Chronic Diseases Together Increase Risk of Depression

Different combinations of chronic physical conditions can increase the risk of developing depression. Using data from thousands of people in the UK, researchers identified groups with similar patterns of illness and found that almost all of these groups were more likely to develop depression over time. The study reinforces the importance of taking care of both physical and mental health at the same time, especially in people with multiple chronic conditions.

As the population ages, it is increasingly common for people to have two or more chronic conditions at the same time. This phenomenon is called multimorbidity. It poses a major challenge to healthcare systems, as it makes treatment more complex and requires more resources and planning.

Multimorbidity is more common in older people, women and those with fewer financial resources.

An important aspect of multimorbidity is that it increases the risk of depression. Depression affects around 6% of people worldwide and is among the most serious illnesses, according to the World Health Organization.

We already know that it usually appears alongside other mental health problems, but it is also common in people with physical illnesses, such as heart problems, multiple sclerosis and inflammatory bowel diseases.

This can happen for a variety of reasons. When someone develops a chronic illness, their life changes dramatically, which can undermine their identity and self-esteem. In addition, the illness can limit a person’s physical and social capabilities.

It is also hypothesized that certain illnesses cause depression through biological mechanisms, such as inflammation in the body, which can affect the brain.

To better understand these patterns, researchers have used statistical methods called cluster analysis, which help identify groups of illnesses that tend to appear together in the same people.

However, many traditional methods do not work well with this type of data, since it usually involves simple answers such as “does or does not” have the disease. Therefore, scientists compared four different clustering techniques that are more suitable for this type of analysis.

The study used data from the UK Biobank, a UK health database with more than 500,000 people. The researchers focused on about 142,000 participants who had at least one recorded physical condition, and were between the ages of 37 and 73.

They separated the data into 77,785 women and 64,220 men, and applied the four methods to identify which patterns of physical illnesses were most linked to the onset of depression over time. They used different methods for the analysis.

The method called “k-modes” performed best. It identified groups of physical illnesses that frequently appeared together. One of the largest groups included diseases related to the heart and metabolism, such as diabetes and hypertension, which was expected.

The k-modes method is a technique used to group categorical data, that is, information that is not continuous numbers, but rather answers such as “yes” or “no”, “has” or “does not have”.

This is especially useful in health studies, where data often indicate only the presence or absence of diseases. k-modes works in a similar way to another, better-known method, k-means, but it was adapted to deal with this type of data.

Instead of calculating averages (which does not make sense with categories), it uses the frequency of responses to form groups with similar characteristics. In the context of the study, k-modes was able to find patterns of chronic diseases that appear together in different people, helping researchers identify which groups of conditions are most associated with the development of depression.

The scientists then looked at whether people in these groups were more likely to develop depression over time. They found that nearly all groups with physical illnesses were at higher risk of depression than people who did not have any chronic conditions.

This image shows how the risk of developing depression increases as a person has more physical illnesses at the same time. The graph is divided into three parts: (a) represents all study participants, (b) shows only women, and (c) shows only men. Each blue dot represents a group of people with specific combinations of illnesses. The further to the right on the graph, the higher the average number of physical illnesses. The higher the dot, the greater the risk of these people developing depression later. The vertical lines (error bars) around each dot indicate the confidence interval, that is, the safety margin for calculating the risk. These results help to understand which combinations of illnesses are most linked to depression.

Finally, the authors conclude that certain combinations of physical illnesses increase the risk of depression, and that this needs to be studied further. In addition to biological factors, they suggest that social and emotional aspects, such as family support, access to health care, and financial stability, may also play an important role in this relationship between physical illnesses and mental health problems.

READ MORE:

Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depression

Lauren Nicole DeLong, Kelly Fleetwood, Regina Prigge, Paola Galdi, Bruce Guthrie, and Jacques D. Fleuriot 

Nature Commun Med 5, 156 (2025). 

https://doi.org/10.1038/s43856-025-00825-7

Abstract:

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.