Australian National University Alzheimer Research Articles

External validation of dementia prediction models in Black or African American and White older adults: A longitudinal population-based study in the United States.

Identifying people at high risk of Alzheimer's disease (AD) dementia allows for timely intervention, which, if successful, will result in preventing or delaying the onset of the disease. Utilizing data from the Chicago Health and Aging Project (CHAP; n=2130), we externally evaluated four risk-prediction models for AD dementia, including Cardiovascular Risk Factors, Aging, and Dementia (CAIDE), Australian National University Alzheimer's Disease Risk Index (ANU-ADRI), Brief Dementia Screening Indicator (BDSI), and Dementia Risk Score (DRS), in Black or African American and White adults. BDSI had the highest discriminate abilities for AD dementia (c-statistics of 0.79 in Black and 0.77 in White adults), followed by ANU-ADRI, within the age range and follow-up period of the original development cohort. CAIDE had the lowest discriminating power (c-statistic≤0.55). With increasing follow-up periods (i.e., 10-15 years), the discrimination abilities for all models declined. Because of racial disparities in AD dementia and longer preclinical and prodromal stages of disease development, race-specific models are needed to predict AD risk over 10 years. Utilizing risk-prediction models to identify individuals at higher risk of Alzheimer's disease (AD) dementia could benefit clinicians, patients, and policymakers. Clinicians could enroll high-risk individuals in clinical trials to test new risk-modifiable treatments or initiate lifestyle modifications, which, if successful, would slow cognitive decline and delay the onset of the disease. Current risk-prediction models had good discriminative power during the first 6 years of follow-up but decreased with longer follow-up time. Acknowledging the longer preclinical phase of AD dementia development and racial differences in dementia risk, there is a need to develop race-specific risk-prediction models that can predict 10 or 20 years of risk for AD and related dementias.

ANU-ADRI scores, tau pathology, and cognition in non-demented adults: the CABLE study

BackgroundIt has been reported that the risk of Alzheimer’s disease (AD) could be predicted by the Australian National University Alzheimer Disease Risk Index (ANU-ADRI) scores. However, among non-demented Chinese adults, the correlations of ANU-ADRI scores with cerebrospinal fluid (CSF) core biomarkers and cognition remain unclear.MethodsIndividuals from the Chinese Alzheimer’s Biomarker and LifestyLE (CABLE) study were grouped into three groups (low/intermediate/high risk groups) based on their ANU-ADRI scores. The multiple linear regression models were conducted to investigate the correlations of ANU-ADRI scores with several biomarkers of AD pathology. Mediation model and structural equation model (SEM) were conducted to investigate the mediators of the correlation between ANU-ADRI scores and cognition.ResultsA total of 1078 non-demented elders were included in our study, with a mean age of 62.58 (standard deviation [SD] 10.06) years as well as a female proportion of 44.16% (n = 476). ANU-ADRI scores were found to be significantly related with MMSE (β = -0.264, P < 0.001) and MoCA (β = -0.393, P < 0.001), as well as CSF t-tau (β = 0.236, P < 0.001), p-tau (β = 0.183, P < 0.001), and t-tau/Aβ42 (β = 0.094, P = 0.005). Mediation analyses indicated that the relationships of ANU-ADRI scores with cognitive scores were mediated by CSF t-tau or p-tau (mediating proportions ranging from 4.45% to 10.50%). SEM did not reveal that ANU-ADRI scores affected cognition by tau-related pathology and level of CSF soluble triggering receptor expressed on myeloid cells 2 (sTREM2).ConclusionANU-ADRI scores were associated with cognition and tau pathology. We also revealed a potential pathological mechanism underlying the impact of ANU-ADRI scores on cognition.

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.

The cardiovascular risk factors, aging and dementia risk score and Australian national university Alzheimer’s disease risk index among Ethiopians

