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挖掘数据算法以识别心血管疾病最佳人体测量预测因子的发展:MASHAD 队列研究。

Development of Data Mining Algorithms for Identifying the Best Anthropometric Predictors for Cardiovascular Disease: MASHAD Cohort Study.

机构信息

International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, 99199-91766, Iran.

Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.

出版信息

High Blood Press Cardiovasc Prev. 2023 May;30(3):243-253. doi: 10.1007/s40292-023-00577-2. Epub 2023 May 19.

DOI:10.1007/s40292-023-00577-2
PMID:37204657
Abstract

INTRODUCTION

Many studies have been published to assess the best anthropometric measurements associated with cardiovascular diseases (CVDs), but controversies still exist.

AIM

Investigating the association between CVDs and anthropometric measurements among Iranian adults.

METHODS

For a total population of 9354 aged 35 to 65, a prospective study was designed. Anthropometric measurements including ABSI (A Body Shape Index), Body Adiposity Index (BAI), Body Mass Index (BMI), Waist to Height Ratio (WHtR), Body Round Index (BRI), HC (Hip Circumference), Demispan, Mid-arm circumference (MAC), Waist-to-hip (WH) and Waist Circumference (WC) were completed. The association between these parameters and CVDs were assessed through logistic regression (LR) and decision tree (DT) models.

RESULTS

During the 6-year follow-up, 4596 individuals (49%) developed CVDs. According to the LR, age, BAI, BMI, Demispan, and BRI, in male and age, WC, BMI, and BAI in female had a significant association with CVDs (p-value < 0.03). Age and BRI for male and age and BMI for female represent the most appropriate estimates for CVDs (OR: 1.07, (95% CI: 1.06, 1.08), 1.36 (1.22, 1.51), 1.14 (1.13, 1.15), and 1.05 (1.02, 1.07), respectively). In the DT for male, those with BRI ≥ 3.87, age ≥ 46 years, and BMI ≥ 35.97 had the highest risk to develop CVDs (90%). Also, in the DT for female, those with age ≥ 54 years and WC ≥ 84 had the highest risk to develop CVDs (71%).

CONCLUSION

BRI and age in male and age and BMI in female had the greatest association with CVDs. Also, BRI and BMI was the strongest indices for this prediction.

摘要

简介

许多研究已经发表,以评估与心血管疾病(CVD)相关的最佳人体测量学指标,但仍存在争议。

目的

调查伊朗成年人 CVD 与人体测量学指标之间的关系。

方法

对 9354 名 35 至 65 岁的人群进行了一项前瞻性研究。完成了包括 ABSI(身体形状指数)、BAI(身体脂肪指数)、BMI(体重指数)、腰高比(WHtR)、BRI(身体圆形指数)、HC(臀围)、Demispan、中臂周长(MAC)、腰臀比(WH)和腰围(WC)在内的人体测量学指标。通过逻辑回归(LR)和决策树(DT)模型评估这些参数与 CVD 之间的关系。

结果

在 6 年的随访期间,4596 人(49%)发生 CVD。根据 LR,男性的年龄、BAI、BMI、Demispan 和 BRI,以及女性的年龄、WC、BMI 和 BAI 与 CVD 有显著关联(p 值<0.03)。男性的年龄和 BRI,以及女性的年龄和 BMI 是 CVD 的最佳预测指标(OR:1.07(95%CI:1.06,1.08)、1.36(1.22,1.51)、1.14(1.13,1.15)和 1.05(1.02,1.07))。在男性的 DT 中,BRI≥3.87、年龄≥46 岁和 BMI≥35.97 的人发生 CVD 的风险最高(90%)。同样,在女性的 DT 中,年龄≥54 岁和 WC≥84 的人发生 CVD 的风险最高(71%)。

结论

男性的 BRI 和年龄以及女性的年龄和 BMI 与 CVD 相关性最强。此外,BRI 和 BMI 是该预测的最强指标。

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2
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3
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