Individuals with multiple physical health conditions may be more likely to develop depression, according to a study involving over 140,000 adults aged 37 to 73 years. The findings, based on UK Biobank data, showed that specific combinations of physical illnesses—known as multimorbidity clusters—were linked to varying levels of depression risk.
Investigators analyzed medical records from 142,005 individuals with at least one long-term physical condition and no prior depression diagnoses. Over an average follow-up of nearly 7 years, 4.2% of the participants were newly diagnosed with depression.
Using clustering analysis, the investigators grouped individuals based on the presence or absence of 69 physical conditions. Eight distinct clusters emerged, including combinations of cardiovascular disease (CVD), diabetes, musculoskeletal disorders, respiratory issues, digestive conditions, and cancer. One cluster, labeled “very extensive morbidity,” included individuals with the highest number of co-occurring conditions.
This group showed the highest risk of depression. In the overall sample, individuals in the “very extensive morbidity” cluster had more than twice the risk of developing depression compared with those without physical conditions (hazard ratio [HR] = 2.42, 95% confidence interval [CI] = 2.17–2.69).
Among women, the risk was even higher (HR = 2.67, 95% CI = 2.24–3.17).
Other clusters also showed elevated risk, though to a lesser degree. For instance, the CVD [plus] diabetes cluster had an HR of 1.78 (95% CI = 1.61–1.97), whereas the mixed including cancer cluster had an HR of 1.62 (95% CI = 1.48–1.77).
“Almost all clusters show a higher association with depression than those without physical conditions,” said lead study author Lauren Nicole DeLong, of the Artificial Intelligence and its Applications Institute at the School of Informatics at the University of Edinburgh, and her colleagues.
The number of health conditions per participant was also a key factor. As the average number of conditions increased, so did the risk of depression—a pattern consistent across both men and women. However, some clusters had multiple conditions but showed only modest increases in depression risk.
To determine the most effective approach to clustering, the investigators compared four statistical methods and found that the k-modes algorithm performed best for binary health data. Analyses were adjusted for baseline age, sex, ethnicity, country of residence, and socioeconomic status.
The findings highlighted the importance of monitoring mental health in patients with multiple chronic diseases. The investigators recommend further studies to examine additional factors—such as social isolation or impaired physical function—that may contribute to depression risk in these populations.
The data set was derived from linked primary care, hospital, and registry records, offering comprehensive coverage of participants’ medical histories.
While the study may enhance the understanding of the link between physical illness and depression, the investigators noted that more research is needed to explore why specific disease combinations pose higher mental health risks.
The authors reported no competing interests.
Source: Communications Medicine