Bibliographic record
Abstract
A recent population-based study indicates that obese children at the time of cancer diagnosis may face an elevated risk of dying. The retrospective study indicates that obesity at diagnosis is an independent predictor of inferior survival, prominently in children with acute lymphoblastic leukemia (ALL) and central nervous system (CNS) tumors (Cancer 2025; https://doi.org/10.1002/cncr.35673). The study was based on information from Cancer in Young People, a Canadian database, including all children with newly diagnosed cancer, ages 2-18 years, across Canada from 2001 to 2020. Obesity was defined as age- and sex-adjusted body mass index at or above the 95th percentile. Among 11,291 children with cancer, 10.5 percent were obese at the time of diagnosis. Investigators assessed 5-year event-free survival (EFS) and overall survival (OS). Compared with patients without obesity at the time of initial cancer diagnosis, those with obesity had lower rates of 5-year EFS (77.5% vs. 79.6%) and OS (83% vs. 85.9%). After adjusting for factors including age, sex, ethnicity, neighborhood income quintile, treatment era, and cancer categories, obesity at diagnosis was linked with a 16 percent increase in the risk of relapse and a 29 percent increase in the risk of death. “Our study highlights the negative impact of obesity among all types of childhood cancers. It provides the rationale to evaluate different strategies to mitigate the adverse risk of obesity on cancer outcomes in future trials,” said co-senior author Thai Hoa Tran, MD, FRCPC, Pediatric Hematologist-Oncologist in the Leukemia Program at the Centre Hospitalier Universitaire Sainte-Justine in Montreal. “It also reinforces the urgent need to reduce the epidemic of childhood obesity, as it can result in significant health consequences.” Obesity increases the risk of various cancers in adults and has been associated with unfavorable outcomes in several cancer types, including breast, colorectal, uterine, prostate, and pancreatic cancers. A growing body of literature suggests that being overweight or obese at diagnosis confers adverse outcomes in children with cancer, particularly those with ALL. Researchers from all across Canada sought to assess the prevalence of obesity and its prognostic significance in children and adolescents diagnosed with cancer. The negative impact of obesity on prognosis was especially pronounced in patients with ALL and CNS tumors. In 3,458 children with ALL, obesity remained associated with inferior EFS (adjusted hazard ratio: 1.55) and OS (adjusted hazard ratio: 1.75) in multivariable analysis. In 2,458 patients with CNS tumors, obesity was also associated with inferior EFS (adjusted hazard ratio: 1.38) and OS (adjusted hazard ratio: 1.47). No statistically significant differences were observed in other cancer types, notably acute myeloid leukemia, lymphomas, and non-CNS solid tumors. “Our findings consolidate the adverse impact of obesity on cancer outcomes in a large population-based cohort of childhood cancers, thereby echoing the growing literature in both pediatric and adult oncology,” the researchers stated. “Obesity at diagnosis remains independently associated with an approximately 55 percent increase in the risk of an event and a 75 percent increase in the risk of death after adjusting for recognized prognostic factors, further strengthening the association between obesity and poor outcomes in childhood ALL.” The study also highlights the risks of obesity in children and adolescents with CNS tumors, a prognostic factor that has not been reported previously. The effect of obesity in children with CNS tumors was more prominent in gliomas and glioneural tumors. The researchers noted that the potential undertreatment and inappropriate dosing of chemotherapeutic agents remain important concerns for obese patients. This may be due to how body composition affects drug absorption, metabolism, protein binding, blood-to-tissue delivery, and drug clearance. “Individualized therapeutic drug monitoring in obese patients during active treatment is urgently needed to inform rationalized chemotherapy dosage and optimize cancer outcomes. In contrast, if obesity causes excessive treatment-related mortality without excess relapse, therapy de-escalation and/or enhanced supportive care would appear to be appropriate measures,” the researchers stated. Further studies are needed to better characterize the pathophysiologic mechanisms linking obesity and survival in pediatric oncology, the researchers concluded. Mark L. Fuerst is a contributing writer.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".