Plasma biomarkers and genetics in the diagnosis and prediction of Alzheimer's disease

Liver as a new target organ in Alzheimer's disease: insight from cholesterol metabolism and its role in amyloid-beta clearance

Alzheimer's disease, the primary cause of dementia, is characterized by neuropathologies, such as amyloid plaques, synaptic and neuronal degeneration, and neurofibrillary tangles. Although amyloid plaques are the primary characteristic of Alzheimer's disease in the central nervous system and peripheral organs, targeting amyloid-beta clearance in the central nervous system has shown limited clinical efficacy in Alzheimer's disease treatment. Metabolic abnormalities are commonly observed in patients with Alzheimer's disease. The liver is the primary peripheral organ involved in amyloid-beta metabolism, playing a crucial role in the pathophysiology of Alzheimer's disease. Notably, impaired cholesterol metabolism in the liver may exacerbate the development of Alzheimer's disease. In this review, we explore the underlying causes of Alzheimer's disease and elucidate the role of the liver in amyloid-beta clearance and cholesterol metabolism. Furthermore, we propose that restoring normal cholesterol metabolism in the liver could represent a promising therapeutic strategy for addressing Alzheimer's disease.

CSF and blood glial fibrillary acidic protein for the diagnosis of Alzheimer's disease: A systematic review and meta-analysis

Recently included in the 2024 new revised diagnostic criteria of Alzheimer's disease (AD), glial fibrillary acidic protein (GFAP) has garnered significant attention. A systematic review and meta-analysis were performed to comprehensively evaluate the diagnostic, differential diagnostic, and prospective diagnostic performance of GFAP in cerebrospinal fluid (CSF) and blood for AD continuum. A literature search using common electronic databases, important websites and historical search way was performed from inception to the beginning of March 2023. The inclusion criteria was studies evaluating the diagnostic accuracy of GFAP in CSF and/or blood for the AD continuum patients, utilizing PET scans, CSF biomarkers and/or clinical criteria. The systematic review and meta-analysis were conducted referring to the Cochrane Handbook. In total, 34 articles were eventually included in the meta-analysis, 29 of which were published within the past three years. Blood GFAP exhibited good diagnostic accuracy across various AD continuum patients, and the summary area under curve for distinguishing PET positive and negative individuals, CSF biomarkers defined positive and negative individuals, clinically diagnosed AD and cognitive unimpaired controls, AD and/or mild cognitive impairment and other neurological diseases, and prospective cases and controls was 0.85[0.81-0.88], 0.77[0.73-0.81], 0.92[0.90-0.94], 0.80[0.77-0.84], and 0.79[0.75-0.82], respectively. Only several studies were recognized to evaluate the diagnostic accuracy of CSF GFAP, which was not as good as that of blood GFAP (paired mixed data: AUC = 0.86 vs. AUC = 0.77), but its accuracy remarkably increased to AUC = 0.91 when combined with other factors like sex, age, and ApoE genotype. In summary, GFAP, particularly in blood, shown good diagnostic, differential diagnostic, and prospective diagnostic accuracy for AD continuum patients, with improved accuracy when used alongside other basic indexes.

Evaluation of cerebrospinal fluid (CSF) and interstitial fluid (ISF) mouse proteomes for the validation and description of Alzheimer's disease biomarkers

BACKGROUND

Mass spectrometry (MS)-based cerebrospinal fluid (CSF) proteomics is an important method for discovering biomarkers of neurodegenerative diseases. CSF serves as a reservoir for interstitial fluid (ISF), and extensive communication between the two fluid compartments helps to remove waste products from the brain.

NEW METHOD

We performed proteomic analyses of both CSF and ISF fluid compartments using intracerebral microdialysis to validate and detect novel biomarkers of Alzheimer's disease (AD) in APPtg and C57Bl/6J control mice.

RESULTS

We identified up to 625 proteins in ISF and 4483 proteins in CSF samples. By comparing the biofluid profiles of APPtg and C57Bl/6J mice, we detected 37 and 108 significantly up- and downregulated candidates, respectively. In ISF, 7 highly regulated proteins, such as Gfap, Aldh1l1, Gstm1, and Txn, have already been implicated in AD progression, whereas in CSF, 9 out of 14 highly regulated proteins, such as Apba2, Syt12, Pgs1 and Vsnl1, have also been validated to be involved in AD pathogenesis. In addition, we also detected new interesting regulated proteins related to the control of synapses and neurotransmission (Kcna2, Cacng3, and Clcn6) whose roles as AD biomarkers should be further investigated.

COMPARISON WITH EXISTING METHODS

This newly established combined protocol provides better insight into the mutual communication between ISF and CSF as an analysis of tissue or CSF compartments alone.

CONCLUSIONS

The use of multiple fluid compartments, ISF and CSF, for the detection of their biological communication enables better detection of new promising AD biomarkers.

Nucleic Acids-Based Biomarkers for Alzheimer's Disease Diagnosis and Novel Molecules to Treat the Disease

Alzheimer's disease (AD) represents the most common form of dementia and affects million people worldwide, with a high social burden and considerable economic costs. AD diagnosis benefits from a well-established panel of laboratory tests that allow ruling-in patients, along with FDG and amyloid PET imaging tools. The main laboratory tests used to identify AD patients are Aβ40, Aβ42, the Aβ42/Aβ40 ratio, phosphorylated Tau 181 (pTau181) and total Tau (tTau). Although they are measured preferentially in the cerebrospinal fluid (CSF), some evidence about the possibility for blood-based determination to enter clinical practice is growing up. Unfortunately, CSF biomarkers for AD and, even more, the blood-based ones, present a few flaws, and twenty years of research in this field did not overcome these pitfalls. The tale even worsens when the issue of treating AD is addressed due to the lack of effective strategies despite the many decades of attempts by pharmaceutic industries and scientists. Amyloid-based drugs failed to stop the disease, and no neuroinflammation-based drugs have been demonstrated to work so far. Hence, only symptomatic therapy is available, with no disease-modifying treatment on hand. Such a desolate situation fully justifies the active search for novel biomarkers to be used as reliable tests for AD diagnosis and molecular targets for treating patients. Recently, a novel group of molecules has been identified to be used for AD diagnosis and follow-up, the nuclei acid-based biomarkers. Nucleic acid-based biomarkers are a composite group of extracellular molecules consisting of DNA and RNA alone or in combination with other molecules, including proteins. This review article reports the main findings from the studies carried out on these biomarkers during AD, and highlights their advantages and limitations.

Cardiometabolic multimorbidity, peripheral biomarkers, and dementia in rural older adults: The MIND-China study

INTRODUCTION

Evidence has emerged that cardiometabolic multimorbidity (CMM) is associated with dementia, but the underlying mechanisms are poorly understood.

METHODS

This population-based study included 5704 older adults. Of these, data were available in 1439 persons for plasma amyloid-β (Aβ), total tau, and neurofilament light chain (NfL) and in 1809 persons for serum cytokines. We defined CMM following two common definitions used in previous studies. Data were analyzed using general linear, logistic, and mediation models.

RESULTS

The presence of CMM was significantly associated with an increased likelihood of dementia, Alzheimer's disease (AD), and vascular dementia (VaD) (p < 0.05). CMM was significantly associated with increased plasma Aβ40, Aβ42, and NfL, whereas CMM that included visceral obesity was associated with increased serum cytokines. The mediation analysis suggested that plasma NfL significantly mediated the association of CMM with AD.

DISCUSSION

CMM is associated with dementia, AD, and VaD in older adults. The neurodegenerative pathway is involved in the association of CMM with AD.

HIGHLIGHTS

The presence of CMM was associated with increased likelihoods of dementia, AD, and VaD in older adults. CMM was associated with increased AD-related plasma biomarkers and serum inflammatory cytokines. Neurodegenerative pathway was partly involved in the association of CMM with AD.

Longitudinal plasma phosphorylated-tau217 and other related biomarkers in a non-demented Alzheimer's risk-enhanced sample

INTRODUCTION

Understanding longitudinal change in key plasma biomarkers will aid in detecting presymptomatic Alzheimer's disease (AD).

METHODS

Serial plasma samples from 424 Wisconsin Registry for Alzheimer's Prevention participants were analyzed for phosphorylated-tau217 (p-tau217; ALZpath) and other AD biomarkers, to study longitudinal trajectories in relation to disease, health factors, and cognitive decline. Of the participants, 18.6% with known amyloid status were amyloid positive (A+); 97.2% were cognitively unimpaired (CU).

