Adiposity and mortality among intensive care patients with COVID-19 and non-COVID-19 respiratory conditions: a cross-context comparison study in the UK.

BACKGROUND: Adiposity shows opposing associations with mortality within COVID-19 versus non-COVID-19 respiratory conditions. We assessed the likely causality of adiposity for mortality among intensive care patients with COVID-19 versus non-COVID-19 by examining the consistency of associations across temporal and geographical contexts where biases vary. METHODS: We used data from 297 intensive care units (ICUs) in England, Wales, and Northern Ireland (Intensive Care National Audit and Research Centre Case Mix Programme). We examined associations of body mass index (BMI) with 30-day mortality, overall and by date and region of ICU admission, among patients admitted with COVID-19 (N = 34,701; February 2020-August 2021) and non-COVID-19 respiratory conditions (N = 25,205; February 2018-August 2019). RESULTS: Compared with non-COVID-19 patients, COVID-19 patients were younger, less often of a white ethnic group, and more often with extreme obesity. COVID-19 patients had fewer comorbidities but higher mortality. Socio-demographic and comorbidity factors and their associations with BMI and mortality varied more by date than region of ICU admission. Among COVID-19 patients, higher BMI was associated with excess mortality (hazard ratio (HR) per standard deviation (SD) = 1.05; 95% CI = 1.03-1.07). This was evident only for extreme obesity and only during February-April 2020 (HR = 1.52, 95% CI = 1.30-1.77 vs. recommended weight); this weakened thereafter. Among non-COVID-19 patients, higher BMI was associated with lower mortality (HR per SD = 0.83; 95% CI = 0.81-0.86), seen across all overweight/obesity groups and across dates and regions, albeit with a magnitude that varied over time. CONCLUSIONS: Obesity is associated with higher mortality among COVID-19 patients, but lower mortality among non-COVID-19 respiratory patients. These associations appear vulnerable to confounding/selection bias in both patient groups, questioning the existence or stability of causal effects.

Testing maternal effects of vitamin-D and omega-3 levels on offspring neurodevelopmental traits in the Norwegian Mother, Father and Child Cohort Study.

BACKGROUND: Maternal vitamin-D and omega-3 fatty acid (DHA) deficiencies during pregnancy have previously been associated with offspring neurodevelopmental traits. However, observational study designs cannot distinguish causal effects from confounding. METHODS: First, we conducted Mendelian randomisation (MR) using genetic instruments for vitamin-D and DHA identified in independent genome-wide association studies (GWAS). Outcomes were (1) GWAS for traits related to autism and ADHD, generated in the Norwegian mother, father, and child cohort study (MoBa) from 3 to 8 years, (2) autism and ADHD diagnoses. Second, we used mother-father-child trio-MR in MoBa (1) to test causal effects through maternal nutrient levels, (2) to test effects of child nutrient levels, and (3) as a paternal negative control. RESULTS: Associations between higher maternal vitamin-D levels on lower ADHD related traits at age 5 did not remain after controlling for familial genetic predisposition using trio-MR. Furthermore, we did not find evidence for causal maternal effects of vitamin-D/DHA levels on other offspring traits or diagnoses. In the reverse direction, there was evidence for a causal effect of autism genetic predisposition on lower vitamin-D levels and of ADHD genetic predisposition on lower DHA levels. CONCLUSIONS: Triangulating across study designs, we did not find evidence for maternal effects. We add to a growing body of evidence that suggests that previous observational associations are likely biased by genetic confounding. Consequently, maternal supplementation is unlikely to influence these offspring neurodevelopmental traits. Notably, genetic predisposition to ADHD and autism was associated with lower DHA and vitamin-D levels respectively, suggesting previous associations might have been due to reverse causation.

Role of Inflammation in Depressive and Anxiety Disorders, Affect, and Cognition: Genetic and Non-Genetic Findings in the Lifelines Cohort Study.

