Life's essential 8 and cardiovascular diseases progression among adults in the United Kingdom.

BACKGROUND: Recently, the American Heart Association introduced Life's Essential 8 (LE8) as a new cardiovascular health (CVH) metric, and studies have reported associations between LE8 and CVH outcomes. However, there is limited understanding of LE8's impact on the risk of transitions between different stages of CVH. The current study investigated whether adhering to LE8 during a healthy stage could mitigate the progression from hypertension (HT) to cardiovascular diseases (CVDs), and consequent death. METHODS: The study included 107,682 participants in the UK Biobank who were initially free of HT and CVDs. CVH were evaluated using LE8 metrics (diet, physical activity, nicotine exposure, sleep duration, body mass index, non-high-density lipoprotein cholesterol, blood glucose, and blood pressure). Multistate models were used to analyse the impacts of LE8 on the progression of CVDs. RESULTS: During a median follow-up of 12.2 years, 5727 participants developed HT, 7243 developed CVDs, and 1183 died afterwards. LE8 was negatively associated with the dynamic disease progression. A per-10 points increase of CVH scores was significantly associated with the reduced risk [Hazard ratios (95 % confidence intervals)] at 0.71 (0.69, 0.72), 0.83 (0.81, 0.85), 0.79 (0.77, 0.82), and 0.91 (0.86, 0.96) in the transition from healthy to HT, CVDs, death, and from CVDs to death, respectively. Mediation analyses indicated that HT significantly mediated LE8-reduced risks of CVDs and mortality. CONCLUSIONS: This study offered evidence that LE8 may influence the stages of CVD progression. The findings underscore the significance of adhering to LE8 in health management and CVDs management.

Healthy Lifestyles Modify the Association of Melatonin Receptor 1B Gene and Ischemic Stroke: A Family-Based Cohort Study in Northern China.

Limited research has reported the association between MTNR1B gene polymorphisms and ischemic stroke (IS), and there is insufficient evidence on whether adopting a healthy lifestyle can mitigate genetic risks in this context. This study aimed to investigate the associations between MTNR1B gene variants (rs10830963 and rs1387153) and IS, examining the potential effect of gene-lifestyle interactions on IS risk. Conducted in northern China, this family-based cohort study involved 5116 initially IS-free subjects. Genotype data for rs10830963 and rs1387153 in MTNR1B were collected. Eight modifiable lifestyle factors, including body mass index (BMI), smoking, alcohol consumption, dietary habits, physical activity, sedentary time, sleep duration, and chronotype, were considered in calculating healthy lifestyle scores. Multilevel Cox models were used to examine the associations between MTNR1B variants and IS. Participants carrying the rs10830963-G and rs1387153-T alleles exhibited an elevated IS risk. Each additional rs10830963-G allele and rs1387153-T allele increased the IS risk by 36% (HR = 1.36, 95% CI, 1.12-1.65) and 32% (HR = 1.32, 95% CI, 1.09-1.60), respectively. Participants were stratified into low, medium, and high healthy lifestyle score groups (1537, 2188, and 1391 participants, respectively). Genetic-lifestyle interactions were observed for rs10830963 and rs1387153 (p for interaction < 0.001). Notably, as the healthy lifestyle score increased, the effect of MTNR1B gene variants on IS risk diminished (p for trend < 0.001). This study underscores the association between the MTNR1B gene and IS, emphasizing that adherence to a healthy lifestyle can mitigate the genetic predisposition to IS.

Prediction of Creep Curves Based on Back Propagation Neural Networks for Superalloys.

Creep deformation is one of the main failure forms for superalloys during service and predicting their creep life and curves is important to evaluate their safety. In this paper, we proposed a back propagation neural networks (BPNN) model to predict the creep curves of MarM247LC superalloy under different conditions. It was found that the prediction errors for the creep curves were within ±20% after using six creep curves for training. Compared with the θ projection model, the maximum error was reduced by 30%. In addition, it is validated that this method is applicable to the prediction of creep curves for other superalloys such as DZ125 and CMSX-4, indicating that the model has a wide range of applicability.