Mild Cognitive Impairment Alzheimer Research Articles

APOE4 and Infectious Diseases Jointly Contribute to Brain Glucose Hypometabolism, a Biomarker of Alzheimer's Pathology: New Findings from the ADNI.

Impaired brain glucose metabolism is a preclinical feature of neurodegenerative diseases such as Alzheimer's disease (AD). Infections may promote AD-related pathology. Therefore, we investigated the interplay between infections and APOE4, a strong genetic risk factor for AD. We analyzed data on 1,509 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using multivariate linear regression models. The outcomes were rank-normalized hypometabolic convergence index (HCI), statistical regions of interest (SROI) for AD, and mild cognitive impairment (MCI). Marginal mean estimates for infections, stratified by APOE4 carrier status, were then computed. Prior infections were associated with greater HCI [β=0.15, 95% CI: 0.03, 0.27, p=0.01]. The combined effects of infections and APOE4 carriers on HCI levels were significantly greater than either variable alone. Among APOE4 carriers, the estimated marginal mean was 0.62, rising to 0.77, with infections (p<0.001), indicating an interaction effect. Carriers with multiple infections showed greater hypometabolism (higher HCI), with an estimate of 0.44 (p=0.01) compared to 0.11 (p=0.08) for those with a single infection, revealing a dose-response relationship. The estimates for the association of infections with SROI AD and SROI MCI were β=-0.01 (p=0.02) and β=-0.01 (p=0.04), respectively. Our findings suggest that infections and APOE4 jointly contribute to brain glucose hypometabolism and AD pathology, supporting a "multi-hit" mechanism in AD development.

A transformer-based unified multimodal framework for Alzheimer's disease assessment

In Alzheimer's disease (AD) assessment, traditional deep learning approaches have often employed separate methodologies to handle the diverse modalities of input data. Recognizing the critical need for a cohesive and interconnected analytical framework, we propose the AD-Transformer, a novel transformer-based unified deep learning model. This innovative framework seamlessly integrates structural magnetic resonance imaging (sMRI), clinical, and genetic data from the extensive Alzheimer's Disease Neuroimaging Initiative (ADNI) database, encompassing 1651 subjects. By employing a Patch-CNN block, the AD-Transformer efficiently transforms image data into image tokens, while a linear projection layer adeptly converts non-image data into corresponding tokens. As the core, a transformer block learns comprehensive representations of the input data, capturing the intricate interplay between modalities. The AD-Transformer sets a new benchmark in AD diagnosis and Mild Cognitive Impairment (MCI) conversion prediction, achieving remarkable average area under curve (AUC) values of 0.993 and 0.845, respectively, surpassing those of traditional image-only models and non-unified multimodal models. Our experimental results confirmed the potential of the AD-Transformer as a potent tool in AD diagnosis and MCI conversion prediction. By providing a unified framework that jointly learns holistic representations of both image and non-image data, the AD-Transformer paves the way for more effective and precise clinical assessments, offering a clinically adaptable strategy for leveraging diverse data modalities in the battle against AD.

Potential Impact of Slowing Disease Progression in Early Symptomatic Alzheimer's Disease on Patient Quality of Life, Caregiver Time, and Total Societal Costs: Estimates Based on Findings from GERAS-US Study.

