The Parkinson's Disease Composite of Executive Functioning: A Measure for Detecting Cognitive Decline in Clinical Trials.

BACKGROUND AND OBJECTIVES: Executive functioning is one of the first domains to be impaired in Parkinson disease (PD), and the majority of patients with PD eventually develop dementia. Thus, developing a cognitive endpoint measure specifically assessing executive functioning is critical for PD clinical trials. The objective of this study was to develop a cognitive composite measure that is sensitive to decline in executive functioning for use in PD clinical trials. METHODS: We used cross-sectional and longitudinal follow-up data from PD participants enrolled in the PD Cognitive Genetics Consortium, a multicenter setting focused on PD. All PD participants with Trail Making Test, Digit Symbol, Letter-Number Sequencing, Semantic Fluency, and Phonemic Fluency neuropsychological data collected from March 2010 to February 2020 were included. Baseline executive functioning data were used to create the Parkinson's Disease Composite of Executive Functioning (PaCEF) through confirmatory factor analysis. We examined the changes in the PaCEF over time, how well baseline PaCEF predicts time to cognitive progression, and the required sample size estimates for PD clinical trials. PaCEF results were compared with the Montreal Cognitive Assessment (MoCA), individual tests forming the PaCEF, and tests of visuospatial, language, and memory functioning. RESULTS: A total of 841 participants (251 no cognitive impairment [NCI], 480 mild cognitive impairment [MCI], and 110 dementia) with baseline data were included, of which the mean (SD) age was 67.1 (8.9) years and 270 were women (32%). Five hundred forty five PD participants had longitudinal neuropsychological data spanning 9 years (mean [SD] 4.5 [2.2] years) and were included in analyses examining cognitive decline. A 1-factor model of executive functioning with excellent fit (comparative fit index = 0.993, Tucker-Lewis index = 0.989, and root mean square error of approximation = 0.044) was used to calculate the PaCEF. The average annual change in PaCEF ranged from 0.246 points per year for PD-NCI participants who remained cognitively unimpaired to -0.821 points per year for PD-MCI participants who progressed to dementia. For PD-MCI, baseline PaCEF, but not baseline MoCA, significantly predicted time to dementia. Sample size estimates were 69%-73% smaller for PD-NCI trials and 16%-19% smaller for PD-MCI trials when using the PaCEF rather than MoCA as the endpoint. DISCUSSION: The PaCEF is a sensitive measure of executive functioning decline in PD and will be especially beneficial for PD clinical trials.

Data Independent Acquisition to Inform the Development of Targeted Proteomics Assays Using a Triple Quadrupole Mass Spectrometer.

Mass spectrometry based targeted proteomics methods provide sensitive and high-throughput analysis of selected proteins. To develop a targeted bottom-up proteomics assay, peptides must be evaluated as proxies for the measurement of a protein or proteoform in a biological matrix. Candidate peptide selection typically relies on predetermined biochemical properties, data from semi-stochastic sampling, or by empirical measurements. These strategies require extensive testing and method refinement due to the difficulties associated with prediction of peptide response in the biological matrix of interest. Gas-phase fractionated (GPF) narrow window data-independent acquisition (DIA) aids in the development of reproducible selected reaction monitoring (SRM) assays by providing matrix-specific information on peptide detectability and quantification by mass spectrometry. To demonstrate the suitability of DIA data for selecting peptide targets, we reimplement a portion of an existing assay to measure 98 Alzheimer's disease proteins in cerebrospinal fluid (CSF). Peptides were selected from GPF-DIA based on signal intensity and reproducibility. The resulting SRM assay exhibits similar quantitative precision to published data, despite the inclusion of different peptides between the assays. This workflow enables development of new assays without additional up-front data acquisition, demonstrated here through generation of a separate assay for an unrelated set of proteins in CSF from the same dataset.

A pathologist-AI collaboration framework for enhancing diagnostic accuracies and efficiencies.

In pathology, the deployment of artificial intelligence (AI) in clinical settings is constrained by limitations in data collection and in model transparency and interpretability. Here we describe a digital pathology framework, nuclei.io, that incorporates active learning and human-in-the-loop real-time feedback for the rapid creation of diverse datasets and models. We validate the effectiveness of the framework via two crossover user studies that leveraged collaboration between the AI and the pathologist, including the identification of plasma cells in endometrial biopsies and the detection of colorectal cancer metastasis in lymph nodes. In both studies, nuclei.io yielded considerable diagnostic performance improvements. Collaboration between clinicians and AI will aid digital pathology by enhancing accuracies and efficiencies.