Inflammation is associated with a range of neuropsychiatric symptoms; however, the nature of the causal relationship is unclear. We used complementary non-genetic, genetic risk score (GRS), and Mendelian randomization (MR) analyses to examine whether inflammatory markers are associated with affect, depressive and anxiety disorders, and cognition. We tested in ≈ 55,098 (59% female) individuals from the Dutch Lifelines cohort the concurrent/prospective associations of C-reactive protein (CRP) with: depressive and anxiety disorders; positive/negative affect; and attention, psychomotor speed, episodic memory, and executive functioning. Additionally, we examined the association between inflammatory GRSs (CRP, interleukin-6 [IL-6], IL-6 receptor [IL-6R and soluble IL-6R (sIL-6R)], glycoprotein acetyls [GlycA]) on these same outcomes (Nmax=57,946), followed by MR analysis examining evidence of causality of CRP on outcomes (Nmax=23,268). In non-genetic analyses, higher CRP was associated with a depressive disorder, lower positive/higher negative affect, and worse executive function, attention, and psychomotor speed after adjusting for potential confounders. In genetic analyses, CRPgrs was associated with any anxiety disorder (ß = 0.002, p = 0.037) whereas GlycAGRS was associated with major depressive disorder (ß = 0.001, p = 0.036). Both CRPgrs (ß = 0.006, p = 0.035) and GlycAGRS (ß = 0.006, p = 0.049) were associated with greater negative affect. Inflammatory GRSs were not associated with cognition, except slL-6RGRS which was associated with poorer memory (ß=-0.009, p = 0.018). There was weak evidence for a CRP-anxiety association using MR (ß = 0.12; p = 0.054). Genetic and non-genetic analyses provide consistent evidence for an association between CRP and negative affect. These results suggest that dysregulated immune physiology may impact a broad range of trans-diagnostic affective symptoms.

The role of body image dissatisfaction in the relationship between body size and disordered eating and self-harm: complimentary Mendelian randomization and mediation analyses.

Disordered eating and self-harm commonly co-occur in young people suggesting potential for shared underlying causes. Body image dissatisfaction (BID) has been recognised as a psychological correlate of body size, associated with both disordered eating and self-harm. However, the investigation into etiological pathways early in the lifecourse to provide detail on how body size and BID may foster disordered eating and self-harm remains largely unexplored. Employing data from two large population-based cohorts, the UK Biobank and the Avon Longitudinal Study of Parents And Children (ALSPAC), we conducted bidirectional Mendelian randomization (MR) to determine the causal direction of effect between genetically predicted prepubertal body size and two measures of BID indicating (i) desire to be smaller, and (ii) desire to be larger. We then used multivariable regression followed by counterfactual mediation analyses. Bidirectional MR indicated robust evidence that increased genetically predicted prepubertal body size increased desire to be smaller and decreased desire to be larger. Evidence for the reverse causal direction was negligible. These findings remained very similar across sensitivity analyses. In females and males, multivariable regression analyses demonstrated that being overweight increased the risk of disordered eating (risk ratio (RR), 95% confidence interval (CI): 1.19, 1.01 to 1.40 and 1.98, 1.28 to 3.05, respectively) and self-harm (RR, 95% CI: 1.35, 1.04 to 1.77 and 1.55, 0.86 to 2.81, respectively), while being underweight was protective against disordered eating (RR, 95% CI: 0.57, 0.40 to 0.81 and 0.81, 0.38 to 1.73, respectively). There was weak evidence of an increase in the risk of self-harm among underweight individuals. Mediation analyses indicated that the relationship between being overweight and subsequent disordered eating was largely mediated by the desire to be smaller. Our research carries important public health implications, suggesting distinct risk profiles for self-harm and disordered eating in relation to weight and body image. In addition, a better understanding of genetically predicted prepubertal BID may be valuable in the prevention and treatment of disordered eating and self-harm in adolescence.

Mapping associations of polygenic scores with autistic and ADHD traits in a single city region.

