bluuebunny/crossref_metadata_2025 huggingface dataset:
Perfusion and cerebrovascular reactivity characterization in Alzheimer’s disease and vascular dementia
Authors: Keeling Elizabeth G. , McElvogue Molly M. , Ott Lauren R. , Burke Anna D. , Sabbagh Marwan N. , Bakkar Nadine , Stokes Ashley M.
Published: January 2025

Background

Cerebrovascular changes are often reported in normal aging, Alzheimer’s disease (AD), and vascular dementia (VaD). Cerebral perfusion and cerebrovascular reactivity (CVR) both decrease with dementia compared to healthy aging; as these changes occur prior to symptomatic onset and in distinct brain regions, perfusion and CVR may act as complementary biomarkers of early cerebrovascular changes. These biomarkers can be measured using MRI methods, yielding macrovascular measures of perfusion and CVR. We recently developed a more advanced method capable of measuring microvascular‐specific measures of perfusion and CVR. Here, we characterized the cerebrovascular profiles of AD and VaD using complementary perfusion and CVR biomarkers representing both macrovascular and microvascular regimes.

Method

MRI data were acquired at 3T (Ingenia, Philips) in three cohorts: non‐cognitively impaired cohort (HC), AD, and VaD (Table 1). Perfusion data were acquired with a multi‐echo, multi‐contrast (SAGE) acquisition (5 echoes, 7.7/26/56/74/92 ms), before, during, and after injection of gadolinium‐based contrast agent. Acquisition parameters include: repetition time (TR) = 1.5 s, voxel size = 2.75×2.75×5 mm, 200 volumes, acquisition time = 5 min. SAGE functional MRI (fMRI) data were acquired in the same cohort with the same TEs and the following acquisition parameters: TR=3.0 s, voxel size = 3 mm3, 160 volumes, acquisition time = 8 min. SAGE data underwent standard pre‐processing. Macro‐ and microvascular cerebral blood flow (CBF), relative cerebral blood volume (rCBV), and relative CVR (rCVR) were calculated using advanced analysis pipelines. The Montreal Cognitive Assessment (MoCA) was administered prior to MRI acquisition.

Result

As expected, microvascular perfusion and rCVR were lower than the corresponding macrovascular metrics for all groups. Macrovascular and microvascular CBF were lower for AD and VaD compared to HC, while there was no difference in CBF between AD and VaD (Figures 1,2). Similar trends were observed for rCBV. There were no differences in macrovascular rCVR between groups; microvascular rCVR was lower for AD and VaD compared to controls.

Conclusion

Macrovascular and microvascular perfusion decreases with AD and VaD. Enrollment is ongoing, and future directions include analysis of perfusion metrics within cortical and subcortical regions and correlation of neuroimaging findings with cognitive testing.
Sleep Biomarker‐Based Characterization of Neurodegenerative Disorder Phenotypical Risk
Authors: Levendowski Daniel J , Walsh Christine M , Cho Yeilim , Tsuang Debby W , Lee‐Iannotti Joyce , Berka Chris , Boeve Brad , Neylan Thomas C. , St. Louis Erik K
Published: January 2025

Background

In this pilot study, the diagnostic agreement for sleep biomarker‐based neurodegenerative disorder (NDD) risk probabilities was evaluated in patients with Alzheimer’s disease dementia (AD), Lewy body disease (LBD), mild cognitive impairment (MCI), and controls (CG) with a Mini‐Mental State Exam scores ≥28.

Methods

Sleep biomarkers recorded with the Sleep Profiler (Advanced Brain Monitoring, Inc.) were used as inputs to a 4‐class machine learning algorithm trained to assign NDD risk probabilities to AD=27, LBD=19, isolated REM sleep behavior disorder=15, and CG=58. Input variables included age, time‐REM, non‐REM hypertonia, autonomic‐activation index, spindle‐duration, atypical‐N3, time‐supine, sleep‐efficiency, relative‐theta, and theta/alpha. Distributions of NDD risk were then assigned to AD=37, LBD=19, MCI=71, and CG=106 records.

Records with CG output probabilities ≥70% were labeled Probably‐Normal, and from 45‐70% Likely‐Normal with indications of a NDD added when the LBD, AD or prodromal synucleinopathy (pSYN) risks exceeded approximately 25%. Similar thresholds were used to assign Likely‐ or Probably‐ LBD, AD, pSYN, or Mixed based on the group probabilities. Likely‐Normal with indications consistent with the diagnosis trended toward agreement.

Results

The proportions of each group with classifications consistent with the diagnosis or trended toward agreement were CG=86%, MCI=75%, LBD=79%, and AD=87%.

In the CG group, NDD risk assignments were Probably‐Normal=68% and Likely‐Normal with NDD indications=18%, Likely‐NDD=10%, and Probable‐NDD=4%.

The MCI group was assigned Probable‐NDD=38% (AD=26%, Mixed=10%, LBD/pSYN=2%), Likely‐NDD=17% (AD=7%, Mixed=6%, LBD=3%, pSYN=1%), Likely‐Normal with indications=20% (AD=9%, pSYN=7%, Mixed=4%) and Probably‐Normal=25%

The LBD group’s assigned risks consistent with the diagnoses included Probable‐NDD=74% and Likely‐Normal with pSYN indications=6% but included inconsistencies from Probable‐AD=5%, Likely‐Normal with AD indications=5%, and Probably‐Normal=11%.

AD group’s consistent risk classifications included Probable‐NDD=60%, Likely‐NDD=11%, Likely‐Normal with AD/Mixed indications=13% and countered by inconsistencies from Probable‐LBD/pSYN=8%, Likely‐Normal with pSYN indications=3%, and Probably‐Normal=5%.

