Low testosterone levels relate to poorer cognitive function in women in an APOE-ε4-dependant manner.

BACKGROUND: Past research suggests that low testosterone levels relate to poorer cognitive function and higher Alzheimer's disease (AD) risk; however, these findings are inconsistent and are mostly derived from male samples, despite similar age-related testosterone decline in females. Both animal and human studies demonstrate that testosterone's effects on brain health may be moderated by apolipoprotein E ε4 allele (APOE-ε4) carrier status, which may explain some previous inconsistencies. We examined how testosterone relates to cognitive function in older women versus men across healthy aging and the AD continuum and the moderating role of APOE-ε4 genotype. METHODS: Five hundred and sixty one participants aged 55-90 (155 cognitively normal (CN), 294 mild cognitive impairment (MCI), 112 AD dementia) from the Alzheimer's Disease Neuroimaging Initiative (ADNI), who had baseline cognitive and plasma testosterone data, as measured by the Rules Based Medicine Human DiscoveryMAP Panel were included. There were 213 females and 348 males (self-reported sex assigned at birth), and 52% of the overall sample were APOE-ε4 carriers. We tested the relationship of plasma testosterone levels and its interaction with APOE-ε4 status on clinical diagnostic group (CN vs. MCI vs. AD), global, and domain-specific cognitive performance using ANOVAs and linear regression models in sex-stratified samples. Cognitive domains included verbal memory, executive function, processing speed, and language. RESULTS: We did not observe a significant difference in testosterone levels between clinical diagnostic groups in either sex, regrardless of APOE-ε4 status. Across clinical diagnostic group, we found a significant testosterone by APOE-ε4 interaction in females, such that lower testosterone levels related to worse global cognition, processing speed, and verbal memory in APOE-ε4 carriers only. We did not find that testosterone, nor its interaction with APOE-ε4, related to cognitive outcomes in males. CONCLUSIONS: Findings suggest that low testosterone levels in older female APOE-ε4 carriers across the aging-MCI-AD continuum may have deleterious, domain-specific effects on cognitive performance. Although future studies including additional sex hormones and longitudinal cognitive trajectories are needed, our results highlight the importance of including both sexes and considering APOE-ε4 carrier status when examining testosterone's role in cognitive health.

Sex differences often suggest a role of sex hormones, and in Alzheimer's Disease (AD) research, women show higher disease prevalence, accelerated cognitive decline, and an enhanced effect of the strongest genetic risk factor for AD, the apolipoprotein E ε4 allele (APOE-ε4). Testosterone, largely regarded as a "male" sex hormone, has demonstrated protective effects against AD in rodent studies including both sexes. However, human research often only includes males, limiting our understanding of testosterone's effect on AD risk and cognitive function. In this study, we investigated whether testosterone levels in the blood relate to cognitive performance measuring overall (global) cognition, verbal memory (remembering word lists or stories), executive function (complex thinking/multitasking), processing speed (how quickly one completes thinking tasks), and language (naming objects) in both sexes. We also tested whether this relationship is influenced by the APOE-ε4 genetic risk factor. We found that in females carrying APOE-ε4, lower testosterone levels related to worse performance on global cognition, processing speed, and verbal memory tests; however, testosterone levels did not relate to cognitive performance on any test in males nor in females without the APOE-ε4 genetic risk factor. Our findings suggest that the lower testosterone levels may be a contributing factor to worse AD outcomes in women, particularly for those at higher genetic risk for AD. Our results also demonstrate the importance of including female participants and considering the APOE-ε4 genetic risk factor when studying testosterone and brain health.

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer's disease dementia.

