and loneliness. Results MR provided evidence consistent with a causal effect of loneliness on SCZ and MDD. Results were also consistent with inflammatory cytokine pathways for IL-1RA, IL-6R, and TNF-R1, partially mediating the loneliness-SCZ and loneliness-MDD causal effect. No significant effects were identified for BD in UVMR or MVMR models. Bidirectional MR suggested evidence of reverse causation between SCZ and loneliness. Conclusions The findings support a causal risk-increasing effect of loneliness on SCZ and MDD, partially mediated by systemic inflammatory signaling, implicating pathways as a plausible mechanistic link between psychosocial stress and mental illness risk and highlighting potential opportunities for prevention and targeted intervention through inflammation and social pathways.

BackgroundCardiometabolic risk factors and conditions are the leading contributors to morbidity and mortality, yet quantifying their causal effects on socioeconomic outcomes using observational data is challenging due to endogeneity. Using genetic variants as instrumental variables, Mendelian randomization (MR) offers a unique approach to strengthen causal inference in this context and has also gained popularity in health economic literature.AimsThis study aimed to: i) map the current landscape of MR studies evaluating the impact of cardiometabolic exposures on healthcare and socioeconomic outcomes; ii) describe how core MR assumptions were tested and reported; iii) summarize how additional assumptions underlying causal interpretation were discussed.MethodsWe searched MEDLINE and EMBASE for studies applying MR to examine the impact of cardiometabolic risk factors or conditions (e.g., obesity, blood pressure, cholesterol, coronary artery disease, type 2 diabetes) on socioeconomic and healthcare outcomes (e.g., education, income, occupational status, social deprivation, healthcare use and costs, health-related quality of life). Study characteristics, MR design choices, and reporting of assumption testing and causal interpretation were extracted and narratively summarized.ResultsSixteen studies were included, covering 79 exposure-outcome pairs. Most studies examined the effects of body mass index on employment or healthcare costs. Only one study assessed home ownership, social income transfers, resource utilization, and quality-adjusted life years as outcomes, respectively. Effects of childhood cardiometabolic exposures were rarely examined beyond educational outcomes. UK Biobank was the predominant data source. None of the core MR assumptions were mentioned across all studies. While weak instrument bias was frequently tested, less than 40% of studies assessed associations between instruments and observable confounders as falsification tests. Only few studies discussed monotonicity or homogeneity assumptions.ConclusionsAlthough MR is a promising identification strategy for assessing causal effects of cardiometabolic risk on healthcare and socioeconomic outcomes, reporting practices for assumption testing and causal interpretation vary widely. This review highlights opportunities to strengthen transparency and coherence in future MR applications. With increasing data availability and clearer methodological guidance, MR could complement conventional observational approaches in supporting policy decisions.

Mendelian randomization has evolved from a niche methodology to a widely adopted research approach. In this Perspective, we briefly present a bibliometric analysis of the Mendelian randomization literature to inform a discussion of how Mendelian randomization studies are conducted and how they do not fully realize the potential of the data and techniques available to empirically examine the reliability of assumptions. We propose that future progress will depend on integrating empirical evidence from molecular, cellular, animal and quasi-experimental studies to assess its assumptions and causal claims. We also highlight how the shifting landscape of genetic and genomic data presents new challenges and opportunities for the Mendelian randomization framework, providing a deeper understanding of causal mechanisms.

