Journal of Alzheimer’s Disease

Detecting Alzheimers Disease (AD) at the earliest possible stage is key in advancing AD prevention and treatment but is challenged by normal aging processes in addition to other confounding neurodegenerative diseases. Recent genome-wide association studies (GWAS) have identified associated alleles, but it has been difficult to transition from non-coding genetic variants to underlying mechanisms of AD. Here, we sought to reveal functional genetic variants and diagnostic biomarkers underlying AD using machine learning techniques. We first developed a Random Forest (RF) classifier using microarray gene expression data sampled from the peripheral blood of 744 participants in the Alzheimers Disease Neuroimaging Initiative (ADNI) cohort. After initial feature selection, 5-fold cross-validation of the 100-gene RF classifier achieved an accuracy of 99.04%. The high accuracy of the RF classifier supports the possibility of a powerful and minimally invasive tool for screening of AD. Next, unsupervised clustering was used to validate and identify relationships among differentially expressed genes (DEGs) the RF selected revealing 3 distinct AD clusters. Results suggest downregulation of global sulfatase and oxidoreductase activities in AD through mutations in SUMF1 and SMOX respectively. Then, we used Greedy Fast Causal Inference (GFCI) to find potential causes of AD within DEGs. In the causal graph, HLA-DPB1 and CYP4A11 emerge as hub genes, furthering the discussion of the immune systems role in AD. Finally, we used Gene Set Enrichment Analysis (GSEA) to determine the biological pathways and processes underlying the DEGs that were highly correlated with AD. Cell activation in the immune system, glycosaminoglycan (GAG) binding, vascular dysfunction, oxidative stress, and the neuronal apoptotic process were revealed to be significantly enriched in AD. This study further advances the possibility of low-cost and noninvasive genetic screening for AD while also providing potential gene targets for further experimentation.

Declines in vascular integrity are potential contributors to Alzheimers disease (AD) as these result in increased blood-brain barrier permeability and, as a consequence, accelerate neuroinflammation and cognitive impairment. Roundabout guidance receptor 4 (Robo4) is primarily expressed in endothelial cells and stabilizes the vasculature, and thus, has the potential to protect the brain in AD. To study the effect of Robo4 on neuroinflammation and cognitive function in the context of AD, we compared Robo4 knockout and wildtype mice crossed with mice with and without AD mutations (APP/tau). We found that the knockout of Robo4 led to greater astrocyte activation, as demonstrated by GFAP content, but this was dependent on the brain region studied. The knockout of Robo4 also led to greater activated microglia, as assessed by Iba1 content, but only in the presence of AD-related mutations. We found that AD mutations, but not Robo4, were associated with cognitive dysfunction measured by a nest-building test. In contrast, Robo4 deletion, but not AD mutations, was associated with impaired motor coordination. Lastly, Robo4 deletion was associated with greater arterial stiffness, but this trend did not reach statistical significance. In summary, these results demonstrate that Robo4 impacts neuroinflammation, motor coordination, and arterial stiffness, however, the impact on neuroinflammation is dependent on the presence/absence of AD-related mutations and the brain region examined.

Large-scale genome wide association studies (GWASs) have been performed in search for risk genes for Alzheimers disease (AD). Despite the significant progress, replicability of genetic findings and their translation into targetable mechanisms related to the disease pathogenesis remains a challenge. Given that bile acids have been suggested in recent metabolic studies as potential age-related metabolic factors associated with AD, we integrated genomic and metabolomic data together with heterogeneous biological networks and investigated the potential cascade of effect of genetic variations to proteins, bile acids and ultimately AD brain phenotypes. Particularly, we leveraged functional protein interaction networks and metabolic networks and focused on the genes directly interacting with AD-altered bile acids and their functional regulators. We examined the association of all the SNPs located in those candidate genes with AD brain imaging phenotypes, and identified multiple AD risk SNPs whose downstream genes and bile acids were also found to be altered in AD. These AD related markers span from genetics to metabolomics, forming functional biological paths connecting across multiple-omics layers, and give valuable insights into the underlying mechanism of AD.

