Analysis of eligibility criteria in Alzheimer's and related dementias clinical trials.

Overly restrictive clinical trial eligibility criteria can reduce generalizability, slow enrollment, and disproportionately exclude historically underrepresented populations. The eligibility criteria for 196 Alzheimer's Disease and Related Dementias (AD/ADRD) trials funded by the National Institute on Aging were analyzed to identify common criteria and their potential to disproportionately exclude participants by race/ethnicity. The trials were categorized by type (48 Phase I/II pharmacological, 7 Phase III/IV pharmacological, 128 non-pharmacological, 7 diagnostic, and 6 neuropsychiatric) and target population (51 AD/ADRD, 58 Mild Cognitive Impairment, 25 at-risk, and 62 cognitively normal). Eligibility criteria were coded into the following categories: Medical, Neurologic, Psychiatric, and Procedural. A literature search was conducted to describe the prevalence of disparities for eligibility criteria for African Americans/Black (AA/B), Hispanic/Latino (H/L), American Indian/Alaska Native (AI/AN) and Native Hawaiian/Pacific Islander (NH/PI) populations. The trials had a median of 15 criteria. The most frequent criterion were age cutoffs (87% of trials), specified neurologic (65%), and psychiatric disorders (61%). Underrepresented groups could be disproportionately excluded by 16 eligibility categories; 42% of trials specified English-speakers only in their criteria. Most trials (82%) contain poorly operationalized criteria (i.e., criteria not well defined that can have multiple interpretations/means of implementation) and criteria that may reduce racial/ethnic enrollment diversity.

Plasma biomarkers of amyloid, tau, axonal, and neuroinflammation pathologies in dementia with Lewy bodies.

BACKGROUND: Increasing evidence supports the use of plasma biomarkers of amyloid, tau, neurodegeneration, and neuroinflammation for diagnosis of dementia. However, their performance for positive and differential diagnosis of dementia with Lewy bodies (DLB) in clinical settings is still uncertain. METHODS: We conducted a retrospective biomarker study in two tertiary memory centers, Paris Lariboisière and CM2RR Strasbourg, France, enrolling patients with DLB (n = 104), Alzheimer's disease (AD, n = 76), and neurological controls (NC, n = 27). Measured biomarkers included plasma Aß40/Aß42 ratio, p-tau181, NfL, and GFAP using SIMOA and plasma YKL-40 and sTREM2 using ELISA. DLB patients with available CSF analysis (n = 90) were stratified according to their CSF Aß profile. RESULTS: DLB patients displayed modified plasma Aß ratio, p-tau181, and GFAP levels compared with NC and modified plasma Aß ratio, p-tau181, GFAP, NfL, and sTREM2 levels compared with AD patients. Plasma p-tau181 best differentiated DLB from AD patients (ROC analysis, area under the curve [AUC] = 0.80) and NC (AUC = 0.78), and combining biomarkers did not improve diagnosis performance. Plasma p-tau181 was the best standalone biomarker to differentiate amyloid-positive from amyloid-negative DLB cases (AUC = 0.75) and was associated with cognitive status in the DLB group. Combining plasma Aß ratio, p-tau181 and NfL increased performance to identify amyloid copathology (AUC = 0.79). Principal component analysis identified different segregation patterns of biomarkers in the DLB and AD groups. CONCLUSIONS: Amyloid, tau, neurodegeneration and neuroinflammation plasma biomarkers are modified in DLB, albeit with moderate diagnosis performance. Plasma p-tau181 can contribute to identify Aß copathology in DLB.

Prescription medication use in the 10 years prior to diagnosis of young onset Alzheimer's disease: a nationwide nested case-control study.

