Alzheimer's is a disease (AD) that affects 10 % of individuals aged ≥ 65, is the most prevalent neurodegenerative disorder. We propose a diagnostic framework integrating plasma attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy with machine learning for AD screening. Four classifiers (SVM, Logistic Regression, XGBoost, LDA) were optimized using a modified Sparrow Search Algorithm (GSSA), benchmarked against its standard version (SSA) and Bayesian methods. GSSA-optimized classifiers demonstrated superior performance, with GSSA-XGBoost achieving peak metrics: 88.51 % accuracy (+2.30 % vs SSA-XGBoost), 95.35 % sensitivity, and 81.82 % specificity. Comparative test-set results revealed consistent improvements: SSA-optimized models attained 83.91 % (SVM), 77.01 % (Logistic), 86.21 % (XGBoost), and 79.31 % (LDA) accuracy, and Bayesian counterparts achieved 85.06 %, 80.46 %, 85.06 %, and 79.31 %,while GSSA-optimized models achieved 86.21 %,80.46 %,88.51 %,80.46 %,respectively. Moreover, GSSA further enhanced sensitivities to 97.67 % (SVM/LDA) and specificities to 81.82 % (XGBoost), outperforming both SSA and Bayesian approaches. This ATR-FTIR/GSSA-machine learning synergy shows significant potential as a minimally invasive AD screening tool, with XGBoost delivering optimal diagnostic balance. Our methodology advances spectroscopic biomarker discovery while demonstrating algorithmic optimization efficacy.

The hypothalamic-pituitary-adrenal axis regulates the secretion of glucocorticoids in response to environmental challenges. In the brain, a nuclear receptor transcription factor, the glucocorticoid receptor, is an important component of the hypothalamic-pituitary-adrenal axis's negative feedback loop and plays a key role in regulating cognitive equilibrium and neuroplasticity. The glucocorticoid receptor influences cognitive processes, including glutamate neurotransmission, calcium signaling, and the activation of brain-derived neurotrophic factor-mediated pathways, through a combination of genomic and non-genomic mechanisms. Protein interactions within the central nervous system can alter the expression and activity of the glucocorticoid receptor, thereby affecting the hypothalamic-pituitary-adrenal axis and stress-related cognitive functions. An appropriate level of glucocorticoid receptor expression can improve cognitive function, while excessive glucocorticoid receptors or long-term exposure to glucocorticoids may lead to cognitive impairment. Patients with cognitive impairment-associated diseases, such as Alzheimer's disease, aging, depression, Parkinson's disease, Huntington's disease, stroke, and addiction, often present with dysregulation of the hypothalamic-pituitary-adrenal axis and glucocorticoid receptor expression. This review provides a comprehensive overview of the functions of the glucocorticoid receptor in the hypothalamic-pituitary-adrenal axis and cognitive activities. It emphasizes that appropriate glucocorticoid receptor signaling facilitates learning and memory, while its dysregulation can lead to cognitive impairment. This provides clues about how glucocorticoid receptor signaling can be targeted to overcome cognitive disability-related disorders.

The integration of autoantibody detection with electrochemical biosensors allows the development of a novel strategy for managing colorectal cancer (CRC) and Alzheimer's disease (AD). This work reports the use, for the first time, of sustainable receptors, autoantigens expressed in mammalian cells, fused to 6xHis at the N-terminus and HaloTag at the C-terminus, immobilized on chloroalkane-modified magnetic beads (MBs) to selectively capture plasma autoantibodies. Detection was based on amperometric measurements using disposable multiplexed screen-printed carbon electrodes (x8 sensing surfaces), horseradish peroxidase (HRP)-conjugated secondary antibodies, and the HQ/H2O2 system. HaloTag immobilization gave rise to a great sensitivity allowing discrimination between patients and healthy individuals in comparison with HisTag or -COOH immobilization. The CRC biosensor integrated seven CRC-specific full-length autoantigens, while the AD bioplatform combined three AD-specific full-length proteins and four AD-specific peptides, which allowed a robust diagnostic performance validated by ROC analyses. These multiplexed biosensors provide minimally invasive, cost-effective, and sustainable alternatives to traditional diagnostic methods, and show ideal for discovery of new targets and for mass screening and early disease detection, supporting personalized medicine approaches.

Unstructured and structured data in electronic health records (EHR) are a rich source of information for research and quality improvement studies. However, extracting accurate information from EHR is labor-intensive. Timely and accurate identification of patients with Alzheimer's Disease, related dementias (ADRD), or mild cognitive impairment (MCI) is critical for improving patient outcomes through early intervention, optimizing care plans, and reducing healthcare system burdens. Here we introduce an automated EHR phenotyping model to streamline this process and enable efficient identification of these conditions.

We analyzed data from 3,626 outpatients seen at two hospitals between February 2015 and June 2022. Through manual chart review, we established ground truth labels for the presence or absence of MCI/ADRD diagnoses. Our model combined three types of data: (1) unstructured clinical notes, from which we extracted single words, two-word phrases (bigrams), and three-word phrases (trigrams) as features, weighted using Term Frequency-Inverse Document Frequency (TF-IDF) to capture their relative importance, (2) International Classification of Diseases (ICD) codes, and (3) medication prescriptions related to MCI/ADRD. We trained a regularized logistic regression model to predict MCI/ADRD diagnoses and evaluated its performance using standard metrics including area under the receiver operating curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, specificity, precision, recall, and F1 score.

Thirty percent of patients in the cohort carried diagnoses of MCI/ADRD based on manual review. When evaluated on a held-out test set, the best model using clinical notes, ICDs, and medications, achieved an AUROC of 0.98, an AUPRC of 0.98, an accuracy of 0.93, a sensitivity (recall) of 0.91, a specificity of 0.96, a precision of 0.96, and an F1 score of 0.93 The estimated overall accuracy for patients randomly selected from EHRs was 99.88%.

Automated EHR phenotyping accurately identifies patients with MCI/ADRD based on clinical notes, ICD codes, and medication records. This approach holds potential for large-scale MCI/ADRD research utilizing EHR databases.

The current study examined whether greater use of technology to help with daily tasks is associated with less subjective cognitive decline (SCD), especially in individuals with a family history of Alzheimer's disease (AD). Individuals over the age of 50 (n = 102; age range 50-85) completed surveys about their digital and analog approaches to daily tasks, physical activity, and SCD. Participants with and without family histories of AD were matched on age, education, sex, and family history of AD using the R package MatchIt. There was no main effect of technology-based behavioral strategies on SCD (p = 0.259). However, a family history of AD moderated the association between technology use and SCD even when controlling for another protective lifestyle factor, physical activity. In individuals with a family history of AD, more reliance on technology-based behavioral strategies was associated with less SCD (p = 0.018), but this relationship was not significant in individuals without family history of AD (p = 0.511). Our findings suggest that technology-based behavioral strategies are associated with less SCD in individuals with a family history of AD, independent of another protective lifestyle factor. Future recommendations provided by healthcare providers to address SCD in cognitively unimpaired older adults might include focusing on technological assistance.

