Adiponectin Appears to Have Different Mechanisms in Type 1 and Type 2 Diabetes with C-peptide as an Important Link
Bibliographic record
Abstract
Adiponectin (ApN) is considered to be responsible for reduction of inflammation and is known to be included in lipid metabolism. This study was designed to assess the role of adiponectin in patients with type 1 and type 2 diabetes and to determine parameters important in the prediction of adiponectin. Adiponectin, high sensitive C-reactive protein, fibrinogen, homocysteine, C-peptide, and lipid panel in addition to clinical and laboratory parameters important for the definition of diabetes, obesity and the metabolic syndrome were measured in 118 patients included in the study. The best model (R2=0.989) for predicting adiponectin in type 1 diabetes included fibrinogen, white blood cell count, uric acid and triglycerides. In type 2 diabetes the best model (R2=0.751) included C-peptide, white blood cell count, systolic blood pressure, fasting blood glucose, glycated hemoglobin and high-density lipoprotein cholesterol. ANOVA showed statistically significant among-group differences in adiponectin (p=0.028), body mass index (p<0.001), fasting blood glucose (p<0.001) and high-density lipoprotein cholesterol (p=0.012) according to the type of diabetes. Significant between-group differences were also observed in adiponectin (p=0.033) and high-density lipoprotein cholesterol (p=0.009) according to sex. Adiponectin correlated significantly (p<0.05) with body mass index, C-peptide, pulse pressure and high-density lipoprotein cholesterol. Adiponectin levels were significantly higher in type 1 diabetes. The association between C-peptide and adiponectin is probably one of the reasons for their different respective levels in different types of diabetes. Interrelations between adiponectin and inflammation, dyslipidemia, C-peptide levels and sex appear to be important for complex adiponectin modulation and action.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".