Dementia Cases Research Articles

Estimating the rates of dementia using administrative data linked to cohort studies.

Current estimates of dementia and Alzheimer's disease incidence and prevalence are required to understand the health needs of the elderly. We used two Australia cohort studies, administrative datasets, and data linkage techniques to estimate dementia rates in Australia. The study used Australian Longitudinal Study on Women's Health and the Health in Men Cohort Study. Records of dementia were obtained from linked sources and incidence and prevalence estimates were produced. Capture-recapture methods were used to estimate numbers of dementia cases not identified through data linkage. There were 3399 (28.5%) men with dementia identified from any source and 3767 (34.8%) women. Rates of dementia incidence and prevalence were similar between sexes but were raised in men once estimates of unidentified cases were included. Cohort studies and linked administrative data can be used together to produce current estimates of dementia prevalence and incidence comparable to other population estimates.

Abstract 4136762: Association Between Circadian Rest/Activity Rhythms and Incident Dementia in Older Adults: The Atherosclerosis Risk in Communities (ARIC) Study

Introduction: Alterations in circadian rest/activity rhythms (RARs) are common among older adults. RARs can be derived from accelerometry data and are routinely used as proxy measures. Evidence suggests that altered (e.g., fragmented, delayed) RARs may be risk factors for dementia. However, results have been mixed. Using data from the ARIC Study, a community-based cohort, we assessed the association between RARs and incident dementia. Methods: ARIC participants without dementia who wore the Zio XT ECG Patch in 2016-17 for > 3 days were included. RARs were obtained from an accelerometer within the Zio XT ECG Patch and measures of interdaily stability (IS; consistency), intradaily variability (IV; fragmentation), and relative amplitude (RA; strength) were derived. Dementia cases were identified through 2020 using in-person and phone-based cognitive assessments, hospitalization discharge codes, or death certificates. Cox proportional hazards models were used. Results: Of the 2,183 participants (mean [SD] age 79 [4.5] years, 58% female, 24% identified as Black), 176 (8%) developed dementia. Median follow-up time was 3 years and mean Zio XT ECG Patch wear time was 12 days. After multivariable adjustment, lower RA and higher IV were associated with incident dementia (HRs [95% CIs] per 1-SD increment: 0.65 [0.56-0.76], 1.19 [1.02-1.38]). No significant association between IS and dementia was noted; however, there was an interaction by race (p=0.03). Stratified results were not significant, but effect estimates were in opposite directions: (HRs [95% CIs] per 1-SD increment in IS: 0.78 [0.59-1.02] in Black participants and 1.17 [0.96-1.41] in white participants, respectively). No interactions by sex or APOEε4 were noted (p>0.10). Conclusion: In this community-based cohort of older adults, weaker circadian rhythm strength and fragmentation were associated with dementia risk. The association between IS and dementia may differ by race; however, this needs to be further examined in future research. Overall, measures of circadian RARs may be risk factors for dementia. Additional research is warranted to determine whether they can be novel targets for dementia prevention.

Patterns of tau, amyloid and synuclein pathology in ageing, Alzheimer's disease and synucleinopathies.

