Daniel Benjamin, Meike Bartels, Philipp Koellinger and colleagues report a genome-wide association meta-analysis of subjective well-being, depressive symptoms and neuroticism. The study leverages a large sample size together with genetic correlations between the phenotypes to identify, with high confidence, loci associated with each phenotype. Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρ^| ≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.

Short and long sleep duration have been linked with poorer cognitive outcomes, but it remains unclear whether these associations are causal. We conducted the first Mendelian randomization (MR) study with 77 single-nucleotide polymorphisms (SNPs) for sleep duration using individual-participant data from the UK Biobank cohort (N = 395 803) and summary statistics from the International Genomics of Alzheimer’s Project (N cases/controls = 17 008/37 154) to investigate the potential impact of sleep duration on cognitive outcomes. Linear MR suggested that each additional hour/day of sleep was associated with 1% [95% confidence interval (CI) = 0–2%; P = 0.008] slower reaction time and 3% more errors in visual-memory test (95% CI = 0–6%; P = 0.05). There was little evidence to support associations of increased sleep duration with decline in visual memory [odds ratio (OR) per additional hour/day of sleep = 1.10 (95% CI = 0.76–1.57); P = 0.62], decline in reaction time [OR = 1.28 (95% CI = 0.49–3.35); P = 0.61], all-cause dementia [OR = 1.19 (95% CI = 0.65–2.19); P = 0.57] or Alzheimer’s disease risk [OR = 0.89 (95% CI = 0.67–1.18); P = 0.41]. Non-linear MR suggested that both short and long sleep duration were associated with poorer visual memory (P for non-linearity = 3.44e^–9) and reaction time (P for non-linearity = 6.66e^–16). Linear increase in sleep duration has a small negative effect on reaction time and visual memory, but the true association might be non-linear, with evidence of associations for both short and long sleep duration. These findings suggest that sleep duration may represent a potential causal pathway for cognition.

Over the past twenty years we have seen a vast number of epidemiological studies emerge on the topic of obesity and sleep duration, with a focus on body mass index, as it is easy and cheap to measure and analyse. Such studies largely observe that cross-sectionally a higher BMI is associated with shorter sleep and that in longitudinal studies shorter sleep duration is associated with increases in BMI over time, but some research has found no relationship between the two. This narrative review is not exhaustive, but appraises the literature on sleep duration and BMI from perspectives that have previously been unexplored in a single paper. As such, I discuss research in these important areas: bidirectionality, objective vs. subjective sleep duration, how meaningful the effect sizes are and how we have begun to address causality in this area. From the evidence appraised in this review, it is clear that: (i) there is some modest evidence of a bidirectional relationship between BMI and sleep duration in both children and adults; (ii) objective measurements of sleep should be used where possible; (iii) it remains difficult to confirm whether the effect sizes are conclusively meaningful in a clinical setting, but at least in adults this so far seems unlikely; (iv) to date, there is no solid evidence that this relationship (in either direction) is in fact causal. In the near future, I would like to see triangulation of these findings and perhaps a move towards focusing on distinct aspects of the relationship between obesity and sleep that have not previously been addressed in detail, for various reasons.

Aim: To understand the relationship between HbA1c and brain health across the entire glycaemic spectrum. Materials and Methods: We used data from the UK Biobank cohort consisting of 500,000 individuals aged 40–69 years. HbA1c and diabetes diagnosis were used to define baseline glycaemic categories. Our outcomes included incident all‐cause dementia, vascular dementia (VD), Alzheimer's dementia (AD), hippocampal volume (HV), white matter hyperintensity (WMH) volume, cognitive function and decline. The reference group was normoglycaemic individuals (HbA1c ≥35 & <42 mmol/mol). Our maximum analytical sample contained 449,973 individuals with complete data. Results: Prediabetes and known diabetes increased incident VD (HR 1.54; 95% CI = 1.04, 2.28 and HR 2.97; 95% CI = 2.26, 3.90, respectively). Known diabetes increased all‐cause and AD risk (HR 1.91; 95% CI = 1.66, 2.21 and HR 1.84; 95% CI = 1.44, 2.36, respectively). Prediabetes and known diabetes elevated the risks of cognitive decline (OR 1.42; 1.48, 2.96 and OR 1.39; 1.04, 1.75, respectively). Prediabetes, undiagnosed and known diabetes conferred higher WMH volumes (3%, 22% and 7%, respectively) and lower HV (36, 80 and 82 mm ³ , respectively), whereas low‐normal HbA1c had 1% lower WMH volume and 12 mm ³ greater HV. Conclusion: Both prediabetes and known diabetes are harmful in terms of VD, cognitive decline and AD risks, as well as lower HV. Associations appeared to be somewhat driven by antihypertensive medication, which implies that certain cardiovascular drugs may ameliorate some of the excess risk. Low‐normal HbA1c levels, however, are associated with more favourable brain health outcomes and warrant more in‐depth investigation.