Recent decades have described parallel neuropathological mechanisms increasing the risk for developing late-onset Alzheimer's dementia (LOAD) in type 2 diabetes mellitus (T2DM); however, still little is known of the role of diabetic encephalopathy and brain atrophy in LOAD. The aim of this systematic review is to provide a comprehensive view on diabetic encephalopathy/cerebral atrophy, taking into account neuroimaging data, neuropathology, metabolic and endocrine mechanisms, amyloid formation, brain perfusion impairments, neuroimmunology, and inflammasome activation. Key switches were identified, to further meta-analyze genomic candidate loci and epigenetic modifications. For the qualitative meta-analysis of genomic bases extracted, human linkage studies were examined; for epigenetic mechanisms, data from both human and animal studies are described. For the systematic review of pathophysiological mechanisms, 1,259 publications were evaluated and 93 gene loci extracted for candidate risk linkages. Sixty-six publications were evaluated for genomic association and descriptions of epigenomic modifications. Overall accumulated results highlight the insulin signaling system, vascular markers, inflammation and inflammasome pathways, amylin interactions, and glycosylation mechanisms. The protocol was registered with PROSPERO (ID: CRD42023440535).

This exploratory study aimed to assess the efficacy of pattern-matching statistical methods within the Meyers Neuropsychological Battery (MNB). It compared neuropsychological test data profiles of Alzheimer's disease (AD) patients from three independent samples against four MNB dementia groups: MNB-AD, MNB-Vascular Dementia (VaD), MNB-Dementia with Lewy bodies (DLB), and MNB-Parkinson's disease dementia (PDD).

Three AD-independent samples completed either the MNB (referred to as I-MNB-AD), Dementia Rating Scale-2 with additional testing (denoted as DRS-Plus-AD), or the Repeatable Battery for the Assessment of Neuropsychological Status (designated as RBANS-AD). Test data profiles were cross-validated with four MNB dementia comparison group datasets. Statistical methods included Pearson correlation, Kullback-Leibler (KL) divergence, pooled effect size (Cohen's d), Configuration, and MNB Code.

Classification accuracy ranged from 40% (Pearson r) to 88% (Cohen's d) in the I-MNB-AD sample, 47% (Cohen's d) to 93% (KL) in the DRS-Plus-AD sample, and 47% (Pearson r) to 78% (Configuration) in the RBANS-AD sample. Some methods showed limited effectiveness depending on the sample and comparison group analyzed, while others demonstrated strong performance. Using a simple majority count of agreement, classification rates for selecting the MNB-AD comparison group were 80% (I-MNB-AD), 85% (DRS-Plus-AD), and 66% (RBANS-AD).

This exploratory study demonstrates that specific statistical methods employed in the MNB for pattern-matching analysis effectively differentiated neuropsychological profiles of individuals with AD from other types of dementia, contributing to improved diagnostic precision. The findings underscore the potential advantages of pattern-matching analysis, advocating for further research to validate and refine its application.

The 4 Mountain Test (4MT) is a test of allocentric spatial working memory and has been proposed as an earlier marker of predementia Alzheimer's disease (AD) than episodic verbal memory. We here compare the 4MT to the CERAD word list memory recall in both cognitively normal (CN) and mild cognitive impairment (MCI) cases with or without cerebrospinal fluid markers (CSF) of Alzheimer's disease pathology. Linear regression was used to assess the influence of CSF determined Aβ-plaque (Aβ-/+) or neurofibrillary tau tangles (Tau-/+) on 4MT and CERAD recall performance. Analyses were performed in the full sample and the CN and MCI sub-samples. Pearson correlations were calculated to examine the relationship between 4MT and tests of psychomotor speed, verbal memory, cognitive flexibility, verbal fluency, and visuo-spatial perception. Analyses showed no significant differences in 4MT scores between Aβ-/Aβ+, nor Tau-/Tau + participants, irrespective of cognitive status. In contrast, CERAD recall scores were lower in both Aβ+ compared to Aβ- (p<.01), and Tau + compared to Tau- participants (p<.01) in the full sample analyses. There were no significant differences in CERAD recall performance between Aβ- vs. Aβ+ and Tau- vs. to Tau + in the in CN/MCI sub-samples. 4MT scores were significantly correlated with tests of psychomotor speed, cognitive flexibility, and visuo-spatial perception in the full sample analyses. In conclusion, the CERAD recall outperformed the 4MT as a cognitive marker of CSF determined AD pathology. This suggests that allocentric working memory, as measured by the 4MT, may not be used as an early marker of predementia AD.

This systematic review investigates the integration of automatic segmentation techniques of magnetic resonance imaging (MRI) with neuropsychological assessments for early diagnosis and prognosis of Alzheimer's Disease (AD).

Focus on studies that utilise automated MRI segmentation and neuropsychological evaluations across the AD spectrum.

A literature search was conducted on the PubMed database on 7 November 2024, using key terms related to MRI, segmentation, brain structures, AD, and cognitive decline.

Studies including individuals with AD, mild cognitive impairment (MCI), or subjective cognitive decline (SCD), utilising structural MRI, focusing on grey matter and automatic segmentation, and reporting cognitive assessments were included.

Data were extracted and synthesised focusing on associations between MRI measures and cognitive tests, and discriminative values for diagnosis or prognosis.

Seven studies were included, showing a significant association between structural changes in the medial temporal lobe and cognitive decline. The combination of MRI volumetric measures and neuropsychological scores enhanced diagnostic accuracy. Neuropsychological measures demonstrated superiority in the identification of patients with MCI and mild AD in comparison to MRI measures alone.

Heterogeneity across studies, selection and measurement bias, and lack of non-response data were noted.

This review emphasises the necessity of integrating automated MRI segmentation with neuropsychological assessments for the diagnosis and prognosis of AD. While MRI is valuable, neuropsychological testing remains essential for early detection. Future studies should focus on developing integrated predictive models and refining neuroimaging techniques.

