bluuebunny/crossref_metadata_2025 huggingface dataset:
Diabetes‐Related Disruption of White Matter Microstructural Integrity is Associated with Faster Late‐Life Cognitive Decline
Authors: Tong Han , Capuano Ana W. , Biswas Roshni , Zhang Shengwei , Bennett David A. , Arfanakis Konstantinos , Arvanitakis Zoe
Published: January 2025

Background

Type‐2 diabetes mellitus is associated with both altered white matter microstructural integrity and increased risk of dementia. However, whether there is a link between the white matter changes in older adults with diabetes and their cognitive decline remains unclear. The objective of this study was to examine the association of diabetes‐related changes in white‐matter microstructural integrity with longitudinally assessed late‐life cognitive function.

Method

A total of 674 older adults were studied (mean age at time of scan: 78.4 years; 125 males; 104 with diabetes), among participants of an ongoing, community‐based cohort study of aging at the Rush Alzheimer’s Disease Center. Annual clinical evaluations included detailed neuropsychological testing from which a global and five domain‐specific cognitive summary measures were derived (mean follow‐up duration: 3.0 years). High‐quality (3T) brain magnetic resonance imaging scans were acquired, and quality‐assured diffusion tensor imaging data were available for analyses. Tract‐based statistical analysis (TBSS) was used to project each fractional anisotropy (FA) image to the IIT white matter skeleton. Voxel‐wise generalized linear regression models were fitted using Permutation Analysis of Linear Models in FSL, with FA and mean diffusivity (MD) as the outcome and diabetes as predictor, and co‐variates of age, sex, education, scanner type, and white matter hyperintensities. Using linear mixed‐effects models and controlling for age, sex, education, and diabetes, we examined the association between median FA of all significant voxels and longitudinally assessed cognitive function data.

Result

Diabetes was associated with lower FA in the rostral body of the corpus callosum (p<0.05, threshold‐free cluster enhancement, family‐wise error corrected). A lower median FA of these voxels was associated with a faster rate of decline in global cognition (estimate=0.121 [SE=0.056], p=0.030) and three cognitive domains including semantic memory (estimate=0.225 [SE=0.090], p=0.013), working memory (estimate=0.205 [SE=0.081], p=0.012) and visuospatial abilities (estimate=0.193 [SE=0.090], p=0.032), but not episodic memory and perceptual speed. No difference was found in MD between older adults with diabetes and those without.

Conclusion

Diabetes‐related disruption of white matter microstructural integrity in the rostral body of corpus callosum in older adults may be associated with faster late‐life cognitive decline.
Cerebrovascular reactivity (CVR) MRI as a biomarker for cerebral small vessel disease (SVD) related cognitive decline: Multi‐site validation in the MarkVCID Consortium
Authors: Liu Peiying , Lin Zixuan , Hazel Kaisha , Pottanat George , Xu Cuimei , Jiang Dengrong , Pillai Jay J , Lucke Emma , Bauer Christopher E. , Gold Brian T , Greenberg Steven M. , Helmer Karl G , Jann Kay , Jicha Gregory A , Kramer Joel H. , Maillard Pauline , Mulavelil Rachel M. , Satizabal Claudia L , Schwab Kristin , Seshadri Sudha , Singh Herpreet , Velarde Angel G , Wang Danny JJ , Kalyani Rita , Moghekar Abhay , Rosenberg Paul B. , Yasar Sevil , Albert Marilyn S. , Lu Hanzhang ,   MarkVCID Consortium
Published: January 2025

Background

Cerebral small vessel disease (SVD) related vascular contributions represent a major factor contributing to cognitive decline and dementia (VCID) in older adults. However, there has not been a validated biomarker for the diagnosis and treatment monitoring of this condition. Recently, the US National Institute on Neurological Disorders and Stroke (NINDS) funded the MarkVCID Consortium to identify and validate clinical‐trial‐ready biomarkers for VCID. Cerebrovascular reactivity (CVR) MRI is one of the selected biomarkers that underwent multi‐site testing in the Consortium. The present study aimed to report the relationship between CVR and cognitive function at independent sites, based on a pre‐specified hypothesis.

Method

CVR, the ability of cerebral small vessels to dilate upon stimulus, is thought to directly reflect physiological function of the brain microvasculature. Based on previous single‐site findings, the pre‐defined hypothesis was that CVR will be associated with the global cognitive function measured by Montreal Cognitive Assessment (MoCA) after adjusting for age, sex, and education, and this association will be observed in data collected and analyzed at each individual site.

A total of 264 older participants from three sites were included (Table 1). Each site performed an identical CVR MRI procedure using 5% CO2 inhalation, and a standardized clinical and cognitive evaluation. Multi‐linear regression analysis was conducted on a site‐by‐site basis to examine the pre‐defined hypothesis.

Result

Gray‐matter CVR showed a positive association with MoCA score after adjustment for age, sex, and education, in which participants with higher CVR had higher MoCA scores. This relationship was reproduced at each site (Figure 1, p<0.05 for each), confirming our pre‐specified hypothesis.

In the secondary analysis of all data together, higher gray‐matter CVR was found to be significantly associated with better executive function measure of item response theory (IRT) score (β=2.87, p=0.003).