AbstractIntroductionThe burden of dementia is highest in LMICs, and the dementia prevention potential is greater than in high‐income countries. However, the burden of modifiable dementia risk factors is not determined in Ethiopia. The objective of this study is to determine the need for personalized dementia risk reduction among high risk patients in Ethiopia using the Cardiovascular Risk Factors, Aging and Dementia Risk Score (CAIDE) and Australian National University Alzheimer’s Disease Risk Index (ANU‐ADRI) for midlife and late life.MethodsOf the 381 participants in the Amharic Dementia Tool –Ethiopia, 182 (48%) were at their midlife (40‐64 years of age) and studied for CAIDE, and 200 (52%) were at their late‐life (65‐90 years of age) and studied for ANU‐ADRI. Baseline data was collected on exposure to 6 of the 7 risk factors included in the CAIDE study and to 10 of the 12 risk factors included in the ANU‐ADRI study.ResultsThe mean midlife CAIDE score was 6.97 (SD 2.1), and a quarter of midlife study participants (25.5%) had a score value of 9‐12. The mean late life ANU‐ADRI score was 15.3 (SD 11.9) for males and 14.2 (SD 8.5) for females respectively.ConclusionA quarter of midlife CAIDE study participants of our study have a higher risk of progressing to dementia. The calculated mean ANU‐ADRI score 15.3 (SD 11.9) for our study was lower than the calculated mean ANU‐ADRI score for the Rush Memory and Aging Project 20.3 (SD 11.78), and the Kungsholmen Project 32.38 (SD 7.67), but higher than the calculated mean for the Cardiovascular Health Cognition Study 8.86 (SD 8.77). We strongly propose the need for the establishment of Brain Health Services (BHS) in Ethiopia alongside with the memory clinic to assess risk, risk profiling and communication, and interventions.

A novel, multidomain, primary care nurse-led and mHealth-assisted intervention for dementia risk reduction in middle-aged adults (HAPPI MIND): study protocol for a cluster randomised controlled trial

IntroductionMiddle-aged multidomain risk reduction interventions targeting modifiable risk factors for dementia may delay or prevent a third of dementia cases in later life. We describe the protocol of a cluster...

MyCOACH (COnnected Advice for Cognitive Health): a digitally delivered multidomain intervention for cognitive decline and risk of dementia in adults with mild cognitive impairment or subjective cognitive decline–study protocol for a randomised controlled trial

IntroductionDigital health interventions are cost-effective and easily accessible, but there is currently a lack of effective online options for dementia prevention especially for people at risk due to mild cognitive...

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

BackgroundCurrent dementia risk scores have had limited success in consistently identifying at-risk individuals across different ages and geographical locations.ObjectiveWe aimed to develop and validate a novel dementia risk score for...

Estimating Dementia Risk Using Multifactorial Prediction Models

The clinical value of current multifactorial algorithms for individualized assessment of dementia risk remains unclear. To evaluate the clinical value associated with 4 widely used dementia risk scores in estimating 10-year dementia risk. This prospective population-based UK Biobank cohort study assessed 4 dementia risk scores at baseline (2006-2010) and ascertained incident dementia during the following 10 years. Replication with a 20-year follow-up was based on the British Whitehall II study. For both analyses, participants who had no dementia at baseline, had complete data on at least 1 dementia risk score, and were linked to electronic health records from hospitalizations or mortality were included. Data analysis was conducted from July 5, 2022, to April 20, 2023. Four existing dementia risk scores: the Cardiovascular Risk Factors, Aging and Dementia (CAIDE)-Clinical score, the CAIDE-APOE-supplemented score, the Brief Dementia Screening Indicator (BDSI), and the Australian National University Alzheimer Disease Risk Index (ANU-ADRI). Dementia was ascertained from linked electronic health records. To evaluate how well each score predicted the 10-year risk of dementia, concordance (C) statistics, detection rate, false-positive rate, and the ratio of true to false positives were calculated for each risk score and for a model including age alone. Of 465 929 UK Biobank participants without dementia at baseline (mean [SD] age, 56.5 [8.1] years; range, 38-73 years; 252 778 [54.3%] female participants), 3421 were diagnosed with dementia at follow-up (7.5 per 10 000 person-years). If the threshold for a positive test result was calibrated to achieve a 5% false-positive rate, all 4 risk scores detected 9% to 16% of incident dementia and therefore missed 84% to 91% (failure rate). The corresponding failure rate was 84% for a model that included age only. For a positive test result calibrated to detect at least half of future incident dementia, the ratio of true to false positives ranged between 1 to 66 (for CAIDE-APOE-supplemented) and 1 to 116 (for ANU-ADRI). For age alone, the ratio was 1 to 43. The C statistic was 0.66 (95% CI, 0.65-0.67) for the CAIDE clinical version, 0.73 (95% CI, 0.72-0.73) for the CAIDE-APOE-supplemented, 0.68 (95% CI, 0.67-0.69) for BDSI, 0.59 (95% CI, 0.58-0.60) for ANU-ADRI, and 0.79 (95% CI, 0.79-0.80) for age alone. Similar C statistics were seen for 20-year dementia risk in the Whitehall II study cohort, which included 4865 participants (mean [SD] age, 54.9 [5.9] years; 1342 [27.6%] female participants). In a subgroup analysis of same-aged participants aged 65 (±1) years, discriminatory capacity of risk scores was low (C statistics between 0.52 and 0.60). In these cohort studies, individualized assessments of dementia risk using existing risk prediction scores had high error rates. These findings suggest that the scores were of limited value in targeting people for dementia prevention. Further research is needed to develop more accurate algorithms for estimation of dementia risk.

