CCL23: A Chemokine Associated with Progression from Mild Cognitive Impairment to Alzheimer's Disease.
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ABSTRACT:
SUBMITTER: Faura J
PROVIDER: S-EPMC8010612 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The preclinical Alzheimer's disease (AD) - amnestic mild cognitive impairment (MCI) - is manifested by phenotypes classified into exclusively memory (single-domain) MCI (sMCI) and multiple-domain MCI (mMCI). We suggest that typical MCI-to-AD progression occurs through the sMCI-to-mMCI sequence as a result of the extension of initial pathological processes. To support this hypothesis, we assess myelin content with a Magnetization Transfer Ratio (MTR) in 21 sMCI and 21 mMCI patients and in 42 age-, sex-, and education-matched controls. A conjunction analysis revealed MTR reduction shared by sMCI and mMCI groups in the medial temporal lobe and posterior structures including white matter (WM: splenium, posterior corona radiata) and gray matter (GM: hippocampus; parahippocampal and lingual gyri). A disjunction analysis showed the spread of demyelination to prefrontal WM and insula GM in executive mMCI. Our findings suggest that demyelination starts in the structures affected by neurofibrillary pathology; its presence correlates with the clinical picture and indicates the method of MCI-to-AD progression. In vivo staging of preclinical AD can be developed in terms of WM/GM demyelination.
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Project description:Genome-wide profiling of DNA methylation in blood leukocytes from Chinese patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The Illumina Infinium MethylationEPIC BeadChip array (850K chip) was used to detect DNA methylation profiles throughout approximately 850,000 CpG sites in peripheral blood white cells of MCI- and AD-affected Chinese patients, as well as cognitively healthy controls. All samples included 20 Chinese patients with MCI, 20 Chinese patients with AD, and 20 cognitively healthy controls.
2023-09-11 | GSE208623 | GEO
Project description:Individuals with mild cognitive impairment (MCI) are clinically heterogeneous, with different risks of progression to Alzheimer's disease. Regular follow-up and examination may be time-consuming and costly, especially for MRI and PET. Therefore, it is necessary to identify a more precise MRI population. In this study, a two-stage screening frame was proposed for evaluating the predictive utility of additional MRI measurements among high-risk MCI subjects. In the first stage, the K-means cluster was performed for trajectory-template based on two clinical assessments. In the second stage, high-risk individuals were filtered out and imputed into prognosis models with varying strategies. As a result, the ADAS-13 was more sensitive for filtering out high-risk individuals among patients with MCI. The optimal model included a change rate of clinical assessments and three neuroimaging measurements and was significantly associated with a net reclassification improvement (NRI) of 0.246 (95% CI 0.021, 0.848) and integrated discrimination improvement (IDI) of 0.090 (95% CI - 0.062, 0.170). The ADAS-13 longitudinal models had the best discrimination performance (Optimism-corrected concordance index = 0.830), as validated by the bootstrap method. Considering the limited medical and financial resources, our findings recommend follow-up MRI examination 1 year after identification for high-risk individuals, while regular clinical assessments for low-risk individuals.
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Project description:IntroductionNo known studies have compared longitudinal characteristics between individuals with incident mild cognitive impairment due to Parkinson's disease (PD-MCI) versus Alzheimer's Disease (AD-MCI).MethodsWe used longitudinal data from the National Alzheimer's Coordinating Center's Uniform Data Set to compare 41 PD-MCI and 191 AD-MCI participants according to their demographics, presence of ≥1 APOE e4 allele, and baseline and change over time in clinical characteristics, neuropsychological test scores, and Clinical Dementia Rating sum of boxes (CDR-SB). Multivariable linear regression models with generalized estimating equations were used to account for clustered data and to test for baseline and longitudinal differences in neuropsychological test scores.ResultsPD-MCI and AD-MCI participants differed by many demographic and clinical characteristics. Significantly fewer PD-MCI participants developed dementia over one year. Compared to AD-MCI participants, PD-MCI participants performed better at baseline and over time on a global measure of cognition (Mini Mental State Exam), memory measures (immediate and delayed Logical Memory), and a language measure (Boston Naming Test), and additionally performed better over time on an attention measure (Digit Span Forward), a language measure (Vegetable List), a processing speed measure (Digit Symbol), and an overall measure of memory and functional impairment (CDR-SB).ConclusionOur study provides further evidence that PD-MCI is clinically distinct from AD-MCI and requires different tools for diagnosis and monitoring clinical progression. More importantly, this study suggests that PD-MCI takes longer to convert into dementia than AD-MCI, findings that require replication by other studies.
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Project description:BackgroundThe retina and brain share many neuronal and vasculature characteristics. We investigated the retinal microvasculature in Alzheimer's disease (AD) and mild cognitive impairment (MCI) using optical coherence tomography angiography (OCTA).MethodsIn this cross-sectional study, 24 AD participants, 37 MCI participants, and 29 controls were diagnosed according to internationally accepted criteria. OCTA images of the superficial and deep capillary plexus (SCP, DCP) of the retinal microvasculature were obtained using a commercial OCTA system (Zeiss Cirrus HD-5000 with AngioPlex, Carl Zeiss Meditec, Dublin, CA). The main outcome measures were vessel density (VD) and fractal dimension (FD) in the SCP and DCP within a 2.5-mm ring around the fovea which were compared between groups. Perfusion density of large vessels and foveal avascular zone (FAZ) area were additional outcome parameters.ResultsAge, gender, and race did not differ among groups. However, there was a significant difference in diabetes status (P = 0.039) and systolic blood pressure (P = 0.008) among the groups. After adjusting for confounders, AD participants showed significantly decreased VD in SCP and DCP (P = 0.006 and P = 0.015, respectively) and decreased FD in SCP (P = 0.006), compared to controls. MCI participants showed significantly decreased VD and FD only in SCP (P = 0.006 and P < 0.001, respectively) and not the DCP (P > 0.05) compared with controls. There was no difference in the OCTA variables between AD and MCI (P > 0.05). Perfusion density of large vessels and FAZ area did not differ significantly between groups (P > 0.05).Conclusions and relevanceEyes of patients with AD have significantly reduced macular VD in both plexuses whereas MCI participants only showed reduction in the superficial plexus. Changes in the retinal microvasculature and capillary network may offer a valuable insight on the brain in AD.
