Machado-Fragua, Marcos D;
Landré, Benjamin;
Chen, Mathilde;
Fayosse, Aurore;
Dugravot, Aline;
Kivimaki, Mika;
Sabia, Séverine;
(2022)
Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study.
BMC Medicine
, 20
, Article 334. 10.1186/s12916-022-02519-6.
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Abstract
BACKGROUND: Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach and determined whether the selected metabolites improved prediction accuracy beyond the effect of age. METHODS: Five thousand three hundred seventy-four participants from the Whitehall II study, mean age 55.8 (standard deviation (SD) 6.0) years in 1997-1999 when 233 metabolites were quantified using nuclear magnetic resonance metabolomics. Participants were followed for a median 21.0 (IQR 20.4, 21.7) years for clinically-diagnosed dementia (N=329). Elastic net penalized Cox regression with 100 repetitions of nested cross-validation was used to select models that improved prediction accuracy for incident dementia compared to an age-only model. Risk scores reflecting the frequency with which predictors appeared in the selected models were constructed, and their predictive accuracy was examined using Royston's R2, Akaike's information criterion, sensitivity, specificity, C-statistic and calibration. RESULTS: Sixteen of the 100 models had a better c-statistic compared to an age-only model and 15 metabolites were selected at least once in all 16 models with glucose present in all models. Five risk scores, reflecting the frequency of selection of metabolites, and a 1-SD increment in all five risk scores was associated with higher dementia risk (HR between 3.13 and 3.26). Three of these, constituted of 4, 5 and 15 metabolites, had better prediction accuracy (c-statistic from 0.788 to 0.796) compared to an age-only model (c-statistic 0.780), all p<0.05. CONCLUSIONS: Although there was robust evidence for the role of glucose in dementia, metabolites measured in midlife made only a modest contribution to dementia prediction once age was taken into account.
| Type: | Article |
|---|---|
| Title: | Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s12916-022-02519-6 |
| Publisher version: | https://doi.org/10.1186/s12916-022-02519-6 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| Keywords: | Biomarkers, C-statistic, Dementia, Longitudinal study, Metabolites, Predictive accuracy, Risk score, Biomarkers, Cohort Studies, Dementia, Follow-Up Studies, Glucose, Humans, Machine Learning, Middle Aged, Risk Factors |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry > Mental Health of Older People UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10156773 |
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