Conclusion

The present study evaluated the relationship between CVR and cognition in a multi‐center setting. CVR was found to be positively associated with global cognitive function measured by the MoCA, which was shown to be reproducible across different sites with diverse cohorts. These findings support the utility of CVR as a biomarker in future clinical trials of SVD and VCID.
Total and Structural Carotid Artery Stiffness are related to Cognitive Decline and ADRD Pathology: The Multi‐Ethnic Study of Atherosclerosis (MESA)
Authors: DeConne Theodore M , Williams Jeremy R , Pewowaruk Ryan , Korcarz Claudia E. , Tanley Jordan E. , Carlsson Cynthia M. , Heckbert Susan R. , Habes Mohamad , Nasrallah Ilya M. , Lockhart Samuel N. , Luchsinger José A. , Ding Jingzhong , Liu Yongmei , Hayden Kathleen M. , Gepner Adam D , Hughes Timothy M.
Published: January 2025

Background

Stiffening of the large elastic arteries is an emerging age‐related risk factor for Alzheimer’s disease (AD) and related dementia (ADRD). Arterial stiffness is associated with pathological changes underlying AD/ADRD, and total arterial stiffness (T‐PWV) can be subdivided into two main mechanisms. Structural stiffening (S‐PWV) is due to intrinsic remodeling of the artery wall, and load‐dependent stiffening (LD‐PWV) is due to increased blood pressure without intrinsic changes to the artery wall. We hypothesized that greater structural stiffness (S‐PWV rather than LD‐PWV) would be associated with faster cognitive decline, and with increased white matter hyperintensity volume (WMHv) and decreased white matter fractional anisotropy (WMFA). Lower WMFA is interpreted as indicating poor WM integrity.

Method

Multi‐Ethnic Study of Atherosclerosis (MESA) participants included in this analysis, represented four racial/ethnic groups that completed carotid arterial stiffness by duplex ultrasound (2010‐2012) from which pulse wave velocity was calculated, repeated cognitive assessments with the Cognitive Abilities Screening Instrument (CASI), Digit Symbol Coding (DSC), and Digit Span (DS) (2010‐12, 2016‐2019, 2019‐2021, Table 1), and 3T MRI (2016‐2021, Table 2). 3T MRI were acquired; T1, FLAIR and diffusion imaging sequences were acquired and processed to generate global WMHv and WMFA. Multivariable linear mixed and regression models related standardized arterial stiffness components to cognitive decline and neuroimaging and were adjusted for age, race and ethnicity, gender, education, site, and language.

Result

Greater T‐PWV was significantly associated with faster declines in DSC and DS (Table 1), greater WMHv, and lower WMFA (Table 2), but not CASI. S‐PWV was significantly associated with faster declines in DSC and DS (Table 1) over a decade. Greater S‐PWV was also significantly associated with greater WMHv, but no WMFA differences (Table 2). Interestingly, LD‐PWV was not associated with changes in any measure of cognitive decline, and was associated with lower WMFA (Table 2).

Conclusion

Higher structural stiffness of the carotid artery drives associations between total arterial stiffness with faster declines in cognitive function and with brain MRI findings. Interventions targeting arterial remodeling rather than blood pressure alone may be the next step in delaying age‐related cognitive decline and structural changes in the brain underlying ADRD.
Identification of CSF proteins in Alzheimer’s Disease highly correlated with [18F]GTP1
Authors: Lee Julie
Published: January 2025

Background

Neurofibrillary tangles (NFTs) are neuropathological hallmarks of Alzheimer’s Disease (AD) and consist of insoluble aggregates of tau protein (MAPT). Tau pathology spreads in the brain with increasing disease severity. Tau is commonly measured in AD trials as it is diagnostic and prognostic of clinical progression, and also a pharmacodynamic biomarker to quantify treatment effects of emerging tau‐targeting therapeutics. Tau PET tracers, like [18F]Genentech Tau Probe 1 ([18F]GTP1), selectively bind to NFTs to enable imaging of tau pathology in AD patients. Tau PET imaging requires special infrastructure at clinical sites and has high costs limiting its broad applicability in trials. The goal of this analysis is to identify CSF proteins highly correlated with [18F]GTP1 to develop fluid surrogate biomarkers of tau PET that may allow greater accessibility to pathological tau measurements in clinical settings.

Method

AD CSF samples were from participants enrolled in Tauriel or Lauriet, two semorinemab PhII trials. Baseline protein levels were measured by analyzing samples using data‐independent acquisition mass spectrometry (DIA‐MS). After data processing and filtering, 3197 unique protein intensities were included in this analysis. Trial participants also completed [18F]GTP1 imaging. Analysis was completed by Invicro where standardized uptake value ratios (SUVR) were reported using inferior cerebellar gray matter as the reference area. The five regions evaluated in this analysis are whole cortical gray matter, temporal, Braak stages I‐II, Braak stages III‐IV, and Braak stages V‐VI. [18F]GTP1 SUVR values for the selected regions were compared against CSF protein intensity measurements to calculate Spearman correlation coefficients and FDR‐adjusted p‐values to identify highly correlated proteins.

Result

CSF proteomic measurements and [18F]GTP1 SUVR values were available from 128 Tauriel or Lauriet participants at baseline. The correlation analysis demonstrated proteins MAPT, SYN1, PAK1, YWHAE, YWHAB, and YWHAZ are highly correlated with [18F]GTP1 across more than one brain region. Proteins with absolute correlation values greater than 0.4 with tau PET are functionally related to the synapse.

Conclusion

Six CSF proteins highly correlated with tau PET were identified. Validation of these results by replicating the analysis on a secondary dataset with PET and CSF protein measurements may establish further confidence in these findings.
Brain age correlates with plasma NfL in amyloid positive individuals
Authors: Sihag Saurabh , Ribeiro Alejandro
Published: January 2025

Background

Brain age prediction using machine learning (ML) models provides novel opportunities to characterize neurobiology of accelerated aging. Brain age gap, i.e., the gap between the predicted age and the chronological age for an individual, is a meaningful metric to assess accelerated or premature aging. Plasma neurofilament light (NfL) is a well‐recognized biomarker that can reflect the severity of structural atrophy and the loss of white matter integrity in Alzheimer's disease. Here, we leveraged an anatomically interpretable ML pipeline for brain age prediction (see ref. [1]) to investigate the correlations between brain age and plasma neurofilament light chain (NfL) in older adults in the ADNI cohort.