High health coaching adherence increases Alzheimer’s protection in at‐risk inviduals following a 4‐month intervention

AbstractBackgroundAlzheimer’s disease and related dementias (ADRD) imposed an estimated financial cost to Americans of $355 billion in 2021 and is projected to rise to $1.1 trillion by 2050. ADRD affects more than 6 million Americans and is expected to rise to more than 13 million by 2050. Health coaching (HC) interventions improve many lifestyle factors also known to be protective against ADRD; however, their ability to provide protection in individuals at‐risk for developing cognitive decline is unknown. The purpose of the present investigation was to examine the effect of HC intervention adherence on ADRD protective factor score after 4 months.MethodAdults aged 45‐75 years (n = 104) with at least two ADRD risk factors and a maximum of one protective factor participated in this study. Participants completed the Australian National University Alzheimer’s Disease Risk Index (ANU‐ADRI) both before and after a 4‐month HC protocol focusing on reducing modifiable risk factors for ADRD in six domains (diet, exercise, sleep, stress, social engagement, and cognitive activity). Participants who completed a number of check‐ins with the health coach greater than the group mean (5.6 check‐ins) were placed in the high adherence (HA) group, and individuals with fewer were placed in the low adherence (LA) group. A mixed factorial ANOVA was conducted to determine if there was a difference in pre‐ and post‐ANU‐ADRI overall protective factor score.ResultAfter the 4‐month HC intervention, ANU‐ADRI scores did not change overall in the pooled sample (F(1,102) = 1.45, p = .23). However, a significant adherence*time interaction effect indicated that HA improved overall protective factor score significantly more than LA (F(1,102) = 4.85, p = .03), and pairwise comparisons revealed that protective factor scores improved only in the HA group (p = .02 for HA versus p = .47 for LA).ConclusionThese results indicate an HC intervention may effect behaviors and emotional states understood to be ADRD protective factors when adherence with the protocol is high. This has the potential to improve the quality of life for individuals at‐risk for ADRD. Further investigation will shed light on whether the ADRD protection derived from HC is iterative and durable.

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).

Improving risk indexes for Alzheimer's disease and related dementias for use in midlife.

Knowledge of a person’s risk for Alzheimer’s disease and related dementias (ADRDs) is required to triage candidates for preventive interventions, surveillance, and treatment trials. ADRD risk indexes exist for this purpose, but each includes only a subset of known risk factors. Information missing from published indexes could improve risk prediction. In the Dunedin Study of a population-representative New Zealand-based birth cohort followed to midlife (N = 938, 49.5% female), we compared associations of four leading risk indexes with midlife antecedents of ADRD against a novel benchmark index comprised of nearly all known ADRD risk factors, the Dunedin ADRD Risk Benchmark (DunedinARB). Existing indexes included the Cardiovascular Risk Factors, Aging, and Dementia index (CAIDE), LIfestyle for BRAin health index (LIBRA), Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI), and risks selected by the Lancet Commission on Dementia. The Dunedin benchmark was comprised of 48 separate indicators of risk organized into 10 conceptually distinct risk domains. Midlife antecedents of ADRD treated as outcome measures included age-45 measures of brain structural integrity [magnetic resonance imaging-assessed: (i) machine-learning-algorithm-estimated brain age, (ii) log-transformed volume of white matter hyperintensities, and (iii) mean grey matter volume of the hippocampus] and measures of brain functional integrity [(i) objective cognitive function assessed via the Wechsler Adult Intelligence Scale-IV, (ii) subjective problems in everyday cognitive function, and (iii) objective cognitive decline measured as residualized change in cognitive scores from childhood to midlife on matched Weschler Intelligence scales]. All indexes were quantitatively distributed and proved informative about midlife antecedents of ADRD, including algorithm-estimated brain age (β's from 0.16 to 0.22), white matter hyperintensities volume (β's from 0.16 to 0.19), hippocampal volume (β's from −0.08 to −0.11), tested cognitive deficits (β's from −0.36 to −0.49), everyday cognitive problems (β's from 0.14 to 0.38), and longitudinal cognitive decline (β's from −0.18 to −0.26). Existing indexes compared favourably to the comprehensive benchmark in their association with the brain structural integrity measures but were outperformed in their association with the functional integrity measures, particularly subjective cognitive problems and tested cognitive decline. Results indicated that existing indexes could be improved with targeted additions, particularly of measures assessing socioeconomic status, physical and sensory function, epigenetic aging, and subjective overall health. Existing premorbid ADRD risk indexes perform well in identifying linear gradients of risk among members of the general population at midlife, even when they include only a small subset of potential risk factors. They could be improved, however, with targeted additions to more holistically capture the different facets of risk for this multiply determined, age-related disease.