Conclusions

Consistencies between the diagnoses and assigned NDD risks agreements exceeded 75% in all four groups. Studies are underway to confirm the test‐retest reliability and/or evaluate NDD risk probabilities in other patient populations.

Support: NIH grants R44AG050326, R44AG054256, RO1AG060477, R33AG064271, ULRR024150, R34AG056639.
Tear Biomarkers as Indicators of Alzheimer's Disease
Authors: Takyar Anshule , Bailey Maisie , Trantow Richard , Coronel Danilo A Sanchez , Garcia Hector Hugo , Gilman Robert H , Diaz Monica M
Published: January 2025

Background

The prevalence of Alzheimer’s disease (AD) is increasing worldwide, particularly in low‐ to middle‐income countries (LMICs). Resource limitations and time constraints in many LMICs make AD screening and diagnosis difficult in the clinical setting. Neurodegenerative biomarkers in human tears may be associated with neurodegenerative diseases, but its potential has yet to be investigated in AD. Amyloid‐beta, tau, α‐synuclein, and neurofilament light (NfL) levels in tears were compared in patients with AD (AD+) and age‐matched negative controls (AD‐).

Methods

Samples were collected from a cohort of AD+ (N=26) and age‐matched AD‐ (N=9) individuals at a public neurological hospital in Lima, Peru. Tears were collected on Schirmer strips, which were dried and frozen. Proteins were then extracted into a PBS‐based buffer and tested for biomarkers using commercial ELISA kits, Luminex assays, and digital ELISA methods from Quanterix and NanoMosaic. Biomarker burdens were compared between AD+ and AD‐ patients using digital ELISA results. Amyloid‐beta 40, Amyloid‐beta 42, total tau, phosphorylated‐tau181 (pTau181), α‐synuclein, and NfL burdens were evaluated using various ELISA methods, and Human TIMP‐1 was used as a control.

Results

Initial analyses using commercial ELISA and Luminex methods proved inconclusive. NfL burden was evaluated using the Quanterix digital ELISA platform showed a detectable protein burden in 3 AD‐ samples and 1 AD+ samples, but remained statistically insignificant (p = 0.45). A custom digital ELISA assay on the NanoMosaic platform showed AD+ status was associated with elevated Total Tau (p = 0.0023) and pTau181 (p = 0.0016) burden in tear proteome as compared to AD‐ samples, shown in Figures 1 and 2.

Conclusions

Due to considerably lower limits of detection, the Quanterix and custom NanoMosaic assays allowed for increased sensitivity of biomarker detection in human tears. Our results indicate a significant association between AD+ status and elevated Total Tau and pTau181 levels in tears, and suggest the potential for tears to be pursued as a potential non‐invasive and accessible addition to AD screening and diagnosis, particularly for LMICs.
Synergistic effects of plasma S100b levels and MRI‐based water exchange rate across the blood‐brain‐barrier on memory performance among older adults
Authors: Pappas Colleen , Zachariou Valentinos , Bauer Christopher E. , Sudduth Tiffany L. , Wilcock Donna M. , Jicha Gregory A , Hartz Anika MS , Shao Xingfeng , Wang Danny JJ , Gold Brian T
Published: January 2025

Background

Non‐invasive biofluid and MRI measures of blood‐brain‐barrier (BBB) dysfunction may aid early detection of cerebral small vessel disease (cSVD). Plasma markers of astrocytic function and injury, such as S100 calcium‐binding protein B (S100b), have gained increased attention in relation to BBB integrity and cognition. Here we explored the inter‐relationships between plasma S100b levels, an MRI measure of water exchange rate across the BBB (kw), and cognitive performance among older adults.

Method

The participant sample consisted of 74 older adults without dementia recruited from the University of Kentucky Sanders Brown Center on Aging. Relationships between S100b and cognition (memory, executive function) and MRI‐based BBB water exchange rate were tested. Plasma S100b levels (pg/mL) were measured using Meso Scale Discovery R‐PLEX assay at the University of Kentucky’s CCTS Biomarker Analysis Lab. Composite scores were created for memory and executive function. A diffusion‐prepared arterial spin labeling (DP‐ASL) MRI sequence was used to estimate water exchange rate across the BBB (expressed as kw). All data (S100b, cognition, MRI) were collected within 1 year of each other. Multiple linear regression models examined the impact of plasma S100b on memory, executive function, and kw. Covariates included age, gender, and education (for cognition models only). Additionally, kw was tested as moderator of the S100b‐cognition relationships using the PROCESS macro.

Result

A negative relationship was observed between S100b and memory, where higher S100b levels were associated with poorer memory performance. A similar relationship was not observed with executive function. S100b was also not associated with kw. However, there was an interaction between S100b levels and kw in the parietal lobe on memory performance such that participants with both lower parietal kw and higher S100b showed the poorest memory performance.

Conclusion

Our results indicate that S100b levels are negatively associated with memory performance, but not MRI‐based BBB kw. However, higher S100b levels coupled with lower MRI‐based water exchange rate further contributed to the strong negative effects observed for memory performance. This suggests that plasma S100b and BBB kw may be different proxies of BBB function and may have synergistic negative effects on cognition.
Detection of Cognitive Impairment through Spontaneous Speech Acoustic Markers Using Transformer‐based Machine Learning Approach
Authors: Mobtahej Pooyan , Gouron Sam , El‐Khoury Annelisse , Leiby Anne‐Marie C , Kawas Claudia H. , Corrada María M. M. , Sajjadi S. Ahmad
Published: January 2025

Background

The increasing prevalence of cognitive impairment and dementia threatens global health, necessitating the development of accessible tools for detection of cognitive impairment. This study explores using a transformer‐based approach to detect cognitive impairment using acoustic markers of spontaneous speech.

