Dataset Information

Improving CSF Biomarkers' Performance for Predicting Progression from Mild Cognitive Impairment to Alzheimer's Disease by Considering Different Confounding Factors: A Meta-Analysis.

ABSTRACT:

Background

Cerebrospinal fluid (CSF) biomarkers' performance for predicting conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is still suboptimal.

Objective

By considering several confounding factors we aimed to identify in which situations these CSF biomarkers can be useful.

Data sources

A systematic review was conducted on MEDLINE, PreMedline, EMBASE, PsycInfo, CINAHL, Cochrane, and CRD (1990-2013).

Eligibility criteria

(1) Prospective studies of CSF biomarkers' performance for predicting conversion from MCI to AD/dementia; (2) inclusion of A?42 and T-tau and/or p-tau. Several meta-analyses were performed.

Results

A?42/p-tau ratio had high capacity to predict conversion to AD in MCI patients younger than 70?years. The p-tau had high capacity to identify MCI cases converting to AD in ?24?months.

Conclusions

Explaining how different confounding factors influence CSF biomarkers' predictive performance is mandatory to elaborate a definitive map of situations, where these CSF biomarkers are useful both in clinics and research.

SUBMITTER: Ferreira D

PROVIDER: S-EPMC4199277 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Improving CSF Biomarkers' Performance for Predicting Progression from Mild Cognitive Impairment to Alzheimer's Disease by Considering Different Confounding Factors: A Meta-Analysis.

Ferreira Daniel D Rivero-Santana Amado A Perestelo-Pérez Lilisbeth L Westman Eric E Wahlund Lars-Olof LO Sarría Antonio A Serrano-Aguilar Pedro P

Frontiers in aging neuroscience 20141016

<h4>Background</h4>Cerebrospinal fluid (CSF) biomarkers' performance for predicting conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is still suboptimal.<h4>Objective</h4>By considering several confounding factors we aimed to identify in which situations these CSF biomarkers can be useful.<h4>Data sources</h4>A systematic review was conducted on MEDLINE, PreMedline, EMBASE, PsycInfo, CINAHL, Cochrane, and CRD (1990-2013).<h4>Eligibility criteria</h4>(1) Prospective stu ...[more]

PMID: 25360114

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Project description:The increasing burden of Alzheimer's disease (AD) emphasizes the need for effective diagnostic and therapeutic strategies. Despite available treatments targeting amyloid beta (Aβ) plaques, disease-modifying therapies remain elusive. Early detection of mild cognitive impairment (MCI) patients at risk for AD conversion is crucial, especially with anti-Aβ therapy. While plasma biomarkers hold promise in differentiating AD from MCI, evidence on predicting cognitive decline is lacking. This study's objectives were to evaluate whether plasma protein biomarkers could predict both cognitive decline in non-demented individuals and the conversion to AD in patients with MCI. This study was conducted as part of the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD), a prospective, community-based cohort. Participants were based on plasma biomarker availability and clinical diagnosis at baseline. The study included MCI (n = 50), MCI-to-AD (n = 21), and cognitively unimpaired (CU, n = 40) participants. Baseline plasma concentrations of six proteins-total tau (tTau), phosphorylated tau at residue 181 (pTau181), amyloid beta 42 (Aβ42), amyloid beta 40 (Aβ40), neurofilament light chain (NFL), and glial fibrillary acidic protein (GFAP)-along with three derivative ratios (pTau181/tTau, Aβ42/Aβ40, pTau181/Aβ42) were analyzed to predict cognitive decline over a six-year follow-up period. Baseline protein biomarkers were stratified into tertiles (low, intermediate, and high) and analyzed using a linear mixed model (LMM) to predict longitudinal cognitive changes. In addition, Kaplan-Meier analysis was performed to discern whether protein biomarkers could predict AD conversion in the MCI subgroup. This prospective cohort study revealed that plasma NFL may predict longitudinal declines in Mini-Mental State Examination (MMSE) scores. In participants categorized as amyloid positive, the NFL biomarker demonstrated predictive performance for both MMSE and total scores of the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet (CERAD-TS) longitudinally. Additionally, as a baseline predictor, GFAP exhibited a significant association with cross-sectional cognitive impairment in the CERAD-TS measure, particularly in amyloid positive participants. Kaplan-Meier curve analysis indicated predictive performance of NFL, GFAP, tTau, and Aβ42/Aβ40 on MCI-to-AD conversion. This study suggests that plasma GFAP in non-demented participants may reflect baseline cross-sectional CERAD-TS scores, a measure of global cognitive function. Conversely, plasma NFL may predict longitudinal decline in MMSE and CERAD-TS scores in participants categorized as amyloid positive. Kaplan-Meier curve analysis suggests that NFL, GFAP, tTau, and Aβ42/Aβ40 are potentially robust predictors of future AD conversion.

Project description:Research shows that late mild cognitive impairment (LMCI) has a high risk of turning into Alzheimer's disease (AD). Due to the invasion of detection methods and physical damage to the patients, it is not a convenient way to diagnose and detect early AD and LMCI by cerebrospinal fluid (CSF) data. So there is an urgent need to find the correlation between peripheral biological data and CSF data in the brain, and to find new diagnostic methods through changes in the peripheral biological data. Studies have shown that during the pathogenesis of LMCI and AD, peripheral immune cells specifically infiltrate into the brain through the blood-brain barrier, causing an imbalance in the brain's immune response and dysregulating the clearance of Aβ in CSF. Therefore, in this paper, canonical correlation analysis (CCA) algorithm is presented to derive the correlation between peripheral and CSF biomarkers based on LMCI peripheral gene expression data and plasma marker information. Firstly, to explore the influence of the infiltration of peripheral blood immune cells on the brain, the abundance of 28 immune cells were calculated by using the gene set enrichment analysis algorithm of LMCI samples. Then, to identify the correlation between biomarkers inside and outside of the brain, we performed CCA to calculate the relationship between CSF and peripheral biomarkers. Results of CCA showed significant correlations between the variable sets of 8 peripheral biomarkers and the variable sets of CSF biomarkers (at 0.794). Finally, according to Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analysis, it was found that the obtained peripheral biomarkers are involved in many immune-related pathways and functions which can be activated in peripheral blood of LMCI patients. Most related genes enriched in immune-related pathways and functions were up-regulated. Through receiver operating characteristic curve (ROC) analysis, it was also found that FP40/FP42 and type 1 T helper can accurately predict the pathological changes of LMCI (at 0.747).

Dataset Information

Improving CSF Biomarkers' Performance for Predicting Progression from Mild Cognitive Impairment to Alzheimer's Disease by Considering Different Confounding Factors: A Meta-Analysis.

Background

Objective

Data sources

Eligibility criteria

Results

Conclusions

Publications

Improving CSF Biomarkers' Performance for Predicting Progression from Mild Cognitive Impairment to Alzheimer's Disease by Considering Different Confounding Factors: A Meta-Analysis.

Similar Datasets

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