Research to date suggests that physical activity (PA) is associated with distinct aspects of sleep, but studies have predominantly focused on sleep quality, been carried out in younger adults, and have not accounted for many covariates. Of particular interest is also the reported relationship between physical activity and depression in older adults and as such, their associations with sleep duration. Here we examine the cross-sectional relation between physical activity and sleep duration in a community-dwelling sample of 5265 older adults from the English Longitudinal Study of Ageing. We analysed the data using multiple regression, with physical activity as a categorical exposure and sleep duration a continuous outcome, as well as testing the interaction between physical activity and depressive symptoms, which was significant (p < 0.001). We therefore stratified our analyses by depressive symptomatology. Our main finding was that, in the group with elevated depressive symptoms only, physical activity was positively associated with sleep duration in models adjusted for all covariates (age, sex, wealth, ethnicity, smoking, alcohol consumption, BMI, long-standing illness) across low [B (mean difference in sleep duration) = 25.22 min, 95% CI = (3.72; 46.72)], moderate [B = 27.92 min, 95% CI = (6.59; 49.26)] and high [B = 31.65 min, 95% CI = (7.36; 55.94)] PA groups, in comparison to the sedentary group. However, we observed no relation between physical activity and sleep duration in respondents who reported no depressive symptoms, irrespective of physical activity level (p > 0.05). Our findings suggest that a potentially effective way of improving sleep in older adults with depressive symptoms is via physical activity interventions.

We investigated the relationship between glycemia and cognitive function, brain structure and incident dementia using bidirectional Mendelian randomization (MR). Data were from the UK Biobank (n = ∼500,000). Our exposures were genetic instruments for type 2 diabetes (157 variants) and HbA_1c (51 variants) and our outcomes were reaction time (RT), visual memory, hippocampal volume (HV), white matter hyperintensity volume (WMHV), and Alzheimer dementia (AD). We also investigated associations between genetic variants for RT (43 variants) and diabetes and HbA_1c. We used conventional inverse-variance–weighted (IVW) MR alongside MR sensitivity analyses. Using IVW, genetic liability to type 2 diabetes was not associated with RT (exponentiated β [expβ] = 1.00 [95% CI 1.00; 1.00]), visual memory (expβ = 1.00 [95% CI 0.99; 1.00]), WMHV (expβ = 0.99 [95% CI 0.97; 1.01]), HV (β-coefficient mm³ = −2.30 [95% CI −12.39; 7.78]) or AD (odds ratio [OR] 1.15 [95% CI 0.87; 1.52]). HbA_1c was not associated with RT (expβ = 1.00 [95% CI 0.99; 1.02]), visual memory (expβ = 0.99 [95% CI 0.96; 1.02]), WMHV (expβ = 1.03 [95% CI 0.88; 1.22]), HV (β = −21.31 [95% CI −82.96; 40.34]), or risk of AD (OR 1.09 [95% CI 0.42; 2.83]). IVW showed that reaction time was not associated with diabetes risk (OR 0.94 [95% CI 0.54; 1.65]), or with HbA_1c (β-coefficient mmol/mol = −0.88 [95% CI = −1.88; 0.13]) after exclusion of a pleiotropic variant. Overall, we observed little evidence of causal association between genetic instruments for type 2 diabetes or peripheral glycemia and some measures of cognition and brain structure in midlife.