DeLong Lauren Nicole, Fleetwood Kelly, Prigge Regina, Galdi Paola, Guthrie Bruce, Fleuriot Jacques D
Artificial Intelligence and its Applications Institute, School of Informatics, University of Edinburgh, Edinburgh, UK.
Usher Institute, University of Edinburgh, Edinburgh, UK.
Commun Med (Lond). 2025 May 13;5(1):156. doi: 10.1038/s43856-025-00825-7.
Multimorbidity, the co-occurrence of two or more conditions within an individual, is a growing challenge for health and care delivery as well as for research. Combinations of physical and mental health conditions are highlighted as particularly important. Here, we investigated associations between physical multimorbidity and subsequent depression.
We performed a clustering analysis upon physical morbidity data for UK Biobank participants aged 37-73. Of 502,353 participants, 142,005 had linked general practice data with at least one baseline physical condition. Following stratification by sex (77,785 women; 64,220 men), we used four clustering methods and selected the best-performing based on clustering metrics. We used Fisher's Exact test to determine significant over-/under-representation of conditions within each cluster. Amongst people with no prior depression, we used survival analysis to estimate associations between cluster-membership and time to subsequent depression diagnosis.
Our results show that the k-modes models perform best, and the over-/under-represented conditions in the resultant clusters reflect known associations. For example, clusters containing an overrepresentation of cardiometabolic conditions are amongst the largest (15.5% of whole cohort, 19.7% of women, 24.2% of men). Cluster associations with depression vary from hazard ratio 1.29 (95% confidence interval 0.85-1.98) to 2.67 (2.24-3.17), but almost all clusters show a higher association with depression than those without physical conditions.
We show that certain groups of physical multimorbidity may be associated with a higher risk of subsequent depression. However, our findings invite further investigation into other factors, such as social considerations, which may link physical multimorbidity with depression.
多重疾病，即个体同时患有两种或更多种疾病，对医疗保健服务以及研究来说，是一个日益严峻的挑战。身体和心理健康状况的组合被特别强调为尤为重要。在此，我们调查了身体多重疾病与后续抑郁症之间的关联。
我们对英国生物银行中年龄在37 - 73岁参与者的身体发病数据进行了聚类分析。在502,353名参与者中，有142,005人的全科医疗数据与至少一种基线身体状况相关联。按性别分层（77,785名女性；64,220名男性）后，我们使用了四种聚类方法，并根据聚类指标选择了表现最佳的方法。我们使用费舍尔精确检验来确定每个聚类中疾病的显著过度/不足代表性。在没有先前抑郁症的人群中，我们使用生存分析来估计聚类成员身份与后续抑郁症诊断时间之间的关联。
我们的结果表明，k - 模式模型表现最佳，并且在所得聚类中过度/不足代表性的疾病反映了已知的关联。例如，包含心血管代谢疾病过度代表性的聚类是最大的聚类之一（占整个队列的15.5%，女性的19.7%，男性的24.2%）。聚类与抑郁症的关联从风险比1.29（95%置信区间0.85 - 1.98）到2.67（2.24 - 3.17）不等，但几乎所有聚类与抑郁症的关联都高于没有身体疾病的聚类。
我们表明，某些身体多重疾病组可能与后续抑郁症的较高风险相关联。然而，我们的研究结果促使进一步调查其他因素，如社会因素，这些因素可能将身体多重疾病与抑郁症联系起来。
