Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depression
Abstract Background 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 as...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-00825-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850269136045735936 |
|---|---|
| author | Lauren Nicole DeLong Kelly Fleetwood Regina Prigge Paola Galdi Bruce Guthrie Jacques D. Fleuriot |
| author_facet | Lauren Nicole DeLong Kelly Fleetwood Regina Prigge Paola Galdi Bruce Guthrie Jacques D. Fleuriot |
| author_sort | Lauren Nicole DeLong |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results 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. Conclusions 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. |
| format | Article |
| id | doaj-art-2032cebe3a8f4fefb679f4a8d576537b |
| institution | OA Journals |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-2032cebe3a8f4fefb679f4a8d576537b2025-08-20T01:53:15ZengNature PortfolioCommunications Medicine2730-664X2025-05-01511910.1038/s43856-025-00825-7Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depressionLauren Nicole DeLong0Kelly Fleetwood1Regina Prigge2Paola Galdi3Bruce Guthrie4Jacques D. Fleuriot5Artificial Intelligence and its Applications Institute, School of Informatics, University of EdinburghUsher Institute, University of EdinburghUsher Institute, University of EdinburghArtificial Intelligence and its Applications Institute, School of Informatics, University of EdinburghAdvanced Care Research Centre, Usher Institute, University of EdinburghArtificial Intelligence and its Applications Institute, School of Informatics, University of EdinburghAbstract Background 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. Methods 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. Results 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. Conclusions 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.https://doi.org/10.1038/s43856-025-00825-7 |
| spellingShingle | Lauren Nicole DeLong Kelly Fleetwood Regina Prigge Paola Galdi Bruce Guthrie Jacques D. Fleuriot Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depression Communications Medicine |
| title | Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depression |
| title_full | Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depression |
| title_fullStr | Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depression |
| title_full_unstemmed | Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depression |
| title_short | Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depression |
| title_sort | cluster and survival analysis of uk biobank data reveals associations between physical multimorbidity clusters and subsequent depression |
| url | https://doi.org/10.1038/s43856-025-00825-7 |
| work_keys_str_mv | AT laurennicoledelong clusterandsurvivalanalysisofukbiobankdatarevealsassociationsbetweenphysicalmultimorbidityclustersandsubsequentdepression AT kellyfleetwood clusterandsurvivalanalysisofukbiobankdatarevealsassociationsbetweenphysicalmultimorbidityclustersandsubsequentdepression AT reginaprigge clusterandsurvivalanalysisofukbiobankdatarevealsassociationsbetweenphysicalmultimorbidityclustersandsubsequentdepression AT paolagaldi clusterandsurvivalanalysisofukbiobankdatarevealsassociationsbetweenphysicalmultimorbidityclustersandsubsequentdepression AT bruceguthrie clusterandsurvivalanalysisofukbiobankdatarevealsassociationsbetweenphysicalmultimorbidityclustersandsubsequentdepression AT jacquesdfleuriot clusterandsurvivalanalysisofukbiobankdatarevealsassociationsbetweenphysicalmultimorbidityclustersandsubsequentdepression |