估计胰岛素耐药性的方法从非目标代谢学数据估计胰岛素耐药性
Fang-Chi Hsu1, Nicholette D Palmer2, Shyh-Huei Chen1
1Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Metabolomics : Official journal of the Metabolomic Society
|August 9, 2023
概括
开发精确的胰岛素抵抗 (IR) 测量方法对于复杂疾病至关重要. 这项研究创建了使用代谢学数据估计胰岛素敏感指数 (SI) 和HOMA-IR的模型,提供了更容易获得的方法.
科学领域:
- 代谢学 代谢学 代谢学
- 生物统计学 生物统计学
- 人类健康 人类健康 人类健康
背景情况:
- 胰岛素耐药性 (IR) 与许多复杂疾病有关.
- 目前用于测量IR的方法,如胰岛素敏感性指数 (SI) 和胰岛素耐药性的恒常模型评估 (HOMA-IR),是侵入性的,昂贵的和耗时的.
- 需要更容易获得和更有效的方法来评估IR.
研究的目的:
- 开发和验证使用代谢学数据对胰岛素抵抗 (SI和HOMA-IR) 关键指标的估计模型.
- 评估使用代谢学数据与临床因素结合用于IR估计的可行性.
- 评估这些模型在不同人群中的表现,包括墨西哥裔美国人和非洲裔美国人.
主要方法:
- 使用最小绝对收缩和选择运算符 (LASSO) 和弹性净回归.
- 整合了1274种代谢物与临床数据 (年龄,性别,BMI) 来构建估计模型.
- 对代谢物数据使用各种数据转换技术 (反向正常,标准化,Box Cox).
- 在不同的招聘站点和种族群体 (墨西哥裔美国人和非洲裔美国人) 中验证了模型.
主要成果:
- 使用代谢学和临床数据开发了SI和HOMA-IR的可靠估计模型.
- 在测试数据集中,在估计和实证SI之间实现了高相关性 (r2=0.77在墨西哥裔美国人中,r2=0.74在非洲裔美国人中).
- 在估计的SI和关键临床标志物 (如BMI,LDL胆固醇和甘油三) 之间展示了一致的关联.
结论:
- 通过使用代谢学数据成功开发了一种用于估计胰岛素耐药性的新方法.
- 开发的模型显示出强大的预测能力和临床相关性.
- 这种方法在生物医学研究和临床实践中具有广泛应用的巨大潜力,用于评估胰岛素耐药性.
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