BACKGROUND: Patients with young onset Alzheimer's disease (YOAD) face long diagnostic delays. Prescription medication use may provide insights into early signs and symptoms, which may help facilitate timely diagnosis. METHODS: In a register-based nested case-control study, we examined medication use for everyone diagnosed with YOAD in a Danish memory clinic during 2016-2020 compared to cognitively healthy controls. Prescription medication use were grouped into 13 overall categories (alimentary tract and metabolism, blood and blood forming organs, cardiovascular system, dermatologicals, genitourinary system and sex hormones, systemic hormonal preparations, antiinfectives for systemic use, antineoplastic and immunomodulating agents, musculo-skeletal system, nervous system, antiparasitic products, respiratory system, and sensory organs). Further stratifications were done for predetermined subcategories with a use-prevalence of at least 5% in the study population. Conditional logistic regression produced odds ratios, which given the use of incidence-density matching is interpretable as incidence rate ratios (IRRs). The association between prescription medication use and subsequent YOAD diagnosis was examined in the entire 10-year study period and in three time-intervals. RESULTS: The study included 1745 YOAD cases and 5235 controls. In the main analysis, several overall categories showed significant associations with YOAD in one or more time-intervals, namely blood and blood forming organs and nervous system. Prescription medication use in the nervous system category was increased for YOAD cases compared to controls already 10->5 years prior to diagnosis (IRR 1.17, 95% CI 1.05-1.31), increasing to 1.57 (95% CI 1.39-1.78) in the year preceding diagnosis. This was largely driven by antidepressant and antipsychotic use, and especially prominent for first-time users. CONCLUSIONS: In this study, medication use in several categories was associated with YOAD. Onset of treatment-requiring psychiatric symptoms such as depression or psychosis in mid-life may serve as potential early indicators of YOAD.

Blood Astrocyte Biomarkers in Alzheimer Disease: A Systematic Review and Meta-Analysis.

BACKGROUND AND OBJECTIVES: Neuroinflammation, particularly early astrocyte reactivity, is a significant driver of Alzheimer disease (AD) pathogenesis. It is unclear how the levels of astrocyte biomarkers change in patients across the AD continuum and which best reflect AD-related change. We performed a systematic review and meta-analysis of 3 blood astrocyte biomarkers (glial fibrillary acidic protein [GFAP], chitinase-3-like protein 1 [YKL-40], and S100B) in patients clinically diagnosed with AD. METHODS: MEDLINE and Web of Science were searched on March 23, 2023, without restrictions on language, time, or study design, for studies reporting blood levels of the astrocyte biomarkers GFAP, YKL-40, or S100B in patients on the AD continuum (including those with mild cognitive impairment [MCI] and dementia) and a cognitively unimpaired (CU) control population. AD diagnosis was based on established diagnostic criteria and/or comprehensive multidisciplinary clinical consensus. Studies reporting indirect biomarker measures (e.g., levels of biomarker autoantibodies) were excluded. Risk of bias assessment was performed using the revised Quality Assessment of Diagnostic Accuracy Studies tool. Pooled effect sizes were determined using the Hedge g method with a random-effects model. The review was prospectively registered on PROSPERO (registration number CRD42023458305). RESULTS: The search identified 1,186 studies; 36 met inclusion criteria (AD continuum n = 3,366, CU n = 4,115). No study was assessed to have a high risk of bias. Compared with CU individuals, patients on the AD continuum had higher GFAP and YKL-40 levels (GFAP effect size 1.15, 95% CI 0.94-1.36, p < 0.0001; YKL-40 effect size 0.38, 95% CI 0.28-0.49, p < 0.0001). Both biomarkers were elevated in more advanced clinical stages of the disease (i.e., in AD dementia compared with MCI due to AD: GFAP effect size 0.48, 95% CI 0.19-0.76, p = 0.0009; YKL-40 effect size 0.34, 95% CI 0.10-0.57, p = 0.0048). No significant differences in blood S100B levels were identified. DISCUSSION: We demonstrated significant elevations in blood GFAP and YKL-40 levels in patients on the AD continuum compared with CU individuals. Furthermore, within the AD clinical spectrum, significant elevation correlated with more advanced disease stage. Our findings suggest that both biomarkers reflect AD-related pathology. Our findings are limited by the lack of cultural and linguistic diversity in the study populations meta-analyzed. Future meta-analyses using a biomarker-defined AD population are warranted.

