Alzheimer's & Dementia: Translational Research & Clinical Interventions

INTRODUCTIONThis retrospective study investigates whether exposition to levodopa/carbidopa (LA/CA) medication is associated with modified Alzheimers disease (AD) trajectories. METHODSMultivariate analysis used cerebrospinal fluid (CSF) biomarker information included in the National Alzheimers Coordinating Center Uniform Data Set for subjects with normal cognition (NC), mild cognitive impairment (MCI) and dementia (DE). Survival analyses examined the progression to MCI/DE and death events. RESULTSLA/CA use is associated with lower levels of CSF amyloid beta, phosphorylated tau (P-tau) and total Tau. After adjusting for age, sex and APOE {varepsilon}4 allele presence, that effect was quantified by negative coefficients of the fitted linear mixed models - P values <0.01 in all cases except for P-tau in the MCI subgroup (P=0.02). No similar effects were identified for other antiparkinsonian drugs. Exposition to LA/CA decreased the progression from MCI to DE (P=0.03). DISCUSSIONThe identified effects of LA/CA exposition on AD biomarkers and progression deserve further investigation in controlled clinical trials.

BackgroundAnticholinergic medications are widely used, however their use in older people has been linked to cognitive decline, dementia and increased mortality. This systematic review examines the literature investigating relationships between anticholinergic burden and risk of dementia, cognitive impairment, and outcomes in dementia. MethodsCochrane database and PubMed searches using the terms "anticholinergic" and "dementia" or "cognition" were performed up to May 2023. Outcomes included: (i) dementia diagnosis, (ii) cognitive outcomes in people without dementia (iii) cognitive outcomes, hospitalisation and death in people with dementia. Inclusion and exclusion criteria were defined, and papers were evaluated for inclusion by two researchers independently. Papers examining these relationships specifically for urinary drugs and antidepressants were also analysed separately. ResultsSixty observational studies met our criteria across the three outcomes of interest. Anticholinergic burden was found to be consistently associated with increased risk of dementia however the relationships with cognitive outcomes were less clear. In people with dementia, there were consistent associations between the anticholinergic burden and mortality (hazard ratio (HR) range: 1.04-1.23) or hospitalisation (HR range: 1.13-4.54) but not for cognitive outcomes. Urological drugs with high anticholinergic burden were associated with a [&ge;]50% increased mortality risk in people with dementia. ConclusionAnticholinergic burden has been consistently associated with increased dementia incidence. Furthermore, in people with existing dementia, anticholinergic burden is associated with increased mortality and hospitalisation. Associations with cognitive outcomes in people without/with dementia remains uncertain. Clinicians should be advised to exercise caution with anticholinergic medication use in older people.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSINTRODUCTIONC_ST_ABSTreatment response in Alzheimers disease (AD) varies substantially across patients, yet no validated frameworks exist to estimate heterogeneous treatment effects (HTE) from observational data while controlling for confounding bias. METHODSWe developed a causal machine learning framework integrating expert-guided causal graphs, complementary HTE estimators, sensitivity analyses, and policy learning. We applied it to cholinesterase inhibitors (ChEIs) in MCI due to AD to patients from the NACC and ADNI cohorts. RESULTSAnalysing 4,049 patients with 12-month and 2,223 with 36-month follow-up, all estimators indicated null or negative long-term ChEI effects on cognitive and functional outcomes, notably on functional measures. ChEIs showed slightly more deleterious effects among men than women. DISCUSSIONThis framework provides a methodology for estimating HTE from observational data. It revealed no beneficial responder subgroups, highlighting the challenge of detecting treatment heterogeneity in moderately sized cohorts. This approach can inform treatment selection for other AD therapies including memantine, anti-amyloid agents, and emerging treatments.

ObjectivesPolygenic hazard score (PHS) models can be used to predict the age-associated risk for complex diseases, including Alzheimers disease (AD). In this study, we present an improved PHS model for AD that incorporates a large number of genetic variants and demonstrates enhanced predictive accuracy for age of onset in European populations compared to alternative models. MethodsWe used the genotyped European Alzheimer & Dementia Biobank (EADB) sample (n=42,120) to develop and evaluate the performance of the PHS model. We developed a PHS model building on 720 genetic variants, including Apolipoprotein E (APOE) {varepsilon}2 and {varepsilon}4 alleles. We used Elastic Net-regularized Cox regression approach to develop the PHS model. ResultsThe new PHS model (EADB720) improved prediction accuracy compared to alternative models in European populations, with the Odds Ratio OR80/20 from the highest quintile of risk (80th risk percentile and above) to the lowest quintile of risk (20th risk percentile and below) varying between 5.10 and 13.15 within the range of age of onset from 65 - 85 years. Our model also improved risk stratification across {varepsilon}3/3 individuals of European ancestry (OR80/20 ranges from 1.95 to 3.52). It was also successfully validated in independent datasets (HUSK, DemGene and ADNI) by achieving OR80/20 up to 10.00 in each independent dataset. ConclusionOur EADB720 model significantly improves the accuracy of age-associated risk of AD across European populations (pval<0.03). Accurately predicting the age of onset of AD is of large clinical importance to implementing new AD medication and early intervention in clinical settings.