Method

Recordings of unstructured interviews from baseline visits were obtained from participants of The 90+ Study, a longitudinal study of individuals older than 90 years. For this analysis, participants without dementia at baseline who had stable cognitive diagnoses over the subsequent two assessments were included. Of 120 total participants (Table 1), 78 had normal cognition (NC) and 42 had cognitive impairment with no dementia (CIND). From baseline interviews, 1,273 segmented 20‐second denoised audio samples were generated. Samples from 80% of participants trained our models while the remaining 20%, unseen data, were test data. After filtering from Mel Spectrogram, Mel‐frequency cepstral coefficients (MFCC), spectral centroid (SC), and Chroma representations, 154 acoustic features were extracted. Features were transformed from 2D images into 1D string representations for interpretation by transformer‐based language models, namely Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre‐Trained Transformer‐2 (GPT‐2). Transformer‐generated feature embeddings were inputted into Random Forest (RF) and Logistic Regression (LR) models. Soft voting ensemble approach combined classifier predictions to maximize prediction power for final classification (Figure 1). To reduce the risk of overfitting, 5‐fold cross‐validation determined evaluation metric averages for baseline models, including random forest and logistic regression.

Result

Our test data comprised 142, 20‐second samples from 16 NC and 113, 20‐second samples from 9 CIND. Our model achieved 100% AUC with either BERT or GPT‐2 feature embeddings. We also investigated F1 scores: BERT reached 99.56% and GPT‐2 achieved 100% (Table 2).

Conclusion

Our approach converts acoustic features from 2D images into 1D patterned value representations interpretable by transformer‐based language models. This renders acoustic markers suitable for detection of cognitive impairment based on language model interpretation. Both GPT‐2 and BERT models outperformed baseline models. The model will be validated with external data and increasing sample size in the next step.
Untargeted metabolomics tools to inform diet and drug exposures of cognitively declined cohorts
Authors: Zhao Haoqi Nina , Mohanty Ipsita , Kaddurah‐Daouk Rima F. ,   Alzheimer's Gut Microbiome Project Consortium (AGMP), Dorrestein Pieter
Published: January 2025

Background

It is now widely acknowledged that diet, lifestyle, and environmental exposures largely affect an individual’s metabolic state in health and disease, including the brain. Metabolomics has demonstrated its potential to enable exciting discoveries in brain health, facilitated by advances in analytical and informatics techniques. Here, we highlighted the use of MS/MS‐based untargeted metabolomics to study the diet and medication exposure of cognitively declined cohorts through the newly developed FoodMASST and DrugMASST tools.

Methods

A food metabolomics reference dataset was created with tandem mass spectrometry data (MS/MS) alongwith detailed and structured metadata for ∼3,500 foods. We used this resource to trace the food origin of important Alzheimer’s disease metabolite biomarkers as well as to investigate the empirical diet patterns of subjects in a Mediterranean diet intervention study (MIND). An MS/MS spectral library for drugs, metabolites, and related molecules was developed by spectral similarity search against ∼1.2 billion public metabolomics spectra. We utilized this resource to perform empirical medication read‐out in 501 brain samples from the Rush Religious Orders and Memory and Aging Project (ROS/MAP) and 637 fecal samples from the Aging and Disability Resource Centers (ADRCs).

Results

We demonstrated the utility of the FoodMASST and DrugMASST tools through several examples. Using FoodMASST we demonstrated that lentil is an important food source of tryptophan betaine, an important AD marker. FoodMASST‐based diet score separated the metabolomics profiles of MIND subjects in the PCoA cohort. DrugMASST detected 102 drugs in brain samples from the ROS/MAP and revealed that neurology/psychiatry and cardiology drugs were the most important categories. The computationally derived drug metabolite library enabled additional annotation of 15 drug metabolites with high confidence. We observed significant separation in the metabolome of patients with and without antibiotic detection. In the ADRC fecal samples, we detected 190 drugs and the neurology/psychiatry and cardiology categories had the highest number of drugs observed. Carbidopa, a dopamine promoter drug given to treat PD, was detected in 62% of the patients.

Conclusions

Untargeted metabolomics tools, such as metadata‐driven metabolomics exemplified by FoodMASST and DrugMASST, create insights into patients' chemical exposure and their role in gut microbiome and Alzheimer’s disease.
Inflammatory role of toll‐like receptors in the hippocampus of 3xTg‐AD animals
Authors: Júnior Carlos Wagner Leal Cordeiro , De‐Paula Vanessa V.J. , de Oliveira Caíque , Couto Portugal
Published: January 2025

Background

Alzheimer's disease (AD) is a progressive neurodegenerative disease with multifactorial etiology. The toxicity of senile plaques and neurofibrillary tangles is associated with changes in the clearance of tau and beta‐amyloid proteins, which are closely related to inflammatory imbalances. Pro‐inflammatory cytokines are present in abnormal details due to the overactivation of immunological pathways, especially toll‐like receptors. Our objective is to determine the difference between the proteins altered in AD and the inflammatory role of toll‐like receptors in the hippocampus of 3xTg‐AD animals chronically treated with lithium at therapeutic and subtherapeutic doses and how lithium acts in this regulation.

Method

48 mice (3xTg‐AD;Tg) were treated with lithium for 3 months and divided into 3 groups (control, 1mM Li and 2mM Li). Hippocampal tissue was subjected to proteome. The inflammatory pathways of the identified proteins were compared by the STRING software and the molecular functions were analyzed by Gene Ontology, using the ClueGO software. The Toll‐Likes receptor family genes were analyzed together with Tau and amyloid beta proteins in the STRING bioinformatics program, in order to study the ontological pathways and molecular function. As parameters, we set significance at 0.7 with a limitation of 50 interactions per gene. The project was submitted to the Ethics Committee under protocol (1876/2022).