Impact of Alzheimer's disease (AD) progression on patient health-related quality of life (HRQoL), caregiver time, and societal costs is not well characterized in early AD. To assess the association of change in cognition with HRQoL, caregiver time, and societal costs over 36 months, and estimate the impact of slowing disease progression on these outcomes. This post-hoc analysis included patients with amyloid-positive mild cognitive impairment (MCI) and mild AD dementia (MILD AD) from the 36-month GERAS-US study. Disease progression was assessed using the Mini-Mental State Examination score. Change in outcomes associated with slowing AD progression was estimated using coefficients from generalized linear models. At baseline, 300 patients had MCI and 317 had MILD AD. Observed natural progression over 36 months was associated with: 5.1 point decline in the Bath Assessment of Subjective Quality of Life in Dementia (BASQID) score (for HRQoL), increase in 1,050 hours of total caregiver time, and $8,504 total societal costs for MCI; 6.6 point decline in the BASQID score, increase in 1,929 hours of total caregiver time, and $12,795 total societal costs for MILD AD per person. Slowing AD progression by 30% could result in per person savings in HRQoL decline, total caregiver time, and total societal costs: for MCI: 1.5 points, 315 hours, and $2,638; for MILD AD: 2.0 points, 579 hours, and $3,974. Slowing AD progression over 36 months could slow decline in HRQoL and save caregiver time and societal cost in patients with MCI and MILD AD.

Effects of sleep deprivation on brain atrophy in individuals with mild cognitive impairment and Alzheimer's disease

Dementia, a prevalent condition in the United States, affecting millions of individuals and their families, underscores the importance of healthy cognitive ageing, which involves maintaining cognitive function and mental wellness as individuals grow older, promoting overall well-being and quality of life. Our original research study investigates the correlation between lifestyle factors and brain atrophy in individuals with mild cognitive impairment (MCI) or Alzheimer's disease (AD), as well as healthy older adults. Conducted over six months in West Texas, the research involved 20 participants aged 62–87. Findings reveal that sleep deprivation in MCI subjects and AD patients correlate with posterior cingulate cortex, hippocampal atrophy and total brain volume, while both groups exhibit age-related hippocampal volume reduction. Notably, fruit/vegetable intake negatively correlates with certain brain regions' volume, emphasizing the importance of diet. Lack of exercise is associated with reduced brain volume and hippocampal atrophy, underlining the cognitive benefits of physical activity. The study underscores lifestyle's significant impact on cognitive health, advocating interventions to promote brain health and disease prevention, particularly in MCI/AD cases. While blood profile data showed no significant results regarding cognitive decline, the study underscores the importance of lifestyle modifications in preserving cognitive function.

Late-onset temporal lobe epilepsy: insights from brain atrophy and Alzheimer's disease biomarkers.

Considering the growing age of the world population, the incidence of epilepsy in older adults is expected to increase significantly. It has been suggested that late-onset temporal lobe epilepsy (LO-TLE) may be neurodegenerative in origin and overlap with Alzheimer's Disease (AD). Herein, we aimed to characterize the pattern of cortical atrophy and cerebrospinal fluid (CSF) biomarkers of AD (total and phosphorylated tau, and β-amyloid) in a selected population of LO-TLE of unknown origin. We prospectively enrolled individuals with temporal lobe epilepsy onset after the age of 50 and no cognitive impairment. They underwent a structural MRI scan and CSF biomarkers measurement. Imaging and biomarkers data were compared to three retrospectively collected groups: (i) age-sex-matched healthy controls, (ii) patients with Mild Cognitive Impairment (MCI) and abnormal CSF AD biomarkers (MCI-AD), and (iii) patients with MCI and normal CSF AD biomarkers (MCI-noAD). From a pool of 52 patients, twenty consecutive eligible LO-TLE patients with a mean disease duration of 1.8 years were recruited. As control populations, 25 patients with MCI-AD, 25 patients with MCI-noAD, and 25 healthy controls were enrolled. CSF biomarkers returned normal values in LO-TLE, significantly different from patients with MCI due to AD. There were no differences in cortico-subcortical atrophy between epilepsy patients and healthy controls, while patients with MCI demonstrated widespread injuries of cortico-subcortical structures. Individuals with a late-onset form of temporal lobe epilepsy, characterized by short disease duration and normal CSF β-amyloid and tau protein levels, showed patterns of cortical thickness and subcortical volumes not significantly different from healthy controls, but highly different from patients with MCI, either due to Alzheimer's Disease or not.