BackgroundSecond-generation antipsychotics (SGAs) are frequently used off-label to manage behavioral symptoms in Alzheimers disease (AD), despite ongoing concerns about their safety. Comparative evidence on mortality risk across specific SGAs remains limited. ObjectiveTo compare all-cause mortality among AD patients treated with commonly prescribed SGAs and to explore treatment effect heterogeneity using causal machine learning. MethodsWe conducted a retrospective cohort study using de-identified electronic health records from the Truveta platform (2018-2024). Patients with incident AD initiating treatment with aripiprazole, risperidone, quetiapine, or olanzapine were identified using an active-comparator, new-user design. Drug exposure was modeled as a time-varying covariate in Cox proportional hazards models, with propensity score matching applied to control for confounding. Causal tree and targeted maximum likelihood estimation (TMLE) were used to identify subgroups with heterogeneous treatment effects. ResultsAmong 17,004 AD patients, aripiprazole was associated with significantly lower mortality than olanzapine (HR = 0.667, 95% CI: 0.472-0.941) and quetiapine (HR = 0.677, 95% CI: 0.462-0.990). Quetiapine was also associated with lower mortality than olanzapine (HR = 0.833, 95% CI: 0.702-0.990) and risperidone (HR = 0.830, 95% CI: 0.705-0.978). Causal tree analysis revealed treatment effect heterogeneity by clinical characteristics, particularly among patients using type 2 diabetes (T2DM) medications. In subgroup analyses, aripiprazole remained protective in T2DM users (HR = 0.604 vs. quetiapine and risperidone, p = 0.002). ConclusionsMortality risks vary substantially across SGAs in AD patients. Aripiprazole and quetiapine were associated with lower mortality compared to olanzapine and risperidone. Treatment effect heterogeneity suggests the need for individualized prescribing based on patient characteristics such as comorbid T2DM.

BackgroundAlzheimers disease is a major health problem, affecting ~4*5% of people aged 60 and older in 2016 with over 43 million affected globally1. The traditional approach for detection evaluates an individual in the presence of symptoms. However, it has been established that amyloid deposits begin to accumulate years before symptoms begin to appear2,3. With improved technology, there is increased focus on risk reduction, timely diagnosis, and early intervention. Early identification of at-risk individuals may enable patients and their families to better prepare for and reduce the impact of this condition.\n\nMethodsWe obtained data for patients from two longitudinal retrospective cohorts (Alzheimers Disease Neuroimaging Initiative: ADNI and National Alzheimers Coordinating Center: NACC), including T1-weighted MRI and genetics data. The polygenic risk score (PRS) used in this study was built based on a published Genome Wide Association Study (GWAS) that identified variants associated with Alzheimers disease. Quantitative MRI features were obtained using a 3D U-Net neural network for brain segmentation. Cox proportional hazards (CPH) regression models were used with subjects censored at death or the last evaluation. Time-to-event was defined as the time it takes for an individual who is dementia-free at the baseline MRI to progress to dementia as defined by the criteria described by ADNI. Time-dependent ROC areas under curve (AUCs) were estimated in the presence of censored data. The time-dependent AUCs were compared among models using the Wilcoxon rank sum test for dependent samples. Data was binned into three groups according to survival probability to eight years after baseline and Kaplan-Meier survival analysis was used to estimate the probability of surviving at least to time t. Calibration for both training and validation cohorts was evaluated using the predicted survival probability, splitting samples into five risk groups of equal size based on the predicted survival probability.\n\nFindingsWe developed a model that predicts the onset of dementia over an eight-year time window in individuals with genetics data and a T1-weighted MRI who were dementia-free at baseline. We then validated the model in an independent multisite cohort.\n\nWe observed that models using PRS in addition to MRI-derived features performed significantly better as measured by time-varying AUC up to eight years in both the training (p = 0*0071) and validation (p = 0*050) cohorts. We observed improved performance of the two modalities versus MRI alone when compared with more invasive amyloid measures. The combined MRI and PRS model showed equivalent performance to cerebral spinal fluid (CSF) amyloid measurement up to eight years prior to disease onset (p = 0*181) and while the MRI only model performed worse (p = 0*040). Finally, we compared to amyloid positron emission tomography (PET) three to four years prior to disease onset with favorable results.\n\nInterpretationOur finding suggests that the two modalities are complementary measures, in that MRI reflects near-term decline and the addition of genetics extends the prediction scope of quantitative MRI by adding additional long-term predictive power.\n\nThe proposed multimodal model shows potential as an alternate solution for early risk assessment given the concordance with CSF amyloid and amyloid PET. Future work will include further comparison with amyloid PET (greater than four years) and with CSF (greater than eight years) as additional long-term data becomes available. Also, the model will be evaluated for its clinical utility in the \"active surveillance\" of individuals who may be concerned about their risk of developing dementia but are not yet eligible for assessment by amyloid PET or CSF.\n\nRESEARCH IN CONTEXT\n\nEvidence before this studyThe most significant known genetic factor in Alzheimers disease (AD) is the {varepsilon}4 allele for the Apolipoprotein E (APOE) gene. Carriers of the allele have a three-fold increased risk of developing AD, whereas individuals who are homozygous have a 15-fold increased risk. Genome-wide association studies (GWASs) have identified many additional genetic variants that are associated with AD. Recent studies have shown that the risk for AD is better predicted by combining effects from several genetic variants into \"polygenic risk scores\" (PRS). Studies have also demonstrated that the age of onset for AD is better predicted using PRS rather than APOE status alone. Regional brain atrophy, as measured using volumetric MRI, is also an important biomarker for evaluating an individuals risk of developing dementia. Previous predictions have shown that medial temporal lobe atrophy, as measured by a Hippocampal Occupancy Score (HOC) is highly associated with progression from MCI to AD.\n\nAdded value of this studyIn the proposed model, the addition of genetics to MRI data lengthens the time over which the model can predict onset of dementia. The two measures appear to be complementary, with MRI showing near-term decline and genetics providing additional predictive power in the long-term. When compared to more invasive measures of amyloid, which have been shown to have long-term predictive power, we observed equivalent performance to CSF amyloid up to 8 years prior to disease onset and equivalent performance to amyloid PET three to four years prior to disease onset.\n\nImplications of all the available evidenceAlthough MRI remains relatively expensive, it is less expensive, less invasive, more accessible, and more commonly available than amyloid PET. Furthermore, MRI is already part of standard clinical practice and this model may be applied to standard clinical MRIs with no additional acquisition required. A recent survey of patients and their caregivers has highlighted a desire for access to better diagnostics, such as amyloid PET, to aid them in long-term legal, financial and healthcare planning. Our model, given the concordance with CSF and amyloid PET could be an alternate solution to fulfill this need. Furthermore, our model could facilitate the \"active surveillance\" of individuals who are high-risk and thereby enhance the possibility of early intervention.

