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Association of N‐terminal pro‐B natriuretic peptide with all‐cause mortality and cardiovascular mortality in obese and non‐obese populations and the development of a machine learning prediction model: National Health and Nutrition Examination Survey (NHANES) 1999–2004

医学 危险系数 体质指数 全国健康与营养检查调查 内科学 比例危险模型 逻辑回归 人口 置信区间 肥胖 环境卫生
作者
Han Zhou,Chen Yang,J.N. Li,Lin Sun
出处
期刊:Diabetes, Obesity and Metabolism [Wiley]
卷期号:26 (12): 5609-5620 被引量:2
标识
DOI:10.1111/dom.15927
摘要

Abstract Aims To explore the potential of N‐terminal pro‐B natriuretic peptide (NTproBNP) in identifying adverse outcomes, particularly cardiovascular adverse outcomes, in a population with obesity, and to establish a risk prediction model. Methods The data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES) for 6772 participants without heart failure, for the years 1999 to 2004. Multivariable Cox regression models, cubic spline restricted models and Kaplan–Meier curves were used to evaluate the relationship between NTproBNP and both all‐cause mortality and cardiovascular mortality. Predictive models were established using seven machine learning methods, and evaluation was conducted using precision, recall, F1 score, accuracy, and area under the curve (AUC) values. Results During the population follow‐up, out of 6772 participants, 1554 died, with 365 deaths attributed to cardiovascular disease. After adjusting for relevant covariates, NTproBNP levels ≥300 pg/mL were positively associated with both all‐cause mortality (hazard ratio [HR] 3.00, 95% confidence interval [CI] 2.48, 3.67) and cardiovascular mortality (HR 6.05, 95% CI 3.67, 9.97), and remained significant across different body mass index (BMI) strata. However, in participants without abdominal obesity, the correlation between NTproBNP and cardiovascular mortality was significantly reduced. Among the seven machine learning methods, logistic regression demonstrated better predictive performance for both all‐cause mortality (AUC 0.86925) and cardiovascular mortality (AUC 0.85115). However, establishing accurate cardiovascular mortality prediction models for non‐abdominal obese individuals proved challenging. Conclusion The study showed that NTproBNP can serve as a predictive factor for all‐cause mortality and cardiovascular mortality in individuals with different BMIs, including obese individuals. However, significant cardiovascular mortality correlation was observed only for NTproBNP levels ≥300 pg/mL, and only among participants with abdominal obesity.
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