RESULTS

In the CU, amyloid-negative (A-) subset, plasma p-tau217 levels increased modestly with age but were unaffected by body mass index and kidney function. In the whole sample, average p-tau217 change rates were higher in those who were A+ (e.g., simple slopes(se) for A+ and A- at age 60 were 0.232(0.028) and 0.038(0.013))). High baseline p-tau217 levels predicted faster preclinical cognitive decline.

DISCUSSION

p-tau217 stands out among markers for its strong association with disease and cognitive decline, indicating its potential for early AD detection and monitoring progression.

HIGHLIGHTS

Phosphorylated-tau217 (p-tau217) trajectories were significantly different in people who were known to be amyloid positive. Subtle age-related trajectories were seen for all the plasma markers in amyloid-negative cognitively unimpaired. Kidney function and body mass index were not associated with plasma p-tau217 trajectories. Higher plasma p-tau217 was associated with faster preclinical cognitive decline.

Incidence and prevalence of Alzheimer's disease in China: a systematic review and meta-analysis

As China faces demographic shifts and socioeconomic changes, the burden of Alzheimer's disease (AD) and associated cognitive impairments is increasing dramatically, with significant implications for public health and the economy. This systematic review and meta-analysis aims to provide a comprehensive assessment of the prevalence and incidence of AD across China. Drawing from an extensive search of international and Chinese databases up to August 27, 2023, including PubMed, Embase, and the Cochrane Library, we synthesized data from 105 studies. Our analysis reveals a combined prevalence of AD of 3.48% within a sample of 626,276 elderly individuals and an incidence rate of 7.90 per 1000 person-years. Subgroup and meta-regression analyses highlight age and gender as pivotal factors influencing these epidemiological patterns. Notably, significant heterogeneity exists due to variations in diagnostic criteria and study quality, impacting the comparability of findings. This meta-analysis underscores the need for continued research into demographic and modifiable risk factors influencing AD, while emphasizing standardized reporting practices to address these limitations and improve the understanding of AD's challenge in China.

Apolipoprotein E in Alzheimer's disease trajectories and the next-generation clinical care pathway

Alzheimer's disease (AD) is a complex, progressive primary neurodegenerative disease. Since pivotal genetic studies in 1993, the ε4 allele of the apolipoprotein E gene (APOE ε4) has remained the strongest single genome-wide associated risk variant in AD. Scientific advances in APOE biology, AD pathophysiology and ApoE-targeted therapies have brought APOE to the forefront of research, with potential translation into routine AD clinical care. This contemporary Review will merge APOE research with the emerging AD clinical care pathway and discuss APOE genetic risk as a conduit to genomic-based precision medicine in AD, including ApoE's influence in the ATX(N) biomarker framework of AD. We summarize the evidence for APOE as an important modifier of AD clinical-biological trajectories. We then illustrate the utility of APOE testing and the future of ApoE-targeted therapies in the next-generation AD clinical-diagnostic pathway. With the emergence of new AD therapies, understanding how APOE modulates AD pathophysiology will become critical for personalized AD patient care.

Brain Injury Biomarkers and Applications in Neurological Diseases

ABSTRACT

Neurological diseases are a major health concern, and brain injury is a typical pathological process in various neurological disorders. Different biomarkers in the blood or the cerebrospinal fluid are associated with specific physiological and pathological processes. They are vital in identifying, diagnosing, and treating brain injuries. In this review, we described biomarkers for neuronal cell body injury (neuron-specific enolase, ubiquitin C-terminal hydrolase-L1, αII-spectrin), axonal injury (neurofilament proteins, tau), astrocyte injury (S100β, glial fibrillary acidic protein), demyelination (myelin basic protein), autoantibodies, and other emerging biomarkers (extracellular vesicles, microRNAs). We aimed to summarize the applications of these biomarkers and their related interests and limits in the diagnosis and prognosis for neurological diseases, including traumatic brain injury, status epilepticus, stroke, Alzheimer's disease, and infection. In addition, a reasonable outlook for brain injury biomarkers as ideal detection tools for neurological diseases is presented.

Considerations for biomarker strategies in clinical trials investigating tau-targeting therapeutics for Alzheimer's disease

The use of biomarker-led clinical trial designs has been transformative for investigating amyloid-targeting therapies for Alzheimer's disease (AD). The designs have ensured the correct selection of patients on these trials, supported target engagement and have been used to support claims of disease modification and clinical efficacy. Ultimately, this has recently led to approval of disease-modifying, amyloid-targeting therapies for AD; something that should be noted for clinical trials investigating tau-targeting therapies for AD. There is a clear overlap of the purpose of biomarker use at each stage of clinical development between amyloid-targeting and tau-targeting clinical trials. However, there are differences within the potential context of use and interpretation for some biomarkers in particular measurements of amyloid and utility of soluble, phosphorylated tau biomarkers. Given the complexities of tau in health and disease, it is paramount that therapies target disease-relevant tau and, in parallel, appropriate assays of target engagement are developed. Tau positron emission tomography, fluid biomarkers reflecting tau pathology and downstream measures of neurodegeneration will be important both for participant recruitment and for monitoring disease-modification in tau-targeting clinical trials. Bespoke design of biomarker strategies and interpretations for different modalities and tau-based targets should also be considered.

Novel Biomarkers for Alzheimer's Disease: Plasma Neurofilament Light and Cerebrospinal Fluid

Neurodegenerative disorders such as Alzheimer's disease (AD) represent an increasingly significant public health concern. As clinical diagnosis faces challenges, biomarkers are becoming increasingly important in research, trials, and patient assessments. While biomarkers like amyloid-β peptide, tau proteins, CSF levels (Aβ, tau, and p-tau), and neuroimaging techniques are commonly used in AD diagnosis, they are often limited and invasive in monitoring and diagnosis. For this reason, blood-based biomarkers are the optimal choice for detecting neurodegeneration in brain diseases due to their noninvasiveness, affordability, reliability, and consistency. This literature review focuses on plasma neurofilament light (NfL) and CSF NfL as blood-based biomarkers used in recent AD diagnosis. The findings revealed that the core CSF biomarkers of neurodegeneration (T-tau, P-tau, and Aβ42), CSF NFL, and plasma T-tau were strongly associated with Alzheimer's disease, and the core biomarkers were strongly associated with mild cognitive impairment due to Alzheimer's disease. Elevated levels of plasma and cerebrospinal fluid NfL were linked to decreased [18F]FDG uptake in corresponding brain areas. In participants with Aβ positivity (Aβ+), NfL correlated with reduced metabolism in regions susceptible to Alzheimer's disease. In addition, CSF NfL levels correlate with brain atrophy and predict cognitive changes, while plasma total tau does not. Plasma P-tau, especially in combination with Aβ42/Aβ40, is promising for symptomatic AD stages. Though not AD-exclusive, blood NfL holds promise for neurodegeneration detection and assessing treatment efficacy. Given the consistent levels of T-tau, P-tau, Aβ42, and NFL in CSF, their incorporation into both clinical practice and research is highly recommended.

Exploring intricate connectivity patterns for cognitive functioning and neurological disorders: incorporating frequency-domain NC method into fMRI analysis

This study extends the application of the frequency-domain new causality method to functional magnetic resonance imaging analysis. Strong causality, weak causality, balanced causality, cyclic causality, and transitivity causality were constructed to simulate varying degrees of causal associations among multivariate functional-magnetic-resonance-imaging blood-oxygen-level-dependent signals. Data from 1,252 groups of individuals with different degrees of cognitive impairment were collected. The frequency-domain new causality method was employed to construct directed efficient connectivity networks of the brain, analyze the statistical characteristics of topological variations in brain regions related to cognitive impairment, and utilize these characteristics as features for training a deep learning model. The results demonstrated that the frequency-domain new causality method accurately detected causal associations among simulated signals of different degrees. The deep learning tests also confirmed the superior performance of new causality, surpassing the other three methods in terms of accuracy, precision, and recall rates. Furthermore, consistent significant differences were observed in the brain efficiency networks, where several subregions defined by the multimodal parcellation method of Human Connectome Project simultaneously appeared in the topological statistical results of different patient groups. This suggests a significant association between these fine-grained cortical subregions, driven by multimodal data segmentation, and human cognitive function, making them potential biomarkers for further analysis of Alzheimer's disease.

Plasma glial fibrillary acidic protein in the visual and language variants of Alzheimer's disease

INTRODUCTION

Glial fibrillary acidic protein (GFAP) in plasma is a proxy for astrocytic activity and is elevated in amyloid-β (Aβ)-positive individuals, making GFAP a potential blood-based biomarker for Alzheimer's disease (AD).

METHODS

We assessed plasma GFAP in 72 Aβ-positive participants diagnosed with the visual or language variant of AD who underwent Aβ- and tau-PET. Fifty-nine participants had follow-up imaging. Linear regression was applied on GFAP and imaging quantities.