BackgroundVascular dementia (VaD) accounts for 15-20% of all dementia cases. It is a syndrome of acquired cognitive impairment with a complex pathophysiological basis. A novel herbal formulation (Sailuotong; SLT) consisting of Panax ginseng, Ginkgo biloba and Crocus sativus extracts was developed to treat VaD. Preclinical animal studies found significant improvements in memory and in pathogenic biochemical parameters. Appropriate safety of SLT was shown in acute and chronic toxicity studies, and early clinical trials of SLT demonstrated enhancements in cognition in VaD patients. A fully powered study with a long intervention period is needed to confirm the efficacy and safety of this novel intervention. MethodsA rigorous phase III clinical trial was developed with the aim of recruiting 238 patients diagnosed with mild to moderate probable VaD, or VaD mixed with Alzheimers disease (where cerebrovascular disease is the clinical dominant contributor to dementia, abbreviated as CVD+AD). Using a permuted block strategy, participants will be randomly allocated to receive SLT (120 mg bd) or placebo capsules for an intervention period of 52 weeks and will be followed-up for an additional 13 weeks. The primary outcome measures are the Vascular Dementia Assessment Scale-cognitive subscale and Alzheimers Disease Cooperative Study-Activities of Daily Living scale. Secondary outcome measures include the Clinicians Interview Based Impression of Change-Plus, CLOX, EXIT-25, Neuropsychiatric Inventory-Clinician rating scale, and Dementia Quality of Life questionnaire. Safety is assessed through adverse event reports and liver, renal, and coagulation studies. DiscussionPrimary and secondary outcome measures will be compared between treatment and placebo groups, using intention to treat and per protocol analyses. We hypothesise that a 52-week treatment of SLT will be clinically effective and well tolerated in participants with VaD or AD+CVD. This project will provide vital efficacy and safety data for this novel treatment approach to VaD. Trial registrationAustralian New Zealand Clinical Trials Registry (ANZCTR), ACTRN 12616000057482. Registered on 20 January 2016. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=369471&isReview=true

ObjectiveAlzheimers Disease (AD) is a debilitating neurodegenerative disorder characterized by memory loss, cognitive decline, and the accumulation of amyloid plaques and neurofibrillary tangles. This study investigates the interplay of various biomarkers and clinical features in diagnosing AD using machine learning (ML) techniques. MethodsWe analyzed data from 191 AD patients and 59 non-AD subjects, employing classifiers including Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). ResultsOur findings indicate that KNN, SVM, RF, and DT achieved high sensitivity (94%) and accuracy (92%), demonstrating their potential as effective diagnostic tools. Notably, significant differences in feature values between AD patients and non-AD subjects suggest that biomarker-driven approaches can enhance diagnostic precision. Key biomarkers such as neprilysin, alpha-secretase, beta-secretase, amyloid plaques and urinary formic acid emerged as critical elements. ConclusionOur results underscore the importance of selecting a targeted subset of features to streamline the diagnostic process, allowing for more efficient and cost-effective screening. While our study reveals valuable insights into AD pathology and diagnosis, future research with larger, longitudinal cohorts is essential to further elucidate these relationships and enhance our understanding of Alzheimers mechanisms, ultimately aiming for innovative therapeutic strategies.

