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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., (© 2023 John Wiley & Sons Ltd.)

Background: Limited studies have explored the association between visceral adipose tissue (VAT) mass and fat-to-muscle mass ratio (FMR) and mortality. We aimed to evaluate the sex-specific association of VAT and FMR with all-cause and cause-specific mortality by age., Methods: A total of 438 896 participants (49.8% men, mean age ± standard deviation: 57 ± 8 years for men; 56 ± 8 years for women) were included from the UK Biobank cohort. The nature of VAT was predictive, as obtained by sex-stratified, non-linear prediction models. Fat and muscle mass were estimated using a bioelectrical impedance assessment device. FMR was calculated as the fat mass divided by the muscle mass in the whole body. VAT and FMRs were divided into quintiles in ascending order, and the 3rd quintile was used as the reference. Cox regression analyses were used to estimate the associations between VAT, FMR and mortality., Results: During a median of 12.4 years of follow-up, we documented 29 903 deaths. After adjusting for various covariates, the individuals in the highest quintiles of VAT and FMR had the highest hazard ratios (HRs) of all-cause mortality [1.24 (95% confidence interval: 1.17-1.33) for VAT and 1.24 (1.17-1.31) for FMR in men; and 1.11 (1.03-1.21) for VAT in women], except that the 1st quintile of FMR in women had the greatest HR [1.18 (1.09-1.27)]. Significant interactions were observed in both sexes according to age category (P for interaction < 0.05). Among men <50 years, participants in the 1st and 5th quintiles of VAT and FMR had significantly higher risks of mortality [1.30 (1.02-1.66) and 1.67 (1.27-2.19) in VAT; 1.25 (0.99-1.56) and 1.41 (1.11-1.79) in FMR, respectively]; in women, this phenomenon was observed in the ≥60 age group [1.16 (1.06-1.27) and 1.19 (1.08-1.31) in VAT; 1.18 (1.08-1.29) and 1.11 (1.01-1.22) in FMR, respectively]. VAT showed a linear positive association with mortality in women <60 years and a J-shaped association from respiratory disease in both sexes ≥60 years. FMR showed a linear positive association with mortality from cancer in men <60 years and a J-shaped association with mortality from cause-specific mortality in both sexes ≥60 years, except for mortality from cardiovascular disease in men., Conclusions: Most associations of VAT and FMR with all-cause mortality were J-shaped and were significantly modified by age status (<50, 50-59 and ≥60 years). The clinical implication is that regarding body composition and VAT mass, different health strategies may be adopted for people of different sexes and ages., (© 2022 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders.)

Background and Aims: 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., Methods: 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., Results: 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., Conclusions: Joint exposure to multiple psychological wellbeing factors was associated with increased risks of incident CVD in an additive manner, regardless of genetic susceptibility., Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background/purpose: Metabolic associated fatty liver disease (MAFLD) was proposed as a new definition to put emphasis on the metabolic aspects of nonalcoholic fatty liver disease (NAFLD). We aim to compare the cardiovascular and renal burden between MAFLD and NAFLD patients., Methods: 12183 participants were enrolled in East China. The cardiovascular burden (Framingham risk score and previous cardiovascular diseases (CVD)) and renal burden (eGFR and chronic kidney disease (CKD)) were measured., Results: The risk of hypertension, dyslipidemia, diabetes, overweight/obesity, and central obesity of MAFLD patients were higher than those of NAFLD. Patients with MAFLD have a similar or higher beta coefficients in Framingham risk score [beta (95%CI): male 0.062 (0.055,0.069) vs 0.041 (0.033,0.048); female 0.014 (0.012,0.016) vs 0.012 (0.01,0.014)], and higher odds ratio in previous CVD [odds ratio (95%CI): male 1.50 (1.22,1.85) vs 1.35 (1.1,1.66); female 1.58 (1.33,1.87) vs 1.45 (1.22,1.72)], compared with those with NAFLD. However, compared with males with MAFLD, the odds ratio of CKD was higher in those with NAFLD [eGFR: -2.731 (-3.422, -2.041) vs-3.578 (-4.268, -2.887). CKD: 1.44 (1.05,1.96) vs 1.56 (1.14,2.12)]. In female, CKD was only marginally associated with NAFLD [0.8 (0.62,1.02), P=0.075], but not MAFLD [0.87 (0.68,1.11), P=0.268]., Conclusions: Patients with MAFLD have a similar or higher risk of future and previous CVD compared with those with NAFLD, but the risk of CKD was higher in male with NAFLD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Yu, Zhang, Chen, Wan, Chen, Xia, Yu, Wang, Ye and Lu.)

Cardiovascular disease (CVD) results in more than half of the mortality and the majority of morbidity in patients with type 2 diabetes. We aim to evaluate the associations of urinary concentrations of phthalate metabolites with CVD in diabetic patients and explore whether CVD risk factors mediate or interact with these associations. A total of 675 type 2 diabetic participants were enrolled from Shanghai, China, in 2018. CVD was defined as a self-reported diagnosis by a physician including coronary heart disease, myocardial infarction, or stroke; it was further reconfirmed in the records from the registration platform. Ten phthalate metabolites were measured in urine. We found positive associations were found among the level of monoethyl phthalate and monoisobutyl phthalate and CVD (OR 1.138, 95% CI 1.032, 1.254; OR 1.369, 95% CI 1.049, 1.786, respectively). Monoisobutyl phthalate and monobenzyl phthalate were marginally and positively associated with carotid intima-media thickness and common carotid artery diameter, respectively. None of the CVD risk factors, including HOMA-IR, body mass index, lipid profile, or blood pressure, significantly mediated the association between the metabolites and CVD. The conditional indirect effect on CVD was significantly stronger for current smoking and dyslipidemia for monoethyl phthalate and for no statin usage and men for monoisobutyl phthalate. In conclusion, phthalate exposure was positively associated with CVD in Chinese with type 2 diabetes. Type 2 diabetic men who are currently smoking, have an uncontrolled lipid profile, and are not using statins might be more susceptible to CVD when exposed to phthalates., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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]