Silvan Licher1, Maarten J G Leening1, Pinar Yilmaz1, Frank J Wolters1, Jan Heeringa1, Patrick J E Bindels1, Meike W Vernooij1, Blossom C M Stephan1, Ewout W Steyerberg1, M Kamran Ikram1, M Arfan Ikram1. 1. The Department of Epidemiology (Licher, Leening, Yilmaz, Wolters, Heeringa, Vernooij, M.K. Ikram, M.A. Ikram), the Department of Neurology (Wolters, M.K. Ikram), the Department of Cardiology (Leening), the Department of Radiology and Nuclear Medicine (Yilmaz, Vernooij), and the Department of General Practice (Bindels), Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands; the Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston (Leening, Wolters); the Institute of Health and Society, Newcastle University, Newcastle, U.K. (Stephan); the Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands (Steyerberg); and the Center for Medical Decision Making, Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands (Steyerberg).
Abstract
OBJECTIVE: Identification of individuals at high risk of dementia is essential for development of prevention strategies, but reliable tools are lacking for risk stratification in the population. The authors developed and validated a prediction model to calculate the 10-year absolute risk of developing dementia in an aging population. METHODS: In a large, prospective population-based cohort, data were collected on demographic, clinical, neuropsychological, genetic, and neuroimaging parameters from 2,710 nondemented individuals age 60 or older, examined between 1995 and 2011. A basic and an extended model were derived to predict 10-year risk of dementia while taking into account competing risks from death due to other causes. Model performance was assessed using optimism-corrected C-statistics and calibration plots, and the models were externally validated in the Dutch population-based Epidemiological Prevention Study of Zoetermeer and in the Alzheimer's Disease Neuroimaging Initiative cohort 1 (ADNI-1). RESULTS: During a follow-up of 20,324 person-years, 181 participants developed dementia. A basic dementia risk model using age, history of stroke, subjective memory decline, and need for assistance with finances or medication yielded a C-statistic of 0.78 (95% CI=0.75, 0.81). Subsequently, an extended model incorporating the basic model and additional cognitive, genetic, and imaging predictors yielded a C-statistic of 0.86 (95% CI=0.83, 0.88). The models performed well in external validation cohorts from Europe and the United States. CONCLUSIONS: In community-dwelling individuals, 10-year dementia risk can be accurately predicted by combining information on readily available predictors in the primary care setting. Dementia prediction can be further improved by using data on cognitive performance, genotyping, and brain imaging. These models can be used to identify individuals at high risk of dementia in the population and are able to inform trial design.
OBJECTIVE: Identification of individuals at high risk of dementia is essential for development of prevention strategies, but reliable tools are lacking for risk stratification in the population. The authors developed and validated a prediction model to calculate the 10-year absolute risk of developing dementia in an aging population. METHODS: In a large, prospective population-based cohort, data were collected on demographic, clinical, neuropsychological, genetic, and neuroimaging parameters from 2,710 nondemented individuals age 60 or older, examined between 1995 and 2011. A basic and an extended model were derived to predict 10-year risk of dementia while taking into account competing risks from death due to other causes. Model performance was assessed using optimism-corrected C-statistics and calibration plots, and the models were externally validated in the Dutch population-based Epidemiological Prevention Study of Zoetermeer and in the Alzheimer's Disease Neuroimaging Initiative cohort 1 (ADNI-1). RESULTS: During a follow-up of 20,324 person-years, 181 participants developed dementia. A basic dementia risk model using age, history of stroke, subjective memory decline, and need for assistance with finances or medication yielded a C-statistic of 0.78 (95% CI=0.75, 0.81). Subsequently, an extended model incorporating the basic model and additional cognitive, genetic, and imaging predictors yielded a C-statistic of 0.86 (95% CI=0.83, 0.88). The models performed well in external validation cohorts from Europe and the United States. CONCLUSIONS: In community-dwelling individuals, 10-year dementia risk can be accurately predicted by combining information on readily available predictors in the primary care setting. Dementia prediction can be further improved by using data on cognitive performance, genotyping, and brain imaging. These models can be used to identify individuals at high risk of dementia in the population and are able to inform trial design.
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Authors: Dallas P Veitch; Michael W Weiner; Paul S Aisen; Laurel A Beckett; Charles DeCarli; Robert C Green; Danielle Harvey; Clifford R Jack; William Jagust; Susan M Landau; John C Morris; Ozioma Okonkwo; Richard J Perrin; Ronald C Petersen; Monica Rivera-Mindt; Andrew J Saykin; Leslie M Shaw; Arthur W Toga; Duygu Tosun; John Q Trojanowski Journal: Alzheimers Dement Date: 2021-09-28 Impact factor: 16.655
Authors: M Arfan Ikram; Guy Brusselle; Mohsen Ghanbari; André Goedegebure; M Kamran Ikram; Maryam Kavousi; Brenda C T Kieboom; Caroline C W Klaver; Robert J de Knegt; Annemarie I Luik; Tamar E C Nijsten; Robin P Peeters; Frank J A van Rooij; Bruno H Stricker; André G Uitterlinden; Meike W Vernooij; Trudy Voortman Journal: Eur J Epidemiol Date: 2020-05-04 Impact factor: 8.082
Authors: William N Whiteley; Sonia Anand; Shrikant I Bangdiwala; Jackie Bosch; Michelle Canavan; Howard Chertkow; Hertzel C Gerstein; Philip Gorelick; Martin O'Donnell; Guillaume Paré; Marie Pigeyre; Sudha Seshadri; Mike Sharma; Eric E Smith; Jeff Williamson; Tali Cukierman-Yaffe; Robert G Hart; Salim Yusuf Journal: Age Ageing Date: 2020-02-27 Impact factor: 10.668
Authors: Blossom C M Stephan; Eduwin Pakpahan; Mario Siervo; Silvan Licher; Graciela Muniz-Terrera; Devi Mohan; Daisy Acosta; Guillermina Rodriguez Pichardo; Ana Luisa Sosa; Isaac Acosta; Juan J Llibre-Rodriguez; Martin Prince; Louise Robinson; Matthew Prina Journal: Lancet Glob Health Date: 2020-04 Impact factor: 38.927