Method

Brain age was derived from the cortical thickness measures (Desikan‐Killiany atlas, available online as part of the ADNI database) for 206 healthy individuals (HC), 372 individuals with MCI diagnosis, and 118 individuals with dementia diagnosis (AD). Further, contributions from individual brain regions to the predicted brain age were characterized for each individual. The amyloid status was available for all individuals and plasma NfL (within 30 days of the MRI scan acquisition) was available for a subset of individuals (Table 1). The ML model used to derive brain age had been trained on the healthy individuals from the OASIS‐3 dataset. We assessed the associations between brain age and plasma NfL for HC and the combined MCI and AD cohorts.

Result

Brain age was significantly elevated in AD cohort with respect to HC cohort (Table 1, Figure 1). No significant differences were found between the brain age gap for amyloid positive and amyloid negative individuals (Figure 1). For HC cohort, brain age was positively correlated with plasma NfL in amyloid positive individuals (with brain age contributions from entorhinal region exhibiting the most significant positive correlation (ρ=0.72, p=2.45e‐6) with plasma NfL), but not amyloid negative individuals (Figure 2). In the combined MCI and AD cohort, brain age was correlated with plasma NfL in only amyloid positive individuals.

Conclusion

Our results suggest that amyloid status may mediate the association between plasma NfL and brain age in older individuals.

[1] Sihag et al., Explainable Brain Age Prediction using coVariance Neural Networks, NeurIPS, 2023.
Prospective Associations of Interleukin‐6 and APOE 4 Allele with Cognitive Decline in Biracial Community‐dwelling Older Adults: the Chicago Health and Aging Project (CHAP)
Authors: Ng Ted K.S. , Beck Todd , Desai Pankaja , Dhana Klodian , Wilson Robert S. , Evans Denis A , Rajan Kumar B
Published: January 2025

Background

It is unclear whether inflammation, i.e., high interleukin‐6 (IL‐6) levels, and genetic risk, i.e., APOE ε4 allele, have a compounding effect on cognitive decline (CD).

Method

We analyzed a subset of participants from the longitudinal cohort study, Chicago Health and Aging Project, comprising 1,120 biracial community‐dwelling older adults (60% African American & 62% women), and mean follow‐up=6.4 years. We ran adjusted mixed‐effects models of longitudinal cognitive decline and explored potential racial differences.

Result

In ε4 carriers, higher serum IL‐6 was not associated with the rate of CD (β=‐0.0091 (SD=0.0165, p=0.5800). Conversely, in non‐ε4 carriers, compared to the lower tertile, those with the upper tertile of serum IL‐6 levels experienced significantly accelerated CD [β=‐0.0257 (SD=0.0084 p=,0.0023)]. These results did not differ between races.

Conclusion

Even without the largest genetic risk factor for late‐onset AD/ADRD, elevated serum IL‐6 still accelerate the rate of CD. Hence, interventions ameliorating inflammation may prevent AD/ADRD. In contrast to the ε4 allele, as serum IL‐6 is a modifiable risk factor, these findings could implicate precision medicine, including AD/ADRD preventions and therapies to prevent AD/ADRD.
Quantifying Deep Learning Interpretability in Alzheimer's Disease Research: Validating Deep Learning Heatmaps through Meta‐Analysis
Authors: Wang Di , Honnorat Nicolas , Nirmala Anoop Benet , Fox Peter T , Seshadri Sudha , Habes Mohamad
Published: January 2025

Background

Deep learning has shown promising results regarding Alzheimer’s disease (AD) studies. In the meantime, heatmap methods have emerged as a popular tool to visualize deep learning models, enhancing the transparency and explainability of deep learning. However, when no ground truth is available, the heatmap methods are particularly difficult to trust. In the neuroimaging field, meta‐analysis is often conducted to reduce heterogeneity and reveal the true positive. Thus, in this work, we aim at validating heatmap methods by quantifying the overlap of a ground truth map provided by a large meta‐analysis and heatmaps derived by three methods, the Layer‐wise Relevance Propagation (LRP) method, the Integrated Gradients (IG) method, and the Guided grad‐CAM (GGC) method from convolutional neural network achieving state‐of‐the‐art classification performance for AD classification on a sample of MRI scans from ADNI.

Method

502 ADNI participants were included in this study with 250 AD participants and 252 healthy controls. Those brain scans were trained by five different 3D CNN architectures. Three CNN heatmap methods were applied on trained models to produce a heatmap indicating which voxels were the most important when classifying AD and control participants. The structural MRI meta‐analysis map used as the ground truth summarized 77 neuroimaging studies reporting 773 locations in the brain affected by AD.

Result

The best CNN 5‐fold cross validated accuracy reached 87.25% for classifying AD and CN. The best Dice overlap measured for LRP, IG, and GGC, using this model was 0.502 for LRP heatmap, 0.55 for the IG heatmap, and 0.54 for the GGC heatmap. SVM activation patterns achieved a dice of 0.363.

Conclusion

We found that the three heatmap methods capture brain regions that overlap well with the meta‐analysis map, and we observed the best overlap for the IG method. All three heatmap methods outperformed linear SVM models suggesting that the deep feature learnt by the most recent deep neural networks can produce more meaningful representations than linear and shallow models.
Comparing neural activity of older adults with and without Parkinson’s disease using mobile electroencephalography in different environments
Authors: Marshall Samantha , Jeyarajan Gianna , Al‐Shaikh Jennifer Hanna , Gabiazon Raphael , Seleem Tia , Nagamatsu Lindsay S
Published: January 2025

Background

Parkinson’s disease is a progressive neurodegenerative disorder that is becoming more prevalent as the population ages. Mobile neuroimaging has made it possible to observe cortical activity in this population outside of standard laboratory environments. Yet, few studies have explored the portability of these devices in a true real‐world environment without a specific task imposed on participants (e.g., dual task, motor demands).