BACKGROUND: The genetic and environmental aetiology of autistic and Attention Deficit Hyperactivity Disorder (ADHD) traits is known to vary spatially, but does this translate into variation in the association of specific common genetic variants? METHODS: We mapped associations between polygenic scores for autism and ADHD and their respective traits in the Avon Longitudinal Study of Parents and Children (N = 4,255-6,165) across the area surrounding Bristol, UK, and compared them to maps of environments associated with the prevalence of autism and ADHD. RESULTS: Our results suggest genetic associations vary spatially, with consistent patterns for autistic traits across polygenic scores constructed at different p-value thresholds. Patterns for ADHD traits were more variable across thresholds. We found that the spatial distributions often correlated with known environmental influences. CONCLUSIONS: These findings shed light on the factors that contribute to the complex interplay between the environment and genetic influences in autistic and ADHD traits.

Exploring pleiotropy in Mendelian randomisation analyses: What are genetic variants associated with 'cigarette smoking initiation' really capturing?

Genetic variants used as instruments for exposures in Mendelian randomisation (MR) analyses may have horizontal pleiotropic effects (i.e., influence outcomes via pathways other than through the exposure), which can undermine the validity of results. We examined the extent of this using smoking behaviours as an example. We first ran a phenome-wide association study in UK Biobank, using a smoking initiation genetic instrument. From the most strongly associated phenotypes, we selected those we considered could either plausibly or not plausibly be caused by smoking. We examined associations between genetic instruments for smoking initiation, smoking heaviness and lifetime smoking and these phenotypes in UK Biobank and the Avon Longitudinal Study of Parents and Children (ALSPAC). We conducted negative control analyses among never smokers, including children. We found evidence that smoking-related genetic instruments were associated with phenotypes not plausibly caused by smoking in UK Biobank and (to a lesser extent) ALSPAC. We observed associations with phenotypes among never smokers. Our results demonstrate that smoking-related genetic risk scores are associated with unexpected phenotypes that are less plausibly downstream of smoking. This may reflect horizontal pleiotropy in these genetic risk scores, and we would encourage researchers to exercise caution this when using these and genetic risk scores for other complex behavioural exposures. We outline approaches that could be taken to consider this and overcome issues caused by potential horizontal pleiotropy, for example, in genetically informed causal inference analyses (e.g., MR) it is important to consider negative control outcomes and triangulation approaches, to avoid arriving at incorrect conclusions.

Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation.

Data within biobanks capture broad yet detailed indices of human variation, but biobank-wide insights can be difficult to extract due to complexity and scale. Here, using large-scale factor analysis, we distill hundreds of variables (diagnoses, assessments and survey items) into 35 latent constructs, using data from unrelated individuals with predominantly estimated European genetic ancestry in UK Biobank. These factors recapitulate known disease classifications, disentangle elements of socioeconomic status, highlight the relevance of psychiatric constructs to health and improve measurement of pro-health behaviours. We go on to demonstrate the power of this approach to clarify genetic signal, enhance discovery and identify associations between underlying phenotypic structure and health outcomes. In building a deeper understanding of ways in which constructs such as socioeconomic status, trauma, or physical activity are structured in the dataset, we emphasize the importance of considering the interwoven nature of the human phenome when evaluating public health patterns.

Implications of the genomic revolution for education research and policy.

Research at the intersection of social science and genomics, 'sociogenomics', is transforming our understanding of the interplay between genomics, individual outcomes and society. It has interesting and maybe unexpected implications for education research and policy. Here we review the growing sociogenomics literature and discuss its implications for educational researchers and policymakers. We cover key concepts and methods in genomic research into educational outcomes, how genomic data can be used to investigate social or environmental effects, the methodological strengths and limitations of genomic data relative to other observational social data, the role of intergenerational transmission and potential policy implications. The increasing availability of genomic data in studies can produce a wealth of new evidence for education research. This may provide opportunities for disentangling the environmental and genomic factors that influence educational outcomes and identifying potential mechanisms for intervention.

Protein Identification for Stroke Progression via Mendelian Randomization in Million Veteran Program and UK Biobank.