Background: International Classification of Diseases (ICD) codes are often used in epidemiological studies to track disease rates over time. Objective: This evaluation of ICD-code-based algorithms for electronic medical record (EMR) studies of Alzheimers disease (AD) and related dementias (ADRD) examines the impact of incorporating Centers for Medicare and Medicaid (CMS) data as an additional source of diagnostic and treatment information in Department of Veterans Affairs (VA) EMR studies. Methods: We performed a chart review of 100 VA Million Veteran Program (MVP) participants to evaluate algorithm performance. We also assessed genetic associations across algorithms in a large MVP cohort (n=396k). Results: Adding CMS data increased the number of detected cases, sensitivity, and positive predictive value, but decreased specificity and negative predictive value. Genetic analyses showed that broader (ADRD/dementia) algorithms with just VA data performed similarly to narrow (AD-focused) algorithms incorporating both VA and CMS ICD codes. Additionally, narrow AD algorithms based solely on VA data yielded the highest ORs, indicating the largest proportion of late-onset AD cases. Conclusions: We recommend using a broad (ADRD) algorithm without CMS or medication data, particularly for epidemiological studies or a strict AD algorithm including CMS and medication cases for genetic discovery of late-onset AD associations in VA EMR, and a strict AD algorithm without CMS data for applications focused solely on AD and sensitive to misspecification. Careful evaluation of algorithm performance is warranted in different EMR systems, as ICD coding practices vary by institution, as demonstrated by this comparison of VA EMR and CMS data.

INTRODUCTIONHispanic/Latino (H/L) adults are more likely than non-Hispanic white adults to have Alzheimers disease (AD), but fewer than 1 in 5 H/L adults has APOE {varepsilon}4. H/L adults are underrepresented in AD research studies and trials, which use genetic data to stratify participants. Successful research programs representative of the entire U.S. population, including over 16 million Spanish speakers, require culturally appropriate educational materials about AD and genetic testing. We sought to learn the culturally salient words Spanish-preferring H/L adults use to talk about AD and genetic testing. METHODSParticipants were community-residing and self-identified as Spanish preferring H/L adults. Fourteen individuals completed freelisting interviews, which yielded lists featuring all the words that came to participants minds about AD-related domains. We performed inductive thematic analysis and calculated theme frequency. RESULTSParticipants were aware of AD as a memory disorder due to advancing age and genes, but were unfamiliar with AD genetic testing. Participants suggested genetic testing was more useful for diagnosis than future risk prediction. They also suggested genetic testing of individuals with intact cognition and no AD family history had limited value. DISCUSSIONFindings suggested individuals are motivated by a technological imperative to participate in AD research, reflecting a responsibility to use genetic testing despite having limited knowledge about it. Interest among H/L adults in AD research could be leveraged to develop educational materials co-created by community members and researchers. Content about primary and secondary findings and the use of AD genetic results to inform a future-oriented disposition to health comprises a useful framework for AD outreach serving diverse populations.

Identifying Alzheimers disease (AD) subtypes is essential for AD diagnosis and treatment. We integrated multiomics data from brain tissues of the ROSMAP and MSBB studies using a subspace merging algorithm and identified two AD patient clusters with notable cognitive and AD pathology differences. Analysis of differentially expressed genes (DEGs) in brain and blood samples pinpointed the LDLR gene as a potential blood biomarker linked to brain gene expression changes. Furthermore, we conducted PheWAS analysis on All of Us Projects EHR and WGS dataset for 105 eQTLs associated with the DEGs and revealed significant associations between these eQTLs and several phenotypes, shedding light on potential regulatory roles of these genes in diverse physiological processes. Our study successfully integrated multiomics data and proposes LDLR as a candidate blood biomarker for AD subtyping. The identified phenotypic signatures provide valuable insights on molecular mechanisms underlying AD heterogeneity, paving the way for personalized AD treatment.