Result

The genes of the Toll‐Like receptor family were analyzed using the STRING bioinformatics program to study ontological pathways and molecular function. As parameters, we set significance at 0.7 with a limitation of 50 interactions per gene. Thus, the results demonstrate that ontological changes include: signal transduction activating the immune response, activation of the immune response, regulation of defense response, positive regulation of the immune response, regulation of the stress response, immune response, positive regulation of the response to stimulus, and the process of the immune system.

Conclusion

The results appear promising for understanding the inflammatory pathways related to toll‐like receptors. Since the identification of positively regulated immune responses suggests that lithium, considering its modulating effect on both proteins involved in neurodegeneration and associated inflammatory processes. However, we emphasize the need for additional clinical studies to validate and extend these findings in animal models.
Transformer‐based Deep Learning Architecture Improves Detection of Associations between Spontaneous Speech Language Markers and Cognition
Authors: Mobtahej Pooyan , Gouron Sam , Javaheri Rojan , El‐Khoury Annelisse , Leiby Anne‐Marie C , Sultzer David L , Sajjadi S. Ahmad
Published: January 2025

Background

Spontaneous speech is easily obtainable and has the potential to become an accessible and low‐cost marker for cognitive function. The time‐consuming and labor‐intensive nature of speech analysis has been a major obstacle to utilizing this promising tool. This study uses a novel transformer‐based methodology to explore associations between spontaneous speech language features and global cognition.

Method

Speech recordings were obtained from participants with clinical diagnoses of mild cognitive impairment (MCI), dementia, and cognitively unimpaired, from the Alzheimer’s Disease Research Center (ADRC) at University of California, Irvine. Audio samples were denoised and transcribed using our transformer‐based model (Figure 1). The model conducted association analyses between global cognition, measured by Montreal Cognitive Assessment (MoCA) scores, and 5 linguistic features pre‐specified based on popularity in language analysis. Features were extracted from transcripts via Bidirectional Encoder Representations from Transformers (BERT), which assigns each word a special “token” (tokenizing), translates “tokens” into a numerical format for computer comprehension (encoding), and generates unique identifiers (embeddings) that mathematically capture meanings and relationships between words. A linear regression (LR) model was then trained on power‐transformed BERT‐extracted linguistic features and its performance was tested against both untransformed and power‐transformed data without BERT feature extraction using the same linguistic features.

Results

In a cohort comprising 73 healthy controls and 12 individuals with MCI or dementia, our analysis, utilizing pre‐specified linguistic features and leveraging BERT for enhancing feature extraction, revealed that vocabulary richness (p = 0.009), average word length (p = 0.005), and semantic coherence (p = 0.047) were significantly associated with MoCA scores (Table 1). The non‐BERT untransformed model found no significant associations with MoCA while the non‐BERT power‐transformed model found only one significant association: average word length (p = 0.016).

Conclusion

Our study's novel approach of employing BERT for linguistic feature extraction in association analysis increased the number of pre‐selected speech features significantly associated with global cognition, namely vocabulary richness, average word length, and semantic coherence.

This improvement in association detection highlights the potential for better deep‐learning dementia detection methods and might lead to increased utility of spontaneous speech as an easily obtainable and scalable cognitive measure.
A genome‐wide association study for clinical dementia phenotypes in an ethnoracially diverse cohort
Authors: Sapkota Shraddha , Almeida Marcio A , Jin Lee‐Way , Farias Sarah Tomaszewski , Decarli Charles
Published: January 2025

Background

Genome‐wide association studies (GWAS) for established clinical dementia phenotypes is often limited to Caucasians from European ancestry. Although the incidence of dementia is higher in African Americans and Hispanics, the inclusion of these groups in GWAS research is less common. We examine GWAS for clinical dementia phenotypes in an ethoracially diverse cohort of older adults.

Method

We used an older‐adult cohort from the University of California, Davis‐Alzheimer’s Disease Research Center (n=1014; mean age= 74.54 (7.50) years; 59.6% female). Participants were diagnosed as cognitively normal (CN; n=329), Mild Cognitive Impairment (MCI; n=448) and Demented (n=234) at final visit. Samples were genotyped and imputed using 1000 genomes as reference. Genetic admixtures components were calculated assuming three original populations resulting in a set of 6078198 high‐quality imputed single nucleotide polymorphisms (SNP) from 1000 participants. Our outcome tested GWAS across clinical dementia phenotypes (CN, MCI, Demented). We further stratified our genetic dataset into non‐synonymous and non‐synonymous high‐deleterious genetic variants. Finally, we performed a replication analysis of the top Alzheimer’s disease‐related genetic risk loci identified in the literature. All genetic association analyses included age, sex, admixture components and education as covariates.

Result

Admixture analysis detected primarily European/Caucasian genetic ancestry and African and Amerindian minorities (Figure 1). We did not observe significant associations for any panel (overall, non‐synonymous, high‐deleterious). Some findings were of interest. Our top genetic risk loci associated with clinical dementia phenotype in the (1) overall panel was rs62344486 (p<4.84e‐07) (Figure 2) known to play a protective role against cognitive decline, (2) non‐synonymous panel was rs17832998 in the DACT1 gene (p<1.0e‐05), and (3)non‐synonymous high‐deleterious panel were rs41267154 (p<3.4e‐03) and rs72697000 (p<1.5e‐03) in the FLG gene. We also identified rs17668923 located downstream of TMEM106B gene (p<2.8e‐05) and rs56220430 (p<1e‐04) near SIGLEC11 gene from our literature review.