Observational evidence proposes a protective effect of having children and an early first pregnancy on breast cancer development; however, the causality of this association remains uncertain. Here, we assess whether parity-related reproductive factors impact breast cancer risk independently of each other and other causally related or genetically correlated factors: adiposity, age at menarche, and age at menopause. We used genetic data from UK Biobank for reproductive factors and adiposity, and the Breast Cancer Association Consortium for risk of overall, estrogen receptor (ER) positive and negative breast cancer, and breast cancer subtypes. We applied univariable and multivariable Mendelian randomization (MR) to estimate genetically predicted direct effects of ever parous status, ages at first birth and last birth, and number of births on breast cancer risk. We found limited evidence for a genetically predicted protective effect of an earlier age at first birth on breast cancer risk. While the univariable analysis revealed later age at first birth decreased ER-negative breast cancer risk (odds ratio (OR): 0.76; 95

Mendelian randomization (MR) is an established epidemiological technique which uses genetic variants to strengthen causal inference regarding modifiable exposures. Non-linear MR is an extension to MR which aims to estimate whether the effect differs across the level of the exposure. Many applications of non-linear MR have focused on Vitamin D as an exposure. Using this technique, the study sample is divided into strata, and separate estimates are calculated in each stratum to estimate causal effects at different levels of the exposure (e.g. Vitamin D). For example, a recent study which applied this method identified an apparent protective effect of Vitamin D on C-reactive protein (CRP) levels for those with poor Vitamin D status. However, recent work has highlighted that the commonly used non-linear MR approaches are susceptible to serious bias, suggesting that further methodological development incorporating extensive simulation and empirical investigation is required. In this paper, we provide a commentary on the sources of bias in non-linear MR methods with a re-examination of the relationship between Vitamin D and CRP as an applied example. We highlight the role of negative controls and non-collider variable-based stratification as potential sensitivity tests to identify potential bias for putative non-linear associations in empirical settings. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The authors work in the Medical Research Council Integrative Epidemiology Unit at the University of Bristol, which is supported by the Medical Research Council: MC\_UU\_00032/1. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All analyses were conducted on individual level UK Biobank data which was accessed via UKB application id: 81499.

Higher childhood adiposity has been reported to lower breast cancer risk across multiple lines of evidence, including lifecourse Mendelian randomization (MR). These MR results show that the apparent univariable effect of higher adulthood adiposity on lower risk of breast cancer is almost entirely accounted for by the childhood adiposity effect when using multivariable analysis. It has been suggested, however, that this protective effect may arise from collider stratification bias, particularly in selective cohorts such as the UK Biobank. To examine the plausibility of this claim we first simulated data assuming no true causal effect of adiposity on breast cancer risk to test whether selection bias alone could generate multivariable MR results of the same magnitude as those obtained empirically. Selection into the analytic sample was modelled with log-additive effects of childhood body size and breast cancer status, together with their interaction. Log-additive selection on body size or cancer status alone produced minimal bias. In contrast, interaction-dependent selection generated appreciable downward bias, with the strongest three-way scenario producing the largest spurious protective effect. However, even under these severe scenarios, the bias fell short of the previous effect observed in MR analyses. Second, we examined if selection bias could induce the joint patterns of univariable and multivariable results observed. We found that across 135k possible models, none strongly overlapped with the observed results, and only 0.7% overlapped with an agreement probability >10%, which mostly relied on a large three-way interaction term between cancer, adulthood body size and childhood body size influencing selection. To conclude, while selection bias, particularly that which is interaction-driven, can generate spurious protective effects, it is unlikely to fully explain the protective effect of childhood adiposity on breast cancer identified in MR studies. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement GMP, ES, GH, and GDS were supported by the Integrative Epidemiology Unit which receives funding from the UK Medical Research Council and the University of Bristol (MC\_UU\_00032/1). GMP was additionally supported by the University of Bristol Cancer research fund for this work. GDS conducts research at the NIHR Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was based entirely on simulated data and did not involve human participants directly; therefore, no ethical approval or informed consent was required. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All code used to generate the simulations and reproduce the results reported here is publicly available at https://github.com/gracemarionpower/selection-bias-simulations/. The data underlying the findings can be derived directly from these simulations.

