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Record W4390194867 · doi:10.1002/alz.072378

Fluid Biomarkers of Neurodegeneration in Mild Cognitive Impairment: A Meta‐Analysis

2023· article· en· W4390194867 on OpenAlexaff
Amish Gaur, Luc Rivet, Ethan Mah, Kritleen K. Bawa, Damien Gallagher, Nathan Herrmann, Krista L. Lanctôt

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicS100 Proteins and Annexins
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsNeurodegenerationInternal medicineMedicineCerebrospinal fluidNeuropathologyMeta-analysisBiomarkerEnolaseGastroenterologyPathologyOncologyDiseaseBiologyImmunohistochemistryBiochemistry

Abstract

fetched live from OpenAlex

Abstract Background Mild cognitive impairment (MCI) is a well‐established prodromal stage of Alzheimer’s disease and is often accompanied by early signs of neurodegeneration (e.g., neuronal loss) that may influence subsequent prognosis. To facilitate a better characterization of the underlying neuropathology, a thorough investigation of proteins associated with neurodegenerative processes in MCI is needed. Thus, the present study systematically assessed the available literature to quantitatively evaluate cerebrospinal fluid (CSF) and peripheral blood concentrations of biomarkers related to neurodegeneration in individuals with MCI compared to healthy controls (HCs). Method Original peer‐reviewed articles that assessed the following CSF and/or peripheral biomarkers: neurofilament light chain (NFL), total‐tau (T‐tau), glial fibrillary acidic protein (GFAP), heart‐type fatty acid binding protein (HFABP), neuron‐specific enolase (NSE), and S100 calcium‐binding protein B (S100B) in both MCI and HCs were included for meta‐analysis. Standardized mean differences (SMDs) and 95% confidence intervals were calculated using a random‐effects model. Heterogeneity was assessed using the I2 statistic. Result A total of 109 study cohorts were included in the meta‐analysis. In CSF, concentrations of NFL (MCI n = 2771, HC n = 4263, SMD [95% CI] = 0.69 [0.56, 0.83], p < 0.001), GFAP (MCI n = 513, HC n = 1620, SMD [95% CI] = 0.41 [0.08, 0.74], p = 0.02), and HFABP (MCI n = 363, HC n = 245, SMD [95% CI] = 0.57 [0.23, 0.55], p < 0.001) were elevated in MCI. In peripheral blood, concentrations of NFL (MCI n = 6520, HC n = 9439, SMD [95% CI] = 0.41 [0.32, 0.49], p < 0.001), T‐tau (MCI n = 4463, HC n = 5921, SMD [95% CI] = 0.19 [0.09, 0.29], p < 0.001), and GFAP (MCI n = 1758, HC n = 3179, SMD [95% CI] = 0.39 [0.23, 0.55], p < 0.001) were increased in MCI. Significant heterogeneity was identified for all comparisons and was explored through meta regression and subgroup analysis. Conclusion The present study identified several fluid biomarkers of neurodegeneration in MCI. Importantly, elevated biomarker levels can be detected in both CSF and less‐invasively through peripheral blood. Future studies should focus on assessing the clinical utility of monitoring these biomarkers as they may provide further insight into neuronal and astroglial pathology occurring in the early stages of cognitive decline.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.028
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0160.052
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.289
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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