Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning.

BACKGROUND: Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a substantial economic burden. The Random forest algorithm is effective in predicting AD; however, the key factors influencing AD onset remain unclear. This study aimed to analyze the key lipoprotein and metabolite factors influencing AD onset using machine-learning methods. It provides new insights for researchers and medical personnel to understand AD and provides a reference for the early diagnosis, treatment, and early prevention of AD. METHODS: A total of 603 participants, including controls and patients with AD with complete lipoprotein and metabolite data from the Alzheimer's disease Neuroimaging Initiative (ADNI) database between 2005 and 2016, were enrolled. Random forest, Lasso regression, and CatBoost algorithms were employed to rank and filter 213 lipoprotein and metabolite variables. Variables with consistently high importance rankings from any two methods were incorporated into the models. Finally, the variables selected from the three methods, with the participants' age, sex, and marital status, were used to construct a random forest predictive model. RESULTS: Fourteen lipoprotein and metabolite variables were screened using the three methods, and 17 variables were included in the AD prediction model based on age, sex, and marital status of the participants. The optimal random forest modeling was constructed with "mtry" set to 3 and "ntree" set to 300. The model exhibited an accuracy of 71.01%, a sensitivity of 79.59%, a specificity of 65.28%, and an AUC (95%CI) of 0.724 (0.645-0.804). When Mean Decrease Accuracy and Gini were used to rank the proteins, age, phospholipids to total lipids ratio in intermediate-density lipoproteins (IDL_PL_PCT), and creatinine were among the top five variables. CONCLUSIONS: Age, IDL_PL_PCT, and creatinine levels play crucial roles in AD onset. Regular monitoring of lipoproteins and their metabolites in older individuals is significant for early AD diagnosis and prevention.

Probing Protein Structural Changes in Alzheimer's Disease via Quantitative Cross-linking Mass Spectrometry.

Alzheimer's disease (AD) is a progressive neurological disorder featuring abnormal protein aggregation in the brain, including the pathological hallmarks of amyloid plaques and hyperphosphorylated tau. Despite extensive research efforts, understanding the molecular intricacies driving AD development remains a formidable challenge. This study focuses on identifying key protein conformational changes associated with the progression of AD. To achieve this, we employed quantitative cross-linking mass spectrometry (XL-MS) to elucidate conformational changes in the protein networks in cerebrospinal fluid (CSF). By using isotopically labeled cross-linkers BS3d0 and BS3d4, we reveal a dynamic shift in protein interaction networks during AD progression. Our comprehensive analysis highlights distinct alterations in protein-protein interactions within mild cognitive impairment (MCI) states. This study accentuates the potential of cross-linked peptides as indicators of AD-related conformational changes, including previously unreported site-specific binding between α-1-antitrypsin (A1AT) and complement component 3 (CO3). Furthermore, this work enables detailed structural characterization of apolipoprotein E (ApoE) and reveals modifications within its helical domains, suggesting their involvement in MCI pathogenesis. The quantitative approach provides insights into site-specific interactions and changes in the abundance of cross-linked peptides, offering an improved understanding of the intricate protein-protein interactions underlying AD progression. These findings lay a foundation for the development of potential diagnostic or therapeutic strategies aimed at mitigating the negative impact of AD.

[EEG changes in patients with Alzheimer's disease]. / Izmeneniya elektroentsefalogrammy u patsientov s bolezn'yu Al'tsgeimera.

The prevalence of cognitive impairment is steadily increasing compared to previous years. According to the World Health Organization, the number of people living with dementia will increase reaching 82 million in 2030 and 152 million in 2050. The most common cause is Alzheimer's disease (AD). The pathophysiological process in AD begins several years before the onset of clinical symptoms; so identifying it at an early stage would likely improve the clinical prognosis. The article presents EEG changes in patients with AD, and discusses the possibility of using EEG as a screening method for examining patients with cognitive impairment.

Comparative studies of hair shaft components between healthy and diseased donors.

