New Dementia Risk Score Targets Factors Modifiable in Midlife

2006 ◽  
Vol 34 (9) ◽  
pp. 38
Author(s):  
Bruce K. Dixon
Keyword(s):  
Risk Score ◽  
Dementia Risk

scholarly journals All-cause Dementia Prediction by Machine Learning: The Health, Aging, and Body Composition Study

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 487-487
Author(s):  
Chenkai Wu ◽  
Xurui Jin
Keyword(s):  
Machine Learning ◽  
Body Composition ◽  
Risk Prediction ◽  
Risk Score ◽  
Body Composition Study ◽  
C Statistic ◽  
Dementia Risk ◽  
Health Aging ◽  
Composition Study ◽  
Traditional Approaches

Abstract There are several shortcomings of the currently available risk prediction models for dementia. We developed a risk prediction model for dementia using machine-learning approach and compared its performance with traditional approaches. Data were from the Health, Aging, and Body Composition Study, comprising 3,075 older adults (at least 70 years). Dementia was defined as (1) use of a prescribed dementia medication, (2) adjudicated dementia diagnosis, or (3) a race-stratified cognitive decline>1.5 SDs from the baseline mean. We selected 275 predictors collected from questionnaires, imaging data, performance testing, and biospecimen. We used random survival forest (RSF) to build the full model and rank the importance of predictors. Subsequently, we built parsimonious models with top-20 predictors using RSF and Cox regression. A dementia risk score was developed using top-ranked variables. We used the C-statistic for performance evaluation. Over a median of 11.4 years of follow-up, 659 dementias (21.4%) occurred. The RSF model (both including all and top-20 variables) showed a higher C-statistic than the regression model. Digit symbol score, physical performance battery, finger tapping score, weight change since age 50, serum adiponectin, and APOE genotype were the top-6 variables. We created a dementia risk score (0-10) using the top-6 variables. A 1-unit increase in the risk score was associated with an 8% higher risk of dementia. The risk score demonstrated good discrimination (C-statistic=0.75). Machine learning methods offered improvement over traditional approaches in predicting dementia. The risk prediction score derived from a parsimonious model had good prediction performance.

scholarly journals Development and Validation of a Novel Dementia Risk Score in the UK Biobank Cohort

2021 ◽  
Author(s):  
Melis Anatürk ◽  
Raihaan Patel ◽  
Georgios Georgiopoulos ◽  
Danielle Newby ◽  
Anya Topiwala ◽  
...  
Keyword(s):  
At Risk ◽  
Cardiovascular Risk ◽  
Risk Score ◽  
Disease Risk ◽  
Risk Index ◽  
Risk Scores ◽  
Uk Biobank ◽  
Dementia Risk ◽  
History Of ◽  
The Uk

INTRODUCTION: Current prognostic models of dementia have had limited success in consistently identifying at-risk individuals. We aimed to develop and validate a novel dementia risk score (DRS) using the UK Biobank cohort.METHODS: After randomly dividing the sample into a training (n=166,487, 80%) and test set (n=41,621, 20%), logistic LASSO regression and standard logistic regression were used to develop the UKB-DRS.RESULTS: The score consisted of age, sex, education, apolipoprotein E4 genotype, a history of diabetes, stroke, and depression, and a family history of dementia. The UKB-DRS had good-to-strong discrimination accuracy in the UKB hold-out sample (AUC [95%CI]=0.79 [0.77, 0.82]) and in an external dataset (Whitehall II cohort, AUC [95%CI]=0.83 [0.79,0.87]). The UKB-DRS also significantly outperformed four published risk scores (i.e., Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI), Cardiovascular Risk Factors, Aging, and Dementia score (CAIDE), Dementia Risk Score (DRS), and the Framingham Cardiovascular Risk Score (FRS) across both test sets.CONCLUSION: The UKB-DRS represents a novel easy-to-use tool that could be used for routine care or targeted selection of at-risk individuals into clinical trials.

scholarly journals Diabetes-specific dementia risk score (DSDRS) predicts cognitive performance in patients with type 2 diabetes at high cardio-renal risk

2020 ◽  
Vol 34 (10) ◽  
pp. 107674
Author(s):  
Chloë Verhagen ◽  
Jolien Janssen ◽  
Lieza G. Exalto ◽  
Esther van den Berg ◽  
Odd Erik Johansen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cognitive Performance ◽  
Risk Score ◽  
Dementia Risk

scholarly journals Global Vascular Risk Score and CAIDE Dementia Risk Score Predict Cognitive Function in the Northern Manhattan Study

2020 ◽  
Vol 73 (3) ◽  
pp. 1221-1231
Author(s):  
Tatjana Rundek ◽  
Hannah Gardener ◽  
Anita Seixas Dias Saporta ◽  
David A. Loewenstein ◽  
Ranjan Duara ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Risk Score ◽  
Vascular Risk ◽  
Dementia Risk

scholarly journals Clinical Applicability of the Specific Risk Score of Dementia in Type 2 Diabetes in the Identification of Patients with Early Cognitive Impairment: Results of the MOPEAD Study in Spain