RESULTS

GFAP did not correlate with Aβ- or tau-PET cross-sectionally. There was a limited positive correlation between GFAP and rates of tau accumulation, particularly in the language variant of AD, although associations were weaker after removing one outlier patient with the highest GFAP level.

DISCUSSION

Among Aβ-positive AD participants with atypical presentations, plasma GFAP did not correlate with levels of AD pathology on PET, suggesting that the associations between GFAP and AD pathology might plateau during the advanced phase of the disease.

Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection

Alzheimer's disease (AD) is a neurodegenerative illness that impairs cognition, function, and behavior by causing irreversible damage to multiple brain areas, including the hippocampus. The suffering of the patients and their family members will be lessened with an early diagnosis of AD. The automatic diagnosis technique is widely required due to the shortage of medical experts and eases the burden of medical staff. The automatic artificial intelligence (AI)-based computerized method can help experts achieve better diagnosis accuracy and precision rates. This study proposes a new automated framework for AD stage prediction based on the ResNet-Self architecture and Fuzzy Entropy-controlled Path-Finding Algorithm (FEcPFA). A data augmentation technique has been utilized to resolve the dataset imbalance issue. In the next step, we proposed a new deep-learning model based on the self-attention module. A ResNet-50 architecture is modified and connected with a self-attention block for important information extraction. The hyperparameters were optimized using Bayesian optimization (BO) and then utilized to train the model, which was subsequently employed for feature extraction. The self-attention extracted features were optimized using the proposed FEcPFA. The best features were selected using FEcPFA and passed to the machine learning classifiers for the final classification. The experimental process utilized a publicly available MRI dataset and achieved an improved accuracy of 99.9%. The results were compared with state-of-the-art (SOTA) techniques, demonstrating the improvement of the proposed framework in terms of accuracy and time efficiency.

Innovative Multivariable Model Combining MRI Radiomics and Plasma Indexes Predicts Alzheimer's Disease Conversion: Evidence from a 2-Cohort Longitudinal Study

To explore the complementary relationship between magnetic resonance imaging (MRI) radiomic and plasma biomarkers in the early diagnosis and conversion prediction of Alzheimer's disease (AD), our study aims to develop an innovative multivariable prediction model that integrates those two for predicting conversion results in AD. This longitudinal multicentric cohort study included 2 independent cohorts: the Sino Longitudinal Study on Cognitive Decline (SILCODE) project and the Alzheimer Disease Neuroimaging Initiative (ADNI). We collected comprehensive assessments, MRI, plasma samples, and amyloid positron emission tomography data. A multivariable logistic regression analysis was applied to combine plasma and MRI radiomics biomarkers and generate a new composite indicator. The optimal model's performance and generalizability were assessed across populations in 2 cross-racial cohorts. A total of 897 subjects were included, including 635 from the SILCODE cohort (mean [SD] age, 64.93 [6.78] years; 343 [63%] female) and 262 from the ADNI cohort (mean [SD] age, 73.96 [7.06] years; 140 [53%] female). The area under the receiver operating characteristic curve of the optimal model was 0.9414 and 0.8979 in the training and validation dataset, respectively. A calibration analysis displayed excellent consistency between the prognosis and actual observation. The findings of the present study provide a valuable diagnostic tool for identifying at-risk individuals for AD and highlight the pivotal role of the radiomic biomarker. Importantly, built upon data-driven analyses commonly seen in previous radiomics studies, our research delves into AD pathology to further elucidate the underlying reasons behind the robust predictive performance of the MRI radiomic predictor.

A critical appraisal of blood-based biomarkers for Alzheimer's disease

Biomarkers that predict the clinical onset of Alzheimer's disease (AD) enable the identification of individuals in the early, preclinical stages of the disease. Detecting AD at this point may allow for more effective therapeutic interventions and optimized enrollment for clinical trials of novel drugs. The current biological diagnosis of AD is based on the AT(N) classification system with the measurement of brain deposition of amyloid-β (Aβ) ("A"), tau pathology ("T"), and neurodegeneration ("N"). Diagnostic cut-offs for Aβ1-42, the Aβ1-42/Aβ1-40 ratio, tau and hyperphosphorylated-tau concentrations in cerebrospinal fluid have been defined and may support AD clinical diagnosis. Blood-based biomarkers of the AT(N) categories have been described in the AD continuum. Cross-sectional and longitudinal studies have shown that the combination of blood biomarkers tracking neuroaxonal injury (neurofilament light chain) and neuroinflammatory pathways (glial fibrillary acidic protein) enhance sensitivity and specificity of AD clinical diagnosis and improve the prediction of AD onset. However, no international accepted cut-offs have been identified for these blood biomarkers. A kit for blood Aβ1-42/Aβ1-40 is commercially available in the U.S.; however, it does not provide a diagnosis, but simply estimates the risk of developing AD. Although blood-based AD biomarkers have a great potential in the diagnostic work-up of AD, they are not ready for the routine clinical use.

The Bio-Hermes Study: Biomarker database developed to investigate blood-based and digital biomarkers in community-based, diverse populations clinically screened for Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) trial participants are often screened for eligibility by brain amyloid positron emission tomography/cerebrospinal fluid (PET/CSF), which is inefficient as many are not amyloid positive. Use of blood-based biomarkers may reduce screen failures.

METHODS

We recruited 755 non-Hispanic White, 115 Hispanic, 112 non-Hispanic Black, and 19 other minority participants across groups of cognitively normal (n = 417), mild cognitive impairment (n = 312), or mild AD (n = 272) participants. Plasma amyloid beta (Aβ)40, Aβ42, Aβ42/Aβ40, total tau, phosphorylated tau (p-tau)181, and p-tau217 were measured; amyloid PET/CSF (n = 956) determined amyloid positivity. Clinical, blood biomarker, and ethnicity/race differences associated with amyloid status were evaluated.

RESULTS

Greater impairment, older age, and carrying an apolipoprotein E (apoE) ε4 allele were associated with greater amyloid burden. Areas under the receiver operating characteristic curve for amyloid status of plasma Aβ42/Aβ40, p-tau181, and p-tau217 with amyloid positivity were ≥ 0.7117 for all ethnoracial groups (p-tau217, ≥0.8128). Age and apoE ε4 adjustments and imputation of biomarker values outside limit of quantitation provided small improvement in predictive power.

DISCUSSION

Blood-based biomarkers are highly associated with amyloid PET/CSF results in diverse populations enrolled at clinical trial sites.

HIGHLIGHTS

Amyloid beta (Aβ)42/Aβ40, phosphorylated tau (p-tau)181, and p-tau 217 blood-based biomarkers predicted brain amyloid positivity. P-tau 217 was the strongest predictor of brain amyloid positivity. Biomarkers from diverse ethnic, racial, and clinical cohorts predicted brain amyloid positivity. Community-based populations have similar Alzheimer's disease (AD) biomarker levels as other populations. A prescreen process with blood-based assays may reduce the number of AD trial screen failures.

Serum NFL and tau, but not serum UCHL-1 and GFAP or CSF SNAP-25, NPTX2, or sTREM2, correlate with delirium in a 3-year retrospective analysis

Delirium represents a common terminal pathway of heterogeneous neurological conditions characterized by disturbances in consciousness and attention. Contemporary theories highlight the acute impairment of synaptic function and network connectivity, driven by neuroinflammation, oxidative stress, and neurotransmitter imbalances. However, established biomarkers are still missing. Innovative diagnostic techniques, such as single-molecule array analysis, enable the detection of biomarkers in blood at picomolar concentrations. This approach paves the way for deeper insights into delirium and potentially therapeutic targets for tailored medical treatments. In a retrospective 3-year study, we investigated seven biomarkers indicative of neuroaxonal damage [neurofilament light chain (NFL), ubiquitin carboxyl-terminal hydrolase (UCHL-1), and tau protein], microglial activation [glial fibrillary acidic protein (GFAP) and soluble triggering receptor expressed on myeloid cells 2 (sTREM2)], and synaptic dysfunction [synaptosomal-associated protein 25 (SNAP-25) and neuronal pentraxin 2 (NPTX2)]. The analysis of 71 patients with delirium, Alzheimer's disease (AD), and non-AD controls revealed that serum NFL levels are higher in delirium cases compared to both AD and non-AD. This suggests that elevated NFL levels in delirium are not exclusively the result of dementia-related damage. Serum tau levels were also elevated in delirium cases compared to controls. Conversely, cerebrospinal fluid (CSF) SNAP-25 showed higher levels in AD patients compared to controls only. These findings add to the increasing body of evidence suggesting that serum NFL could be a valuable biomarker of neuroaxonal damage in delirium research. Although SNAP-25 and NPTX2 did not exhibit significant differences in delirium, the exploration of synaptic biomarkers remains promising for enhancing our understanding of this condition.