BACKGROUND: Transcriptome-wide association study (TWAS) is an influential tool for identifying genes associated with complex diseases whose genetic effects are likely mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate effect sizes of genetic variants on gene expression (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are employed as variant weights in gene-based association tests, facilitating the mapping of risk genes with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia are limited to studying only cis-eQTL proximal to the test gene. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method to leveraging both cis- and trans- eQTL of brain and blood tissues, in order to enhance mapping risk genes for AD dementia. METHODS: We first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate cis- and trans- eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per gene per tissue type. Then we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene. RESULTS: We identified 85 significant genes in prefrontal cortex, 82 in cortex, and 76 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 141 significant risk genes including 34 genes primarily due to trans-eQTL and 35 mapped risk genes in GWAS Catalog. With these 141 significant risk genes, we detected functional clusters comprised of both known mapped GWAS risk genes of AD in GWAS Catalog and our identified TWAS risk genes by protein-protein interaction network analysis, as well as several enriched phenotypes related to AD. CONCLUSION: We applied BGW-TWAS and aggregated Cauchy test methods to integrate both cis- and trans- eQTL data of brain and blood tissues with GWAS summary data, identifying 141 TWAS risk genes of AD dementia. These identified risk genes provide novel insights into the underlying biological mechanisms of AD dementia and potential gene targets for therapeutics development.

Association of Plasma Amyloid, P-Tau, GFAP, and NfL With CSF, Clinical, and Cognitive Features in Patients With Dementia With Lewy Bodies.

BACKGROUND AND OBJECTIVES: Plasma ß-amyloid-1-42/1-40 (Aß42/40), phosphorylated-tau (P-tau), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) have been widely examined in Alzheimer disease (AD), but little is known about their reflection of copathologies, clinical importance, and predictive value in dementia with Lewy bodies (DLB). We aimed to evaluate associations of these biomarkers with CSF amyloid, cognition, and core features in DLB. METHODS: This cross-sectional multicenter cohort study with prospective component included individuals with DLB, AD, and healthy controls (HCs), recruited from 2002 to 2020 with an annual follow-up of up to 5 years, from the European-Dementia With Lewy Bodies consortium. Plasma biomarkers were measured by single-molecule array (Neurology 4-Plex E kit). Amyloid status was determined by CSF Aß42 concentrations, and cognition was assessed by Mini-Mental State Examination (MMSE). Biomarker differences across groups, associations with amyloid status, and clinical core features were assessed by analysis of covariance. Associations with cognitive impairment and decline were assessed by linear regression and linear mixed-effects models. RESULTS: In our cohort consisting of 562 individuals (HC n = 89, DLB n = 342, AD n = 131; 250 women [44.5%], mean [SD] age of 71 [8] years), sex distribution did not differ between groups. Patients with DLB were significantly older, and had less years of education and worse baseline cognition than HC, but not AD. DLB participants stratified for amyloid status differed significantly in plasma Aß42/40 ratio (decreased in amyloid abnormal: ß = -0.008, 95% CI -0.016 to -0.0003, p = 0.01) and P-tau (increased in amyloid abnormal, P-tau181: ß = 0.246, 95% CI 0.011-0.481; P-tau231: ß = 0.227, 95% CI 0.035-0.419, both p < 0.05), but not in GFAP (ß = 0.068, 95% CI -0.018 to 0.153, p = 0.119), and NfL (ß = 0.004, 95% CI -0.087 to 0.096, p = 0.923) concentrations. Higher baseline GFAP, NfL, and P-tau concentrations were associated with lower MMSE scores in DLB, and GFAP and NfL were associated with a faster cognitive decline (GFAP: annual change of -2.11 MMSE points, 95% CI -2.88 to -1.35 MMSE points, p < 0.001; NfL: annual change of -2.13 MMSE points, 95% CI -2.97 to -1.29 MMSE points, p < 0.001). DLB participants with parkinsonism had higher concentrations of NfL (ß = 0.08, 95% CI 0.02-0.14, p = 0.006) than those without. DISCUSSION: Our study suggests a possible utility of plasma Aß42/40, P-tau181, and P-tau231 as a noninvasive biomarkers to assess amyloid copathology in DLB, and plasma GFAP and NfL as monitoring biomarkers for cognitive symptoms in DLB.

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.