2020 ◽  
Vol 9 (9) ◽  
pp. 2726
Author(s):  
Angel Michael Ortiz Zuñiga ◽  
Rafael Simó ◽  
Octavio Rodriguez-Gómez ◽  
Cristina Hernández ◽  
Adrian Rodrigo ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cognitive Impairment ◽  
Risk Score ◽  
Screening Tool ◽  
Memory Clinic ◽  
Dementia Risk ◽  
High Prevalence ◽  
Level Hospital ◽  
Main Factor

Introduction: Although the Diabetes Specific Dementia Risk Score (DSDRS) was proposed for predicting risk of dementia at 10 years, its usefulness as a screening tool is unknown. For this purpose, the European consortium MOPEAD included the DSDRS within the specific strategy for screening of cognitive impairment in type 2 diabetes (T2D) patients attended in a third-level hospital. Material and Methods: T2D patients > 65 years, without known cognitive impairment, attended in a third-level hospital, were evaluated. As per MOPEAD protocol, patients with MMSE ≤ 27 or DSDRS ≥ 7 were referred to the memory clinic for complete neuropsychological assessment. Results: 112 T2D patients were recruited. A total of 82 fulfilled the criteria for referral to the memory unit (43 of them declined referral: 48.8% for associated comorbidities, 37.2% lack of interest, 13.95% lack of social support). At the Fundació ACE’s Memory Clinic, 34 cases (87.2%) of mild cognitive impairment (MCI) and 3 cases (7.7%) of dementia were diagnosed. The predictive value of DSDRS ≥ 7 as a screening tool of cognitive impairment was AUROC = 0.739, p 0.024, CI 95% (0.609–0.825). Conclusions: We found a high prevalence of unknown cognitive impairment in TD2 patients who attended a third-level hospital. The DSDRS was found to be a useful screening tool. The presence of associated comorbidities was the main factor of declining referral.

P2-298: CAIDE DEMENTIA RISK SCORE, ALZHEIMER, AND VASCULAR NEUROPATHOLOGY IN THE VANTAA 85+ STUDY

2014 ◽  
Vol 10 ◽  
pp. P586-P587
Author(s):  
Babak Hooshmand ◽  
Tuomo Polvikoski ◽  
Miia Kivipelto ◽  
Maarit Tanskanen ◽  
Liisa Myllykangas ◽  
...  
Keyword(s):  
Risk Score ◽  
Dementia Risk

O1-02-07: The CAIDE dementia risk score: A practical tool to predict dementia risk in 20 years among middle aged persons

2007 ◽  
Vol 3 (3S_Part_3) ◽  
pp. S170-S170
Author(s):  
Miia Kivipelto ◽  
Tiia Ngandu ◽  
Rachel Whitmer ◽  
Tiina Laatikainen ◽  
Bengt Winblad ◽  
...  
Keyword(s):  
Risk Score ◽  
Middle Aged ◽  
Dementia Risk ◽  
Practical Tool

Dementia-Predicting Cognitive Risk Score and Its Correlation with Cortical Thickness in Parkinson Disease

2017 ◽  
Vol 44 (3-4) ◽  
pp. 203-212 ◽  
Author(s):  
Byoung Seok Ye ◽  
Seun Jeon ◽  
Jee Hyun Ham ◽  
Jae Jung Lee ◽  
Jong Min Lee ◽  
...  
Keyword(s):  
Parkinson Disease ◽  
Risk Score ◽  
Neuropsychological Tests ◽  
Cox Regression ◽  
Cingulate Cortex ◽  
Data Set ◽  
Score System ◽  
Cognitive Risk ◽  
Dementia Risk ◽  
Risk Score System

Background: We developed a risk score system to predict risks of developing dementia in individual Parkinson disease (PD) patients using baseline neuropsychological tests. Methods: A total of 216 nondemented PD patients underwent a baseline neuropsychological evaluation and were followed up for a mean of 2.7 (±1.1) years. Univariate Cox regression models controlled for age, gender, and education selected neuropsychological tests individually predicting dementia risk. Then, a multivariate Cox regression model combined them into a cognitive risk score system. Cortical areas correlating with cognitive risk score were investigated using a separate MRI data set from 207 nondemented PD patients. Results: Fifty-two patients (23.9%) developed dementia. The univariate Cox regression analyses identified the confrontational naming and semantic fluency tests, frontal/executive function tests, immediate verbal memory test, and visuospatial function test as predicting dementia risk. The calculated cognitive risk score (range 53-188) predicted future dementia with moderate accuracy (integrated area under the curve = 0.79; 95% CI: 0.73-0.85). A higher cognitive risk score correlated with cortical thinning in the right anteromedial temporal cortex, bilateral posterior cingulate cortex, right anterior cingulate cortex, left parahippocampal gyrus, and right superior frontal cortex in a separate MRI data set. Conclusion: The cognitive risk score system is a useful approach to predict the dementia risk among PD patients.

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