Alzheimer's disease (AD) is neuropathologically defined by deposits of misfolded hyperphosphorylated tau (HP-tau) and β-amyloid. Lewy body (LB) dementia, which includes dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), is characterised pathologically by α-synuclein aggregates. HP-tau and β-amyloid can also occur as copathologies in LB dementia, and a diagnosis mixedAD/DLB can be made if present in sufficient quantities. We hypothesised the spread of these abnormal proteins selectively affects vulnerable areas, resulting in pathologic regional covariance that differentially associates with pre-mortem clinical characteristics. Our aims were to map regional quantitative pathology (HP-tau, β-amyloid, α-synuclein) and investigate the spatial distributions from tissue microarray (TMA) post-mortem samples across healthy aging, AD and LB dementia. The study involved 159 clinico-pathologically diagnosed human post-mortem brains (48 controls, 47 AD, 25 DLB, 20 mixedAD/DLB, 19 PDD). The burden of HP-tau, β-amyloid and α-synuclein was quantitatively assessed in cortical and subcortical areas. Principal components (PC) analysis was applied across all cases to determine the pattern nature of HP-tau, β-amyloid and α-synuclein. Further analyses explored the relationships of these pathological patterns with cognitive and symptom variables. Cortical (tauPC1) and temporolimbic (tauPC2) patterns were observed for HP-tau. For β-amyloid, a cortical-subcortical pattern (amylPC1) was identified. For α-synuclein, four patterns emerged: 'posterior temporal - occipital (synPC1)', 'anterior temporal-frontal (synPC2)', 'parieto-cingulate-insula (synPC3)', and 'frontostriatal-amygdala (synPC4)'. Distinct synPC scores were apparent among DLB, mixedAD/DLB and PDD, and may relate to different spreading patterns of α-synuclein pathology. In dementia, cognitive measures correlated with tauPC1, tauPC2 and amylPC1 pattern scores (P≤0.02), whereas such variables did not relate to α-synuclein parameters in these or combined LB dementia cases. Mediation analysis then revealed that in the presence of amylPC1, tauPC1 had a direct effect on global cognition in dementia (n=65, P=0.04), while tauPC1 mediated the relationship between amylPC1 and cognition through the indirect pathway (amylPC1→ tauPC1 → global cognition) (P<0.05). Lastly, in synucleinopathies, synPC1 and synPC4 pattern scores were associated with visual hallucinations and motor impairment, respectively (P=0.02). In conclusion, distinct patterns of α-synuclein pathology were apparent in LB dementia, which could explain some of the disease heterogeneity and differing spreading patterns among these conditions. Visual hallucinations and motor severity were associated with specific α-synuclein topographies in LB dementia that may be important to the clinical phenotype, and could, after necessary testing/validation, be integrated into semi-quantitative routine pathological assessment.

Personhood, Dementia, and Bioethics.

Jennifer Blumenthal-Barby (2024) has called for bioethics to end talk about personhood, asserting that such talk has the tendency to confuse and offend. It will be argued that this has only limited application for (largely) private settings. However, in other settings, theorizing about personhood leaves a gap in which there is the risk that the offending concept will get uptake elsewhere, and so the problem Blumenthal-Barby nominates may not be completely avoided. In response to this risk, an argument is presented in support of the idea that the role of philosophers and bioethicists, far from ending talk of personhood, ought to be to clarify the concept, and to do so in nuanced ways, given its application for specific kinds of impairments. The case of dementia is used to illustrate this in the context of person-centered care. Ironically, given the stigma attached to dementia, far from the need to end talk of personhood, bioethicists are needed to rescue the concept and clarify its role.

Frailty Trajectories Preceding Dementia in the US and UK.

An accessible marker of both biological age and dementia risk is crucial to advancing dementia prevention and treatment strategies. Although frailty is a candidate for that role, the nature of the relationship between frailty and dementia is not well understood. To clarify the temporal relationship between frailty and incident dementia by investigating frailty trajectories in the years preceding dementia onset. Participant data came from 4 prospective cohort studies: the English Longitudinal Study of Ageing, the Health and Retirement Study, the Rush Memory and Aging Project, and the National Alzheimer Coordinating Center. Data were collected between 1997 and 2024 and were analyzed from July 2023 to August 2024. The settings were retirement communities, national-level surveys, and a multiclinic-based cohort. Included individuals were 60 years or older and without cognitive impairment at baseline. Included individuals also had data on age, sex, education level, and ethnicity and a frailty index score calculated at baseline. Frailty was the main exposure, with participants' degrees of frailty quantified using retrospectively calculated frailty index scores. Incident all-cause dementia ascertained through physician-derived diagnoses, self- and informant-report, and estimated classifications based on combinations of cognitive tests. The participant number before exclusions was 87 737. After exclusions, data from 29 849 participants (mean [SD] age, 71.6 [7.7] years; 18 369 female [62%]; 257 963 person-years of follow-up; 3154 cases of incident dementia) were analyzed. Bayesian generalized linear mixed regression models revealed accelerations in frailty trajectories 4 to 9 years before incident dementia. Overall, frailty was positively associated with dementia risk (adjusted hazard ratios [aHRs] ranged from 1.18; 95% CI, 1.13-1.24 to 1.73; 95% CI, 1.57-1.92). This association held among participants whose time between frailty measurement and incident dementia exceeded the identified acceleration period (aHRs ranged from 1.18; 95% CI, 1.12-1.23 to 1.43; 95% CI, 1.14-1.80). These findings suggest that frailty measurements may be used to identify high-risk population groups for preferential enrolment into clinical trials for dementia prevention and treatment. Frailty itself may represent a useful upstream target for behavioral and societal approaches to dementia prevention.