BackgroundDelaying the transition from minimal cognitive impairment to Alzheimers dementia is a major concern in Alzheimers disease (AD) therapeutics. Pathological signs of AD occur years before the onset of clinical dementia. Thus, long-term therapeutic approaches, with safe, minimally invasive, and yet effective substances are recommended. There is a need to develop new drugs to delay Alzheimers dementia. We have taken a nutritional supplement approach with genistein, a chemically defined polyphenol that acts by multimodal specific mechanisms. Our group previously showed that genistein supplementation is effective to treat the double transgenic (APP/PS1) AD animal model. MethodsIn this double-blind, placebo-controlled, bicentric clinical trial we evaluated the effect of daily oral supplementation with 120 mg of genistein for 12 months on 24 early symptomatic Alzheimers patients. We used a battery of validated neurocognitive tests: Mini-Mental State Exam (MMSE), Memory Alteration Test (M@T) Clock-drawing test, Complutense Verbal Learning Test (TAVEC), Barcelona Test-Revised (TBR), and Rey Complex Figure Test. ResultsWe report that genistein treatment results in a significant improvement in two of the tests used (dichotomized direct TAVEC, p=0.031; dichotomized delayed centil REY copy p=0.002 and a tendency to improve in all the rest of them. The amyloid-beta deposition was analyzed using 18F-flutemetamol uptake which showed that genistein-treated patients did not increase their uptake in the anterior cingulate gyrus after treatment (p = 0.878) while placebo-treated did increase it (p=0.036) We did not observe significant changes in other brain areas studied ConclusionsThis study shows that genistein may have a role in therapeutics to delay the onset of Alzheimers dementia in patients with mild cognitive impairment. These encouraging results indicate that this should be followed up by a new study with more patients to further validate the conclusion that arises from this study. Trial registrationNCT01982578

IntroductionAffordable tools for early Alzheimers disease (AD) detection could support drug development and early intervention. Subtle motor changes may indicate preclinical AD, but hand response selection and initiation speeds are understudied. This study assessed whether unsupervised, online visuomotor reaction time (RT) tests relate to subjective cognitive impairment (SCI), a validated high risk state for future conversion to AD MethodsA total of 910 participants (age 66.3{+/-}7.5, 70.8% female) completed assessments of simple and choice visuomotor RT tests at home as part of the online TAS Test protocol; they also completed the Cambridge Neuropsychological Test Automated Battery (CANTAB) episodic memory and executive function tests. Among them, 142 participants reported SCI. ResultsOn the TAS Test visuomotor tests, SCI was associated with 8.4% [1.4%, 15.4%] longer RT ( (p = .008; adjusted for task complexity), greater odds of time-out failure (OR = 1.35 [1.01, 1.81]; p = .037), and greater variance in RT (log-variance (SCI - comparison) = .094 [.028, .159]; p < .001). There were no significant differences between the SCI and comparison groups on any of the CANTAB tests. After adjusting for SCI status, none of the CANTAB tests were significantly associated with RT. DiscussionSCI was associated with longer and more variable visuomotor RT, and greater odds of time-out failure, while not being associated with tests of memory and executive function. Cognitive test scores did not explain a significant amount of variance in visuomotor RT. Taken together, these results support a hypothesis that people with SCI may be experiencing earlier visuomotor deficits that are distinct from (or precede) decline in episodic memory and executive function. Visuomotor tasks that record RT may be more sensitive to preclinical manifestations of cognitive decline than more traditional tests of cognitive function.

