Association of the novel cardiovascular risk factors paraoxonase 1 and cystatin C in type 2 diabetes
Bibliographic record
Abstract
Paraoxonase 1 (PON1) has been reported to be associated with proteinuria in subjects with type 2 diabetes mellitus (T2DM). Plasma cystatin C is more accurate than creatinine for identifying stage 3 kidney disease in T2DM. We tested the hypothesis that PON1 and cystatin C would be associated in T2DM subjects from an Aboriginal Canadian community, who are at high risk for the development of nephropathy. PON1 A(-162)G and PON2 Ala148Gly genotypes, cystatin C, HbA1c, high density lipoprotein cholesterol (HDLC), waist circumference (waist), and duration of diabetes were included in the regression analysis with log(e) (ln) of PON1 mass as the dependent variable. A regression model including PON2 Ala148Gly genotype, HDLC, and ln cystatin C explained 25.8% of the variance in PON1 mass. Conversely, waist, age, ln HbA1c, ln duration of diabetes, and ln PON1 mass, but not PON2 genotype, explained 38% of the variance in cystatin C. Subjects with cystatin C estimated glomerular filtration rate (eGFR) <60 ml/min per 1.73 m(2) (stage 3 kidney disease) had significantly lower PON1 mass compared with subjects with cystatin C-eGFR >60 ml/min per 1.73 m(2). The lower mass of PON1, an anti-inflammatory HDL-associated enzyme, in T2DM with cystatin C-eGFR <60 ml/min per 1.73 m(2) may contribute to their increased risk for cardiovascular disease.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".