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Background: Life's Simple 7, the former construct of cardiovascular health (CVH) has been used to evaluate adverse non-communicable chronic diseases (NCDs). However, some flaws have been recognized in recent years and Life's Essential 8 has been established. In this study, we aimed to analyze the association between CVH defined by Life's Essential 8 and risk of 44 common NCDs and further estimate the population attributable fractions (PAFs) of low-moderate CVH scores in the 44 NCDs. Methods: In the UK Biobank, 170,726 participants free of 44 common NCDs at baseline were included. The Life's Essential 8 composite measure consists of four health behaviours (diet, physical activity, nicotine exposure, and sleep) and four health factors (body mass index, non-high density lipoprotein cholesterol, blood glucose, and blood pressure), and the maximum CVH score was 100 points. CVH score was categorized into low, moderate, and high groups. Participants were followed up for 44 NCDs diagnosis across 10 human system disorders according to the International Classification of Diseases 10th edition (ICD-10) code using linkage to national health records until 2022. Cox proportional hazard models were used in this study. The hazard ratios (HRs) and PAFs of 44 NCDs associated with CVH score were examined. Results: During the median follow-up of 10.85 years, 58,889 incident NCD cases were documented. Significant linear dose-response associations were found between higher CVH score and lower risk of 25 (56.8%) of 44 NCDs. Low-moderate CVH (<80 points) score accounted for the largest proportion of incident cases in diabetes (PAF: 80.3%), followed by gout (59.6%), sleep disorder (55.6%), chronic liver disease (45.9%), chronic kidney disease (40.9%), ischemic heart disease (40.8%), chronic obstructive pulmonary disease (40.0%), endometrium cancer (35.8%), lung cancer (34.0%), and heart failure (34.0%) as the top 10. Among the eight modifiable factors, overweight/obesity explained the la

Objective: Prediabetes and lifestyle factors have been associated with the risks of multiple adverse outcomes, but the effect of a healthy lifestyle on prediabetes-related complications remains unknown. We aimed to investigate whether the risks of multiple adverse outcomes including incident type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), and chronic kidney disease (CKD) among individuals with prediabetes can be offset by a broad combination of healthy lifestyle factors. Methods: This prospective study used data from the UK Biobank cohort. An overall lifestyle score ranging from 0 to 6 was created with 1 point for each of the 6 healthy lifestyle factors: no current smoking, moderate alcohol consumption, regular physical activity, healthy diet, no overweight or obese, and adequate sleep duration. T2DM, CVD, and CKD were ascertained during a median follow-up of 14 years. Cox proportional hazard regression models were used to estimate the associations. Sensitivity analyses were performed to test the robustness of the results. Results: We included 202,993 participants without T2DM, CVD, and CKD at baseline (mean age 55.5 years [SD 8.1]; 54.7% were women). Among these participants, 6,745, 16,961, and 6,260 participants eventually developed T2DM, CVD, and CKD, respectively. Compared with the participants with normoglycaemia, those with prediabetes showed a higher risk of these adverse outcomes. In addition, those prediabetic participants with a lifestyle score of 0-1 had a significantly higher risk of T2DM (hazard ratio [HR] 16.73, 95% CI 14.24, 19.65), CVD (HR 1.96, 95% CI 1.74, 2.21), and CKD (HR 1.92, 95% CI 1.58, 2.34) compared with those with no prediabetes and a score of 5-6. Moreover, among the participants with prediabetes, the HRs for T2DM, CVD, and CKD comparing a lifestyle score of 5-6 versus 0-1 decreased to 0.43 (95% CI 0.36, 0.51), 0.52 (95% CI 0.44, 0.62), and 0.60 (95% CI 0.46, 0.79), respectively. Conclusions: Combined healthy lifestyle fac

