Biomarker For Mild Cognitive Impairment Research Articles

Plasma ADAM10 Levels and their Association with Alzheimer's Disease Diagnosis in Older Adults with Fewer Years of Formal Education.

Low educational attainment is a potential risk factor for Alzheimer's disease (AD) development. Alpha-secretase ADAM10 plays a central role in AD pathology, attenuating the formation of beta-amyloid peptides and, therefore, their aggregation into senile plaques. This study seeks to investigate ADAM10 as a blood-based biomarker in mild cognitive impairment (MCI) and AD in a diverse group of community-dwelling older adults, focusing on those with limited educational attainment. Participants were recruited from public health services. Cognition was evaluated using MMSE and ACE-R batteries. Blood samples were collected to analyze plasma ADAM10 levels. A logistic regression was conducted to verify the influence of plasma ADAM10 on the AD diagnosis. Significant differences in age, years of education, prescribed medications, and cognitive test scores were found between the MCI and AD groups. Regarding cognitive performance, both ACE-R and MMSE scores displayed significant differences between groups, with post-hoc analyses highlighting these distinctions, particularly between AD and cognitively healthy individuals. Elevated plasma ADAM10 levels were associated with a 4.5-fold increase in the likelihood of a diagnosis of MCI and a 5.9-fold increase in the likelihood of a diagnosis of AD. These findings suggest that ADAM10 levels in plasma serve as a valuable biomarker for assessing cognitive status in older individuals with low education attainment. This study underscores the potential utility of plasma ADAM10 levels as a blood-based biomarker for cognitive status, especially in individuals with low educational backgrounds, shedding light on their relevance in AD development and diagnosis.

Alterations in brain functional connectivity in patients with mild cognitive impairment: A systematic review and meta-analysis of functional near-infrared spectroscopy studies.

Emerging evidences suggest that cognitive deficits in individuals with mild cognitive impairment (MCI) are associated with disruptions in brain functional connectivity (FC). This systematic review and meta-analysis aimed to comprehensively evaluate alterations in FC between MCI individuals and healthy control (HC) using functional near-infrared spectroscopy (fNIRS). Thirteen studies were included in qualitative analysis, with two studies synthesized for quantitative meta-analysis. Overall, MCI patients exhibited reduced resting-state FC, predominantly in the prefrontal, parietal, and occipital cortex. Meta-analysis of two studies revealed a significant reduction in resting-state FC from the right prefrontal to right occipital cortex (standardized mean difference [SMD]=-.56; p<.001), left prefrontal to left occipital cortex (SMD=-.68; p<.001), and right prefrontal to left occipital cortex (SMD=-.53; p<.001) in MCI patients compared to HC. During naming animal-walking task, MCI patients exhibited enhanced FC in the prefrontal, motor, and occipital cortex, whereas a decrease in FC was observed in the right prefrontal to left prefrontal cortex during calculating-walking task. In working memory tasks, MCI predominantly showed increased FC in the medial and left prefrontal cortex. However, a decreased in prefrontal FC and a shifted in distribution from the left to the right prefrontal cortex were noted in MCI patients during a verbal frequency task. In conclusion, fNIRS effectively identified abnormalities in FC between MCI and HC, indicating disrupted FC as potential markers for the early detection of MCI. Future studies should investigate the use of task- and region-specific FC alterations as a sensitive biomarker for MCI.

Dual-task turn velocity - a novel digital biomarker for mild cognitive impairment and dementia.

Disorders associated with cognitive impairment impose a significant burden on both families and society. Previous studies have indicated that gait characteristics under dual-task as reliable markers of early cognitive impairment. Therefore, digital gait detection has great potential for future cognitive screening. However, research on digital biomarkers based on smart devices to identify cognitive impairment remains limited. The aim of this study is to explore digital gait biomarkers by utilizing intelligent wearable devices for discriminating mild cognitive impairment and dementia. This study included 122 subjects (age: 74.7 ± 7.7 years) diagnosed with normal cognition (NC, n = 38), mild cognitive impairment (MCI, n = 42), or dementia (n = 42). All subjects underwent comprehensive neuropsychological assessments and cranial Magnetic Resonance Imaging (MRI). Gait parameters were collected using validated wearable devices in both single-task and dual-task (DT). We analyzed the ability of gait variables to predict MCI and dementia, and examined the correlations between specific DT-gait parameters and sub-cognitive functions as well as hippocampal atrophy. Our results demonstrated that dual-task could significantly improve the ability to predict cognitive impairment based on gait parameters such as gait speed (GS) and stride length (SL). Additionally, we discovered that turn velocity (TV and DT-TV) can be a valuable novel digital marker for predicting MCI and dementia, for identifying MCI (DT-TV: AUC = 0.801, sensitivity 0.738, specificity 0.842), and dementia (DT-TV: AUC = 0.923, sensitivity 0.857, specificity 0.842). The correlation analysis and linear regression analysis revealed a robust association between DT-TV and memory function, as well as the hippocampus atrophy. This study presents a novel finding that DT-TV could accurately identify varying degrees of cognitive impairment. DT-TV is strongly correlated with memory function and hippocampus shrinkage, suggests that it can accurately reflect changes in cognitive function. Therefore, DT-TV could serve as a novel and effective digital biomarker for discriminating cognitive impairment.

