Alzheimer's Disease Risk Index Research Articles

Adaptation of the Australian National University Alzheimer's Disease Risk Index-Short Form (ANU-ADRI-SF) into Turkish.

The 'Australian National University Alzheimer's Disease Risk Index' (ANU-ADRI) assesses the risk of developing Alzheimer's disease (AD) and is a potential tool for its prevention. The aim of this study is to adapt the ANU-ADRI-SF (the short version of ANU-ADRI) into the Turkish language and Turkish cultural context. The study was methodological and involved the translation and intercultural adaptation of the ANU-ADRI-SF into the Turkish language. The study included 384 community-based participants from a province in the Western Black Sea Region of Türkiye. Data was collected via an online form prepared using Google Forms. The index was translated from its original language, English, into Turkish and then retranslated to English by bilingual translators. It was then reviewed and evaluated for possible issues related to translation and degrees of equivalence. When TR-ANU-ADRI-SF levels were compared according to sex, the mean risk scores were found to be 11.25 ± 7.02 for males and 11.69 ± 7.99 for females. After cross-cultural adaptation, the TR-ANU-ADRI-SF was conceptually intelligible to Turkish adults. The TR-ANU-ADRI-SF is a valid and reliable AD risk assessment tool. Given the increase in AD and its impact on people's health, there is a great need for strategies to be implemented by health professionals to improve the lifestyle of the adult population. For use in conjunction with these strategies, a localised AD risk assessment tool that can be applied by clinicians or by individual patients has been adapted and introduced to the Turkish literature.

Development of a novel dementia risk prediction model in the general population: A large, longitudinal, population-based machine-learning study.

SummaryBackgroundThe existing dementia risk models are limited to known risk factors and traditional statistical methods. We aimed to employ machine learning (ML) to develop a novel dementia prediction model by leveraging a rich-phenotypic variable space of 366 features covering multiple domains of health-related data.MethodsIn this longitudinal population-based cohort of the UK Biobank (UKB), 425,159 non-demented participants were enrolled from 22 recruitment centres across the UK between March 1, 2006 and October 31, 2010. We implemented a data-driven strategy to identify predictors from 366 candidate variables covering a comprehensive range of genetic and environmental factors and developed the ML model to predict incident dementia and Alzheimer's Disease (AD) within five, ten, and much longer years (median 11.9 [Interquartile range 11.2–12.5] years).FindingsDuring a follow-up of 5,023,337 person-years, 5287 and 2416 participants developed dementia and AD, respectively. A novel UKB dementia risk prediction (UKB-DRP) model comprising ten predictors including age, ApoE ε4, pairs matching time, leg fat percentage, number of medications taken, reaction time, peak expiratory flow, mother's age at death, long-standing illness, and mean corpuscular volume was established. Our prediction model was internally evaluated based on five-fold cross-validation on discrimination and calibration, and it was further compared with existing prediction scales. The UKB-DRP model can achieve high discriminative accuracy in dementia (AUC 0.848 ± 0.007) and even better in AD (AUC 0.862 ± 0.015). The model was well-calibrated (Hosmer-Lemeshow goodness-of-fit p-value = 0.92), and the predictive power was solid in different incidence time groups. More importantly, our model presented an apparent superiority over existing models like Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score (AUC 0.705 ± 0.008), the Dementia Risk Score (AUC 0.752 ± 0.007), and the Australian National University Alzheimer's Disease Risk Index (AUC 0.584 ± 0.017). The model was internally validated in the general population of European ancestry and White ethnicity; thus, further validation with independent datasets is necessary to confirm these findings.InterpretationOur ML-based UKB-DRP model incorporated ten easily accessible predictors with solid predictive power for incident dementia and AD within five, ten, and much longer years, which can be used to identify individuals at high risk of dementia and AD in the general population.FundingThis study was funded by grants from the Science and Technology Innovation 2030 Major Projects (2022ZD0211600), National Key R&D Program of China (2018YFC1312904, 2019YFA070950), National Natural Science Foundation of China (282071201, 81971032, 82071997), Shanghai Municipal Science and Technology Major Project (2018SHZDZX01), Research Start-up Fund of Huashan Hospital (2022QD002), Excellence 2025 Talent Cultivation Program at Fudan University (3030277001), Shanghai Rising-Star Program (21QA1408700), Medical Engineering Fund of Fudan University (yg2021-013), and the 111 Project (No. B18015).