Dementia risk reduction in the African context: Multi-national implementation of multimodal strategies to promote healthy brain aging in Africa (the Africa-FINGERS project).

Dementia prevention in Africa is critically underexplored, despite the continent's high prevalence of modifiable risk factors. With a predominantly young and middle-aged population, Africa presents a prime opportunity to implement evidence-based strategies that could significantly reduce future dementia cases and mitigate its economic impact. The multinational Africa-FINGERS program offers an innovative solution, pioneering culturally sensitive, multidomain interventions tailored to the unique challenges of the region. Leveraging insights from landmark global studies such as Worldwide-FINGERS and Alzheimer's Disease Neuroimaging Initiative, the program employs a multideterminant precision prevention framework, grounded in community based systems dynamics. Africa-FINGERS further integrates cutting-edge state-of-the-art multimodal biomarker evaluations tailored to regional contexts, with the goal of advancing brain health and establishing a global standard for dementia prevention. This groundbreaking initiative highlights the potential for scalableand sustainable interventions, thus is poised to transform dementia risk reduction efforts across the continent. HIGHLIGHTS: Dementia rates are escalating in Africa, largely due to longer life spans and increased prevalence of modifiable risk factors. Yet, few regional interventions have directly targeted lifestyle factors to reduce dementia risk. The multinational Africa-FINGERS study will address this gap by adapting the successful FINGERS lifestyle intervention to African populations. Africa-FINGERS will pioneer a culturally informed, multidomain dementia risk reduction intervention in the African region through feasibility dementia prevention trials in rural and urban sites across Kenya and Nigeria in the first instance, enrolling 600 at-risk adults (≥ 50 years). The program adoptsparticipatory research methods to develop culturally appropriate interventions and build infrastructure to evaluate dementia biomarkers from ante and post mortem samples. A cost-effectiveness analysis will be conducted to guide the strategic implementation of Africa-FINGERS into regional health systems. The Africa-FINGERS strategy aligns with the Worldwide-FINGERS framework and integrates the global Alzheimer's Disease Neuroimaging Initiative approach, emphasizing multimodal analysis.

Facial aging, cognitive impairment, and dementia risk

BackgroundFacial aging, cognitive impairment, and dementia are all age-related conditions. However, the temporal relation between facial age and future risk of dementia was not systematically examined.ObjectivesTo investigate the relationship between facial age (both subjective/perceived and objective) and cognitive impairment and/or dementia risk.MethodsThe study included 195,329 participants (age ≥ 60 y) from the UK Biobank (UKB) with self-perceived facial age and 612 participants from the Nutrition and Health of Aging Population in China Project (NHAPC) study (age ≥ 56 y) with objective assessment of facial age. Cox proportional hazards model was used to prospectively examine the hazard ratios (HRs) and their 95% confidence intervals (CIs) of self-perceived facial age and dementia risk in the UKB, adjusting for age, sex, education, APOE ε4 allele, and other potential confounders. Linear and logistic regressions were performed to examine the cross-sectional association between facial age (perceived and objective) and cognitive impairment in the UKB and NHAPC, with potential confounders adjusted.ResultsDuring a median follow-up of 12.3 years, 5659 dementia cases were identified in the UKB. The fully-adjusted HRs comparing high vs. low perceived facial age were 1.61 (95% CI, 1.33 ~ 1.96) for dementia (P-trend ≤ 0.001). Subjective facial age and cognitive impairment was also observed in the UKB. In the NHAPC, facial age, as assessed by three objective wrinkle parameters, was associated with higher odds of cognitive impairment (P-trend < 0.05). Specifically, the fully-adjusted OR for cognitive impairment comparing the highest versus the lowest quartiles of crow’s feet wrinkles number was 2.48 (95% CI, 1.06 ~ 5.78).ConclusionsHigh facial age was associated with cognitive impairment, dementia and its subtypes after adjusting for conventional risk factors for dementia. Facial aging may be an indicator of cognitive decline and dementia risk in older adults, which can aid in the early diagnosis and management of age-related conditions.

Multiomic profiling reveals timing of menopause predicts prefrontal cortex aging and cognitive function.

