Exploring the impact of selection bias in observational studies of COVID-19: a simulation study

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

Integrating multiple lines of evidence to assess the effects of maternal BMI on pregnancy and perinatal outcomes

This is the final version. Available from BMC via the DOI in this record. Availability of data and materials: In order to protect participant confdentiality, supporting data cannot be made openly available. Bona fide researchers can apply for access to study specifc executive committees. Summary association data for FinnGen is publicly available at https://www.fnngen.f/en/access_results. Researchers can apply for access to the UK Biobank data via the Access Management System (AMS) (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access).BACKGROUND: Higher maternal pre-pregnancy body mass index (BMI) is associated with adverse pregnancy and perinatal outcomes. However, whether these associations are causal remains unclear. METHODS: We explored the relation of maternal pre-/early-pregnancy BMI with 20 pregnancy and perinatal outcomes by integrating evidence from three different approaches (i.e. multivariable regression, Mendelian randomisation, and paternal negative control analyses), including data from over 400,000 women. RESULTS: All three analytical approaches supported associations of higher maternal BMI with lower odds of maternal anaemia, delivering a small-for-gestational-age baby and initiating breastfeeding, but higher odds of hypertensive disorders of pregnancy, gestational hypertension, preeclampsia, gestational diabetes, pre-labour membrane rupture, induction of labour, caesarean section, large-for-gestational age, high birthweight, low Apgar score at 1 min, and neonatal intensive care unit admission. For example, higher maternal BMI was associated with higher risk of gestational hypertension in multivariable regression (OR = 1.67; 95% CI = 1.63, 1.70 per standard unit in BMI) and Mendelian randomisation (OR = 1.59; 95% CI = 1.38, 1.83), which was not seen for paternal BMI (OR = 1.01; 95% CI = 0.98, 1.04). Findings did not support a relation between maternal BMI and perinatal depression. For other outcomes, evidence was inconclusive due to inconsistencies across the applied approaches or substantial imprecision in effect estimates from Mendelian randomisation. CONCLUSIONS: Our findings support a causal role for maternal pre-/early-pregnancy BMI on 14 out of 20 adverse pregnancy and perinatal outcomes. Pre-conception interventions to support women maintaining a healthy BMI may reduce the burden of obstetric and neonatal complications. FUNDING: Medical Research Council, British Heart Foundation, European Research Council, National Institutes of Health, National Institute for Health Research, Research Council of Norway, Wellcome Trust.Medical Research CouncilBritish Heart FoundationEuropean Research CouncilEuropean Research CouncilEuropean Union’s Horizon 2020National Institutes of HealthNational Institutes of HealthResearch Council of NorwayWellcome Trus

High-density lipoprotein characteristics and coronary artery disease: a Mendelian randomization study

Background To assess whether genetically determined quantitative and qualitative HDL characteristics were independently associated with coronary artery disease (CAD). Methods We designed a two-sample multivariate Mendelian randomization study with available genome-wide association summary data. We identified genetic variants associated with HDL cholesterol and apolipoprotein A-I levels, HDL size, particle levels, and lipid content to define our genetic instrumental variables in one sample (Kettunen et al. study, n = 24,925) and analyzed their association with CAD risk in a different study (CARDIoGRAMplusC4D, n = 184,305). We validated these results by defining our genetic variables in another database (METSIM, n = 8372) and studied their relationship with CAD in the CARDIoGRAMplusC4D dataset. To estimate the effect size of the associations of interest adjusted for other lipoprotein traits and minimize potential pleiotropy, we used the Multi-trait-based Conditional & Joint analysis. Results Genetically determined HDL cholesterol and apolipoprotein A-I levels were not associated with CAD. HDL mean diameter (β = 0.27 [95%CI = 0.19; 0.35]), cholesterol levels in very large HDLs (β = 0.29 [95%CI = 0.17; 0.40]), and triglyceride content in very large HDLs (β = 0.14 [95%CI = 0.040; 0.25]) were directly associated with CAD risk, whereas the cholesterol content in medium-sized HDLs (β = −0.076 [95%CI = -0.10; −0.052]) was inversely related to this risk. These results were validated in the METSIM-CARDIoGRAMplusC4D data. Conclusions Some qualitative HDL characteristics (related to size, particle distribution, and cholesterol and triglyceride content) are related to CAD risk while HDL cholesterol levels are not

Biological Age is a predictor of mortality in Ischemic Stroke

Age and stroke severity are the main mortality predictors after ischemic stroke. However, chronological age and biological age are not exactly concordant. Age-related changes in DNA methylation in multiple CpG sites across the genome can be used to estimate biological age, which is influenced by lifestyle, environmental factors, and genetic variation. We analyzed the impact of biological age on 3-month mortality in ischemic stroke. We assessed 594 patients with acute ischemic stroke in a cohort from Hospital del Mar (Barcelona) and validated the results in an independent cohort. Demographic and clinical data, including chronological age, vascular risk factors, initial stroke severity (NIHSS score), recanalization treatment, and previous modified Rankin scale were registered. Biological age was estimated with an algorithm based on DNA methylation in 71 CpGs. Biological age was predictive of 3-month mortality (p = 0.041; OR = 1.05, 95% CI 1.00-1.10), independently of NIHSS score, chronological age, TOAST, vascular risk factors, and blood cell composition. Stratified by TOAST classification, biological age was associated with mortality only in large-artery atherosclerosis etiology (p = 0.004; OR = 1.14, 95% CI 1.04-1.25). As estimated by DNA methylation, biological age is an independent predictor of 3-month mortality in ischemic stroke regardless of chronological age, NIHSS, previous modified Rankin scale, and vascular risk factors

Integrating multiple lines of evidence to assess the effects of maternal BMI on pregnancy and perinatal outcomes

BACKGROUND: Higher maternal pre-pregnancy body mass index (BMI) is associated with adverse pregnancy and perinatal outcomes. However, whether these associations are causal remains unclear.METHODS: We explored the relation of maternal pre-/early-pregnancy BMI with 20 pregnancy and perinatal outcomes by integrating evidence from three different approaches (i.e. multivariable regression, Mendelian randomisation, and paternal negative control analyses), including data from over 400,000 women.RESULTS: All three analytical approaches supported associations of higher maternal BMI with lower odds of maternal anaemia, delivering a small-for-gestational-age baby and initiating breastfeeding, but higher odds of hypertensive disorders of pregnancy, gestational hypertension, preeclampsia, gestational diabetes, pre-labour membrane rupture, induction of labour, caesarean section, large-for-gestational age, high birthweight, low Apgar score at 1 min, and neonatal intensive care unit admission. For example, higher maternal BMI was associated with higher risk of gestational hypertension in multivariable regression (OR = 1.67; 95% CI = 1.63, 1.70 per standard unit in BMI) and Mendelian randomisation (OR = 1.59; 95% CI = 1.38, 1.83), which was not seen for paternal BMI (OR = 1.01; 95% CI = 0.98, 1.04). Findings did not support a relation between maternal BMI and perinatal depression. For other outcomes, evidence was inconclusive due to inconsistencies across the applied approaches or substantial imprecision in effect estimates from Mendelian randomisation.CONCLUSIONS: Our findings support a causal role for maternal pre-/early-pregnancy BMI on 14 out of 20 adverse pregnancy and perinatal outcomes. Pre-conception interventions to support women maintaining a healthy BMI may reduce the burden of obstetric and neonatal complications.FUNDING: Medical Research Council, British Heart Foundation, European Research Council, National Institutes of Health, National Institute for Health Research, Research Council of Norway, Wellcome Trust.</p