COVID-19 induces acute and long-term neurological symptoms. Links between COVID-19 neurological disturbance and Alzheimer's disease (AD) have been hypothesized because neuroinflammation plays a significant role in both diseases. However, it is unknown if COVID-19 patients with neurological disturbance present molecular alterations related to AD pathology. A better understanding of possible molecular links between COVID-19-induced neurological disease and AD would lead to improved patient follow-up and late-onset disease prevention. Here, we analyze early AD biomarkers in a Brazilian cohort of COVID-19 patients with neurological symptoms. We compared COVID-19 patients' neuroinflammatory and AD biomarker levels to controls, amnestic mild cognitive impairment (aMCI), and AD.

We analyzed cerebrospinal (CSF) biomarkers of neuroinflammation (interleukin-6 (IL6)), amyloid-beta (Aβ) proteinopathy (Aβ42/40), phosphorylated Tau (pTau181), and the neurodegeneration-associated biomarker total Tau in controls (n = 36), COVID-19 patients presenting neurological alterations (n = 35), aMCI (n = 19), and AD patients (n = 20). Comparisons were corrected by possible sex, age, and comorbidities confounding effects. CSF biomarkers were correlated with systemic and neuro-inflammation markers.

We found that severe COVID-19 patients presented higher CSF Tau than controls, comparable to alterations observed in AD patients. However, we did not find changes in CSF Aβ42/40, pTau-181/Aβ42, or Tau/Aβ42 ratios. Severe COVID-19 patients presented higher Tau, Tau/Aβ42, and pTau181/Aβ42 than mild patients. In COVID-19 patients, CSF pro-inflammatory cytokine IL6 and AD biomarkers correlated with systemic inflammatory index (SII).

Collectively, our findings reveal that CSF tau levels are comparably elevated in COVID-19 neurological patients and AD, suggesting ongoing neurodegeneration in COVID-19 neurological disease, but no biomarker alterations related to AD pathology. Furthermore, CNS AD-related biomarker levels in COVID-19 patients change in association with disease severity and systemic inflammation. Considering that inflammation may persist post-COVID, our findings urge the assessment of possible AD-related biomarker changes in COVID-19 survivors with lingering symptoms.

Investigation of olfactory impairment, an early manifestation of Alzheimer disease (AD), in relation to plasma biomarkers of AD and neurodegeneration could provide insights into the disease's pathophysiology. Because few such studies based on large, diverse, community-based populations exist, we investigated associations of odor identification ability with plasma biomarkers of AD and other neurodegenerative pathologies in community-dwelling Black and White older adults.

This cross-sectional investigation included participants from the Atherosclerosis Risk in Communities study who attended visit 5 (2011-2013) and who completed olfactory testing and brain MRI examinations and had plasma biomarkers measured (namely, amyloid-beta [Aβ]42/Aβ40 ratio, phosphorylated-tau at threonine-181 [p-tau181], p-tau181/Aβ42 ratio, glial fibrillary acidic protein [GFAP], and neurofilament light chain [NfL]). Odor identification ability was measured by the 12-item Sniffin' Sticks test. Separate linear regression models were used to examine the association of continuous olfaction score and olfaction categories (anosmia: score 6; hyposmia: 7-8; moderate-normal: 9-10; good-normal: 11-12) with each biomarker (all were log-transformed), adjusting for sociodemographic and cardiovascular factors, head injury, APOE-ε4 status, and estimated glomerular filtration rate. We further examined whether any observed associations are explained by total and regional brain volumes.

Among 1,545 participants (age: 76 ± 5 years, 60% women, 27% self-reported Black participants), the mean olfaction score was 9.2 ± 2.3; 14% had anosmia. Consistent with our hypotheses, poorer olfactory scores were associated with higher plasma p-tau181 (β per 1-unit lower score: 0.026 [95% CI 0.012-0.040]), p-tau181/Aβ42 (β: 0.027 [95% CI 0.011- 0.044]), GFAP (β: 0.024 [95% CI 0.009-0.040]), and NfL (β: 0.034 [95% CI 0.019-0.050] and lower Aβ42/Aβ40 ratio (β: -0.007 [95% CI -0.015 to 0.000]). Likewise, compared with good-normal olfaction, anosmia showed associations with all biomarker levels indicative of greater neuropathology (e.g., β for plasma p-tau181/Aβ42: 0.235 [95% CI 0.113-0.358] and β for plasma NfL: 0.210 [95% CI 0.102-0.317]), although the association with Aβ42/Aβ40 ratio was not statistically significant (β: -0.054 [95% CI -0.108 to 0.001]). These biomarkers were not significantly associated with hyposmia or moderate-normal olfaction. Smaller medial-temporal lobe volumes partly explained olfaction's link with plasma p-tau181, p-tau181/Aβ42, GFAP, and NfL.

Our findings suggest that poor olfaction is associated with multiple AD-related and other neurodegenerative processes. Future studies should investigate how longitudinal changes in both olfaction and biomarkers relate to each other.

Alzheimer's disease (AD), the most prevalent neurodegenerative disorder in aging populations, demands minimally invasive biomarkers for early diagnosis and monitoring. Circulating microRNAs (miRNAs) show promise as such biomarkers. In this study, we examined the levels of five selected miRNAs, implicated in neurodegenerative processes, in plasma and neuron-derived extracellular vesicles (EVs) from cognitively healthy controls (n = 5), and patients with mild (n = 10) and moderate AD (n = 10), stratified by Mini-Mental State Examination (MMSE). miR-23a-3p, miR-223a-3p, and miR-132-3p were significantly downregulated in both plasma and EVs of AD patients, with miR-132-3p emerging as the strongest biomarker candidate for mild AD. Plasma miRNA levels strongly correlated with EV cargo, supporting plasma-based assessments. To validate these findings, miR-132-3p levels were analyzed in expanded cohorts, including cognitively healthy subjects (n = 36), mild AD (n = 37), and moderate AD (n = 40), as well as a cohort of subjects with mild cognitive impairment (MCI, n = 31) and an additional external cohort of cognitively healthy subjects (CTR external, n = 37). Results confirmed miR-132-3p downregulation in AD patients and revealed a significant elevation in MCI individuals, suggesting a potential neuroprotective role in AD early stages. These findings highlight miR-132-3p as a promising, minimally invasive biomarker for early AD diagnosis and disease progression monitoring.

The high heterogeneity of neuropsychiatric symptoms (NPSs) hinders further exploration of their role in neurobiological mechanisms and Alzheimer's disease (AD). We aimed to delineate NPS patterns based on brain macroscale connectomics to understand the biological mechanisms of NPSs on the AD continuum.

