识别与肉症相关的长期疾病组合:英国生物银行研究中的横截面决策树分析
Susan J Hillman1,2, Richard M Dodds1,2, Antoneta Granic1,2
1AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
BMJ open
|September 6, 2024
概括
机器学习确定了多种长期疾病 (MLTC) 的特定组合,与较高的肉类风险相关. 这种知识可以帮助针对性干预,并了解肉病的原因.
科学领域:
- 老年学和计算健康科学.
- 研究慢性疾病和与年龄有关的肌肉损失之间的相互作用.
背景情况:
- 多重长期疾病 (MLTC) 增加了sarcopenia风险,但特定的有助于疾病组合仍然不清楚.
- 标志着肌肉质量和力量损失的萨尔科佩尼亚严重影响老年人的健康和运动能力.
研究的目的:
- 采用机器学习来确定与高风险相关的长期疾病 (LTC) 的特定组合.
- 解决关于哪些LTC组合导致MLTC患者中观察到的 Sarcopenia风险增加的证据缺口.
主要方法:
- 利用决策树分析来识别英国生物银行队列中与增加的肉症风险相关的LTC组合.
- 根据握力值 (男性<32公斤,女性<19公斤) 划分了患风险的参与者.
- 来自决策树的三角化发现与逻辑回归分析进行验证.
主要成果:
- 对140,001名接受MLTC的英国生物库参与者的分析显示,有21.0%的人面临着沙科佩尼亚的风险.
- 决策树确定了与增加的肉类风险相关的显著LTC组合,包括与男性骨关节炎和结缔组织疾病与骨质疏松症相关的药物/酒精滥用.
- 特定的组合表明了显著的相对过度的相互作用风险,如药物/酒精滥用和男性骨关节炎的3.91.
结论:
- 识别与肉症风险相关的特定LTC组合可以促进有针对性的干预.
- 这项研究有助于招募参与者参加肉症研究,并增强对肉症潜在原因的理解.
- 这些发现为临床实践和未来的研究方向提供了有价值的见解,用于治疗肉类和MLTC.
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