BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. We aimed to investigate whether neural network-derived ECG features could be used to predict future cardiovascular disease and mortality and have phenotypic and genotypic associations. METHODS: We extracted 5120 neural network-derived ECG features from an artificial intelligence-enabled ECG model trained for 6 simple diagnoses and applied unsupervised machine learning to identify 3 phenogroups. Using the identified phenogroups, we externally validated our findings in 5 diverse cohorts from the United States, Brazil, and the United Kingdom. Data were collected between 2000 and 2023. RESULTS: In total, 1 808 584 patients were included in this study. In the derivation cohort, the 3 phenogroups had significantly different mortality profiles. After adjusting for known covariates, phenogroup B had a 20% increase in long-term mortality compared with phenogroup A (hazard ratio, 1.20 [95% CI, 1.17-1.23]; P<0.0001; phenogroup A mortality, 2.2%; phenogroup B mortality, 6.1%). In univariate analyses, we found phenogroup B had a significantly greater risk of mortality in all cohorts (log-rank P<0.01 in all 5 cohorts). Phenome-wide association study showed phenogroup B had a higher rate of future atrial fibrillation (odds ratio, 2.89; P<0.00001), ventricular tachycardia (odds ratio, 2.00; P<0.00001), ischemic heart disease (odds ratio, 1.44; P<0.00001), and cardiomyopathy (odds ratio, 2.04; P<0.00001). A single-trait genome-wide association study yielded 4 loci. SCN10A, SCN5A, and CAV1 have roles in cardiac conduction and arrhythmia. ARHGAP24 does not have a clear cardiac role and may be a novel target. CONCLUSIONS: Neural network-derived ECG features can be used to predict all-cause mortality and future cardiovascular diseases. We have identified biologically plausible and novel phenotypic and genotypic associations that describe mechanisms for the increased risk identified.

• Klíčová slova
• cardiovascular diseases, electrocardiography, neural networks, computer, supervised machine learning, unsupervised machine learning,
• MeSH
• časové faktory MeSH
• elektrokardiografie * MeSH
• fenotyp * MeSH
• hodnocení rizik MeSH
• kardiovaskulární nemoci diagnóza   mortalita   genetika   patofyziologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• neuronové sítě * MeSH
• prediktivní hodnota testů * MeSH
• prognóza MeSH
• reprodukovatelnost výsledků MeSH
• rizikové faktory MeSH
• senioři MeSH
• srdeční frekvence MeSH
• strojové učení bez učitele MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• Geografické názvy
• Spojené státy americké epidemiologie   MeSH

Work stress has been extensively supported to predict health outcomes like health behaviors. Evidence has linked work stress and personality independently to health, but the interrelationships between work stress and personality and their joint effects on health might deserve more attention in research. This study attempts to integrate recent developments in psychological research (diverse roles of personality in stress processes) into the well-established Effort-Reward Imbalance (ERI) model for work stress. Based on the ERI model, this population-based cohort study aims to investigate the relationships between work stress, personality and alcohol consumption; it particularly focuses on potential roles of overcommitment (OC) personality in ERI-drinking relations, including modifying, antecedent, mediator or direct effects. This two-wave cohort study was conducted in population samples of 3782 men and 3731 women (aged 45-69 years) from Czech Republic, Poland and Russia. Alcohol consumption was assessed by three drinking outcomes: binge drinking, heavy drinking and problem drinking. To assess modifying effect of OC in ERI-drinking relations, logistic regression was used. To assess antecedent or mediator role of OC in ERI-drinking relations, path analysis with the autoregressive and cross-lagged model was conducted. The results showed that OC had no significantly modifying effect in ERI-drinking relations. OC and ERI might have bidirectional relationships in the average follow-up period of 3.5 years; the effect of OC on ERI was remarkably stronger than the reversed causation. Antecedent role of OC in ERI-drinking relationship was significant, but mediator role of OC was not. In conclusion, our findings imply that "antecedent role" of OC in ERI-drinking relations is significant and promising as a potential target for individual intervention; future interventions are suggested to identify and target potential cognitive-behavioral mechanisms via which personality might influence work stress and subsequently health behaviors.

• Klíčová slova
• Alcohol consumption, Drinking, ERI, Effort–Reward Imbalance, Effort–Reward Imbalance, OC, Overcommitment, Overcommitment, Personality, Work stress,
• Publikační typ
• časopisecké články MeSH

Background Increased vagal modulation is a mechanism that may partially explain the protective effect of healthy lifestyles. However, it is unclear how healthy lifestyles relate to vagal regulation longitudinally. We prospectively examined associations between a comprehensive measure of 4 important lifestyle factors and vagal modulation, indexed by heart rate variability (HRV) over 10 years. Methods and Results The fifth (1997-1999), seventh (2002-2004), and ninth (2007-2009) phases of the UK Whitehall II cohort were analyzed. Analytical samples ranged from 2059 to 3333 (mean age: 55.7 years). A healthy lifestyle score was derived by giving participants 1 point for each healthy factor: physically active, not smoking, moderate alcohol consumption, and healthy body mass index. Two vagally mediated HRV measures were used: high-frequency HRV and root mean square of successive differences of normal-to-normal R-R intervals. Cross-sectionally, a positively graded association was observed between the healthy lifestyle score and HRV at baseline (Poverall≤0.001). Differences in HRV according to the healthy lifestyle score remained relatively stable over time. Compared with participants who hardly ever adhered to healthy lifestyles, those with consistent healthy lifestyles displayed higher high-frequency HRV (β=0.23; 95% CI, 0.10-0.35; P=0.001) and higher root mean square of successive differences of normal-to-normal R-R intervals (β=0.15; 95% CI, 0.07-0.22; P≤0.001) at follow-up after covariate adjustment. These differences in high-frequency HRV and root mean square of successive differences of normal-to-normal R-R intervals are equivalent to ≈6 to 20 years differences in chronological age. Compared with participants who reduced their healthy lifestyle scores, those with stable scores displayed higher subsequent high-frequency HRV (β=0.24; 95% CI, 0.01-0.48; P=0.046) and higher root mean square of successive differences of normal-to-normal R-R intervals (β=0.15; 95% CI, 0.01-0.29; P=0.042). Conclusions Maintaining healthy lifestyles is positively associated with cardiac vagal functioning, and these beneficial adaptations may be lost if not sustained.

• Klíčová slova
• autonomic nervous system, heart rate variability, lifestyle,
• MeSH
• časové faktory MeSH
• chování snižující riziko * MeSH
• cvičení MeSH
• dospělí MeSH
• hodnocení rizik MeSH
• index tělesné hmotnosti MeSH
• kardiovaskulární nemoci epidemiologie   patofyziologie   prevence a kontrola   MeSH
• lidé středního věku MeSH
• lidé MeSH
• nekuřáci MeSH
• nervus vagus patofyziologie   MeSH
• ochranné faktory MeSH
• pití alkoholu epidemiologie   MeSH
• prospektivní studie MeSH
• rizikové faktory MeSH
• senioři MeSH
• srdce inervace   MeSH
• srdeční frekvence * MeSH
• zdravotní stav MeSH
• zdravý životní styl * MeSH
• zvyky MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Geografické názvy
• Londýn epidemiologie   MeSH