Predicting clinical progression trajectories of early Alzheimer's disease patients

BACKGROUND

Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring.

METHODS

Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures.

RESULTS

The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%.

DISCUSSION

Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.

A hybrid multimodal machine learning model for Detecting Alzheimer's disease

Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has limitations. Multimodal fusion of complementary biomarkers may improve diagnostic performance. This study proposes a multimodal machine learning framework integrating magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF) assays for enhanced AD characterization. The model incorporates a hybrid algorithm combining enhanced Harris Hawks Optimization (HHO) algorithm referred to as ILHHO, with Kernel Extreme Learning Machine (KELM) classifier for simultaneous feature selection and classification. ILHHO enhances HHO's search efficiency by integrating iterative mapping (IM) to improve population diversity and local escaping operator (LEO) to balance exploration-exploitation. Comparative analysis with other improved HHO algorithms, classic meta-heuristic algorithms (MHAs), and state-of-the-art MHAs on IEEE CEC2014 benchmark functions indicates that ILHHO achieves superior optimization performance compared to other comparative algorithms. The synergistic ILHHO-KELM model is evaluated on 202 AD Neuroimaging Initiative (ADNI) subjects. Results demonstrate superior multimodal classification accuracy over single modalities, validating the importance of fusing heterogeneous biomarkers. MRI + PET + CSF achieves 99.2 % accuracy for AD vs. normal control (NC), outperforming conventional and proposed methods. Discriminative feature analysis provides further insights into differential AD-related neurodegeneration patterns detected by MRI and PET. The differential PET and MRI features demonstrate how the two modalities provide complementary biomarkers. The neuroanatomical relevance of selected features supports ILHHO-KELM's potential for extracting sensitive AD imaging signatures. Overall, the study showcases the advantages of capitalizing on complementary multimodal data through advanced feature learning techniques for improving AD diagnosis.

14-Week exercise training modifies the DNA methylation levels at gene sites in non-Alzheimer's disease women aged 50 to 70 years

Exercise training emerges as a key strategy in lifestyle modification, capable of reducing the risk of developing Alzheimer's disease (AD) due to risk factors such as age, family history, genetics and low level of education associated with AD. We aim to analyze the effect of a 14-week combined exercise training (CT) on the methylation of genes associated with AD in non-alzheimer's disease women. CT sessions lasted 60 min, occurring three times a week for 14 weeks. Forty non-Alzheimer's disease women aged 50 to 70 years (60.7 ± 4.1 years) with a mean height of 1.6 ± 0.1 m, mean weight of 73.12 ± 9.0 kg and a mean body mass index of 29.69 ± 3.5 kg/m2, underwent two physical assessments: pre and post the 14 weeks. DNA methylation assays utilized the EPIC Infinium Methylation BeadChip from Illumina. We observed that 14 weeks of CT led to reductions in systolic (p = 0.001) and diastolic (p = 0.017) blood pressure and improved motor skills post-intervention. Among 25 genes linked to AD, CT induced differentially methylated sites in 12 genes, predominantly showing hypomethylated sites (negative β values). Interestingly, despite hypomethylated sites, some genes exhibited hypermethylated sites (positive β values), such as ABCA7, BDNF, and WWOX. A 14-week CT regimen was adequate to induce differential methylation in 12 CE-related genes in healthy older women, alongside improvements in motor skills and blood pressure. In conclusion, this study suggest that combined training can be a strategy to improve physical fitness in older individuals, especially able to induce methylation alterations in genes sites related to development of AD. It is important to highlight that training should act as protective factor in older adults.

HDAC9-mediated calmodulin deacetylation induces memory impairment in Alzheimer's disease

AIMS

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive dysfunction and memory impairment. AD pathology involves protein acetylation. Previous studies have mainly focused on histone acetylation in AD, however, the roles of nonhistone acetylation in AD are less explored.

METHODS

The protein acetylation and expression levels were detected by western blotting and co-immunoprecipitation. The stoichiometry of acetylation was measured by home-made and site-specific antibodies against acetylated-CaM (Ac-CaM) at K22, K95, and K116. Hippocampus-dependent learning and memory were evaluated by using the Morris water maze, novel object recognition, and contextual fear conditioning tests.

RESULTS

We showed that calmodulin (CaM) acetylation is reduced in plasma of AD patients and mice. CaM acetylation and its target Ca2+ /CaM-dependent kinase II α (CaMKIIα) activity were severely impaired in AD mouse brain. The stoichiometry showed that Ac-K22, K95-CaM acetylation were decreased in AD patients and mice. Moreover, we screened and identified that lysine deacetylase 9 (HDAC9) was the main deacetylase for CaM. In addition, HDAC9 inhibition increased CaM acetylation and CaMKIIα activity, and hippocampus-dependent memory in AD mice.

CONCLUSIONS

HDAC9-mediated CaM deacetylation induces memory impairment in AD, HDAC9, or CaM acetylation may become potential therapeutic targets for AD.

A multiclass extreme gradient boosting model for evaluation of transcriptomic biomarkers in Alzheimer's disease prediction

BACKGROUND

Patients with young-onset Alzheimer's disease (AD) (before the age of 50 years old) often lack obvious imaging changes and amyloid protein deposition, which can lead to misdiagnosis with other cognitive impairments. Considering the association between immunological dysfunction and progression of neurodegenerative disease, recent research has focused on identifying blood transcriptomic signatures for precise prediction of AD.

METHODS

In this study, we extracted blood biomarkers from large-scale transcriptomics to construct multiclass eXtreme Gradient Boosting models (XGBoost), and evaluated their performance in distinguishing AD from cognitive normal (CN) and mild cognitive impairment (MCI).

RESULTS

Independent testing with external dataset revealed that the combination of blood transcriptomic signatures achieved an area under the receiver operating characteristic curve (AUC of ROC) of 0.81 for multiclass classification (sensitivity = 0.81; specificity = 0.63), 0.83 for classification of AD vs. CN (sensitivity = 0.72; specificity = 0.73), and 0.85 for classification of AD vs. MCI (sensitivity = 0.77; specificity = 0.73). These candidate signatures were significantly enriched in 62 chromosome regions, such as Chr.19p12-19p13.3, Chr.1p22.1-1p31.1, and Chr.1q21.2-1p23.1 (adjusted p < 0.05), and significantly overrepresented by 26 transcription factors, including E2F2, FOXO3, and GATA1 (adjusted p < 0.05). Biological analysis of these signatures pointed to systemic dysregulation of immune responses, hematopoiesis, exocytosis, and neuronal support in neurodegenerative disease (adjusted p < 0.05).

CONCLUSIONS

Blood transcriptomic biomarkers hold great promise in clinical use for the accurate assessment and prediction of AD.

Protein Misfolding in Pregnancy: Current Insights, Potential Mechanisms, and Implications for the Pathogenesis of Preeclampsia

Protein misfolding disorders are a group of diseases characterized by supra-physiologic accumulation and aggregation of pathogenic proteoforms resulting from improper protein folding and/or insufficiency in clearance mechanisms. Although these processes have been historically linked to neurodegenerative disorders, such as Alzheimer's disease, evidence linking protein misfolding to other pathologies continues to emerge. Indeed, the deposition of toxic protein aggregates in the form of oligomers or large amyloid fibrils has been linked to type 2 diabetes, various types of cancer, and, in more recent years, to preeclampsia, a life-threatening pregnancy-specific disorder. While extensive physiological mechanisms are in place to maintain proteostasis, processes, such as aging, genetic factors, or environmental stress in the form of hypoxia, nutrient deprivation or xenobiotic exposures can induce failure in these systems. As such, pregnancy, a natural physical state that already places the maternal body under significant physiological stress, creates an environment with a lower threshold for aberrant aggregation. In this review, we set out to discuss current evidence of protein misfolding in pregnancy and potential mechanisms supporting a key role for this process in preeclampsia pathogenesis. Improving our understanding of this emerging pathophysiological process in preeclampsia can lead to vital discoveries that can be harnessed to create better diagnoses and treatment modalities for the disorder.