Globally, the rapid aging of the population is predicted to become even more severe in the second half of the 21st century. Thus, it is expected to establish a growing expectation for innovative, non-invasive health indicators and diagnostic methods to support disease prevention, care, and health promotion efforts. In this study, we aimed to establish a new health index and disease diagnosis method by analyzing the minerals and free amino acid components contained in hair shaft. We first evaluated the range of these components in healthy humans and then conducted a comparative analysis of these components in subjects with diabetes, hypertension, androgenetic alopecia, major depressive disorder, Alzheimer's disease, and stroke. In the statistical analysis, we first used a student's t test to compare the hair components of healthy people and those of patients with various diseases. However, many minerals and free amino acids showed significant differences in all diseases, because the sample size of the healthy group was very large compared to the sample size of the disease group. Therefore, we attempted a comparative analysis based on effect size, which is not affected by differences in sample size. As a result, we were able to narrow down the minerals and free amino acids for all diseases compared to t test analysis. For diabetes, the t test narrowed down the minerals to 15, whereas the effect size measurement narrowed it down to 3 (Cr, Mn, and Hg). For free amino acids, the t test narrowed it down to 15 minerals. By measuring the effect size, we were able to narrow it down to 7 (Gly, His, Lys, Pro, Ser, Thr, and Val). It is also possible to narrow down the minerals and free amino acids in other diseases, and to identify potential health indicators and disease-related components by using effect size.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches.

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

Validity of claims-based definition of number of remaining teeth in Japan: Results from the Longevity Improvement and Fair Evidence Study.

BACKGROUND: Secondary healthcare data use has been increasing in the dental research field. The validity of the number of remaining teeth assessed from Japanese dental claims data has been reported in several studies, but has not been tested in the general population in Japan. OBJECTIVES: To evaluate the validity of the number of remaining teeth assessed from Japanese dental claims data and assess its predictability against subsequent health deterioration. METHODS: We used the claims data of residents of a municipality that implemented oral health screening programs. Using the number of teeth in the screening records as the reference standard, we assessed the validity of the claims-based number of teeth by calculating the mean differences. In addition, we assessed the association between the claims-based number of teeth and pneumococcal disease (PD) or Alzheimer's disease (AD) in adults aged ≥65 years using Cox proportional hazards analyses. RESULTS: Of the 10,154 participants, the mean number of teeth assessed from the claims data was 20.9, that in the screening records was 20.5, and their mean difference was 0.5. During the 3-year follow-up, PD or AD onset was observed in 10.4% (3,212/30,838) and 5.3% (1,589/30,207) of participants, respectively. Compared with participants with ≥20 teeth, those with 1-9 teeth had a 1.29 (95% confidence interval [CI]: 1.17-1.43) or 1.19 (95% CI: 1.04-1.36) times higher risk of developing PD or AD, respectively. CONCLUSION: High validity of the claims-based number of teeth was observed. In addition, the claims-based number of teeth was associated with the risk of PD and AD.

Plasma biomarkers increase diagnostic confidence in patients with Alzheimer's disease or frontotemporal lobar degeneration.

BACKGROUND: The recent development of techniques to assess plasma biomarkers has changed the way the research community envisions the future of diagnosis and management of Alzheimer's disease (AD) and other neurodegenerative disorders. This work aims to provide real world evidence on the clinical impact of plasma biomarkers in an academic tertiary care center. METHODS: Anonymized clinical reports of patients diagnosed with AD or Frontotemporal Lobar Degeneration with available plasma biomarkers (Aß42, Aß42/Aß40, p-tau181, p-tau231, NfL, GFAP) were independently assessed by two neurologists who expressed diagnosis and diagnostic confidence three times: (T0) at baseline based on the information collected during the first visit, (T1) after plasma biomarkers, and (T2) after traditional biomarkers (when available). Finally, we assessed whether clinicians' interpretation of plasma biomarkers and the consequent clinical impact are consistent with the final diagnosis, determined after the conclusion of the diagnostic clinical and instrumental work-up by the actual managing physicians who had complete access to all available information. RESULTS: Clinicians assessed 122 reports, and their concordance ranged from 81 to 91% at the three time points. At T1, the presentation of plasma biomarkers resulted in a change of diagnosis in 2% (2/122, p = 1.00) of cases, and in increased diagnostic confidence in 76% (91/120, p < 0.001) of cases with confirmed diagnosis. The change in diagnosis and the increase in diagnostic confidence after plasma biomarkers were consistent with the final diagnosis in 100% (2/2) and 81% (74/91) of cases, respectively. At T2, the presentation of traditional biomarkers resulted in a further change of diagnosis in 13% (12/94, p = 0.149) of cases, and in increased diagnostic confidence in 88% (72/82, p < 0.001) of cases with confirmed diagnosis. CONCLUSIONS: In an academic tertiary care center, plasma biomarkers supported clinicians by increasing their diagnostic confidence in most cases, despite a negligible impact on diagnosis. Future prospective studies are needed to assess the full potential of plasma biomarkers on clinical grounds.