Effect of Metformin on Plasma and Cerebrospinal Fluid Biomarkers in Non-Diabetic Older Adults with Mild Cognitive Impairment Related to Alzheimer's Disease.

Alzheimer's disease (AD) is a complicated condition involving multiple metabolic and immunologic pathophysiological processes that can occur with the hallmark pathologies of amyloid-β, tau, and neurodegeneration. Metformin, an anti-diabetes drug, targets several of these disease processes in in vitro and animal studies. However, the effects of metformin on human cerebrospinal fluid (CSF) and plasma proteins as potential biomarkers of treatment remain unexplored. Using proteomics data from a metformin clinical trial, identify the impact of metformin on plasma and CSF proteins. We analyzed plasma and CSF proteomics data collected previously (ClinicalTrials.gov identifier: NCT01965756, conducted between 2013 and 2015), and conduced bioinformatics analyses to compare the plasma and CSF protein levels after 8 weeks of metformin or placebo use to their baseline levels in 20 non-diabetic patients with mild cognitive impairment (MCI) and positive AD biomarkers participants. 50 proteins were significantly (unadjusted p < 0.05) altered in plasma and 26 in CSF after 8 weeks of metformin use, with 7 proteins in common (AZU1, CASP-3, CCL11, CCL20, IL32, PRTN3, and REG1A). The correlation between changes in plasma and CSF levels of these 7 proteins after metformin use relative to baseline levels was high (r = 0.98). The proteins also demonstrated temporal stability. Our pilot study is the first to investigate the effect of metformin on plasma and CSF proteins in non-diabetic patients with MCI and positive AD biomarkers and identifies several candidate plasma biomarkers for future clinical trials after confirmatory studies.

Serum neurofilament light chain and inflammatory cytokines as biomarkers for early detection of mild cognitive impairment

To investigate the association between serum neurofilament light chain (NfL) levels, inflammatory cytokines, and cognitive function to assess their utility in the early detection of mild cognitive impairment (MCI). We conducted a cross-sectional study involving 157 community-dwelling individuals aged 55 years and above, categorized into healthy controls, MCI, and probable Alzheimer's disease (AD). Serum levels of NfL, inflammatory cytokines, and AD pathology markers were measured using enzyme-linked immunosorbent assay (ELISA). Correlations between these biomarkers and cognitive function were analyzed, and the diagnostic performance of the cognitive assessment scales and serum biomarker concentrations was evaluated using receiver operating characteristic (ROC) curve analysis. Serum NfL levels were significantly elevated in MCI and probable AD groups compared to healthy controls. Positive correlations were found between serum NfL and inflammatory cytokines IL-1β, IL-6, and Aβ40. Combining serum NfL with p-tau217 and the Boston Naming Test significantly enhanced the predictive accuracy for MCI. However, combining serum NfL with inflammatory markers did not improve MCI prediction accuracy. Elevated serum NfL is associated with cognitive impairment and inflammatory markers, suggesting its potential as a peripheral serum biomarker for MCI detection. The combination of serum NfL with p-tau217 and cognitive tests could offer a more accurate prediction of MCI, providing new insights for AD treatment strategies.

The impact of physical exercise on hippocampal atrophy in mild cognitive impairment and Alzheimer's disease: a meta-analysis.

Physical activity (PA) is a promising therapeutic for Alzheimer's disease (AD). Only a handful of meta-analyses have studied the impact of PA interventions on regional brain volumes, and none to date has solely included studies on effect of PA on regional brain volumes in individuals with cognitive impairment (CI). In this meta-analysis, we examined whether there is support for the hypothesis that PA interventions positively impact hippocampal volume (HV) in individuals with CI. We also assessed whether the level of CI [mild CI (MCI) vs. AD] impacted this relationship. We identified six controlled trials that met inclusion criteria. These included 236 participants with AD, MCI, or preclinical AD. Data were extracted and analyzed following Cochrane guidelines. We used a random-effects model to estimate the mean change in HV pre- and post-exercise intervention. Forest plots, Hedges' g funnel plots, and Egger's test were used to assess unbiasedness and visualize intervention effects, and Tau 2 , Cochran's Q, and I 2 were calculated to assess heterogeneity. The primary analysis revealed a significant positive effect of PA on total HV. However, sub-group analyses indicated a significant preservation of HV only in individuals with MCI, but not in those with AD. Egger's test indicated no evidence of publication bias. Subgroup analyses also revealed significant heterogeneity only within the MCI cohort for the total and left HV. PA demonstrated a moderate, significant effect in preserving HV among individuals with MCI, but not AD, highlighting a therapeutic benefit, particularly in earlier disease stages.