Peripheral blood amyloid-β involved in the pathogenesis of Alzheimer's disease via impacting on peripheral innate immune cells

A key pathological factor of Alzheimer's disease (AD), the most prevalent form of age-related dementia in the world, is excessive β-amyloid protein (Aβ) in extracellular aggregation in the brain. And in the peripheral blood, a large amount of Aβ is derived from platelets. So far, the causality between the levels of peripheral blood Aβ and its aggregation in the brain, particularly the role of the peripheral blood Aβ in the pathology of AD, is still unclear. And the relation between the peripheral blood Aβ and tau tangles of brain, another crucial pathologic factor contributing to the pathogenesis of AD, is also ambiguous. More recently, the anti-Aβ monoclonal antibodies are approved for treatment of AD patients through declining the peripheral blood Aβ mechanism of action to enhance plasma and central nervous system (CNS) Aβ clearance, leading to a decrease Aβ burden in brain and improving cognitive function, which clearly indicates that the levels of the peripheral blood Aβ impacted on the Aβ burden in brain and involved in the pathogenesis of AD. In addition, the role of peripheral innate immune cells in AD remains mostly unknown and the results obtained were controversial. In the present review, we summarize recent studies on the roles of peripheral blood Aβ and the peripheral innate immune cells in the pathogenesis of AD. Finally, based on the published data and our own work, we believe that peripheral blood Aβ plays an important role in the development and progression of AD by impacting on the peripheral innate immune cells.

Predicting amyloid-PET status in a memory clinic: The role of the novel antero-posterior index and visual rating scales

INTRODUCTION

Visual rating scales are increasingly utilized in clinical practice to assess atrophy in crucial brain regions among patients with cognitive disorders. However, their capacity to predict Alzheimer's disease (AD)-related pathology remains unexplored, particularly within a heterogeneous memory clinic population. This study aims to assess the accuracy of a novel visual rating assessment, the antero-posterior index (API) scale, in predicting amyloid-PET status. Furthermore, the study seeks to determine the optimal cohort-based cutoffs for the medial temporal atrophy (MTA) and parietal atrophy (PA) scales and to integrate the main visual rating scores into a predictive model.

METHODS

We conducted a retrospective analysis of brain MRI and high-resolution TC scans from 153 patients with cognitive disorders who had undergone amyloid-PET assessments due to suspected AD pathology in a real-world memory clinic setting.

RESULTS

The API scale (cutoff ≥1) exhibited the highest accuracy (AUC = 0.721) among the visual rating scales. The combination of the cohort-based MTA and PA threshold with the API yielded favorable accuracy (AUC = 0.787). Analyzing a cohort of MCI/Mild dementia patients below 75 years of age, the API scale and the predictive model improved their accuracy (AUC = 0.741 and 0.813, respectively), achieving excellent results in the early-onset population (AUC = 0.857 and 0.949, respectively).

CONCLUSION

Our study emphasizes the significance of visual rating scales in predicting amyloid-PET positivity within a real-world memory clinic. Implementing the novel API scale, alongside our cohort-based MTA and PA thresholds, has the potential to substantially enhance diagnostic accuracy.

Trans-ethnic genomic informed risk assessment for Alzheimer's disease: An International Hundred K+ Cohorts Consortium study

BACKGROUND

As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD).

METHODS

The GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure.

RESULTS

We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD.

CONCLUSIONS

As the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.

Cerebrospinal and Blood Biomarkers in Alzheimer's Disease: Did Mild Cognitive Impairment Definition Affect Their Clinical Usefulness?

Despite Alzheimer's Disease (AD) being known from the times of Alois Alzheimer, who lived more than one century ago, many aspects of the disease are still obscure, including the pathogenesis, the clinical spectrum definition, and the therapeutic approach. Well-established biomarkers for AD come from the histopathological hallmarks of the disease, which are Aβ and phosphorylated Tau protein aggregates. Consistently, cerebrospinal fluid (CSF) Amyloid β (Aβ) and phosphorylated Tau level measurements are currently used to detect AD presence. However, two central biases affect these biomarkers. Firstly, incomplete knowledge of the pathogenesis of diseases legitimates the search for novel molecules that, reasonably, could be expressed by neurons and microglia and could be detected in blood simpler and earlier than the classical markers and in a higher amount. Further, studies have been performed to evaluate whether CSF biomarkers can predict AD onset in Mild Cognitive Impairment (MCI) patients. However, the MCI definition has changed over time. Hence, the studies on MCI patients seem to be biased at the beginning due to the imprecise enrollment and heterogeneous composition of the miscellaneous MCI subgroup. Plasma biomarkers and novel candidate molecules, such as microglia biomarkers, have been tentatively investigated and could represent valuable targets for diagnosing and monitoring AD. Also, novel AD markers are urgently needed to identify molecular targets for treatment strategies. This review article summarizes the main CSF and blood AD biomarkers, underpins their advantages and flaws, and mentions novel molecules that can be used as potential biomarkers for AD.

Development of a diagnostic and risk prediction model for Alzheimer's disease through integration of single-cell and bulk transcriptomic analysis of glutamine metabolism

Background

In this study, we present a novel system for quantifying glutamine metabolism (GM) to enhance the effectiveness of Alzheimer's disease (AD) diagnosis and risk prediction.

Methods

Single-cell RNA sequencing (scRNA-seq) analysis was utilized to comprehensively assess the expression patterns of GM. The WGCNA algorithm was applied to investigate the most significant genes related to GM. Subsequently, three machine learning algorithms (Boruta, LASSO, and SVM-RFE) were employed to identify GM-associated characteristic genes and develop a risk model. Patients were divided into high- and low-risk groups based on this model. Moreover, we explored biological properties, distinct signaling pathways, and immunological characteristics of AD patients at different risk levels. Finally, in vitro and in vivo models of AD were constructed to validate the characteristics of the feature genes.

Results

Both scRNA-seq and bulk transcriptomic analyses revealed increased GM activity in AD patients, specifically in certain cell subsets (pDC, Tem/Effector helper T cells (LTB), and plasma cells). Cells with higher GM scores demonstrated more significant numbers and strengths of interactions with other cell types. The WGCNA algorithm identified 360 genes related to GM, and a risk score was constructed based on nine characteristic genes (ATP13A4, PIK3C2A, CD164, PHF1, CES2, PDGFB, LCOR, TMEM30A, and PLXNA1) identified through multiple machine learning algorithms displayed reliable diagnostic efficacy for AD onset. Nomograms, calibration curves, and decision curve analysis (DCA) based on these characteristic genes provided significant clinical benefits for AD patients. High-risk AD patients exhibited higher levels of immune-related functions and pathways, increased immune cell infiltration, and elevated expressions of immune modulators. RT-qPCR analysis revealed that the majority of the nine characteristic genes were differentially expressed in AD-induced rat neurons. Knocking down PHF1 could protect against neurite loss and alleviate cell injury in AD neurons. In vivo, down-regulation of PHF1 in AD models decreases GM metabolism levels and modulates the immunoinflammatory response in the brain.

Conclusion

This comprehensive identification of gene expression patterns contributes to a deeper understanding of the underlying pathological mechanisms driving AD pathogenesis. Furthermore, the risk model based on the nine-gene signature offers a promising theoretical foundation for developing individualized treatments for AD patients.

Clinical and biological relevance of glial fibrillary acidic protein in Alzheimer's disease

INTRODUCTION

There is a tremendous need for identifying reliable blood-based biomarkers for Alzheimer's disease (AD) that are tied to the biological ATN (amyloid, tau and neurodegeneration) framework as well as clinical assessment and progression.

METHODS

One hundred forty-four elderly participants underwent 18F-AV45 positron emission tomography (PET) scan, structural magnetic resonance imaging (MRI) scan, and blood sample collection. The composite standardized uptake value ratio (SUVR) was derived from 18F-AV45 PET to assess brain amyloid burden, and the hippocampal volume was determined from structural MRI scans. Plasma glial fibrillary acidic protein (GFAP), phosphorylated tau-181 (ptau-181), and neurofilament light (NfL) measured by single molecular array (SIMOA) technology were assessed with respect to ATN framework, genetic risk factor, age, clinical assessment, and future functional decline among the participants.

RESULTS

Among the three plasma markers, GFAP best discriminated participants stratified by clinical diagnosis and brain amyloid status. Age was strongly associated with NfL, followed by GFAP and ptau-181 at much weaker extent. Brain amyloid was strongly associated with plasma GFAP and ptau-181 and to a lesser extent with plasma NfL. Moderate association was observed between plasma markers. Hippocampal volume was weakly associated with all three markers. Elevated GFAP and ptau-181 were associated with worse cognition, and plasma GFAP was the most predictive of future functional decline. Combining GFAP and ptau-181 together was the best model to predict brain amyloid status across all participants (AUC = 0.86) or within cognitively impaired participants (AUC = 0.93); adding NfL as an additional predictor only had a marginal improvement.

CONCLUSION

Our findings indicate that GFAP is of potential clinical utility in screening amyloid pathology and predicting future cognitive decline. GFAP, NfL, and ptau-181 were moderately associated with each other, with discrepant relevance to age, sex, and AD genetic risk, suggesting their relevant but differential roles for AD assessment. The combination of GFAP with ptau-181 provides an accurate model to predict brain amyloid status, with the superior performance of GFAP over ptau-181 when the prediction is limited to cognitively impaired participants.

