搜索结果：NPJ cardiovascular health

While Life's Essential 8 (LE8) provides a comprehensive measure of cardiovascular health (CVH), its association with mortality among the oldest-old, including centenarians, remains unclear. This study evaluated the relationship between LE8-defined CVH and all-cause mortality across adulthood using data from the China Kadoorie Biobank (Hainan cohort) and the China Hainan Centenarian Cohort Study, including 31,473 individuals aged 30-116. Participants were categorized by life stage and CVH score (low, moderate, high). Higher CVH scores were associated with significantly reduced mortality risk at all life stages, including among centenarians, who experienced a 54.8% lower risk with high CVH. A near-linear dose-response relationship was observed. Population-attributable fractions for mortality reached 36.8% in centenarians. Physical activity and body mass were particularly important in reducing mortality among centenarians. These findings challenge therapeutic nihilism in the oldest-old while underscoring the need for age-specific strategies tailored to distinct physiological profiles is crucial for extending healthy lifespan across the adult life course.

Obesity is a risk factor for cardiovascular diseases (CVD). We evaluated a 52-week digital Health Behaviour Change Support System (HBCSS) with a one-year follow-up to treat overweight and Class I obesity. A total of 532 participants (BMI 27-35&#x2009;kg/m2) were randomized into three groups: CBT-based group counselling and self-help guidance (SHG) delivered face-to-face, and usual care. These groups were further divided into HBCSS and non-HBCSS groups. The 10-year CVD risks were assessed using the FINRISK calculator. Baseline median overall CVD risks were similar between groups. In two-group analyses comparing HBCSS and non-HBCSS groups, after 12 months, CVD risks decreased in both groups. However, after 24 months, only the HBCSS group maintained significant reductions in overall risk (-0.40%, p&#x2009;<&#x2009;0.001). Among participants with obesity, the HBCSS group demonstrated a sustained decrease in overall and coronary artery disease risk, while there were no CVD risk reductions in the non-HBCSS group. However, the differences between HBCSS and non-HBCSS groups were not significant in any analyses conducted, and there were no significant differences in six group analyses between intervention groups. The HBCSS group showed significant reductions in weight, BMI, and waist circumference (at 12 and 24 months) and in LDL cholesterol (at 12 months), compared with controls (p&#x2009;<&#x2009;0.05). The HBCSS results in a decrease in CVD risk factors, which is reflected by a sustained reduction in calculated CVD risks, especially among participants with obesity.

Cardiovascular, kidney, and metabolic (CKM) diseases are leading causes of mortality in the United States (US), with geographic and socioeconomic disparities. The community food environment, including food accessibility and security, is a modifiable determinant of CKM health. We systematically examined associations between county-level multidimensional food environment indicators and CKM-related mortality across US counties. We conducted an ecological analysis integrating data from the County Health Rankings and CDC WONDER. Generalized linear models estimated rate ratios (RR) for age-adjusted CKM mortality across quartiles of the food environment index, limited access to healthy foods, and food insecurity. Adjusting for demographic and socioeconomic factors, the most favorable quartile of the food environment index was associated with reduced mortality from cardiovascular disease (RR 0.85, 95% CI 0.84-0.86), diabetes (RR 0.76, 0.73-0.79), renal failure (RR 0.73, 0.69-0.77), and overall CKM disease (RR 0.83, 0.82-0.84) compared with the least favorable quartile. Conversely, limited access to healthy foods (RR 1.11, 1.09-1.12) and food insecurity (RR 1.19, 1.18-1.21) correlated with higher overall CKM mortality. Counties with less favorable food environments are linked to higher CKM-related mortality. Improving structural food access and security may be essential for reducing disease burden and promoting health equity.

The metabolic vulnerability index (MVX) captures metabolic-inflammatory vulnerability, but its joint relevance with Life's Essential 8 (LE8) for major adverse cardiovascular events (MACE) is unclear. We analyzed 239,135 UK Biobank participants free of baseline MACE. MVX was calculated from six NMR-based biomarkers. LE8 scores were classified as low (<60), moderate (60-79), or high (&#x2265;80) cardiovascular health (CVH). Cox models evaluated associations of MVX and LE8 with incident MACE; joint-effect, interaction, counterfactual, and mediation analyses were conducted. Over a median 13.6 years, 17,146 MACE occurred. Higher MVX was associated with higher MACE risk (HR&#x2009;=&#x2009;1.08, 95% CI: 1.07, 1.10), whereas high CVH was associated with lower risk compared to low CVH (HR&#x2009;=&#x2009;0.44, 95% CI: 0.41, 0.47). Participants with low CVH and MVX Q4 had the highest risk (HR&#x2009;=&#x2009;2.84, 95% CI: 2.60, 3.12). Additive interaction was evident for MACE and myocardial infarction. Counterfactual and mediation analyses suggested that better CVH could prevent a substantial proportion of events, partly through lower metabolic vulnerability. Combined MVX and LE8 assessment may improve cardiovascular risk stratification and support targeted prevention.