Plasma GFAP, NfL and pTau 181 detect preclinical stages of dementia.

Plasma biomarkers are preferable to invasive and expensive diagnostic tools, such as neuroimaging and lumbar puncture that are gold standard in the clinical management of Alzheimer's Disease (AD). Here, we investigated plasma Glial Fibrillary Acidic Protein (GFAP), Neurofilament Light Chain (NfL) and Phosphorylated-tau-181 (pTau 181) in AD and in its early stages: Subjective cognitive decline (SCD) and Mild cognitive impairment (MCI). This study included 152 patients (42 SCD, 74 MCI and 36 AD). All patients underwent comprehensive clinical and neurological assessment. Blood samples were collected for Apolipoprotein E (APOE) genotyping and plasma biomarker (GFAP, NfL, and pTau 181) measurements. Forty-three patients (7 SCD, 27 MCI, and 9 AD) underwent a follow-up (FU) visit after 2 years, and a second plasma sample was collected. Plasma biomarker levels were detected using the Simoa SR-X technology (Quanterix Corp.). Statistical analysis was performed using SPSS software version 28 (IBM SPSS Statistics). Statistical significance was set at p < 0.05. GFAP, NfL and pTau 181 levels in plasma were lower in SCD and MCI than in AD patients. In particular, plasma GFAP levels were statistically significant different between SCD and AD (p=0.003), and between MCI and AD (p=0.032). Plasma NfL was different in SCD vs MCI (p=0.026), SCD vs AD (p<0.001), SCD vs AD FU (p<0.001), SCD FU vs AD (p=0.033), SCD FU vs AD FU (p=0.011), MCI vs AD (p=0.002), MCI FU vs AD (p=0.003), MCI FU vs AD FU (p=0.003) and MCI vs AD FU (p=0.003). Plasma pTau 181 concentration was significantly different between SCD and AD (p=0.001), MCI and AD (p=0.026), MCI FU and AD (p=0.020). In APOE ϵ4 carriers, a statistically significant increase in plasma NfL (p<0.001) and pTau 181 levels was found (p=0.014). Moreover, an association emerged between age at disease onset and plasma GFAP (p = 0.021) and pTau181 (p < 0.001) levels. Plasma GFAP, NfL and pTau 181 are promising biomarkers in the diagnosis of the prodromic stages and prognosis of dementia.

The Contribution of Functional Near-Infrared Spectroscopy (fNIRS) to the Study of Neurodegenerative Disorders: A Narrative Review.

Functional near-infrared spectroscopy (fNIRS) is an innovative neuroimaging method that offers several advantages over other commonly used modalities. This narrative review investigated the potential contribution of this method to the study of neurodegenerative disorders. Thirty-four studies involving patients with Alzheimer's disease (AD), mild cognitive impairment (MCI), frontotemporal dementia (FTD), Parkinson's disease (PD), or amyotrophic lateral sclerosis (ALS) and healthy controls were reviewed. Overall, it was revealed that the prefrontal cortex of individuals with MCI may engage compensatory mechanisms to support declining brain functions. A rightward shift was suggested to compensate for the loss of the left prefrontal capacity in the course of cognitive decline. In parallel, some studies reported the failure of compensatory mechanisms in MCI and early AD; this lack of appropriate hemodynamic responses may serve as an early biomarker of neurodegeneration. One article assessing FTD demonstrated a heterogeneous cortical activation pattern compared to AD, indicating that fNIRS may contribute to the challenging distinction of these conditions. Regarding PD, there was evidence that cognitive resources (especially executive function) were recruited to compensate for locomotor impairments. As for ALS, fNIRS data support the involvement of extra-motor networks in ALS, even in the absence of measurable cognitive impairment.