Beyond CSF and Neuroimaging Assessment: Evaluating Plasma miR-145-5p as a Potential Biomarker for Mild Cognitive Impairment and Alzheimer's Disease.

Alzheimer's disease (AD) is the most common cause of dementia. New strategies for the early detection of MCI and sporadic AD are crucial for developing effective treatment options. Current techniques used for diagnosis of AD are invasive and/or expensive, so they are not suitable for population screening. Cerebrospinal fluid (CSF) biomarkers such as amyloid β1-42 (Aβ1-42), total tau (T-tau), and phosphorylated tau181 (P-tau181) levels are core biomarkers for early diagnosis of AD. Several studies have proposed the use of blood-circulating microRNAs (miRNAs) as potential novel early biomarkers for AD. We therefore applied a novel approach to identify blood-circulating miRNAs associated with CSF biomarkers and explored the potential of these miRNAs as biomarkers of AD. In total, 112 subjects consisting of 28 dementia due to AD cases, 63 MCI due to AD cases, and 21 cognitively healthy controls were included. We identified seven Aβ1-42-associated plasma miRNAs, six P-tau181-associated plasma miRNAs, and nine Aβ1-42-associated serum miRNAs. These miRNAs were involved in AD-relevant biological processes, such as PI3K/AKT signaling. Based on this signaling pathway, we constructed an miRNA-gene target network, wherein miR-145-5p has been identified as a hub. Furthermore, we showed that miR-145-5p performs best in the prediction of both AD and MCI. Moreover, miR-145-5p also improved the prediction performance of the mini-mental state examination (MMSE) score. The performance of this miRNA was validated using different datasets including an RT-qPCR dataset from plasma samples of 23 MCI cases and 30 age-matched controls. These findings indicate that blood-circulating miRNAs that are associated with CSF biomarkers levels and specifically plasma miR-145-5p alone or combined with the MMSE score can potentially be used as noninvasive biomarkers for AD or MCI screening in the general population, although studies in other AD cohorts are necessary for further validation.

Metabolic and Environmental Biomarkers in Mild Cognitive Impairment and Dementia: An Exploratory Study.

Objective: To determine the frequency with which suspected pathogenic factors, including metals and metabolites that might contribute to Alzheimer's disease (AD), may be found in patients with cognitive impairment through commonly available blood tests. Methods: A variety of serum studies, including metals, ammonia, homocysteine, vitamin B12, folate, thyroid tests, metabolic products, and inflammatory markers, were measured in two cohorts: one meeting mild cognitive impairment (MCI) criteria and the other meeting mild-to-moderate dementia (DE) criteria. Medications these patients received were reviewed. Results: Metal abnormalities were detected in over half the subjects, including evidence of mercury, lead, and arsenic elevation as well as instances of excessive essential metals, iron (Fe), and copper. Some metal aberration was detected in 64% of the DE group and 66% of the MCI group. Females were more likely to have elevated copper, consistent with hormonal effects on copper excretion. Homocysteinemia was the most common abnormality, detected in 71% with DE and 67% with MCI, while methylmalonic acid was not elevated. Slight hyperammonemia was moderately common (38%) suggesting a hepatic factor in this subset. Findings of moderate insulin resistance were present in nearly half (44% DE, 52% MCI). Sixty of 65 (92%) had at least one abnormal biomarker and 60% had two or more. The most common drug taken by the total cohort was proton pump inhibitors at 22% DE and 38% MCI. Conclusions: This study suggests that both toxic metals and excessive vital metals such as copper and iron, as well as common metabolic and hepatic factors are detectable at both stages of MCI and DE. There appears to be a multiplicity of provocative factors leading to DE. Individualized interventions based on these parameters may be a means to reduce cognitive decline leading to DE. A more comprehensive prospective study of these environmental and metabolic factors with corrective early interventions appears warranted.

Comparison of Commonly Measured Plasma and Cerebrospinal Fluid Proteins and Their Significance for the Characterization of Cognitive Impairment Status.