This study explores the causal role of multiple correlated risk factors in coronary heart disease (CHD) and ischemic stroke, using Mendelian randomization (MR) analyses with GWAS summary data from both prevalent and incident stroke cases. Thirteen candidate risk factors were considered, including age at menarche, adiposity, lipid fractions, blood pressure, and smoking. Univariable MR identified seven exposures significantly associated with CHD risk, including BMI, blood pressure, LDL, triglycerides, type-II diabetes, and smoking. Notably, HDL showed a protective effect (OR = 0.77, 95% CI: 0.72–0.83), while type-II diabetes was positively associated with CHD (OR = 1.10, 95% CI: 1.05–1.16). For ischemic stroke subtypes, diastolic and systolic blood pressure showed consistent effects across both small vessel and large artery stroke (e.g., DBP OR = 2.27, 95% CI: 1.77–2.89 for small vessel stroke), and HDL again demonstrated protective effects. Multivariable MR (MVMR) further confirmed these associations, though estimates were attenuated. In summary, both univariable and MVMR analyses identified robust associations of lipid fractions and blood pressure with cardiovascular outcomes, highlighting their importance in CHD and ischemic stroke risk across multiple stroke subtypes.

INTRODUCTION:We tested whether genetically proxied non-high-density lipoprotein cholesterol (non-HDL-C)-lowering drug targets reduce risk of all-cause dementia. METHODS:We included 1,091,775 individuals from three prospective general population cohorts with individual-level data and two consortia with summary-level data. We selected genetic variants within HMGCR, NPC1L1, PCSK9, ANGPTL4, LPL, and CETP associated with non-HDL-C. These variants were used as exposures in Cox regression and one- and two-sample Mendelian randomization. Results were meta-analyzed. RESULTS:Meta-analysis of one-sample Mendelian randomization odds ratios per 1 mmol/L (39 mg/dL) lower non-HDL-C was 0.24 (0.18-0.31) for HMGCR, 0.18 (0.12-0.25) for NPC1L1, 0.97 (0.70-1.35) for PCSK9, 1.66 (0.52-5.36) for ANGPTL4, 1.41 (0.63-3.16) for LPL, and 0.30 (0.26-0.34) for CETP. Cox regression and two-sample Mendelian randomization results were mostly directionally consistent. DISCUSSION:Genetic lowering of non-HDL cholesterol via HMGCR, NPC1L1, and CETP reduces the risk of dementia. This reflects the effect of lifelong differences in non-HDL cholesterol on risk of dementia. HIGHLIGHTS:Variants in HMGCR, NPC1L1, and CETP reduce the risk of dementia via non-high-density lipoprotein cholesterol (non-HDL-C). An effect of PCSK9, ANGPTL4, and LPL variants on dementia risk cannot be excluded. This reflects the effect of lifelong lower non-HDL-C on risk of dementia.

Abstract Background Obesity particularly during childhood is considered a global public health crisis and has been linked with later life health consequences including mental health. However, there is lack of causal understanding if childhood body size has a direct effect on mental health or has an indirect effect after accounting for adulthood body size. Methods Two-sample Mendelian randomization (MR) was performed to estimate the total effect and direct effect (accounting for adulthood body size) of childhood body size on anxiety and depression. We used summary statistics from a genome-wide association study (GWAS) of UK Biobank (n = 453,169) and large-scale consortia of anxiety (Million Veteran Program) and depression (Psychiatric Genomics Consortium) (n = 175,163 and n = 173,005, respectively). Results Univariable MR did not indicate genetically predicted effects of childhood body size with later life anxiety (beta = − 0.05, 95% CI = − 0.13, 0.02) and depression (OR = 1.06, 95% CI = 0.94, 1.20). However, using multivariable MR, we observed that the higher body size in childhood reduced the risk of later life anxiety (beta = − 0.19, 95% CI = − 0.29, − 0.08) and depression (OR = 0.83, 95% CI = 0.71, 0.97) upon accounting for the effect of adulthood body size. Both univariable and multivariable MR indicated that higher body size in adulthood increased the risk of later life anxiety and depression. Conclusions Higher body size in adulthood may increase the risk of anxiety and depression, independent of childhood higher body size. In contrast, higher childhood body size does not appear to be a risk factor for later life anxiety and depression.