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

Abstract 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 . 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, 1 st follow-up (n= 20,257) and 1 st 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. Summary Box What is already known about this subject? 3-4 bullet points Poor mental health is associated with cognitive deficits. One in four older adults experience a decline in affective state with increasing age. ML approaches have certain advantages in identifying patterns of information useful for the prediction of an outcome. What are the new findings? 3-4 bullet points Psychiatric disorders are important comorbid disorders of long-term cognitive change. Machine-learning methods such as sequence learning based methods are able to offer non-parametric joint modelling, allow for multiplicity of factors and provide prediction models that are more robust and accurate for longitudinal data The outcome of the RNN analysis found that anxiety and depression were stronger predictors of change IIV over time than either cardiovascular disease and diabetes or the covariate variables. 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