Plasma pTau181 Reveals a Pathological Signature that Predicts Cognitive Outcomes in Lewy Body Disease.

OBJECTIVE: To determine whether plasma phosphorylated-Tau181 (pTau181) could be used as a diagnostic biomarker of concurrent Alzheimer's disease neuropathologic change (ADNC) or amyloidosis alone, as well as a prognostic, monitoring, and susceptibility/risk biomarker for clinical outcomes in Lewy body disease (LBD). METHODS: We studied 565 participants: 94 LBD with normal cognition, 83 LBD with abnormal cognition, 114 with Alzheimer's disease, and 274 cognitively normal. Plasma pTau181 levels were measured with the Lumipulse G platform. Diagnostic accuracy for concurrent ADNC and amyloidosis was assessed with Receiver Operating Characteristic curves in a subset of participants with CSF pTau181/Aß42, and CSF Aß42/Aß40 or amyloid-ß PET, respectively. Linear mixed effects models were used to examine the associations between baseline and longitudinal plasma pTau181 levels and clinical outcomes. RESULTS: Plasma pTau181 predicted concurrent ADNC and amyloidosis in LBD with abnormal cognition with 87% and 72% accuracy, respectively. In LBD patients with abnormal cognition, higher baseline plasma pTau181 was associated with worse baseline MoCA and CDR-SB, as well as accelerated decline in CDR-SB. Additionally, in this group, rapid increases in plasma pTau181 over 3 years predicted a faster decline in CDR-SB and memory. In LBD patients with normal cognition, there was no association between baseline or longitudinal plasma pTau181 levels and clinical outcomes; however, elevated pTau181 at baseline increased the risk of conversion to cognitive impairment. INTERPRETATION: Our findings suggest that plasma pTau181 is a promising biomarker for concurrent ADNC and amyloidosis in LBD. Furthermore, plasma pTau181 holds potential as a prognostic, monitoring, and susceptibility/risk biomarker, predicting disease progression in LBD. ANN NEUROL 2024.

regionalpcs: improved discovery of DNA methylation associations with complex traits.

We have developed the regional principal components (rPCs) method, a novel approach for summarizing gene-level methylation. rPCs address the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease (AD). In contrast to traditional averaging, rPCs leverage principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrated a 54% improvement in sensitivity over averaging in simulations, offering a robust framework for identifying subtle epigenetic variations. Applying rPCs to the AD brain methylation data in ROSMAP, combined with cell type deconvolution, we uncovered 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci (meQTL) with genome-wide association studies (GWAS) identified 17 genes with potential causal roles in AD, including MS4A4A and PICALM. Our approach is available in the Bioconductor package regionalpcs, opening avenues for research and facilitating a deeper understanding of the epigenetic landscape in complex diseases.

Exposure to autoimmune disorders increases Alzheimer's disease risk in a multi-site electronic health record analysis.

Molecular studies of Alzheimer's disease (AD) implicate potential links between autoimmunity and AD, but the underlying clinical relationships between these conditions remain poorly understood. Electronic health records (EHRs) provide an opportunity to determine the clinical risk relationship between autoimmune disorders and AD and understand whether specific disorders and disorder subtypes affect AD risk at the phenotypic level in human populations. We evaluated relationships between 26 autoimmune disorders and AD across retrospective observational case-control and cohort study designs in the EHR systems at UCSF and Stanford. We quantified overall and sex-specific AD risk effects that these autoimmune disorders confer. We identified significantly increased AD risk in autoimmune disorder patients in both study designs at UCSF and at Stanford. This pattern was driven by specific autoimmunity subtypes including endocrine, gastrointestinal, dermatologic, and musculoskeletal disorders. We also observed increased AD risk from autoimmunity in both women and men, but women with autoimmune disorders continued to have a higher AD prevalence than men, indicating persistent sex-specificity. This study identifies autoimmune disorders as strong risk factors for AD that validate across several study designs and EHR databases. It sets the foundation for exploring how underlying autoimmune mechanisms increase AD risk and contribute to AD pathogenesis.

Mediterranean diet protects against a neuroinflammatory cortical transcriptome: Associations with brain volumetrics, peripheral inflammation, social isolation, and anxiety in nonhuman primates (Macaca fascicularis).