Background: Prenatal and postnatal factors may have joint effects on cardiovascular health, and we aimed to assess the joint association of birth weight and ideal cardiovascular health metrics (ICVHMs) prospectively in adulthood with incident cardiovascular disease (CVD). Methods: In the UK Biobank, 227,833 participants with data on ICVHM components and birth weight and without CVD at baseline were included. The ICVHMs included smoking, body mass index, physical activity, diet information, total cholesterol, blood pressure, and hemoglobin A1c. The Cox proportional hazards model was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) in men and women. Results: Over a median follow-up period of 13.0 years (2,831,236 person-years), we documented 17,477 patients with incident CVD. Compared with participants with birth weights of 2.5–4.0 kg, the HRs (95% CIs) of CVD among those with low birth weights was 1.08 (1.00–1.16) in men and 1.23 (1.16–1.31) in women. The association between having a birth weight <2.5 kg and CVD risk in men was more prominent for those aged <50 years than for those of older age (P for interaction = 0.026). Lower birth weight and non-ideal cardiovascular health metrics were jointly related to an increased risk of CVD. Participants with birth weights <2.5 kg and ICVHMs score 0–1 had the highest risk of incident CVD (HR [95% CI]: 3.93 [3.01–5.13] in men; 4.24 [3.33–5.40] in women). The joint effect (HR [95% CI]: 1.36 [1.17–1.58]) could be decomposed into 24.7% (95% CI: 15.0%–34.4%) for a lower birth weight, 64.7% (95% CI: 56.7%–72.6%) for a lower ICVHM score, and 10.6% (95% CI: 2.7%–18.6%) for their additive interaction in women. Conclusions: Birth weight and ICVHMs were jointly related to CVD risk. Attaining a normal birth weight and ideal ICVHMs may reduce the risk of CVD, and a simultaneous improvement of both prenatal and postnatal factors could further prevent additional cases in women.

Objectives Disorders of immune system may impact cardiovascular health; however, comprehensive study is lacking. We aimed to analyse the association of total and 20 individual immune-mediated diseases (IMDs) with risk of incident cardiovascular disease (CVD). Methods In this prospective cohort study, 414 495 participants (55.6% women; mean age 55.9 years) from UK Biobank with baseline assessment at 2006-10 were included. Among them, 21 784 participants had prevalent IMDs. Information on IMDs at baseline and incidence of CVDs during follow-up were recorded. Cox proportional hazard models were used to estimate the association between IMDs and CVDs risk. Results During the median follow-up of 12.1 years, there were 6506 cases of CVDs in participants with IMDs (29.9%) and 77 699 cases in those without IMDs (19.8%). After multivariable adjustment, participants with IMDs were significantly associated with an increased risk of total CVD [hazard ratio (HR) 1.57; 95% CI 1.52-1.61]. Among the 20 IMDs, 16 showed significant associations with CVD (all P < 0.0025 after Bonferroni correction), with HR ranging from 1.34 (1.16-1.54) for celiac disease to 2.75 (2.10-3.61) for SLE. Participants with any IMD exposure had a higher risk of all individual CVD events, with HR ranging from 1.34 (1.14-1.58) for cerebral hemorrhage to 1.80 (1.54-2.11) for pericardium diseases. IMD duration <5, 5-10 and >10 years was associated with 55%, 59% and 56% increased risk of total CVD, respectively. Conclusion Total and individual IMDs were associated with an increased risk of overall CVDs. It is important to consider primary prevention of CVD in patients with IMD and dysregulation of immune system in the cardiovascular health.

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 (Pinteraction < .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 (Pinteraction < .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% i

AIMS: To evaluate the association of cardiovascular health (CVH), measured by Life's Essential 8 score, with the risk of premature mortality and to determine the patterns of CVH-related differences in life expectancy among people with and without type 2 diabetes (T2D). MATERIALS AND METHODS: This prospective study included 309,789 participants (age 56.6 ± 8.1 years; 46% men) enroled in the UK Biobank. The Life's Essential 8 composite measure consists of four health behaviours (diet, physical activity, nicotine exposure, and sleep) and four health factors (BMI, non-HDL cholesterol, blood glucose, and blood pressure), and the maximum CVH score was 100 points. CVH was categorised into low, moderate, and high groups. Premature death was defined as death before the age of 75. Cox proportional hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated, and life expectancy was estimated. RESULTS: During a median follow-up of 12.7 years, 13,683 cases of premature death were documented. Compared to participants with low CVH, the multivariable HRs (95% CIs) of premature death were 0.59 (0.56-0.62) and 0.42 (0.39-0.45) for the moderate and high CVH groups, respectively. This association was stronger in participants with T2D compared with those without T2D. At the age of 50 years, compared to low CVH groups, high CVH was associated with a gain of 9.79 (9.70-9.87) and 5.58 (5.48-5.67) additional life years for men with and without T2D, respectively. The corresponding life gain for women with and without T2D was 24.21 (24.13-24.27) and 10.18 (10.10-10.27), respectively. CONCLUSIONS: Maintaining an ideal Life's Essential 8 score may provide more benefits for people with T2D than for those without T2D, including a lower risk of premature death and an increased lifespan.