Method

Mobile electroencephalography (EEG) was utilized to examine and compare cortical activity during sitting in different environments across older adults with and without Parkinson’s disease. We used the Muse S Generation 2 Brain‐Sensing Headband to record brain function. The first condition involved participants sitting in a standard laboratory environment while their brain activity was recorded. In the second condition, participants sat in an indoor space that included a living green wall, natural light, and other people while their brain activity was recorded. Fast Fournier Transform (FFT) analysis was performed on the raw EEG data to obtain corresponding waves for each electrode to examine power (uV^2) at each frequency (Hz).

Result

Preliminary findings demonstrate significant differences in mean cortical activations across older adults with and without Parkinson’s disease. Differences in cortical activations were also observed when comparing the laboratory and real‐world environment.

Conclusion

These findings suggest that elicited brain activity may differ across older adults with and without Parkinson’s disease and across different environments. These findings expand current knowledge on Parkinson’s disease, brain function, and cognition using real‐world methods and technology, which may inform interventions to increase quality of life among this population. As our findings suggest that environmental factors may modulate cortical activity, we also highlight the potential and importance for real‐world methods to supplement standard research practices to increase the ecological validity of studies conducted across the scientific community.
Relationship of Mito‐Nuclear Genetics to FDG‐PET Alzheimer’s Disease Biomarkers in Aging
Authors: Honea Robyn A , Vidoni Eric D , Lepping Rebecca J , Morris Jill K , Wilkins Heather M , Burns Jeffrey M , Swerdlow Russell H
Published: January 2025

Background

A growing amount of data has implicated the various roles of nuclear genes involved in mitochondrial function, and mitochondrial genes, on risk for Alzheimer’s disease (AD) and AD neuroimaging biomarkers. To date, no studies have investigated the relationship of mitochondrial haplogroups or the APOE and TOMM40 genes on brain glucose metabolism, a sensitive early marker of metabolic decline and possible mitochondrial dysfunction in AD.

Method

We analyzed regional standard uptake value ratio (SUVR) differences in 18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) using SPM12 and CAT12 software between nondemented (ND, n=69), mild cognitively impaired (MCI, n=19) and AD (n=18) groups in a sample of individuals from the University of Kansas Alzheimer’s Disease Research Center Cohort, controlling for age, sex, and education. We then tested whether genetic variation in APOE (ε4‐carriers vs. non‐carriers), TOMM40 rs2075650 (‘650 G‐allele carriers vs. A‐Allele) or mitochondrial haplogroup significantly influenced regional differences in glucose metabolism in nondemented individuals, as they made up the bulk of our sample of individuals with these scans.

Result

We found that, as expected, individuals with AD had significantly decreased glucose metabolism across all regions except the motor cortex and the cerebellum compared to ND individuals (Figure 1). Our results across various gene groups, controlling for age, sex, education, and with and without APOE ε4 showed that there were no significant differences in FDG‐PET SUVR between ND TOMM40 ‘650 G‐allele carriers, or mitochondrial haplogroups (H, JT, and UK groups combined), however APOE ε4‐carriers had significantly lower glucose metabolism in the anterior cingulate, superior medial frontal, posterior cingulate, medial frontal, right middle frontal, and bilateral superior frontal cortices than ε4‐non carriers (Figure 2). There were no significant effects of APOE ε4 on glucose metabolism in limbic regions.

Conclusion

The APOE ε4 allele may have more impact on FDG‐PET in default mode regions (posterior and anterior cingulate, medial frontal cortex) than TOMM40 '650, a larger study in cognitively healthy individuals will be needed to confirm these results.
PreSiBOGNN: Multi‐Modal Graph Neural Network for Prediction of Cognitive Improvement in Medication Usage in UK Biobank
Authors: Priyadarshi Dhawal , Huang Heng , Sahelijo Nathan J , Shirkavand Reza , Jun Gyungah R
Published: January 2025

Background

The prohibitive costs of drug development for Alzheimer’s Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti‐modal information to predict cognitive improvement at the subject level for precision medicine in AD.

Method

The graph neural network framework integrates four layers of input data including transcriptome, proteome, drug, and subject to connect to bipartite graphs in the United Kingdom Biobank (UKBB). Medication usage, clinical, and GWAS data were downloaded for 48187 subjects with first and second cognitive exams from the UKBB. The protein and transcriptome layers were constructed using the String database and gene coexpression networks generated from single nuclei RNA data (Sahelijo et al. 2022). Layers were connected by binary bipartite graphs constructed using drug information from the UniProt and DrugCentral databases. Sequential Graph Attention Networks convoluted embedded features generated by each layer in a hierarchical order: 1. gene‐gene, 2. gene‐protein, 3. protein‐protein, 4. protein‐drug, 5. drug‐subj. Feature embeddings were decoded using a multilayer perceptron to predict cognitive improvement between the first and second cognitive exams. Two models compared methods of data aggregation. The first model follows a strict hierarchy, aggregating inter‐layer data in a single direction (gene > protein > drug > subj). The second model allows for fluid message passing between inter‐layers. We used 60% of the UKBB subjects for training and 30% for validation. We assessed the model with the greatest training accuracy using the remaining 10% of subjects.

Result

We observed that the performance of the strict message‐passing model attained validation and test accuracy of 57.7% and 51.2%, respectively. Performance of the fluid message passing model improved prediction accuracy with 61.3% and 58.1% in the test set.