Irregular word reading as a marker of semantic decline in Alzheimer's disease: implications for premorbid intellectual ability measurement.

BACKGROUND: Irregular word reading has been used to estimate premorbid intelligence in Alzheimer's disease (AD) dementia. However, reading models highlight the core influence of semantic abilities on irregular word reading, which shows early decline in AD. The primary objective of this study is to ascertain whether irregular word reading serves as an indicator of cognitive and semantic decline in AD, potentially discouraging its use as a marker for premorbid intellectual abilities. METHOD: Six hundred eighty-one healthy controls (HC), 104 subjective cognitive decline, 290 early and 589 late mild cognitive impairment (EMCI, LMCI) and 348 AD participants from the Alzheimer's Disease Neuroimaging Initiative were included. Irregular word reading was assessed with the American National Adult Reading Test (AmNART). Multiple linear regressions were conducted predicting AmNART score using diagnostic category, general cognitive impairment and semantic tests. A generalized logistic mixed-effects model predicted correct reading using extracted psycholinguistic characteristics of each AmNART words. Deformation-based morphometry was used to assess the relationship between AmNART scores and voxel-wise brain volumes, as well as with the volume of a region of interest placed in the left anterior temporal lobe (ATL), a region implicated in semantic memory. RESULTS: EMCI, LMCI and AD patients made significantly more errors in reading irregular words compared to HC, and AD patients made more errors than all other groups. Across the AD continuum, as well as within each diagnostic group, irregular word reading was significantly correlated to measures of general cognitive impairment / dementia severity. Neuropsychological tests of lexicosemantics were moderately correlated to irregular word reading whilst executive functioning and episodic memory were respectively weakly and not correlated. Age of acquisition, a primarily semantic variable, had a strong effect on irregular word reading accuracy whilst none of the phonological variables significantly contributed. Neuroimaging analyses pointed to bilateral hippocampal and left ATL volume loss as the main contributors to decreased irregular word reading performances. CONCLUSIONS: While the AmNART may be appropriate to measure premorbid intellectual abilities in cognitively unimpaired individuals, our results suggest that it captures current semantic decline in MCI and AD patients and may therefore underestimate premorbid intelligence. On the other hand, irregular word reading tests might be clinically useful to detect semantic impairments in individuals on the AD continuum.

Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction.

Predicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (ß-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice.

Diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease.

The purpose of this study was to explore the diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease. In this study, we first collected 161 samples from the GEO database (including 87 in the AD group and 74 in the normal group). Subsequently, through differential expression analysis and the iUUCD 2.0 database, we obtained 3450 Differentially Expressed Genes (DEGs) and 806 Ubiquitin-related genes (UbRGs). After taking the intersection, we obtained 128 UbR-DEGs. Secondly, by conducting GO and KEGG enrichment analysis on these 128 UbR-DEGs, we identified the main molecular functions and biological pathways related to AD. Furthermore, through the utilization of GSEA analysis, we have gained insight into the enrichment of functions and pathways within both the AD and normal groups. Further, using lasso regression analysis and cross-validation techniques, we identified 22 characteristic genes associated with AD. Subsequently, we constructed a logistic regression model and optimized it, resulting in the identification of 6 RUbR-DEGs: KLHL21, WDR82, DTX3L, UBTD2, CISH, and ATXN3L. In addition, the ROC result showed that the diagnostic model we built has excellent accuracy and reliability in identifying AD patients. Finally, we constructed a lncRNA-miRNA-mRNA (competing endogenous RNA, ceRNA) regulatory network for AD based on six RUbR-DEGs, further elucidating the interaction between UbRGs and lncRNA, miRNA. In conclusion, our findings will contribute to further understanding of the molecular pathogenesis of AD and provide a new perspective for AD risk prediction, early diagnosis and targeted therapy in the population.

