联合多家族史和多基因评分预测重度抑郁障碍

IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY European Neuropsychopharmacology Pub Date : 2024-10-01 DOI:10.1016/j.euroneuro.2024.08.064
Rujia Wang , Helena Davies , Sanghyuck Lee , Jonathan Coleman , Raquel Iniesta , Thalia Eley , Gerome Breen
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引用次数: 0

摘要

重度抑郁障碍(MDD)是一种受遗传、社会和环境因素影响的复杂精神疾病。多发性抑郁症及相关精神障碍的家族史和多基因风险评分是预测多发性抑郁症的有力因素,而儿童创伤(CT)也起着至关重要的作用。本研究旨在联合模拟多家族史(mFH)、多PRS(mPRS)和童年创伤对 MDD 发病和 MDD 发作次数的预测作用。我们的目的是确定预测模型,以便对强化或非强化治疗计划和干预措施进行分层。数据来自美国国立卫生研究院生物资源与焦虑和抑郁的遗传联系(GLAD)研究和英国生物库(UKB)。MDD诊断遵循DSM-V标准,使用GLAD和UKB中相同的在线心理健康问卷数据。报告了多达 22 种精神疾病的家族史(是/否)。MegaPRS 用于计算基于大型全基因组关联研究的 PRS。报告的童年创伤通过 5 项童年创伤筛查问卷确定。在 GLAD(9927 例 MDD 病例,4452 例对照)中,mFH 解释了 16.85% 的 MDD 变异,其次是 CT(10.62%)、人口统计学(9.92%)和 mPRS(7.73%)。所有预测因子加在一起可解释 33.87% 的 MDD 变异,相应的接收器操作特征曲线下面积 (AUC) 为 0.84,阳性预测值 (PPV) 为 0.81。在 UKB(40667 例 MDD 病例,70755 例对照)中,mFH 可解释 13.56% 的 MDD 变异,其次是人口统计学(5.95%)、CT(5.87%)和 mPRS(3.69%)。所有预测因子加在一起可解释 23.68% 的变异(AUC=0.74,PPV=0.66)。在两个队列中,最强的个体预测因子是抑郁症家族史,其次是 CT、性别、焦虑症家族史和抑郁症 PRS。在GLAD中,MDD病例的平均发作次数≥13次,而在UKB中为1次。此外,GLAD 的平均发病年龄为 21 岁,UKB 为 33 岁。当该模型应用于其他 MDD 表型时,在 GLAD 中,所有预测因子分别占 MDD 发作次数方差的 25.80%和发病年龄方差的 8.41%,在 UKB 中分别占 11.92% 和 6.01%。该预测模型在严重MDD队列(GLAD)和基于人群的队列(UKB)中都表现良好,这表明该模型可能适用于更广泛的人群。最强的预测因素是抑郁症家族史和童年创伤,这两个因素在临床环境中都很容易测量。此外,为预测 MDD 而训练的模型对 MDD 的发作次数和发病年龄也有很强的预测作用,这表明该模型在预测 MDD 的严重程度方面非常有效。
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JOINT MULTI-FAMILY HISTORY AND MULTI-POLYGENIC SCORE PREDICTION OF MAJOR DEPRESSIVE DISORDER
Major depressive disorder (MDD) is a complex psychiatric disorder influenced by genetic, social, and environmental factors. Family history and polygenic risk scores of MDD and related psychiatric disorders are strong predictors for MDD, while childhood trauma (CT) also plays a crucial role. This study aimed to jointly model the predictive effect of multi-family history (mFH), multi-PRS (mPRS), and childhood trauma on the development of MDD and the number of MDD episodes experienced. Our aim was to identify predictive model useful for stratification to more or less intensive treatment plans and interventions.
Data were obtained from the NIHR BioResource Genetic Links to Anxiety and Depression (GLAD) study and UK Biobank (UKB). MDD diagnosis followed DSM-V criteria using the same online mental health questionnaire data in GLAD and UKB. Family history (Yes/No) was reported for up to 22 psychiatric disorders. MegaPRS was used to calculate PRSs based on large genome-wide association studies. Reported childhood trauma was identified via the5-item childhood trauma screener questionnaire. Elastic net regression with nested cross-validation was applied.
In GLAD (9,927 MDD cases, 4,452 controls), mFH explained 16.85% of MDD variance, followed by CT (10.62%), demographics (9.92%), and mPRS (7.73%). All predictors together explained 33.87% of MDD variance, with corresponding areas under the receiver operating characteristic curve (AUC) of 0.84 and a positive predictive value (PPV) of 0.81. In UKB (40,667 MDD cases, 70,755 controls), mFH explained 13.56% of MDD variance, followed by demographics (5.95%), CT (5.87%), and mPRS (3.69%). Together, all predictors explained 23.68% of variance (AUC=0.74, PPV=0.66). The strongest individual predictor in both cohorts is family history of depression, followed by CT, sex, family history of anxiety, and PRS for depression. The modal number of MDD episodes among MDD cases is ≥ 13 episodes in GLAD, compared to 1 episode in UKB. Additionally, the mean age of onset is 21 years in GLAD and 33 years in UKB. When the model was applied to other MDD phenotypes, all predictors accounted for 25.80% of the variances for the number of MDD episodes and 8.41% for age of onset in GLAD, and 11.92% and 6.01% in UKB, respectively.
Integrating multi-family history, multi-PRS, childhood trauma, and demographics enhances MDD prediction. The prediction model performs effectively in both severe MDD cohort (GLAD) and population-based cohort (UKB), suggesting its potential generalizability to broader populations. The strongest predictors are family history of depression and childhood trauma, both of which are easily measurable in clinical settings. Furthermore, the model trained for MDD prediction also proves to be a strong predictor for the number of MDD episodes and age of onset, indicating its effectiveness in predicting the severity of MDD.
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来源期刊
European Neuropsychopharmacology
European Neuropsychopharmacology 医学-精神病学
CiteScore
10.30
自引率
5.40%
发文量
730
审稿时长
41 days
期刊介绍: European Neuropsychopharmacology is the official publication of the European College of Neuropsychopharmacology (ECNP). In accordance with the mission of the College, the journal focuses on clinical and basic science contributions that advance our understanding of brain function and human behaviour and enable translation into improved treatments and enhanced public health impact in psychiatry. Recent years have been characterized by exciting advances in basic knowledge and available experimental techniques in neuroscience and genomics. However, clinical translation of these findings has not been as rapid. The journal aims to narrow this gap by promoting findings that are expected to have a major impact on both our understanding of the biological bases of mental disorders and the development and improvement of treatments, ideally paving the way for prevention and recovery.
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