We aim to estimate and compare the cost-effectiveness of statins, berberine, and their combined use for primary cardiovascular disease (CVD) prevention. The Scottish CVD Policy Model was used to predict long-term health and cost outcomes in Scottish adults aged 40 years or older without pre-existing CVD. Intervention and cost inputs were sourced from published literature and health service cost data. The primary outcome measure was the lifetime incremental cost-effectiveness ratio (ICER), evaluated as cost per quality-adjusted life year (QALY) gained. Five strategies were analyzed for individuals with ASSIGN risk scores &#x2265;20% and &#x2265;10%: no intervention, atorvastatin 20&#x2009;mg/day, berberine 1000&#x2009;mg/day, simvastatin 20&#x2009;mg plus berberine 1500&#x2009;mg/day, and simvastatin 20&#x2009;mg plus berberine 900&#x2009;mg/day. All intervention strategies were cost-effective, compared to no intervention, at the threshold of ICER of &#xa3;20,000 per QALY. Compared to statins, berberine was less cost-effective, but the combined interventions remained cost-effective. Notably, when using drug costs from China (reflecting lower berberine prices), berberine and the combined interventions were preferable to statins alone. Statins, berberine, and combined interventions are all cost-effective options for primary CVD prevention. Berberine could be considered a valuable alternative or complementary therapy, particularly if its price decreases below that of statins.

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality in women, yet remains under-studied and under-recognised. Pregnancy acts as a 'cardiovascular stress test', with haemodynamic and hormonal changes that may unmask subclinical disease or precipitate decompensation in those with existing cardiac disease. Pregnancy-related metabolic complications are under-studied risk factors for future CVD. This review explores the complex interplay between pregnancy and CVD.

Venous thromboembolism (VTE) is a leading cause of preventable death among patients undergoing systemic treatment for cancer. Studies suggest that treatment strategies such as direct oral anticoagulant administration can significantly reduce the likelihood of VTE. Therefore, identifying people at high risk is of critical importance. Leveraging electronic health records (EHRs) from the U.S. Veterans Affairs (VA) healthcare system, we developed a transformer model to predict VTE risk in 80,808 cancer patients following the initiation of systemic treatment. The model uses longitudinal diagnostic codes, laboratory values, and demographic data. The proposed transformer model dynamically predicts VTE risk in 3-month quarterly intervals over the year following systemic treatment, achieving progressively improved performance across quarters (AUC: 0.68-0.77). The model is similarly performant on the external validation cohort from the Harris Health System (HHS) with 9752 patients (AUC: 0.68-0.74). By improving its predictions as a patient's history evolves, this dynamic model surpasses prior static risk scores and better supports actionable decisions deeper into the treatment course.

Psychological stress is a key driver of short-term blood pressure (BP) elevations and cardiovascular risk, yet its moment-to-moment impact in daily life remains difficult to predict. In this longitudinal observational study, we collected multimodal data from 20 adults with self-reported hypertension, including continuous wearable-derived heart rate and activity, ecological momentary assessment (EMA) stress ratings, and ambulatory BP measurements in free-living conditions. The dataset comprised 3694 EMA responses and 3812 BP measurements collected over approximately four weeks per participant (mean 24.1&#x2009;&#xb1;&#x2009;8.5 days). We evaluated whether participant-specific ("personalized") models outperform a single pooled population model. Two prediction tasks were examined: (i) prediction of near-term BP elevations from wearable signals and stress EMA responses and (ii) prediction of self-reported stress from wearable signals and BP. Across both tasks, personalized models consistently improved predictive performance. For BP prediction, personalized models achieved a mean AUROC of 0.803, exceeding the population model by 0.235, while for stress prediction they achieved a mean AUROC of 0.849, exceeding the population model by 0.208. These findings suggest that personalized wearable-based models can capture individual patterns of stress and BP dynamics, with direct implications for precision mental health assessment and just-in-time adaptive intervention design in future work.