Genetic risk scores enhance the diagnostic value of plasma biomarkers of brain amyloidosis

Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrolment and treatment monitoring in Alzheimer's disease (AD). However, further advances are needed before these biomarkers can achieve wider deployment beyond selective research studies and specialty memory clinics, including the development of frameworks for optimal interpretation of biomarker profiles. We hypothesized that integrating Alzheimer's disease genetic risk score (AD-GRS) data would enhance the diagnostic value of plasma AD biomarkers by better capturing extant disease heterogeneity. Analysing 962 individuals from a population-based sample, we observed that an AD-GRS was independently associated with amyloid PET levels (an early marker of AD pathophysiology) over and above APOE ε4 or plasma p-tau181, amyloid-β42/40, glial fibrillary acidic protein or neurofilament light chain. Among individuals with a high or moderately high plasma p-tau181, integrating AD-GRS data significantly improved classification accuracy of amyloid PET positivity, including the finding that the combination of a high AD-GRS and high plasma p-tau181 outperformed p-tau181 alone in classifying amyloid PET positivity (88% versus 68%; P = 0.001). A machine learning approach incorporating plasma biomarkers, demographics and the AD-GRS was highly accurate in predicting amyloid PET levels (90% training set; 89% test set) and Shapley value analyses (an explainer method based in cooperative game theory) indicated that the AD-GRS and plasma biomarkers had differential importance in explaining amyloid deposition across individuals. Polygenic risk for AD dementia appears to account for a unique portion of disease heterogeneity, which could non-invasively enhance the interpretation of blood-based AD biomarker profiles in the population.

The link between metabolic syndrome and Alzheimer disease: A mutual relationship and long rigorous investigation

It has been illustrated that metabolic syndrome (MetS) is associated with Alzheimer disease (AD) neuropathology. Components of MetS including central obesity, hypertension, insulin resistance (IR), and dyslipidemia adversely affect the pathogenesis of AD by different mechanisms including activation of renin-angiotensin system (RAS), inflammatory signaling pathways, neuroinflammation, brain IR, mitochondrial dysfunction, and oxidative stress. MetS exacerbates AD neuropathology, and targeting of molecular pathways in MetS by pharmacological approach could a novel therapeutic strategy in the management of AD in high risk group. However, the underlying mechanisms of these pathways in AD neuropathology are not completely clarified. Therefore, this review aims to elucidate the association between MetS and AD regarding the oxidative and inflammatory mechanistic pathways.

Association of Plasma Biomarkers of Alzheimer Disease With Cognition and Medical Comorbidities in a Biracial Cohort

BACKGROUND AND OBJECTIVES

Recent advances in blood-based biomarkers offer the potential to revolutionize the diagnosis and management of Alzheimer disease (AD), but additional research in diverse populations is critical. We assessed the profiles of blood-based AD biomarkers and their relationships to cognition and common medical comorbidities in a biracial cohort.

METHODS

Participants were evaluated through the Mayo Clinic Jacksonville Alzheimer Disease Research Center and matched on age, sex, and cognitive status. Plasma AD biomarkers (β-amyloid peptide 1-42 [Aβ42/40], plasma tau phosphorylated at position 181 [p-tau181], glial fibrillary acidic protein [GFAP], and neurofilament light) were measured using the Quanterix SiMoA HD-X analyzer. Cognition was assessed with the Mini-Mental State Examination. Wilcoxon rank sum tests were used to assess for differences in plasma biomarker levels by sex. Linear models tested for associations of self-reported race, chronic kidney disease (CKD), and vascular risk factors with plasma AD biomarker levels. Additional models assessed for interactions between race and plasma biomarkers in predicting cognition.

RESULTS

The sample comprised African American (AA; N = 267) and non-Hispanic White (NHW; N = 268) participants, including 69% female participants and age range 43-100 (median 80.2) years. Education was higher in NHW participants (median 16 vs 12 years, p < 0.001) while APOE ε4 positivity was higher in AA participants (43% vs 34%; p = 0.04). We observed no differences in plasma AD biomarker levels between AA and NHW participants. These results were unchanged after stratifying by cognitive status (unimpaired vs impaired). Although the p-tau181-cognition association seemed stronger in NHW participants while the Aβ42/40-cognition association seemed stronger in AA participants, these findings did not survive after excluding individuals with CKD. Female participants displayed higher GFAP (177.5 pg/mL vs 157.73 pg/mL; p = 0.002) and lower p-tau181 (2.62 pg/mL vs 3.28 pg/mL; p = 0.001) levels than male participants. Diabetes was inversely associated with GFAP levels (β = -0.01; p < 0.001).

DISCUSSION

In a biracial community-based sample of adults, we observed that sex differences, CKD, and vascular risk factors, but not self-reported race, contributed to variation in plasma AD biomarkers. Although some prior studies have reported primary effects of race/ethnicity, our results reinforce the need to account for broad-based medical and social determinants of health (including sex, systemic comorbidities, and other factors) in effectively and equitably deploying plasma AD biomarkers in the general population.

Advanced brain imaging for the diagnosis of Alzheimer disease

PURPOSE OF REVIEW

The purpose is to review the latest advances of brain imaging for the diagnosis of Alzheimer disease (AD).

RECENT FINDINGS

Brain imaging techniques provide valuable and complementary information to support the diagnosis of Alzheimer disease in clinical and research settings. The recent FDA accelerated approvals of aducanumab, lecanemab and donanemab made amyloid-PET critical in helping determine the optimal window for anti-amyloid therapeutic interventions. Tau-PET, on the other hand, is considered of key importance for the tracking of disease progression and for monitoring therapeutic interventions in clinical trials. PET imaging for microglial activation, astrocyte reactivity and synaptic degeneration are still new techniques only used in the research field, and more studies are needed to validate their use in the clinical diagnosis of AD. Finally, artificial intelligence has opened new prospective in the early detection of AD using MRI modalities.

SUMMARY

Brain imaging techniques using PET improve our understanding of the different AD-related pathologies and their relationship with each other along the course of disease. With more robust validation, machine learning and deep learning algorithms could be integrated with neuroimaging modalities to serve as valuable tools for clinicians to make early diagnosis and prognosis of AD.

Bibliometric review on biomarkers for Alzheimer's disease between 2000 and 2023

BACKGROUND

Alzheimer's disease (AD) is a common cause of dementia and frailty. Therefore, it is important to develop biomarkers that can diagnose these changes to improve the likelihood of monitoring and treating potential causes. Therefore, this study aimed to examine the relationship between biomarkers and AD, identify journal publications and collaborators, and analyze keywords and research trends using a bibliometric method.

METHODS

We systematically searched for papers published in the Web of Science Core Collection database on biomarkers and AD. The search strategy was as follows: (TS) = (Alzheimer's OR Alzheimer's OR Alzheimer OR "Alzheimer's disease" OR "Alzheimer disease") AND TS = (biomarker OR biomarkers). Only articles and reviews were included as document types, with English as the primary language. The CiteSpace software was used to analyze the retrieved data on countries/regions, institutions, authors, published journals, and keywords. Simultaneously, the co-occurrence of the keywords was constructed.

RESULTS

There were 2625 articles on biomarkers and AD research published by 51 institutions located in 41 countries in 75 journals; the number of articles has shown an increasing trend over the past 20 years. Keywords analysis showed that Alzheimer's disease, cerebrospinal fluid, mild cognitive impairment, amyloid beta, and tau were also highly influential.

CONCLUSION

This was the first study to provide an overview of the current status of development, hot spots of study, and future trends in biomarkers for AD. These findings will provide useful information for researchers to explore trends and gaps in the field of biomarkers and AD.

COPI-regulated mitochondria-ER contact site formation maintains axonal integrity

Coat protein complex I (COPI) is best known for its role in Golgi-endoplasmic reticulum (ER) trafficking, responsible for the retrograde transport of ER-resident proteins. The ER is crucial to neuronal function, regulating Ca2+ homeostasis and the distribution and function of other organelles such as endosomes, peroxisomes, and mitochondria via functional contact sites. Here we demonstrate that disruption of COPI results in mitochondrial dysfunction in Drosophila axons and human cells. The ER network is also disrupted, and the neurons undergo rapid degeneration. We demonstrate that mitochondria-ER contact sites (MERCS) are decreased in COPI-deficient axons, leading to Ca2+ dysregulation, heightened mitophagy, and a decrease in respiratory capacity. Reintroducing MERCS is sufficient to rescue not only mitochondrial distribution and Ca2+ uptake but also ER morphology, dramatically delaying neurodegeneration. This work demonstrates an important role for COPI-mediated trafficking in MERC formation, which is an essential process for maintaining axonal integrity.