Assessment of the Correlation and Diagnostic Accuracy between Cerebrospinal Fluid and Plasma Alzheimer's Disease Biomarkers: A Comparison of the Lumipulse and Simoa Platforms.

We compared the clinical and analytical performance of Alzheimer's disease (AD) plasma biomarkers measured using the single-molecule array (Simoa) and Lumipulse platforms. We quantified the plasma levels of amyloid beta 42 (Aß42), Aß40, phosphorylated tau (Ptau181), and total tau biomarkers in 81 patients with mild cognitive impairment (MCI), 30 with AD, and 16 with non-AD dementia. We found a strong correlation between the Simoa and Lumipulse methods. Concerning the clinical diagnosis, Simoa Ptau181/Aß42 (AUC 0.739, 95% CI 0.592-0.887) and Lumipulse Aß42 and Ptau181/Aß42 (AUC 0.735, 95% CI 0.589-0.882 and AUC 0.733, 95% CI 0.567-0.900) had the highest discriminating power. However, their power was significantly lower than that of CSF Aß42/Aß40, as measured by Lumipulse (AUC 0.879, 95% CI 0.766-0.992). Simoa Ptau181 and Lumipulse Ptau181/Aß42 were the markers most consistent with the CSF Aß42/Aß40 status (AUC 0.801, 95% CI 0.712-0.890 vs. AUC 0.870, 95% CI 0.806-0.934, respectively) at the ≥2.127 and ≥0.084 cut-offs, respectively. The performance of the Simoa and Lumipulse plasma AD assays is weaker than that of CSF AD biomarkers. At present, the analysed AD plasma biomarkers may be useful for screening to reduce the number of lumbar punctures in the clinical setting.

Unlocking Preclinical Alzheimer's: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics.

Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods.

Clinical validity of the Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB) in Mild Cognitive Impairment and Alzheimer's Disease.

BACKGROUND: The identification and staging of Alzheimer's Disease (AD) represent a challenge, especially in the prodromal stage of Mild Cognitive Impairment (MCI), when cognitive changes can be subtle. Worldwide efforts were dedicated to select and harmonize available neuropsychological instruments. In Italy, the Italian Network of Neuroscience and Neuro-Rehabilitation has promoted the adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB), collecting normative data from 433 healthy controls (HC). Here, we aimed to explore the ability of I-UDSNB to differentiate between a) MCI and HC, b) AD and HC, c) MCI and AD. METHODS: One hundred thirty-seven patients (65 MCI, 72 AD) diagnosed after clinical-neuropsychological assessment, and 137 HC were included. We compared the I-UDSNB scores between a) MCI and HC, b) AD and HC, c) MCI and AD, with t-tests. To identify the test(s) most capable of differentiating between groups, significant scores were entered in binary logistic and in stepwise regressions, and then in Receiver Operating Characteristic curve analyses. RESULTS: Two episodic memory tests (Craft Story and Five Words test) differentiated MCI from HC subjects; Five Words test, Semantic Fluency (vegetables), and TMT-part B differentiated AD from, respectively, HC and MCI. CONCLUSIONS: Our findings indicate that the I-UDSNB is a suitable tool for the harmonized and concise assessment of patients with cognitive decline, showing high sensitivity and specificity for the diagnosis of MCI and AD.

[Features of the clinical and neuroimaging picture in patients with early-onset Alzheimer's disease]. / Osobennosti klinicheskoi i neirovizualizatsionnoi kartiny u patsientov s bolezn'yu Al'tsgeimera s rannim nachalom.