Although cerebrospinal fluid (CSF) amyloid-β42 peptide (Aβ42) and phosphorylated tau (p-tau) and blood p-tau are valuable for differential diagnosis of Alzheimer's disease (AD) from cognitively normal (CN) there is a lack of validated biomarkers for mild cognitive impairment (MCI). This study sought to determine how plasma and CSF protein markers compared in the characterization of MCI and AD status. This cohort study included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who had baseline levels of 75 proteins measured commonly in plasma and CSF (257 total, 46 CN, 143 MCI, and 68 AD). Logistic regression, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF) methods were used to identify the protein candidates for the disease classification. We observed that six plasma proteins panel (APOE, AMBP, C3, IL16, IGFBP2, APOD) outperformed the seven CSF proteins panel (VEGFA, HGF, PRL, FABP3, FGF4, CD40, RETN) as well as AD markers (CSF p-tau and Aβ42) to distinguish the MCI from AD [area under the curve (AUC) = 0.75 (plasma proteins), AUC = 0.60 (CSF proteins) and AUC = 0.56 (CSF p-tau and Aβ42)]. Also, these six plasma proteins performed better than the CSF proteins and were in line with CSF p-tau and Aβ42 in differentiating CN versus MCI subjects [AUC = 0.89 (plasma proteins), AUC = 0.85 (CSF proteins) and AUC = 0.89 (CSF p-tau and Aβ42)]. These results were adjusted for age, sex, education, and APOEϵ4 genotype. This study suggests that the combination of 6 plasma proteins can serve as an effective marker for differentiating MCI from AD and CN.

The plasma derived exosomal miRNA-483-5p/502-5p serve as potential MCI biomarkers in aging

Alzheimer's disease (AD) is the leading cause of dementia and is rapidly becoming one of the most costly, fatal diseases, which is typically discovered in the late stage of molecular pathology, at which point medication intervention is irreversible. As a result, there is an urgent need for a low-cost, least-invasive way of screening cognitive impairment, with the goal of identifying persons at risk of AD. Mild cognitive impairment (MCI) has been described as a transitional state between normal cognitive aging and AD. Early detection and timely tracking of MCI can to some extent prevent the progression towards AD. We found a population in Northwestern China has a comparatively high prevalence of MCI. Continued education, consistent exercise, and a secure financial situation can all help older people maintain cognitive function. Due to the critical role of circulating microRNAs in intercellular signaling and the perturbations thereof, their investigation has assumed paramount significance in elucidating various pathological conditions. Numerous investigations have substantiated the significance of circulating miRNAs specifically in MCI. Here, we evaluated miR-483-5p (Area Under the Curve (AUC) is 0.901, sensitivity 79.2 % and specificity 100 %) and miR-502-5p (AUC is 0.872, sensitivity 79.2 % and specificity 83.3 %), which were derived from plasma exosomes and maintained at high levels in elderly people with MCI, could be employed as promising noninvasive biomarkers.

Red Cell Distribution Width is Related to Mild Cognitive Impairment: A Cross-Sectional Study of Community Residents.

Previous literature has reported that red cell distribution width (RDW) correlated with Alzheimer's disease (AD), but the correlation with mild cognitive impairment (MCI) was not clear. This study aimed to investigate MCI in the residents aged ≥65 living in the suburban of Shanghai, China. A total of 550 participants were recruited as MCI (MCI group, 226) and normal (NC group, 284) groups and received blood examination voluntarily. Blood routine indexes were tested by blood tests using Sysmex XT-4000i (Japan). The Chi-square test, t-test, and linear regression analysis were used to find the statistical difference and correlation of data, respectively. Each cognition domain of MCI was found to be impaired, the weight of which, however, was different in integral damage. Most MCI people had impairment of attention among cognitive domains (235, 88.3%). According to the results of the binary logistic regression, the highest weight among impaired cognitive domains was for attention in MCI, and the Wald value of attention was higher than those of others (Wald = 51.83). Additionally, RDW had the greatest negative correlation with attention score (P < 0.05). Increased RDW may be considered as a biomarker of MCI.

Risk factors and metabolomics of mild cognitive impairment in type 2 diabetes mellitus.