Neutrophil-Lymphocytes Ratio as Potential Early Marker for Alzheimer's Disease.

Background: Neutrophil-lymphocyte ratio (NLR) is a noninvasive, inexpensive, and easily applicable marker of inflammation. Since immune dysregulation leading to inflammation is regarded as a hallmark of dementia, in particular Alzheimer's disease (AD), we decided to investigate the potentials of NLR as a diagnostic and predictive biomarker in this clinical setting. Materials and Methods: NLR was measured in the blood of patients with AD (n = 103), amnestic type mild cognitive impairment (aMCI, n = 212), vascular dementia (VAD, n = 34), and cognitively healthy Controls (n = 61). One hundred twelve MCI patients underwent a regular clinical follow-up. Over a 36-months median follow-up, 80 remained stable, while 32 progressed to overt dementia. Results: NLR was higher in patients with aMCI or dementia compared to Controls; however, the difference was statistically significant only for aMCI (+13%, p=0.04) and AD (+20%, p=0.03). These results were confirmed by multivariate logistic analysis, which showed that high NLR was associated with an increase in the likelihood of receiving a diagnosis of aMCI (odd ratio (OR): 2.58, 95% confidence interval (CI): 1.36-4.89) or AD (OR: 3.13, 95%CI: 1.47-6.70), but not of VAD. NLR did not differ when comparing stable vs. progressing aMCI. Conclusions: This is the first report showing that NLR is significantly increased in MCI and AD but not in VAD. We also found that NLR was unable to predict the conversion from aMCI to AD. Further research on larger cohorts is warranted to definitely ascertain the application of NLR as a possible marker for aMCI and AD.

Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study.

BACKGROUND: Alzheimer disease and related dementias (ADRD) rank as the sixth leading cause of death in the United States, underlining the importance of accurate ADRD risk prediction. While recent advancements in ADRD risk prediction have primarily relied on imaging analysis, not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes. OBJECTIVE: The study aims to use graph neural networks (GNNs) with claim data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative, self-explainable method to evaluate relationship importance and its influence on ADRD risk prediction. METHODS: We used a variationally regularized encoder-decoder GNN (variational GNN [VGNN]) integrated with our proposed relation importance method for estimating ADRD likelihood. This self-explainable method can provide a feature-important explanation in the context of ADRD risk prediction, leveraging relational information within a graph. Three scenarios with 1-year, 2-year, and 3-year prediction windows were created to assess the model's efficiency, respectively. Random forest (RF) and light gradient boost machine (LGBM) were used as baselines. By using this method, we further clarify the key relationships for ADRD risk prediction. RESULTS: In scenario 1, the VGNN model showed area under the receiver operating characteristic (AUROC) scores of 0.7272 and 0.7480 for the small subset and the matched cohort data set. It outperforms RF and LGBM by 10.6% and 9.1%, respectively, on average. In scenario 2, it achieved AUROC scores of 0.7125 and 0.7281, surpassing the other models by 10.5% and 8.9%, respectively. Similarly, in scenario 3, AUROC scores of 0.7001 and 0.7187 were obtained, exceeding 10.1% and 8.5% than the baseline models, respectively. These results clearly demonstrate the significant superiority of the graph-based approach over the tree-based models (RF and LGBM) in predicting ADRD. Furthermore, the integration of the VGNN model and our relation importance interpretation could provide valuable insight into paired factors that may contribute to or delay ADRD progression. CONCLUSIONS: Using our innovative self-explainable method with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data.

Basic, Translational, and Clinical Research on Dementia.

The global impact of dementia is an increasing area of concern and, according to the Alzheimer's Disease International (ADI) World Alzheimer Report 2021, up to 90% of dementia patients in low- and middle-income countries are not diagnosed [...].

Comparative Analysis of Retinal Structural and Angiographic Parameters in Diabetic Patients, Alzheimer's Disease Patients, and Healthy Controls: An OCT-Based Study.