We constructed regional radiomics similarity networks for 550 participants (AD with NPSs [n = 376], AD without NPSs [n = 111], and normal control participants [n = 63]) from the CIBL (Chinese Imaging, Biomarkers, and Lifestyle) study. We identified regional radiomics similarity network connections associated with NPSs and then clustered distinct subtypes of AD with NPSs. An independent dataset (n = 189) and internal validation were performed to assess the robustness of the NPS subtypes. Subsequent multiomics analysis was performed to assess the distinct clinical phenotype and biological mechanisms in each NPS subtype.

AD patients with NPSs were clustered into severe (n = 187), moderate (n = 87), and mild (n = 102) NPS subtypes, each exhibiting distinct brain network dysfunction patterns. A high level of consistency in clustering NPSs was internally and externally validated. Severe and moderate NPS subtypes were associated with significant cognitive impairment, increased plasma p-tau181 (tau phosphorylated at threonine 181) levels, extensive decreased brain volume and cortical thickness, and accelerated cognitive decline. Gene set enrichment analysis revealed enrichment of differentially expressed genes in ion transport and synaptic transmission with variations for each NPS subtype. Genome-wide association study analysis defined the specific gene loci for each subtype of AD with NPSs (e.g., logical memory), consistent with clinical manifestations and progression patterns.

This study identified and validated 3 distinct NPS subtypes, underscoring the role of NPSs in neurobiological mechanisms and progression of the AD continuum.

Cognitive impairment is prevalent among the elderly population. Cerebrospinal fluid (CSF) biomarker tests are employed to facilitate timely and differential diagnosis of cognitive dysfunction.

To investigate incidence and risk factors of post-lumbar puncture headache (PLPH) in Chinese geriatric patients with cognitive impairment.

A total of 254 consecutive patients with cognitive impairment were recruited between March and September 2023 and categorized into two groups: mild cognitive impairment (MCI) and dementia. Different scales, such as the short-form of the McGill pain questionnaire (SF-MPQ), facial visual analogue scale (F-VAS), revised Wong-Baker Assessment of Facial Expression Pain (FPS-R), and Chinese version of Pain Assessment Scale for Advanced Dementia (C-PAINAD), were used to evaluate the incidence of headache after lumbar puncture. Univariate and multivariate factor analyses were conducted to identify potential risk factors, with the most influential predictors included in regression models.

Among patients with cognitive disorders, the incidence of PLPH was 24.8%. The incidence and duration of headache did not differ significantly between the MCI and dementia groups. The average time of post-lumbar puncture headache (PLPH) was 22.9 h. There was no significant difference in headache scale scores between MCI patients. The incidence of PLPH in women with cognitive impairment was higher than that in men. The incidence of PLPH in cognitive impairment patients with a history of headache was significantly higher than that in cognitive impairment patients without a history of headache. However, no significant differences were observed in years of education, number of lumbar punctures, length of bed rest, and oral rehydration volume.

PLPH is more commonly observed in women, individuals with a history of headache, younger age, or lower body mass index (BMI). C-PAINAD and SF-MPQ are the preferred assessment tools for evaluating PLPH in patients with cognitive impairment.

There is an urgent need for the precise prediction of cerebral amyloidosis using noninvasive and accessible indicators to facilitate the early diagnosis of individuals with the preclinical stage of Alzheimer's disease (AD). Two hundred and four individuals with subjective cognitive decline (SCD) were enrolled in this study. All subjects completed neuropsychological assessments and underwent 18F-florbetapir PET, structural MRI, and functional MRI. A total of 315 features were extracted from the MRI, demographics, and neuropsychological scales and selected using the least absolute shrinkage and selection operator (LASSO). The logistic regression (LR) model, based on machine learning, was trained to classify SCD as either β-amyloid (Aβ) positive or negative. A nomogram was established using a multivariate LR model to predict the risk of Aβ+. The performance of the prediction model and nomogram was assessed with area under the curve (AUC) and calibration. The final model was based on the right rostral anterior cingulate thickness, the grey matter volume of the right inferior temporal, the ReHo of the left posterior cingulate gyrus and right superior temporal gyrus, as well as MoCA-B and AVLT-R. In the training set, the model achieved a good AUC of 0.78 for predicting Aβ+, with an accuracy of 0.72. The validation of the model also yielded a favorable discriminatory ability with an AUC of 0.88 and an accuracy of 0.83. We have established and validated a model based on cognitive, sMRI, and fMRI data that exhibits adequate discrimination. This model has the potential to predict amyloid status in the SCD group and provide a noninvasive, cost-effective way that might facilitate early screening, clinical diagnosis, and drug clinical trials.

Neurodegenerative diseases, such as Alzheimer's Disease (AD), Multiple Sclerosis (MS), Parkinson's Disease (PD), and Amyotrophic Lateral Sclerosis (ALS) are increasingly prevalent as global populations age. Fluid biomarkers, derived from cerebrospinal fluid (CSF), blood, saliva, urine, and exosomes, offer a promising solution for early diagnosis, prognosis, and disease monitoring. These biomarkers can reflect critical pathological processes like amyloid-beta (Aβ) deposition, tau protein hyperphosphorylation, α-syn misfolding, TDP-43 mislocalization and aggregation, and neuronal damage, enabling detection long before clinical symptoms emerge. Recent advances in blood-based biomarkers, particularly plasma Aβ, phosphorylated tau, and TDP-43, have shown diagnostic accuracy equivalent to CSF biomarkers, offering more accessible testing options. This review discusses the current challenges in fluid biomarker research, including variability, standardization, and sensitivity issues, and explores how combining multiple biomarkers with clinical symptoms improves diagnostic reliability. Ethical considerations, future directions involving extracellular vehicles (EVs), and the integration of artificial intelligence (AI) are also highlighted. Continued research efforts will be key to overcoming these obstacles, enabling fluid biomarkers to become crucial tools in personalized medicine for neurodegenerative diseases.

The functional connectivity (FC) of the amygdala in Alzheimer's disease (AD) and its relationship to cognitive impairment is still not well established. Thus, we examined resting-state FC changes in the amygdala among 21 patients with AD dementia (ADD) and 34 individuals with amnestic mild cognitive impairment (aMCI), compared to 33 individuals with subjective cognitive impairment (SCI), to provide insights into the association between amygdala FC and cognitive decline in different clinical stages of Alzheimer's disease. We conducted seed-to-voxel FC analysis, focused on two cognitive functions, episodic memory, and face recognition, and examined the correlations between changes in FC of the amygdala and cognitive test scores. We demonstrated that the left amygdala exhibits progressive disruption in FC, especially with the frontal regions in aMCI and ADD. We further identified that this disrupted FC in the left amygdala showed significant positive correlations with cognitive test scores from the MCI stage onward. Our results indicate that FC changes in the left amygdala may serve as an early marker of AD and this FC pattern of amygdala influence detrimentally affects episodic memory and face recognition functions. These findings highlight that the amygdala may be a critical anatomical region for detecting the early stages of AD.