Objective: This study aimed to explore the risk factors, metabolic characteristics, and potential biomarkers of mild cognitive impairment in type 2 diabetes mellitus (T2DM-MCI) and to provide potential evidence for the diagnosis, prevention, and treatment of mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM). Methods: A total of 103 patients with T2DM were recruited from the Endocrinology Department of The Second Affiliated Hospital of Dalian Medical University for inclusion in the study. The Montreal Cognitive Assessment (MoCA) was utilized to evaluate the cognitive functioning of all patients. Among them, 50 patients were categorized into the T2DM-MCI group (MoCA score < 26 points), while 53 subjects were classified into the T2DM without cognitive impairment (T2DM-NCI) group (MoCA score ≥ 26 points). Serum samples were collected from the subjects, and metabolomics profiling data were generated by Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). These groups were analyzed to investigate the differences in expression of small molecule metabolites, metabolic pathways, and potential specific biomarkers. Results: Comparison between the T2DM-MCI group and T2DM-NCI group revealed significant differences in years of education, history of insulin application, insulin resistance index, insulin-like growth factor-binding protein-3 (IGFBP-3), and creatinine levels. Further binary logistic regression analysis of the variables indicated that low educational level and low serum IGFBP-3 were independent risk factor for T2DM-MCI. Metabolomics analysis revealed that differential expression of 10 metabolites between the T2DM-MCI group and T2DM-NCI group (p < 0.05 and FDR<0.05, VIP>1.5). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis revealed that fatty acid degradation was the most significant pathway. Receiver operating characteristic (ROC) analysis shows that lysophosphatidylcholine (LPC) 18:0 exhibited greater diagnostic efficiency. Conclusion: This study revealed that a shorter duration of education and lower serum IGFBP-3 levels are independent risk factors for T2DM-MCI. Serum metabolites were found to be altered in both T2DM-MCI and T2DM-NCI groups. T2DM patients with or without MCI can be distinguished by LPC 18:0. Abnormal lipid metabolism plays a significant role in the development of MCI in T2DM patients.

Exploring Frequency Band-Based Biomarkers of EEG Signals for Mild Cognitive Impairment Detection.

Mild Cognitive Impairment (MCI) is often considered a precursor to Alzheimer's disease (AD), with a high likelihood of progression. Accurate and timely diagnosis of MCI is essential for halting the progression of AD and other forms of dementia. Electroencephalography (EEG) is the prevalent method for identifying MCI biomarkers. Frequency band-based EEG biomarkers are crucial for identifying MCI as they capture neuronal activities and connectivity patterns linked to cognitive functions. However, traditional approaches struggle to identify precise frequency band-based biomarkers for MCI diagnosis. To address this challenge, a novel framework has been developed for identifying important frequency sub-bands within EEG signals for MCI detection. In the proposed scheme, the signals are first denoised using a stationary wavelet transformation and segmented into small time frames. Then, four frequency sub-bands are extracted from each segment, and spectrogram images are generated for each sub-band as well as for the full filtered frequency band signal segments. This process produces five different sets of images for five separate frequency bands. Afterwards, a convolutional neural network is used individually on those image sets to perform the classification task. Finally, the obtained results for the tested four sub-bands are compared with the results obtained using the full bandwidth. Our proposed framework was tested on two MCI datasets, and the results indicate that the 16-32 Hz sub-band range has the greatest impact on MCI detection, followed by 4-8 Hz. Furthermore, our framework, utilizing the full frequency band, outperformed existing state-of-the-art methods, indicating its potential for developing diagnostic tools for MCI detection.

Alpha Asymmetry as biomarker for Mild Cognitive Impairment

AbstractBackgroundEarly screening of dementia at its pre‐clinical stage is crucial, seeing as how deteriorated cognitive functions can be recovered through appropriate medicinal practices. The pre‐clinical stage, which we refer to as mild cognitive impairment (MCI) is known to affect the alpha rhythm of the brain activity. Hence, our study aims to establish quantitative EEG (qEEG)‐based alpha asymmetry biomarker that aids the diagnoses of MCI.MethodThe qEEG dataset were composed of age‐ and sex‐ matched subjects (N = 634, 317 healthy control (HC), 317 MCI), acquired in accordance with the 10‐20 system and under resting state with their eyes being closed. Subjects were recruited by Chung‐Ang University (153 HC, 111 MCI), iMediSync(164 HC, 76 MCI), Dong‐a Medical Center(84 MCI) and Pusan Medical Center(46 MCI).The features were classified into four types: the absolute and relative region alpha power (AAP/RAP) of six regions (Prefrontal, Frontal, Temporal, Central, Parietal, Occipital); alpha power asymmetry (APA) between regions that align in anterior‐posterior and lateral planes; occipital alpha peak frequency (OPF) that analyzes peak frequencies.In order to highlight the alpha variability, EEG signals were bandpass filtered (6.5‐12Hz). Filtered signal at each channel was categorized into slow (6.5‐8Hz), middle (8‐10Hz) and fast alpha (10‐12Hz). The mean and standard deviation of their FFT were computed through moving time window. Henceforth, the feature set were used to train machine learning algorithms. Data were split into 8(training N = 507) to 2(test N = 127) ratio. 101 subjects were randomly selected and used as validation set. The algorithm that exhibited the best classification performance were determined through 5‐fold cross validation (CV) results.ResultSupport vector machine (SVM) showed the best 5‐fold CV results – 84.61% average accuracy, 74.02% sensitivity, 95.25% specificity, 94.15% precision and F1‐Score of 0.8273. The RAP decreased for all regions and OPF were slower in MCI group when compared to HC. APA patterns of the right intra‐hemisphere exhibited most significant differences.ConclusionThis study illustrates the potential of APA as a biomarker for MCI. Current applications of APA are confined to emotional‐related studies, such as depressive disorder. The reported cases of MCI‐associated depression also uphold the validity of APA as a biomarker for MCI.