Electroencephalogram-Based Metastability in Mild Cognitive Impairment Alzheimer's Disease.

Background: In this study, we analyze metastability, a feature of brain dynamics in subjects experiencing mild cognitive impairment Alzheimer's disease (MCI-AD) under eyes open (EO) and eyes closed (EC) conditions. Alzheimer's disease (AD) is a critically prolonged brain disorder that interrupts neural synchronization and desynchronization. Thus, studying metastability under EO and EC conditions would help in understanding the cortical dynamics and its impact in early-stage AD. Methods: Metastability is investigated using three methods namely frequency variance analysis, Kuramoto order parameter, and through meta-state activation patterns. Frequency variance estimated from 21 electroencephalogram (EEG) channels was clustered into three regions namely anterior, central, and posterior to study the regional metastability analysis. Global metastability was assessed from Kuramoto order parameter and meta-state activation patterns by collating the 21 EEG channels. Results: Reduction in metastability was observed in central regions of MCI-AD subjects through the study of frequency variance analysis. There was a marked reduction in global metastability in the patient group under the resting EO condition. Reduction in meta-state activation properties such as temporal activation sequence complexity, modularity, and leap size in MCI-AD condition under the EO condition indicates an overall reduction in brain flexibility. Conclusion: Taken together, the study infers an underlying structural change in neuronal dynamics influencing the reduction of metastability under the MCI-AD condition. The study further revealed that this reduction in metastability is more pronounced in the EO condition.

Olfactory deficit: a potential functional marker across the Alzheimer's disease continuum.

Alzheimer's disease (AD) is a prevalent form of dementia that affects an estimated 32 million individuals globally. Identifying early indicators is vital for screening at-risk populations and implementing timely interventions. At present, there is an urgent need for early and sensitive biomarkers to screen individuals at risk of AD. Among all sensory biomarkers, olfaction is currently one of the most promising indicators for AD. Olfactory dysfunction signifies a decline in the ability to detect, identify, or remember odors. Within the spectrum of AD, impairment in olfactory identification precedes detectable cognitive impairments, including mild cognitive impairment (MCI) and even the stage of subjective cognitive decline (SCD), by several years. Olfactory impairment is closely linked to the clinical symptoms and neuropathological biomarkers of AD, accompanied by significant structural and functional abnormalities in the brain. Olfactory behavior examination can subjectively evaluate the abilities of olfactory identification, threshold, and discrimination. Olfactory functional magnetic resonance imaging (fMRI) can provide a relatively objective assessment of olfactory capabilities, with the potential to become a promising tool for exploring the neural mechanisms of olfactory damage in AD. Here, we provide a timely review of recent literature on the characteristics, neuropathology, and examination of olfactory dysfunction in the AD continuum. We focus on the early changes in olfactory indicators detected by behavioral and fMRI assessments and discuss the potential of these techniques in MCI and preclinical AD. Despite the challenges and limitations of existing research, olfactory dysfunction has demonstrated its value in assessing neurodegenerative diseases and may serve as an early indicator of AD in the future.

Peripapillary Optical Coherence Tomography Angiography in Alzheimer's Disease, Mild Cognitive Impairment, and Normal Cognition.