A new case of dementia is diagnosed every 3 s. Beyond age, risk prediction of dementia is challenging. There is growing evidence of underlying processes that connect aging across organ systems and may provide insight for early detection, and there is a need to identify early biomarkers at an age when action can be taken to mitigate cognitive decline. We hypothesized that timing of menopause, a marker of ovarian aging, predicts brain age decades later. We used 2086 subjects with multiple "omics" measurements from post-mortem brain samples. Age at menopause (AAM) is positively correlated with cognitive function and negatively correlated with pre-frontal cortex aging acceleration (calculated as estimated biological age from DNA methylation minus chronological age). Genetic correlations showed that at least part of these associations is derived from shared heritability. To dissect the mechanism linking AAM to cognitive decline, we turned to transcriptomic data which confirmed that later AAM was associated with gene expression in pre-frontal cortex consistent with better cognition, and among those who reached menopause naturally, decreased gene expression of pathways implicated in aging. Those with surgical menopause displayed different molecular changes, including perturbed nicotinamide adenine dinucleotide (NAD+) activity, validated by metabolomics. Bile acid metabolism was perturbed in both groups, although different bile acid ratios were associated with AAM in each. Together, our data suggest that AAM is predictive of brain aging and cognition, with potential mediation by the gut, although through different mechanisms depending on the type of menopause.

A novel light GBM framework combined with gain optimization for predicting dementia severity of Alzheimer’s disease

ABSTRACT Introduction Alzheimer’s disease (AD) is a severe brain disorder, which comprises almost 75% of all types of dementia cases. As per the latest report, the global count of Alzheimer’s disease patients is 131 million, due to which the treatment of Alzheimer’s disease is becoming a major burden to the world healthcare system. However, Alzheimer’s disease still remains incurable due to its multifactorial nature of symptoms. Therefore, early-stage diagnosis of Alzheimer’s disease is essential, which helps in the treatment and recovery of patients to a greater extent. Background In the existing literature, few research attempts are carried out for Alzheimer’s disease prediction using machine learning algorithms; yet, most of the existing techniques fail to focus on aspects such as gain-optimisation, dementia severity identification and accuracy enhancement. Methodology To solve these issues, this article presents a light gradient boosting machine-based framework, which employs a gain-optimisation algorithm using multi-category factors for the early stage prediction of Alzheimer’s disease. Further, the proposed framework introduces a detailed feature analysis for identifying the key features needed for the disease prediction and thereby classifies the Alzheimer’s disease patients into different dementia severity types. Results The extensive evaluations of the proposed framework on different dataset sizes using real-world MRI datasets of patients, clearly demonstrate the performance of the proposed work in terms of gain-optimised accuracy metrics. Further, experimental evaluations clearly prove the better performance of the proposed framework when compared to that of other ML techniques including LR, RFC, SVR, SVC and existing methods in terms of Recall, F1-score and accuracy metrics.

Association of dietary choline intake with incidence of dementia, Alzheimer's disease and mild cognitive impairment: a large population-based prospective cohort study

ObjectiveExplore the associations between dietary choline intake and the incidence of dementia, Alzheimer's disease (AD), mild cognitive impairment (MCI), and current cognitive performance in the UK Biobank cohort. MethodsDietary choline intake was categorized into quartiles of consumption based on 24-hour dietary recalls, with units expressed as milligrams per day. Diagnoses of dementia, AD, and MCI were identified using ICD-9/10 codes. Current cognitive performance was assessed via the computerized touchscreen interface. After adjusting for sociodemographic factors, dietary and lifestyle behaviors, and comorbid conditions, Cox proportional hazards regression, logistic regression, and restricted cubic splines were used to analyze the association between choline intake and dementia or cognitive performance. ResultsAmong 125,594 participants (55.8% female), with a mean age of 56.1 years (range: 40 to 70 years) at baseline, and a median follow-up of 11.8 years, 1,103 cases of dementia (including 385 AD) and 87 cases of MCI were recorded. U-shaped associations were observed between choline intake and dementia and AD. Participants in the 2nd quartile of total choline intake had lower risks compared to those in the lowest quartile, with HR of 0.80 (95% CI: 0.67, 0.96) for dementia and 0.76 (0.58, 1.00) for AD. Moderate intake of choline derivative, including free choline (HR, 0.77; 95%CI, 0.65, 0.92), phosphatidylcholine (0.82; 0.68, 0.98), sphingomyelin (0.82; 0.69, 0.98) and glycerophosphocholine (0.83; 0.70, 1.00), were associated with a 17% to 23% lower odds of dementia. Additionally, moderate total choline intake was associated with an 8% to 13% lower odds of poor cognitive performance in visual attention (OR, 0.92; 95%CI, 0.86, 0.99), fluid intelligence (0.87; 0.82, 0.92), and complex processing speed (0.90; 0.84, 0.95). ConclusionsIn conclusion, our findings suggest that moderate dietary choline intake, ranging from 332.89 mg/d to 353.93 mg/d, is associated with lower odds of dementia and better cognitive performance.