Nanomechanical spectroscopy (NMS) is a recently developed approach to determine optical absorption spectra of nanoscale materials via mechanical measurements. It is based on measuring changes in the resonance frequency of a membrane resonator vs. the photon energy of incoming light. This method is a direct measurement of absorption, which has practical advantages compared to common optical spectroscopy approaches. In the case of two-dimensional (2D) materials, NMS overcomes limitations inherent to conventional optical methods, such as the complications associated with measurements at high magnetic fields and low temperatures. In this work, we develop a protocol for NMS of 2D materials that yields two orders of magnitude improved sensitivity compared to previous approaches, while being simpler to use. To this end, we use mechanical sample actuation, which simplifies the experiment and provides a reliable calibration for greater accuracy. Additionally, the use of low-stress silicon nitride membranes as our substrate reduces the noise-equivalent power to NEP = 890 fW H z − 1 , comparable to commercial semiconductor photodetectors. We use our approach to spectroscopically characterize a 2D transition metal dichalcogenide (WS2), a layered magnetic semiconductor (CrPS4), and a plasmonic super-crystal consisting of gold nanoparticles., QN/vanderSarlab, QN/van der Zant Lab

Context Whether the psychological wellbeing status could be a risk factor for type 2 diabetes is unclear. Objective We aimed to measure the association between combined psychological wellbeing factors and type 2 diabetes and investigate whether this association was modified by genetic predisposition. Methods Prospective cohort study from the UK Biobank. In total, 127 496 participants who completed a psychological wellbeing questionnaire and did not have type 2 diabetes at baseline (2006-2010) were included; among them, 88 584 (69.5%) were analyzed to determine their genetic predisposition. The main outcome measure was incident type 2 diabetes. Results During the median follow-up of 10.0 years, 2547 incident type 2 diabetes cases were documented. Moderate to extreme unhappiness, satisfaction score <= 3, presence of broad depression, and a neuroticism score >= 3 were all significantly and independently associated with an increased risk of diabetes. When considered as a combination indicator, compared with individuals in the highest quartile of the psychological wellbeing score, the fully adjusted hazard ratios (95% CI) of type 2 diabetes were 1.41 (1.21-1.65) in the third quartile, 1.45 (1.24-1.69) in the second quartile, and 1.73 (1.48-2.01) in the lowest quartile. In the stratified analysis, we observed significant interactions between age and physical activity, and type 2 diabetes (P-interaction < .001 and 0.049, respectively). However, there was no significant interaction between the psychological wellbeing score and genetic susceptibility to diabetes (P-interaction = .980). Conclusion Worse overall psychological wellbeing was associated with a significantly increased risk of type 2 diabetes in a dose-response fashion regardless of genetic predisposition.

CONTEXT: Increasing evidence suggests that sleep is important for fat metabolism. However, the causal relationship between sleep duration and visceral adipose tissue (VAT) needs to be further clarified. OBJECTIVE: This study investigated the linear and nonlinear causal association between sleep duration and VAT. METHODS: This study used one-sample and two-sample Mendelian randomization MR). Single-nucleotide polymorphisms (SNPs) associated with sleep duration at genome-wide significance were obtained from published genome-wide association studies. We also recalculated the correlation between each SNP and sleep duration in the UK Biobank. The associations of SNPs with predicted VAT (396 858 participants) were conducted in the UK Biobank. RESULTS: A total of 396 858 eligible participants (54.10% females, 57 ± 8 years old) were included in the study. The participants slept 7.17 ± 1.04 hours and stored 1.25 ± 0.88 kg of VAT on average. Genetically predicted sleep duration was significantly associated with VAT. For each 1-hour increase in genetically predicted sleep duration, the reduction in predicted VAT mass was 0.11 kg (P = 8.18E-16) in total, 0.17 kg (P = 3.30E-11) in men and 0.07 kg (P = 1.94E-06) in women. Nonlinear MR analyses demonstrated nonlinearity (L-shaped associations) between genetically predicted sleep duration and VAT in all participants, men, and women. Complementary analyses provided confirmative evidence of the adverse effects of genetically predicted short sleep duration on the increased VAT. In contrast, no clear evidence on the causal effect of genetically predicted long sleep duration on VAT mass was found. CONCLUSION: The causal association of sleep duration with VAT was L-type. Our findings support that short sleep duration is a risk factor for increasing VAT, thus reinforcing the probability that increasing sleep duration may decrease VAT.