Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample

Abstract

Background: Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research was to combine machine-learning (ML) algorithms with traditional regression techniques by utilising self-reported medical symptoms to identify and describe clusters of individuals with increased rates of depression from a large cross-sectional community based population epidemiological study.

Methods: A multi-staged methodology utilising ML and traditional statistical techniques was performed using the community based population National Health and Nutrition Examination Study (2009-2010) (N = 3,922). A Self-organised Mapping (SOM) ML algorithm, combined with hierarchical clustering, was performed to create participant clusters based on 68 medical symptoms. Binary logistic regression, controlling for sociodemographic confounders, was used to then identify the key clusters of participants with higher levels of depression (PHQ-9≥10, n = 377). Finally, a Multiple Additive Regression Tree boosted ML algorithm was run to identify the important medical symptoms for each key cluster within 17 broad categories: heart, liver, thyroid, respiratory, diabetes, arthritis, fractures and osteoporosis, skeletal pain, blood pressure, blood transfusion, cholesterol, vision, hearing, psoriasis, weight, bowels and urinary.

Results: Five clusters of participants, based on medical symptoms, were identified to have significantly increased rates of depression compared to the cluster with the lowest rate: odds ratios ranged from 2.24 (95% CI 1.56, 3.24) to 6.33 (95% CI 1.67, 24.02). The ML boosted regression algorithm identified three key medical condition categories as being significantly more common in these clusters: bowel, pain and urinary symptoms. Bowel-related symptoms was found to dominate the relative importance of symptoms within the five key clusters.

Conclusion: This methodology shows promise for the identification of conditions in general populations and supports the current focus on the potential importance of bowel symptoms and the gut in mental health research.

Fig 1. Flowchart of Methods, Testing and Results.

Fig 2. Training progress and SOM plots.

Note: The “Training progress” graph indicates as the SOM training iterations distance from each node's weights to the samples represented by that node reduces and plateaus to indicate no more iterations were required. The “Counts plots” indicates reasonable samples were mapped to each node on the map. The “Neighbour distance plot” or U-Matrix indicates the distance between each node and its neighbours.

Fig 3. Hierarchical Cluster Options for SOM.

Note: Clusters 3 to 12 solutions mapped onto the SOM grid. Colours indicate different clusters. The final 10 cluster solution selected for further analysis has been highlighted with a red border.

Fig 4. Mean depression scores and percent depression across final depression clusters.

Note: “Mean Depression Score” is the average total PHQ-9 score which ranged from 0 to 27. “Percent Depressed” based on a total PHQ-9 ≥ 10.

Fig 5. Predicted probability of depression across age and family income poverty ratio for each cluster.

Fig 6. Importance of medical categories that make up the key significant clusters.

Note: Based on total boosted relative importance percentage across all clusters. Summed percentage from boosted regression across all five key significant clusters, thus total >100%.

Fig 7. Total percentage importance of medical conditions for each key significant cluster.

Note: Clusters presented in order of percent depressed. Note: Percentage sum does not take account of direction of relationship.