Psychiatric comorbid disorders of cognition: A machine learning approach using 1,175 UK Biobank participants

Background Conceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as ‘chronic’ and, although they may be pathologically related, they may also act independently[1][1]. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition. Objectives To examine whether anxiety and/or depression are important longitudinal predictors of cognitive change. Methods UK Biobank participants used at three time points (n= 502,664): baseline, 1st follow-up (n= 20,257) and 1st imaging study (n=40,199). Participants with no missing data were 1,175 participants aged 40 to 70 years, 41% female. Machine learning (ML) was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used. Findings Using the area under the Receiver Operating Characteristic (ROC) curve, the anxiety model achieves the best performance with an Area Under the Curve (AUC) of 0.68, followed by the depression model with an AUC of 0.63. The cardiovascular and diabetes model, and the covariates model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.56, respectively. Conclusions Outcomes suggest psychiatric disorders are more important comorbidities of long-term cognitive change than diabetes and cardiovascular disease, and demographic factors. Findings suggest that psychiatric disorders (anxiety and depression) may have a deleterious effect on long-term cognition and should be considered as an important comorbid disorder of cognitive decline. Clinical implications Important predictive effects of poor mental health on longitudinal cognitive decline should be considered in secondary and also primary care. What is already known about this subject? 3-4 bullet points What are the new findings? 3-4 bullet points How might it impact on clinical practice in the foreseeable future? The important predictive effect of mental health on longitudinal cognition should be noted and, its comorbidity relationship with other conditions such as cardiovascular disease likewise to be considered in primary care and other clinical settings ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This is a DPUK supported project with all analyses conducted on the DPUK Data Portal, constituting part 1 of DPUK Application 0132. The Medical Research Council supports DPUK through grant MR/L023784/2 ### Author Declarations All relevant ethical guidelines have been followed and any necessary IRB and/or ethics committee approvals have been obtained. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes Any clinical trials involved have been registered with an ICMJE-approved registry such as ClinicalTrials.gov and the trial ID is included in the manuscript. Not Applicable I have followed all appropriate research reporting guidelines and uploaded the relevant Equator, ICMJE or other checklist(s) as supplementary files, if applicable. Not Applicable All data is available on the Dementias Platform UK Data Portal after permission from UK Biobank [1]: #ref-1