Correlation Between Resistin Level and Metabolic Syndrome Component: A Review
Bibliographic record
Abstract
Metabolic syndrome (MetS) has a collection of some abnormal and pathological conditions that cause many critical diseases. Resistin is one of the possible candidates for these pathologies but there are not enough data to prove if resistin has positive, neutral, or negative effects on one or some components of MetS. This review summarizes data about comparing the effects and contribution of resistin in initiation and progression of MetS components and also its different actions between human and other mammalians. This summarized data about the relationship of resistin and MetS components have been obtained from clinical researches and in some cases even animal studies. To find the relevant studies, the search in PubMed, Science Direct, and Scopus were performed. Human and animal studies on relationships between resistin and MetS (initiation and progression of components) were included in our search. In experiments reported among different human genetic groups as well as the patients with various disease such as diabetes, no significant correlation is shown between FBG and resistin level. Furthermore, this review shows that the results of correlation between resistin and TG, HDL, and central or abdominal obesity were inconsistent. These inconsistencies can arise from different sample size or genetic groups, gender, and also from experimental studies. Therefore, to obtain precise results systematic review and meta-analyses are required.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".