Body Mass Index and Mortality in Kidney Transplant Recipients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: A higher body mass index (BMI) seems to be linked to survival advantage in maintenance hemodialysis patients. However, it is uncertain if this 'obesity survival paradox' is also observed in kidney transplant recipients. Hence, we systematically reviewed the literature on the impact of pre-transplantation BMI on all-cause mortality in this population. METHODS: We searched MEDLINE, EMBASE, Web of Science, CINAHL, and Cochrane CENTRAL for relevant studies up to July 2013. Two investigators independently selected the studies using predefined criteria, abstracted the data from the included studies, and independently assessed each study's quality using the Newcastle-Ottawa Quality Assessment Scale. In addition to the qualitative synthesis, we quantitatively pooled the results of the studies with clinical, methodological, and statistical homogeneity. RESULTS: We screened 7,123 records, from which we included 11 studies (with a total of 305,392 participants) in this systematic review and 4 studies in the meta-analyses. In the only study that included children, obesity was linked to higher mortality in children of 6-12 years old. For adults, our meta-analyses indicated that compared to normal BMI, underweight [Hazard Ratio (HR): 1.09; 95% Confidence Interval (CI): 1.02-1.20], overweight (HR: 1.07; 95% CI: 1.04-1.12), and obese (HR: 1.20; 95% CI: 1.14-1.23) levels of BMI were associated with higher mortality. CONCLUSION: The presence of the obesity survival paradox is unlikely in kidney transplant recipients since both extremes of pre-transplantation BMI are linked to higher mortality in this population.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".