Imaging diagnosis and emotion recognition relationship in mild cognitive impairment

AbstractBackgroundRecent research has focused attention on social cognition impairment in Alzheimer Disease (AD). Specifically, emotion recognition can be evaluated with Reading the Mind in the Eyes Test (RMET). Some neuroanatomical changes could be related with social and cognitive deficits in patients. Therefore, future research combining cognitive indicators (RMET) with brain structure and function parameters (volumes, glucose metabolism, blood flow, amyloid deposits…) is required.MethodParticipants are patients from the Neurology Unit (Hospital Universitari i Politècnic La Fe). They were neuropsychologically evaluated, and CSF samples were obtained to determine some biomarkers (β‐amyloid‐42, β‐amyloid‐40, p‐Tau…). Also, brain imaging techniques (magnetic resonance (MR) and Positron Emission Tomography with [18F]Fluorodeoxyglucose (FDG‐PET/CT)) were applied to obtain brain structural and functional information, and to evaluate the relationship between them and emotion recognition evaluation (RMET). Imaging data were processed following SPM and CAT12 pipelines, a brain parcellation based on the Hammers atlas was used. Finally, 166 patients (with MR and/or FDG‐PET/CT) diagnosed with mild cognitive impairment (MCI) were included in the study.ResultThe correlation analysis between FDG‐PET/CT and RMET results showed significant correlations. Specifically, with prefrontal medial L and R (r = 0.36), posterior cingulate L and R (r = 0.34), parietal inferior L and R (r = 0.31), anterior cingulate L and R (r = 0.32), prefrontal lateral L and R (r = 0.32), insula R (r = 0.31) and basal ganglia R (r = 0.3). So, they are considered potential brain function biomarkers in MCI. Regarding the correlation analysis between MR (GM volumes (TIV‐corrected)) and RMET results, some significant correlations were observed. Specifically, with the straight gyrus L (r = 0.40), superior frontal gyrus L (r = 0.37), insula anterior inferior cortex R (r = 0.36), fusiform gyrus R (r = 0.36), anterior cingulate gyrus R (r = 0.36), insula anterior short gyrus L (r = 0.36), posterior and anterior cingulate gyrus L (r = 0.36, 0.35), and anterior orbital gyrus L (r = 0.35). So, they are considered potential brain structure biomarkers in MCI.ConclusionThe relationship between brain imaging parameters and emotion recognition impairment variables could provide important information for further biomarkers identification, as well as to identify potential physiopathological mechanisms involved in neurodegenerative development since early steps.

Blood biomarkers and lifestyle factors in dementia prevention

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder with preclinical period that may begin more than 15‐20 years before onset without any clinical symptoms. Risk factors for intervention include obesity in middle age, lack of exercise, hypertension in middle age, diabetes mellitus, smoking, and hearing loss in middle age. Our research indicated that plasma proteins, such as apoA1, transthyretin, and complement C3, may serve as potential biomarkers for mild cognitive impairment (MCI) and Alzheimer’s disease. In this study, we analyzed the associations between risk and protective factors and these plasma protein levels to establish a risk assessment method for dementia.MethodIn present study, we measured blood biomarkers, including apoA1, transthyretin, and complement C3, in 1,736 subjects. We collected medical data from annual health examinations and clinical records of the participants. Additionally, lifestyle questionnaires were incorporated into the research.ResultOur analyses revealed that the risk values in the composite score indicated by the three blood biomarkers were higher in those with a history of dementia and diabetes than in those without a history of dementia and diabetes. The odds ratios (OR) for MCI were 5.27 (CI: 2.14‐13.2) and 1.59 (CI: 1.04‐2.45), respectively. Smoking exhibited a high‐risk value (OR: 1.87, CI: 1.15‐3.01), while smoking cessation led to higher apoA1 levels than in those who continued to smoke. Utilizing the composite score as an index, we analyzed factors that increase or decrease the composite score based on the receipt diagnosis history, health checkup data, and lifestyle questionnaires. We found that the high‐risk group in the composite score was associated with low HDL cholesterol (OR 0.94), high HbA1c (OR 1.6), and right high frequency hearing loss (OR 2.97). Furthermore, we identified several protective factors against MCI, including regular walking and strong physical exercise, drinking 100% fruit and vegetable juices, and avoiding sweetened beverages, and eating sashimi and miso soup.ConclusionOur findings suggest that a firm commitment to lifestyle‐related disease treatment and high‐impact exercise from middle age can help prevent dementia. Additionally, the blood test we used may be useful in presenting specific methods of dementia prevention and evaluating their effectiveness.

