Association of Erythrocytes Indices and Interleukin-1 beta with Metabolic Syndrome Components : Erythrocyte indices, IL-1β and metabolic syndrome
Bibliographic record
Abstract
Background and Aim: The hematological studies and cytokines are of great importance in metabolic syndrome pathophysiology. The study aimed to illuminate the association between red blood cell (RBCs) indices and interleukin-1beta (IL-1β) with MetS components among adults' Egyptian patients. Material and Methods: A total of 100 healthy subjects and 200 patients diagnosed with MetS components were enrolled. Eligible patients were classified into four groups (50 patients). Group1 included patients with 2 MetS criteria, group2 those had 3 MetS criteria, group3 those with 4 MetS criteria and group 4 those had 5 MetS criteria. Results: Among MetS patients, the data revealed that RBCs count and hematocrit % showed an obvious higher in both group 2 and 3, however, hemoglobin (HB) and mean corpuscular volume levels showed a significant elevation in group 3 compared to healthy controls. Group 4 observed a noticeable lower in both RBCs count and HB concentration compared healthy controls. Moreover, red blood cell distribution width value was significantly higher in all groups compared to healthy controls. Additionally, IL-1β was significantly higher in all patients with MetS components compared to the healthy group. Concerning group 3, a positive correlation between both RBCs count and hematocrit level with systolic blood pressure was recorded, while HB concentration was correlated with diastolic blood pressure. Conclusions: Erythrocyte indices and IL-1β levels are associated with an increase in MetS components, and this study provides additional evidence for the use of hematological and cytokine markers in early identification of individuals at risk of MetS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".