Objective: This study aimed to investigate the potential connections between Alzheimer's Disease (AD) and diabetes. Methods: This is a cross-sectional study in which AD and diabetes patients sent by the Psychiatry and Diabetes Departments for ophthalmological screening were observed for inclusion/exclusion criteria. Patients were divided into two comparison groups. The first group (n=3) consisted of the age-matched normal and diabetic patient of the stage 3 AD disease participant. The second group (n=3) was for the stage 5 AD patient with diabetes and normal age-matched controls. Each patient underwent a full ophthalmological examination and SS-OCT (Swept Source-Ocular Computer Tomography) for retinal evaluation. Results: A total of 6 patients (12 eyes) were obtained, three men and three women. In the early AD group, the patient with diabetes showed lower macular thickness compared to other groups. In the nasal-inferior (NI) and temporal-superior (TS) ganglion cell layer (GCL), the AD patient showed statistically significant lower values compared to the other patients. In the moderately severe AD group, we found that the AD patient had lower retinal nerve fiber layer (RNFL) thickness on the temporal side compared to the rest of the patients and both the AD patient and diabetes patient showed lower RNFL thickness in the nasal-superior (NS) quadrant. Also, the foveal avascular zone (FAZ) area was statistically significantly lower for both the diabetes and AD patients compared to the healthy control. Conclusions: In conclusion, distinct retinal findings associated with AD and diabetes in young and elderly patients were revealed in our study. The clinical implications and potential interplay between these conditions need to be elucidated by further research. Abbreviations: AD = Alzheimer's Disease, SS-OCT = Swept Source - Ocular Computer Tomography, GCL = Ganglion cell layer, RNFL = Retinal nerve fiber layer, FAZ = foveal avascular zone.

Evaluation of core Biomarkers of Alzheimer's disease in saliva and plasma measured by chemiluminescent enzyme immunoassays on a fully automated platform.

Cerebrospinal fluid (CSF) core biomarkers of Alzheimer's disease (AD), including amyloid peptide beta-42 (Aß42), Aß42/40 ratio, and phosphorylated tau (pTau), are precious tools for supporting AD diagnosis. However, their use in clinical practice is limited due to the invasiveness of CSF collection. Thus, there is intensive research to find alternative, noninvasive, and widely accessible biological matrices to measure AD core biomarkers. In this study, we measured AD core biomarkers in saliva and plasma by a fully automated platform. We enrolled all consecutive patients with cognitive decline. For each patient, we measured Aß42, Aß40, and pTau levels in CSF, saliva, and plasma by Lumipulse G1200 (Fujirebio). We included forty-two patients, of whom 27 had AD. Levels of all biomarkers significantly differed in the three biofluids, with saliva having the lowest and CSF the highest levels of Aß42, Aß40, and pTau. A positive correlation of pTau, Aß42/40 ratio, and pTau/Aß42 ratio levels in CSF and plasma was detected, while no correlation between any biomarker in CSF and saliva was found. Our findings suggest that plasma but not saliva could represent a surrogate biofluid for measuring core AD biomarkers. Specifically, plasma Aß42/40 ratio, pTau/Aß42 ratio, and pTau could serve as surrogates of the corresponding CSF biomarkers.

Predicting Longitudinal Cognitive Decline and Alzheimer's Conversion in Mild Cognitive Impairment Patients Based on Plasma Biomarkers.

