Serum leptin and adiponectin levels and risk of renal cell carcinoma
Bibliographic record
Abstract
OBJECTIVE: The incidence of renal cell carcinoma (RCC) has increased rapidly in the U.S., particularly among African Americans. Despite a well-established link between obesity and RCC, the mechanism through which obesity increases cancer risk has yet to be established. Adipokines, such as leptin and adiponectin, may link obesity and cancer, with different quantitative effects by race. DESIGN AND METHODS: We evaluated the association between leptin and adiponectin concentrations and RCC risk among Caucasians (581 cases, 558 controls) and African Americans (187 cases, 359 controls) in a case-control study conducted in Detroit and Chicago. Odds ratios (ORs) and 95% confidence intervals (95%CIs) were estimated using unconditional logistic regression. RESULTS: Among controls, Caucasians had higher median adiponectin than African Americans (males: 8.2 vs. 7.0 μg/ml, P = 0.001; females: 13.4 vs. 8.4 μg/ml, P < 0.0001), and lower median leptin than African Americans (males: 11.8 vs. 14.1 ng/ml, P = 0.04; females: 28.3 vs. 45.9 ng/ml, P < 0.0001). Among Caucasians, the ORs for RCC comparing the highest (Q4) to the lowest (Q1) sex-specific quartile of leptin were 3.2 (95% CI: 1.9-5.2) for males and 4.7 (95% CI: 2.6-8.6) for females. Serum leptin was not significantly associated with RCC among African American males (OR 1.5, 95% CI: 0.7-3.1) or females (OR 2.1, 95% CI: 0.8-5.5). Higher adiponectin was associated with RCC risk among African American males (Q4 vs. Q1: OR 2.3, 95% CI: 1.1-4.6) and females (OR 2.1, 95% CI: 1.2-6.7), but not significantly among Caucasian males (OR 1.6, 95% CI: 0.99-2.7) and females (OR 1.6, 95% CI: 0.9-3.1). CONCLUSION: We observed an association between both leptin and adiponectin concentrations and risk of RCC, which may differ by race. Confirmation in further investigations is needed.
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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 | 0.000 | 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".