Comparison of sleep and wake EEG biomarkers in mild cognitive impairment and Alzheimer’s disease

AbstractBackgroundEEG slowing, a biomarker of cognitive decline in Alzheimer’s disease (AD), has been reported in wake or sleep EEG [1,2,3]. However, the relation between wake and sleep EEG and their comparative sensitivity to early stage of cognitive decline is less understood. In this study we compared sleep and wake EEG biomarkers using a common set of endpoints.MethodsParticipants included individuals with mild cognitive impairment (MCI, n = 30, ages 53‐88), Alzheimer’s disease dementia (ADem, n = 15, ages 60‐84), and controls with normal cognition (HC, n = 55, ages 50‐84). Twenty‐channel resting‐state EEG with eyes‐closed was collected during 5‐minutes of wakefulness (STAT X24). Sleep EEG recordings from AF7‐AF8 were collected in‐home and staged using Sleep ProfilerTM. Power spectral densities (PSD) and the derived Theta‐Alpha ratios were computed for wake EEG sites (counterpart bipolar montage), with the wake FP1‐FP2 compared by stage to the sleep EEG at AF7‐AF8. Significant differences in Theta‐Alpha ratios between MCI or ADem vs. HC groups were identified using independent t‐test (p&lt;0.05) with the normalized effect size measured using Hedges‐g (E.S.).ResultsCorrelation coefficients between wake Alpha, Theta power and non‐REM sleep were: r = 0.83, 0.75 for stage N1, r = 0.71, 0.66 for stage N2, and r = 0.55, 0.50 for stage N3. Based on the frontal Theta‐Alpha ratios, the ADem group showed significant EEG slowing during both wake (E.S. = 0.73) and sleep (E.S. = 1.65, 1.31, 0.61, 1.71 for N1, N2, N3, and REM) as compared to the HC, while MCI group only exhibited EEG slowing during sleep (E.S. = 0.56, 0.77, 0.51 for N1, N2 and REM). Temporal (T5‐T6) wake EEG manifested significant slowing in both ADem (E.S. = 1.21) and MCI (E.S. = 0.56) groups. Compared to the HC, ADem patients exhibited significantly less sleep time (E.S. = 0.91, p = 0.002) and less stage REM (E.S. = 1.0, p = 0.001).ConclusionsCognitive decline in the AD spectrum is associated with common signatures in both sleep and wake EEG. However, the magnitude of these effects and their spatial distribution may be different in sleep versus wakefulness. These findings support a multi modal approach to neurophysiological assessment using both sleep and wakefulness data.

Lower serum carotenoid levels are associated with incident cognitive decline independent of traditional risk factors in a prospective study of older men from Northern Ireland