Association of Vascular Endothelial Growth Factor Levels with Risk of Alzheimer's Disease: A Systematic Review and Meta-Analysis.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative illness that leads to impairment of cognitive functions and memory loss. Even though there is a plethora of research reporting the abnormal regulation of VEGF expression in AD pathogenesis, whether the CSF and serum VEGF are increased in AD is an open question yet. In this study, the association of CSF and serum VEGF concentrations with the risk of Alzheimer's disease was investigated using systematic review and meta-analysis. METHODS: A systematic literature search was carried out using online specialized biomedical databases of Web of Science, Pubmed, Scopus, Embase, and Google Scholar until Feb 2023 without restriction to the beginning time. The meta-analysis was performed using the random-effects model and only case-control publications describing VEGF concentrations in Alzheimer's patients were considered for calculating the pooled effect size. RESULTS: In the systematic literature search, 6 and 13 studies met the inclusion criteria to evaluate CSF and serum VEGF concentrations of Alzheimer's patients, respectively. This meta-analysis retrieved a total number of 2380 Alzheimer's patients and 5368 healthy controls. Under the random-effects model in the meta-analysis, the pooled SMD for CSF and serum VEGF concentrations of Alzheimer's patients were -0.13 (95%CI,-0.42-0.16) and 0.23 (95%CI,-0.27-0.73), respectively. Results of meta-regression analysis showed that the quality scores of papers and female sex ratios of participants did not affect the associations of VEGF concentrations with the risk of Alzheimer's disease. However, the age average of patients significantly affects the associations of CSF VEGF concentrations with the risk of Alzheimer's disease (P=0.051). There was a statistically significant subgroup effect for the disease severity of Alzheimer's patients which modifies the associations of serum VEGF concentrations with the risk of Alzheimer's disease (P<0.01) and subgroup analysis shows that study location modifies the associations of CSF and serum VEGF concentrations with the risk of Alzheimer's disease (P<0.01). CONCLUSION: The results show that the serum VEGF concentrations increased for Alzheimer's patients in accordance with the increased expression of VEGF and the VEGF levels of Alzheimer's patients decreased by increasing their disease severities. Therefore, in addition to detecting AD in the earliest stages of the disease, serum VEGF could be a promising biomarker to follow up on the disease and evaluate the clinical course of the disease.

Nucleic acid aptamer-based electrochemical sensor for the detection of serum P-tau231 and the instant screening test of Alzheimer's disease.

The instant screening of patients with a tendency towards developing Alzheimer's disease (AD) is significant for providing preventive measures and treatment. However, the current imaging-based technology cannot meet the requirements in the early stage. Developing biosensor-based liquid biopsy technology could be overcoming this bottleneck problem. Herein, we developed a simple, low-cost, and sensitive electrochemical aptamer biosensor for detecting phosphorylated tau protein threonine 231 (P-tau231), the earliest and one of the most efficacious abnormally elevated biomarkers of AD. Gold nanoparticles (AuNPs) were electrochemically synthesized on a glassy carbon electrode as the transducer, exhibiting excellent conductivity, and were applied to amplify the electrochemical signal. A nucleic acid aptamer was designed as the receptor to capture the P-tau231 protein, specifically through the formation of an aptamer-antigen complex. The proposed biosensor showed excellent sensitivity in detecting P-tau 231, with a broad linear detection range from 10 to 107 pg/mL and a limit of detection (LOD) of 2.31 pg/mL. The recoveries of the biosensor in human serum ranged from 97.59 to 103.26%, demonstrating that the biosensor could be used in complex practical samples. In addition, the results showed that the developed biosensor has good repeatability, reproducibility, and stability, which provides a novel method for the early screening of AD.

Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (µPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on µPADs can further facilitate the realization of smartphone µPADs platforms for efficient disease detection. RESULTS: This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on µPADs. Our platform successfully applied sandwich c-ELISA for detecting the ß-amyloid peptide 1-42 (Aß 1-42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach. SIGNIFICANCE: This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aß 1-42, particularly in areas with low resources and limited communication infrastructure.