Alzheimer's polygenic risk scores, APOE, Alzheimer's disease risk, and dementia-related blood biomarker levels in a population-based cohort study followed over 17 years

BACKGROUND

In order to utilize polygenic risk scores (PRSs) for Alzheimer's disease (AD) in a meaningful way, influential factors (i.e. training set) and prediction across groups such as APOE e4 (APOE4) genotype as well as associations to dementia-related biomarkers should be explored. Therefore, we examined the association of APOE4 and various PRSs, based on training sets that utilized differing AD definitions, with incident AD and all-cause dementia (ACD) within 17 years, and with levels of phosphorylated tau181 (P-tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) in blood. Secondarily, effect modification by APOE4 status and sex was examined.

METHODS

In this prospective, population-based cohort study and nested case-control study, 9,940 participants in Germany were enrolled between 2000 and 2002 by their general practitioners and followed for up to 17 years. Participants were included in this study if dementia status and genetic data were available. A subsample of participants additionally had measurements of P-tau181, NfL, and GFAP obtained from blood samples. Cox and logistic regression analyses were used to assess the association of genetic risk (APOE genotype and PRSnoAPOE) with incident ACD/AD and log-transformed blood levels of P-tau181, NfL, and GFAP.

RESULTS

Five thousand seven hundred sixty-five participants (54% female, aged 50-75years at baseline) were included in this study, of whom 464 received an all-cause dementia diagnosis within 17 years. The PRSs were not more predictive of dementia than APOE4. An APOE4 specific relationship was apparent with PRSs only exhibiting associations to dementia among APOE4 carriers. In the nested case-control study including biomarkers (n = 712), APOE4 status and polygenic risk were significantly associated to levels of GFAP in blood.

CONCLUSIONS

The use of PRSs may be beneficial for increased precision in risk estimates among APOE4 carriers. While APOE4 may play a crucial etiological role in initial disease processes such as Aβ deposition, the PRS may be an indicator of further disease drivers as well as astrocyte activation. Further research is necessary to confirm these findings, especially the association to GFAP.

Phosphorylated tau in Alzheimer's disease

There is a need for blood biomarkers to detect individuals at different Alzheimer's disease (AD) stages because obtaining cerebrospinal fluid-based biomarkers is invasive and costly. Plasma phosphorylated tau proteins (p-tau) have shown potential as such biomarkers. This systematic review was conducted according to the PRISMA guidelines and aimed to determine whether quantification of plasma tau phosphorylated at threonine 181 (p-tau181), threonine 217 (p-tau217) and threonine 231 (p-tau231) is informative in the diagnosis of AD. All p-tau isoforms increase as a function of Aβ-accumulation and discriminate healthy individuals from those at preclinical AD stages with high accuracy. P-tau231 increases earliest, followed by p-tau181 and p-tau217. In advanced stages, all p-tau isoforms are associated with the clinical classification of AD and increase with disease severity, with the greatest increase seen for p-tau217. This is also reflected by a better correlation of p-tau217 with Aβ scans, whereas both, p-tau217 and p-tau181 correlated equally with tau scans. However, at the very advanced stages, p-tau181 begins to plateau, which may mirror the trajectory of the Aβ pathology and indicate an association with a more intermediate risk of AD. Across the AD continuum, the incremental increase in all biomarkers is associated with structural changes in widespread brain regions and underlying cognitive decline. Furthermore, all isoforms differentiate AD from non-AD neurodegenerative disorders, making them specific for AD. Incorporating p-tau181, p-tau217 and p-tau231 in clinical use requires further studies to examine ideal cut-points and harmonize assays.

GFAP as a Potential Biomarker for Alzheimer's Disease: A Systematic Review and Meta-Analysis

Blood biomarkers have been considered tools for the diagnosis, prognosis, and monitoring of Alzheimer's disease (AD). Although amyloid-β peptide (Aβ) and tau are primarily blood biomarkers, recent studies have identified other reliable candidates that can serve as measurable indicators of pathological conditions. One such candidate is the glial fibrillary acidic protein (GFAP), an astrocytic cytoskeletal protein that can be detected in blood samples. Increasing evidence suggests that blood GFAP levels can be used to detect early-stage AD. In this systematic review and meta-analysis, we aimed to evaluate GFAP in peripheral blood as a biomarker for AD and provide an overview of the evidence regarding its utility. Our analysis revealed that the GFAP level in the blood was higher in the Aβ-positive group than in the negative groups, and in individuals with AD or mild cognitive impairment (MCI) compared to the healthy controls. Therefore, we believe that the clinical use of blood GFAP measurements has the potential to accelerate the diagnosis and improve the prognosis of AD.

Plasma Biomarkers of Alzheimer's Disease: A Review of Available Assays, Recent Developments, and Implications for Clinical Practice

Recently, low-sensitive plasma assays have been replaced by new ultra-sensitive assays such as single molecule enzyme-linked immunosorbent assay (Simoa), the Mesoscale Discovery (MSD) platform, and immunoprecipitation-mass spectrometry (IP-MS) with higher accuracy in the determination of plasma biomarkers of Alzheimer's disease (AD). Despite the significant variability, many studies have established in-house cut-off values for the most promising available biomarkers. We first reviewed the most used laboratory methods and assays to measure plasma AD biomarkers. Next, we review studies focused on the diagnostic performance of these biomarkers to identify AD cases, predict cognitive decline in pre-clinical AD cases, and differentiate AD cases from other dementia. We summarized data from studies published until January 2023. A combination of plasma Aβ_42/40 ratio, age, and APOE status showed the best accuracy in diagnosing brain amyloidosis with a liquid chromatography-mass spectrometry (LC-MS) assay. Plasma p-tau217 has shown the best accuracy in distinguishing Aβ-PET+ from Aβ-PET-even in cognitively unimpaired individuals. We also summarized the different cut-off values for each biomarker when available. Recently developed assays for plasma biomarkers have undeniable importance in AD research, with improved analytical and diagnostic performance. Some biomarkers have been extensively used in clinical trials and are now clinically available. Nonetheless, several challenges remain to their widespread use in clinical practice.

A global view of the genetic basis of Alzheimer disease

The risk of Alzheimer disease (AD) increases with age, family history and informative genetic variants. Sadly, there is still no cure or means of prevention. As in other complex diseases, uncovering genetic causes of AD could identify underlying pathological mechanisms and lead to potential treatments. Rare, autosomal dominant forms of AD occur in middle age as a result of highly penetrant genetic mutations, but the most common form of AD occurs later in life. Large-scale, genome-wide analyses indicate that 70 or more genes or loci contribute to AD. One of the major factors limiting progress is that most genetic data have been obtained from non-Hispanic white individuals in Europe and North America, preventing the development of personalized approaches to AD in individuals of other ethnicities. Fortunately, emerging genetic data from other regions - including Africa, Asia, India and South America - are now providing information on the disease from a broader range of ethnicities. Here, we summarize the current knowledge on AD genetics in populations across the world. We predominantly focus on replicated genetic discoveries but also include studies in ethnic groups where replication might not be feasible. We attempt to identify gaps that need to be addressed to achieve a complete picture of the genetic and molecular factors that drive AD in individuals across the globe.

Development and validation of a 13-gene signature associated with immune function for the detection of Alzheimer's disease

Current knowledge of Alzheimer's disease (AD) etiology and effective therapy remains limited. Thus, the identification of biomarkers is crucial to improve the detection and treatment of patients with AD. Using robust rank aggregation method to analyze the microarray data from Gene Expression Omnibus database, we identified 1138 differentially expressed genes in AD. We then explored 13 hub genes by weighted gene co-expression network analysis, least absolute shrinkage, and selection operator, and logistic regression in the training dataset. The detection model, which composed of CD163, CDC42SE1, CECR6, CSF1R, CYP27A1, EIF4E3, H2AFJ, IFIT2, IL10RA, KIAA1324, PSTPIP1, SLA, and TBC1D2 genes, along with APOE gene, showed that the area under the curve for detecting AD was 0.821 (95% confidence interval [CI] = 0.782-0.861) and the model was validated in ADNI dataset (area under the curve = 0.776; 95%CI = 0.686-0.865). Notably, the 13 genes in the model were highly enriched in immune function. These findings have implications for the detection and therapeutic target of AD.