Artery structural properties and Alzheimers disease (AD) pathology are individually associated with impaired cerebrovascular function; however, the interaction of these factors is unclear. Furthermore, while elastin haploinsufficient (Eln+/-) mice are known to have impaired cerebrovascular function, sex differences for this effect have not been previously studied. To answer these questions, we crossed middle-aged and old Eln+/-mice with 3xTg-AD mice. We measured cerebral blood flow (CBF) using arterial spin labeling MRI at rest and during hypercapnia to calculate cerebrovascular reactivity (CVR). We also assessed neuroinflammation by microglia and astrocyte cell counts. We found that Eln+/- mice had lower resting blood flow rate in the cerebral cortex compared with Eln+/+ mice, but Eln+/- mice had an intact hypercapnic response, resulting in better CVR compared with Eln+/+ in hippocampus. Sex did not impact resting blood flow or CVR. 3xTg-AD mice had a lower resting CBF than non-AD mice, and there was an interaction between Eln genotype and AD mutations on CVR, such that Eln+/-x 3xTg-AD mice had the poorest hippocampal CVR of all groups. Glia cell counts were highly dependent on brain region, with Eln+/- having more microglia but fewer astrocytes, while 3xTg-AD having higher both microglia and astrocytes. While sex also impacted glial cell counts, we found no interactions between sex and Eln genotype. Our results demonstrate that elastin haploinsufficiency and AD mutations individually result in lower resting CBF, and the combination of these leads to impaired CVR. NEW & NOTEWORTHYThe findings of this study demonstrate that elastin haploinsufficiency leads to lower resting cerebral blood flow, but also greater cerebrovascular reactivity. However, elastin haploinsufficiency interacts with Alzheimers disease mutations to impair cerebrovascular reactivity. These results suggest that multiple insults, such as changes to the extracellular matrix combined with genetic risk factors, are needed to impact cerebrovascular reactivity.

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.

IntroductionSeveral Mendelian randomization studies have been conducted, which identified multiple risk factors for Alzheimers disease (AD). However, they typically focus on a few pre-selected risk factors. MethodsTwo-sample Mendelian randomization (MR) study was used to systematically examine the potential causal associations of 1,054 risk factors/medical conditions and 28 drugs with the risk of late- onset AD. To correct for multiple comparisons, the false discovery rate was set at <0.05. ResultsThere were strong evidence of a causal association between glioma risk, reduced trunk fat-free mass, lower education levels, lower intelligence and a higher risk of AD. For 28 investigated treatments (such as antihypertensive drugs), we found limited evidence for their associations. ConclusionMR found robust evidence of causal associations between glioma, trunk fat-free and AD. Our study also confirms that higher educational attainment and higher intelligence are associated with a reduced risk of AD.

BACKGROUND: Neuropsychiatric symptoms (NPS) are prevalent and debilitating in Alzheimer's disease (AD). Existing pharmacologic treatments are often ineffective and associated with serious adverse events. Identifying modifiable risk factors (MRFs) is critical for prevention and treatment. METHODS: Capitalizing on data from 14,497 individuals with AD from the National Alzheimer's Coordinating Center (NACC) database, we examined longitudinal associations between modifiable risk factors, APOE genotype and NPI-Q-assessed NPS using Cox proportional hazards models adjusted for demographics. RESULTS: Diabetes, alcohol consumption, smoking, and TBI were associated with an increased risk of specific NPS in AD. APOE{varepsilon}4 carrier status was linked to multiple NPS, showing a dose-response relationship. Education, LDL-C, and corrective lenses were protective; hypertension showed no associations. CONCLUSION: These findings strongly suggest that individual MRFs are associated with specific NPS in line with a complex etiology underlying these symptoms. Early detection and management of vascular, lifestyle and sensory factors could reduce NPS.

Alzheimers disease (AD) is a neurogenerative disease that affects millions worldwide with no effective treatment. Several studies have been conducted to decipher to genomic underpinnings of AD. Due to its complex nature, many genes have been found to be associated with AD. Despite these findings, the pathophysiology of the disease is still elusive. To discover new putative AD-associated genes, in this study, we integrated multimodal gene and phenotype datasets of AD using network biology methods to prioritize potential AD-related genes. We constructed a multiplex heterogeneous network composed of patient and gene similarity networks utilizing phenotypic and omics datasets of AD patients from the Alzheimers Disease Neuroimaging Initiative (ADNI) database. We applied PhenoGeneRanker to traverse this network to discover potential AD-associated genes. To assess the impact of each network layer and seed gene, we also run PhenoGeneRanker on different variants of the network and seed genes. Our results showed that top-ranked genes captured several known AD-related genes and were enriched in Gene Ontology (GO) terms related to AD. We also observed that several top-ranked genes that are not in AD-associated gene list had literature supporting their potential relevance to AD.