Conclusion

Our investigation suggests the feasibility of the PreSiBOGNN framework to infer cognitive improvement of existing drugs by integrating medication, multi‐omics, and clinical data. Future work will focus on model optimizations and the integration of additional modalities including compound‐specific fingerprint data.
Using plasma biomarkers to distinguish TDP43 associated pathologies
Authors: Foley Joseph P , Zacco Anna , Towle‐Miller Lorin , Ong Rachel M Y , Ganji Gopinath
Published: January 2025

Background

Mislocalization and aggregation of TAR DNA‐binding protein (TDP43) leads to loss of function and pathogenesis in 40‐45% Frontotemporal dementia (FTD) and >95% amyotrophic lateral sclerosis (ALS) patients. While TDP43 pathology has been studied in post‐mortem tissues, it has been challenging to evaluate pathology using reliable fluid biomarkers with clinical utility. We examined levels of plasma biomarkers, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), phosphorylated Tau (pTau181) and total Tau (Tau), and followed up on previous results1 showing lower levels of GFAP:NfL in differentiating FTD‐TDP from FTD‐Tau subpopulations.

Method

40 ALS and 32 non‐disease age‐matched control plasma samples were procured from Northeast ALS Consortium (NEALS) biorepository and PrecisionMed (Carlsbad, CA, USA). Quanterix digital immunoassays, Neuro 2‐Plex B assay, SIMOA pTau‐181 and SIMOA Tau Advantage kits, were used to determine concentrations of NfL, GFAP, pTau181 and Tau, respectively, on the SR‐X platform. Ratios, correlations and statistical analyses (unpaired t test with Welch’s correction or one‐way ANOVA with Dunnett’s test) were performed across all plasma biomarkers analyzed. Cutoffs from prior studies1 were examined to classify disease vs control samples. Furthermore, the ALLFTD dataset is being analyzed to evaluate relationships between some of these biomarkers and relevant clinical covariates in subgroups of interest.

Result

Plasma NfL and GFAP levels were significantly (p<0.0001) elevated in ALS samples compared to non‐disease controls. NfL was elevated (∼7x) relative to GFAP (∼1.8x) in cases versus controls. Furthermore, GFAP:NFL ratio showed a notable separation with lower values being significantly (p<0.0001) associated with disease samples (mean=3.4) versus controls (mean=9.7), as previously reported1. Similarly, higher (∼1.8x) pTau181 and lower (∼0.7x) Tau levels were significantly (p<0.05) observed in ALS versus control samples and higher (mean=20.2) pTau181:Tau ratios achieved a markedly significant (p<0.0001) differentiation of ALS from controls. Interestingly, 80% of ALS plasma samples showed a consistent correlation of higher pTau181:Tau with lower GFAP:NFL ratios while the control samples showed the exact opposite result.

Conclusion

Plasma biomarkers measured as GFAP:NfL and/or pTau181:Tau ratios have the potential to distinguish TDP43 associated pathologies such as ALS and FTD‐TDP.

1Cousins et al., JAMA Neurol. 2022 Nov 1;79(11):1155‐1164
GDF15 as a Prognostic Biomarker of Cognitive Decline in Frontotemporal Dementia
Authors: Chen Coty , Paolillo Emily W. , Saloner Rowan , VandeBunte Anna M. , Cadwallader Claire J. , Lee Shannon Y. , Lago Argentina Lario , Gendron Tania F , Petrucelli Leonard , Webb Julia D , Staffaroni Adam M. , Heuer Hilary W. , Boeve Brad , Forsberg Leah K. , Rosen Howard J. , Rojas Julio C. , Kramer Joel H. , Boxer Adam L. , Casaletto Kaitlin B. ,   ALLFTD Investigators
Published: January 2025

Background

Growth Differentiation Factor 15 (GDF15) is a TGF‐beta superfamily protein upregulated during immune‐mediated stress responses. Peripheral GDF15 is linked to adverse aging across organ systems including the brain. Immune dysregulation is implicated in frontotemporal dementia (FTD) pathogenesis. We examined how plasma GDF15 levels associated with longitudinal cognitive and neurofilament light chain (NfL) trajectories in adults with FTD and controls. Furthermore, we tested the utility of GDF15 as a prognostic biomarker against common inflammatory proteins (CRP and IL6).

Method

231 adults with FTD (24.35% sporadic FTD syndromes; 19.19% MAPT; 19.56% GRN; 22.14% C9orf72 pathogenic carriers) and 40 normal controls from the ALLFTD Consortium completed longitudinal neuropsychological testing and a blood draw with plasma analyzed on SomaScan V4.0 (baseline only: GDF15, CRP, and IL6) and Simoa (longitudinal: NfL). CRP and IL6 were selected as control markers to test the specificity of GDF15. Linear mixed effects models separately examined longitudinal cognition (global, executive functioning, memory, and language z‐scores) and plasma NfL levels as a function of baseline plasma GDF15, time, and their interaction, controlling for baseline age, sex, and education. Parallel models were conducted for CRP and IL6.

Result

In the whole cohort, higher baseline plasma GDF15 levels associated with steeper global cognitive decline (b = ‐3.91, p = 0.0092) and NfL accumulation (b = 3.88, p = 0.00088) over time. When examining cognitive domains, we found GDF15 only associated with worse executive functioning trajectories (b = ‐3.19, p = 0.0021). In contrast, baseline levels of CRP and IL6 did not display statistically significant relationships with cognitive or NfL trajectories. Prognostic effects of GDF15 remained statistically significant when restricting analyses to those with FTD pathogenic variants (b = ‐2.98, p = 0.0054 [global cognition], b = 2.69, p = 0.0098 [NfL]).

