Accuracy of plasma interleukin-18 and adiponectin concentrations in predicting metabolic syndrome and cardiometabolic disease risk in middle-age Brazilian men
Bibliographic record
Abstract
The aims of this cross-sectional study were to explore the ability of serum interleukin 18 (IL-18) and adiponectin to identify metabolic syndrome (MetS), and to verify their association with an index of central lipid overaccumulation (lipid accumulation product (LAP)) and cardiometabolic risk factors in a population of middle-aged Brazilian men. A group of 218 apparently healthy middle-aged Brazilian men (age, 50.3 ± 4.97 years) underwent anthropometric, clinical, sociodemographic, and standard serum biochemical assessments. LAP was calculated and the study participants were categorized into 3 groups according to serum IL-18 and adiponectin cut-points tertiles to verify the association of these biomarkers with cardiometabolic risk factors. The MetS group had more less active (p = 0.03) and obese (p < 0.01) individuals who exhibited higher IL-18 (p < 0.01) and lower adiponectin (p < 0.01) than did those in the group with no MetS. After adjustments (age, smoking, alcohol consumption, physical activity level, and total body fat), serum IL-18 ≥ 336.4 pg/mL was an independent factor for MetS occurrence and it was directly associated with LAP (≥51.28), central obesity, hypertriglyceridemia, and hypertension (p < 0.05), but not with high-density lipoprotein cholesterol (HDL-C). Serum adiponectin ≥ 7.02 μg/mL was negatively associated with MetS occurrence, LAP, hypertriglyceridemia, and low HDL-C (p < 0.05), but not with central obesity and hypertension. In conclusion, both IL-18 and adiponectin demonstrated the ability to identify MetS in this population, with IL-18 being more accurate. The association of these biomamarkers with LAP and cardiometabolic risk factors highlights its relevance as a diagnostic tool.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".