A single risk assessment for the most common diseases of ageing, developed and validated on 10 cohort studies.

Autor: Huque MH; School of Psychology, University of New South Wales, Kensington, NSW, Australia.; Neuroscience Research Australia, Randwick, NSW, Australia.; University of New South Wales Ageing Futures Institute, University of NSW, Kensington, NSW, Australia., Kootar S; The Sydney Children's Hospital Network, Sydney, Australia., Kiely KM; School of Mathematics and Applied Statistics, and, School of Health and Society , University of Wollongong, Wollongong, NSW, Australia., Anderson CS; Faculty of Medicine, The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia., van Boxtel M; Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands., Brodaty H; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia., Sachdev PS; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia., Carlson M; Johns Hopkins Center On Aging and Health, Johns Hopkins University, Baltimore, USA., Fitzpatrick AL; Departments of Family Medicine and Epidemiology, University of Washington, Seattle, WA, USA., Whitmer RA; Department of Public Health Sciences, University of California, Davis, CA, USA., Kivipelto M; Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.; The Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK.; Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland., Jorm L; Centre for Big Data Research in Health, School of Medicine and Health, University of New South Wales, Sydney, NSW, Australia., Köhler S; Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands.; Research Institute for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands., Lautenschlager NT; Department of Psychiatry, University of Melbourne, Melbourne, VIC, Australia.; Older Adult Mental Health Program, Royal Melbourne Hospital Mental Health Service, Parkville, Australia., Lopez OL; Departments of Neurology and Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA., Shaw JE; Department of Clinical Diabetes and Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Australia., Matthews FE; Population Health Sciences Institute, Newcastle University, Newcastle, UK.; Institute for Clinical and Applied Health Research (ICAHR), University of Hull, Hull, UK., Peters R; University of New South Wales Ageing Futures Institute, University of NSW, Kensington, NSW, Australia.; Faculty of Medicine, The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia., Anstey KJ; School of Psychology, University of New South Wales, Kensington, NSW, Australia. k.anstey@unsw.edu.au.; Neuroscience Research Australia, Randwick, NSW, Australia. k.anstey@unsw.edu.au.; University of New South Wales Ageing Futures Institute, University of NSW, Kensington, NSW, Australia. k.anstey@unsw.edu.au.
Jazyk: angličtina
Zdroj: BMC medicine [BMC Med] 2024 Oct 31; Vol. 22 (1), pp. 501. Date of Electronic Publication: 2024 Oct 31.
DOI: 10.1186/s12916-024-03711-6
Abstrakt: Background: We aimed to develop risk tools for dementia, stroke, myocardial infarction (MI), and diabetes, for adults aged ≥ 65 years using shared risk factors.
Methods: Data were obtained from 10 population-based cohorts (N = 41,755) with median follow-up time (years) for dementia, stroke, MI, and diabetes of 6.2, 7.0, 6.8, and 7.4, respectively. Disease-free participants at baseline were included, and 22 risk factors (sociodemographic, medical, lifestyle, laboratory biomarkers) were evaluated. Two risk tools (DemNCD and DemNCD-LR based on Fine and Gray sub-distribution and logistic regression [LR], respectively) were developed and validated. Predictive accuracies of these risk tools were assessed using Harrel's C-statistics and area under the curve (AUC) and 95% confidence interval (CI). Model calibration was conducted using Hosmer-Lemeshow goodness of fit test along calibration plots.
Results: Both the DemNCD and DemNCD-LR resulted in similar predictive accuracy for each outcome. The overall AUC (95% CI) for dementia, stroke, MI, and diabetes risk tool were 0·68 (0·65, 0·70), 0·58 (0·54, 0·61), 0·65 (0·61, 0·68), and 0·68 (0·64, 0·72), respectively, for males. For females, these figures were 0·65 (0·63, 0·67), 0·55 (0·52, 0·57), 0·65 (0·62, 0·68), and 0·61 (0·57, 0·65).
Conclusions: The DemNCD is the first tool to predict both dementia and multiple cardio-metabolic diseases using comprehensive risk factors and provided similar predictive accuracy to existing risk tools. It has similar predictive accuracy as tools designed for single outcomes in this age-group. DemNCD has the potential to be used in community and clinical settings as it includes self-reported and routinely available clinical measures.
(© 2024. The Author(s).)
Databáze: MEDLINE
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