Modifiable risk factors for dementia in India: a cross-sectional study revisiting estimates and reassessing prevention potential and priorities

BackgroundAbout 16% of worldwide dementia cases are in India. Evaluating the prospects for dementia prevention in India requires knowledge of context-specific risk factors, as relationships between risk factors and dementia...

Metabolomic profiling identifies signatures and biomarkers linking air pollution to dementia risk: a prospective cohort study

Exposure to air pollution has been associated with increased dementia. However, it remains unknown what specific metabolic mechanisms play a role in this relationship. We included 192,300 dementia-free participants from the UK Biobank cohort study. Annual concentrations of air pollution were assessed based on the residential address. Elastic net regression was performed to identify air pollution-related metabolites, and metabolic score was constructed. Cox regression models and covariate balancing generalized propensity scores (CBGPS) regression models were conducted to explore the longitudinal associations between air pollution/metabolic signatures and dementia risk. The underlying mechanisms between air pollution and dementia driven by metabolic signature or specific metabolites were also investigated. A total of 2,592 incident dementia cases were documented. We identified the metabolite profiles in response to air pollution exposure, including 87 metabolites for PM2.5, 65 metabolites for PM10, 76 metabolites for NO2, and 71 metabolites for NOx. The air pollution-related metabolic signatures were associated with increased risk of dementia, with hazard ratios (HR) of 1.17 (95% CI: 1.12, 1.22), 1.06 (95% CI: 1.02, 1.11), 1.16 (95% CI: 1.10, 1.21), and 1.17 (95% CI: 1.12, 1.22) for PM2.5, PM10, NO2 and NOx, respectively. The associations persisted using causal models. Metabolic signatures mediated the associations between air pollution exposure and dementia risk, with mediation proportions ranging from 6.57% to 12.71%. Additionally, we observed that a metabolite known as free cholesterol in medium VLDL (M-VLDL-FC) played a crucial mediating role. Our study provides novel insights into the metabolic mechanisms linking air pollution exposure to dementia risk.

Knowledge, Attitudes and Confidence in Providing Dementia Care to Older Adults Among Nurses Practicing in Hanoi, Vietnam: A Cross-Sectional Study.

Nurses' competencies are crucial in providing effective dementia care in healthcare settings for older people. Understanding nurses' current knowledge, attitudes and confidence in this area is essential for developing education programmes for healthcare professionals to improve patient care. The purpose of this study was to assess the knowledge, attitudes and confidence related to providing dementia care among nurses practicing in geriatric hospital wards and nursing homes in Hanoi, Vietnam. A total of 269 out of 313 (response rate was 86%) full-time nurses working at six geriatric wards in hospitals and nursing homes in Hanoi were surveyed using three self-administered questionnaires: the Dementia Knowledge Assessment Scale (DKAS), Dementia Attitude Scale (DAS) and the Confidence in Dementia Scale (CODE). Multiple regression models were constructed to identify factors associated with dementia care knowledge, attitudes and confidence. The overall mean scores of nurse's knowledge, attitudes and confidence were 28.1 ± 8.0, 102.1 ± 13.4 and 28.3 ± 6.4, respectively. A positive correlation was reported between the knowledge and attitude scores and between the attitudes and confidence scores. Greater seniority (β: 0.29; 95% CI: 0.03-0.56) and having learned information through colleagues or experts (β: 3.02; 95% CI: 0.88-5.16) were associated with better dementia knowledge. A higher level of dementia training desirability was associated with increased knowledge (β: 0.74; 95% CI: 0.28-1.20) and favourable attitudes (β: 0.94; 95% CI: 0.15-1.74), whereas frequent exposure to dementia cases was associated with higher confidence (β: 3.56; 95% CI: 1.39-5.73) and more favourable attitudes (β: 3.96; 95% CI: 0.27-7.66). Our study highlights deficits in knowledge, low levels of social comfort in nurses' attitudes towards people with dementia and a lack of confidence in providing effective care among nurses practicing in healthcare settings for older adults in Hanoi, Vietnam. With the ageing of the population and with increasing numbers of persons living with dementia, our findings suggest the importance of improving the training of nurses to specifically address these deficits. Multidisciplinary consultation meetings need to be encouraged in the healthcare workplace setting as well as ensuring the presence of qualified counsellors for care teams working with older adults in non-hospital settings. Training about non-cognitive symptoms of dementia and demonstrating effective verbal and non-verbal communication skills is critical and should be integrated into nurse's educational training.