The most common cause of severe cognitive impairment in adults is Alzheimer's disease (AD). Depending on the age of onset, AD is divided into early (<65 years) and late (≥65 years) forms. Early-onset AD (EOAD) is significantly less common than later-onset AD (LOAD) and accounts for only about 5-10% of cases. However, its medical and social significance, as a disease leading to loss of ability to work and legal capacity, as well as premature death in patients aged 40-64 years, is extremely high. Patients with EOAD compared with LOAD have a greater number of atypical clinical variants - 25% and 6-12.5%, respectively, which complicates the differential diagnosis of EOAD with other neurodegenerative diseases. However, the typical classical amnestic variant predominates in both EOAD and LOAD. Also, patients with EOAD have peculiarities according to neuroimaging data: when performing MRI of the brain, patients with EOAD often have more pronounced parietal atrophy and less pronounced hippocampal atrophy compared to patients with LOAD. The article pays attention to the features of the clinical and neuroimaging data in patients with EOAD; a case of a patient with EOAD is presented.

Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases.

Cerebrospinal fluid (CSF) biomarkers reflect brain pathophysiology and are used extensively in translational research as well as in clinical practice for diagnosis of neurological diseases, e.g., Alzheimer's disease (AD). However, CSF biomarker concentrations may be influenced by non-disease related inter-individual variability. Here we use a data-driven approach to demonstrate the existence of inter-individual variability in mean standardized CSF protein levels. We show that these non-disease related differences cause many commonly reported CSF biomarkers to be highly correlated, thereby producing misleading results if not accounted for. To adjust for this inter-individual variability, we identified and evaluated high-performing reference proteins which improved the diagnostic accuracy of key CSF AD biomarkers. Our reference protein method attenuates the risk for false positive findings, and improves the sensitivity and specificity of CSF biomarkers, with broad implications for both research and clinical practice.

DMA-HPCNet: Dual Multi-Level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer's Disease Classification.

Computer-aided diagnosis (CAD) plays a crucial role in the clinical application of Alzheimer's disease (AD). In particular, convolutional neural network (CNN)-based methods are highly sensitive to subtle changes caused by brain atrophy in medical images (e.g., magnetic resonance imaging, MRI). Due to computational resource constraints, most CAD methods focus on quantitative features in specific regions, neglecting the holistic nature of the images, which poses a challenge for a comprehensive understanding of pathological changes in AD. To address this issue, we propose a lightweight dual multi-level hybrid pyramid convolutional neural network (DMA-HPCNet) to aid clinical diagnosis of AD. Specifically, we introduced ResNet as the backbone network and modularly extended the hybrid pyramid convolution (HPC) block and the dual multi-level attention (DMA) module. Among them, the HPC block is designed to enhance the acquisition of information at different scales, and the DMA module is proposed to sequentially extract different local and global representations from the channel and spatial domains. Our proposed DMA-HPCNet method was evaluated on baseline MRI slices of 443 subjects from the ADNI dataset. Experimental results show that our proposed DMA-HPCNet model performs efficiently in AD-related classification tasks with low computational cost.

Future perspective and clinical applicability of the combined use of plasma phosphorylated tau 181 and neurofilament light chain in Subjective Cognitive Decline and Mild Cognitive Impairment.

We aimed to assess diagnostic accuracy of plasma p-tau181 and NfL separately and in combination in discriminating Subjective Cognitive Decline (SCD) and Mild Cognitive Impairment (MCI) patients carrying Alzheimer's Disease (AD) pathology from non-carriers; to propose a flowchart for the interpretation of the results of plasma p-tau181 and NfL. We included 43 SCD, 41 MCI and 21 AD-demented (AD-d) patients, who underwent plasma p-tau181 and NfL analysis. Twenty-eight SCD, 41 MCI and 21 AD-d patients underwent CSF biomarkers analysis (Aß1-42, Aß1-42/1-40, p-tau, t-tau) and were classified as carriers of AD pathology (AP+) it they were A+/T+ , or non-carriers (AP-) when they were A-, A+/T-/N-, or A+/T-/N+ according to the A/T(N) system. Plasma p-tau181 and NfL separately showed a good accuracy (AUC = 0.88), while the combined model (NfL + p-tau181) showed an excellent accuracy (AUC = 0.92) in discriminating AP+ from AP- patients. Plasma p-tau181 and NfL results were moderately concordant (Coehn's k = 0.50, p < 0.001). Based on a logistic regression model, we estimated the risk of AD pathology considering the two biomarkers: 10.91% if both p-tau181 and NfL were negative; 41.10 and 76.49% if only one biomarker was positive (respectively p-tau18 and NfL); 94.88% if both p-tau181 and NfL were positive. Considering the moderate concordance and the risk of presenting an underlying AD pathology according to the positivity of plasma p-tau181 and NfL, we proposed a flow chart to guide the combined use of plasma p-tau181 and NfL and the interpretation of biomarker results to detect AD pathology.