This study aimed to identify peripapillary microvascular changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI). In this prospective study, 66 eyes of 36 subjects with AD, 119 eyes of 63 with MCI, and 513 eyes of 265 controls with normal cognition were enrolled. Peripapillary capillary perfusion density (CPD), capillary flux index (CFI), and retinal nerve fiber layer (RNFL) thickness were determined. Average CPD differed significantly between all three groups (P = 0.001), being significantly greater in AD vs controls (0.446 ± 0.015 vs 0.439 ± 0.017, P = 0.001) and MCI vs controls (0.443 ± 0.020 vs 0.439 ± 0.017, P = 0.007) but not AD vs MCI (P = 0.69). CFI and average RNFL thickness did not significantly differ among groups (all P > 0.05). Peripapillary CPD is increased in eyes with AD or MCI compared to controls despite similar RNFL thickness. [Ophthalmic Surg Lasers Imaging Retina 2024;55:78-84.].

Diagnosis Framework for Probable Alzheimer's Disease and Mild Cognitive Impairment Based on Multi-Dimensional Emotion Features.

Emotion and cognition are intercorrelated. Impaired emotion is common in populations with Alzheimer's disease (AD) and mild cognitive impairment (MCI), showing promises as an early detection approach. We aim to develop a novel automatic classification tool based on emotion features and machine learning. Older adults aged 60 years or over were recruited among residents in the long-term care facilities and the community. Participants included healthy control participants with normal cognition (HC, n = 26), patients with MCI (n = 23), and patients with probable AD (n = 30). Participants watched emotional film clips while multi-dimensional emotion data were collected, including mental features of Self-Assessment Manikin (SAM), physiological features of electrodermal activity (EDA), and facial expressions. Emotional features of EDA and facial expression were abstracted by using continuous decomposition analysis and EomNet, respectively. Bidirectional long short-term memory (Bi-LSTM) was used to train classification model. Hybrid fusion was used, including early feature fusion and late decision fusion. Data from 79 participants were utilized into deep machine learning analysis and hybrid fusion method. By combining multiple emotion features, the model's performance of AUC value was highest in classification between HC and probable AD (AUC = 0.92), intermediate between MCI and probable AD (AUC = 0.88), and lowest between HC and MCI (AUC = 0.82). Our method demonstrated an excellent predictive power to differentiate HC/MCI/AD by fusion of multiple emotion features. The proposed model provides a cost-effective and automated method that can assist in detecting probable AD and MCI from normal aging.

Comparing neuropsychological, typical, and ADNI criteria for the diagnosis of mild cognitive impairment in Vietnam-era veterans.

Neuropsychological criteria for mild cognitive impairment (MCI) more accurately predict progression to Alzheimer's disease (AD) and are more strongly associated with AD biomarkers and neuroimaging profiles than ADNI criteria. However, research to date has been conducted in relatively healthy samples with few comorbidities. Given that history of traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD) are risk factors for AD and common in Veterans, we compared neuropsychological, typical (Petersen/Winblad), and ADNI criteria for MCI in Vietnam-era Veterans with histories of TBI or PTSD. 267 Veterans (mean age = 69.8) from the DOD-ADNI study were evaluated for MCI using neuropsychological, typical, and ADNI criteria. Linear regressions adjusting for age and education assessed associations between MCI status and AD biomarker levels (cerebrospinal fluid [CSF] p-tau181, t-tau, and Aβ42) by diagnostic criteria. Logistic regressions adjusting for age and education assessed the effects of TBI severity and PTSD symptom severity simultaneously on MCI classification by each criteria. Agreement between criteria was poor. Neuropsychological criteria identified more Veterans with MCI than typical or ADNI criteria, and were associated with higher CSF p-tau181 and t-tau. Typical and ADNI criteria were not associated with CSF biomarkers. PTSD symptom severity predicted MCI diagnosis by neuropsychological and ADNI criteria. History of moderate/severe TBI predicted MCI by typical and ADNI criteria. MCI diagnosis using sensitive neuropsychological criteria is more strongly associated with AD biomarkers than conventional diagnostic methods. MCI diagnostics in Veterans would benefit from incorporation of comprehensive neuropsychological methods and consideration of the impact of PTSD.