The increasing burden of Alzheimer's disease (AD) emphasizes the need for effective diagnostic and therapeutic strategies. Despite available treatments targeting amyloid beta (Aß) plaques, disease-modifying therapies remain elusive. Early detection of mild cognitive impairment (MCI) patients at risk for AD conversion is crucial, especially with anti-Aß therapy. While plasma biomarkers hold promise in differentiating AD from MCI, evidence on predicting cognitive decline is lacking. This study's objectives were to evaluate whether plasma protein biomarkers could predict both cognitive decline in non-demented individuals and the conversion to AD in patients with MCI. This study was conducted as part of the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD), a prospective, community-based cohort. Participants were based on plasma biomarker availability and clinical diagnosis at baseline. The study included MCI (n = 50), MCI-to-AD (n = 21), and cognitively unimpaired (CU, n = 40) participants. Baseline plasma concentrations of six proteins-total tau (tTau), phosphorylated tau at residue 181 (pTau181), amyloid beta 42 (Aß42), amyloid beta 40 (Aß40), neurofilament light chain (NFL), and glial fibrillary acidic protein (GFAP)-along with three derivative ratios (pTau181/tTau, Aß42/Aß40, pTau181/Aß42) were analyzed to predict cognitive decline over a six-year follow-up period. Baseline protein biomarkers were stratified into tertiles (low, intermediate, and high) and analyzed using a linear mixed model (LMM) to predict longitudinal cognitive changes. In addition, Kaplan-Meier analysis was performed to discern whether protein biomarkers could predict AD conversion in the MCI subgroup. This prospective cohort study revealed that plasma NFL may predict longitudinal declines in Mini-Mental State Examination (MMSE) scores. In participants categorized as amyloid positive, the NFL biomarker demonstrated predictive performance for both MMSE and total scores of the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet (CERAD-TS) longitudinally. Additionally, as a baseline predictor, GFAP exhibited a significant association with cross-sectional cognitive impairment in the CERAD-TS measure, particularly in amyloid positive participants. Kaplan-Meier curve analysis indicated predictive performance of NFL, GFAP, tTau, and Aß42/Aß40 on MCI-to-AD conversion. This study suggests that plasma GFAP in non-demented participants may reflect baseline cross-sectional CERAD-TS scores, a measure of global cognitive function. Conversely, plasma NFL may predict longitudinal decline in MMSE and CERAD-TS scores in participants categorized as amyloid positive. Kaplan-Meier curve analysis suggests that NFL, GFAP, tTau, and Aß42/Aß40 are potentially robust predictors of future AD conversion.

Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease.

Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer's disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.

Novel diagnostic and prognostic approach for rapidly progressive dementias: Indicators based on amyloid/tau/neurodegeneration (ATN) framework.

AIMS: Apply established cerebrospinal fluid (CSF) and serum biomarkers and novel combined indicators based on the amyloid/tau/neurodegeneration (ATN) framework to improve diagnostic and prognostic power in patients with rapidly progressive dementias (RPDs). METHODS: CSF and serum biomarkers of Alzheimer's disease (AD) common neuropathology including Aß42, Aß40, p-Tau, and t-Tau were measured in cognitively normal (CN) controls (n = 33) and three RPD groups with rapidly progressive AD (rpAD, n = 23), autoimmune encephalitis (AE, n = 25), and Creutzfeldt-Jakob disease (CJD, n = 28). Logistic regression and multiple linear regression were used for producing combined indicators and prognostic assessment, respectively, including A&T, A&N, T&N, A&T&N, etc. RESULTS: Combined diagnostic indicator with A&T&N had the potential for differentiating AE from other types of RPDs, identifying 62.51% and 75% of AE subjects based on CSF and serum samples, respectively, compared to 39.13% and 37.5% when using autoantibodies. CSF t-Tau was associated with survival in the CJD group (adjusted R-Square = 0.16, p = 0.02), and its prognosis value improved when using combined predictors based on the ATN framework (adjusted R-Square = 0.273, p = 0.014). CONCLUSION: Combined indicators based on the ATN framework provide a novel perspective for establishing biomarkers for early recognition of RPDs due to treatment-responsive causes.

An Integrative Review about Electrophysiological Biomarkers of Cognitive Impairment in Alzheimer's Disease: A Developing Relationship.