Assessing the Predictive Validity of Simple Dementia Risk Models in Harmonized Stroke Cohorts.

Stroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity. Data were harmonized from 4 stroke studies (follow-up range, ≈12-18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests. The predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53). Dementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution). Future work is needed to develop simple and cost-effective risk prediction models specific to poststroke dementia.

Prediction of dementia risk in low-income and middle-income countries (the 10/66 Study): an independent external validation of existing models

SummaryBackgroundTo date, dementia prediction models have been exclusively developed and tested in high-income countries (HICs). However, most people with dementia live in low-income and middle-income countries (LMICs), where dementia risk prediction research is almost non-existent and the ability of current models to predict dementia is unknown. This study investigated whether dementia prediction models developed in HICs are applicable to LMICs.MethodsData were from the 10/66 Study. Individuals aged 65 years or older and without dementia at baseline were selected from China, Cuba, the Dominican Republic, Mexico, Peru, Puerto Rico, and Venezuela. Dementia incidence was assessed over 3–5 years, with diagnosis according to the 10/66 Study diagnostic algorithm. Discrimination and calibration were tested for five models: the Cardiovascular Risk Factors, Aging and Dementia risk score (CAIDE); the Study on Aging, Cognition and Dementia (AgeCoDe) model; the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI); the Brief Dementia Screening Indicator (BDSI); and the Rotterdam Study Basic Dementia Risk Model (BDRM). Models were tested with use of Cox regression. The discriminative accuracy of each model was assessed using Harrell's concordance (c)-statistic, with a value of 0·70 or higher considered to indicate acceptable discriminative ability. Calibration (model fit) was assessed statistically using the Grønnesby and Borgan test.Findings11 143 individuals without baseline dementia and with available follow-up data were included in the analysis. During follow-up (mean 3·8 years [SD 1·3]), 1069 people progressed to dementia across all sites (incidence rate 24·9 cases per 1000 person-years). Performance of the models varied. Across countries, the discriminative ability of the CAIDE (0·52≤c≤0·63) and AgeCoDe (0·57≤c≤0·74) models was poor. By contrast, the ANU-ADRI (0·66≤c≤0·78), BDSI (0·62≤c≤0·78), and BDRM (0·66≤c≤0·78) models showed similar levels of discriminative ability to those of the development cohorts. All models showed good calibration, especially at low and intermediate levels of predicted risk. The models validated best in Peru and poorest in the Dominican Republic and China.InterpretationNot all dementia prediction models developed in HICs can be simply extrapolated to LMICs. Further work defining what number and which combination of risk variables works best for predicting risk of dementia in LMICs is needed. However, models that transport well could be used immediately for dementia prevention research and targeted risk reduction in LMICs.FundingNational Institute for Health Research, Wellcome Trust, WHO, US Alzheimer's Association, and European Research Council.

Comparison of predictive power of different dementia risk assessment tools for Alzheimer's disease in Chinese elders

Objective To compare the predictive and applicable power of 3 dementia risk assessment tools for Alzheimer's disease in Chinese elders. Methods In this case-control study, totally 80 elderly patients with Alzheimer's disease hospitalized in 5 hospitals in Beijing from May 2013 to August 2018 were selected by convenient sampling as the case group, while 80 elders with normal cognitive function were randomly selected as the control group from the same region. Clinical data of both groups were collected, and the subjects' morbidity risk of Alzheimer's disease was assessed using Barnes Model, Australian National University Alzheimer's Disease Risk Index (ANU-ADRI) and Chinese Alzheimer's Disease Risk Assessment Model (C-ADRA) . The predictive power of different assessment tools was thus compared. Results There was significant difference in family history of dementia, craniocerebral injury, hearing disorder, depression, lifestyle as well as some physiological and biochemical criteria between case and control groups. The accuracy, sensitivity and specificity of C-ADRA in predicting Alzheimer's disease in Chinese elders was better than that of Barnes and ANU-ADRI models. Conclusions C-ADRA has a better predictive power for Alzheimer's disease in Chinese elders. Targeted prevention may be provided for patients according to the results of risk assessment, thus promising a guarantee for the prevention and control of Alzheimer's disease in clinical nursing and prevention. Key words: Alzheimer's disease; Aged; Dementia; Risk assessment; Forecasting

Alzheimer's Environmental and Genetic Risk Scores are Differentially Associated With General Cognitive Ability and Dementia Severity.