Advancements and Challenges in Antiamyloid Therapy for Alzheimer's Disease: A Comprehensive Review.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder caused by the accumulation of amyloid-beta (Aβ) proteins and neurofibrillary tangles in the brain. There have been recent advancements in antiamyloid therapy for AD. This narrative review explores the recent advancements and challenges in antiamyloid therapy. In addition, a summary of evidence from antiamyloid therapy trials is presented with a focus on lecanemab. Lecanemab is the most recently approved monoclonal antibody that targets Aβ protofibrils for the treatment of patients with early AD and mild cognitive impairment (MCI). Lecanemab was the first drug shown to slow cognitive decline in patients with MCI or early onset AD dementia when administered as an infusion once every two weeks. In the Clarity AD trial, lecanemab was associated with infusion-site reactions (26.4%) and amyloid-related imaging abnormalities (12.6%). The clinical relevance and long-term side effects of lecanemab require further longitudinal observation. However, several challenges must be addressed before the drug can be routinely used in clinical practice. The drug's route of administration, need for imaging and genetic testing, affordability, accessibility, infrastructure, and potential for serious side effects are some of these challenges. Lecanemab's approval has fueled interest in the potential of other antiamyloid therapies, such as donanemab. Future research must focus on developing strategies to prevent AD; identify easy-to-use validated plasma-based assays; and discover newer user-friendly, and cost-effective drugs that target multiple pathways in AD pathology.

Association between clinical dementia rating and clinical outcomes in Alzheimer's disease.

We examined associations between the Clinical Dementia Rating Scale (CDR) and function (Functional Assessment Scale [FAS]), neuropsychiatric symptoms (Neuropsychiatric Inventory Questionnaire [NPI-Q]), and cognitive impairment in Alzheimer's disease (AD). We used data from the National Alzheimer's Coordinating Center Uniform Data Set and defined cognitively unimpaired and AD stages using CDR-global. Functional and neuropsychiatric symptoms occur as early as the mild cognitive impairment (MCI) phase. The adjusted lest square mean FAS (95% confidence interval [CI]) was lowest in cognitively unimpaired (3.88 [3.66, 4.11] to 5.01 [4.76, 5.26]) and higher with more advanced AD (MCI: 8.17 [6.92, 9.43] to 20.87 [19.53, 22.20]; mild: 18.54 [17.57, 19.50] to 28.13 [27.14, 29.12]; moderate: 26.01 [25.31, 26.70] to 29.42 [28.73, 30.10]). FAS and NPI-Q scores increased steeply with MCI (NPI-Q: 5.55 [4.89, 6.20] to 7.11 [6.43, 7.78]) and mild AD dementia (NPI-Q: 6.66 [5.72, 7.60] to 8.32 [7.32, 9.33]). CDR-global staged AD by capturing differences in relevant outcomes along AD progression. There were strong associations among CDR and the various outcomes relevant to healthcare providers, patients, and their care givers, such as activities of daily living.Overall, activities of daily living, neuropsychiatric symptoms, and cognitive function outcomes deteriorated over time and can be observed in early stages of AD (MCI or mild dementia).Our findings directly inform the current understanding of AD progression and can aid in care planning and benefit assessments of early AD interventions to delay the progression of AD to more advanced stages.

Association of soluble TREM2 with Alzheimer's disease and mild cognitive impairment: a systematic review and meta-analysis.

