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Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants.
BMJ Mental Health ( IF 5.2 ) Pub Date : 2020-11-01 , DOI: 10.1136/ebmental-2020-300147
Chenlu Li 1 , Delia A Gheorghe 2 , John E Gallacher 2 , Sarah Bauermeister 3
Affiliation  

Background Conceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as ‘chronic’ and, although they may be pathologically related, they may also act independently. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition. Objectives To examine whether anxiety and/or depression are/is important longitudinal predictors of cognitive change. Methods UK Biobank participants used at three time points (n=502 664): baseline, first follow-up (n=20 257) and first imaging study (n=40 199). Participants with no missing data were 1175 participants aged 40–70 years, 41% women. Machine learning was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used. Findings Using the area under the receiver operating characteristic curve, the anxiety model achieves the best performance with an area under the curve (AUC) of 0.68, followed by the depression model with an AUC of 0.63. The cardiovascular and diabetes model, and the covariates model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.56, respectively. Conclusions Outcomes suggest that psychiatric disorders are more important comorbidities of long-term cognitive change than diabetes and cardiovascular disease, and demographic factors. Findings suggest that psychiatric disorders (anxiety and depression) may have a deleterious effect on long-term cognition and should be considered as an important comorbid disorder of cognitive decline. Clinical implications Important predictive effects of poor mental health on longitudinal cognitive decline should be considered in secondary and also primary care.

中文翻译:

精神病共病认知障碍:使用 1175 名英国生物银行参与者的机器学习方法。

背景 对合并症进行概念化是复杂的,并且该术语的使用方式多种多样。在这里,两种或多种诊断的共存可能被定义为“慢性”,尽管它们可能在病理上相关,但它们也可能独立起作用。这里有趣的是常见精神疾病和认知障碍的合并症。目的 研究焦虑和/或抑郁是否是/是认知变化的重要纵向预测因素。UK Biobank 参与者在三个时间点 (n=502 664) 使用的方法:基线、首次随访 (n=20 257) 和首次影像学研究 (n=40 199)。没有缺失数据的参与者是 1175 名年龄在 40-70 岁之间的参与者,其中 41% 是女性。应用机器学习,并使用反应时间的个体差异(认知)的主要结果测量。结果 使用受试者工作特征曲线下面积,焦虑模型的曲线下面积 (AUC) 为 0.68,表现最佳,其次是抑郁模型,AUC 为 0.63。心血管和糖尿病模型以及协变量模型在预测认知方面的表现较弱,AUC 分别为 0.60 和 0.56。结论 结果表明,与糖尿病和心血管疾病以及人口因素相比,精神疾病是更重要的长期认知改变的合并症。研究结果表明,精神疾病(焦虑和抑郁)可能对长期认知产生有害影响,应被视为认知能力下降的重要共病。
更新日期:2020-10-30
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