Growing evidence links ultrafine particles (UFP) to neurotoxicity, but human studies remain limited. Various mobile monitoring approaches have been used to develop air pollution exposure models. However, whether design choices impact epidemiology, including for UFP and cognitive function, remains unclear. We evaluated the adjusted association between 5-year average UFP number concentration (PNC) and late-life cognitive function (Cognitive Abilities Screening Instrument - Item Response Theory [CASI-IRT]) in the Adult Changes in Thought cohort (N = 5283) by leveraging an extensive roadside mobile monitoring campaign specifically designed for epidemiology. To assess the impact of reduced monitoring approaches on this association, we repeatedly subsampled UFP measures from the campaign, developed exposure models, and evaluated the degree to which associations were impacted. In the primary analysis, each 1900 pt/cm3 increment in PNC was associated with an adjusted mean baseline CASI-IRT score that was 0.002 (95 % CI: -0.016, 0.020) higher, which was not statistically significant. Point estimates were consistent across sampling designs with fewer visits per site (≤12), fewer seasons (1-3), and unbalanced visit frequency across sites. Sampling designs restricted to rush hours were more similar (median point estimate 0.002, IQR of point estimates: 0.000, 0.003) than business hour designs (0.006, IQR: 0.005, 0.007), but the opposite was true when temporal adjustments were applied (rush: -0.003, IQR: -0.005, -0.001; business: 0.002, IQR: 0.001, 0.004). We observed similar results in sensitivity and secondary analyses. We did not find evidence of an association between UFP and cognitive function in fully adjusted models. Monitoring design had minimal impact on the inferential results in this setting, which may have been caused by the lack of association. Secondary analyses in a reduced model that is potentially confounded suggest that monitoring design might have a greater impact in other datasets. Further research is needed, particularly in contexts with robust statistically significant health associations.

Alzheimer's disease (AD) might impact the complexity of cerebral cortex, and the underlying biological mechanisms responsible for cortical changes in the AD cortex remain unclear.

Fifty-eight participants with AD and 67 normal controls underwent high-resolution 3 T structural brain MRI. Using surface-based morphometry (SBM), we created vertex-wise maps for group comparisons in terms of five measures: cortical thickness, fractal dimension, gyrification index, Toro's gyrification index and sulcal depth respectively. Five machine learning (ML) models combining SBM parameters were established to predict AD. In addition, transcription-neuroimaging association analyses, as well as Mendelian randomization of AD and cortical thickness data, were conducted to investigate the genetic mechanisms and biological functions of AD.

AD patients exhibited topological changes in cortical complexity, with increased complexity in the frontal and temporal cortex and decreased complexity in the insula cortex, alongside extensive cortical atrophy. Combining different SBM measures could aid disease diagnosis. The genes involved in cell structure support and the immune response were the strongest contributors to cortical anatomical features in AD patients. The identified genes associated with AD cortical morphology were overexpressed or underexpressed in excitatory neurons, oligodendrocytes, and astrocytes.

Complexity alterations of the cerebral surface may be associated with a range of biological processes and molecular mechanisms, including immune responses. The present findings may contribute to a more comprehensive understanding of brain morphological patterns in AD patients.

In research on Alzheimer's disease (AD) individuals experiencing cognitive problems but not satisfying criteria for mild cognitive impairment (MCI) or AD are referred to as having 'Subjective Cognitive Decline' (SCD). A small subset of them will progress to a state justifying an AD diagnosis. AD research trying to identify prognostic biomarkers aims to inform individuals labelled with SCD about the future development of their complaints. This paper explores how persons experiencing these cognitive complaints currently anticipate the future and how they would value emerging biomarker-based prognostic tests.

Semi-structured interviews with 10 individuals experiencing cognitive complaints, some accompanied by their partners. The interviews were coded using ATLAS.ti 22. The exploratory qualitative analysis was conducted using thematic content analysis (TA).

The interviewees experience pervasive uncertainty concerning their persistent but medically unexplained cognitive symptoms. Lacking a diagnosis and a prognosis, they anticipate their future considering various scenarios, while mainly acting upon the scenario of developing further cognitive decline/AD dementia. Interviewees often interpret questions about prognostication as asking about predicting the likelihood of a future diagnosis, clearly valuing the latter. Most interviewees are positive, assuming it would help them and their relatives prepare for the future. They have specific ideas about what type of prognostic information would be helpful, usually focusing on how cognitive decline would impact their lives, for example, what activities they will be able to continue to engage in, when they will no longer recognise loved ones or what character they will have as a patient.

People experiencing unexplained cognitive complaints experience pervasive uncertainty about their situation and future. They clearly value the idea of a test predicting with high certainty whether they will be diagnosed with AD in the future. Regarding prognostication, most interviewees expect that such tests could help prepare for the future. However, there is a discrepancy between the outcomes usually measured in prognostic research (like the expected speed of decline) and the specific outcomes meaningful to our interviewees. In view of this gap, it is important to reconsider whether and how the development of prognostic biomarker tests for people suffering from unexplained cognitive complaints can be better tailored to the needs of these individuals.

During our interviews, we gathered the perspectives of 10 individuals suffering from medically unexplained cognitive complaints on the value of biomarker-based prognostics. These interviews were the primary data source. Individuals were also involved in the interpretation of our findings and critical evaluation of our preliminary conclusions.

At autopsy, around half of the Alzheimer's disease (AD) brains exhibit Lewy body pathology, and the main component of Lewy body pathology is α-synuclein aggregates. This study investigated the prevalence of cerebrospinal fluid (CSF) α-synuclein aggregation and its association with demographic factors and cognitive decline among 1619 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), with the test for α-synuclein aggregation by seed amplification assay (SAA). This cohort consisted of 595 cognitively normal (CN) individuals, 765 with mild cognitive impairment (MCI), and 259 with AD dementia. The results showed a higher prevalence of positive α-synuclein aggregation status in the AD dementia group (37.07 %) and the MCI group (22.75 %) compared to CN controls (16.13 %). Additionally, APOE ε4 carriers exhibited a higher prevalence of α-synuclein aggregation compared to non-carriers: 20.12 % for APOE ε4-/- (non-carriers), 24.82 % for APOE ε4 + /-, and 30.92 % for APOE ε4 + /+ . Longitudinally, positive CSF α-synuclein aggregation associated with accelerated cognitive decline, especially in the MCI and AD groups. Notably, positive aggregation status did not significantly affect cognitive trajectories in CN individuals. Moreover, APOE ε4 carriers with positive CSF α-synuclein aggregation experienced more pronounced cognitive decline. This study provides evidence that CSF α-synuclein aggregation is associated with cognitive function and the APOE ε4 allele. These findings suggest that CSF α-synuclein SAA, in combination with APOE ε4 status, could serve as biomarkers for predicting cognitive decline in AD.

