新型肥胖子组及其与结果的关联:数据驱动的集群分析
Saki Takeshita1,2, Yuichi Nishioka1,2, Yuko Tamaki2
1Department of Public Health, Health Management and Policy, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan.
BMC public health
|January 9, 2024
概括
这项研究使用集群分析确定了七个不同的肥胖子组,揭示了干预措施的具体目标和代谢健康的肥胖群体. 这些发现有助于个性化肥胖管理和改善患者的治疗结果.
科学领域:
- 代谢和腹部手术 代谢和腹部手术
- 临床流行病学临床流行病学
- 公共卫生 公共卫生
背景情况:
- 肥胖会带来严重的健康风险和各种各样的并发症,但其子组仍然没有定义.
- 了解肥胖异质性对于有效的干预策略至关重要.
- 之前的研究还没有明确划分明显的肥胖子组.
研究的目的:
- 通过集群分析识别肥胖个体的不同子组.
- 根据已识别的肥胖子组,定义临床干预的目标.
- 描述每个子组内的并发症和预后的流行情况.
主要方法:
- 无监督的数据驱动集群分析应用于9,494名肥胖个体 (BMI ≥35 kg/m2).
- 利用来自医疗保健索赔的数据,包括ICD-10代码,药物处方和医疗程序.
- 综合症 (例如糖尿病),脏和终身预后的患病率在各个集群中进行了评估.
主要成果:
- 确定了七个不同的肥胖子组,每个都有独特的并发症概况.
- 某些并发症仅限于特定的子组.
- 患有心血管并发症和糖尿病的个体以及不经常与医疗保健互动的人群的预后最差.
结论:
- 以人口为基础的,数据驱动的集群分析成功地确定了7个肥胖个体的子组.
- 显然表明了干预的不同目标子组.
- 确定了一个代谢健康的肥胖子组,为有针对性的管理策略提供了潜力.
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