Background: Electrophysiological biomarkers are being examined as potential diagnostic measures of cognitive impairment and its manifestations for psychiatric nurses' use in the care of Alzheimer's disease (AD). However, there is no integrative review describing the themes from the current research about electrophysiological biomarkers and the developing relationship among the themes. Characterizing this developing relationship is imperative for any possible integration of biomarkers into the care of AD by psychiatric nurses. Objective: The purpose of this integrative review is to identify themes from the current research about electrophysiological biomarkers of AD and the developing relationship among the themes, the conceivable relational premise for psychiatric nurses to integrate electrophysiological biomarkers into the screening, assessment, diagnosis, and treatment of AD for the care of persons with AD. Methods: A literature search was executed with PUBMED (accessing Medline and Elsevier) and CINAHL databases that focused on studies about electrophysiological biomarkers of AD from 2015 to 2022. Twenty-seven peer-reviewed studies met this review's inclusion criteria. Results: Five themes emerged: (1) assessing/screening, (2) assessment differential, (3) diagnosing, (4) diagnostic accuracy, and (5) treating. These themes related sequentially and linearly, establishing a developing relationship about the risk, the onset, and the progression of AD. Discussion: Electrophysiological biomarkers associated to cognitive impairment in AD, supporting the accepted understanding of the symptoms of AD. Changes in behavior and functioning were not examined, limiting the possible integration of electrophysiological biomarkers. Further investigations are warranted with an expansion of the clinical symptoms and diverse study populations.

[An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion].

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.

Exploration of mitochondrial autophagy related genes in the diagnosis model construction and molecular marker mining of Alzheimer's disease based on multi-omics integration.

Key features of Alzheimer's disease include neuronal loss, accumulation of beta-amyloid plaques, and formation of neurofibrillary tangles. These changes are due in part to abnormal protein metabolism, particularly the accumulation of amyloid beta. Mitochondria are the energy production centers within cells and are also the main source of oxidative stress. In AD, mitochondrial function is impaired, leading to increased oxidative stress and the production of more reactive oxidative substances, further damaging cells. Mitophagy is an important mechanism for maintaining mitochondrial health, helping to clear damaged mitochondria, prevent the spread of oxidative stress, and reduce abnormal protein aggregation. To this end, this article conducts an integrated analysis based on DNA methylation and transcriptome data of AD. After taking the intersection of the genes where the differential methylation sites are located and the differential genes, machine learning methods were used to build an AD diagnostic model. This article screened five diagnostic genes ATG12, CSNK2A2, CSNK2B, MFN1 and PGAM5 and conducted experimental verification. The diagnostic genes discovered and the diagnostic model constructed in this article can provide reference for the development of clinical diagnostic models for AD.

Development of a real-world simulated instrument for evaluating visuospatial working memory: a preliminary psychometric study on older adults.

BACKGROUND: A prevalent challenge in neuropsychological assessment, particularly when utilizing instruments designed for controlled laboratory environments, is that the outcomes may not correspond to an individual's real-life status. Accordingly, assessments of visuospatial working memory (VSWM) conducted in such settings might fail to capture certain facets of this function, as it operates in real life. On the other hand, entirely ecological assessments may risk compromising internal validity. This study aimed to develop an intermediate mode of assessment that measures VSWM in older adults by employing a setting, a task, and a response format that aligns closely with both laboratory and ecological assessments. Furthermore, a preliminary investigation was carried out to study the variations in spatial cognition among different demographic groups. METHODS: In a two-session study, 77 healthy older adults, eight patients with mild cognitive impairment (MCI), and seven patients with Alzheimer's disease (AD) were recruited to complete the wayfinding questionnaire (WQ), the Corsi block-tapping task (CBTT), and the Spatial Memory Table (SMT). The SMT is a novel instrument developed specifically for this study, aiming to provide a more accurate measure of VSWM performance in older adults' everyday life. Test-retest and split-half reliabilities, as well as the face, content, concurrent, convergent, and known-groups validities, were analyzed to investigate the psychometric properties of the SMT. RESULTS: The analyses were mainly centered on studying the psychometric properties of the SMT. Test-retest reliability (r = .753, p < .001) and split-half reliability (ρSC = 0.747) were found to be acceptable. Concurrent validity using CBTT (r = .264, p = .021), convergent validity using WQ subscales (navigation and orientation: r = .282, p = .014; distance estimation: r = .261, p = .024), and known-groups validity using the SMT scores among people with MCI and AD (χ2 = 35.194, df = 2, p < .001) were also indicative of the instrument's good validity. Data analysis also revealed acceptable levels of face validity (U = 4.50; p = .095) and content validity (CVR ≥ 0.60). As a result of comparing VSWM and wayfinding variables across genders and education levels, a significant difference was observed for navigation and orientation and spatial anxiety between women and men (p < .05). None of the variables were different among education levels. CONCLUSION: The SMT was found to be a reliable and valid tool for measuring VSWM performance in older adults. Given these findings, the SMT can be regarded as a measure that sufficiently approximates both laboratory and real-life demands for VSWM. Additionally, the instrument demonstrated a preliminary acceptable capacity to differentiate between healthy individuals and those with MCI and AD.