Conclusion

Adults with FTD and higher baseline peripheral GDF15 levels exhibit accelerated cognitive decline and neurodegeneration. Our results support GDF15 as a potential prognosticator of adverse clinical outcomes in FTD and highlight the relevance of GDF15‐specific pathways in FTD progression. Plasma GDF15 may provide utility over commonly examined inflammatory proteins and further work to explore its clinical value is indicated.
Quantitative Systems Pharmacology Model for Therapies Targeting Aβ and Tau Pathologies in Alzheimer’s Disease
Authors: Lin Lin , Minucci Sarah , DiBartolo Sarah , Grant Joshuaine , Muralidharan Kumar Kandadi , Hutchison Matthew , Collins Jessica , Edwards Amanda , Hering Heike , Bussière Thierry , Graham Danielle , Hua Fei , Roberts John
Published: January 2025

Background

Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by the formation of amyloid‐beta (Aβ) plaques and neurofibrillary tangles (NFTs) composed of tau aggregates. Research in animal models has generated hypotheses on the underlying mechanisms of the interaction between Aβ and tau pathology. In support of this interaction, results from clinical trials have shown that treatment with anti‐Aβ monoclonal antibodies (mAbs) affects tau pathology. Meanwhile, preliminary results from a phase 1b study showed that BIIB080, a tau‐targeting antisense oligonucleotide (ASO), reduced tau biomarkers in early AD patients. Building upon our previous work of a quantitative systems pharmacology (QSP) model for anti‐Aβ mAbs, we have developed a more comprehensive model incorporating the interplay between Aβ and tau pathways and therapies targeting Aβ and tau.

Method

A QSP model was developed that accounts for changes in Aβ and tau biomarkers in the progression of AD. It includes the production, aggregation, and inter‐compartment transport of both Aβ and tau species, as well as changes in phospho‐tau species and spreading of aggregated tau pathology. The model captures the interaction between Aβ and tau pathology by Aβ plaque‐dependent tau hyperphosphorylation that drives NFT formation in Braak region I/II and tau pathology spreading to Braak regions III/IV and V/VI. Furthermore, the model incorporates mechanisms of action for anti‐Aβ mAbs, including aducanumab, lecanemab, donanemab and gantenerumab, and the tau‐targeting ASO, BIIB080. The model was calibrated using Aβ and tau biomarker data (Aβ42/Aβ40, t‐tau and p‐tau181 in CSF and plasma, amyloid and tau PET) from natural history studies as well as pharmacokinetic (PK) data and biomarker response data from the clinical trials with these therapies.

Result

Model simulations were in good agreement to the available clinical data. The calibrated model was able to match observed drug PK and Aβ and tau biomarker data, including amyloid re‐accumulation after depletion. In addition, the model was able to reasonably capture the effects of anti‐Aβ mAbs on tau pathology.

Conclusion

A QSP model was developed to integrate therapies targeting Aβ and tau pathologies in AD. The model can be utilized to explore dosing regimens to support the development of combination therapy.
Lower slow wave sleep and rapid eye‐movement sleep are associated with brain atrophy of AD‐vulnerable regions
Authors: Cho Gawon , Mecca Adam P , Buxton Orfeu M. , Liu Xiao , Miner Brienne
Published: January 2025

Background

Sleep deficiency is associated with an increased risk of Alzheimer’s disease (AD), warranting research on underlying mechanisms. This study examined the association of sleep architecture with anatomical features frequently observed in AD: (1) atrophy of cuneus, hippocampus, entorhinal, inferior parietal, parahippocampal, and precuneus regions (henceforth referred to as “AD‐vulnerable regions”) and (2) the presence of cerebral microbleeds.

Method

In 271 participants of the Atherosclerosis Risk in the Communities Study, we examined the prospective association of baseline sleep architecture with anatomical features of the brain identified on MRI conducted ∼17 years later. Sleep architecture was quantified as the proportion of time spent in slow wave sleep (SWS), proportion of time spent in rapid eye‐movement sleep (REM), and the number of arousals per hour of total sleep time using polysomnography. Outcomes included (1) volumetric measurements of each AD‐vulnerable region and (2) the presence of any cerebral microbleeds (CMBs) and that of lobar CMBs, which are more specifically associated with AD. We analyzed the association of each sleep predictor with each brain outcome, adjusting for demographics, baseline cognitive function, APOE genotype, intracranial volume, other covariates, and false discovery rate (FDR; family‐wise p<.05).

Result

Having less SWS was associated with smaller volumes of the inferior parietal region (β=‐41.56 mm3 per percentage point (PP) [95%CI=‐73.97, ‐9.15]) and cuneus (β=‐11.56 mm3 per PP [‐20.51,‐2.62]) after adjustments for covariates. Having less REM was associated with smaller volumes of the inferior parietal region (β=‐73.13 mm3 per PP [‐126.95, ‐19.30]) and precuneus (β=‐32.46 mm3 per PP [‐64.48,‐0.44]). After FDR adjustments, lower SWS and REM, respectively, were associated with lower volumes of the inferior parietal region only. Arousal index was not associated with the volumes of AD‐vulnerable regions. None of the sleep architecture variables were associated with the presence of CMBs or lobar CMBs.

Conclusion

Sleep deficiency is associated with the atrophy of the inferior parietal region, which is often observed in early AD. Sleep architecture may be a modifiable risk factor for AD pathogenesis.
Clinical effects of amyloid positivity upon positron emission tomography in an Asian cohort with Alzheimer’s disease and other dementias
Authors: Chun Min Young , Jang Hyemin , Yun Jihwan , Kim Jun Pyo , Kang Sung Hoon , Weiner Michael S. W. , Kim Hee Jin , Na Duk L , Hong Chang‐Hyung , Roh Hyung Woong , Lee Tae‐Kyeong , Lee Eek‐Sung , LEE EUN HYE , Shin Daeun , Ham Hongki , Gu Yuna , Kim Yeshin , Kim Chi‐Hun , Woo Sook‐Young , Seo Sang Won
Published: January 2025

Background

Ethnic differences in amyloid‐β (Aβ) characteristics and cognitive trajectories should be considered, when designing Aβ‐targeted therapies. The clinical effects of co‐pathologies of Aβ have not been evaluated extensively across various dementias. We investigated the prevalence of Aβ+ and cognitive trajectories in Koreans and non‐Hispanic whites (NHWs) with Alzheimer’s clinical syndrome (ACS) and the clinical effects of Aβ+ in other dementias.