Unraveling the genetic architecture of blood unfolded p-53 among non-demented elderlies: novel candidate genes for early Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a heritable neurodegenerative disease whose long asymptomatic phase makes the early diagnosis of it pivotal. Blood U-p53 has recently emerged as a superior predictive biomarker for AD in the early stages. We hypothesized that genetic variants associated with blood U-p53 could reveal novel loci and pathways involved in the early stages of AD. RESULTS: We performed a blood U-p53 Genome-wide association study (GWAS) on 484 healthy and mild cognitively impaired subjects from the ADNI cohort using 612,843 Single nucleotide polymorphisms (SNPs). We performed a pathway analysis and prioritized candidate genes using an AD single-cell gene program. We fine-mapped the intergenic SNPs by leveraging a cell-type-specific enhancer-to-gene linking strategy using a brain single-cell multimodal dataset. We validated the candidate genes in an independent brain single-cell RNA-seq and the ADNI blood transcriptome datasets. The rs279686 between AASS and FEZF1 genes was the most significant SNP (p-value = 4.82 × 10-7). Suggestive pathways were related to the immune and nervous systems. Twenty-three candidate genes were prioritized at 27 suggestive loci. Fine-mapping of 5 intergenic loci yielded nine cell-specific candidate genes. Finally, 15 genes were validated in the independent single-cell RNA-seq dataset, and five were validated in the ADNI blood transcriptome dataset. CONCLUSIONS: We underlined the importance of performing a GWAS on an early-stage biomarker of AD and leveraging functional omics datasets for pinpointing causal genes in AD. Our study prioritized nine genes (SORCS1, KIF5C, TMEFF2, TMEM63C, HLA-E, ATAT1, TUBB, ARID1B, and RUNX1) strongly implicated in the early stages of AD.

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.

Blood biomarkers of neurodegeneration associate differently with amyloid deposition, medial temporal atrophy, and cerebrovascular changes in APOE ε4-enriched cognitively unimpaired elderly.

BACKGROUND: Alzheimer's disease (AD) is characterized by the accumulation of amyloid-ß (Aß) plaques, neurofibrillary tau tangles, and neurodegeneration in the brain parenchyma. Here, we aimed to (i) assess differences in blood and imaging biomarkers used to evaluate neurodegeneration among cognitively unimpaired APOE ε4 homozygotes, heterozygotes, and non-carriers with varying risk for sporadic AD, and (ii) to determine how different cerebral pathologies (i.e., Aß deposition, medial temporal atrophy, and cerebrovascular pathology) contribute to blood biomarker concentrations in this sample. METHODS: Sixty APOE ε4 homozygotes (n = 19), heterozygotes (n = 21), and non-carriers (n = 20) ranging from 60 to 75 years, were recruited in collaboration with Auria biobank (Turku, Finland). Participants underwent Aß-PET ([11C]PiB), structural brain MRI including T1-weighted and T2-FLAIR sequences, and blood sampling for measuring serum neurofilament light chain (NfL), plasma total tau (t-tau), plasma N-terminal tau fragments (NTA-tau) and plasma glial fibrillary acidic protein (GFAP). [11C]PiB standardized uptake value ratio was calculated for regions typical for Aß accumulation in AD. MRI images were analysed for regional volumes, atrophy scores, and volumes of white matter hyperintensities. Differences in biomarker levels and associations between blood and imaging biomarkers were tested using uni- and multivariable linear models (unadjusted and adjusted for age and sex). RESULTS: Serum NfL concentration was increased in APOE ε4 homozygotes compared with non-carriers (mean 21.4 pg/ml (SD 9.5) vs. 15.5 pg/ml (3.8), p = 0.013), whereas other blood biomarkers did not differ between the groups (p > 0.077 for all). From imaging biomarkers, hippocampal volume was significantly decreased in APOE ε4 homozygotes compared with non-carriers (6.71 ml (0.86) vs. 7.2 ml (0.7), p = 0.029). In the whole sample, blood biomarker levels were differently predicted by the three measured cerebral pathologies; serum NfL concentration was associated with cerebrovascular pathology and medial temporal atrophy, while plasma NTA-tau associated with medial temporal atrophy. Plasma GFAP showed significant association with both medial temporal atrophy and Aß pathology. Plasma t-tau concentration did not associate with any of the measured pathologies. CONCLUSIONS: Only increased serum NfL concentrations and decreased hippocampal volume was observed in cognitively unimpaired APOEε4 homozygotes compared to non-carriers. In the whole population the concentrations of blood biomarkers were affected in distinct ways by different pathologies.