At the present time, clinical detection of individuals who have amyloid in their brain is not possible without expensive biomarkers. The objective of the study was to test whether Cognivue Clarity® can differentiate True Controls, preclinical Alzheimer's disease (pAD), mild cognitive impairment (MCI) due to Alzheimer's disease (MCI-AD), AD, and MCI and dementia due to non-AD etiologies enrolled in the Bio-Hermes Study.

A total of 887 individuals completed Cognivue Clarity, amyloid PET scan, and blood-based AD biomarkers. Three Cognivue Clarity subtests differentiated between True Controls and pAD, and between cognitive impairment due to AD versus non-AD processes. This finding was leveraged to develop an amyloid-specific marker, combining the three subtests with age using machine learning to create the 4-point Cognivue Amyloid Risk Measure (CARM).

Cognivue Clarity discriminated cognitively normal from cognitively impaired individuals (p < 0.001, Cohen's d = 0.732). The CARM differentiated between individuals with amyloid and without amyloid by PET (p < 0.001, Cohen's d = 0.618) and blood-based biomarkers (p's < 0.001). Amyloid positivity and cognitive impairment increased across four CARM thresholds (p < 0.001). Dichotomizing CARM thresholds into low (CARM1/CARM2) and high (CARM3/CARM4) likelihood provided excellent discrimination for amyloid PET positivity (OR: 3.67; 95% CI 2.76-4.89). CARM categories differentiated between True Controls, pAD, MCI-AD, AD, and cognitive impairment due to non-AD etiologies (χ2 = 137.6, p < 0.001) with the majority of True Controls and non-AD etiologies being in CARM1/CARM2, and the majority of pAD, MCI-AD, and AD being in CARM3/CARM4.

Cognivue Clarity detects individuals with cognitive impairment, and a derivation benchmarked against amyloid PET was used to develop the CARM to predict the presence of amyloid. Combining the CARM and the Cognivue Clarity overall score could help identify individuals with and without cognitive impairment due to AD or non-AD etiologies, help screen for treatment protocols with anti-amyloid therapies, enrich clinical trial recruitment, and help to identify pAD for prevention studies.

ClinicalTrials. gov identifier, NCT04733989.

To investigate systemic inflammation and the effect of dapagliflozin treatment in (type 2 diabetes mellitus) T2DM patients with mild cognitive impairment (MCI).

Between January and December 2023, 200 participants were recruited from the Department of Endocrinology of Hefei First People's Hospital. Baseline data collected included medical history, fasting blood glucose, HbA1c, liver and kidney function, lipid profiles, IL-1β, TNF-α, sVCAM-1 level, and the urinary albumin-creatinine ratio (uACR). Based on their Montreal Cognitive Assessment Scale (MoCA) scores, these participants were categorized into two groups: 127 in the MCI group and 73 in the non-MCI group. MCI group received dapagliflozin (10 mg daily) alongside standard treatment.

The MCI group showed higher age, height, weight, BMI, HbA1c, FBG, disease duration, carotid plaques, stenosis rates, and elevated IL-1β, TNF-α, and sVCAM-1. MoCA scores were significantly lower in the MCI group. Correlation analysis showed a negative correlation of MoCA scores with IL-1β, TNF-α, sVCAM-1, plaques, stenosis, FBG, and HbA1c, and a positive correlation with height. Binary logistic regression identified age, BMI, IL-1β, sVCAM-1, and FBG as predictors of cognitive impairment in T2DM. Dapagliflozin treatment reduced BMI, HbA1c, inflammatory markers, and FBG, improving MoCA scores.

Dapagliflozin treatment may improve cognitive function by reducing inflammation.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by intracellular neurofibrillary tangles with tau protein and extracellular β-amyloid plaques. Early and accurate diagnosis is crucial for effective treatment and management.

The purpose of this review is to investigate new technologies that improve diagnostic accuracy while looking at the current diagnostic criteria for AD, such as clinical evaluations, cognitive testing, and biomarker-based techniques.

A thorough review of the literature was done in order to assess both conventional and contemporary diagnostic methods. Multimodal strategies integrating clinical, imaging, and biochemical evaluations were emphasised. The promise of current developments in biomarker discovery was also examined, including mass spectrometry and artificial intelligence.

Current diagnostic approaches include cerebrospinal fluid (CSF) biomarkers, imaging tools (MRI, PET), cognitive tests, and new blood-based markers. Integrating these technologies into multimodal diagnostic procedures enhances diagnostic accuracy and distinguishes dementia from other conditions. New technologies that hold promise for improving biomarker identification and diagnostic reliability include mass spectrometry and artificial intelligence.

Advancements in AD diagnostics underscore the need for accessible, minimally invasive, and cost-effective techniques to facilitate early detection and intervention. The integration of novel technologies with traditional methods may significantly enhance the accuracy and feasibility of AD diagnosis.

Understanding the heterogeneity of Alzheimer's disease (AD) is crucial for advancing precision medicine specifically tailored to this disorder. Recent research has deepened our understanding of AD heterogeneity; however, translating these insights from bench to bedside via neuroimaging heterogeneity frameworks presents significant challenges. In this review, we systematically revisit prior studies and summarize the existing methodology of data-driven neuroimaging studies for AD heterogeneity. We organized the current methodology into 1) a subtyping clustering strategy for patients with AD, and we also subdivided it into subtyping analysis based on cross-sectional multimodal neuroimaging profiles and the identification of long-term disease progression from short-term datasets; 2) a stratified strategy that integrates neuroimaging measures with biomarkers; and 3) individual-specific abnormal patterns based on the normative model. Then, we evaluated the characteristics of these studies along 2 dimensions: 1) the understanding of pathology and 2) clinical application. We systematically address the limitations, challenges, and future directions of research into AD heterogeneity. Our goal is to enhance the neuroimaging heterogeneity framework for AD, thereby facilitating its transition from bench to bedside.

Heterogeneity in clinical phenotypes has led to the description of different phenotypes of Alzheimer's disease (AD). Besides the most frequent amnestic variant of AD (aAD), patients presenting with language deficits are diagnosed with logopenic variant primary progressive aphasia (lvPPA), whereas patients presenting with visual deficits are classified as posterior cortical atrophy (PCA).