Cardiometabolic risk factors predict executive function scores in high-risk individuals

Alzheimer’s disease (AD) is expected to triple by 2050, affecting 16 million Americans. As a result, it is essential to combat this alarming increase in cognitive impairment through early detection. Cardiometabolic risk factors have shown to be associated with higher risk of AD. The purpose of this study was to determine if cardiometabolic risk factors could predict executive function scores in a high-risk population. Fifty (60.9±8.8 years) high-risk adults (classified by the Australian National University Alzheimer’s Disease Risk Index) were enrolled in this study. Participants completed a 6-minute walking test, venous blood draw, blood pressure measurement, and the digit coding symbol test (DCS). Results were examined through a multiple linear regression with DCS as the dependent variable and age, sex, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), glucose, 6-minute walking test, systolic blood pressure (SBP), and diastolic blood pressure (DBP) as predictor variables. The model explained 42% of the variance of DCS (p = .04) with SBP (45%; p = .003) as a significant predictor. LDL (p = .087) and DBP (p = .123) accounted for 24% and 22% of the variance for this model, respectively. These results suggest cardiometabolic risk factors predict executive function values in high-risk individuals. Higher SBP was significantly associated with lower DCS scores indicating SBP as a valuable tool for practitioners when evaluating cognitive decline. Further research should expand sample size and track values longitudinally to substantiate these claims.

An Internet-Based Intervention Augmented With a Diet and Physical Activity Consultation to Decrease the Risk of Dementia in At-Risk Adults in a Primary Care Setting: Pragmatic Randomized Controlled Trial.

BackgroundThere is a need to develop interventions to reduce the risk of dementia in the community by addressing lifestyle factors and chronic diseases over the adult life course.ObjectiveThis study aims to evaluate a multidomain dementia risk reduction intervention, Body Brain Life in General Practice (BBL-GP), targeting at-risk adults in primary care.MethodsA pragmatic, parallel, three-arm randomized trial involving 125 adults aged 18 years or older (86/125, 68.8% female) with a BMI of ≥25 kg/m2 or a chronic health condition recruited from general practices was conducted. The arms included (1) BBL-GP, a web-based intervention augmented with an in-person diet and physical activity consultation; (2) a single clinician–led group, Lifestyle Modification Program (LMP); and (3) a web-based control. The primary outcome was the Australian National University Alzheimer Disease Risk Index Short Form (ANU-ADRI-SF).ResultsBaseline assessments were conducted on 128 participants. A total of 125 participants were randomized to 3 groups (BBL-GP=42, LMP=41, and control=42). At immediate, week 18, week 36, and week 62 follow-ups, the completion rates were 43% (18/42), 57% (24/42), 48% (20/42), and 48% (20/42), respectively, for the BBL-GP group; 71% (29/41), 68% (28/41), 68% (28/41), and 51% (21/41), respectively, for the LMP group; and 62% (26/42), 69% (29/42), 60% (25/42), and 60% (25/42), respectively, for the control group. The primary outcome of the ANU-ADRI-SF score was lower for the BBL-GP group than the control group at all follow-ups. These comparisons were all significant at the 5% level for estimates adjusted for baseline differences (immediate: difference in means −3.86, 95% CI −6.81 to −0.90, P=.01; week 18: difference in means −4.05, 95% CI −6.81 to −1.28, P<.001; week 36: difference in means −4.99, 95% CI −8.04 to −1.94, P<.001; and week 62: difference in means −4.62, 95% CI −7.62 to −1.62, P<.001).ConclusionsA web-based multidomain dementia risk reduction program augmented with allied health consultations administered within the general practice context can reduce dementia risk exposure for at least 15 months. This study was limited by a small sample size, and replication on a larger sample with longer follow-up will strengthen the results.Trial RegistrationAustralian clinical trials registration number (ACTRN): 12616000868482; https://anzctr.org.au/ACTRN12616000868482.aspx.