BACKGROUND: Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke. METHODS: We performed genome-wide association studies for subsequent major adverse cardiovascular events (MACE; ncases=51 929; ncontrols=39 980) and subsequent arterial ischemic stroke (AIS; ncases=45 120; ncontrols=46 789) after the first incident stroke within the Million Veteran Program and UK Biobank. We then used genetic variants associated with proteins (protein quantitative trait loci) to determine the effect of 1463 plasma protein abundances on subsequent MACE using Mendelian randomization. RESULTS: Two variants were significantly associated with subsequent cardiovascular events: rs76472767 near gene RNF220 (odds ratio, 0.75 [95% CI, 0.64-0.85]; P=3.69×10-8) with subsequent AIS and rs13294166 near gene LINC01492 (odds ratio, 1.52 [95% CI, 1.37-1.67]; P=3.77×10-8) with subsequent MACE. Using Mendelian randomization, we identified 2 proteins with an effect on subsequent MACE after a stroke: CCL27 ([C-C motif chemokine 27], effect odds ratio, 0.77 [95% CI, 0.66-0.88]; adjusted P=0.05) and TNFRSF14 ([tumor necrosis factor receptor superfamily member 14], effect odds ratio, 1.42 [95% CI, 1.24-1.60]; adjusted P=0.006). These proteins are not associated with incident AIS and are implicated to have a role in inflammation. CONCLUSIONS: We found evidence that 2 proteins with little effect on incident stroke appear to influence subsequent MACE after incident AIS. These associations suggest that inflammation is a contributing factor to subsequent MACE outcomes after incident AIS and highlights potential novel targets.

Childhood adiposity underlies numerous adult brain traits commonly attributed to midlife obesity.

Obese adults are often reported to have smaller brain volumes than their non-obese peers. Whether this represents evidence of accelerations in obesity-driven atrophy or is instead a legacy of developmental differences established earlier in the lifespan remains unclear. This study aimed to investigate whether early-life differences in adiposity explain differences in numerous adult brain traits commonly attributed to mid-life obesity. We utilised a two-sample lifecourse Mendelian randomization study in 37,501 adults recruited to UK Biobank (UKB) imaging centers from 2014, with secondary analyses in 6,996 children assessed in the Adolescent Brain Cognitive Development Study (ABCD) recruited from 2018. Exposures were genetic variants for childhood (266 variants) and adult (470 variants) adiposity derived from a GWAS of 407,741 UKB participants. Primary outcomes were adult total brain volume; grey matter volume, thickness, and surface area; white matter volume and hyperintensities; and hippocampus, amygdala, and thalamus volumes at mean age 55 in UKB. Secondary outcomes were equivalent childhood measures collected at mean age 10 in ABCD. In UKB, individuals who were genetically-predicted to have had higher levels of adiposity in childhood were found to have multiple smaller adult brain volumes relative to intracranial volume (e.g. z-score difference in normalised brain volume per category increase in adiposity [95%CI] = -0.20 [-0.28, -0.12]; p = 4 × 10-6). These effect sizes remained essentially unchanged after accounting for birthweight or current adult obesity in multivariable models, whereas most observed adult effects attenuated towards null (e.g. adult z-score [95%CI] for total volume = 0.06 [-0.05,0.17]; p = 0.3). Observational analyses in ABCD showed a similar pattern of changes already present in those with a high BMI by age 10 (z-score [95%CI] = -0.10 [-0.13, -0.07]; p = 8 × 10-13), with follow-up genetic risk score analyses providing some evidence for a causal effect already at this early age. Sensitivity analyses revealed that many of these effects were likely due to the persistence of larger head sizes established in those who gained excess weight in childhood (childhood z-score [95%CI] for intracranial volume = 0.14 [0.05,0.23]; p = 0.002), rather than smaller brain sizes per se. Our data suggest that persistence of early-life developmental differences across the lifecourse may underlie numerous neuroimaging traits commonly attributed to obesity-related atrophy in later life.

Challenging the current hypothesis that thrombosis is responsible for the post-COVID-19 condition.

