New-onset Obesity After Lung Transplantation: Incidence, Risk Factors, and Clinical Outcomes
Bibliographic record
Abstract
BACKGROUND: Lung transplant (LTx) recipients who gain weight after transplantation may experience an upward shift in body mass index (BMI) that places them in the obese category. The incidence, risk factors, and impact on metabolic health and mortality of new-onset obesity have not been documented in the LTx setting. METHODS: This single-center retrospective study included 564 LTx recipients. Individuals were stratified according to their BMI trajectories from pretransplant evaluation up to 10 y posttransplant. New-onset obesity was defined as a pretransplant BMI <30 kg/m 2 and posttransplant BMI >30 kg/m 2 . The incidence, risk factors, and posttransplant diabetes mellitus, metabolic syndrome, and mortality of recipients with new-onset obesity were compared with those of nonobese (BMI <30 kg/m 2 , pre/post-LTx), consistently obese (BMI >30 kg/m 2 , pre/post-LTx), and obese recipients with weight loss (BMI >30 kg/m 2 pre-LTx, BMI <30 kg/m 2 post-LTx). RESULTS: We found that 14% of recipients developed obesity after transplantation. Overweight individuals (odds ratio [OR]: 9.01; 95% confidence interval [CI] [4.86-16.69]; P < 0.001) and candidates with chronic obstructive pulmonary disease (OR: 6.93; 95% CI [2.30-20.85]; P = 0.001) and other diagnoses (OR: 4.28; 95% CI [1.22-14.98]; P = 0.023) were at greater risk. Multivariable regression analysis showed that new-onset obesity was associated with a greater risk of metabolic syndrome (hazard ratio: 1.70; 95% CI [1.17-2.46]; P = 0.005), but not of posttransplant diabetes mellitus, than nonobesity. Recipients with new-onset obesity had a survival comparable to that of consistently obese individuals. CONCLUSIONS: A greater understanding of the multifaceted nature of post-LTx obesity may lead to interventions that are better tailored to the characteristics of these individuals.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".