The Associations Between Serum Insulin-like Growth Factor-I, Brain White Matter Volumes, and Cognition in Mild Cognitive Impairment and Alzheimer's Disease.

Background: Insulin-like growth factor-I (IGF-I) regulates myelin, but little is known whether IGF-I associates with white matter functions in subjective and objective mild cognitive impairment (SCI/MCI) or Alzheimer's disease (AD). Objective: To explore whether serum IGF-I is associated with magnetic resonance imaging - estimated brain white matter volumes or cognitive functions. Methods: In a prospective study of SCI/MCI (n = 106) and AD (n = 59), we evaluated the volumes of the total white matter, corpus callosum (CC), and white matter hyperintensities (WMHs) as well as Mini-Mental State Examination (MMSE), Trail Making Test A and B (TMT-A/B), and Stroop tests I-III at baseline, and after 2 years. Results: IGF-I was comparable in SCI/MCI and AD (113 versus 118 ng/mL, p = 0.44). In SCI/MCI patients, the correlations between higher baseline IGF-I and greater baseline and 2-year volumes of the total white matter and total CC lost statistical significance after adjustment for intracranial volume and other covariates. However, after adjustment for covariates, higher baseline IGF-I correlated with better baseline scores of MMSE and Stroop test II in SCI/MCI and with better baseline results of TMT-B and Stroop test I in AD. IGF-I did not correlate with WMH volumes or changes in any of the variables. Conclusions: Both in SCI/MCI and AD, higher IGF-I was associated with better attention/executive functions at baseline after adjustment for covariates. Furthermore, the baseline associations between IGF-I and neuropsychological test results in AD may argue against significant IGF-I resistance in the AD brain.

Plasma Neurofilament Light Relates to Divergent Default and Salience Network Connectivity in Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia.

Background: Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) show differential vulnerability to large-scale brain functional networks. Plasma neurofilament light (NfL), a promising biomarker of neurodegeneration, has been linked in AD patients to glucose metabolism changes in AD-related regions. However, it is unknown whether plasma NfL would be similarly associated with disease-specific functional connectivity changes in AD and bvFTD. Objective: Our study examined the associations between plasma NfL and functional connectivity of the default mode and salience networks in patients with AD and bvFTD. Methods: Plasma NfL and neuroimaging data from patients with bvFTD (n = 16) and AD or mild cognitive impairment (n = 38; AD + MCI) were analyzed. Seed-based functional connectivity maps of key regions within the default mode and salience networks were obtained and associated with plasma NfL in these patients. RESULTS: We demonstrated divergent associations between NfL and functional connectivity in AD + MCI and bvFTD patients. Specifically, AD + MCI patients showed lower default mode network functional connectivity with higher plasma NfL, while bvFTD patients showed lower salience network functional connectivity with higher plasma NfL. Further, lower NfL-related default mode network connectivity in AD + MCI patients was associated with lower Montreal Cognitive Assessment scores and higher Clinical Dementia Rating sum-of-boxes scores, although NfL-related salience network connectivity in bvFTD patients was not associated with Neuropsychiatric Inventory Questionnaire scores. CONCLUSIONS: Our findings indicate that plasma NfL is differentially associated with brain functional connectivity changes in AD and bvFTD.

Search in the Periphery for Potential Inflammatory Biomarkers of Dementia with Lewy Bodies and Alzheimer's Disease.