Mediterranean diets may be neuroprotective and prevent cognitive decline relative to Western diets; however, the underlying biology is poorly understood. We assessed the effects of Western versus Mediterranean-like diets on RNAseq-generated transcriptional profiles in lateral temporal cortex and their relationships with longitudinal changes in neuroanatomy, circulating monocyte gene expression, and observations of social isolation and anxiety in 38 socially-housed, middle-aged female cynomolgus macaques (Macaca fascicularis). Diet resulted in differential expression of seven transcripts (FDR < 0.05). Cyclin dependent kinase 14 (CDK14), a proinflammatory regulator, was lower in the Mediterranean group. The remaining six transcripts [i.e., "lunatic fringe" (LFNG), mannose receptor C type 2 (MRC2), solute carrier family 3 member 2 (SLCA32), butyrophilin subfamily 2 member A1 (BTN2A1), katanin regulatory subunit B1 (KATNB1), and transmembrane protein 268 (TMEM268)] were higher in cortex of the Mediterranean group and generally associated with anti-inflammatory/neuroprotective pathways. KATNB1 encodes a subcomponent of katanin, important in maintaining microtubule homeostasis. BTN2A1 is involved in immunomodulation of γδ T-cells which have anti-neuroinflammatory and neuroprotective effects. CDK14, LFNG, MRC2, and SLCA32 are associated with inflammatory pathways. The latter four differentially expressed cortex transcripts were associated with peripheral monocyte transcript levels, neuroanatomical changes determined by MRI, and with social isolation and anxiety. These results provide important insights into the potential mechanistic processes linking diet, peripheral and central inflammation, and behavior. Collectively, our results provide evidence that, relative to Western diets, Mediterranean diets confer protection against peripheral and central inflammation which is reflected in preserved brain structure and socioemotional behavior. Ultimately, such protective effects may confer resilience to the development of neuropathology and associated disease.

Neuropathologic changes at age 90+ related to sleep duration 19 to 40 years earlier: The 90+ Study.

INTRODUCTION: We investigated the association between sleep duration and neuropathologic changes 19 to 40 years later in oldest-old (age 90+) participants of The 90+ Study. METHODS: Participants self-reported sleep duration and underwent neuropathologic evaluation. We categorized sleep duration as < 7, 7 to 8 = reference, > 8 hours and dichotomized neuropathologic changes as present/absent. We estimated odds ratio (OR) and 95% confidence intervals (CI) using logistic regression. RESULTS: In 264 participants, mean age at sleep self-report was 69 years, mean age at autopsy was 98 years, and mean interval between sleep self-report and autopsy was 29 years (range: 19-40). Those reporting > 8 hours of sleep had lower likelihood of limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) inclusions (OR = 0.18; CI = 0.04-0.82) and amyloid beta deposits (OR = 0.34; 95% CI = 0.12-0.94). DISCUSSION: Long self-reported sleep is associated with lower odds of neurodegenerative neuropathologic changes 19 to 40 years later in the oldest-old, suggesting a potential role of sleep in accumulation of dementia-related neuropathologies. HIGHLIGHTS: Association of self-reported sleep with non-Alzheimer's disease neuropathologic changes has not been explored. Whether sleep duration is related to dementia neuropathologic changes decades later is unclear. Long self-reported sleep is associated with lower odds of Alzheimer's disease neuropathologic change 19 to 40 years later in the oldest-old. Long self-reported sleep is associated with lower odds of limbic-predominant age-related TDP-43 encephalopathy neuropathologic change 19 to 40 years later in the oldest-old.

Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome.

Membrane-bound particles in plasma are composed of exosomes, microvesicles, and apoptotic bodies and represent ~1-2% of the total protein composition. Proteomic interrogation of this subset of plasma proteins augments the representation of tissue-specific proteins, representing a "liquid biopsy," while enabling the detection of proteins that would otherwise be beyond the dynamic range of liquid chromatography-tandem mass spectrometry of unfractionated plasma. We have developed an enrichment strategy (Mag-Net) using hyper-porous strong-anion exchange magnetic microparticles to sieve membrane-bound particles from plasma. The Mag-Net method is robust, reproducible, inexpensive, and requires <100 µL plasma input. Coupled to a quantitative data-independent mass spectrometry analytical strategy, we demonstrate that we can collect results for >37,000 peptides from >4,000 plasma proteins with high precision. Using this analytical pipeline on a small cohort of patients with neurodegenerative disease and healthy age-matched controls, we discovered 204 proteins that differentiate (q-value < 0.05) patients with Alzheimer's disease dementia (ADD) from those without ADD. Our method also discovered 310 proteins that were different between Parkinson's disease and those with either ADD or healthy cognitively normal individuals. Using machine learning we were able to distinguish between ADD and not ADD with a mean ROC AUC = 0.98 ± 0.06.

