BACKGROUND Persons with multiple sclerosis (PwMS) are disproportionately burdened by depression compared to the general population. While several factors associated with depression and depression severity in PwMS have been identified, a prediction model for depression risk has not been developed. In addition, it is unknown if depression-related genetic variants, including Apolipoprotein E (APOE), would be informative for predicting depression in PwMS. OBJECTIVE To develop a depression prediction model for PwMS who did not have a history of depression prior MS onset. METHODS The study population included 917 non-Hispanic white PwMS. An optimized multivariable Cox proportional hazards model for time to depression was generated using non-genetic variables, to which APOE and a depression-related genetic risk score were included. RESULTS Having a mother who had a history of depression, having obstructive pulmonary disease, obesity and other physical disorders at MS onset, and affect-related symptoms at MS onset predicted depression risk (hazards ratios (HRs): 1.6-2.3). Genetic variables improved the prediction model's performance. APOE ε4/ε4 and ε2/x conferred increased (HR = 2.5, p = 0.026) and decreased (HR = 0.65, p = 0.046) depression risk, respectively. CONCLUSION We present a prediction model aligned with The Precision Medicine Initiative, which integrates genetic and non-genetic predictors to inform depression risk stratification after MS onset.

中文翻译：

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使用遗传和非遗传因素预测多发性硬化症发病后自我报告的抑郁症

背景与一般人群相比，患有多发性硬化症（PwMS）的人不成比例地受到抑郁症的负担。虽然已经确定了与 PwMS 中的抑郁症和抑郁症严重程度相关的几个因素，但尚未开发出抑郁症风险的预测模型。此外，尚不清楚与抑郁症相关的遗传变异，包括载脂蛋白 E (APOE)，是否对预测 PwMS 中的抑郁症有帮助。目的 为在 MS 发病前没有抑郁病史的 PwMS 开发抑郁预测模型。方法 研究人群包括 917 名非西班牙裔白人 PwMS。使用非遗传变量生成抑郁时间的优化多变量 Cox 比例风险模型，其中包括 APOE 和抑郁相关遗传风险评分。结果 母亲有抑郁症史、阻塞性肺病、肥胖和其他 MS 发病时的身体疾病，以及 MS 发病时的情感相关症状可预测抑郁风险（风险比 (HR)：1.6-2.3）。遗传变量提高了预测模型的性能。APOE ε4/ε4 和 ε2/x 分别增加（HR = 2.5，p = 0.026）和降低（HR = 0.65，p = 0.046）抑郁风险。结论 我们提出了一个与精准医学计划相一致的预测模型，该模型整合了遗传和非遗传预测因素，以告知 MS 发病后的抑郁风险分层。遗传变量提高了预测模型的性能。APOE ε4/ε4 和 ε2/x 分别增加（HR = 2.5，p = 0.026）和降低（HR = 0.65，p = 0.046）抑郁风险。结论 我们提出了一个与精准医学计划相一致的预测模型，该模型整合了遗传和非遗传预测因素，以告知 MS 发病后的抑郁风险分层。遗传变量提高了预测模型的性能。APOE ε4/ε4 和 ε2/x 分别增加（HR = 2.5，p = 0.026）和降低（HR = 0.65，p = 0.046）抑郁风险。结论 我们提出了一个与精准医学计划相一致的预测模型，该模型整合了遗传和非遗传预测因素，以告知 MS 发病后的抑郁风险分层。