Body Mass Index, Waist Circumference and Mortality in Kidney Transplant Recipients
Bibliographic record
Abstract
Higher body mass index (BMI) appears paradoxically associated with better outcomes in patients with chronic kidney disease. Whereas higher BMI reflects both increased visceral and subcutaneous fat and/or muscle mass, a combined assessment of BMI and waist circumference may enable differentiation of visceral adiposity from muscle and/or nonvisceral fat mass. We examined the association of BMI and waist circumference with all-cause mortality in a prospective cohort of 993 kidney transplant recipients. Associations were examined in Cox models with adjustment for demographic and comorbid conditions and for inflammatory markers. Unadjusted death hazard ratios (95%CI) associated with one standard deviation higher BMI and waist circumference were 0.94 (0.78, 1.13), p = 0.5 and 1.20 (1.00, 1.45), p = 0.05, respectively. Higher BMI was associated with lower mortality after adjustment for waist circumference (0.48 [0.34, 0.69], p < 0.001), and higher waist circumference was more strongly associated with higher mortality after adjustment for BMI (2.18 [1.55-3.08], p < 0.001). The associations of waist circumference with mortality remained significant after additional multivariable adjustments. Higher BMI and waist circumference display opposite associations with mortality in kidney transplant recipients. Waist circumference appears to be a better prognostic marker for obesity than BMI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".