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.

IF 5.4 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM Diabetes, Obesity & Metabolism Pub Date : 2024-09-06 DOI:10.1111/dom.15927
Han Zhou, Chen Yang, Jingjie Li, Lin Sun
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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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肥胖和非肥胖人群中 N 端前 B 型钠尿肽与全因死亡率和心血管死亡率的关系以及机器学习预测模型的开发:国家健康与营养调查(NHANES)1999-2004。
目的:探讨 N-末端前 B 型钠尿肽(NTproBNP)在识别肥胖人群不良结局(尤其是心血管不良结局)方面的潜力,并建立风险预测模型:本研究的数据来自美国国家健康与营养调查(NHANES),涉及 6772 名无心力衰竭的参与者,时间跨度为 1999 年至 2004 年。采用多变量 Cox 回归模型、三次样条曲线限制模型和 Kaplan-Meier 曲线来评估 NTproBNP 与全因死亡率和心血管死亡率之间的关系。使用七种机器学习方法建立了预测模型,并使用精确度、召回率、F1得分、准确度和曲线下面积(AUC)值进行评估:在人群随访期间,6772 名参与者中有 1554 人死亡,其中 365 人死于心血管疾病。调整相关协变量后,NTproBNP水平≥300 pg/mL与全因死亡率(危险比[HR]3.00,95%置信区间[CI]2.48,3.67)和心血管死亡率(HR 6.05,95%置信区间[CI]3.67,9.97)呈正相关,且在不同体重指数(BMI)分层中仍具有显著性。然而,在没有腹部肥胖的参与者中,NTproBNP 与心血管死亡率之间的相关性显著降低。在七种机器学习方法中,逻辑回归对全因死亡率(AUC 0.86925)和心血管死亡率(AUC 0.85115)的预测效果更好。然而,为非腹型肥胖者建立准确的心血管死亡率预测模型具有挑战性:研究表明,NTproBNP 可作为不同体重指数(包括肥胖者)的全因死亡率和心血管死亡率的预测因子。然而,仅在 NTproBNP 水平≥300 pg/mL 且腹型肥胖的参与者中观察到了明显的心血管死亡率相关性。
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来源期刊
Diabetes, Obesity & Metabolism
Diabetes, Obesity & Metabolism 医学-内分泌学与代谢
CiteScore
10.90
自引率
6.90%
发文量
319
审稿时长
3-8 weeks
期刊介绍: Diabetes, Obesity and Metabolism is primarily a journal of clinical and experimental pharmacology and therapeutics covering the interrelated areas of diabetes, obesity and metabolism. The journal prioritises high-quality original research that reports on the effects of new or existing therapies, including dietary, exercise and lifestyle (non-pharmacological) interventions, in any aspect of metabolic and endocrine disease, either in humans or animal and cellular systems. ‘Metabolism’ may relate to lipids, bone and drug metabolism, or broader aspects of endocrine dysfunction. Preclinical pharmacology, pharmacokinetic studies, meta-analyses and those addressing drug safety and tolerability are also highly suitable for publication in this journal. Original research may be published as a main paper or as a research letter.
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