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Background Non-random selection of analytic subsamples could introduce selection bias in observational studies. We explored the potential presence and impact of selection in studies of SARS-CoV-2 infection and COVID-19 prognosis. Methods We tested the association of a broad range of characteristics with selection into COVID-19 analytic subsamples in the Avon Longitudinal Study of Parents and Children (ALSPAC) and UK Biobank (UKB). We then conducted empirical analyses and simulations to explore the potential presence, direction and magnitude of bias due to this selection (relative to our defined UK-based adult target populations) when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19. Results In both cohorts, a broad range of characteristics was related to selection, sometimes in opposite directions (e.g. more-educated people were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB). Higher BMI was associated with higher odds of SARS-CoV-2 infection and death-with-COVID-19. We found non-negligible bias in many simulated scenarios. Conclusions Analyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depend on the outcome definition, the true effect of the risk factor and the assumed selection mechanism; these are likely to differ between studies with different target populations. Bias due to sample selection is a key concern in COVID-19 research based on national registry data, especially as countries end free mass testing. The framework we have used can be applied by other researchers assessing the extent to which their results may be biased for their research question of interest.

Background Non-random sampling could bias estimates of association in observational studies but the extent to which this occurs in studies of SARS-CoV-2 infection/COVID-19 severity is not well established. Methods Using ALSPAC and UK Biobank we investigated pre-pandemic predictors of selection (i.e. having data on SARS-CoV-2 infection and COVID-19 severity from self-report and/or health record linkage). We conducted empirical analyses and simulations to explore the potential presence, direction and magnitude of bias due to selection when estimating the association of BMI with SARS-CoV-2 infection and COVID-19 severity. Results A broad range of characteristics related to selection in both cohorts, sometimes in opposite directions. We found bias in all simulated scenarios, mostly of small magnitude. Both the direction and magnitude of bias was influenced by the presence of an effect of BMI on SARS-CoV-2 infection and COVID-19 severity and the control group definition used (e.g. assuming no effect of BMI on SARS-CoV-2 infection our main simulation showed bias equivalent to an estimated odds ratio of 0.99 when using non-infected controls but 1.16 when using controls combining non-infected and non-assessed). Conclusions Despite small amounts of bias in most scenarios, a control group definition including those non-assessed (e.g. non-tested) can induce more bias. In large samples such as UK Biobank the statistical power means incorrect conclusions could be made. Key messages Observational studies estimating associations of factors with SARS-CoV-2 or COVID-19 may be biased due to non-random selection into the analytic sample.

Background Mendelian randomisation uses genetic variants randomly allocated at conception as instrumental variables for an exposure. Methodological advances allow for mediation analysis to be carried out using Mendelian randomisation using either multivariable Mendelian randomisation or two-step Mendelian randomisation. Methods We use simulations and an applied example to demonstrate when multivariable Mendelian randomisation and two-step Mendelian randomisation methods are valid and how they relate to traditional phenotypic regression-based approaches to mediation. We demonstrate how Mendelian randomisation methods can relax assumptions required for causal inference in phenotypic mediation, as well as which Mendelian randomisation specific assumptions are required. We illustrate our methods in data from UK Biobank, estimating the role of body mass index mediating the association between education and cardiovascular outcomes. Results Both multivariable Mendelian randomization and two-step Mendelian randomization are unbiased when estimating the total effect, direct effect, indirect effect and proportion mediated when both confounding, and measurement error are present. Multivariable Mendelian Randomization can be used when multiple mediators are to be investigated in a single model. Conclusions Mendelian randomisation provides an opportunity to improve causal inference in mediation analysis. Although Mendelian randomisation specific assumptions apply, such as no weak instrument bias and no pleiotropic pathways, strong phenotypic assumptions of no confounding and no measurement error can be relaxed. Key messages Mendelian randomisation offers an opportunity to address bias by unmeasured confounding, measurement error and reverse causality in mediation analysis.

Background Low socio-economic position (SEP) is a risk factor for multiple health outcomes, but its molecular imprints in the body remain unclear. Methods We examined SEP as a determinant of serum nuclear magnetic resonance metabolic profiles in ∼30 000 adults and 4000 children across 10 UK and Finnish cohort studies. Results In risk-factor-adjusted analysis of 233 metabolic measures, low educational attainment was associated with 37 measures including higher levels of triglycerides in small high-density lipoproteins (HDL) and lower levels of docosahexaenoic acid (DHA), omega-3 fatty acids, apolipoprotein A1, large and very large HDL particles (including levels of their respective lipid constituents) and cholesterol measures across different density lipoproteins. Among adults whose father worked in manual occupations, associations with apolipoprotein A1, large and very large HDL particles and HDL-2 cholesterol remained after adjustment for SEP in later life. Among manual workers, levels of glutamine were higher compared with non-manual workers. All three indicators of low SEP were associated with lower DHA, omega-3 fatty acids and HDL diameter. At all ages, children of manual workers had lower levels of DHA as a proportion of total fatty acids. Conclusions Our work indicates that social and economic factors have a measurable impact on human physiology. Lower SEP was independently associated with a generally unfavourable metabolic profile, consistent across ages and cohorts. The metabolites we found to be associated with SEP, including DHA, are known to predict cardiovascular disease and cognitive decline in later life and may contribute to health inequalities.