Correlation of circulating full‐length visfatin (PBEF/NAMPT) with metabolic parameters in subjects with and without diabetes: a cross‐sectional study
Bibliographic record
Abstract
OBJECTIVE: Here we use a novel ELISA that is specific for full-length visfatin (PBEF/NAMPT), compare it with the existing C-terminal based assay and use it to investigate associations of visfatin with metabolic parameters. DESIGN, PATIENTS AND MEASUREMENTS: We established the specificity and effectiveness of the new ELISA and evaluated the associations of full-length visfatin with clinical, anthropometric and metabolic parameters in a cross-sectional study of 129 Thai subjects, consisting of 50 outpatients with type 2 diabetes and 79 healthy volunteers. RESULTS: The new ELISA accurately recovered full-length recombinant visfatin and detected visfatin secreted by primary human and rat adipocytes. We found serum full-length visfatin was significantly higher in subjects with diabetes compared to their nondiabetic peers (median 2.75 vs. 2.22 ng/ml, P = 0.0142). After adjustment for age, gender and traditional metabolic risk factors, adjusted mean visfatin remained significantly higher in the diabetes group (3.80 vs. 2.10 ng/ml, P = 0.0021). On Spearman univariate correlation analysis, visfatin was significantly associated with resistin (r = 0.30, P = 0.0011), but not with any other anthropometric or metabolic variables, including adiponectin multimers. On multiple linear regression analysis, the only covariates independently associated with visfatin were diabetes (t = 3.11, P = 0.0024) and log resistin (t = 2.68, P = 0.0086). CONCLUSIONS: Circulating visfatin is independently associated with diabetes and resistin concentration, but is not related to adiponectin multimers or other metabolic covariates. These data are suggestive of a potential role of visfatin in subclinical inflammatory states.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".