Conclusion

We identified two novel SNPs near TMEM106B and SIGLEC11 genes which have been associated with frontotemporal dementia and immunosuppressive signals, respectively. Preliminary GWAS for clinical dementia phenotypes in diverse populations may (1) identity novel genetic risk loci underlying the dynamic neurobiological underpinnings across the dementia continuum and (2) be further validated in larger minority population studies.
Digital Cognitive Screening and ML‐Enabled Random Forest Modeling for the Detection of Cognitive Impairment
Authors: Banks Russell , Thompson Karl , Jannati Ali , Tobyne Sean , Showalter John , Bates David , Pascual‐Leone Alvaro
Published: January 2025

Background

Mild cognitive impairment (MCI), is characterized by cognitive dysfunction not severe enough to affect one’s activities of daily living (ADLs)1. Annually, approximately 15‐20% adults 65 and older will present with MCI 1. MCI is considered a significant risk factor and a robust predictor for developing dementia. The time course for progression to dementia can vary substantially between individuals and is impacted by the specific pathology underlying the MCI, and the cognitive deficits associated with cognitive impairment (CI) subtypes 2,3. Despite the conversion risk of MCI to dementia and the effectiveness of early lifestyle interventions to mitigate the conversion risk, many investigations do not account for MCI in their CI prediction models. This research investigates binary and 3‐class ML‐enabled modeling to classify CI status leveraging multiple modalities of cognition extracted from the Digital Clock and Recall (DCR), a brief digital cognitive assessment.

Method

Data from 983 participants in the Bio‐Hermes‐001 multi‐site study (age mean±SD=72±6.7; 56% female; years of education mean±SD=15±2.7; primary language English), a priori classified as cognitively unimpaired (CU; n=417), mild cognitively impaired (n=309), or probable Alzheimer’s dementia (n=257) based on expert consensus clinical diagnosis and neuropsychological evaluation were analyzed. A random forest model was trained on DCTclock and word recall data to classify cognitive impairment using a binary (CI and CU) and 3‐tier (CI, Indeterminate, and CU) prediction thresholding schemes.

Result

The 3‐tier model predictions performed well (AUC=0.887; accuracy=0.834; NPV=0.801; PPV=0.859) outperforming the binary predictions (AUC=0.865; accuracy=0.79; NPV=0.737; PPV=0.837) when measured on Biohermes’ cohort diagnosis. The sensitivity and specificity of the 3‐tier predictions were 0.858 and 0.8, respectively.

Conclusion

The DCR, a 3‐minute digital cognitive assessment can be used to classify MCI and probable Alzheimer’s dementia with high accuracy, NPV, and PPV.
Impact of race/ethnicity and area deprivation index on Alzheimer’s disease and related disorders biomarkers in the POINTER Cohort
Authors: Toledo Jon B. , Pascual Belen , Landau Susan M. , Harrison Theresa M. , Finn Quentin , Demesie Yitbarek N , Yu Melissa , Decarli Charles , Vemuri Prashanthi , Baker Laura D , Lovato Laura , Snyder Heather M , Masdeu Joseph C. , Pavlik Valory
Published: January 2025

Background

There is limited information on biomarker‐defined Alzheimer’s disease (AD) pathology in community‐recruited individuals of diverse racial and ethnic groups. Here, we assessed the association of race/ethnicity with baseline biomarkers and cognitive measures and hypothesized a lower impact of AD pathology in non‐Hispanic White (nHW) participants in the U.S. POINTER study clinical trial, which evaluates the benefits of a multidomain lifestyle intervention on cognition of community‐recruited at‐risk elderly individuals.

Method

We included 879 participants from the U.S. POINTER study enrolled in the imaging substudy (∼50% of participants) who self‐identified as nHW (n=644), African American/Black (AA/B, n=132), Asian (n=29), and Hispanic (n=74). Outcomes measures included global Aβ pathology (18F‐florbetaben), temporal meta‐ROI tau (18F‐MK‐6240), MRI‐measured white matter hyperintensity volume (WMH) volumes, global clinical dementia rating (gCDR), and global, executive, and memory standardized cognitive scores. The nHW participants were selected as reference. Power transformations normalized skewed distributions. Multivariable linear regressions include age, sex, education, Framingham risk score, and area deprivation index (ADI) as covariates.

Result

AA/B harbored lower WMHs, and meta‐temporal tau ROI values. In the full model, 0.5 gCDR participants had higher centiloid values, were more likely to be AA/B and Hispanic, and had a greater burden of WMHs. In addition, higher centiloid values were associated with a 0.5 gCDR in all groups except for the AA/B participants. Hispanic, AA/B, and Asian participants had lower cognitive scores than the nHW group. A higher ADI score was associated with lower global and executive cognitive scores but not with WMH volume or Aβ centiloid score.

Conclusion

The analyses of the community‐based U.S. POINTER indicate that AA/B individuals present differences in AD and related dementia biomarker values. nHW had higher fully adjusted cognitive scores. These results indicate a significant heterogeneity in the cognitive testing performance and biomarker values of individuals at risk for dementia in the community.
Physical activity and sex modify the influence of diabetic‐level insulin resistance on cognitive function in a cohort enriched with risk for Alzheimer’s disease
Authors: Mamlouk Gabriella M , Edmunds Kyle J , Breidenbach Brianne M. , McIntyre Clayton Connor , Lose Sarah R , Johnson Sterling C. , Okonkwo Ozioma C
Published: January 2025

Background

Physical activity (PA) and peripheral insulin resistance are two promising targets for delaying the onset of cognitive impairment in preclinical Alzheimer’s disease (AD). Understanding how these factors interact and whether their influence on cognitive outcomes is sex‐dependent may be crucial for designing effective lifestyle interventions to protect aging brain health. This study examines whether PA and sex modify the relationship between diabetic‐level peripheral insulin resistance and cognitive function in a sample enriched with risk for AD.

