Association of Red Blood Cell and Platelet Parameters with Metabolic Syndrome: A Systematic Review and Meta-Analysis of 170,000 Patients
Bibliographic record
Abstract
This systematic review and meta-analysis aim to establish associations between metabolic syndrome (MetS) and erythrocyte and platelet markers, contributing to improved diagnostic tests for identifying individuals at risk. Observational studies and Randomized Controlled Trials (RCTs) were included. The standardized mean difference (SMD) and 95% confidence intervals (CI) of erythrocyte and platelet markers between individuals with and without MetS were used as effect size (inverse variance model). Methodological quality assessment was conducted using the Newcastle-Ottawa scale (NOS) for observational studies and the Cochrane Risk of Bias tool 2.0 for RCTs. The analysis included 51 articles. Compared to controls, individuals with MetS exhibited significantly higher concentrations of mean red blood cell count [Standardized Mean Difference (95% CI): 0.15 (0.13-0.18); p<0.00001], hemoglobin [0.24 (0.18-0.31); p<0.00001], blood platelet count [5.49 (2.78-8.20); p<0.0001], and red blood cell distribution width [(0.55 (0.05-1.04); p=0.03]. Regarding mean platelet volume [0.16 (- 0.03 to 0.35); p=0.10] and platelet-to-lymphocyte ratio (PLR) [7.48 (-2.85-17.81); p=0.16], a non-significant difference was observed in patients with MetS. There was no statistically significant difference in hematocrit counts between the two groups [0.47 (-0.40 to -1.34); p=0.29]. Biomarkers such as mean red blood cell count, hemoglobin, blood platelet count, and RDW are associated with higher levels in patients in MetS, whereas mean platelet volume and PLR tend to be lower. These markers can potentially provide new avenues for early diagnosis of MetS.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".