INTRODUCTIONAlzheimers disease (AD) is a leading cause of disability worldwide. Alterations in cerebral blood flow (CBF) and AD blood biomarkers are fundamental at early stages of AD. Exercise shows promise in delaying physiological changes, but its mechanisms for enhancing brain health remains unclear. FlADex aims to examine the acute effects of different exercise types on CBF and blood biomarkers in older adults. This protocol describes the methodology and rationale of flADex. METHODSFlADex is a counterbalanced crossover trial that will include 20 older adults aged 68 to 83 years old with negative brain amyloid status (<12 centiloid) and APOE{varepsilon}4 noncarriers. Participants will complete a 30-minute session of each condition in a randomized order: (i) moderate-intensity aerobic exercise (60-70% maximal heart rate), (ii) moderate-intensity resistance exercise (rating of perceived exertion: 4-6 points out of 10), and (iii) resting condition. Changes in the CBF is the primary outcome and will be assessed by magnetic resonance imaging using pseudo-continuous arterial spin labeling at pre- and at 3 timepoints post-condition (starting at 20, 27, 34 minutes). Secondary outcomes are biomarkers of AD pathology and neurodegeneration (A{beta}42, A{beta}40, p-tau217, p-tau181, BD-tau, GFAP, NfL) and growth factors (BDNF, IGF-1), measured through blood samples collected at pre- and post-condition (at 3, 50, 70 minutes). Moreover, cognitive outcomes and mood status will be measured pre- and post-condition. DISCUSSIONFlADex will highlight the acute effects of exercise types on CBF and biomarkers before beta-amyloid accumulation. Acute effects on CBF dynamics and blood biomarkers are expected to be greater with aerobic than resistance exercise when compared to resting. CBF is expected to vary by brain region, and biomarkers to fluctuate dynamically post-exercise. This will provide critical insights into exercise impact on vascular and molecular pathways associated with AD pathology, and potential recommendations for standardized blood sampling to enhance diagnostic accuracy. HighlightsO_LIExercise may delay the development of AD, but its physiological mechanisms are unclear. C_LIO_LIflADex explores exercise-induced changes in CBF and blood biomarkers. C_LIO_LI20 older adults will undergo a bout of aerobic, resistance exercise, and resting. C_LIO_LICBF may increase more after aerobic exercise, with changes varying by brain region. C_LIO_LIStandardized rest conditions before blood sampling may enhance diagnostic accuracy. C_LI Declarations of interestTKK has consulted for Quanterix Corp., has received honoraria from the NIH for study section membership, and honoraria for speaker/grant review engagements from UPENN, UW-Madison, Advent Health, Brain Health conference, Barcelona-Pittsburgh conference and CQDM Canada, all outside of the submitted work. TKK has received blood biomarker data on defined research cohorts from Janssen and Alamar Biosciences for independent analysis and publication, with no financial incentive and/or research funding included. TKK is an inventor on patent #WO2020193500A1 and patent applications #2450702-2, #63/693,956, #63/679,361, and 63/672,952.

IntroductionMetabolic syndrome (MetS) is associated with increased risk of dementia in high-income countries. Given the different etiologic processes and population conditions driving MetS prevalence, it is unclear if MetS and dementia will show similar associations in low- and middle-income settings. MethodsMixed effects linear regression models were used to estimate the association between dichotomous MetS status and memory decline for individuals in the South African HAALSA Indepth cohort and by age and sex strata. An interaction term between MetS and time allowed the slope to vary by MetS status. ResultsMetS was associated with higher baseline memory scores ({beta} = 0.07 SD units, 95% CI = 0.02, 0.13) and faster memory decline over time ({beta} = -0.01 SD units/year, 95% CI = -0.02, 0.00). DiscussionOur findings suggest that MetS status could be an important marker for identifying groups at higher risk of dementia in low-resource settings.

IntroductionThis study investigates the effects of vortioxetine on cognitive performance in patients with mild cognitive impairment (MCI) in a randomized controlled trial (RCT). MCI is characterized by memory impairment and deficits in other cognitive domains and has been found to progress into Alzheimers dementia (AD) in some cases. In a recent open-label, single-arm study, vortioxetine, an antidepressant, was associated with improved cognitive function in patients with MCI, however, no placebo-controlled studies have been conducted to support these findings. MethodsForty-seven subjects diagnosed with MCI were randomly assigned to receive treatment with either 10 mg vortioxetine, 20 mg vortioxetine, or placebo over 12 weeks, in a randomized, double-blind study design. Neuropsychological tests were conducted several times during the trial, including at a baseline screening visit, consisting of the Alzheimers Disease Assessment Scale - Cognitive Subscale (ADAS-cog), a German version of the Rey Auditory Verbal Learning Test ("Verbaler Lern-und Merkfahigkeitstest", VLMT), the Digit Symbol Substitution Test (DSST), the Mini-Mental State Examination (MMSE), and the Quality of Life Scale (QOLS). Statistical analysis of neuropsychological data was performed using linear mixed models (LMM) and subsequent post-hoc testing to account for multiple comparisons. ResultsLMM results showed a significant increase in VLMT test scores from baseline to week 12 in the 10 mg (p = 0.02) and 20 mg vortioxetine groups (p = 0.017). The placebo group showed a significant increase in test scores from baseline to week 4 (p = 0.009), followed by a significant decrease in test performance observed from week 4 to week 8 (p = 0.006). Both the vortioxetine and placebo groups showed statistically significant DSST test score changes over 12 weeks (10mg: p = 0.004; 20mg: p <.0001; placebo: p <.0001). Similarly, ADAS-Cog test performance significantly increased from baseline to week 12 in both the 10 mg vortioxetine group (p = 0.02) and the placebo group (p = 0.002). MMSE test results showed significant improvement over 8 weeks in the 20 mg vortioxetine group (p = 0.011), compared to no significant findings in the 10 mg and placebo groups. A significant improvement of QOLS scores was observed from week 8 to week 12 in the 10 mg vortioxetine group only (p = 0.033). ConclusionThe results of this study indicate a potential treatment effect of vortioxetine on verbal learning and memory processes in patients with MCI. However, the effects of vortioxetine could not be found at the group level; thus, further research and larger RCTs are needed to elucidate its underlying mechanisms and potential in MCI and early AD treatment.