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Project description:Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell's C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.
| S-EPMC6338093 | biostudies-literature
Project description:We investigated the expression of the Alzheimer's disease-related metabolic brain pattern (ADRP) in 18F-FDG-PET scans of 44 controls, 27 patients with mild cognitive impairment (MCI) who did not convert to Alzheimer's disease (AD) after five or more years of clinical follow-up, 95 MCI patients who did develop AD dementia on clinical follow-up, and 55 patients with mild-to-moderate AD. The ADRP showed good sensitivity (84%) and specificity (86%) for MCI-converters when compared to controls, but limited specificity when compared to MCI non-converters (66%). Assessment of 18F-FDG-PET scans on a case-by-case basis using the ADRP may be useful for quantifying disease progression.
| S-EPMC5718332 | biostudies-other
Project description:IntroductionHeterogeneity in risk of conversion to Alzheimer's disease (AD) among individuals with mild cognitive impairment (MCI) is well known. Novel statistical methods that are based on partially ordered set (poset) models can be used to create models that provide detailed and accurate information about performance with specific cognitive functions. This approach allows for the study of direct links between specific cognitive functions and risk of conversion to AD from MCI. It also allows for further delineation of multi-domain amnestic MCI, in relation to specific non-amnestic cognitive deficits, and the modeling of a range of episodic memory functioning levels.MethodsFrom the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, conversion at 24 months of 268 MCI subjects was analyzed. It was found that 101 of those subjects (37.7%) converted to AD within that time frame. Poset models were then used to classify cognitive performance for MCI subjects. Respective observed conversion rates to AD were calculated for various cognitive subgroups, and by APOE e4 allele status. These rates were then compared across subgroups.ResultsThe observed conversion rate for MCI subjects with a relatively lower functioning with a high level of episodic memory at baseline was 61.2%. In MCI subjects who additionally also had relatively lower perceptual motor speed functioning and at least one APOE e4 allele, the conversion rate was 84.2%. In contrast, the observed conversion rate was 9.8% for MCI subjects with a relatively higher episodic memory functioning level and no APOE e4 allele. Relatively lower functioning with cognitive flexibility and perceptual motor speed by itself also appears to be associated with higher conversion rates.ConclusionsAmong MCI subjects, specific baseline cognitive profiles that were derived through poset modeling methods, are clearly associated with differential rates of conversion to AD. More precise delineation of MCI by such cognitive functioning profiles, including notions such as multidomain amnestic MCI, can help in gaining further insight into how heterogeneity arises in outcomes. Poset-based modeling methods may be useful for providing more precise classification of cognitive subgroups among MCI for imaging and genetics studies, and for developing more efficient and focused cognitive test batteries.
| S-EPMC3707057 | biostudies-literature
Project description:To explore the microRNAs associated with pathology of early Alzheimer's disease, we detected the microRNA profiles in the plasma of subjects with mild cognitive impairment due to Alzheimer's disease and gender-, age-, education-matched normal control elderly.
2020-03-20 | GSE147232 | GEO
Project description:In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals. To this end, we collected EEG data from individuals with AD (n = 26), MCI (n = 53), and cognitively normal healthy controls stratified by age into three groups: 18-40 (n = 129), 40-60 (n = 62) and 60-90 (= 55) years old. For each participant, we computed power spectral density at each channel and spectral coherence between pairs of channels. Compared to age matched controls, in the AD group, we found increases in both spectral power and coherence at the slower frequencies (Delta, Theta). A smaller but significant increase in power of slow frequencies was observed for the MCI group, localized to temporal areas. These effects on slow frequency spectral power opposed that of normal aging observed by a decrease in the power of slow frequencies in our control groups. The AD group showed a significant decrease in the spectral power and coherence in the Alpha band consistent with the same effect in normal aging. However, the MCI group did not show any significant change in the Alpha band. Overall, Theta to Alpha ratio (TAR) provided the largest and most significant differences between the AD group and controls. However, differences in the MCI group remained small and localized. We proposed a novel method to quantify these small differences between Theta and Alpha bands' power using empirically derived distributions of spectral power across the time domain as opposed to averaging power across time. We defined Power Distribution Distance Measure (PDDM) as a distance measure between probability distribution functions (pdf) of Theta and Alpha power. Compared to average TAR, using PDDF enhanced the statistical significance, the effect size, and the spatial distribution of significant effects in the MCI group. We designed classifiers for differentiating individual MCI and AD participants from age-matched controls. The classification performance measured by the area under ROC curve after cross-validation were AUC = 0.85 and AUC = 0.6, for AD and MCI classifiers, respectively. Posterior probability of AD, TAR, and the proposed PDDM measure were all significantly correlated with MMSE score and neuropsychological tests in the AD group.
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