Plasma NfL is associated with the APOE ε4 allele, brain imaging measurements of neurodegeneration, and lower recall memory scores in cognitively unimpaired late-middle-aged and older adults

BACKGROUND

Plasma neurofilament light (NfL) is an indicator of neurodegeneration and/or neuroaxonal injury in persons with Alzheimer's disease (AD) and a wide range of other neurological disorders. Here, we characterized and compared plasma NfL concentrations in cognitively unimpaired (CU) late-middle-aged and older adults with two, one, or no copies of the APOE ε4 allele, the major genetic risk factor for AD. We then assessed plasma NfL associations with brain imaging measurements of AD-related neurodegeneration (hippocampal atrophy and a hypometabolic convergence index [HCI]), brain imaging measurements of amyloid-β plaque burden, tau tangle burden and white matter hyperintensity volume (WMHV), and delayed and total recall memory scores.

METHODS

Plasma NfL concentrations were measured in 543 CU 69 ± 9 year-old participants in the Arizona APOE Cohort Study, including 66 APOE ε4 homozygotes (HM), 165 heterozygotes (HT), and 312 non-carriers (NC). Robust regression models were used to characterize plasma NfL associations with APOE ε4 allelic dose before and after adjustment for age, sex, and education. They were also used to characterize plasma NfL associations with MRI-based hippocampal volume and WMHV measurements, an FDG PET-based HCI, mean cortical PiB PET measurements of amyloid-β plaque burden and meta-region-of-interest (meta-ROI) flortaucipir PET measurements of tau tangle burden, and Auditory Verbal Learning Test (AVLT) Delayed and Total Recall Memory scores.

RESULTS

After the adjustments noted above, plasma NfL levels were significantly greater in APOE ε4 homozygotes and heterozygotes than non-carriers and significantly associated with smaller hippocampal volumes (r =  - 0.43), greater tangle burden in the entorhinal cortex and inferior temporal lobes (r = 0.49, r = 0.52, respectively), and lower delayed (r =  - 0.27), and total (r =  - 0.27) recall memory scores (p < 0.001). NfL levels were not significantly associated with PET measurements of amyloid-β plaque or total tangle burden.

CONCLUSIONS

Plasma NfL concentrations are associated with the APOE ε4 allele, brain imaging biomarkers of neurodegeneration, and less good recall memory in CU late-middle-aged and older adults, supporting its value as an indicator of neurodegeneration in the preclinical study of AD.

WWOX P47T partial loss-of-function mutation induces epilepsy, progressive neuroinflammation, and cerebellar degeneration in mice phenocopying human SCAR12

WWOX gene loss-of-function (LoF) has been associated with neuropathologies resulting in developmental, epileptic, and ataxic phenotypes of varying severity based on the level of WWOX dysfunction. WWOX gene biallelic germline variant p.Pro47Thr (P47T) has been causally associated with a new form of autosomal recessive cerebellar ataxia with epilepsy and intellectual disability (SCAR12, MIM:614322). This mutation affecting the WW1 protein binding domain of WWOX, impairs its interaction with canonical proline-proline-X-tyrosine motifs in partner proteins. We generated a mutant knock-in mouse model of Wwox P47T mutation that phenocopies human SCAR12. WwoxP47T/P47T mice displayed epilepsy, profound social behavior and cognition deficits, and poor motor coordination, and unlike KO models that survive only for 1 month, live beyond 1 year of age. These deficits progressed with age and mice became practically immobile, suggesting severe cerebellar dysfunction. WwoxP47T/P47T mice brains revealed signs of progressive neuroinflammation with elevated astro-microgliosis that increased with age. Cerebellar cortex displayed significantly reduced molecular and granular layer thickness and a strikingly reduced number of Purkinje cells with degenerated dendrites. Transcriptome profiling from various brain regions of WW domain LoF mice highlighted widespread changes in neuronal and glial pathways, enrichment of bioprocesses related to neuroinflammation, and severe cerebellar dysfunction. Our results show significant pathobiological effects and potential mechanisms through which WWOX partial LoF leads to epilepsy, cerebellar neurodegeneration, neuroinflammation, and ataxia. Additionally, the mouse model described here will be a useful tool to understand the role of WWOX in common neurodegenerative conditions in which this gene has been identified as a novel risk factor.

Neurofilament-light chain quantification by Simoa and Ella in plasma from patients with dementia: a comparative study

Neurofilament light chains (NfL) are neuron-specific cytoskeletal proteins whose plasmatic concentrations have been explored as a clinically useful marker in several types of dementia. Plasma concentrations of NfL are extremely low, and just two assays are commercially available for their study: one based on the SiMoA technology and one based on Ella. We thus studied plasma levels of NfL with both platforms to check the correlation between them and to assess their potential in the diagnosis of neurodegeneration. Plasma NfL levels were measured on 50 subjects: 18 healthy controls, 20 Alzheimer's disease, and 12 frontotemporal dementia patients. Ella returned plasmatic NfL levels significantly higher than SiMoA, however the results were strongly correlated (r = 0.94), and a proportional coefficient of 0.58 between the two assays was calculated. Both assays detected higher plasma NfL levels in patients with dementia than in the control group (p < 0.0001) and allowed their discrimination with excellent diagnostic performance (AUC > 0.95). No difference was found between Alzheimer's and Frontotemporal dementia either using SiMoA or Ella. In conclusion, both the analytical platforms resulted effective in analysing plasma levels of NfL. However, the correct interpretation of results requires the precise knowledge of the assay used.

Blood and Cerebrospinal Fluid Biomarkers in Vascular Dementia and Alzheimer's Disease: A Brief Review

The Maintenance of brain health is a lifelong process whereby potentially deleterious exposures such as cardiovascular risks, amyloid beta, and phosphorylated tau may adversely affect the brain decades before there are clinical manifestations. Thus, the early structural and neuropathological foundation for the development of cognitive impairment and its allied features later in life may provide precursor targets such that interventions may be applied to prevent or slow cognitively impairing processes if the underlying mechanism(s) can be addressed in time.

Pathogenesis, Animal Models, and Drug Discovery of Alzheimer's Disease

Alzheimer's disease (AD), the most common cause of dementia, is a chronic neurodegenerative disease induced by multiple factors. The high incidence and the aging of the global population make it a growing global health concern with huge implications for individuals and society. The clinical manifestations are progressive cognitive dysfunction and lack of behavioral ability, which not only seriously affect the health and quality of life of the elderly, but also bring a heavy burden to the family and society. Unfortunately, almost all the drugs targeting the classical pathogenesis have not achieved satisfactory clinical effects in the past two decades. Therefore, the present review provides more novel ideas on the complex pathophysiological mechanisms of AD, including classical pathogenesis and a variety of possible pathogenesis that have been proposed in recent years. It will be helpful to find out the key target and the effect pathway of potential drugs and mechanisms for the prevention and treatment of AD. In addition, the common animal models in AD research are outlined and we examine their prospect for the future. Finally, Phase I, II, III, and IV randomized clinical trials or on the market of drugs for AD treatment were searched in online databases (Drug Bank Online 5.0, the U.S. National Library of Medicine, and Alzforum). Therefore, this review may also provide useful information in the research and development of new AD-based drugs.

Serum GFAP - reference interval and preanalytical properties in Danish adults

OBJECTIVES

Glial fibrillary acidic protein (GFAP) is a promising biomarker that could potentially contribute to diagnosis and prognosis in neurological diseases. The biomarker is approaching clinical use but the reference interval for serum GFAP remains to be established, and knowledge about the effect of preanalytical factors is also limited.

METHODS

Serum samples from 371 apparently healthy reference subjects, 21-90 years of age, were measured by a single-molecule array (Simoa) assay. Continuous reference intervals were modelled using non-parametric quantile regression and compared with traditional age-partitioned non-parametric reference intervals established according to the Clinical and Laboratory Standards Institute (CLSI) guideline C28-A3. The following preanalytical conditions were also examined: stability in whole blood at room temperature (RT), stability in serum at RT and -20 °C, repeated freeze-thaw cycles, and haemolysis.

RESULTS

The continuous reference interval showed good overall agreement with the traditional age-partitioned reference intervals of 25-136 ng/L, 34-242 ng/L, and 5-438 ng/L for the age groups 20-39, 40-64, and 65-90 years, respectively. Both types of reference intervals showed increasing levels and variability of serum GFAP with age. In the preanalytical tests, the mean changes from baseline were 2.3% (95% CI: -2.4%, 6.9%) in whole blood after 9 h at RT, 3.1% (95% CI: -4.5%, 10.7%) in serum after 7 days at RT, 10.4% (95% CI: -6.0%, 26.8%) in serum after 133 days at -20 °C, and 10.4% (95% CI: 9.5%, 11.4%) after three freeze-thaw cycles.

CONCLUSIONS

The study establishes age-dependent reference ranges for serum GFAP in adults and demonstrates overall good stability of the biomarker.