Identification of high-performing antibodies for the reliable detection of Tau proteoforms by Western blotting and immunohistochemistry.

Antibodies are essential research tools whose performance directly impacts research conclusions and reproducibility. Owing to its central role in Alzheimer's disease and other dementias, hundreds of distinct antibody clones have been developed against the microtubule-associated protein Tau and its multiple proteoforms. Despite this breadth of offer, limited understanding of their performance and poor antibody selectivity have hindered research progress. Here, we validate a large panel of Tau antibodies by Western blot (79 reagents) and immunohistochemistry (35 reagents). We address the reagents' ability to detect the target proteoform, selectivity, the impact of protein phosphorylation on antibody binding and performance in human brain samples. While most antibodies detected Tau at high levels, many failed to detect it at lower, endogenous levels. By WB, non-selective binding to other proteins affected over half of the antibodies tested, with several cross-reacting with the related MAP2 protein, whereas the "oligomeric Tau" T22 antibody reacted with monomeric Tau by WB, thus calling into question its specificity to Tau oligomers. Despite the presumption that "total" Tau antibodies are agnostic to post-translational modifications, we found that phosphorylation partially inhibits binding for many such antibodies, including the popular Tau-5 clone. We further combine high-sensitivity reagents, mass-spectrometry proteomics and cDNA sequencing to demonstrate that presumptive Tau "knockout" human cells continue to express residual protein arising through exon skipping, providing evidence of previously unappreciated gene plasticity. Finally, probing of human brain samples with a large panel of antibodies revealed the presence of C-term-truncated versions of all main Tau brain isoforms in both control and tauopathy donors. Ultimately, we identify a validated panel of Tau antibodies that can be employed in Western blotting and/or immunohistochemistry to reliably detect even low levels of Tau expression with high selectivity. This work represents an extensive resource that will enable the re-interpretation of published data, improve reproducibility in Tau research, and overall accelerate scientific progress.

Appendicular lean mass and the risk of stroke and Alzheimer's disease: a mendelian randomization study.

BACKGROUND: Appendicular lean mass (ALM) is a good predictive biomarker for sarcopenia. And previous studies have reported the association between ALM and stroke or Alzheimer's disease (AD), however, the causal relationship is still unclear, The purpose of this study was to evaluate whether genetically predicted ALM is causally associated with the risk of stroke and AD by performing Mendelian randomization (MR) analyses. METHODS: A two-sample MR study was designed. Genetic variants associated with the ALM were obtained from a large genome-wide association study (GWAS) and utilized as instrumental variables (IVs). Summary-level data for stroke and AD were generated from the corresponding GWASs. We used random-effect inverse-variance weighted (IVW) as the main method for estimating causal effects, complemented by several sensitivity analyses, including the weighted median, MR-Egger, and MR-pleiotropy residual sum and outlier (MR-PRESSO) methods. Multivariable analysis was further conducted to adjust for confounding factors, including body mass index (BMI), type 2 diabetes mellitus (T2DM), low density lipoprotein-C (LDL-C), and atrial fibrillation (AF). RESULTS: The present MR study indicated significant inverse associations of genetically predicted ALM with any ischemic stroke ([AIS], odds ratio [OR], 0.93; 95% confidence interval [CI], 0.89-0.97; P = 0.002) and AD (OR, 090; 95% CI 0.85-0.96; P = 0.001). Regarding the subtypes of AIS, genetically predicted ALM was related to the risk of large artery stroke ([LAS], OR, 0.86; 95% CI 0.77-0.95; P = 0.005) and small vessel stroke ([SVS], OR, 0.80; 95% CI 0.73-0.89; P < 0.001). Regarding multivariable MR analysis, ALM retained the stable effect on AIS when adjusting for BMI, LDL-C, and AF, while a suggestive association was observed after adjusting for T2DM. And the estimated effect of ALM on LAS was significant after adjustment for BMI and AF, while a suggestive association was found after adjusting for T2DM and LDL-C. Besides, the estimated effects of ALM were still significant on SVS and AD after adjustment for BMI, T2DM, LDL-C, and AF. CONCLUSIONS: The two-sample MR analysis indicated that genetically predicted ALM was negatively related to AIS and AD. And the subgroup analysis of AIS revealed a negative causal effect of genetically predicted ALM on LAS or SVS. Future studies are required to further investigate the underlying mechanisms.

