Neurological and renal complications in obese children with cancer: a systematic review of cardiovascular risk factors
Bibliographic record
Abstract
Obesity in children, especially those with cancer, is a growing concern due to its impact on health outcomes. These children are at increased risk for neurological, renal, and cardiovascular complications, which can worsen their prognosis. This systematic review aims to examine the role of obesity in the development of these complications in children with cancer, highlighting the associated cardiovascular risk factors. A comprehensive literature search was conducted across databases such as PubMed, Scopus, Web of Science, Embase, and Google Scholar for studies published between 2014 and 2025. Eligible studies included interventional, cohort, case-control, and observational studies that examined the impact of cancer treatments on neurological and renal outcomes in obese pediatric patients. The review followed PRISMA guidelines to ensure methodological rigor, with quality assessment using validated tools such as the Newcastle-Ottawa Scale and STROBE checklist. Thirteen studies involving 14,723 participants met the inclusion criteria. Obesity was associated with poorer survival outcomes, particularly in children with ALL and CNS tumors, showing lower EFS and OS rates. Obese children undergoing chemotherapy had higher incidences of treatment-related toxicities, including hepatotoxicity, nephrotoxicity, and thrombotic events. Renal complications, including acute kidney injury and electrolyte imbalances, were more prevalent in obese patients. Obesity also increased cardiovascular risk, with higher rates of hypertension and insulin resistance. Additionally, it contributed to neurocognitive impairments and poor psychosocial outcomes. Lastly, obesity affected growth trajectories, with many survivors remaining obese long-term. Early weight management and personalized treatment strategies are crucial to mitigate these risks. Addressing obesity in pediatric cancer care is essential to improve treatment outcomes and long-term survivorship, with further research needed to develop effective interventions.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".