BackgroundDementia screening tools typically involve face-to-face cognitive testing. Indeed, this introduces an increasing burden on the clinical staff, particularly in low-resource settings. The objective of our study is to develop an integrated online platform for efficient dementia screening, using a brief and cost-effective assessment. MethodsWe used the Longitudinal Ageing Study in India dataset (LASI-DAD, n=2528) to predict dementia diagnosis based on the Clinical Dementia Rating (CDR). Using feature selection algorithms and principal component analysis (PCA), we identified key predictive features. We compared the performance of six machine learning (ML) classifiers that were trained on the 42 selected features (full model) and the two components identified by PCA (minimal model). The best-performing model was selected for our web platform. ResultsSelected features mapped onto two distinct, interpretable domains: a cognitive domain and an informant domain. The first two principal components cumulatively explained 90.2% of the variance and included questions from the Mini-Mental State Exam (MMSE) and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). Classifiers trained on the minimal model performed on par with the full model, with Support Vector Machine performing best (93.4%). The model did not reliably predict Parkinsons disease (67% accuracy) or stroke (53.1% accuracy), suggesting dementia specificity. The respective questions from MMSE and IQCODE (27 items) were incorporated into our online platform. ConclusionWe built an online platform enabling end-to-end screening for dementia from assessment to prediction, based on patient and caregiver reports. Web App code is available at GitHub: https://github.com/sanjaysinghrathi/READi-Dem & Web App link is available at Web Page: https://researchmind.co.uk/readi-dem. For the convenience of researchers, a video summarizing our work is also accessible on the Web App Page and YouTube Link.

BackgroundLifestyle factors have been studied for dementia risk, but few have comprehensively assessed both Alzheimers disease (AD) and cerebrovascular disease (CBVD) pathologies. ObjectiveOur research aims to determine the relationships between lifestyle and various dementia pathologies, challenging conventional research paradigms. MethodsAnalyzing 1231 Wisconsin Registry for Alzheimers Prevention (WRAP) study participants, we focused on Life Simple Seven (LS7) score calculations from questionnaire data and clinical vitals. We assessed brain health indicators including CBVD, AD, and cognition. ResultsHigher (healthier) LS7 scores were associated with better CBVD outcomes, including lower percent white matter hyperintensities and higher cerebral blood flow, and higher Preclinical Alzheimers Composite 3 and Delayed Recall scores. No significant associations were observed between LS7 scores and AD markers of amyloid and tau accumulation. ConclusionThis study provides evidence that the beneficial effects of LS7 on cognition are primarily through cerebrovascular pathways rather than direct influences on AD pathology.