INTRODUCTIONCognitive SuperAgers (SA) are individuals aged 80+ with exceptional episodic memory performance for their age, exceeding middle-aged adult norms. This study integrates family- and association-based methods to identify genetic variants associated with SA in the Midwestern Amish population. METHODS83 Amish SA were grouped into 16 pedigrees for parametric and non-parametric linkage analysis. Variants in linked regions (HLOD/LOD*[&ge;]3) were tested for association with SA using two contrasts: SA vs. Alzheimers disease (AD; n=40), and SA vs. cognitively unimpaired (CU), age-matched non-SA individuals (CU80+; n=157). RESULTSEvidence of linkage for SA was observed on chromosomes 1, 2, 7, 16, and 20, with the strongest signal around the AD-associated locus WDR12 on chromosome 2. Association analysis for SA vs. AD identified eight variants in HIVEP3 (chromosome 1) that were nominally significant when comparing SA vs. CU80+. DISCUSSIONWDR12 and HIVEP3 are potential candidate genes contributing to SA in the Amish population.

BackgroundGenetic studies of Alzheimers disease (AD) have made remarkable progress in identifying genetic factors that increase the risk of AD. However, the genetic determinants that contribute to preserved cognition at older ages, particularly those that mitigate AD risk, are less understood. In the Midwestern Amish community, certain older adults maintain normal cognitive abilities despite carrying a relatively high genetic risk of AD. Our goal was to identify shared genetic factors among high-risk Amish sibships enriched with cognitively unimpaired individuals. We hypothesized that ranking sibships by their mean genetic risk of AD would provide greater evidence of genetic linkage in cognitively preserved sibships at higher risk, identifying genomic regions containing protective loci. MethodsCognitive status in adult Amish participants (n = 1,855) was evaluated using the Modified Mini Mental Status Exam (3MS). Those aged [&ge;] 75 with education-adjusted 3MS [&ge;] 87 were classified as cognitively unimpaired (CU), while those with education-adjusted 3MS < 87 were classified as cognitively impaired (CI). Genome-wide non-parametric linkage analysis was performed on 143 sibships with [&ge;] 2 CU members. To account for known genetic risk, ordered-subsets analysis (OSA) was used to rank families by the mean AD genetic risk score (GRS) of their CU members using effect sizes for 25 established European AD risk loci. Regions exhibiting significant or suggestive increases in the LOD* score during OSA were further evaluated using single-variant and interaction association tests in a sample of 715 CU and 460 CI participants. ResultsSignificant evidence of linkage following OSA adjustment was detected on chromosome 2 at SNP rs6719884 ([~] 59.0 Mb) within the lincRNA LINC01122 (LOD* = 3.08), suggesting a shared genetic factor influencing preserved cognition in high-risk individuals. Additionally, a significant interaction effect was observed on chromosome 12 at SNP rs11063479 ([~] 5.1 Mb) near the KCNA5 gene. The variant showed increased odds of being cognitively unimpaired in the high-GRS stratum (OR = 2.07) and decreased odds in the low-GRS stratum (OR = 0.70), indicating a potential synergistic effect with genetic risk. ConclusionsOur findings highlight specific genomic regions--particularly on chromosomes 2 and 12--that may harbor genetic loci contributing to preserved cognitive function in aged individuals at high genetic risk for AD. The identification of LINC01122 and KCNA5 as candidate genes underscores the potential role of brain-expressed lincRNAs and potassium channel genes in preserving cognitive function. These results contribute to the understanding of cognitive preservation and may inform future research on protective genetic mechanisms against cognitive decline.

A comprehensive understanding of factors associated with Alzheimers disease (AD) and AD-related dementias (AD/ADRD) will significantly enhance efforts when designing new studies to develop new treatments and identify high-risk populations for prevention. We reviewed existing meta-analyses and review articles on AD/ADRD risk and preventive factors, extracting 477 risk factors across 10 categories from 537 studies. An interactive knowledge graph was created to share our study findings. Most risk factors can be found in structured Electronic Health Records (EHRs), with clinical narratives also providing valuable information. However, assessing genomic risk factors using real-world data (RWD) like EHRs remains a challenge, as genetic testing for AD/ADRD is still not a common practice and poorly documented in both structured and unstructured EHRs. Given the constant and rapid evolution of AD/ADRD research, using natural language processing (NLP) for literature mining emerges as a viable method to continually and automatically update our knowledge graph.