Comprehensive proteomics of CSF, plasma, and urine identify DDC and other biomarkers of early Parkinson's disease.

Parkinson's disease (PD) starts at the molecular and cellular level long before motor symptoms appear, yet there are no early-stage molecular biomarkers for diagnosis, prognosis prediction, or monitoring therapeutic response. This lack of biomarkers greatly impedes patient care and translational research-L-DOPA remains the standard of care more than 50 years after its introduction. Here, we performed a large-scale, multi-tissue, and multi-platform proteomics study to identify new biomarkers for early diagnosis and disease monitoring in PD. We analyzed 4877 cerebrospinal fluid, blood plasma, and urine samples from participants across seven cohorts using three orthogonal proteomics methods: Olink proximity extension assay, SomaScan aptamer precipitation assay, and liquid chromatography-mass spectrometry proteomics. We discovered that hundreds of proteins were upregulated in the CSF, blood, or urine of PD patients, prodromal PD patients with DAT deficit and REM sleep behavior disorder or anosmia, and non-manifesting genetic carriers of LRRK2 and GBA mutations. We nominate multiple novel hits across our analyses as promising markers of early PD, including DOPA decarboxylase (DDC), also known as L-aromatic acid decarboxylase (AADC), sulfatase-modifying factor 1 (SUMF1), dipeptidyl peptidase 2/7 (DPP7), glutamyl aminopeptidase (ENPEP), WAP four-disulfide core domain 2 (WFDC2), and others. DDC, which catalyzes the final step in dopamine synthesis, particularly stands out as a novel hit with a compelling mechanistic link to PD pathogenesis. DDC is consistently upregulated in the CSF and urine of treatment-naïve PD, prodromal PD, and GBA or LRRK2 carrier participants by all three proteomics methods. We show that CSF DDC levels correlate with clinical symptom severity in treatment-naïve PD patients and can be used to accurately diagnose PD and prodromal PD. This suggests that urine and CSF DDC could be a promising diagnostic and prognostic marker with utility in both clinical care and translational research.

Multiple biomarkers improve diagnostic accuracy across Lewy body and Alzheimer's disease spectra.

OBJECTIVE: More than half of neurodegenerative disease patients have multiple pathologies at autopsy; however, most receive one diagnosis during life. We used the α-synuclein seed amplification assay (αSyn-SAA) and CSF biomarkers for amyloidosis and Alzheimer's disease (AD) neuropathological change (ADNC) to determine the frequency of co-pathologies in participants clinically diagnosed with Lewy body (LB) disease or AD. METHODS: Using receiver operating characteristic analyses on retrospective CSF samples from 150 participants determined αSyn-SAA accuracy, sensitivity, and specificity for identifying clinically defined LB disease and predicting future change in clinical diagnosis. CSF biomarkers helped determine the frequency of concomitant Lewy body pathology, ADNC, and/or amyloidosis in participants with LB disease and AD, across clinical spectra. RESULTS: Following a decade-long follow-up, the clinically or autopsy-defined diagnosis changed for nine participants. αSyn-SAA demonstrated improved accuracy (91.3%), sensitivity (89.3%), and specificity (93.3%) for identifying LB disease compared to all non-LB disease, highlighting the limitations of clinical diagnosis alone. When examining biomarkers of co-pathology, amyloidosis was present in 18%, 48%, and 71% (χ2(2) = 13.56, p = 0.001) and AD biomarkers were present in 0%, 8.7%, and 42.9% (χ2(2) = 18.44, p < 0.001) of LB disease participants with different stages of cognitive impairment respectively. Co-occurring biomarkers for αSyn-SAA and amyloidosis were present in 12% and 14% of AD compared to 43% and 57% LB disease participants with different stages of cognitive impairment (χ2(3) = 13.87, p = 0.003). INTERPRETATION: Our study shows that using a combination of αSyn-SAA and AD biomarkers can identify people with αSyn, ADNC, and co-pathology better and earlier than traditional clinical diagnostic criteria alone.

