Blood biomarkers in MCI conversion to Alzheimer’s disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The predictive effects of blood biomarkers (BBMs) in the progression of Alzheimer’s disease (AD) have been reported recently. However, controversies still exist. In the present study, we aim to identify the predictive performances of BBMs in the conversion from Mild cognitive impairment (MCI) to AD. Methods: PubMed, Embase, Cochrane Library, and Web of Science from inception to June 10, 2023 were searched. Predictive potentials were evaluated by pooling the ratio of means (ROMs), relative risks (RRs), and diagnostic indexes from MCI-converters (MCI-c: MCI patients who convert to AD) and MCI-non converters (MCI-nc) based on fixed-effects or random-effects. Newcastle–Ottawa Quality Assessment Scale (NOS) was applied for quality assessment. Results: A total of 44 studies with 9343 participants from 28 cohorts were included in the meta-analysis, whereas the other 45 articles were included in the qualitative review. The average score of 44 studies included in the meta-analysis was 7.125. In pooled ROMs, plasma Aβ42/Aβ40 was lower, whereas Aβ40, T-tau, P-tau 181, P-tau 217, NFL, and GFAP were higher in MCI-c than MCI-nc. In pooled RRs, P-tau (RR=2.50, 95%CI: 2.04-3.06) as a continuous variable, Aβ42/Aβ40 as a categorical variable (RR=1.28, 95%CI: 1.01-1.61) could predict future conversion risk of MCI patients. In diagnostic indexes, the diagnostic odds ratio (DOR) was 42 for P-tau 217 (sensitivity: 91%; specificity: 81%), 15 for P-tau 181 (sensitivity: 81%; specificity: 78%), 12.71 for GFAP (sensitivity: 71%; specificity: 86%), 6 for Aβ42/Aβ40 (sensitivity: 86%; specificity: 49%, and 6 for NFL (sensitivity: 80%; specificity: 61%). Conclusion: Here, our results indicated that blood biomarkers held promising potential in predicting MCI conversion. However, more prospective cohorts based on particular MCI types and high-sensitivity assays are warranted to validate the results next.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".