BackgroundPre-existing long-term health conditions are highly prevalent in people living with dementia. There is an established relationship between these conditions, trajectories of decline and outcomes in dementia post-diagnosis. An understanding of when these conditions first occur and how they manifest over time within dementia populations remains unclear. The pathophysiology across major bodily systems changes before and after the clinical onset of dementia. However, the timing of various complex, chronic conditions, how they interact and their association with dementia are not yet fully understood. Understanding the complex relationship between multiple long-term conditions and dementia can enhance our understanding of the mechanisms of disease. This understanding can help identify "windows of opportunity" for early interventions in at-risk populations. Long-term conditions also continue to affect individual decline as the disease progresses. We set out to map these key comorbidities chronologically, specific to diagnoses of Alzheimers Disease and Vascular Dementia. MethodWe map comorbidities based on International Classification of Diseases (10th revision) system, in a population of people with Alzheimers Disease and Vascular Dementia from 20 years before through to 10 years after diagnosis. We used inpatient hospital electronic healthcare record data from 10,696 UK Biobank participants, with records spanning a median of 16.90 years (IQR = 10.10 years) for participants with dementia and 13.36 years (IQR = 13.52) for controls (no diagnosis of dementia). Controls were validated with lower polygenic risk scores than either dementia group. ResultsCharacteristic comorbidity signatures were observed in individuals with Alzheimers Disease and Vascular Dementia. Up to 20 years before diagnosis, depressive episodes, osteoporosis, arthritis and irritable bowel syndrome are characteristic of Alzheimers Disease, but not Vascular Dementia or control cohorts. From 15 years before diagnosis, type 1 diabetes, functional intestinal disorder and chronic obstructive pulmonary disease begin to emerge. Up to 10 years before, symptoms involving fluid and food intake are uniquely associated with the cohort of people diagnosed with Alzheimers Disease. In Vascular Dementia, 20 years before diagnosis cerebral infarctions, type 1 diabetes, cerebrovascular disease, peripheral vascular disease, intestinal disorders and rheumatoid arthritis are early conditions in the Vascular Dementia cohort, not seen in control or Alzheimers Disease cohorts. 15 years before Vascular Dementia, an increase in mental health conditions emerge such as depressive episodes as well as rheumatoid arthritis. 10 years before Vascular Dementia, an additional burden of cerebrovascular diseases, hypotension and dorsalgia. From 5 years up to diagnosis of Vascular Dementia, intra-cerebral haemorrhage, mental and behavioural disorders due to tobacco begin to emerge within this population. In the years following Alzheimers Disease diagnosis, acute lower respiratory infections and skin conditions such as decubitus ulcer emerge up to 2 years after diagnosis. After a diagnosis of Vascular Dementia, pneumonitis and decubitus ulcer and pressure area, are unique to this cohort. ConclusionIn this study, comorbid conditions were mapped in relation to Alzheimers Disease and Vascular Dementia pre- and post-diagnosis. Understanding prodromal and progressive changes in health over the course of these dementia sub-types could uncover opportunities for targeted screening, preventative measures and early interventions. SummaryDementia develops alongside multiple long-term health conditions1-3, yet the timing and subtype-specific patterns of these comorbidities remain unclear. Using hospital records from 10,696 UK Biobank participants, we mapped comorbidities from 20 years before to 10 years after diagnosis of Alzheimers Disease or Vascular Dementia. We show that distinct comorbidity signatures precede each dementia subtype: depression, osteoporosis and intestinal disorders are early features of Alzheimers Disease, while Vascular Dementia is characterised by cerebrovascular and peripheral vascular diseases, type 1 diabetes and arthritis up to two decades before diagnosis. Post-diagnosis, conditions such as respiratory infections and pressure ulcers show divergent trajectories. These findings reveal temporally distinct, disease-specific comorbidity profiles, suggesting differing pathological pathways and windows for targeted intervention. Understanding the longitudinal burden of comorbidities in dementia can inform screening, risk stratification, and preventative strategies long before clinical onset.

BackgroundAlzheimers disease, type 2 diabetes mellitus, and depression are significant challenges facing public health. Research has demonstrated common comorbidities amongst these three conditions, typically focusing on two of them at a time. ObjectivesThe goal of this study, however, was to assess the interrelationships between the three conditions, focusing on mid-life risk before the emergence of dementia caused by Alzheimers Disease. MethodsIn the current study, we used data from 665 participants from the prospective cohort study, PREVENT. FindingsUsing structural equation modelling, we showed that (i) insulin resistance predicts executive dysfunction in older but not younger adults in midlife, that (ii) insulin resistance predicts self-reported depression in both older and younger middle-aged adults, and that (iii) depression predicts deficits in visuospatial memory in older but not younger adults in midlife. ConclusionsTogether, we demonstrate the interrelations between three common non-communicable diseases in middle-aged adults. Clinical ImplicationsWe emphasise the need for combined interventions and the utilisation of resources to help adults in midlife to modify risk factors for cognitive impairment, such as depression and diabetes. FundingThe PREVENT study was funded by the Alzheimers Society (grant numbers 178 and 264), the Alzheimers Association (grant number TriBEKa-17-519007) and philanthropic donations. GR acknowledges funding for this work for his research programme funded by the Medical Research council (Dementias Platform UK) and Five Lives Ltd. IK declares funding for this project through Medical Research Council (Dementias Platform UK), NIHR Oxford Health Biomedical Research Centre and NIHR personal awards. SG acknowledges funding for salary from the Medical Research Council Nutrition Research Partnership Collaboration Award (MR/T001852/1). Key MessagesO_ST_ABSWhat is already known on this topicC_ST_ABSMood disorders and metabolic diseases are known to be frequently comorbid. Furthermore, both conditions are known to be associated with cognitive impairment and cognitive decline. There has been some evidence that the risk of cognitive impairment associated with diabetes and depression is most notable in midlife. However, studies focusing on this period of life have been sparse and most research has modelled bivariate correlations amongst cognitive impairment, depression, and diabetes. As such, this study was conducted in order to model the interrelations between the three conditions in a large cohort, whilst focusing on midlife as depression and diabetes in this period are thought to carry higher risk for cognitive impairment. What this study addsWhilst insulin resistance, as a core feature of diabetes, was related to depression across all stages of midlife, the relationship with cognitive functioning was more complex. In the current study, we found that the stage of midlife in which middle-aged adults find themselves moderates the relationship between insulin resistance and cognition and depression and cognition. That is, only in older middle aged adults does insulin resistance predict impaired cognition (i.e., executive function) and does depression predict impaired cognition (i.e., visuospatial memory). How this study might affect research, practice or policyClinicians should be mindful of the impact of comorbidities between cognitive impairment, metabolic diseases, such as diabetes, and mood disorders, such as depression in midlife. Given the risk of intractable dementia in individuals with cognitive impairment, available resources for intervening in modifiable risk factors, such as depression and diabetes, should be considered for adults in the middle period of life.