Predicting mid- and late-life dementia risk in primary care: A prognostic study from a national health screening cohort

The objective of this study is to develop and validate dementia risk prediction models for mid- and late-life individuals that are based on accessible variables within a primary care setting. Using the Korean National Health Insurance Service database (2010–2019), we analyzed 6,696,073 individuals aged 40 and older who participated in a national health screening program over a mean follow-up of 8.95 years. Potential predictors were selected based on a literature review and the available data. Dementia cases were identified using claim-based codes and validated by corresponding prescription information. 5-year dementia risk prediction models for mid-life (40–59 years), and late-life (60+ years) stages were developed using the Cox proportional hazards model. Model performance was assessed through discrimination and calibration. Both models included age, sex, body mass index, smoking, alcohol consumption, physical activity, diabetes, hypertension, dyslipidemia, and chronic kidney disease. The models’ AUROCs were 0.764 for mid-life and 0.743 for late-life. The impact of predictors on dementia risk was consistently stronger in mid-life than in late-life stages. Our models showed good calibration with low-risk estimates in mid-life and overall in late-life. These findings underscore the crucial role of managing modifiable risk factors, particularly during mid-life to reduce dementia risk.

Combined Effect of tDCS and Motor or Cognitive Activity in Patients with Alzheimer’s Disease: A Proof-of-Concept Pilot Study

(1) Background: Alzheimer’s disease (AD) accounts for 70% of dementia cases and with no effective pharmacological treatments, new rehabilitation methods are needed. Motor and cognitive activities and transcranial direct current stimulation (tDCS) have shown promise in stabilizing and enhancing cognitive functions. Objective: we want to investigate the effects of tDCS combined with motor or cognitive activity on cognitive functions in AD patients. (2) Methods: Patients with mild or moderate AD were randomized between anodic tDCS groups (MotA or CogA) and sham tDCS groups (MotS or CogS). They received two weeks of treatment (45 min, five days/week), with the first 15 min using tDCS stimulation on the dorsolateral prefrontal cortex. Cognitive assessments were conducted pre-treatment (T0), post-treatment (T1), and one week after (T2). (3) Results: Twenty-three patients were included. Statistical analysis showed significant differences between anodic tDCS groups (MotA + CogA) and sham tDCS groups (MotS + CogS) with advantages for the first in improving global cognitive status (p = 0.042), selective attention (p = 0.012), and sustained attention (p = 0.012). Further analysis indicated no differences between the two anodic tDCS groups between T0 and T1. (4) Conclusions: combined anodal tDCS with motor or cognitive activity could improve global cognitive state and attention, slowing cognitive decline in AD patients. The trial was registered on Clinical Trials: NCT06619795.

A novel electrocardiogram-based model for prediction of dementia: the Atherosclerosis Risk in Communities (ARIC) study

