A pioneering new way of analysing UK Biobank data has helped researchers to reveal the relationship between depression and physical disorders.
Depression commonly occurs in people with physical disorders such as migraine or diabetes. Such examples of disease co-morbidity are generally seen as an understandable reaction to physical symptoms such as pain. However, some physical disorders may be co-morbid with depression because they share similar underlying risk factors with depression such as similar genetic make up, diet or environmental stress. An unknown proportion of the many co-morbidities among physical disorders may also have risk factors in common. Unfortunately standard statistical and epidemiological methods are not able to separate direct comorbidities that may share underlying mechanisms from apparent co-morbidities that arise from indirect ‘mediated’ connections with a third or more distantly related disorders.
Now a collaboration between biostatisticians and clinical researchers in Hungary and the UK have developed a new statistical method that filters out the indirect mediated co-mobidities. Applying the method to 247 diseases of 117,392 participants in the UK Biobank, they have published a multimorbidity map showing the directly related disorders.
The multimorbidity map shows that the well-known psychiatric comorbidities of depression appear to be direct (anxiety, stress, nervous breakdown, postnatal depression etc). Furthermore, the association of several physical disorders such as cardiovascular diseases with depression appears to be indirectly mediated through a direct co-morbidity between obesity and depression.
The Bayesian direct multimorbidity mapping method was developed by the Department of Measurement and Information Systems at Budapest University of Technology and Economics, and applied to the UK Biobank dataset in collaboration with MTA-SE-NAP B Genetic Brain Imaging Migraine Research Group and MTA-SE Neuropsychopharmacology and Neurochemistry Research Group at Semmelweis University together with the Neuroscience and Psychiatry Unit at The University of Manchester.
Gabriella Juhasz, Docent of the Semmelweis University’s Department of Pharmacodynamics, and Senior Clinical Research Fellow at The University of Manchester said:
“Our findings highlight the different pathways that can lead to depression, indicated by different comorbid conditions, and demonstrate how our method may contribute to the understanding of the pathobiology of common complex multifactorial disorders, such as depression.”
“In the future, using the advantages of probabilistic graphical modelling, we will investigate the relations between depression, environmental factors, lifestyle data, and genetic factors with the ultimate aim to improve disease prevention and enhance precision medicine.”
The work – published in PLOS Computational Biology – was carried out in collaboration of the Semmelweis University (Budapest, Hungary), University of Technology and Economics (Budapest, Hungary) and The University of Manchester (Manchester, UK):