AbstractBackgroundNutritional influences, and antioxidant status in particular, have been implicated in the aetiology of cognitive decline. This study investigated the neuroprotective effects of serum antioxidants in a prospective cohort of older men to identify associations with cognitive outcomes, independent of traditional risk factors.MethodThis cohort study included 873 Northern Irish men nested within PRIME (Prospective Epidemiological Study of Myocardial Infarction) who were followed‐up for at least 15 years. Mini Mental State Examination (MMSE) was carried out at baseline. The Addenbrooke’s Cognitive Examination‐R and MMSE were carried out at follow‐up. A consensus diagnosis of normal cognition for age, mild cognitive impairment (MCI) or dementia was made using accepted clinical criteria. Serum concentrations of retinol, two forms of vitamin E (α‐ and ϒ‐tocopherol) and six carotenoids measured from baseline samples were quantified by high‐performance liquid chromatography and assessed by multiple logistic regression analyses.ResultThe case definition included the 163 men characterised with MCI and 46 men with dementia; 664 participants were considered cognitively normal controls (Table 1). In unadjusted analyses, serum levels of the carotenoids α‐carotene, β‐carotene, lycopene and β‐cryptoxanthin were significantly lower in MCI/dementia cases than controls, in contrast to ϒ‐tocopherol, which was significantly higher (Table 2). Following adjustment for traditional risk factors including height, APOε4 genotype, diastolic blood pressure, smoking status and the presence of (pre)diabetes or depression, the effect size of those carotenoids significant in the unadjusted analysis was attenuated somewhat; only α‐carotene remained significantly associated with MCI/dementia (odds ratio = 0.55; confidence interval: 0.31‐0.99; Table 2).ConclusionOur data implicates lower levels of serum carotenoids as potential early biomarkers of MCI/ dementia before overt manifestation of symptoms, independent of traditional risk factors. These findings are consistent with hypotheses supportive of a neuroprotective role of dietary antioxidants and corroborate previous associations of higher serum carotenoid levels with better cognition in aging subjects. There is emerging evidence that carotenoids, through their potent antioxidant and anti‐inflammatory properties, may positively impact cognitive function. Limiting cognitive decline through lifestyle, exercise, and a higher carotenoid intake, may prove beneficial.

Novel Fluid Biomarkers for Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.

Mild cognitive impairment (MCI) is a well-established prodromal stage of dementia (e.g., Alzheimer's disease) that is often accompanied by early signs of neurodegeneration. To facilitate a better characterization of the underlying pathophysiology, we assessed the available literature to evaluate potential fluid biomarkers in MCI. Peer-reviewed articles that measured cerebrospinal fluid (CSF) and/or peripheral biomarkers of neuronal injury (total-tau [T-tau], neurofilament light chain [NfL], heart-type fatty acid binding protein [HFABP], neuron-specific enolase, ubiquitin C-terminal hydrolase L1) and/or astroglial pathology (glial fibrillary acidic protein [GFAP], S100 calcium-binding protein B) in MCI and healthy controls were assessed. Group differences were summarized by standardized mean differences (SMDs) and 95% confidence intervals calculated using a random-effects model. Heterogeneity was quantified using I2. A total of 107 studies were included in the meta-analysis and 10 studies were qualitatively reviewed. In CSF, concentrations of NfL (SMD = 0.69 [0.56, 0.83]), GFAP (SMD = 0.41 [0.07, 0.75]), and HFABP (SMD = 0.57 [0.26, 0.89]) were elevated in MCI. In blood, increased concentrations of T-tau (SMD = 0.19 [0.09, 0.29]), NfL (SMD = 0.41 [0.32, 0.49]), and GFAP (SMD = 0.39 [0.23, 0.55]) were found in MCI. Heterogeneity that was identified in all comparisons was explored using meta-regression and subgroup analysis. Elevated NfL and GFAP can be detected in both CSF and peripheral blood. Monitoring these biomarkers in clinical settings may provide important insight into underlying neurodegenerative processes in MCI.

Hippocampal subfield volumes in mild cognitive impairment and alzheimer's disease: a systematic review and meta-analysis.

The hippocampus is a complex structure that consists of several subfields with distinct and specialized functions. Although numerous studies have been performed to explore hippocampal atrophy at the sub-regional level in mild cognitive impairment (MCI) and Alzheimer's disease (AD), the results have been inconsistent especially for whether and which subfields can be served as the most potential biomarkers in MCI and AD. Herein, we used a meta-analytic approach to synthesize the extant literatures on hippocampal subfields in MCI and AD through PubMed, Web of Science, and Embase (PROSPERO CRD42021257586). As a result, a total of twenty studies using Freesurfer 5 and Freesurfer 6 were included in this investigation. These studies revealed that at the sub-regional level, hippocampal subfield volume reductions in MCI and AD were not restricted to specific subfields, and subiculum and presubiculum had the largest z-scores across most comparisons. However, none of the subfield performed much better in discriminating MCI and HC, AD and MCI, AD and HC as compared to whole hippocampus volume. These results suggested that we should explore the changes in the hippocampal subfields in subtypes of MCI or even at an earlier stage, that is subjective cognitive impairment.