Lewy body dementia: Overcoming barriers and identifying solutions.

Despite its high prevalence among dementias, Lewy body dementia (LBD) remains poorly understood with a limited, albeit growing, evidence base. The public-health burden that LBD imposes is worsened by overlapping pathologies, which contribute to misdiagnosis, and lack of treatments. For this report, we gathered and analyzed public-domain information on advocacy, funding, research outputs, and the therapeutic pipeline to identify gaps in each of these key elements. To further understand the current gaps, we also conducted interviews with leading experts in regulatory/governmental agencies, LBD advocacy, academic research, and biopharmaceutical research, as well as with funding sources. We identified wide gaps across the entire landscape, the most critical being in research. Many of the experts participated in a workshop to discuss the prioritization of research areas with a view to accelerating therapeutic development and improving patient care. This white paper outlines the opportunities for bridging the major LBD gaps and creates the framework for collaboration in that endeavor. HIGHLIGHTS: A group representing academia, government, industry, and consulting expertise was convened to discuss current progress in Dementia with Lewy Body care and research. Consideration of expert opinion,natural language processing of the literature as well as publicly available data bases, and Delphi inspired discussion led to a proposed consensus document of priorities for the field.

Neuropathologic Burden and Dementia in Nonagenarians and Centenarians: Comparison of 2 Community-Based Cohorts

BACKGROUND AND OBJECTIVES: The aim of this study was to compare 2 large clinicopathologic cohorts of participants aged 90+ and to determine whether the association between neuropathologic burden and dementia in these older groups differs substantially from those seen in younger-old adults. METHODS: Autopsied participants from The 90+ Study and Adult Changes in Thought (ACT) Study community-based cohort studies were evaluated for dementia-associated neuropathologic changes. Associations between neuropathologic variables and dementia were assessed using logistic or linear regression, and the weighted population attributable fraction (PAF) per type of neuropathologic change was estimated. RESULTS: The 90+ Study participants (n = 414) were older (mean age at death = 97.7 years) and had higher amyloid/tau burden than ACT <90 (n = 418) (mean age at death = 83.5 years) and ACT 90+ (n = 401) (mean age at death = 94.2 years) participants. The ACT 90+ cohort had significantly higher rates of limbic-predominant age-related TDP-43 encephalopathy (LATE-NC), microvascular brain injury (µVBI), and total neuropathologic burden. Independent associations between individual neuropathologic lesions and odds of dementia were similar between all 3 groups, with the exception of µVBI, which was associated with increased dementia risk in the ACT <90 group only (odds ratio 1.5, 95% CI 1.2-1.8, p < 0.001). Weighted PAF scores indicated that eliminating µVBI, although more prevalent in ACT 90+ participants, would have little effect on dementia. Conversely, eliminating µVBI in ACT <90 could theoretically reduce dementia at a similar rate to that of AD neuropathologic change (weighted PAF = 6.1%, 95% CI 3.8-8.4, p = 0.001). Furthermore, reducing LATE-NC in The 90+ Study could potentially reduce dementia to a greater degree (weighted PAF = 5.1%, 95% CI 3.0-7.3, p = 0.001) than either ACT cohort (weighted PAFs = 1.69, 95% CI 0.4-2.7). DISCUSSION: Our results suggest that specific neuropathologic features may differ in their effect on dementia among nonagenarians and centenarians from cohorts with different selection criteria and study design. Furthermore, microvascular lesions seem to have a more significant effect on dementia in younger compared with older participants. The results from this study demonstrate that different populations may require distinct dementia interventions, underscoring the need for disease-specific biomarkers.

Quantitative estimate of cognitive resilience and its medical and genetic associations.

