发生2型糖尿病的多组预测
Julia Carrasco-Zanini1,2,3, Maik Pietzner1,2,3, Eleanor Wheeler1
1MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Institute of Metabolic Science, Cambridge, UK.
Diabetologia
|October 27, 2023
概括
结合多个omics生物标志物,比临床模型稍微改善了2型糖尿病预测. 虽然多基因风险得分有助于在正常血糖人群中进行预测,但其低绝对风险限制了预防基因查的可行性.
科学领域:
- 基因组学和个性化医学
- 发现了代谢疾病的生物标志物.
- 2型糖尿病的流行病学
背景情况:
- 识别2型糖尿病高风险个体对于人口层面的预防至关重要.
- 之前的研究已经评估了omics测量 (代谢物,蛋白质,多基因分数) 单独用于糖尿病预测.
- 与现有临床模型相比,组合的奥米克生物标志物的附加预测价值仍然不清楚.
研究的目的:
- 评估基因组,蛋白质组,代谢组和临床生物标志物的预测性能.
- 确定组合omics生物标志物是否改善了已建立的2型糖尿病临床预测模型.
主要方法:
- 一个前性,嵌套的病例和队列研究,包括1105名参与者和375例2型糖尿病病例.
- 利用最少绝对收缩和选择操作员 (LASSO) 回归来从基因组,蛋白质组,代谢组和临床生物标志物中选择特征.
- 对比了omics衍生预测因子的预测性能与临床模型,包括剑桥糖尿病风险评分和HbA1c.
主要成果:
- 单一的奥米克模型显示蛋白质是最有信息的层 (C指数=0.82),没有临床因素.
- 在2型糖尿病预测方面,最大的改善 (ΔC指数=0.05,p=0.045) 是通过将前十个特征在多个OMIC层中结合起来来实现的.
- 这种组合的奥米克方法显著改善了HbA1c <42 mmol/mol (糖尿病前期范围) 个体的预测,多基因风险评分是主要的贡献者.
结论:
- 奥米克方法在预测发生的2型糖尿病方面提供了微不足道的改善.
- 多基因风险评分提高了正常血糖症个体的预测,但显示了2型糖尿病的绝对风险较低.
- 这些发现表明,实施广泛的,针对性的人口基因查以预防2型糖尿病的可行性有限.
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