Non-invasive biomarkers for mild cognitive impairment and Alzheimer's disease

Alzheimer's disease is the most common type of dementia in the elderly. It is a progressive degenerative disorder that may begin to develop up to 15 years before clinical symptoms appear. The identification of early biomarkers is crucial to enable a prompt diagnosis and to start effective interventions. In this work, we conducted a metabolomic study using proton Nuclear Magnetic Resonance (1H NMR) spectroscopy in serum samples from patients with neuropathologically confirmed Alzheimer's disease (AD, n = 51), mild cognitive impairment (MCI, n = 27), and cognitively healthy controls (HC, n = 50) to search for metabolites that could be used as biomarkers. Patients and controls underwent yearly clinical follow-ups for up to six years. MCI group included samples from three subgroups of subjects with different disease progression rates. The first subgroup included subjects that remained clinically stable at the MCI stage during the period of study (stable MCI, S-MCI, n = 9). The second subgroup accounted for subjects which were diagnosed with MCI at the moment of blood extraction, but progressed to clinical dementia in subsequent years (MCI-to-dementia, MCI-D, n = 14). The last subgroup was composed of subjects that had been diagnosed as dementia for the first time at the moment of sample collection (incipient dementia, Incp-D, n = 4). Partial Least Square Discriminant Analysis (PLS-DA) models were developed. Three models were obtained, one to discriminate between AD and HC samples with high sensitivity (93.75%) and specificity (94.75%), another model to discriminate between AD and MCI samples (100% sensitivity and 82.35% specificity), and a last model to discriminate HC and MCI with lower sensitivity and specificity (67% and 50%). Differences within the MCI group were further studied in an attempt to determine those MCI subjects that could develop AD-type dementia in the future. The relative concentration of metabolites, and metabolic pathways were studied. Alterations in the pathways of alanine, aspartate and glutamate metabolism, pantothenate and CoA biosynthesis, and beta-alanine metabolism, were found when HC and MCI- D patients were compared. In contrast, no pathway was found disturbed in the comparison of S-MCI with HC groups. These results highlight the potential of 1H NMR metabolomics to support the diagnosis of dementia in a less invasive way, and set a starting point for the study of potential biomarkers to identify MCI or HC subjects at risk of developing AD in the future.

Resting-state electroencephalographic characteristics related to mild cognitive impairments.

Alzheimer's disease (AD) causes a rapid deterioration in cognitive and physical functions, including problem-solving, memory, language, and daily activities. Mild cognitive impairment (MCI) is considered a risk factor for AD, and early diagnosis and treatment of MCI may help slow the progression of AD. Electroencephalography (EEG) analysis has become an increasingly popular tool for developing biomarkers for MCI and AD diagnosis. Compared with healthy elderly, patients with AD showed very clear differences in EEG patterns, but it is inconclusive for MCI. This study aimed to investigate the resting-state EEG features of individuals with MCI (n = 12) and cognitively healthy controls (HC) (n = 13) with their eyes closed. EEG data were analyzed using spectral power, complexity, functional connectivity, and graph analysis. The results revealed no significant difference in EEG spectral power between the HC and MCI groups. However, we observed significant changes in brain complexity and networks in individuals with MCI compared with HC. Patients with MCI exhibited lower complexity in the middle temporal lobe, lower global efficiency in theta and alpha bands, higher local efficiency in the beta band, lower nodal efficiency in the frontal theta band, and less small-world network topology compared to the HC group. These observed differences may be related to underlying neuropathological alterations associated with MCI progression. The findings highlight the potential of network analysis as a promising tool for the diagnosis of MCI.

Using a dual-stream attention neural network to characterize mild cognitive impairment based on retinal images

Mild cognitive impairment (MCI) is a critical transitional stage between normal cognition and dementia, for which early detection is crucial for timely intervention. Retinal imaging has been shown as a promising potential biomarker for MCI. This study aimed to develop a dual-stream attention neural network to classify individuals with MCI based on multi-modal retinal images. Our approach incorporated a cross-modality fusion technique, a variable scale dense residual model, and a multi-classifier mechanism within the dual-stream network. The model utilized a residual module to extract image features and employed a multi-level feature aggregation method to capture complex context information. Self-attention and cross-attention modules were utilized at each convolutional layer to fuse features from optical coherence tomography (OCT) and fundus modalities, resulting in multiple output losses. The neural network was applied to classify individuals with MCI, Alzheimer's disease, and control participants with normal cognition. Through fine-tuning the pre-trained model, we classified community-dwelling participants into two groups based on cognitive impairment test scores. To identify retinal imaging biomarkers associated with accurate prediction, we used the Gradient-weighted Class Activation Mapping technique. The proposed method achieved high precision rates of 84.96% and 80.90% in classifying MCI and positive test scores for cognitive impairment, respectively. Notably, changes in the optic nerve head on fundus photographs or OCT images among patients with MCI were not used to discriminate patients from the control group. These findings demonstrate the potential of our approach in identifying individuals with MCI and emphasize the significance of retinal imaging for early detection of cognitive impairment.