This study set out to investigate the value of a multi-parametric [18F]PI-2620 tau PET/MRI protocol to distinguish aAD, lvPPA and PCA to support clinical diagnosis in 32 patients. Phenotype-specific information about tau accumulation, relative perfusion, grey matter density, functional network alterations and white matter microstructural alterations was collected.

The aAD patients showed significantly higher tau accumulation, relative hypoperfusion and grey matter density loss in the temporal lobes compared to PCA and lvPPA patients. PCA patients, on the other hand, showed significantly higher tau accumulation in the occipital lobe as compared to aAD patients. Relative hypoperfusion in the occipital lobe and loss of functional connectivity of the posterior cingulate cortex to supplementary visual cortical regions helped to distinguish PCA from lvPPA. Tau accumulation in the cerebellum and microstructural changes in the cingulum were found to help differentiate lvPPA from aAD.

This study highlights structural and functional differences between patients with different AD phenotypes. Differences in regional tau PET signals suggest that refinements in the Braak staging system are needed for the non-aAD cases. These patterns of tau accumulation align with the cascading network failure hypothesis, though more research is needed to warrant the here presented results in larger patient cohorts.

Neuroinflammation is associated with both early and late stages of the pathophysiology of Alzheimer's disease (AD). Fluid biomarkers are gaining significance in clinical practice for diagnosis in presymptomatic stages, monitoring, and disease prognosis. This systematic literature review (SLR) aimed to identify fluid biomarkers for neuroinflammation related to clinical stages across the AD continuum and examined long-term outcomes associated with changes in biomarkers.

The SLR was conducted per the Cochrane Handbook for Systematic Reviews of Interventions and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We used PubMed®, Embase®, and Cochrane Collaboration databases to search for articles in English (between 2012 and 2022) on AD or mild cognitive impairment due to AD, using "neuroinflammation" or other "immune" search strings. Two independent reviewers screened titles and examined data from full-text articles for the SLR.

After the initial screening, 54 studies were prioritized for data extraction based upon their relevance to the SLR research questions. Nine studies for YKL-40, seven studies for sTREM2, and 11 studies for GFAP examined the relationship between the neuroinflammatory biomarkers and the clinical stage of the disease. Nine longitudinal studies further explored the association of fluid biomarkers with long-term clinical outcomes of disease. Cerebrospinal fluid (CSF) levels of YKL-40 were elevated in patients with AD dementia, while CSF sTREM2 levels were more strongly associated with preclinical and early symptomatic stages of AD. Plasma GFAP levels remained consistently elevated both in patients with AD dementia and individuals in preclinical stages with β-amyloid pathology. Longitudinal changes in plasma GFAP appeared to be predictive of cognitive decline in patients over time.

Neuroinflammatory biomarkers are associated with AD progression. More longitudinal studies in the preclinical and MCI stages of AD are needed to validate fluid biomarkers for diagnosis, disease monitoring, and prognosis in clinical practice.

Although previous studies have shown that specific platelet indices had correlations with cognitive impairment, the associations between platelet indices and cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathology remain unclear.

Our study included 1,047 cognitively normal participants from the Chinese Alzheimer's Biomarker and LifestylE (CABLE) study. The total participants had an average age of 58.33 years, a female proportion of 41.5% and average educational attainment of 9.58 years. Multiple linear regression models were used to analyze the associations of five platelet indices (plateletcrit [PCT], platelet count [PLT], mean platelet volume [MPV], platelet distribution width [PDW], and platelet large cell ratio [PLCR]) with CSF AD biomarkers after adjusting for age, gender, education and APOE ε4 allele status. Furthermore, the interactive, stratified and sensitivity analyses were further conducted to verify their relationships.

In the total participants, we found higher PCT levels were significantly correlated with higher CSF P-tau/Aβ42 (β = 0.102, P = 0.008) and T-tau/Aβ42 (β = 0.102, P = 0.008), as well as lower CSF Aβ42 (β = -0.089, P = 0.018) and Aβ42/Aβ40 (β = -0.093, P = 0.018). Moreover, other four platelet indices (PLT, MPV, PDW, PLCR) demonstrated moderate correlations with CSF AD biomarkers. The interaction analyses revealed that age affected the correlations between PCT and PLT with CSF Aβ42. Importantly, the associations between PCT and the aforementioned CSF AD biomarkers became more significant in the late-life group, but turned non-significant in the mid-life group. Besides, sensitivity analyses confirmed the robustness of our findings.

Our study provided preliminary evidence suggesting potential associations between platelet indices (especially PCT) and CSF AD biomarkers in cognitively intact adults. Nonetheless, more studies are needed to further validate these findings.

The lack of a definitive cure for Alzheimer's disease (AD) is fueling the search for innovative therapeutic strategies. Having revolutionized cancer immunotherapy, immune cell engineering with chimeric antigen receptors (CAR) is being explored to target AD. Whether CARs can recognize distinct amyloid-β (Aβ) species and tau neurofibrillary tangles (NFTs)-hallmark pathologies of AD-remains unclear.

To investigate this, we engineered a series of CARs using single-chain fragment variable (scFv) derived from the variable light and heavy chains of antibodies tested in AD clinical trials. These included E2814 (E2814-CAR), targeting tau; Lecanemab (Lec-CAR) and Aducanumab (Adu-CAR), targeting Aβ; and Donanemab (Don-CAR) and Remternetug (Rem-CAR), targeting the truncated pyroglutamated Aβ species Aβp3-42. To evaluate CAR function, we utilized the murine DO11.10 CD4⁺ T-cell hybridoma line as a scalable and reproducible platform. CAR activation was assessed in response to tau preformed fibrils (PFFs), Aβ1-42 oligomer-enriched aggregates, and Aβp3-42 aggregates, using flow cytometry for CD69 expression and ELISA for IL-2 secretion. To validate this platform, we tested Adu-CAR in primary mouse CD4⁺ T cells treated with Aβ1-42 aggregates and assessed activation via flow cytometry for CD69 and CD25 expression.

DO11.10 cells expressing E2814-CAR-but not Lec-CAR-responded to tau PFFs. In contrast, cells expressing Adu-CAR, and to a lesser extent Lec-CAR-but not E2814-CAR-responded to Aβ1-42 aggregates. For Aβp3-42 aggregates, Rem-CAR elicited the strongest response, followed by Adu-CAR, while E2814-CAR and Don-CAR showed no activation. The activation of Adu-CAR by Aβ1-42 aggregates was recapitulated in primary CD4⁺ T cells, as measured by CD69 expression.

