Lower total and percent of high-molecular-weight adiponectin concentration in South Asian kidney transplant recipients
Bibliographic record
Abstract
Ethnicity is an important determinant of post-renal transplant outcomes. Limited data are available on cardiovascular risk differences in kidney transplant recipients (KTR) based on ethnicity. A group of 129 clinically stable age-matched KTR [43 South Asian (SA), 86 Caucasian]) were assessed for plasma total and high-molecular-weight (HMW) adiponectin, cystatin C, apolipoproteins A1 and B, C-reactive protein, uric acid, urine albumin-to-creatinine ratio, estimated glomerular filtration rate (eGFR) and transplant-specific plus traditional Framingham risk factors. SA and Caucasians were compared by t-tests, Wilcoxon rank-sum or chi-square testing. Accounting for the matched design, multivariable linear regression was performed to determine predictors of adiponectin concentrations. SA did not differ from Caucasians in background cardiac disease or cardioprotective medication use or risk factors other than smoking (26 versus 56%, P = 0.001). Total adiponectin (9.5 ± 3.5 versus 12.9 ± 6.7 μmg/mL, P < 0.001) and HMW adiponectin (22 ± 9 versus 29 ± 11%, P < 0.001) were significantly lower in SA. Determinants of total adiponectin included SA ethnicity (P = 0.02), cystatin C-eGFR (P < 0.001), high-density lipoprotein (HDL) cholesterol (P < 0.0001) and waist-to-hip ratio (P < 0.001), while those of HMW adiponectin included SA ethnicity (P < 0.001), cystatin C-eGFR (P = 0.03) and HDL cholesterol (P = 0.001). There were no important differences in the other measured biomarkers. Total and HMW adiponectin concentrations are lower in SA KTR and may be promising exploratory biomarkers of post-transplant cardiovascular risk.
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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 | 0.000 | 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 | 0.000 |
| 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".