The impact of body mass index (BMI) on overall survival (OS) among patients receiving immune checkpoint inhibitors (ICIs): A population-based study.
Bibliographic record
Abstract
12041 Background: While obesity is a risk factor for cancer, BMI has been previously identified as a potential prognostic marker in different solid tumors. Prior studies have identified that among cancer survivors receiving ICIs, higher BMI may be associated with better OS, but there have been heterogeneous results among studies. Here we use population-level administrative data to evaluate the association between BMI and OS among patients receiving ICIs. Methods: We used administrative data deterministically linked across databases to identify a cohort of solid tumors patients initiating ICI therapy in Ontario, Canada from June 2012 to October 2018 and obtained information on socio-demographics including BMI at start of ICI, clinical covariates, and OS. We applied multivariable Cox proportional hazards models to evaluate the impact of BMI on OS, adjusting for sex, age, cancer center, autoimmune history, recent hospitalization and comorbidity score. Subgroup analyses were performed based on disease site and sex. Results: Among 4863 patients, median age was 67, 57% male; 46% had lung cancer, 35% melanoma, 9% renal cancers; 40% received nivolumab, 36% pembrolizumab, 17% ipilimumab. Median BMI was 26.1, with 3% low, 29% normal, 27% overweight, 19% obese. Median OS 317 days. Overall, greater BMI was associated with better OS (aHR=0.98 per unit, 95% CI [0.97-0.99] p<0.001). When compared to normal BMI, obese (aHR=0.77 [0.70-0.85] P<0.001) and overweight patients (aHR=0.85 [0.78-0.93] p<0.001) had better OS while those with low BMI had poorer OS (aHR=1.39 [1.16-1.66] P<0.001). Among melanoma patients, those who were obese had better OS (aHR=0.84 [0.71-0.98] p=0.03) and low BMI patients had poorer OS (aHR=1.80 [1.20-2.69] p=0.004) when compared to normal BMI. For lung and renal patients, increased BMI was associated with better OS when BMI was evaluated continuously (aHRlung=0.99 per unit [0.98-1.00] p=0.05; aHRrenal=0.98 per unit [0.95-0.99] p=0.04), but no significant associations were observed when BMI was evaluated categorically (p>0.05). Among males, patients who were obese (aHR=0.70 [0.62-0.80] p<0.001) and overweight (aHR=0.80 [0.72-0.90] p<0.001) had better OS compared to those with normal BMI, while those with low BMI had poorer OS (aHR=1.58 [1.22-2.06] p<0.001). However, among females, low BMI were associated with poorer OS (aHR=1.29 [1.01-1.65] p=0.04) compared to normal BMI, while no significant associations with OS were observed for overweight (aHR=0.93,p=0.30) or obese (aHR=0.89, p=0.12) patients. Conclusions: BMI was identified as a potential prognostic factor among cancer survivors receiving ICIs where greater BMI was associated with better OS. This association varied by cancer type and sex and is particularly notable in melanoma and among males. Further studies understanding these prognostic associations are warranted.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".