We investigated the association of the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI) and an Alzheimer disease (AD) genetic risk score (GRS) with cognitive performance. The ANU-ADRI (composed of 12 risk factors for AD) and GRS (composed of 25 AD risk loci) were computed in 1061 community-dwelling older adults. Participants were assessed on 11 cognitive tests and activities of daily living. Structural equation modeling was used to evaluate the association of the ANU-ADRI and GRS with: (1) general cognitive ability (g), (2) dementia-related variance in cognitive performance (δ), and (3) verbal ability (VA), episodic memory (EM), executive function (EF), and processing speed (PS). A worse ANU-ADRI score was associated with poorer performance in "g" [β (SE)=-0.40 (0.02), P<0.001], δ [-0.40 (0.04), P<0.001], and each cognitive domain [VA=-0.29 (0.04), P<0.001; EM=-0.34 (0.03), P<0.001; EF=-0.38 (0.03), P<0.001; and PS=-0.40 (0.03), P<0.001]. A worse GRS was associated with poorer performance in δ [-0.08 (0.03), P=0.041] and EM [-0.10 (0.03), P=0.035]. The ANU-ADRI was broadly associated with worse cognitive performance, including general ability and dementia severity, validating its further use in early dementia risk assessment.

Cross-cultural adaptation of the "Australian National University Alzheimer's Disease Risk Index" for the Brazilian population.

Alzheimer's disease (AD) represents a major public health problem and it is therefore crucial that modifiable risk factors be known prior to onset of dementia in late-life. The "Australian National University - Alzheimer's Disease Risk Index" (ANU-ADRI) is one of the potential tools for primary prevention of the disease.ObjectiveThe aim of this study was to devise an adapted version of the ANU-ADRI for use in Brazil.MethodsThe instrument was translated from its original language of English into Portuguese and then back-translated into English by bilingual translators. It was subsequently reviewed and evaluated as to the degree of translation issues and equivalence. In this study, the ANU-ADRI was applied using individual (face-to-face) interviews in a public hospital, unlike the original version which is applied online by self-report. The final version (pretest) was evaluated in a sample of 10 participants with a mean age of 60 years (±11.46) and mean education of 11 years (±6.32).ResultsThe intraclass correlation coefficient (ICC) (inter-rater) was 0.954 (P<0.001 for a confidence interval (CI) of 95%=[0.932; 0.969]). Cultural equivalence was performed without the need for a second instrument application step.ConclusionAfter cross-cultural adaptation, the language of the resultant questionnaire was deemed easily understandable by the Brazilian population.

The Australian National University Alzheimer's disease Risk Index (ANU-ADRI) predicts cognitive impairment in a community study of older adults

The Australian National University Alzheimer's disease Risk Index (ANU-ADRI) predicts cognitive impairment in a community study of older adults

Assessing reliability of short and tick box forms of the ANU-ADRI: Convenient alternatives of a self-report Alzheimer's disease risk assessment

IntroductionTo assess the reliability of short versions of the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI). MethodsA short form of the ANU-ADRI (ANU-ADRI-SF) was developed by assessing risk and protective factors with single questions where possible and with short forms of sub-questionnaires where available. The tick box form of the ANU-ADRI (ANU-ADRI-TB) was developed with unique questions for each risk and protective factor for Alzheimer's disease. The short versions were evaluated in an independent community sample of 504 participants with a mean age of 45.01 (SD = 14.85, range = 18–81). ResultsThe short versions demonstrated high reliabilities when compared with the ANU-ADRI. However, the proportion of misclassification was high for some risk factors and particularly for the ANU-ADRI-TB. DiscussionThe ANU-ADRI-SF may be considered if less reliable questions from the ANU-ADRI-SF can be replaced with more reliable questions from the ANU-ADRI for risk/protective factors with high misclassification.