Aims To quantify the association of combined sleep behaviours and genetic susceptibility with the incidence of cardiovascular disease (CVD). Methods and results This study included 385 292 participants initially free of CVD from UK Biobank. We newly created a healthy sleep score according to five sleep factors and defined the low-risk groups as follows: early chronotype, sleep 7–8 h per day, never/rarely insomnia, no snoring, and no frequent excessive daytime sleepiness. Weighted genetic risk scores of coronary heart disease (CHD) or stroke were calculated. During a median of 8.5 years of follow-up, we documented 7280 incident CVD cases including 4667 CHD and 2650 stroke cases. Compared to those with a sleep score of 0–1, participants with a score of 5 had a 35% (19–48%), 34% (22–44%), and 34% (25–42%) reduced risk of CVD, CHD, and stroke, respectively. Nearly 10% of cardiovascular events in this cohort could be attributed to poor sleep pattern. Participants with poor sleep pattern and high genetic risk showed the highest risk of CHD and stroke. Conclusion In this large prospective study, a healthy sleep pattern was associated with reduced risks of CVD, CHD, and stroke among participants with low, intermediate, or high genetic risk.

BackgroundIn the ongoing COVID-19 pandemic, the susceptibility of patients with rheumatic diseases to COVID-19 remains unclear. We aimed to investigate susceptibility to COVID-19 in patients with autoimmune rheumatic diseases during the ongoing COVID-19 pandemic. MethodsWe did a multicentre retrospective study of patients with autoimmune rheumatic diseases in Hubei province, the epicentre of the COVID-19 outbreak in China. Patients with rheumatic diseases were contacted through an automated telephone-based survey to investigate their susceptibility to COVID-19. Data about COVID-19 exposure or diagnosis were collected. Families with a documented history of COVID-19 exposure, as defined by having at least one family member diagnosed with COVID-19, were followed up by medical professionals to obtain detailed information, including sex, age, smoking history, past medical history, use of medications, and information related to COVID-19. FindingsBetween March 20 and March 30, 2020, 6228 patients with autoimmune rheumatic diseases were included in the study. The overall rate of COVID-19 in patients with an autoimmune rheumatic disease in our study population was 0•43% (27 of 6228 patients). We identified 42 families in which COVID-19 was diagnosed between Dec 20, 2019, and March 20, 2020, in either patients with a rheumatic disease or in a family member residing at the same physical address during the outbreak. Within these 42 families, COVID-19 was diagnosed in 27 (63%) of 43 patients with a rheumatic disease and in 28 (34%) of 83 of their family members with no rheumatic disease (adjusted odds ratio [OR] 2•68 [95% CI 1•14-6•27]; p=0•023). Patients with rheumatic disease who were taking hydroxychloroquine had a lower risk of COVID-19 infection than patients taking other disease-modifying anti-rheumatic drugs (OR 0•09 [95% CI 0•01-0•94]; p=0•044). Additionally, the risk of COVID-19 was increased with age (adjusted OR 1•04 [95% CI 1•01-1•06]; p=0•0081).Interpretation Patients with autoimmune rheumatic disease might be more susceptible to COVID-19 infection than the general population.

Rationale: In observational studies, type 2 diabetes mellitus (T2D) has been associated with an increased risk of hypertension, and vice versa; however, the causality between these conditions remains to be determined. Objectives: This population-based prospective cohort study sought to investigate the bidirectional causal relations of T2D with hypertension, systolic and diastolic blood pressure (BP) using Mendelian randomization (MR) analysis. Methods and Results: After exclusion of participants free of a history of heart failure, cardiovascular disease, cardiac procedures, and non-T2D diabetes mellitus, a total of 318 664 unrelated individuals with qualified genotyping data of European descent aged 37 to 73 from UK Biobank were included. The genetically instrumented T2D and hypertension were constructed using 134 and 233 single nucleotide polymorphisms, respectively. Seven complementary MR methods were applied, including inverse-variance weighted method, 2 median-based methods (simple and weighted), MR-Egger, MR-robust adjusted profile scores, MR-Pleiotropy Residual Sum and Outlier, and multivariate MR. The genetically instrumented T2D was associated with risk of hypertension (odds ratio, 1.07 [95% CI, 1.04–1.10], P =3.4×10 −7 ), whereas the genetically determined hypertension showed no relationship with T2D (odds ratio, 0.96 [0.88–1.04], P =0.34). Our MR estimates from T2D to BP showed that the genetically instrumented T2D was associated with a 0.67 mm Hg higher systolic BP (95% CI, 0.41–0.93, P =5.75×10 –7 ) but not with a higher diastolic BP. There was no clear evidence showing a causal effect of elevated systolic BP or diastolic BP on T2D risk. Positive pleiotropic bias was indicated in the hypertension→T2D relation (odds ratio, of MR-Egger intercept 1.010 [1.004–1.016], P =0.001) but not from T2D to hypertension (1.001 [0.998–1.004], P =0.556). Conclusions: T2D may causally affect hypertension, whereas the relationship from hypertension to T2D is unlikely to be causal. These findings suggest the importance of keeping an optimal glycemic profile in general populations, and BP screening and monitoring, especially systolic BP, in patients with T2D.

Aims  Little is known about the relation between the long-term joint exposure to various ambient air pollutants and the incidence of heart failure (HF). We aimed to assess the joint association of various air pollutants with HF risk and examine the modification effect of the genetic susceptibility. Methods and results  This study included 432 530 participants free of HF, atrial fibrillation, or coronary heart disease in the UK Biobank study. All participants were enrolled from 2006 to 2010 and followed up to 2018. The information on particulate matter (PM) with diameters ≤2.5 µm (PM2.5), ≤10 µm (PM10), and between 2.5 and 10 µm (PM2.5–10) as well as nitrogen oxides (NO2 and NOx) was collected. We newly proposed an air pollution score to assess the joint exposure to the five air pollutants through summing each pollutant concentration weighted by the regression coefficients with HF from single-pollutant models. We also calculated the weighted genetic risk score of HF. During a median of 10.1 years (4 346 642 person-years) of follow-up, we documented 4201 incident HF. The hazard ratios (HRs) [95% confidence interval (CI)] of HF for a 10 µg/m3 increase in PM2.5, PM10, PM2.5–10, NO2, and NOx were 1.85 (1.34–2.55), 1.61 (1.30–2.00), 1.13 (0.80–1.59), 1.10 (1.04–1.15), and 1.04 (1.02–1.06), respectively. We found that the air pollution score was associated with an increased risk of incident HF in a dose–response fashion. The HRs (95% CI) of HF were 1.16 (1.05–1.28), 1.19 (1.08–1.32), 1.21 (1.09–1.35), and 1.31 (1.17–1.48) in higher quintile groups compared with the lowest quintile of the air pollution score (P trend <0.001). In addition, we observed that the elevated risk of HF associated with a higher air pollution score was strengthened by the genetic susceptibility to HF. Conclusion  Our results indicate that the long-term joint exposure to various air pollutants including PM2.5, PM10, PM2.5–10, NO2, and NOx is associated with an elevated risk of incident HF in an additive manner. Our findings highlight the importance to comprehensively assess various air pollutants in relation to the HF risk.