JACC. Advances · 2025
Identifying Patterns of Tobacco Use and Associated Cardiovascular Disease Risk Through Machine Learning Analysis of Urine Biomarkers.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Siegel Noah A, Zhao Juan, Benjamin Emelia J, Bhatnagar Aruni, Hall Jennifer L, Stokes Andrew C
Аннотация
BACKGROUND: Tobacco use remains a leading cause of disability-adjusted life years lost in the United States. Cardiovascular harm varies by tobacco product type and usage patterns, yet reliable methods for assessing exposure and harm across different products, especially novel tobacco products, are limited.
OBJECTIVES: The authors aimed to identify distinct biomarker exposure patterns associated with different tobacco products using cluster analysis and validate this approach through longitudinal analysis of cardiovascular disease risk.
METHODS: Using the Population Assessment of Tobacco and Health data set, we performed cluster analysis and geometric mean modeling of tobacco-related biomarkers, followed by a longitudinal retrospective cohort study with Cox proportional hazard modeling used to examine associations between clusters and a primary composite outcome of heart failure, myocardial infarction, or stroke.
RESULTS: Examining 6,463 individuals, we identified 5 clusters: never users (cluster 1), predominant e-cigarette users (cluster 4), cigarette/dual users (cluster 2), and mixed tobacco users (clusters 3 and 5). All clusters showed elevated biomarkers of oxidative stress and inflammation compared to cluster 1, with clusters 2 and 3 showing the highest levels. Multivariable analysis revealed significantly higher cardiovascular disease risk in cluster 2 vs cluster 1 (HR: 2.24; 95% CI: 1.17-4.30), while other clusters showed elevated but nonsignificant risks.
CONCLUSIONS: Our categorization of exposure through cluster analysis provides a potential tool for evaluating the use of emerging tobacco products and establishing a connection between novel exposures and cardiovascular risk. This approach may contribute to the validation of a valuable tool for assessing the risk associated with the use of different tobacco products.
OBJECTIVES: The authors aimed to identify distinct biomarker exposure patterns associated with different tobacco products using cluster analysis and validate this approach through longitudinal analysis of cardiovascular disease risk.
METHODS: Using the Population Assessment of Tobacco and Health data set, we performed cluster analysis and geometric mean modeling of tobacco-related biomarkers, followed by a longitudinal retrospective cohort study with Cox proportional hazard modeling used to examine associations between clusters and a primary composite outcome of heart failure, myocardial infarction, or stroke.
RESULTS: Examining 6,463 individuals, we identified 5 clusters: never users (cluster 1), predominant e-cigarette users (cluster 4), cigarette/dual users (cluster 2), and mixed tobacco users (clusters 3 and 5). All clusters showed elevated biomarkers of oxidative stress and inflammation compared to cluster 1, with clusters 2 and 3 showing the highest levels. Multivariable analysis revealed significantly higher cardiovascular disease risk in cluster 2 vs cluster 1 (HR: 2.24; 95% CI: 1.17-4.30), while other clusters showed elevated but nonsignificant risks.
CONCLUSIONS: Our categorization of exposure through cluster analysis provides a potential tool for evaluating the use of emerging tobacco products and establishing a connection between novel exposures and cardiovascular risk. This approach may contribute to the validation of a valuable tool for assessing the risk associated with the use of different tobacco products.