Circulating leptin levels are associated with adiposity in survivors of childhood brain tumors
Bibliographic record
Abstract
Survivors of Childhood Brain Tumors (SCBT) are at a higher risk of developing cardiovascular disease and type 2 diabetes compared to the general population. Adiposity is an important risk factor for the development of these outcomes, and identifying biomarkers of adiposity may help the stratification of survivors based on their cardiovascular risk or allow for early screening and interventions to improve cardiometabolic outcomes. Leptin is an adipokine that positively correlates with the adipose mass in the general population and is a predictor of adverse cardiometabolic outcomes, yet its association with adiposity in SCBT has not been studied. The aim of this study was to determine if leptin levels are associated with the adipose mass in SCBT, and to define its predictors. This cross-sectional study included 74 SCBT (n = 32 females) with 126 non-cancer controls (n = 59 females). Total adiposity was measured using Bioelectrical Impendence Analysis (BIA) and central adiposity was measured using waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR). We used multivariable linear regression analysis to determine if leptin predicts adiposity in SCBT and adjusted for age, sex, puberty, and cancer status. Leptin correlated strongly with total (p < 0.001) and central (WHR p = 0.001; WHtR p < 0.001) adiposity in SCBT and non-cancer controls. In conclusion, leptin is a potential biomarker for adiposity in SCBT, and further investigation is needed to clarify if leptin is a predictor of future cardiometabolic risk in SCBT.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".