Associations of caffeine (the most routinely consumed bioactive compound worldwide) and caffeine metabolites (CAF/CAFMs) with cardiovascular-kidney-metabolic (CKM) syndrome (a newly-introduced high-burden clinical condition) and role of biological aging and underlying mechanisms are uncharted territories warranting pressing decryption. Among 3020 adults, 15 urinary CAF/CAFMs were determined to investigate their single-analyte (by weighted logistic regression) and mixture (by Bayesian kernel machine regression [BKMR] and weighted quantile sum [WQS]) relationships between advanced CKM syndrome. Role of emerging biological aging metric-Gompertz-Law-Based Biological Age Difference (GOLD BioAgeDiff)-was explored by mediation analysis. Underlying mechanisms were explored by network pharmacology. Specific CAF/CAFMs (1-methylxanthine, theophylline, paraxanthine, and theobromine) were negatively linked to advanced CKM syndrome (P and false discovery rate&#x2009;<&#x2009;0.05). WQS and BKMR uncovered negative CAF/CAFMs mixture-advanced CKM syndrome association (odds ratio&#x2009;=&#x2009;0.782; 95% confidential interval: 0.670, 0.912). Exploratorily, GOLD BioAgeDiff mediated 13.85-20.06% of the CAF/CAFMs-advanced CKM syndrome associations. Metabolic, mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase-protein kinase B (PI3K-Akt), and apoptosis signaling pathways were exploratorily enriched in the CAF/CAFMs-CKM syndrome relationships. Overall, several CAF/CAFMs and, especially, their mixture showed protective associations with CKM syndrome, which was exploratorily mediated by delayed biological aging, while metabolic, MAPK, PI3K-Akt, and apoptosis signaling pathways might be involved in the underlying mechanisms.

Large language models (LLMs) are poised to transform physician interactions with electronic health records (EHRs) by assisting clinical documentation, drafting preliminary diagnostic reports, and supporting patient communication. While LLMs reduce administrative burden, their integration in clinical workflows introduces the risk of blending AI- and human-generated content within EHRs. This perspective reviews technological and policy solutions to ensure traceability of AI-generated content in EHRs to preserve clinical integrity.

Classical statistics are commonly used to find differences between distributions of average skin temperature across populations. However, skin temperature is affected by many endogenous (within body) and exogenous (outside body) factors, and these factors induce causal changes in longitudinal skin temperature that can obfuscate the interpretation of average population differences. Moreover, interpretations are increasingly difficult to make when using temperature signals sampled longitudinally in uncontrolled settings. A potential way to better handle the inherent complexity of skin temperature dynamics in uncontrolled settings is to explicitly account for the effects of causal factors on the short- and long-term trajectories of temperature. In this work, we find that a physics-informed model of skin temperature and activity during sleep accounts for significantly more variance than an equally parsimonious linear model. Furthermore, this model enables separation of cohorts with cardiovascular conditions that are known to affect skin thermoregulation, an important improvement over classic statistical modeling.

Breast arterial calcification (BAC), frequently visible on screening mammography, is a potent biomarker for cardiovascular disease risk in women. Unlike atherosclerosis, BAC represents medial arterial calcification characterized by arterial stiffness rather than narrowing. BAC has been linked to myocardial infarction, stroke, and heart failure and may offer a scalable risk stratification tool through AI-driven quantification. Standardizing BAC measurement and reporting is essential to improving long-term clinical outcomes for women.

Obstructive sleep apnea (OSA) is highly prevalent among patients with cardiovascular (CV) risk factors, yet early detection in primary care remains difficult, particularly in individuals with subtle or absent symptoms, in whom screening questionnaires may have limited accuracy. This study aimed to evaluate the diagnostic performance of five validated questionnaires and the triglyceride-glucose (TyG) index, alone and in combination, for detecting moderate-to-severe OSA in primary care patients stratified by CV risk. In this prospective study, 189 adults aged 18-75 years with hypertension, type 2 diabetes, or dyslipidemia were consecutively recruited from a primary care center in Spain. Participants completed the Berlin, STOP, STOP-BANG, NoSAS, and BASH-GN questionnaires, and the TyG index was calculated from fasting glucose and triglyceride levels. OSA was assessed using home sleep apnea testing, with moderate-to-severe OSA defined as an apnea-hypopnea index (AHI)&#x2009;&#x2265;&#x2009;15 events/h. CV risk was categorized using SCORE charts. Overall OSA prevalence was 57.7%, and 23.8% of participants had moderate-to-severe disease. Individually, questionnaires and TyG showed modest discrimination (AUC range 0.575-0.675). Diagnostic accuracy improved when strategies were tailored to CV risk: in low-to-moderate CV risk patients, TyG combined with the Berlin questionnaire achieved the best performance (AUC 0.740), whereas in high-to-very-high CV risk patients, the TyG plus STOP-BANG combination performed best (AUC 0.732). Notably, high-risk patients had more severe OSA but fewer typical symptoms, suggesting a "silent" phenotype. Integrating TyG with selected questionnaires may modestly enhance detection of clinically significant OSA in primary care, particularly when adapted to CV risk.