Method

We included 5,856 Koreans who underwent Aβ positron emission tomography (PET) in South Korea. The participants were categorized into the following groups: ACS, including cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia of the Alzheimer type (DAT); subcortical vascular cognitive impairment (SVCI); and frontotemporal dementia (FTD). Data from 929 NHWs were collected from the Alzheimer’s Disease Neuroimaging Initiative. Mini‐Mental State Examination (MMSE) scores performed at least twice were used to compare cognitive trajectories. We performed multivariable analyses and stabilized inverse probability of treatment weights based on the propensity scores to mitigate imbalances across ethnic groups.

Result

The prevalence of Aβ+ was lower in CU Koreans than in CU NHWs (odds ratio 0·6); however, this pattern was not observed in the MCI and DAT stages. Moreover, Aβ+ Koreans in the CU and MCI stages showed a more rapid decline in MMSE scores than Aβ+ NHWs (CU: B= ‐0·3136, p=0·004; MCI: B= ‐0·3847, p<0·001). Compared to CU, SVCI had a higher prevalence of Aβ+ in the 75–90 years age group (p<0·001). Aβ+ was predictive of a more rapid decrease in MMSE scores in SVCI (B= ‐0·6424, p<0·001) but not in FTD.

Conclusion

Our findings contribute to understanding Aβ biomarker traits in various dementias in Asians and highlight distinct ethnic differences when compared to NHWs.
Brain Networks Associated with Alzheimer’s Disease Biomarkers
Authors: Mahzarnia Ali , Stout Jacques Andrew , Anderson Robert J , Moon Hae Sol , Lutz Michael W , Staicu Ana‐Maria , Badea Alexandra
Published: January 2025

Background

While we do not yet have the means to detect early Alzheimer’s disease (AD), studying subjects at risk conferred by the presence of the APOE4 allele, can provide useful information before clinical onset. We show that using symmetric bilinear regression with L1 penalty (SBL) of individual (DTI, fMRI) and fused connectomes, we can identify vulnerable regions changing in association with hallmark AD biomarkers measured in cerebrospinal fluid: amyloid beta Aβ42/40, phosphorylated tau (PTAU), and neurofibrillary light (NfL) as a proxy for neurodegeneration.

Methods

We use structural connectomes derived from diffusion‐weighted MRI (DTI) and functional connectomes (fMRI) from 57 subjects, 45 normal controls and 12 cognitively impaired to predict CSF Aβ42/40, PTAU, and NfL to reflect neurodegeneration. To fuse connectomes, we first ranked them and then used the SNFtool. We run SBL for the three types of connectomes (DTI, fMRI, and fusion) and the three biomarkers. We compared the models using a k‐fold cross‐validations scheme and standardized root mean square errors (SRMSE) by dividing RMSE by the standard deviation of the biomarker outcome.

Results

Linear modeling approaches showed that APOE is a significant factor with respect to NfL biomarker measures and that the interaction between APOE and age, APOE and sex, sex and age, were significant for PTAU (Figure 1). The SBL results showed that the best predictions were for NfL, PTAU and Aβ ratio based on DTI connectomes (Table 1.) The common sub‐network across biomarkers via DTI connectomes included the right inferior parietal, inferior temporal, superior parietal and caudal middle frontal, postcentral; and left superior frontal, right precentral, right superior frontal. The common connections across biomarkers via fMRI included the right putamen, left frontal pole and left accumbens, and right transversetemporal gyrus (Figure 2).

Conclusions

DTI stood out as the best performer in predicting key biomarkers for AD (NfL, PTAU, Ab ratio). This research enhances our understanding of vulnerable brain networks, including in subjects at risk. Future work will explore strategies for data fusion to preserve common and unique contributions from multiple data sources.
A blood‐based multi‐modal assay for diagnosing Alzheimer's disease
Authors: Modi Souvik , Vergara Hernando M. , Thekkan Shareefa , Chouliaras Leonidas , Malpetti Maura , Swann Peter , Rowe James B , O'Brien John T
Published: January 2025

Background

Combinations of blood‐based biomarkers have been used to detect Alzheimer’s disease (AD). While these markers provide information about neuropathology, they fail to integrate the cellular dysfunction, such as disease‐associated defects in lysosomal ion homeostasis. To understand cellular dysfunction in AD and its relation to the pathophysiology of the disease, we developed a multi‐modal biomarker diagnostic platform that incorporates lysosomal ionic pH and Ca2+ and plasma levels of Amyloid beta1‐42 (Aβ1‐42), Amyloid beta1‐40 (Aβ1‐40), phosphorylated Tau181 (pTau181), Neurofilament light (NfL) and Glial fibrillary acidic protein (GFAP).

Method

This multi‐modal blood‐based platform incorporates the concentrations of H+ and Ca2+ ions with single lysosome resolution in monocytes measured using DNA nanodevices from 33 participants (20 Controls and 13 AD patients). It also includes a panel of pathological biomarkers, including Aβ1‐40, Aβ1‐42, pTau181, NfL, and GFAP, which are measured using the Simoa platform from plasma across seven different cohorts totalling 323 participants (146 Controls, 92 AD patients, 34 amyloid negative and 51 amyloid positive mild cognitive impairment).

