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Background and Aims Atrial fibrillation (AF) is the most common sustained arrhythmia in adults. Investigations of risk factor profiles for AF according to age and genetic risk groups are essential to promote individualized strategies for the prevention and control of AF. Methods A total of 409 661 participants (mean age, 56 years; 46% men) free of AF at baseline and with complete information about risk factors were included from the UK Biobank cohort. The hazard ratios and population-attributable risk (PAR) percentages of incident AF associated with 23 risk factors were examined, including 3 social factors, 7 health behaviours, 6 cardiometabolic factors, 6 clinical comorbidities, and the genetic risk score (GRS), across 3 age groups (40–49, 50–59, and 60–69 years) and 3 genetic risk groups (low, moderate, and high GRS). Results After a follow-up of 5 027 587 person-years, 23 847 participants developed AF. Most cardiometabolic factors and clinical comorbidities showed a significant interaction with age, whereby the associations were generally strengthened in younger groups (P interaction <.002). However, only low LDL cholesterol, renal dysfunction, and cardiovascular disease showed a significant interaction with genetic risk, and the associations with these factors were stronger in lower genetic risk groups (P interaction <.002). Cardiometabolic factors consistently accounted for the largest number of incident AF cases across all age groups (PAR: 36.2%–38.9%) and genetic risk groups (34.0%–41.9%), with hypertension and overweight/obesity being the two leading modifiable factors. Health behaviours (PAR: 11.5% vs. 8.7%) and genetic risk factors (19.1% vs. 14.3%) contributed to more AF cases in the 40–49 years group than in the 60–69 years group, while the contribution of clinical comorbidities remained relatively stable across different age groups. The AF risk attributable to overall cardiometabolic factors (PAR: 41.9% in the low genetic risk group and 34.0% in the high genetic risk group) and clinical comorbidities (24.7% and 15.9%) decreased with increasing genetic risk. The impact of social factors on AF was relatively low across the groups by age and genetic risk. Conclusions This study provided comprehensive information about age- and genetic predisposition-related risk factor profiles for AF in a cohort of UK adults. Prioritizing risk factors according to age and genetic risk stratifications may help to achieve precise and efficient prevention of AF. [ABSTRACT FROM AUTHOR]

Aims: To investigate whether the association between sleep patterns and cardiovascular disease (CVD) risk differs according to glucose tolerance status. Materials and Methods: This prospective study included 358,805 participants initially free of CVD from the UK Biobank. We created a sleep score based on five sleep factors (sleep duration, chronotype, insomnia, snoring, and daytime sleepiness) with one point for each unhealthy factor. Cox proportional hazards models were used to examine the association between sleep and incident CVD, including coronary heart disease (CHD) and stroke, according to normal glucose tolerance (NGT), prediabetes, and diabetes. Results: During a median follow‐up of 12.4 years, 29,663 incident CVD events were documented. There was a significant interaction between sleep score and glucose tolerance status on CVD (P for interaction = 0.002). Each 1 point increment in sleep score was associated with a 7% (95% confidence interval 6%–9%), 11% (8%–14%), and 13% (9%–17%) higher risk of CVD among participants with NGT, prediabetes, and diabetes, respectively. Similar interaction patterns were observed for CHD and stroke. Among the individual sleep factors, sleep duration and insomnia significantly interacted with glucose tolerance status on CVD outcomes (all P for interaction <0.05). All five unhealthy sleep factors accounted for 14.2% (8.7%–19.8%), 19.5% (7.4%–31.0%), and 25.1% (9.7%–39.3%) of incident CVD cases among participants with NGT, prediabetes, and diabetes, respectively. Conclusions: The CVD risk associated with a poor sleep pattern was exacerbated across glucose intolerance status. Our findings emphasise the importance of integrating sleep management into a lifestyle modification programme, particularly in people with prediabetes or diabetes. [ABSTRACT FROM AUTHOR]