Method

Our sample featured n=1,125 cognitively unimpaired participants from the Wisconsin Registry for Alzheimer’s Prevention (WRAP). An index for PA was defined from self‐reported weekly minutes of moderate and vigorous physical activity, where active participants were defined by reporting at least 150 weekly minutes of moderate or vigorous aerobic exercise. Cognitive composite scores were based on neuropsychological examination by trained technicians, and outcomes were standardized to z‐scores across the WRAP database and indexed. The Homeostatic Model Assessment for Insulin Resistance (HOMA‐IR) quantified insulin resistance status, and scores exceeding 3.63 were classified as diabetic‐level. Cross‐sectional linear regression models were assembled in a moderation framework to test the three‐way interaction between PA, sex, and diabetic‐level insulin resistance on cognitive outcomes, and all model covariates included age, years of education, Apolipoprotein E4 (APOE‐ε4) carriage, and familial history of AD.

Result

73.6% of participants had family history of AD and 37.9% were APOE‐ε4 carriers. The sample was 69.9% female, and participants had a mean age of 64.5 ± 7 years and a mean of 16 ± 3 years of education. Models demonstrated significant interactions between PA, sex, and diabetic‐level insulin resistance on three cognitive composite scores: executive function (β=‐0.73, SE=0.30, p=0.017), preclinical Alzheimer's cognitive composite 3 (PACC3) (β=‐0.66, SE=0.27, p=0.014), and PACC4 (β=‐0.50, SE=0.24, p=0.037). In individuals with diabetic‐level HOMA‐IR, the effect of sex on cognitive performance was moderated by PA, with female sex being more strongly linked to higher cognitive performance in physically active subjects.

Conclusion

In older adults at risk for AD, sex differences in cognitive function in individuals with diabetic‐level insulin resistance may depend on physical activity.
A Comparison of DTI‐Based Measures as Predictors of Future Cerebrovascular Degeneration
Authors: Reid Robert I. , Przybelski Scott A. , Lesnick Timothy G. , Fought Angela J. , Raghavan Sheelakumari , Kamykowski Michael G. , Graff‐Radford Jonathan , Knopman David S. , Petersen Ronald C. , Jack Clifford R. , Vemuri Prashanthi
Published: January 2025

Background

There has been a recent proliferation of various quantities based on single shell Diffusion Tensor Imaging (DTI) for capturing overall cerebrovascular health, especially Small Vessel Disease (SVD) as a single number. The existing literature has gaps in the comparison of these measures, so we evaluated them as predictors of both current and future White Matter Hyperintensity (WMH) as a proxy of SVD.

Method

Using baseline 3T MRI examinations (T1, DTI, FLAIR scans) of 598 participants (>60 years, amyloid negative, to limit to those on the SVD pathway) from the Mayo Clinic Study of Aging, we calculated five summary DTI quantities with varying degree of complexity. We estimated their partial R2 from cross‐sectional linear regression models and computed semi‐partial R2 from mixed models predicting WMH volume as a percentage of Total Intracranial Volume (TIV). All models were adjusted for age, sex, and scanner model, and the WMH and based were log‐transformed before fitting. We also conducted subset analyses with high (WMH+) vs. low (WMH‐) baseline WMH load (cut at 1.62%) to compare the performance of the measures in early vs. widespread WMH.

Result

All MD‐based quantities computed on the whole brain, including FWF, performed similarly to each other, but Genu FA underperformed at explaining WMH variance (Table 1). PSMD (skeletonized or atlas based) was more sensitive in WMH+ but dramatically less so in WMH‐. In WMH‐ and overall cohort, FWF and JHU MD (which is a simple averaging of MD in the JHU atlas) performed well.

Conclusion

All MD‐based measures performed similarly to each other in the whole dataset but dichotomizing by WMH load showed interesting differences. PSMD measurements are driven by the extreme end (95th percentile) of the MD distribution and therefore more sensitive in WMH+. Genu FA, a regional measure, was not useful for predicting whole brain WMH change. We noticed some variation in how the quantities are affected by scanner model (GE Signa HDxt and Discovery 750) which needs broader investigation. Simply averaging MD over WM avoids additional processing and is more sensitive to WM degeneration that is not (yet) severe.
Complex relationship between serum IGFBP2 and FGF21, dysmetabolism and cognitive function across the clinical spectrum of Alzheimer’s disease
Authors: Dallaire‐Théroux Caroline , Denis Hélèna , Provencher Amélie , Valentin‐Escalera Josue , Leclerc Manon , Tremblay Cyntia , Loiselle Andréanne , Tournissac Marine , Belleville Sylvie , Picard Frédéric , Zetterberg Henrik , Duchesne Simon ,   CIMA‐Q Group, Calon Frederic
Published: January 2025

Background

Chronic conditions such as obesity, dyslipidemia, insulin resistance and metabolic syndrome are found with increased prevalence in older adults and are investigated as risk factors of Alzheimer’s disease (AD). The exact mechanisms by which such conditions may act on the brain to promote AD pathogenesis are still unknown. We aimed to investigate the relationship between peripheral markers of metabolic function, including adiponectin, fibroblast growth factor 21 (FGF21) and insulin‐like growth factor binding protein 2 (IGFBP2), and AD‐associated cognitive decline.

Method

Samples from 288 participants (mean age, 74 [SD, 6] years; 37% males) were obtained through the CIMA‐Q cohort. Participants were classified as cognitively healthy (CH; n=56), subjective cognitive decline (SCD; n=115), mild cognitive impairment (MCI; n=86), and AD (n=31). Demographics, APOE4 status, cognitive testing, anthropometrics, blood and cerebrospinal fluid biochemical parameters and MRI data were compared between clinical groups using one‐way ANOVA and Kruskal‐Wallis tests. Multiple linear regressions were performed to evaluate the association between serum levels of adiponectin, FGF21 and IGFBP2 and clinical, biochemical and neuroimaging data, with adjustment for age and sex.

