Metabolic syndrome definitions and components in predicting major adverse cardiovascular events after kidney transplantation
Bibliographic record
Abstract
Metabolic syndrome (MetS) associates with cardiovascular risk post-kidney transplantation, but its ambiguity impairs understanding of its diagnostic utility relative to components. We compared five MetS definitions and the predictive value of constituent components of significant definitions for major adverse cardiovascular events (MACE) in a cohort of 1182 kidney transplant recipients. MetS definitions were adjusted for noncomponent traditional Framingham risk factors and relevant transplant-related variables. Kaplan-Meier, logistic regression, and Cox proportional hazards analysis were utilized. There were 143 MACE over 7447 patient-years of follow-up. Only the World Health Organization (WHO) 1998 definition predicted MACE (25.3 vs 15.5 events/1000 patient-years, P = 0.019). Time-to-MACE was 5.5 ± 3.5 years with MetS and 6.8 ± 3.9 years without MetS (P < 0.0001). MetS was independent of pertinent MACE risk factors except age and previous cardiac disease. Among MetS components, dysglycemia provided greatest hazard ratio (HR) for MACE (1.814 [95% confidence interval 1.26-2.60]), increased successively by microalbuminuria (HR 1.946 [1.37-2.75]), dyslipidemia (3.284 [1.72-6.26]), hypertension (4.127 [2.16-7.86]), and central obesity (4.282 [2.09-8.76]). MetS did not affect graft survival. In summary, although the WHO 1998 definition provides greatest predictive value for post-transplant MACE, most of this is conferred by dysglycemia and is overshadowed by age and previous cardiac 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 | 0.006 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".