Psychologic wellbeing can impact cardiovascular health. We aimed to evaluate the joint association of multiple psychologic wellbeing factors with cardiovascular diseases (CVD) and examine whether this association was modified by genetic susceptibility. In the UK Biobank, 126,255 participants free of CVD (coronary heart disease [CHD], stroke, and heart failure [HF]) at baseline, who completed a questionnaire on psychological factors, were included. The psychological wellbeing score was calculated by four factors: happiness, life satisfaction, broad depression, and neuroticism. Cox proportional hazard models were used to assess the association between the psychological wellbeing score and CVD risk. During the median follow-up of 11.5 years, 10,815 participants had newly diagnosed CVDs. Low life satisfaction, the presence of depression, and neuroticism score ≥1 were significantly associated with an increased risk of CVD in the multivariable-adjusted model. Through decreasing the psychological wellbeing score, there were significant increasing linear trends in the risk of CVD, CHD, stroke, and HF (all p for trend < 0.001). Participants with the lowest psychological wellbeing score had the highest risk for CVD (HR 1.51, 95% CI 1.42–1.61). Women were more susceptible to worse psychological wellbeing status for CVD than men (p for interaction = 0.009). The associations of the psychological wellbeing score with CVD were consistent across genetic risk (p for interaction >0.05). When considered jointly, participants exposed to high-risk psychological wellbeing and genetic status had a 2.70-fold (95% CI 2.25–3.24) risk for CHD. Joint exposure to multiple psychological wellbeing factors was associated with increased risks of incident CVD in an additive manner, regardless of genetic susceptibility. [Display omitted] • Multiple psychological wellbeing factors are associated with risk of cardiovascular diseases (CVD). • Psychological wellbeing score could be used in large populations as a combination indicator. • Lower psychologic wellbeing score is associated with increased CVD risk, regardless of genetic risk. • In CVD events, women are more vulnerable to poor psychological status than men. [ABSTRACT FROM AUTHOR]

Background and Aim: Non‐alcoholic fatty liver disease (NAFLD) is associated with incident type 2 diabetes; however, the extent to which NAFLD may confer its risk remains uncertain, especially in Europeans. Emerging evidence suggests that sleep behaviors are linked to NAFLD and diabetes. We aimed to measure whether sleep behaviors modified the association between NAFLD and incident type 2 diabetes. Methods: This prospective cohort study included 365 339 participants without type 2 diabetes at baseline in UK Biobank data. Five sleep behaviors, including sleep duration, insomnia, snoring, chronotype, and daytime sleepiness, were collected from the questionnaire. Overall sleep patterns were created by summing the five scores. Liver steatosis was based on the fatty liver index. Results: During a median follow up of 11.0 years, we documented 8774 patients with incident type 2 diabetes. NAFLD was significantly associated with increased diabetes risk. Sleeping 7–8 h/day, no insomnia, no self‐reported snoring, and no frequent daytime sleepiness were independently associated with incident type 2 diabetes, with a 20%, 18%, 16%, and 31% lower risk, respectively. About 33.8% and 33.5% of type 2 diabetes events in this cohort could be attributed to NAFLD and poor sleep pattern, respectively. Participants with NAFLD and poor sleep pattern showed the highest risk of type 2 diabetes (relative risk 3.17, 95% confidence interval 2.80, 3.59). Sleep pattern (healthy, intermediate, and poor) did not significantly modify the association between NAFLD and type 2 diabetes. However, when studying separately, we found a significant interaction between NAFLD and insomnia on the risk of incident type 2 diabetes (P for interaction = 0.003). Conclusion: In this large prospective study, both NAFLD and some sleep behaviors were risk factors for type 2 diabetes. Although overall sleep pattern did not modify the association between NAFLD and type 2 diabetes, certain sleep behavior, especially insomnia, showed the modification effect. [ABSTRACT FROM AUTHOR]