People with the post-COVID-19 condition suffer symptoms that persist beyond 12 weeks following acute COVID-19 infection. Fatigue, shortness of breath, and cognitive dysfunction ("brain fog") are common. Scientists, clinicians, and patients debate the pathophysiology. One pathophysiological hypothesis is that prothrombotic changes associated with acute COVID-19 persist, causing clots that lead to symptoms. This theory, arising from a research team in South Africa and supported by a paper in Nature Medicine, has been widely disseminated on social media and entered the public narrative as a cause of the post-COVID-19 condition. We describe the development of this theory, examine the findings of a Cochrane review that critically appraises the "microclot" beliefs, and critically appraise the influential study relating clotting biomarkers to cognitive deficits. We conclude the inferences for the hypothesis are not based on evidence, unlicensed use of antithrombotic medication is not justified, and apheresis should not be considered outside of a well-designed clinical trial.

Familial confounding or measurement error? How to interpret findings from sibling and co-twin control studies.

Epidemiological researchers often examine associations between risk factors and health outcomes in non-experimental designs. Observed associations may be causal or confounded by unmeasured factors. Sibling and co-twin control studies account for familial confounding by comparing exposure levels among siblings (or twins). If the exposure-outcome association is causal, the siblings should also differ regarding the outcome. However, such studies may sometimes introduce more bias than they alleviate. Measurement error in the exposure may bias results and lead to erroneous conclusions that truly causal exposure-outcome associations are confounded by familial factors. The current study used Monte Carlo simulations to examine bias due to measurement error in sibling control models when the observed exposure-outcome association is truly causal. The results showed that decreasing exposure reliability and increasing sibling-correlations in the exposure led to deflated exposure-outcome associations and inflated associations between the family mean of the exposure and the outcome. The risk of falsely concluding that causal associations were confounded was high in many situations. For example, when exposure reliability was 0.7 and the observed sibling-correlation was r = 0.4, about 30-90% of the samples (n = 2,000) provided results supporting a false conclusion of confounding, depending on how p-values were interpreted as evidence for a family effect on the outcome. The current results have practical importance for epidemiological researchers conducting or reviewing sibling and co-twin control studies and may improve our understanding of observed associations between risk factors and health outcomes. We have developed an app (SibSim) providing simulations of many situations not presented in this paper.

Non-linear Mendelian randomization: detection of biases using negative controls with a focus on BMI, Vitamin D and LDL cholesterol.

Mendelian randomisation (MR) is an established technique in epidemiological investigation, using the principle of random allocation of genetic variants at conception to estimate the causal linear effect of an exposure on an outcome. Extensions to this technique include non-linear approaches that allow for differential effects of the exposure on the outcome depending on the level of the exposure. A widely used non-linear method is the residual approach, which estimates the causal effect within different strata of the non-genetically predicted exposure (i.e. the "residual" exposure). These "local" causal estimates are then used to make inferences about non-linear effects. Recent work has identified that this method can lead to estimates that are seriously biased, and a new method-the doubly-ranked method-has been introduced as a possibly more robust approach. In this paper, we perform negative control outcome analyses in the MR context. These are analyses with outcomes onto which the exposure should have no predicted causal effect. Using both methods we find clearly biased estimates in certain situations. We additionally examined a situation for which there are robust randomised controlled trial estimates of effects-that of low-density lipoprotein cholesterol (LDL-C) reduction onto myocardial infarction, where randomised trials have provided strong evidence of the shape of the relationship. The doubly-ranked method did not identify the same shape as the trial data, and for LDL-C and other lipids they generated some highly implausible findings. Therefore, we suggest there should be extensive simulation and empirical methodological examination of performance of both methods for NLMR under different conditions before further use of these methods. In the interim, use of NLMR methods needs justification, and a number of sanity checks (such as analysis of negative and positive control outcomes, sensitivity analyses excluding removal of strata at the extremes of the distribution, examination of biological plausibility and triangulation of results) should be performed.