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来自设计用于预测阿片类药物使用障碍遗传风险的算法候选基因的实用性

Christal N Davis^1,2, Zeal Jinwala^1,2, Alexander S Hatoum³

¹Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania.

JAMA network open

|January 9, 2025

概括

经FDA批准的算法的遗传变异在预测阿片类药物使用障碍 (OUD) 风险方面具有有限的实用性. 这个算法就是算法.

科学领域:

药物基因组学和精准医学
计算生物学和生物信息学
临床遗传学和遗传流行病学

背景情况:

美国食品和药物管理局 (FDA) 批准了一种用于识别阿片类药物使用障碍 (OUD) 的遗传风险的算法.
这种算法中遗传变异的临床实用性缺乏独立验证.
准确识别具有OUD遗传风险的个体对于有针对性的干预至关重要.

研究的目的:

评估用于OUD风险评估的算法中15个特定遗传变异的预测效用.
确定这些遗传变异与发展阿片类药物使用障碍的风险之间的关联.

主要方法:

一个用电子健康记录从百万退伍军人计划 (n=452,664) 与阿片类药物暴露的病例控制研究.
分析了15种候选遗传变异及其与OUD的关联,通过ICD代码识别.
使用后勤回归和机器学习模型来评估预测性能.

主要成果:

15种遗传变异总体上只解释了OUD风险变化的0.40%,远远低于年龄和性别 (3.27%).
使用这些变体的整体机器学习模型在独立测试集中实现了52.83%的分类准确性.
研究样本的祖先多样化,包括欧洲,非洲和混合美国祖先的个人.

结论:

经批准的算法中的候选遗传变异不符合识别OUD风险的可接受的疗效标准.
算法的有限的预测准确性可能会导致显著的错误阳性和错误阴性临床发现.
开发更强大的和临床上有用的模型是有效预测OUD风险的必要条件.

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