Integrating Electronic Health Records and Polygenic Risk to Identify Genetically Unrelated Comorbidities of Schizophrenia That May Be Modifiable

IF 4 Q2 NEUROSCIENCES
Tess Vessels , Nicholas Strayer , Hyunjoon Lee , Karmel W. Choi , Siwei Zhang , Lide Han , Theodore J. Morley , Jordan W. Smoller , Yaomin Xu , Douglas M. Ruderfer
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引用次数: 0

Abstract

Background

Patients with schizophrenia have substantial comorbidity that contributes to reduced life expectancy of 10 to 20 years. Identifying modifiable comorbidities could improve rates of premature mortality. Conditions that frequently co-occur but lack shared genetic risk with schizophrenia are more likely to be products of treatment, behavior, or environmental factors and therefore are enriched for potentially modifiable associations.

Methods

Phenome-wide comorbidity was calculated from electronic health records of 250,000 patients across 2 independent health care institutions (Vanderbilt University Medical Center and Mass General Brigham); associations with schizophrenia polygenic risk scores were calculated across the same phenotypes in linked biobanks.

Results

Schizophrenia comorbidity was significantly correlated across institutions (r = 0.85), and the 77 identified comorbidities were consistent with prior literature. Overall, comorbidity and polygenic risk score associations were significantly correlated (r = 0.55, p = 1.29 × 10−118). However, directly testing for the absence of genetic effects identified 36 comorbidities that had significantly equivalent schizophrenia polygenic risk score distributions between cases and controls. This set included phenotypes known to be consequences of antipsychotic medications (e.g., movement disorders) or of the disease such as reduced hygiene (e.g., diseases of the nail), thereby validating the approach. It also highlighted phenotypes with less clear causal relationships and minimal genetic effects such as tobacco use disorder and diabetes.

Conclusions

This work demonstrates the consistency and robustness of electronic health record–based schizophrenia comorbidities across independent institutions and with the existing literature. It identifies known and novel comorbidities with an absence of shared genetic risk, indicating other causes that may be modifiable and where further study of causal pathways could improve outcomes for patients.

整合电子健康记录和多基因风险,识别可能可以改变的与基因无关的精神分裂症并发症
背景精神分裂症患者有很多合并症,导致预期寿命缩短 10 到 20 年。确定可改变的并发症可以提高过早死亡率。经常与精神分裂症并发但缺乏共同遗传风险的疾病更有可能是治疗、行为或环境因素的产物,因此可以丰富潜在的可改变关联。方法根据两家独立医疗机构(范德比尔特大学医学中心和麻省总医院)25万名患者的电子健康记录计算出全表型的合并症;根据链接生物库中的相同表型计算出与精神分裂症多基因风险评分的关联。总体而言,合并症与多基因风险评分之间存在明显的相关性(r = 0.55,p = 1.29 × 10-118)。然而,通过直接检测是否存在遗传效应,发现有 36 种合并症在病例和对照组之间的精神分裂症多基因风险评分分布明显相当。这组疾病包括已知由抗精神病药物(如运动障碍)或疾病(如指甲疾病)引起的表型,从而验证了该方法的有效性。结论这项工作证明了基于电子健康记录的精神分裂症合并症在各独立机构和现有文献中的一致性和稳健性。它确定了缺乏共同遗传风险的已知和新型合并症,指出了可能可以改变的其他原因,进一步研究这些原因的途径可以改善患者的预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biological psychiatry global open science
Biological psychiatry global open science Psychiatry and Mental Health
CiteScore
4.00
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