与传统肥胖指数相比,基于数据的现象映射用于心血管结果的新型分类:德黑兰脂质和葡萄糖研究
Elahe Zare Borzeshi1, Majid Valizadeh2, Mohammad Hossein Panahi1
1Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
BMJ open
|June 14, 2023
概括
一个新的数据驱动的框架,现象映射,改善了心血管风险分类,超出了传统的肥胖和代谢综合征指标. 这种方法用于确定有针对性的预防和干预策略的不同人口子组.
科学领域:
- 心血管疾病的研究研究.
- 在医疗保健中的数据科学.
- 代谢综合征和肥胖研究的研究.
背景情况:
- 心血管结果受到肥胖和代谢综合征的显著影响.
- 传统的风险分层方法可能无法完全捕捉人口异质性.
- 需要更精细的分类框架来改善个性化的心血管风险评估.
研究的目的:
- 提出一个数据驱动的框架,用于对心血管疾病风险的个体进行分类.
- 整合肥胖症和代谢综合征数据,以提高风险分层.
- 为改善心血管疾病预防开发新的内分类型.
主要方法:
- 利用了来自德黑兰脂质和葡萄糖研究 (TLGS) 队列 (n=12,808) 的数据,随访时间超过15年.
- 采用特征工程和层次聚类来识别不同的人口群和内基类型.
- 应用考克斯回归来验证现象映射,并使用Akaike/贝叶斯信息标准比较性能.
主要成果:
- 确定了八种不同的内分类型,具有显著不同的临床特征和心血管结果.
- 低风险集群与高风险集群在多个人口,临床和代谢参数上有所不同.
- 现象映射揭示了新的分类,与传统方法相比,更好地分层个人.
结论:
- 现象映射为心血管结果提供了一个新的分类,使得更好的分层成同质的子类.
- 这种数据驱动的方法是传统方法的替代方案,仅基于肥胖或代谢状态.
- 这些发现对中东人口具有重大临床影响,解决了西方衍生风险评估工具的局限性.
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