This study challenges the conventional psycholinguistic view that the distinction between nouns and verbs is pivotal in understanding language impairments in neurological disorders. Traditional views link frontal brain region damage with verb processing deficits and posterior temporoparietal damage with noun difficulties. However, this perspective is contested by findings from patients with Alzheimers disease (pwAD), who show impairments in both word classes despite their typical temporoparietal atrophy. Notably, pwAD tend to use semantically lighter verbs in their speech than healthy individuals. By examining English-speaking pwAD and comparing them with Persian-speaking pwAD, this research aims to demonstrate that language impairments in Alzheimers disease (AD) stem from the distributional properties of words within a language rather than distinct neural processing networks for nouns and verbs. We propose that the primary deficit in AD language production is an overreliance on high-frequency words. English has a set of particularly high-frequency verbs that surpass most nouns in usage frequency. Since pwAD tend to use high-frequency words, the byproduct of this word distribution in the English language would be an over-usage of high-frequency verbs. In contrast, Persian features complex verbs with an overall distribution lacking extremely high-frequency verbs like those found in English. As a result, we hypothesize that Persian-speaking pwAD would not have a bias toward the overuse of high-frequency verbs. We analyzed language samples from 95 English-speaking pwAD and 91 healthy controls, along with 27 Persian-speaking pwAD and 27 healthy controls. Employing uniform automated natural language processing methods, we measured the usage rates of nouns, verbs, and word frequencies across both cohorts. Our findings showed that English-speaking pwAD use higher-frequency verbs than healthy individuals, a pattern not mirrored by Persian-speaking pwAD. Crucially, we found a significant interaction between the frequencies of verbs used by English and Persian speakers with and without AD. Moreover, regression models that treated noun and verb frequencies as separate predictors did not outperform models that considered overall word frequency alone in classifying AD. In conclusion, this study suggests that language abnormalities among English-speaking pwAD reflect the unique distributional properties of words in English rather than a universal noun-verb class distinction. Beyond offering a new understanding of language abnormalities in AD, the study highlights the critical need for further investigation across diverse languages to deepen our insight into the mechanisms of language impairments in neurological disorders.

INTRODUCTIONAlzheimers disease (AD) is a common and costly neurodegenerative disorder. A large proportion of risk is heritable and many genetic risk factors for AD have been identified. The cumulative genetic risk of known markers has not been benchmarked for dementia in a population-based sample. METHODSIn the United States population-based Health and Retirement Study (HRS) (waves 1995-2014), we evaluated the role of cumulative genetic risk for AD, with and without the APOE-{varepsilon}4 alleles, on dementia status (dementia, cognitive impairment without dementia, borderline cognitive impairment without dementia, cognitively normal). We used logistic regression, accounting for demographic covariates and genetic principal components, and analyses were stratified by European and African genetic ancestry. RESULTSIn the European ancestry sample (n=8399), both AD polygenic score excluding the APOE genetic region (odds ratio (OR)=1.10; 95% confidence interval (CI): 1.00, 1.20) and the presence of any APOE-{varepsilon}4 alleles (OR=2.42; 95% CI: 1.99, 2.95) were associated with the odds of dementia relative to normal cognition in a mutually-adjusted model. In the African ancestry sample (n=1605), the presence of any APOE-{varepsilon}4 alleles was associated with 1.77 (95% CI: 1.20, 2.61) times higher odds of dementia, while the AD polygenic score excluding the APOE genetic region was not significantly associated with the odds of dementia relative to normal cognition 1.06 (95% CI: 0.97, 1.30). DISCUSSIONCumulative genetic risk for AD and APOE-{varepsilon}4 are both independent predictors of dementia. This study provides important insight into the polygenic nature of dementia and demonstrates the utility of polygenic scores in dementia research.

