Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 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. Of interest here is the comorbidity of common...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2020-11-01
|
| Series: | BMJ Mental Health |
| Online Access: | https://ebmh.bmj.com/content/23/4/140.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846164549089099776 |
|---|---|
| author | Chenlu Li Delia A Gheorghe John E Gallacher Sarah Bauermeister |
| author_facet | Chenlu Li Delia A Gheorghe John E Gallacher Sarah Bauermeister |
| author_sort | Chenlu Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-709aff600f5e4140b94020ad6bc9be33 |
| institution | Kabale University |
| issn | 2755-9734 |
| language | English |
| publishDate | 2020-11-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Mental Health |
| spelling | doaj-art-709aff600f5e4140b94020ad6bc9be332024-11-18T04:05:07ZengBMJ Publishing GroupBMJ Mental Health2755-97342020-11-0123410.1136/ebmental-2020-300147Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participantsChenlu Li0Delia A Gheorghe1John E Gallacher2Sarah Bauermeister31 Department Risk Advisory, Deloitte LLP, London, UK2 Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK2 Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK2 Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UKBackground 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.https://ebmh.bmj.com/content/23/4/140.full |
| spellingShingle | Chenlu Li Delia A Gheorghe John E Gallacher Sarah Bauermeister Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants BMJ Mental Health |
| title | Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants |
| title_full | Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants |
| title_fullStr | Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants |
| title_full_unstemmed | Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants |
| title_short | Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants |
| title_sort | psychiatric comorbid disorders of cognition a machine learning approach using 1175 uk biobank participants |
| url | https://ebmh.bmj.com/content/23/4/140.full |
| work_keys_str_mv | AT chenluli psychiatriccomorbiddisordersofcognitionamachinelearningapproachusing1175ukbiobankparticipants AT deliaagheorghe psychiatriccomorbiddisordersofcognitionamachinelearningapproachusing1175ukbiobankparticipants AT johnegallacher psychiatriccomorbiddisordersofcognitionamachinelearningapproachusing1175ukbiobankparticipants AT sarahbauermeister psychiatriccomorbiddisordersofcognitionamachinelearningapproachusing1175ukbiobankparticipants |