Multiomics Blood-Based Biomarkers Predict Alzheimer's Predementia with High Specificity in a Multicentric Cohort Study.

BACKGROUND: The primary criteria for diagnosing mild cognitive impairment (MCI) due to Alzheimer's Disease (AD) or probable mild AD dementia rely partly on cognitive assessments and the presence of amyloid plaques. Although these criteria exhibit high sensitivity in predicting AD among cognitively impaired patients, their specificity remains limited. Notably, up to 25% of non-demented patients with amyloid plaques may be misdiagnosed with MCI due to AD, when in fact they suffer from a different brain disorder. The introduction of anti-amyloid antibodies complicates this scenario. Physicians must prioritize which amyloid-positive MCI patients receive these treatments, as not all are suitable candidates. Specifically, those with non-AD amyloid pathologies are not primary targets for amyloid-modifying therapies. Consequently, there is an escalating medical necessity for highly specific blood biomarkers that can accurately detect pre-dementia AD, thus optimizing amyloid antibody prescription. OBJECTIVES: The objective of this study was to evaluate a predictive model based on peripheral biomarkers to identify MCI and mild dementia patients who will develop AD dementia symptoms in cognitively impaired population with high specificity. DESIGN: Peripheral biomarkers were identified in a gene transfer-based animal model of AD and then validated during a retrospective multi-center clinical study. SETTING: Participants from 7 retrospective cohorts (US, EU and Australia). PARTICIPANTS: This study followed 345 cognitively impaired individuals over up to 13 years, including 193 with MCI and 152 with mild dementia, starting from their initial visits. The final diagnoses, established during their last assessments, classified 249 participants as AD patients and 96 as having non-AD brain disorders, based on the specific diagnostic criteria for each disorder subtype. Amyloid status, assessed at baseline, was available for 82.9% of the participants, with 61.9% testing positive for amyloid. Both amyloid-positive and negative individuals were represented in each clinical group. Some of the AD patients had co-morbidities such as metabolic disorders, chronic diseases, or cardiovascular pathologies. MEASUREMENTS: We developed targeted mass spectrometry assays for 81 blood-based biomarkers, encompassing 45 proteins and 36 metabolites previously identified in AAV-AD rats. METHODS: We analyzed blood samples from study participants for the 81 biomarkers. The B-HEALED test, a machine learning-based diagnostic tool, was developed to differentiate AD patients, including 123 with Prodromal AD and 126 with mild AD dementia, from 96 individuals with non-AD brain disorders. The model was trained using 70% of the data, selecting relevant biomarkers, calibrating the algorithm, and establishing cutoff values. The remaining 30% served as an external test dataset for blind validation of the predictive accuracy. RESULTS: Integrating a combination of 19 blood biomarkers and participant age, the B-HEALED model successfully distinguished participants that will develop AD dementia symptoms (82 with Prodromal AD and 83 with AD dementia) from non-AD subjects (71 individuals) with a specificity of 93.0% and sensitivity of 65.4% (AUROC=81.9%, p<0.001) during internal validation. When the amyloid status (derived from CSF or PET scans) and the B-HEALED model were applied in association, with individuals being categorized as AD if they tested positive in both tests, we achieved 100% specificity and 52.8% sensitivity. This performance was consistent in blind external validation, underscoring the model's reliability on independent datasets. CONCLUSIONS: The B-HEALED test, utilizing multiomics blood-based biomarkers, demonstrates high predictive specificity in identifying AD patients within the cognitively impaired population, minimizing false positives. When used alongside amyloid screening, it effectively identifies a nearly pure prodromal AD cohort. These results bear significant implications for refining clinical trial inclusion criteria, facilitating drug development and validation, and accurately identifying patients who will benefit the most from disease-modifying AD treatments.