Clonal haematopoiesis (CH) refers to the clonal expansion of haematopoietic stem cells in the absence of overt haematological malignancy and is common in older individuals. Recent evidence has heralded CH as a novel determinant of cardiovascular disease. This review explores the associations of CH with ischaemic heart disease, heart failure, atrial fibrillation and cardio-oncology, highlighting biases in the literature, exploring effects of mutation and clone size, concluding with clinical implications.

Cardiovascular-Kidney-Metabolic (CKM) syndrome is uncharacterized in Chinese adults across ages, including centenarians. From three cohorts, age- and sex-standardized CKM prevalence showed nearly 90% stage&#x2265;1 and >18% advanced. Prevalence rose with age; advanced CKM reached 100% in male centenarians. Age, sex, smoking, and BMI were risk factors for advanced CKM, with faster male progression. These findings highlight growing disease burden in China's aging population and inform future CKM prevention.

Artificial intelligence (AI) is progressively utilized in cardiology; nonetheless, the overarching advantages across various care domains remain ambiguous. We conducted a search of PubMed, Embase, CINAHL, and trial registries for randomized controlled trials up to January 16, 2026, assessing prospectively applied interventions based on machine/deep-learning algorithms while excluding rule-based systems. Endpoints were categorized according to NICE evidence tiers: workflow efficiency (Tier A), patient engagement/health promotion (Tier B), and clinical outcomes (Tier C). The risk of bias was evaluated using RoB 2.0. In 32 randomized controlled trials (27 of which were meta-analyzed), artificial intelligence improved all levels. Tier A: workflow time reduced (SMD&#x2009;-&#x2009;0.71; 95% CI&#x2009;-&#x2009;1.04 to -0.39), corresponding to a diagnostic time that is 30-120&#x2009;s shorter and a decrease of 1.0-4.2 hospital days in trials reporting length of stay. Tier B: Behavioral nudging enhanced medication adherence (RR 1.59; 95% CI 1.01-2.50; NNT&#x2009;=&#x2009;12). Tier C: decision-support implementations decreased all-cause mortality (RR 0.84; 95% CI 0.75-0.94; I&#xb2;&#x2009;=&#x2009;8%; NNT&#x2009;=&#x2009;32). Limitations encompassed restricted blinding and insufficient sham-AI controls. Data-driven clinical AI yields quantifiable efficiency improvements, enhances engagement, and reduces adverse outcomes when integrated with actionable decision support, hence informing a structured framework for governance and implementation.

Previous studies have demonstrated that exercise can influence motor skill learning. However, the specific components of learning primed by exercise remain unclear. This study examined the effect of a preceding bout of high intensity interval training (HIIT) on the acquisition of a novel motor skill. The investigation focused on whether improvement in skill across the session was attributable to online gains during active practice or offline rest periods between practice blocks. Whether common polymorphisms of the BDNF and DRD2/ANKK1 genes that regulate plasticity, learning, and memory, influenced the relationship between exercise and motor learning was also investigated. HIIT enhanced skill acquisition, but the effects of HIIT priming were not specifically attributable to within-session online or offline learning processes. Contrary to research on overnight consolidation, there was no interaction between BDNF, nor DRD2/ANKK1 genotype, with exercise primed skill learning. This builds our understanding of how exercise benefits skill leaning over a single session.

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This paper proposes a multifunctional robotic micromanipulation system for automated microinjection and cardiac rhythm monitoring of zebrafish larvae. An indirect localization method for zebrafish larval hearts is introduced, and a visual algorithm based on modulo operation is devised to locate the zebrafish atrium and ventricle accurately. For the first time, a batch of zebrafish larval yolk is injected and their cardiac rhythm is monitored during the entire developmental stages of zebrafish larvae, which is enabled by the developed robotic system. The system has been applied to investigate the effects of different concentrations of Tricaine (MS222) on zebrafish larvae and the influences of Aspirin on cardiovascular activities. It decreases the dosage by over 60% for heart disease treatment compared to traditional water-based administration. Experimental results verified the functionality and accuracy of the reported system, suggesting that the robotic micromanipulation system can effectively liberate human labor from complex and repetitive tasks.