INTRODUCTION: The Diabetes Prevention Program (DPP) was a randomized clinical trial designed to prevent type 2 diabetes (T2D) in adults with prediabetes. The DPP Outcomes Study (DPPOS) is the 30-year follow-up of this cohort, focusing on T2D, prediabetes, and related complications. Cognitive assessments began in 2009 and expanded in 2022 to examine cognitive impairment, including Alzheimer's disease (AD) and AD related dementias (ADRD), in the surviving cohort. To support these aims, the National Alzheimer's Coordinating Center Uniform Data Set version 3 (NACC-UDSv3), the standardized framework used by Alzheimer's Disease Research Centers, was implemented in DPPOS in 2022 to enable data sharing with NACC. These forms were complemented by cognitive tests administered in DPPOS. We aimed to integrate the NACC-UDSv3 into the existing longitudinal DPPOS framework while maintaining fidelity to its structure and developing automated reports to streamline cognitive outcomes adjudication. METHODS: Items from the 16 NACC-UDSv3 data forms were compared with those already collected within DPPOS to integrate overlapping similar items, add missing NACC-UDSv3 items, and create a dataset harmonized with NACC-UDSv3. Forms were adapted for electronic data capture (EDC) using the MIDAS (Multimodal Integrated Data Acquisition System, George Washington University). Automated reports integrated current and prior neuropsychological scores to support adjudications. In the first wave of the DPPOS-AD/ADRD study, 1561 cognitive adjudications were successfully completed using the harmonized DPPOS and NACC-UDSv3 data implemented into MIDAS. DISCUSSION: The DPPOS-AD/ADRD project demonstrated that NACC-UDSv3 can be successfully integrated into a long-standing longitudinal cohort not originally designed for AD/ADRD research. The harmonization, electronic capture, and automated adjudication processes may provide a practical framework for other cohorts seeking to incorporate NACC-UDSv3 to align with national AD/ADRD research standards.

Background: Electronic health record (EHR)-linked biorepositories provide opportunities to advance epidemiological research in Alzheimer's disease (AD) and related dementias. Objective: Evaluate the extraction, curation, and associative validity of Mini Mental State Examination (MMSE) scores from the VA EHR for participants in the VA Million Veteran Program (MVP). Methods: The sample (N = 49,555; 7.4% women) included a multiethnic cohort (European [68.3%], African [20.4%], Hispanic [9.0%]) with EHR-extracted MMSE scores; 30.7% were apolipoprotein E (APOE) {epsilon}4 carriers, and 25.8% had multiple scores. Linear regressions examined cross-sectional associations between {epsilon}4 dosage (0, 1, 2) and first and lowest MMSE scores. MMSE scores were also evaluated against MVP dementia diagnostic algorithms in participants aged [&ge;]65 years. Results: Among participants of European ancestry, there was a significant {epsilon}4 dose-response relationship (ps < .001) with MMSE scores. Homozygote carriers scored lower than heterozygote carriers (Mdiff: first = -0.5; lowest = -0.9), who scored lower than non-carriers (Mdiff: first = -0.4; lowest = -0.6). Among Veterans of African and Hispanic ancestry, no dose-response relationship was observed, although {epsilon}4 carriers had lower scores than non-carriers (ps [&le;] .04). MMSE scores corresponded strongly with dementia case/control status across phenotypes: mild impairment on the MMSE was strongly associated with AD (odds ratio [OR] = 11.48), with more severe MMSE impairment showing stronger associations (moderate OR = 17.95; severe OR = 27.83). Conclusion: This study demonstrated MMSE scores can be systematically extracted and curated from the VA EHR. Findings offer a scalable framework for future studies on risk stratification, highlighting the potential for harnessing MVP to explore genetic and clinical factors contributing to cognitive and dementia outcomes in diverse samples.

INTRODUCTIONThis research investigates the psycholinguistic origins of language impairments in Alzheimers Disease (AD), questioning if these impairments result from language-specific structural disruptions or from a universal deficit in generating meaningful content. METHODSCross-linguistic analysis was conducted on language samples from 184 English and 52 Persian speakers, comprising both AD patients and healthy controls, to extract various language features. Furthermore, we introduced a machine learning-based metric, Language Informativeness Index (LII), to quantify informativeness. RESULTSIndicators of AD in English were found to be highly predictive of AD in Persian, with a 92.3% classification accuracy. Additionally, we found robust correlations between the typical linguistic abnormalities of AD and language emptiness (low LII) across both languages. DISCUSSIONFindings suggest AD linguistics impairments are attributed to a core universal difficulty in generating informative messages. Our approach underscores the importance of incorporating biocultural diversity into research, fostering the development of inclusive diagnostic tools.

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.