Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights

被引:15
作者
Tang, Alice S. [1 ,2 ,3 ]
Rankin, Katherine P. [1 ,4 ]
Cerono, Gabriel [5 ]
Miramontes, Silvia [1 ]
Mills, Hunter [1 ]
Roger, Jacquelyn [1 ]
Zeng, Billy [1 ]
Nelson, Charlotte [5 ]
Soman, Karthik [5 ]
Woldemariam, Sarah [1 ]
Li, Yaqiao [1 ]
Lee, Albert [1 ]
Bove, Riley [5 ]
Glymour, Maria [6 ]
Aghaeepour, Nima [6 ,7 ,8 ]
Oskotsky, Tomiko T. [1 ]
Miller, Zachary [4 ]
Allen, Isabel E. [9 ]
Sanders, Stephan J. [1 ,10 ,11 ]
Baranzini, Sergio [5 ]
Sirota, Marina [1 ,12 ]
机构
[1] Univ Calif San Francisco, Bakar Computat Hlth Sci Inst, San Francisco, CA 94143 USA
[2] Univ Calif San Francisco, Grad Program Bioengn, San Francisco, CA 94143 USA
[3] Univ Calif Berkeley, Berkeley, CA 94720 USA
[4] Univ Calif San Francisco, Dept Neurol, Memory & Aging Ctr, San Francisco, CA USA
[5] Univ Calif San Francisco, Dept Neurol, Weill Inst Neurosci, San Francisco, CA USA
[6] Stanford Univ, Dept Anesthesiol Pain & Perioperat Med, Palo Alto, CA USA
[7] Stanford Univ, Dept Pediat, Palo Alto, CA USA
[8] Stanford Univ, Dept Biomed Data Sci, Palo Alto, CA USA
[9] Univ Calif San Francisco, Dept Epidemiol & Biostat, San Francisco, CA USA
[10] Univ Oxford, Inst Dev & Regenerat Med, Dept Paediat, Oxford, England
[11] Univ Calif San Francisco, Weill Inst Neurosci, Dept Psychiat & Behav Sci, San Francisco, CA USA
[12] Univ Calif San Francisco, Dept Pediat, San Francisco, CA 94143 USA
来源
NATURE AGING | 2024年 / 4卷 / 03期
基金
美国国家卫生研究院;
关键词
DEPRESSIVE SYMPTOMS; INSULIN-RESISTANCE; APOLIPOPROTEIN-E; RISK; DEMENTIA; ASSOCIATION; HYPERTENSION; POPULATION; METABOLISM; FRAMEWORK;
D O I
10.1038/s43587-024-00573-8
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses. Identifying individuals at risk of developing Alzheimer's disease is an important task. Here Tang et al. leverage electronic health records to predict Alzheimer's disease onset, and utilize knowledge networks to prioritize shared genes behind the clinical data as well as facilitate contextualization based on sex.
引用
收藏
页码:379 / 395
页数:29
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