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Inflammatory Markers and Cancer Risk in Overweight Adults: A U-Shaped Paradox
AI Transparency Notice: This post was generated by an AI assistant acting as an expert oncology curator. It is for informational purposes only and does not constitute medical advice.
Key Facts
| Metric | Value | Context |
|---|---|---|
| Study Population | 11,348 overweight adults | BMI 25–29.9 kg/m² PMID: 42701956 |
| Top Incidence Predictor | BLR (OR 1.343) | Independent predictor in fully adjusted models PMID: 42701956 |
| Best Mortality Panel | MLR_SII_SIRI (AUC 0.7697) | Triple-marker combination for death prediction PMID: 42701956 |
| Correlation Shape | U-shaped | Both low and high values associated with risk PMID: 42701956 |
Why It Matters / Context
Chronic inflammation is a well-established driver of carcinogenesis, particularly in the context of obesity. However, the predictive utility of routine hematologic inflammatory markers in the overweight population (BMI 25–29.9), as distinct from the obese population, has remained controversial. Clinicians often rely on standard blood counts, yet the specific indices that best stratify cancer risk in this intermediate BMI group were previously unclear.
This study addresses a critical gap by evaluating six hematologic indices: Baseline Lymphocyte Ratio (BLR), Neutrophil-to-Lymphocyte Ratio (NLR), Monocyte-to-Lymphocyte Ratio (MLR), Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), and Platelet-to-Lymphocyte Ratio (PLR). The findings suggest that the relationship between inflammation and cancer is not linear. Instead, a U-shaped correlation was observed for all six markers, implying that both suppressed and elevated inflammatory states may confer increased risk. This nuance is vital for risk stratification, as it challenges the simplistic assumption that “higher inflammation always equals higher risk.”
Details
The study utilized cross-sectional data from the National Health and Nutrition Examination Survey (NHANES), analyzing 11,348 overweight subjects and 9,181 normal-weight controls PMID: 42701956. For mortality assessment, 823 overweight cancer patients were included, with cancer-specific death defined by ICD-10 codes C00-C97 PMID: 42701956.
Predictive performance was evaluated using logistic regression, Cox proportional hazards models, and XGBoost machine learning with five-fold cross-validation. The following table summarizes the key predictive metrics for the most significant models:
| Model Type | Biomarker(s) | Metric | Value | 95% CI / Notes |
|---|---|---|---|---|
| Incidence (Logistic) | BLR | Odds Ratio (OR) | 1.343 | 1.063–1.689 (P = 0.012) PMID: 42701956 |
| Mortality (Cox) | NLR | Hazard Ratio (HR) | 1.150 | Significant only in partially adjusted models PMID: 42701956 |
| Mortality (Cox) | MLR | Hazard Ratio (HR) | 3.388 | Significance lost after full covariate correction PMID: 42701956 |
| Incidence (ML) | PLR_SII | Accuracy | 0.7339 | Highest accuracy for cancer prediction PMID: 42701956 |
| Incidence (ML) | PLR_SIRI | AUC | 0.8124 | Maximum AUC among dual-marker panels PMID: 42701956 |
| Mortality (ML) | MLR_SII_SIRI | AUC | 0.7697 | Optimal mortality forecasting PMID: 42701956 |
| Mortality (ML) | MLR_SII_SIRI | Accuracy | 0.7273 | Mild gains over single biomarkers PMID: 42701956 |
Subgroup analyses revealed disparities for MLR and SIRI across age, race, and socioeconomic groups. Kaplan-Meier curves indicated that elevated MLR and SIRI corresponded to worse survival outcomes. Notably, while machine learning models identified complex multi-marker panels (such as PLR_SIRI and MLR_SII_SIRI) as having the highest statistical performance, the abstract notes that these combinations delivered only “limited predictive improvement” compared to single biomarkers PMID: 42701956.
Limitations
Critical Limitation: Observational Design and Self-Reported Data
This study is cross-sectional and observational, meaning it cannot establish causality. Cancer status was self-reported by participants, which introduces potential misclassification bias. Furthermore, the study lacks external validation in independent cohorts. The authors explicitly state that “only correlational conclusions can be drawn” and that longitudinal prospective studies are required before these markers can be used for clinical early cancer screening PMID: 42701956.
Clinical Takeaway
While multi-marker inflammatory panels show promise for population-level risk stratification in overweight adults, clinicians should not yet use these indices for individual diagnostic decisions due to the lack of prospective validation and the complex U-shaped risk profile.
Full Reference
Dang X, Zhang T, Li H, Liu Y. Cancer risk and mortality assessment in the overweight population: evaluation of single and integrated models based on inflammatory markers. Clin Transl Oncol. 2026. PMID: 42701956 [DOI: 10.1007/s12094-026-04483-6]