BACKGROUND: We have proposed that cognitive resilience (CR) counteracts brain damage from Alzheimer's disease (AD) or AD-related dementias such that older individuals who harbor neurodegenerative disease burden sufficient to cause dementia remain cognitively normal. However, CR traditionally is considered a binary trait, capturing only the most extreme examples, and is often inconsistently defined. METHODS: This study addressed existing discrepancies and shortcomings of the current CR definition by proposing a framework for defining CR as a continuous variable for each neuropsychological test. The linear equations clarified CR's relationship to closely related terms, including cognitive function, reserve, compensation, and damage. Primarily, resilience is defined as a function of cognitive performance and damage from neuropathologic damage. As such, the study utilized data from 844 individuals (age = 79 ± 12, 44% female) in the National Alzheimer's Coordinating Center cohort that met our inclusion criteria of comprehensive lesion rankings for 17 neuropathologic features and complete neuropsychological test results. Machine learning models and GWAS then were used to identify medical and genetic factors that are associated with CR. RESULTS: CR varied across five cognitive assessments and was greater in female participants, associated with longer survival, and weakly associated with educational attainment or APOE ε4 allele. In contrast, damage was strongly associated with APOE ε4 allele (P value < 0.0001). Major predictors of CR were cardiovascular health and social interactions, as well as the absence of behavioral symptoms. CONCLUSIONS: Our framework explicitly decoupled the effects of CR from neuropathologic damage. Characterizations and genetic association study of these two components suggest that the underlying CR mechanism has minimal overlap with the disease mechanism. Moreover, the identified medical features associated with CR suggest modifiable features to counteract clinical expression of damage and maintain cognitive function in older individuals.

Mediterranean Diet Protects Against a Neuroinflammatory Cortical Transcriptome: Associations with Brain Volumetrics, Peripheral Inflammation, Social Isolation and Anxiety.

INTRODUCTION: Mediterranean diets may be neuroprotective and prevent cognitive decline relative to Western diets, however the underlying biology is poorly understood. METHODS: We assessed the effects of Western vs. Mediterranean-like diets on RNAseq generated transcriptional profiles in temporal cortex and their relationships with changes in MRI neuroimaging phenotypes, circulating monocyte gene expression, and observations of social isolation and anxiety in 38 socially-housed, middle-aged female cynomolgus macaques. RESULTS: Diet resulted in differential expression of seven transcripts (FDR<0.05). Cyclin dependent kinase 14 ( CDK14 ), a proinflammatory regulator, was lower in the Mediterranean group. The remaining six transcripts [i.e., "lunatic fringe" ( LFNG ), mannose receptor C type 2 ( MRC2 ), solute carrier family 3 member 2 ( SLCA32 ), butyrophilin subfamily 2 member A1 ( BTN2A1 ), katanin regulatory subunit B1 ( KATNB1 ), and transmembrane protein 268 ( TMEM268 )] were higher in cortex of the Mediterranean group and generally associated with anti-inflammatory/neuroprotective pathways. KATNB1 encodes a subcomponent of katanin, important in maintaining microtubule homeostasis. BTN2A1 is involved in immunomodulation of γδ T-cells which have anti-neuroinflammatory and neuroprotective effects. CDK14 , LFNG , MRC2, and SLCA32 are associated with inflammatory pathways. The latter four differentially expressed cortex transcripts were associated with monocyte transcript levels, changes in AD-relevant brain volumes determined by MRI over the course of the study, and social isolation and anxiety. CDK14 was positively correlated with monocyte inflammatory transcripts, changes in total brain, gray matter, cortical gray matter volumes, and time alone and anxious behavior, and negatively correlated with changes in total white matter and cerebrospinal fluid (CSF) volumes. In contrast, LFNG , MRC2 , and SLCA32 were negatively correlated with monocyte inflammatory transcripts and changes in total gray matter volume, and positively correlated with CSF volume changes, and SLCA32 was negatively correlated with time alone. DISCUSSION: Collectively, our results suggest that relative to Western diets, Mediterranean diets confer protection against peripheral and central inflammation which is reflected in preserved brain structure and behavior.

Unveiling Resilience to Alzheimer's Disease: Insights From Brain Regional Proteomic Markers.

Studying proteomics data of the human brain could offer numerous insights into unraveling the signature of resilience to Alzheimer's disease. In our previous study with rigorous cohort selection criteria that excluded 4 common comorbidities, we harnessed multiple brain regions from 43 research participants with 12 of them displaying cognitive resilience to Alzheimer's disease. Based on the previous findings, this work focuses on 6 proteins out of the 33 differentially expressed proteins associated with resilience to Alzheimer's disease. These proteins are used to construct a decision tree classifier, enabling the differentiation of 3 groups: (i) healthy control, (ii) resilience to Alzheimer's disease, and (iii) Alzheimer's disease with dementia. Our analysis unveiled 2 important regional proteomic markers: Aß peptides in the hippocampus and PA1B3 in the inferior parietal lobule. These findings underscore the potential of using distinct regional proteomic markers as signatures in characterizing the resilience to Alzheimer's disease.