Background: Neuroinflammation, with altered peripheral proinflammatory cytokine production, plays a major role in the pathogenesis of neurodegenerative diseases, such as Alzheimer's disease (AD), while the role of inflammation in dementia with Lewy bodies (DLB) is less known and the results of different studies are often in disagreement. Objective: The present study aimed to investigate the levels of TNFα and IL-6 in serum and supernatants, and the related DNA methylation in patients affected by DLB and AD compared to healthy controls (HCs), to clarify the role of epigenetic mechanisms of DNA promoter methylation on of pro-inflammatory cytokines overproduction. Methods: Twenty-one patients with DLB and fourteen with AD were frequency-matched for age and sex with eleven HCs. Clinical evaluation, TNFα and IL-6 gene methylation status, cytokine gene expression levels and production in serum and peripheral blood mononuclear cell (PBMC) supernatants were performed. Results: In AD and DLB patients, higher serum levels of IL-6 and TNFα were detected than in HCs. Differences in LPS-stimulated versus spontaneous PBMCs were observed between DLB, AD, and HC in the levels of TNFα (p = 0.027) and IL-6 (p < 0.001). Higher levels were also revealed for sIL-6R in DLB (p < 0.001) and AD (p < 0.001) in comparison with HC.DNA hypomethylation in IL-6 and TNFα CpG promoter sites was detected for DLB and AD patients compared to the corresponding site in HCs. Conclusions: Our preliminary study documented increased levels of IL-6 and TNFα in DLB and AD patients to HCs. This overproduction can be due to epigenetic mechanisms regarding the hypomethylation of DNA promoters.

Prognostic Power? Do the Plasma Biomarkers, Neurofilament Light and Phospho-Tau 181, Improve Prediction of Progression to Alzheimer's Disease Using a Machine Learning Approach in the ADNI Cohort?

With the advent of therapeutics with potential to slow Alzheimer's disease progression the necessity of understanding the diagnostic value of plasma biomarkers is critical, not only for understanding the etiology and progression of Alzheimer's disease, but also for access and response to potentially disease modifying therapeutic agents. Multiple studies are currently assessing the sensitivity and specificity of plasma biomarkers in large cohorts such as the Alzheimer's Disease Neuroimaging Initiative. This study uses machine learning to predict the progression from mild cognitive impairment using plasma biomarkers in conjunction with well-established cerebrospinal fluid and imaging biomarkers of disease progression.

Relationship between Plasma Lipid Profile and Cognitive Status in Early Alzheimer Disease.

Alzheimer disease (AD) is a heterogeneous and complex disease in which different pathophysiological mechanisms are involved. This heterogenicity can be reflected in different atrophy patterns or clinical manifestations. Regarding biochemical pathways involved in early AD, lipid metabolism plays an important role; therefore, lipid levels have been evaluated as potential AD diagnosis biomarkers, and their levels could be related to different AD clinical manifestations. Therefore, the aim of this work is to study AD lipid profiles from early AD patients and evaluate their clinical significance. For this purpose, untargeted plasma lipidomic analysis was carried out in early AD patients (n = 31) diagnosed with cerebrospinal fluid (CSF) biomarkers. Cluster analysis was carried out to define early AD subgroups according to the lipid levels. Then, the clinical significance of each lipid profile subgroup was studied, analyzing differences for other variables (cognitive status, CSF biomarkers, medication, comorbidities, age, and gender). The cluster analysis revealed two different groups of AD patients. Cluster 1 showed higher levels of plasma lipids and better cognitive status than Cluster 2. However, no differences were found for the other variables (age, gender, medication, comorbidities, cholesterol, and triglycerides levels) between both groups. Plasma lipid levels could differentiate two early AD subgroups, which showed different cognitive statuses. However, further research with a large cohort and longitudinal study evaluating the clinical evolution of these patients is required. In general, it would involve a relevant advance in the knowledge of AD pathological mechanisms, potential treatments, and precision medicine.

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.

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.