BackgroundGlobally, one new case of Dementia develops in every three seconds. Patients with Cognitive Impairment usually have additional comorbidities which may accelerate progression towards a state of Cognitive Impairment. Furthermore, most suspected dementia patients visit primary physicians first instead of neurologists. This necessitates targeted screening of at-risk individuals for early intervention and prevention of Dementia. AimThis study aimed to assess the Prevalence and Factors Contributing to Dementia among Geriatric Patients attending Medical Outpatient Clinic at MZRH. MethodsA cross-sectional study was conducted in April, 2024 at MZRH, in 239 patients attending medical clinic, obtained by simple random sampling. The ethical approval was obtained from UDSM-MCHAS ethics committee, which presented approval letter to MZRH administration. Identification of Dementia for Elderly Africans (IDEA) cognitive screening questionnaire was used to assess Cognitive Impairment. From a range of 0-15, the scores were grouped into three categories; [&le;] 7 indicating severe Cognitive Impairment (Dementia), 8-14 indicating mild to moderate Cognitive Impairment and 15 indicating normal cognitive function. Bivariate and Multivariate analysis was performed to assess factors associated with Cognitive Impairment. Results1 in 2 patients attending medical outpatient clinic had Cognitive Impairment. However, majority of patients had mild to moderate Cognitive Impairment (49.15%) as compared to severe Cognitive Impairment/Dementia (0.85%). Being [&ge;]65 years old (p=0.04), lack of formal education(p=0.02), and being male (p=0.001) were significantly associated with Cognitive Impairment. ConclusionA high proportion of Cognitive Impairment was observed among elderly medical outpatients, with majority of them in early stages of Cognitive Impairment. Age, educational level, and sex of the participants significantly affected Cognitive Impairment among study participants. Cognitive screening should form part of assessment for elderly patients attending medical clinic. There is a critical need to identify new interventions that can slow progression of Cognitive Impairment advancing to Dementia, particularly in patients with additional comorbidities.

Structured AbstractO_ST_ABSObjectiveC_ST_ABSMild Behavioral Impairment (MBI) is a syndrome of late-life-onset persistent neuropsychiatric symptoms. Anticholinergic medication is commonly prescribed in older adults. Both MBI and anticholinergic exposure are associated with increased dementia risk. We sought to understand the association of anticholinergic burden (ACB) with MBI. Design, Setting, participantsWe mapped ratings on the Neuropsychiatric Inventory Questionnaire to the MBI checklist (MBI-C) using an established algorithm to define MBI status in cognitively unimpaired individuals in the National Alzheimers Coordinating Center database. We then assessed the association between time-varying ACB ratings and risk of incident MBI. Results4865 participants met inclusion criteria and were followed for a mean (SD) of 5.64 (3.92) years. ACB scores ranged from 0-11. 63.3% of participants had a score of 0, 27.7% had a score of 1-2, and 9% had a score of [&ge;]3. Higher maximum total ACB score was associated with a higher likelihood of developing MBI (p=<0.001). When assessed as a time varying covariate, ACB score was associated with incident MBI (HR 1.12, 95% CI 1.05-1.19, p=<0.001). This association remained significant when adjusted for 10-year mortality risk. ConclusionsMBI risk should be considered when prescribing anticholinergic medication in older adults.

ObjectiveRecombinant monoclonal therapeutic antibodies like lecanemab, which target amyloid beta in Alzheimers disease, offer a promising approach for modifying the disease progression. Due to its relatively short half-life, Lecanemab, administered as a bi-monthly infusion (typically 10mg/kg) has a relatively brief half-life. Interaction with abundant plasma proteins binder in the bloodstream can affect pharmacokinetics of drugs, including their half-life. In this study we investigated potential plasma protein binding interaction to lecanemab using lecanemab biosimilar. MethodsLecanemab biosimilar used in this study was based on publicly available sequences. ELISA and Western blotting were used to assess lecanemab biosimilar immunoreactivity in the fractions human plasma sample obtained through size exclusion chromatography. The binding of lecanemab biosimilar to candidate binders was confirmed by Western blotting, ELISA, and surface plasmon resonance analysis. ResultsUsing a combination of equilibrium dialysis, ELISA, and Western blotting in human plasma, we first describe the presence of likely plasma protein binding partner to lecanemab biosimilar, and then identify fibrinogen as one of them. Utilizing surface plasmon resonance, we confirmed that lecanemab biosimilar does bind to fibrinogen, although with lower affinity than to monomeric amyloid beta. InterpretationIn the context of lecanemab therapy, these results imply that fibrinogen levels could impact the levels of free antibodies in the bloodstream and that fibrinogen might serve as a reservoir for lecanemab. More broadly, these results indicate that plasma protein binding may be an important consideration when clinically utilizing therapeutic antibodies in neurodegenerative disease.