Paradigm Shift: Multiple Potential Pathways to Neurodegenerative Dementia.

Neurodegenerative dementia can result from multiple underlying abnormalities, including neurotransmitter imbalances, protein aggregation, and other neurotoxic events. A major complication in identifying effective treatment targets is the frequent co-occurrence of multiple neurodegenerative processes, occurring either in parallel or sequentially. The path towards developing effective treatments for Alzheimer's disease (AD) and other dementias has been relatively slow and until recently has focused on disease symptoms. Aducanumab and lecanemab, recently approved by the FDA, are meant to target disease structures but have only modest benefit on symptom progression and remain unproven in reversing or preventing dementia. A third, donanemab, appears more promising but awaits FDA approval. Ongoing trials include potential cognition enhancers, new combinations of known drugs for synergistic effects, prodrugs with less toxicity, and increasing interest in drugs targeting neuroinflammation or microbiome. Scientific and technological advances offer the opportunity to move in new therapy directions, such as modifying microglia to prevent or suppress underlying disease. A major challenge, however, is that underlying comorbidities likely influence the effectiveness of therapies. Indeed, the full range of comorbidity, today only definitively identified postmortem, likely contributes to failed clinical trials and overmedication of older adults, since it is difficult to exclude (during life) people unlikely to respond. Our current knowledge thus signals that a paradigm shift towards individualized and multimodal treatments is necessary to effectively advance the field of dementia therapeutics.

Characterizing Limbic-Predominant Age-Related TDP-43 Encephalopathy Without Alzheimer's Disease and Lewy Body Dementia in the Oldest Old: A Case Series.

BACKGROUND: Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) is a clinicopathological construct proposed to facilitate studying TDP-43 pathology in older individuals. OBJECTIVE: Our aim was to describe clinical and cognitive characteristics of LATE-NC without Alzheimer's disease neuropathologic change (ADNC) and Lewy body (LB) and to compare this with ADNC and primary age related tauopathy (PART). METHODS: In 364 autopsies of the oldest old of The 90+ Study, we identified those with LATE-NC without ADNC and LB. Control groups were participants with ADNC and PART. RESULTS: Of 31% of participants who had LATE-NC, only 5 (1.4%) had LATE-NC without ADNC and LB, all of whom had tau. These participants had a gradual and progressive cognitive decline. Four (80%) had dementia at death, a rate that was higher than ADNC (50%) and PART (21.7%). Mean duration of cognitive impairment was twice as long in LATE-NC without ADNC and LB (6.2 years) compared to ADNC (2.9 years) and PART (3 years). LATE-NC without ADNC and LB group had a higher prevalence of syncope, depression, and extrapyramidal signs than the ADNC and PART groups. CONCLUSIONS: Despite the high prevalence of LATE-NC, LATE-NC without ADNC and LB was rare in this large oldest-old cohort, highlighting the very high prevalence of multiple pathologic changes in the oldest old. Slowly progressive cognitive decline, ubiquitous memory impairment, history of syncope and depression, and extrapyramidal signs were prominent features among our LATE-NC without ADNC and LB group.

Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature.

Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.

A visual-language foundation model for pathology image analysis using medical Twitter.

The lack of annotated publicly available medical images is a major barrier for computational research and education innovations. At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter. Here we harness these crowd platforms to curate OpenPath, a large dataset of 208,414 pathology images paired with natural language descriptions. We demonstrate the value of this resource by developing pathology language-image pretraining (PLIP), a multimodal artificial intelligence with both image and text understanding, which is trained on OpenPath. PLIP achieves state-of-the-art performances for classifying new pathology images across four external datasets: for zero-shot classification, PLIP achieves F1 scores of 0.565-0.832 compared to F1 scores of 0.030-0.481 for previous contrastive language-image pretrained model. Training a simple supervised classifier on top of PLIP embeddings also achieves 2.5% improvement in F1 scores compared to using other supervised model embeddings. Moreover, PLIP enables users to retrieve similar cases by either image or natural language search, greatly facilitating knowledge sharing. Our approach demonstrates that publicly shared medical information is a tremendous resource that can be harnessed to develop medical artificial intelligence for enhancing diagnosis, knowledge sharing and education.