Abstract 4361408: Amyloid Pathway Markers and Cardiovascular Outcomes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Amyloid beta 1–40 (Aβ1–40), a cleavage product of amyloid precursor protein, is emerging as a vascular inflammation marker with prognostic value in cardiovascular disease (CVD). Its predictive utility, however, remains inconsistent across clinical settings. BACE1-AS, a regulatory long non-coding RNA of Aβ1–40, has also been implicated in adverse cardiovascular outcomes. We aimed to synthesize available evidence and quantify the prognostic strength of these markers across CVD populations. Methods: We conducted a systematic search of PubMed, Embase, Scopus, and Google Scholar using predefined PICO criteria. Filters: human studies, English language, adults (≥18), observational cohorts or clinical trials. Outcomes of interest included mortality, heart failure, and major adverse cardiovascular events (MACE). Risk of bias was assessed via the Newcastle-Ottawa Scale. Pooled hazard ratios were calculated using the generic inverse variance method. Sensitivity analyses were performed to assess heterogeneity. Results: Four studies (n = 7,154). Pooled hazard ratio for all-cause mortality: HR = 1.19 (95% CI: 1.04–1.36; p = 0.01). Heterogeneity was high (I 2 = 94%). One-out sensitivity analysis identified Aleksova et al. as the primary driver; excluding it reduced I 2 to 59%. This study uniquely combined STEMI and NSTEMI patients in a high-risk cohort with longer follow-up, enriched for older age, renal dysfunction, and systolic failure—conditions known to amplify Aβ1–40 levels. Another study reported a 32% increased heart failure risk per 1-SD rise in Aβ1–40. Elevated BACE1-AS tertiles predicted a 1.86-fold increase in MACE risk. Conclusion: Aβ1–40 is a significant prognostic biomarker for mortality and heart failure in CVD populations. The heterogeneity across studies is largely explained by the Aleksova et al. study, which captured a more acute, high-risk phenotype with STEMI dominance and systemic inflammation. Its removal in sensitivity analysis reveals more consistent effect sizes across stable cohorts. These results reinforce the pathobiological relevance of the amyloid axis in cardiovascular disease and call for standardized measurement protocols and phenotype-stratified validation. Aβ1–40 and BACE1-AS should be investigated further as tools for early risk stratification in precision cardiology.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".