Soluble triggering receptor expressed on myeloid cells 2 (sTREM2) is a potential neuroinflammatory biomarker linked to the pathogenesis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Previous studies have produced inconsistent results regarding sTREM2 levels in various clinical stages of AD. This study aims to establish the correlation between sTREM2 levels and AD progression through a meta-analysis of sTREM2 levels in cerebrospinal fluid (CSF) and blood. Comprehensive searches were conducted in PubMed, Embase, Web of Science, and the Cochrane Library to identify observational studies reporting CSF and blood sTREM2 levels in AD patients, MCI patients, and healthy controls. A random effects meta-analysis was used to calculate the standardized mean difference (SMD) and 95% confidence intervals (CIs). Thirty-six observational studies involving 3,016 AD patients, 3,533 MCI patients, and 4,510 healthy controls were included. CSF sTREM2 levels were significantly higher in both the AD [SMD = 0.28, 95% CI (0.15, 0.41)] and MCI groups [SMD = 0.30, 95% CI (0.13, 0.47)] compared to the healthy control group. However, no significant differences in expression were detected between the AD and MCI groups [SMD = 0.09, 95% CI (-0.09, 0.26)]. Furthermore, increased plasma sTREM2 levels were associated with a higher risk of AD [SMD = 0.42, 95% CI (0.01, 0.83)]. CSF sTREM2 levels are positively associated with an increased risk of AD and MCI. Plasma sTREM2 levels were notably higher in the AD group than in the control group and may serve as a promising biomarker for diagnosing AD. However, sTREM2 levels are not effective for distinguishing between different disease stages of AD. Further investigations are needed to explore the longitudinal changes in sTREM2 levels, particularly plasma sTREM2 levels, during AD progression. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024514593.

Dominating Alzheimer's disease diagnosis with deep learning on sMRI and DTI-MD.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has become one of the major health concerns for the elderly. Computer-aided AD diagnosis can assist doctors in quickly and accurately determining patients' severity and affected regions. In this paper, we propose a method called MADNet for computer-aided AD diagnosis using multimodal datasets. The method selects ResNet-10 as the backbone network, with dual-branch parallel extraction of discriminative features for AD classification. It incorporates long-range dependencies modeling using attention scores in the decision-making layer and fuses the features based on their importance across modalities. To validate the effectiveness of our proposed multimodal classification method, we construct a multimodal dataset based on the publicly available ADNI dataset and a collected XWNI dataset, which includes examples of AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN). On this dataset, we conduct binary classification experiments of AD vs. CN and MCI vs. CN, and demonstrate that our proposed method outperforms other traditional single-modal deep learning models. Furthermore, this conclusion also confirms the necessity of using multimodal sMRI and DTI data for computer-aided AD diagnosis, as these two modalities complement and convey information to each other. We visualize the feature maps extracted by MADNet using Grad-CAM, generating heatmaps that guide doctors' attention to important regions in patients' sMRI, which play a crucial role in the development of AD, establishing trust between human experts and machine learning models. We propose a simple yet effective multimodal deep convolutional neural network model MADNet that outperforms traditional deep learning methods that use a single-modality dataset for AD diagnosis.

Association of plasma biomarkers with cognition, cognitive decline, and daily function across and within neurodegenerative diseases: Results from the Ontario Neurodegenerative Disease Research Initiative.

We investigated whether novel plasma biomarkers are associated with cognition, cognitive decline, and functional independence in activities of daily living across and within neurodegenerative diseases. Glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), phosphorylated tau (p-tau)181 and amyloid beta (Aβ)42/40 were measured using ultra-sensitive Simoa immunoassays in 44 healthy controls and 480 participants diagnosed with Alzheimer's disease/mild cognitive impairment (AD/MCI), Parkinson's disease (PD), frontotemporal dementia (FTD) spectrum disorders, or cerebrovascular disease (CVD). GFAP, NfL, and/or p-tau181 were elevated among all diseases compared to controls, and were broadly associated with worse baseline cognitive performance, greater cognitive decline, and/or lower functional independence. While GFAP, NfL, and p-tau181 were highly predictive across diseases, p-tau181 was more specific to the AD/MCI cohort. Sparse associations were found in the FTD and CVD cohorts and for Aβ42/40 . GFAP, NfL, and p-tau181 are valuable predictors of cognition and function across common neurodegenerative diseases, and may be useful in specialized clinics and clinical trials.