Abstract Background Current dementia prediction models, such as the UK Biobank Dementia Risk Score (UKBDRS, 12 variables), are limited by having variables that are difficult to obtain, subjectively measured, and self-reported. Although individual ECG parameters are linked to higher dementia risk, no study has combined multiple ECG parameters to create an ECG model for dementia prediction. Purpose Using data from the ARIC study, a prospective community-based cohort of Black and White participants in the USA that started in 1987-89, we created an ECG model (based on 7 pre-defined ECG parameters linked to dementia) and compared its model discrimination with the UKBDRS. Methods Participants without prevalent dementia from 2 baselines were included: Visit 4 (V4, 1996-98) and V5 (2011-13). Seven ECG parameters were examined: heart rate, abnormal P-wave terminal force in lead V1 (PTFV1), prolonged P wave duration, abnormal P wave axis, advanced interatrial block, left ventricular hypertrophy (LVH, Cornell voltage criteria), and corrected QT interval (QTc). Dementia cases were identified through 2019 using in-person and phone cognitive assessments, hospitalisation codes, and death certificates. Using a backward selection method (keeping variables with P&amp;lt;0.10 and age, since it is an important dementia risk factor), parsimonious Cox regression models were constructed for prediction of dementia over the longest available follow-up (V4: 23 years, V5: 8 years). Model discrimination of the ECG models and UKBDRS variables was evaluated by the Harrell’s C-statistic (95% CI). Results At V4, 11,520 participants (mean age, 62.8 ± 5.7 years; 56% women; 22% Black) were included; 2,427 developed dementia. At V5, 5,667 participants (mean age, 75.4 ± 5.1 years; 59% women; 22% Black) were included; 830 developed dementia. Figures 1 and 2 show the C-statistic (95%) and area under the ROC curve for the ECG model and UKBDRS variables at V4 and V5, respectively. The C-statistic (95% CI) for both ECG models were &amp;gt;0.70 and comparable with the corresponding UKBDRS models. Conclusion For middle-aged and older adults, a dementia prediction model comprising age and 4 ECG variables has good discrimination for dementia that is comparable with the 12-variable UKBDRS model. Since ECG variables are easily obtainable and objectively measured, these ECG models will be easier to adopt than the UKBDRS model. Further work to validate this model in an external cohort and develop a risk score will facilitate clinical translation.

Preventing dementia is possible. The fraction of cases attributable to modifiable factors in Italy

Abstract Background Worldwide, approximately 50 million people live with dementia. The Lancet Commission, in its latest report in 2020, identifies 12 ‘modifiable’ risk factors associated with dementia, estimating that they account for about 40% of overall dementia cases. The study aimed to estimate the population attributable fraction (PAF) of dementia cases in Italy due to these factors. Methods Relative risks (RR) of each factor and formulas for estimating PAF are derived from the Lancet Commission’s report. Prevalence data were obtained from the Italian surveillance systems PASSI and PASSI d’Argento (PdA), which provide information on 11 of the 12 identified risk factors. The total sample 2017-19 included 86,494 individuals aged 18-64 and 48,516 individuals aged over 65. The Communality, used for ‘weighted’ estimates of PAFs, was calculated via principal components analysis of the inter-risk-factor tetrachoric correlation matrix, using data derived from the overall PASSI and PdA sample. Results In Italy, it is estimated that 39.5% of dementia cases are attributable to 11 of the 12 modifiable risk factors, equivalent to 445,150 out of 1,126,961 estimated cases as of January 1, 2023. Among the risk factors with the greatest impact are hypertension before the age of 65 (weighted PAF=6.3%); sedentariness after the age of 65 (weighted PAF=5.9%); low education in youth (weighted PAF = 5%); depression and social isolation after the age of 65 (weighted PAF=4.5% and 4.4%), as well as diabetes (weighted PAF=3.9%) and smoking (weighted PAF=2.4%). Conclusions Although it is necessary to to gather further evidence on the role and mechanisms of action of each factor, the importance of prevention and promotion of health and social well-being in the field of dementia is now evident. Key messages • Having an overall national sample of adults and elderly people is a unique tool that allows an estimation of dementia cases attributable to modifiable risk factors. • Dementia prevention may also be possible by acting on modifiable risk factors.

Genetic and lifestyle risks for coronary artery disease and risk of incident dementia

