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Digital mental health
Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants
  1. Chenlu Li1,
  2. Delia A Gheorghe2,
  3. John E Gallacher2,
  4. Sarah Bauermeister2
  1. 1Department Risk Advisory, Deloitte LLP, London, UK
  2. 2Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK
  1. Correspondence to Dr Sarah Bauermeister, Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK; sarah.bauermeister{at}psych.ox.ac.uk

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. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition.

Objectives To examine whether anxiety and/or depression are/is important longitudinal predictors of cognitive change.

Methods UK Biobank participants used at three time points (n=502 664): baseline, first follow-up (n=20 257) and first imaging study (n=40 199). Participants with no missing data were 1175 participants aged 40–70 years, 41% women. Machine learning was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used.

Findings Using the area under the receiver operating characteristic 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 that 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.

  • psychiatry
  • anxiety disorders
  • depression & mood disorders
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Footnotes

  • Twitter @S_Bauermeister

  • Contributors CL, JEG and SDB conceptualised the idea. CL analysed the data and wrote the first draft of the manuscript. All authors interpreted the data. DG and SB commented, edited and proofread the manuscript. All authors read and approved the final manuscript.

  • Funding This is a DPUK supported project, all analyses were conducted on the DPUK Data Portal, constituting part 1 of DPUK Application 0132. The Medical Research Council supports DPUK through grant MR/L023784/2. CL was funded by DPUK to complete this analysis.

  • Competing interests No, there are no competing interests.

  • Patient consent for publication Not required.

  • Ethics approval Ethical approval was granted to Biobank from the Research Ethics Committee (REC) reference 11/NW/0382.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data may be obtained from a third party and are not publicly available. Data were uploaded onto the DPUK Data Portal https://portal.dementiasplatform.uk/. All analyses were conducted on the Data Portal. Access to the data may occur through UK Biobank but all scripts are available to view on the Data Portal. UK Biobank application 15008 was used for this paper.

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