Objectives: Unhealthy sleep behaviors may be potential risk factors for chronic kidney disease (CKD). We aimed to examine the associations of combined sleep patterns and genetic susceptibility with incident CKD. Methods: This large-scale prospective cohort study included 370,671 participants without CKD at baseline (2006–2010) in UK Biobank data. Five sleep behaviors were made up of sleep duration, insomnia, snoring, chronotype, and daytime sleepiness according to questionnaire. Overall sleep patterns by summing the five scores were created. Weighted genetic risk score of kidney function was calculated. Incident CKD was recorded from death register, primary care, and hospital inpatient records. A subset of 41,130 individuals who participated both the initial assessment visit and follow-up visit (2012+) was also used. Results: During a median follow-up of 10.6 years (about 3.9 million person-years), we documented 6,365 patients with incident CKD. In five sleep behaviors, sleep 7–8 h/day, free of insomnia and no frequent daytime sleepiness were independently associated with incident CKD, with a 12% (95%CI 7–16), 9% (3–14), 13% (9–18) lower risk, respectively. Compared to those with a sleep score of 0–1, participants with a score of 5 had a 21% (10–31%) lower risk of CKD. 17.1% of CKD in this cohort could be attributed to total poor sleep pattern. Participants with high genetic risk and intermediate or poor sleep pattern showed the highest risk of CKD (OR = 2.58, 95%CI 2.24–2.96; OR = 2.59, 95%CI 2.02–3.32, respectively), although there was no significant interaction between sleep patterns and genetic risk categories. Among individuals who participated both the initial assessment visit and follow-up visit, we found that the association between amelioration of sleep pattern and risk of CKD was significant after fully adjustment (OR = 0.60, 95%CI 0.36–0.99), compared with group of stable sleep pattern. Conclusion: In this large prospective study, participants with a healthy sleep pattern was associated with a significant reduction of incident CKD risk no matter they had a high, intermediate, or low genetic risk. [ABSTRACT FROM AUTHOR]

Background: The fat‐to‐muscle mass ratio (FMR) might be an indicator to assess type 2 diabetes risk independent of general obesity. However, no longitudinal studies have explored the extent to which total and regional FMRs may confer risks. We aimed to measure the sex‐specific associations between FMRs of the arm, leg, trunk and whole body and incident type 2 diabetes. Methods: A total of 464 817 participants (207 286 men and 257 531 women, mean age 56.5 ± 8.2 and 56.2 ± 8.0 years old, respectively) free of diabetes at baseline were included in this prospective cohort study with UK Biobank data. Fat mass and muscle mass were estimated using a bioelectrical impedance assessment device (Tanita BC 418MA). FMR was calculated as fat mass divided by muscle mass in corresponding body parts (total body, arm, leg and trunk). Cox proportional hazard models were used to estimate the aforementioned associations among men and women. Interaction analyses were performed between FMRs and body mass index (BMI) categories (BMI < 25 kg/m2 and BMI ≥ 25 kg/m2). Results: Over the median 11.0 years (5 057 534 person‐years) of follow‐up, we documented 11 618 cases of type 2 diabetes. There was a significantly positive association between total and regional FMR and incident type 2 diabetes, even after adjusting for BMI and other covariates. Compared with other body parts, FMRs of the whole body and leg showed the strongest relationship among men and women, respectively (hazard ratio per 1 SD, 95% confidence interval: 1.67, 1.55–1.80; 1.45, 1.39–1.53). A significant interaction (P for interaction < 0.001) between BMI category and FMRs of different body parts was observed. In the stratified analysis by BMI category and tertiles of FMRs, overweight/obese individuals with a high FMR tertile tended to have the highest hazard ratio, ranging from 5.91 to 7.94 in whole body and regional areas. Conclusions: In this large prospective study, higher total and regional FMRs were associated with a higher risk of developing type 2 diabetes, independent of BMI. This association was markedly strengthened in participants with BMI ≥ 25 kg/m2. [ABSTRACT FROM AUTHOR]