Development and validation of a dementia risk score in the UK Biobank and Whitehall II cohorts

被引:14
作者
Anaturk, Melis [1 ,2 ]
Patel, Raihaan [2 ,3 ,12 ]
Ebmeier, Klaus P. [2 ]
Georgiopoulos, Georgios [4 ]
Newby, Danielle [2 ]
Topiwala, Anya [2 ,5 ]
de Lange, Ann-Marie G. [2 ,6 ,7 ]
Cole, James H. [1 ,8 ]
Jansen, Michelle G. [9 ]
Singh-Manoux, Archana [10 ,11 ]
Kivimaki, Mika [11 ]
Suri, Sana [2 ,3 ]
机构
[1] UCL, Ctr Med Image Comp, Dept Comp Sci, London, England
[2] Univ Oxford, Dept Psychiat, Oxford, England
[3] Univ Oxford, Oxford Ctr Human Brain Act, Wellcome Ctr Integrat Neuroimaging, Oxford, England
[4] Kings Coll London, Sch Biomed Engn & Imaging Sci, London, England
[5] Univ Oxford, Big Data Inst, Oxford, England
[6] Univ Lausanne, Dept Clin Neurosci, Lausanne, Switzerland
[7] Univ Oslo, Dept Psychol, Oslo, Norway
[8] UCL, Inst Neurol, Dementia Res Ctr, London, England
[9] Radboud Univ Nijmegen, Donders Ctr Cognit, Donders Inst Brain Cognit & Behav, Nijmegen, Netherlands
[10] Univ Paris, Inserm U1153, Epidemiol Ageing & Neurodegenerat Dis, Paris, France
[11] UCL, Fac Brain Sci, London, England
[12] Univ Oxford, Psychiat, Oxford OX1 4BH, England
来源
BMJ MENTAL HEALTH | 2023年 / 26卷 / 01期
关键词
PSYCHIATRY; Delirium & cognitive disorders; Adult psychiatry; MODELS; PREVENTION; PREDICTION;
D O I
10.1136/bmjment-2023-300719
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Background Current dementia risk scores have had limited success in consistently identifying at-risk individuals across different ages and geographical locations. Objective We aimed to develop and validate a novel dementia risk score for a midlife UK population, using two cohorts: the UK Biobank, and UK Whitehall II study. Methods We divided the UK Biobank cohort into a training (n=176 611, 80%) and test sample (n=44 151, 20%) and used the Whitehall II cohort (n=2934) for external validation. We used the Cox LASSO regression to select the strongest predictors of incident dementia from 28 candidate predictors and then developed the risk score using competing risk regression. Findings Our risk score, termed the UK Biobank Dementia Risk Score (UKBDRS), consisted of age, education, parental history of dementia, material deprivation, a history of diabetes, stroke, depression, hypertension, high cholesterol, household occupancy, and sex. The score had a strong discrimination accuracy in the UK Biobank test sample (area under the curve (AUC) 0.8, 95% CI 0.78 to 0.82) and in the Whitehall cohort (AUC 0.77, 95% CI 0.72 to 0.81). The UKBDRS also significantly outperformed three other widely used dementia risk scores originally developed in cohorts in Australia (the Australian National University Alzheimer's Disease Risk Index), Finland (the Cardiovascular Risk Factors, Ageing, and Dementia score), and the UK (Dementia Risk Score). Clinical implications Our risk score represents an easy-to-use tool to identify individuals at risk for dementia in the UK. Further research is required to determine the validity of this score in other populations.
引用
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页码:1 / 7
页数:7
相关论文
共 30 条
[1]   Dementia Risk Scores and Their Role in the Implementation of Risk Reduction Guidelines [J].
Anstey, Kaarin J. ;
Zheng, Lidan ;
Peters, Ruth ;
Kootar, Scherazad ;
Barbera, Mariagnese ;
Stephen, Ruth ;
Dua, Tarun ;
Chowdhary, Neerja ;
Solomon, Alina ;
Kivipelto, Miia .
FRONTIERS IN NEUROLOGY, 2022, 12
[2]   A Self-Report Risk Index to Predict Occurrence of Dementia in Three Independent Cohorts of Older Adults: The ANU-ADRI [J].
Anstey, Kaarin J. ;
Cherbuin, Nicolas ;
Herath, Pushpani M. ;
Qiu, Chengxuan ;
Kuller, Lewis H. ;
Lopez, Oscar L. ;
Wilson, Robert S. ;
Fratiglioni, Laura .
PLOS ONE, 2014, 9 (01)
[3]   Estimation of the Absolute Risk of Cardiovascular Disease and Other Events: Issues With the Use of Multiple Fine- Gray Subdistribution Hazard Models [J].
Austin, Peter C. ;
Putter, Hein ;
Lee, Douglas S. ;
Steyerberg, Ewout W. .
CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES, 2022, 15 (02) :161-168
[4]   Quantifying and Comparing Dynamic Predictive Accuracy of Joint Models for Longitudinal Marker and Time-to-Event in Presence of Censoring and Competing Risks [J].
Blanche, Paul ;
Proust-Lima, Cecile ;
Loubere, Lucie ;
Berr, Claudine ;
Dartigues, Jean-Francois ;
Jacqmin-Gadda, Helene .
BIOMETRICS, 2015, 71 (01) :102-113
[5]   A proportional hazards model for the subdistribution of a competing risk [J].
Fine, JP ;
Gray, RJ .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1999, 94 (446) :496-509
[6]   Regularization Paths for Generalized Linear Models via Coordinate Descent [J].
Friedman, Jerome ;
Hastie, Trevor ;
Tibshirani, Rob .
JOURNAL OF STATISTICAL SOFTWARE, 2010, 33 (01) :1-22
[7]   Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population [J].
Fry, Anna ;
Littlejohns, Thomas J. ;
Sudlow, Cathie ;
Doherty, Nicola ;
Adamska, Ligia ;
Sprosen, Tim ;
Collins, Rory ;
Allen, Naomi E. .
AMERICAN JOURNAL OF EPIDEMIOLOGY, 2017, 186 (09) :1026-1034
[8]   Sex differences in the association between major cardiovascular risk factors in midlife and dementia: a cohort study using data from the UK Biobank [J].
Gong, Jessica ;
Harris, Katie ;
Peters, Sanne A. E. ;
Woodward, Mark .
BMC MEDICINE, 2021, 19 (01)
[9]   Models for predicting risk of dementia: a systematic review [J].
Hou, Xiao-He ;
Feng, Lei ;
Zhang, Can ;
Cao, Xi-Peng ;
Tan, Lan ;
Yu, Jin-Tai .
JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY, 2019, 90 (04) :373-379
[10]   Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study [J].
Kivipelto, Miia ;
Ngandu, Tiia ;
Laatikainen, Tiina ;
Winblad, Bengt ;
Soininen, Hilkka ;
Tuomilehto, Jaakko .
LANCET NEUROLOGY, 2006, 5 (09) :735-741