Result

The minimalistic panel of GFAP and pTau181/Aβ1‐42 ratio performs significantly better (area under the curve [AUC] = 85%) in predicting Amyloid PET status than either NfL, GFAP, Aβ1‐42/Aβ1‐40 ratio or pTau181 alone, with AUCs ranging from 68‐77%. The performance of these individual biomarkers or their combinations were further enhanced when lysosomal ionic information was incorporated, differentiating AD subjects from healthy individuals with an accuracy of 88% (Sensitivity 85%; Specificity 95%).

Conclusion

The multi‐modal blood platform described here is highly accurate, specific, and sensitive in evaluating AD, representing a promising avenue to explore the connections between cellular biology and the pathological hallmarks of AD, and the diagnosis of this disease.
Effect of the therapeutic modulation of APT20TTMG on U1 snRNP complex assembly in Alzheimer’s disease
Authors: Bottos Rafael , Zimmer Camila , Sinatti Vanessa , Soares Ericks , Rafii Michael , Buee Luc , Leal Caio
Published: January 2025

Background

Alzheimer’s disease (AD) is the most common cause of dementia worldwide. It is characterized by dysfunction in the U1 small nuclear ribonucleoproteins (snRNPs) complex, which may precede TAU aggregation, enhancing premature polyadenylation, spliceosome dysfunction, and causing cell cycle reentry and death. Thus, we evaluated the effects of a synthetic single‐stranded cDNA, called APT20TTMG, in induced pluripotent stem cells (iPSC) derived neurons from healthy and AD donors and in the Senescence Accelerated Mouse‐Prone 8 (SAMP8) model.

Method

Following a 7‐day treatment with 5 concentrations of APT20TTMG in iPSC‐derived healthy and AD neurons, mitochondrial activity, glutamate release, and TAU levels were analyzed. The most effective concentration was chosen for the analysis of RNA‐seq differential expression and 3’ untranslated region and coding sequence (UTR/CDS) expression ratios for U1‐70K, SNRP200, MAPT, and APP genes. Levels of U1‐70K, TAU, Aβ, and GFAP were analyzed in 20‐week‐old female SAMP8 mice (Animal Welfare Act, Austria: BGBl. II Nr. 542/2020, BGBl. I Nr. 76/2020, BGBl. I Nr. 86/2018, BGBl. I Nr. 8/2022), treated with 0.3 µg APT20TTMG infused continuously for 42 days (i.c.v.).

Result

In addition to unaffected mitochondrial activity and glutamate release, treatment decreased TAU in AD neurons, with no changes in healthy neurons. Metabolic pathway enrichment and network analysis identified four prominent clusters containing differentially expressed genes (DEGs) associated with pathways like cell cycle, PI3K‐Akt signaling pathway, fatty acid metabolism, MAPK signaling pathway, and steroid biosynthesis. APT20TTMG also corrected target assembly, avoiding premature polyadenylation of all analyzed genes. In vivo, treatment decreased U1‐70K in both cortex and hippocampus, as well as Aβ in cortex, and insoluble pTau in hippocampus. It also reduced gliosis (GFAP) in the cortex of animals.

Conclusion

APT20TTMG specifically decreases TAU protein in AD neurons, with a wide safe effect for human brain cells. Its ability to correct the U1 snRNPs dysfunction prevents premature polyadenylation in key genes and leads to a favorable enrichment of DEGs related to pathways deregulated in AD. It also decreases important hallmarks of the disease, in addition to controlling severe astrogliosis. These data evidence the great potential of utilizing APT20TTMG as a disruptive approach for neuronal protection in AD.
Deep learning‐based estimation of future brain atrophy using baseline MRI and PET
Authors: Le Linh N. N. , Fletcher Evan , Qi Jinyi , Fan Audrey P.
Published: January 2025

Background

Cortical brain atrophy is an excellent marker of clinical decline and can support future clinical course prediction in cognitive impairment. We used a U‐Net image‐generation deep learning network to predict future cortical atrophy rates in elderly populations, with initial T1‐weighted (T1w‐) MRI or baseline amyloid‐PET serving as inputs to the model.

Method

MRI and PET data were retrospectively collected from Alzheimer’s Disease Imaging Initiative (ADNI) and all participants had two serial T1w‐MRI scans (Figure 1A,B). The ground truth was an atrophy map (in log‐Jacobian values) generated from Tensor Based Morphometry (TBM) of the two longitudinal MRIs (Figure 1E). A U‐Net architecture was used in this study to predict maps of cortical atrophy. We used mean squared error (MSE) for loss function and five‐fold cross‐validation (Figure 1C,D). The testing set was randomly chosen (n=30). Separate models were trained for T1w‐MRI data as input (T1‐model) and amyloid‐PET as input (PET‐model). The predicted atrophy rate was evaluated across diagnosis groups in the test set using the whole‐brain structural similarity index measure (SSIM) to the TBM ground truth. Each model's average atrophy at selected brain regions was compared with TBM using paired t‐tests.

Result

Figure 2 shows average predicted atrophy maps and corresponding error for each diagnosis group. In the whole brain, for cognitively normal (CN) and mild‐cognitive‐impaired (MCI) groups, T1 model (with SSIMs of 0.67±0.07 and 0.64±0.06, respectively) slightly outperformed PET model (with SSIMs of 0.70±0.06 and 0.69±0.06, respectively). In selected regions, especially gray matter, we observed a larger rate of atrophy in Alzheimer’s Disease (AD) compared to MCI and in MCI compared to CN groups in both deep learning models, which was consistent with the ground truth. There was no statistical difference in average atrophy predicted by either model and TBM values for all diagnostic groups in all selected regions, with relatively small absolute error and MSE in both models (Table 1).

Conclusion

A U‐Net deep learning model trained using only baseline T1 weighted MRI or amyloid PET data could be used to predict future atrophy rates. Both models predicted cortical volume loss and ventricle expansion that differed between AD, MCI, and CN.