Our findings demonstrate that CARs can detect and discriminate between tau PFFs, Aβ1-42, and Aβp3-42 aggregates. This highlights the potential of repurposing AD antibodies for CAR-based therapies to selectively target tau NFTs and distinct forms of Aβ senile plaques.

Emerging evidence highlights the bidirectional communication between the gut microbiota and the brain, suggesting a potential role for gut dysbiosis in Alzheimer's disease (AD) pathology and cognitive decline. Existing literature on gut microbiota lacks species-level insights. This study investigates gut microbiota alterations in mild cognitive impairment (MCI), focusing on their association with comprehensive AD biomarkers, including amyloid burden, tau pathology, neurodegeneration, and cognitive performance.

We analyzed fecal samples from 119 individuals with MCI and 320 cognitively normal controls enrolled in the Taiwan Precision Medicine Initiative on Cognitive Impairment and Dementia cohort. Shotgun metagenomic sequencing was conducted with taxonomic profiling using MetaPhlAn4. Amyloid burden and plasma pTau181 were quantified via PET imaging and Simoa assays, respectively, while APOE genotyping was performed using TaqMan assays. Microbial diversity, differential abundance analysis, and correlation mapping with neuropsychological and neuroimaging measures were conducted to identify gut microbiota species signatures associated with MCI and AD biomarkers.

We identified 59 key microbial species linked to MCI and AD biomarkers. Notably, species within the same genera, such as Bacteroides and Ruminococcus, showed opposing effects, while Akkermansia muciniphila correlated with reduced amyloid burden, suggesting a protective role. Functional profiling revealed microbial pathways contributing to energy metabolism and neuroinflammation, mediating the relationship between gut microbes and brain health. Co-occurrence network analyses demonstrated complex microbial interactions, indicating that the collective influence of gut microbiota on neurodegeneration.

Our findings challenge genus-level microbiome analyses, revealing species-specific modulators of AD pathology. This study highlights gut microbial activity as a potential therapeutic target to mitigate cognitive decline and neurodegeneration.

While anatomical white matter (WM) alterations in Alzheimer's disease (AD) are well-established, functional WM dysregulation remains rarely investigated. The current study examines WM functional connectivity and network properties alterations in AD and mild cognitive impairment (MCI) and further describes their spatially correlated genes. AD and MCI shared decreased functional connectivity, clustering coefficient, and local efficiency within WM regions involved in impaired sensory-motor, visual-spatial, language, or memory functions. AD-specific dysfunction (i.e., AD vs. MCI and cognitively unimpaired participants) was predominantly located in WM, including anterior and posterior limb of internal capsule, corona radiata, and left tapetum. This WM dysfunction spatially correlates with specific genes, which are enriched in multiple biological processes related to synaptic function and development, and are mostly active in neurons and astrocytes. These findings may contribute to understanding molecular, cellular, and functional signatures associated with WM damage in AD.

Postmortem neuropathological examination, while the gold standard for diagnosing neurodegenerative diseases, often relies on limited regional sampling that may miss critical areas affected by Alzheimer's disease and related disorders. Ultra-high resolution postmortem MRI can help identify regions that fall outside the diagnostic sampling criteria for additional histopathologic evaluation. However, there are no standardized guidelines for integrating histology and MRI in a traditional brain bank. We developed a comprehensive protocol for whole hemisphere postmortem 7T MRI-guided histopathological sampling with whole-slide digital imaging and histopathological analysis, providing a reliable pipeline for high-volume brain banking in heterogeneous brain tissue. Our method uses patient-specific 3D printed molds built from postmortem MRI, allowing standardized tissue processing with a permanent spatial reference frame. To facilitate pathology-MRI association studies, we created a semi-automated MRI to histology registration pipeline and developed a quantitative pathology scoring system using weakly supervised deep learning. We validated this protocol on a cohort of 29 brains with diagnosis on the AD spectrum that revealed correlations between cortical thickness and phosphorylated tau accumulation. This pipeline has broad applicability across neuropathological research and brain banking, facilitating large-scale studies that integrate histology with neuroimaging. The innovations presented here provide a scalable and reproducible approach to studying postmortem brain pathology, with implications for advancing diagnostic and therapeutic strategies for Alzheimer's disease and related disorders.

Alzheimer's disease (AD) is a degenerative condition of the nervous system that causes severe damage to patients' daily activities and quality of life. Amyloid beta 1-40 protein (Aβ40), which is involved in the formation of cerebral plaques, is one of the crucial biomarkers related to AD. Herein, a novel and highly sensitive immunosensor for the detection of Aβ40 is developed. Using a reinforced indium tin oxide-coated glass with a nanocomposite of gold nanoparticle-enhanced CoSn(OH)6 (AuNPs@CoSn(OH)6) to trigger the electrochemiluminescence (ECL) of luminol as the sensing signal, the immunosensor is fabricated by immobilizing the Aβ40 antibody onto it. By integrating the high immune specificity, excellent conductivity and catalytic activity of the nanocomposite, the resultant immunosensor can be successfully employed to detect the target in real samples. The formation of the immune complex leads to increased steric hindrance and electron transfer resistance, which in turn causes a declined ECL output when the target Aβ40 binds to the antibody on the sensor surface. Under optimized conditions, the developed ECL immunosensor exhibits a linear response for Aβ40 ranging from 1 to 800 pg mL-1 and a low detection limit of 0.47 pg mL-1. Experimentally, it is demonstrated to be highly sensitive, specific, reproducible and stable. This work extends the application of the perovskite CoSn(OH)6 and AuNPs in the field of ECL immunosensing and provides a novel strategy for clinical research on Alzheimer's disease.

Cross-sectional studies suggest a limited relationship between accelerated epigenetic aging derived from epigenetic clocks, and Alzheimer's disease (AD) pathophysiology or risk. However, most prior analyses have not utilized longitudinal analyses or whole-brain neuroimaging biomarkers of AD. Herein, we employed longitudinal modeling and structural neuroimaging analyses to test the hypothesis that accelerated epigenetic aging would predict AD progression. Using survival analyses, we found that two second-generation epigenetic clocks, DNAmPhenoAge and DNAmGrimAge, predicted progression from cognitively normal aging to mild cognitive impairment or AD and worse longitudinal cognitive outcomes. Epigenetic age was also strongly associated with cortical thinning in AD-relevant regions and white matter disease burden. Thus, in contrast to earlier work suggesting limited applicability of blood-based epigenetic clocks in AD, our novel analytic framework suggests that second-generation epigenetic clocks have broad utility and may represent promising predictors of AD risk and pathophysiology.