Plasma Leptin and Alzheimer Protein Pathologies Among Older Adults.

Importance: Many epidemiologic studies have suggested that low levels of plasma leptin, a major adipokine, are associated with increased risk of Alzheimer disease (AD) dementia and cognitive decline. Nevertheless, the mechanistic pathway linking plasma leptin and AD-related cognitive decline is not yet fully understood. Objective: To examine the association of plasma leptin levels with in vivo AD pathologies, including amyloid-beta (Aß) and tau deposition, through both cross-sectional and longitudinal approaches among cognitively unimpaired older adults. Design, Setting, and Participants: This was a longitudinal cohort study from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer Disease. Data were collected from January 1, 2014, to December 31, 2020, and data were analyzed from July 11 to September 6, 2022. The study included a total of 208 cognitively unimpaired participants who underwent baseline positron emission tomography (PET) scans for brain Aß deposition. For longitudinal analyses, 192 participants who completed both baseline and 2-year follow-up PET scans for brain Aß deposition were included. Exposure: Plasma leptin levels as assessed by enzyme-linked immunosorbent assay. Main Outcomes and Measures: Baseline levels and longitudinal changes of global Aß and AD-signature region tau deposition measured by PET scans. Results: Among the 208 participants, the mean (SD) age was 66.0 (11.3) years, 114 were women (54.8%), and 37 were apolipoprotein E ε4 carriers (17.8%). Lower plasma leptin levels had a significant cross-sectional association with greater brain Aß deposition (ß = -0.04; 95% CI, -0.09 to 0.00; P = .046), while there was no significant association between plasma leptin levels and tau deposition (ß = -0.02; 95% CI, -0.05 to 0.02; P = .41). In contrast, longitudinal analyses revealed that there was a significant association between lower baseline leptin levels and greater increase of tau deposition over 2 years (ß = -0.06; 95% CI, -0.11 to -0.01; P = .03), whereas plasma leptin levels did not have a significant association with longitudinal change of Aß deposition (ß = 0.006; 95% CI, 0.00-0.02; P = .27). Conclusions and Relevance: The present findings suggest that plasma leptin may be protective for the development or progression of AD pathology, including both Aß and tau deposition.

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.

Altering plasma lipids and liver enzyme activities via hippocampal injections of hen Lysozyme amyloid aggregates in an Alzheimer's disease mouse model: Insights into the therapeutic role of Bis (Indolyl) phenylmethane.

Alzheimer's disease (AD) is a prevalent form of dementia in the elderly. There is currently no effective treatment available for this disease. Diagnosis of AD typically relies on clinical manifestations and specific biomarkers. The present study investigated the impact of inducing Alzheimer's disease (AD) in mice through the injection of lysozyme amyloids formed in the presence or absence of Bis (Indolyl) phenylmethane (BIPM) on alterations in plasma lipid profiles and liver enzyme activities. 24 adult Wistar rats were divided into control, Scopolamine, Lysozyme, BIPM groups and the blood samples were obtained from the groups for biochemical analysis. The findings of the study revealed significant changes in the plasma lipid profiles and liver enzyme markers of the Lysozyme group compared to the control group. The Lysozyme group exhibited elevated triglycerides (n = 6, P < 0.02) and LDL levels (n = 6, P < 0.02), reduced HDL (n = 6, P < 0.05) and cholesterol levels (n = 6, P < 0.02), and altered serum glutamic oxaloacetic transaminase (SGOT) level (n = 6, P < 0.05) compared to controls. While the level of serum glutamic pyruvic transaminase (SGPT) did not change significantly compared to the control. BIPM groups showed no significant changes in lipid or enzyme levels compared to controls. Overall, our research has shown that BIPM has the ability to modify the structure of HEWL aggregates, thereby improving the detrimental effects associated with AD caused by these aggregates. Analyzing lipid profiles and liver enzyme markers presents a promising avenue for targeted therapeutic approaches. These alterations observed in the plasma may potentially serve as candidate biomarkers for diagnosing this disease.