Abstract Background Lifestyle risk factors for coronary artery disease (CAD) have been associated with neurodegeneration and dementia. Whether an underlying genetic risk for the development of CAD further increases this risk, however, remains unclear. Purpose To examine whether an increased genetic risk for CAD is associated with an increased risk of dementia, and to investigate whether lifestyle factors modify these associations. Methods A prospective cohort study of 457,353 participants of European ancestry without dementia at baseline (mean [SD], age 57 [8] years; 55% were female) was conducted within the UK Biobank study. Exposures were a genome-wide polygenic risk score (PRS) for CAD and a modified version of the American Heart Association’s Life’s Essential 8 (LE8) cardiovascular health score comprising four biological (BMI, BP, lipids, glucose) and four lifestyle (smoking status, diet, physical activity, and sleep duration) risk factors previously linked to both CAD and dementia. Dementia diagnoses were obtained from death register and inpatient records. Results 9,337 cases of all-cause dementia were observed over a median 13.9-year follow-up. There were no significant interactions between genetic risk for CAD and lifestyle factors for all-cause dementia, or any other dementia subtype. After adjusting for potential demographic confounders, a higher genetic risk for CAD was associated with elevated risk of developing all-cause dementia (hazard ratio (HR) per 1-SD increase in PRS = 1.08 [95% CI, 1.05-1.10]), Alzheimer’s disease (HR 1.08 [1.03-1.12]), and vascular dementia (HR 1.13 [1.06-1.19]). An unfavourable LE8 cardiovascular health score was also associated with an increased risk of developing all-cause dementia (HR 1.05 [1.02-1.08]), predominantly through its association with vascular dementia (HR 1.14 [1.06-1.22]), as we found no evidence for any associations between LE8 and Alzheimer’s disease (HR 1.00 [0.97-1.02]). Conclusion These findings indicate that a high genetic risk for CAD is associated with an increased risk of developing major dementia subtypes. Regardless of this underlying genetic risk, however, our results suggest that lifestyle interventions designed to reduce the risk of CAD may also reduce one’s risk of developing dementia, particularly in those with underlying vascular pathology.

Detecting Unrecognized Dementia Using Deep Learning Methods with Korean National Claims Data

Abstract Background Dementia is one of the leading causes of death in elderly people. In Korea, the government supports dementia screening for people aged 60 and older, but it is not a population-based organized screening program. It is difficult to achieve early diagnosis and early treatment. The objective of this study is to detect unrecognized dementia using Korean National Claims Data for early diagnosis. Methods This study used the National Health Insurance Claims data in Korea. The case group is the new dementia visit as an unrecognized group, while the control group was randomly selected from the general population without dementia. The predictors included health utilization, socioeconomic and demographic, procedure codes, diagnostic codes and health screenings data. The health utilization variables included length of stay, the number of outpatient visits, health expenditures and other encounter information. All medical utilization data was aggregated monthly. In the case group, 12 months of data from 6-month or 2-years before the onset of dementia were used. In the control group, 12 months of data from randomly selected time point were included. Transformer, embedding methods, and Time2Vec were used as the deep learning methods. This study included 453,306 incidents of dementia, representing almost all cases reported from 2010 to 2019 in Korea. The control group consisted of 669,873. Results After training the deep learning prediction model, the AUC for 6-month dementia prediction on the test dataset was 0.87, and for 2-year dementia prediction, it was 0.76. The most important variables were health utilization variables, especially health expenditures. The health utilization trajectories of the case group were significantly different from those of the control group. Conclusions Based on the claims data, we could detect unrecognized dementia case. We can extend the model to other chronic disease such as cancer and stroke, which are significant burden of disease. Key messages • Screening in healthcare facilities or using lab test results would be more accurate, but it would be difficult to apply to a large population due to the cost-effectiveness and timeliness constraints. • On the other hand, the detection model using the existing claims data would be helpful to to develop cost-effective mass screening program. It can cover the weakness of current screening program.

Longitudinal Changes in Peak Expiratory Flow Predict Risk for Incident Dementia.

Impaired respiratory function, measured via peak expiratory flow (PEF), has been associated with increased dementia risk. However, much of the current literature uses cross-sectional measures of PEF, whereas longitudinal relationships between changes in respiratory function and dementia risk are underexplored. Using 10 years of data (2011-2021) from 2,439 adults ages 65 and older in the National Health and Aging Trends Study (NHATS), we examined whether 5-year changes in PEF (2011-2016) were associated with risk for incident dementia over the subsequent 5-year period (2017-2021). PEF slopes for each participant were estimated using linear mixed-effects models and then grouped into quartiles: rapid, moderate, mild, and no declines. Discrete-time Cox proportional hazards models were used to estimate the risk for incident dementia by PEF slope category, while controlling for several health and sociodemographic characteristics. After excluding dementia cases during the exposure window (2011-2016), we identified 338 cases of incident dementia (13.9%) between 2017-2021. Rapid PEF declines between 2011-2016 were associated with 85% higher risk for incident dementia between 2017-2021 compared to those with no declines in PEF (HR=1.85; 95% CI [1.24, 2.76]). Results were robust to several sensitivity analyses. These findings demonstrate that declines in PEF may precede declines in cognition, suggesting that respiratory function may be an important dementia risk factor in older adults. Additionally, these findings highlight the utility of measuring PEF via a peak flow meter, which is a simple and inexpensive measure of respiratory function.