Lifestyle Risk Factors and Cognitive Outcomes from the Multidomain Dementia Risk Reduction Randomized Controlled Trial, Body Brain Life for Cognitive Decline (BBL-CD).

To evaluate the efficacy of a multidomain intervention to reduce lifestyle risk factors for Alzheimer's disease (AD) and improve cognition in individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI). The study was an 8-week two-arm single-blind proof-of-concept randomized controlled trial. Community-dwelling individuals living in Canberra, Australia, and surrounding areas. Participants were 119 individuals (intervention n = 57; control n = 62) experiencing SCD or MCI. The control condition involved four educational modules covering dementia and lifestyle risk factors, Mediterranean diet, physical activity, and cognitive engagement. Participants were instructed to implement this information into their own lifestyle. The intervention condition included the same educational modules and additional active components to assist with the implementation of this information into participants' lifestyles: dietitian sessions, an exercise physiologist session, and online brain training. Lifestyle risk factors for AD were assessed using the Australian National University-Alzheimer's Disease Risk Index (ANU-ADRI), and cognition was assessed using Alzheimer's Disease Assessment Scale-Cognitive subscale, Pfeffer Functional Activities Questionnaire, Symbol Digit Modalities Test (SDMT), Trail Making Test-B, and Category Fluency. The primary analysis showed that the intervention group had a significantly lower ANU-ADRI score (χ2 = 10.84; df = 3; P = .013) and a significantly higher cognition score (χ2 = 7.28; df = 2; P = .026) than the control group. A secondary analysis demonstrated that the changes in lifestyle were driven by increases in protective lifestyle factors (χ2 = 12.02; df = 3; P = .007), rather than a reduction in risk factors (χ2 = 2.93; df = 3; P = .403), and cognitive changes were only apparent for the SDMT (χ2 = 6.46; df = 2; P = .040). Results were robust to intention-to-treat analysis controlling for missing data. Results support the hypothesis that improvements in lifestyle risk factors for dementia can lead to improvements in cognition over a short time frame with a population experiencing cognitive decline. Outcomes from this trial support the conduct of a larger and longer trial with this participant group.

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.

Protocol for a pragmatic randomised controlled trial of Body Brain Life—General Practice and a Lifestyle Modification Programme to decrease dementia risk exposure in a primary care setting

IntroductionIt has been estimated that a 10%–25% reduction in seven key risk factors could potentially prevent 1.1–3.0 million Alzheimer’s disease cases globally. In addition, as dementia is preceded by more...

General Practice Clinical Data Help Identify Dementia Hotspots: A Novel Geospatial Analysis Approach.

We have a poor understanding of whether dementia clusters geographically, how this occurs, and how dementia may relate to socio-demographic factors. To shed light on these important questions, this study aimed to compute a dementia risk score for individuals to assess spatial variation of dementia risk, identify significant clusters (hotspots), and explore their association with socioeconomic status. We used clinical records from 16 general practices (468 Statistical Area level 1 s, N = 14,746) from the city of west Adelaide, Australia for the duration of 1 January 2012 to 31 December 2014. Dementia risk was estimated using The Australian National University-Alzheimer's Disease Risk Index. Hotspot analyses were applied to examine potential clusters in dementia risk at small area level. Significant hotspots were observed in eastern and southern areas while coldspots were observed in the western area within the study perimeter. Additionally, significant hotspots were observed in low socio-economic communities. We found dementia risk scores increased with age, sex (female), high cholesterol, no physical activity, living alone (widow, divorced, separated, or never married), and co-morbidities such as diabetes and depression. Similarly, smoking was associated with a lower dementia risk score. The identification of dementia risk clusters may provide insight into possible geographical variations in risk factors for dementia and quantify these risks at the community level. As such, this research may enable policy makers to tailor early prevention strategies to the correct individuals within their precise locations.