Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression
Fig 2
A matrix, tree and network view of comorbid relations.
Sparsity and correspondence of pairwise associative measure of comorbidity and co=morbidity posteriors of the Bayesian direct multimorbidity maps (BDMM) using three subsets (clusters) of disorders, namely metabolic syndromes (red), diseases of the nervous system (blue) and mental and behavioural disorders (green), reported in the UK Biobank dataset. Top figure a. shows the p-values of the comorbidity associations by χ2 test in purple as pairwise statistical associations, while the posterior probabilities of co=morbidities derived from the BDMM are in gold below the gray diagonal. Middle figure b. as intermediate step towards structural dependencies represents the hierarchical clustering of diseases based on the pairwise associations (χ2 p-values as distances are used by the Ward method to compute a hierarchical clustering) resulting three main clusters, which follows the expected disease groups. Bottom figure c. represents the disease networks, where the gold edges show the sparse co=morbidities in BDMM while the purple dashed lines show indirect links defined by pairwise methods. We used the following abbreviations for the disease names: ANXIETY: anxiety/panic attacks, CTS: carpal tunnel syndrome, CFS: chronic fatigue syndrome, DIAB EYE: diabetic eye disease, HEADACHES NM: headaches (not migraine), HEAD INJ: head injury, HIGH CHOL: high cholesterol, BD: mania/bipolar disorder/manic depression, MS: multiple sclerosis, NERVOUS BREAK: nervous breakdown, ONP: other neurological problem, PD: Parkinson’s disease, PN: peripheral neuropathy, POLIO: polio/poliomyelitis, PN DEP: post-natal depression, TGN: trigeminal neuralgia.