Result

Higher levels of FGF21 (p=0.01) and IGFBP2 (p<0.001) were found with increasing age. Amongst the three metabolic markers, IGFBP2 was significantly elevated in SCD and AD (p=0.04) compared to CH. Clinical and biochemical evidence of obesity, insulin resistance and dyslipidemia were associated with lower serum adiponectin and IGFBP2, but higher FGF21 (p<0.0001). Significant inverse associations between metabolic markers and clinical scores included FGF21 with Rey Auditory Verbal Learning Test (RAVLT; p=0.002) and IGFBP2 with Montreal Cognitive Assessment (MoCA; p=0.02). Adiponectin levels were not linked to clinical diagnosis nor cognition but were higher in women. Finally, IGFBP2 also correlated with markers of neurodegeneration including higher plasma pTau181 and lower amygdala volume (p<0.05).

Conclusion

While FGF21 and IGFBP2 exhibit opposing trends in metabolically impaired individuals, both markers increase with age and are inversely associated with cognitive performance. However, serum IGFBP2 is more strongly associated with clinical diagnosis and cognitive function than FGF21. These results unveil complex relationships between fluid‐based biomarkers of dysmetabolism and AD, offering new opportunities for diagnosis and treatment.
Mapping alterations in the hippocampal subfields in the ARTE10 mouse model of Alzheimer’s disease using dMRI derived soma and neurite density maps
Authors: Shahid Syed Salman , Jarvis Erin E , Butch Elizabeth R , Snyder Scott E , Wu Yu‐Chien
Published: January 2025

Background

The hippocampus, a region vital for memory and cognition, is prone to abnormal deposition of beta‐amyloid (Aβ) during the early stages of the Alzheimer’s disease. Aβ‐associated pathophysiological mechanisms instigate dendritic deficit, neuronal loss, and neuroinflammation, leading to abnormal functional and behavioral changes. These factors directly impact tissue microstructures. Histological studies suggest subfield specific Aβ accumulation. The aim of the current study is to map the changes in soma‐ and neurite‐density in the hippocampal‐subfields due to Aβ load in the ARTE10 mouse‐model of AD using a compartment‐specific diffusion model.

Method

Ten‐month‐old male ARTE10 (N=8) and age‐matched littermate controls underwent MRI on a 9.4T scanner (Bruker BioSpin, GmbH, Germany) equipped with an 1H cryogenic surface coil. DWIs were acquired using a 2D multi‐shot spin‐echo echo planar imaging sequence using the following parameters: TE/TR=26/4000 ms; voxel size=125x125x400 µm3, 20 slices, 4 segments, b values= (500, 1000,2500, 4000,5500, 7000) s/mm2 and 5 b0 per b‐shell. The averaged b0 image was non‐linearly registered to Badhwar hippocampus atlas. The hippocampal‐subfields (CA1, CA2, CA3, CA4‐DG, subiculum, and stratum‐granulosum of hippocampus (SGH)) in atlas space were inverse transformed to diffusion space. Multi‐compartment microstructural estimation was performed using soma and neurite density imaging (SANDI) model. This model provided volume fractions for extra‐cellular (fec), intra‐neurite (fin), and intra‐soma (fsoma) compartments (Figure 1). The regional mean values of microstructural parameters of individual subfields were extracted and used in a general linear model for group comparisons.

Result

The group‐level differences (WT vs. ARTE10) in fin were observed in CA1 (p=0.02; d=‐1.25), CA2 (p=0.03; d=‐1.20), CA4‐DG (0.01; d=‐1.34), and SGH (p=0.03; d=‐1.22) Figure 2. Group differences in fsoma were significant in CA1 (p=0.04; d=1.08), CA2 (p=0.02; d=1.22), CA4DG (p=0.04; d=1.10), and SGH (p=0.03; d=0.99). In general, the ARTE10 AD mice had reduced neurite density and elevated soma density in the hippocampal subfields.

Conclusion

The results demonstrated that the SANDI model is sensitive to the microstructural alterations in the hippocampal subfields of the animal model with profound AD pathologies.
Treatment effects of vitamin D3 and marine omega‐3 fatty acids on plasma ADRD biomarkers: Exploring sex and race differences
Authors: Vyas Chirag M. , Ostaszewski Beth L. , Anderson Amirah K. , Cook Nancy R. , Kang Jae H. , Gatchel Jennifer R. , Mischoulon David , Chang Grace , Reynolds Charles F. , Manson JoAnn E. , Selkoe Dennis J. , Okereke Olivia I.
Published: January 2025

Background

There is an urgent need to identify novel, accessible and affordable strategies to prevent cognitive decline and progression in the Alzheimer disease and related dementias (ADRD) continuum. Vitamin D3 and marine omega‐3 fatty acids (omega‐3s) supplements show promise for cognitive protection, with potential variations in their effects by sex or race. However, to date, no randomized clinical trials (RCTs) have tested their impact on emerging plasma‐based biomarkers with potential utility to predict ADRD pathogenesis.

Methods

Vitamin D and Omega‐3 Trial (VITAL) is a completed, nation‐wide, 2x2 factorial placebo‐controlled RCT testing vitamin D3 (2000 IU/d) and omega‐3s (1 g/d) for cancer and cardiovascular disease prevention. We included a Boston‐area sub‐cohort of 929 randomized VITAL participants who provided blood samples at baseline, 2‐year, and/or 4‐year follow‐up. Plasma ADRD biomarkers, including an N‐terminal tau fragment (NT1), amyloid‐β (Aβ)‐40, Aβ‐42, neurofilament‐light (NfL), and glial fibrillary acidic protein (GFAP), were measured. We used multivariable‐adjusted repeated measures models for data analysis; sex and race were pre‐specified effect modifiers.