Matrix Remodeling Enzymes as Potential Fluid Biomarkers of Neurodegeneration in Alzheimer's Disease.

This study investigated the diagnostic accuracy of plasma biomarkers-specifically, matrix metalloproteinase (MMP-9), tissue inhibitor of metalloproteinase (TIMP-1), CD147, and the MMP-/TIMP-1 ratio in patients with Alzheimer's disease (AD) dementia. The research cohort comprised patients diagnosed with probable AD dementia and a control group of cognitively unimpaired (CU) individuals. Neuroradiological assessments included brain magnetic resonance imaging (MRI) following dementia protocols, with subsequent volumetric analysis. Additionally, cerebrospinal fluid (CSF) AD biomarkers were classified using the A/T/N system, and apolipoprotein E (APOE) ε4 carrier status was determined. Findings revealed elevated plasma levels of MMP-9 and TIMP-1 in AD dementia patients compared to CU individuals. Receiver operating characteristic (ROC) curve analysis demonstrated significant differences in the areas under the curve (AUC) for MMP-9 (p < 0.001) and TIMP-1 (p < 0.001). Notably, plasma TIMP-1 levels were significantly lower in APOE ε4+ patients than in APOE ε4- patients (p = 0.041). Furthermore, APOE ε4+ patients exhibited reduced hippocampal volume, particularly in total, right, and left hippocampal measurements. TIMP-1 levels exhibited a positive correlation, while the MMP-9/TIMP-1 ratio showed a negative correlation with hippocampal volume parameters. This study sheds light on the potential use of TIMP-1 as a diagnostic marker and its association with hippocampal changes in AD.

Potential prognostic value of CSF-targeted proteomics across the Alzheimer's disease continuum.

BACKGROUND: Core biomarkers for Alzheimer's disease (AD), such as Aß42 and tau, have demonstrated high prognostic accuracy but do not fully capture the complex pathophysiology of AD. In this study, our objective was to identify novel cerebrospinal fluid (CSF) biomarkers using proteomics across the entire AD continuum to predict conversion to AD and explore their involvement in AD pathogenesis. METHODS: A cohort of 186 cognitively normal (CN), 127 subjective memory complaint (SMC), 79 early mild cognitive impairment (EMCI), 249 late MCI (LMCI), and 132 AD individuals was analyzed, with a follow-up period of over 3 years for non-AD participants. CSF 65 peptides, as well as hippocampal and entorhinal volumes were analyzed, and cognitive function was evaluated using the 13-item cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-Cog 13). Cox proportional hazards models and mediation analysis were performed to investigate associations and causal relationships. RESULTS: During the follow-up, approximately one-fourth (146/580) of the non-AD participants progressed to AD. After adjusting for baseline diagnosis (CN to LMCI) and other variables, multivariable Cox regression analysis identified three peptides (VAELEDEK, VSFELFADK, and VVSSIEQK) as significant predictors of conversion to AD. Incorporating these three peptides into the initial model significantly improved the C-statistic from 0.82 to 0.85 for predicting AD conversion, surpassing the predictive ability of Aß42 and P-tau. Moreover, hippocampal and entorhinal volumes mediated 30.3-53.8% of the association between the three peptides and ADAS-Cog 13 scores. CONCLUSIONS: These findings underscore the potential of these three peptides as robust prognostic biomarker candidates for AD conversion across the entire AD continuum, with a mechanism involving the mediation of hippocampal and entorhinal volumes.