Results

Among 929 participants, the mean age was 65 years; 49.2% were females; 17.3% were from racial and/or ethnic minority backgrounds, including 8.2% Black adults. Neither vitamin D3 nor omega‐3s, compared to placebo, significantly reduced ADRD biomarkers overall across 4 years of treatment; however, there was a trend for reduction in Aβ‐40:Aβ‐42 ratio over 4 years for vitamin D3 versus placebo [percent difference (95% confidence interval [CI]): ‐1.2 (‐2.7, 0.3)]. Subgroup analyses uncorrected for multiple‐testing suggested interactions by sex and race. Vitamin D3 versus placebo resulted in a reduction in NT1 among males (‐2.7%) but not females (p‐interaction=0.08). Among Black participants, vitamin D3 versus placebo resulted in a 16% reduction in NfL levels [95% CI, ‐30.5% to 1.1%; p‐interaction=0.06], while omega‐3s versus placebo showed a 12.4% reduction in GFAP levels [95% CI, ‐21.5% to ‐2.2%; p‐interaction=0.049].

Conclusion

In this RCT sub‐cohort of 929 older adults, neither vitamin D3 nor omega‐3s supplements significantly reduced selected plasma ADRD biomarkers over 4 years. We observed potential differences by sex and race in reductions of some ADRD biomarkers in response to these supplements which warrant further investigation in a larger sample.
Comparing regional and global diffusion MRI markers as biomarkers of VCID
Authors: Raghavan Sheelakumari , Reid Robert I. , Przybelski Scott A. , Kamykowski Michael G. , Lesnick Timothy G. , Graff‐Radford Jonathan , Lowe Val J. , Knopman David S. , Jack Clifford R. , Petersen Ronald C. , Vemuri Prashanthi
Published: January 2025

Background

Quantifying white matter using diffusion MRI (dMRI) has been proposed for measuring early microstructural tissue changes due to cerebral small vessel disease and aid in quantifying vascular contributions to cognitive impairment and dementia (VCID). Our goal was to compare the usefulness of longitudinal white matter changes in the commonly available diffusion MRI measures for VCID prevention trials.

Method

We included 718 participants over 50 years of age (mean age: 71.1(9.6) years) from the Mayo Clinic Study of Aging, a population‐based sample, with at least two dMRI scans and structural imaging. We computed the commonly available single‐shell dMRI measures (fractional anisotropy, mean diffusivity, and two MarkVCID measures ‐ free water and peak‐width skeletonized mean diffusivity [PSMD]) at each time point. We tested for voxel‐wise associations between dMRI markers and vascular risk measured by cardiovascular metabolic condition (CMC) and observed a region‐specific dependance across all dMRI measures. Using both global and regional dMRI measures, we (i) examined the longitudinal association of dMRI measures with cognition and (ii) computed sample size estimates for a hypothetical clinical trial. We also included white matter hyperintensities (WMH) and our previously proposed composite vascular white matter score (combination of WMH and fractional anisotropy of the genu) as a comparison.

Result

Vascular risk was associated with all single shell dMRI measures in the genu of the corpus callosum, which we included as a regional dMRI marker for comparison (Figure 1). All dMRI markers correlated with cognitive performance longitudinally (Table 1) and had comparable sample size estimates required for hypothetical VCID clinical trials (Figure 2). Further, global free water and the composite vascular white matter score had the smallest sample size estimates.

Conclusion

All commonly used dMRI markers had significant frontal lobe changes due to vascular risk. Both global and regional corpus callosum dMRI markers were sensitive to longitudinal cognitive decline, suggesting their utility in measuring the slowing down of VCID. The composite vascular white matter score, global free water, and WMH show promise as VCID biomarkers. Further work is needed to validate these markers on multiple populations.
Discovery of half‐curcumin‐based chemiluminescence probes and their applications in detecting quasi‐stable oxidized proteins
Authors: Ran Chongzhao , Yang Jun
Published: January 2025

Background

Oxidative stress, which refers to an imbalance between the production of reactive oxygen/nitrogen species (ROS/RNS) and the body's antioxidant defense mechanisms, is implicated in the pathogenesis of various chronic diseases by promoting cellular damage, inflammation, and dysfunction. Numerous methods have been reported for detecting ROS/RNS in vitro and in vivo; however, detecting methods for the secondary products of the ROS/RNS reactions, particularly quasi‐stable oxidized products, have been much less explored.

Method

In this report, we discovered that acetylacetone, a core moiety of curcumins, is a new scaffold for generating chemiluminescence in the presence of oxidants. With this new scaffold, we designed a series of half‐curcumins CRANAD‐Xs and demonstrated that CRANAD‐164 could be used to detect quasi‐stable oxidized proteins (QSOP) in patient serum samples and for in vivo imaging in animal models.

Result

We found that the QSOP levels were much higher in the disease serum samples, including Alzheimer’s disease, diabetes, multiple sclerosis, and pulmonary fibrosis, compared to the samples from healthy controls. Moreover, our results revealed that the sera chemiluminescence intensities were higher in aged healthy controls (60‐year old) compared to young healthy subjects (20‐year old), suggesting that CRANAD‐164 can be used to monitor the increase of QSOP in the course of aging.

Conclusion

Our results suggest that acetylacetone‐based chemiluminescence probes can potentially reflect the health status of a patient’s serum (blood) via in vitro diagnosis. In addition, the probes’ in vivo imaging capacity can be harnessed to facilitate fundamental studies and drug discovery of inflammatory diseases.