BMC cancer · 2026
Hemoglobin-to-red blood cell distribution width ratio is inversely associated with prevalent gastrointestinal cancers in NHANES and serves as an informative feature in machine-learning models.
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Chen Shiming, Zhao Kexin, Zhang Jingxia, Niu Yingxue, Zou Ran, Wang Min, Li Yifang
Аннотация
BACKGROUND: The hemoglobin-to-red blood cell distribution width ratio (HRR) is an inexpensive index derived from routine blood tests, but its relationship with gastrointestinal (GI) cancers in the general population remains unclear.
METHODS: We pooled NHANES 2001-2018 data (adults ≥ 20 years). GI cancer history was screened using MCQ220 and site-confirmed using site-specific follow-up items for colon, rectal, stomach, esophageal, liver, and pancreatic cancers. HRR was calculated as hemoglobin (g/dL) divided by RDW (%). Survey-weighted logistic regression estimated odds ratios (ORs) and 95% CIs for prevalent GI cancer across HRR (per 0.1-unit increase and tertiles), with restricted cubic splines and subgroup interaction tests. In parallel, eight machine-learning (ML) algorithms were trained and evaluated (70/30 split) to assess predictive performance (AUC, calibration, and decision-curve analysis), with SHAP for interpretability.
RESULTS: Among 39,663 participants, 344 had site-confirmed GI cancer. Higher HRR was inversely associated with GI cancers in the fully adjusted model (per 0.1-unit increase: OR 0.86, 95% CI 0.77-0.95; P = 0.003). Participants in the highest HRR tertile had lower odds than the lowest tertile (OR 0.57, 95% CI 0.35-0.93; P = 0.026; P for trend = 0.028). Sex modified the association (P for interaction = 0.002), with an inverse association in men (OR 0.74) but not women. ML models showed similar discrimination (top AUC ≈ 0.82), and SHAP highlighted HRR as an important contributor alongside age.
CONCLUSIONS: Higher HRR was inversely associated with prevalent, site-confirmed GI cancers in NHANES. In machine-learning analyses, HRR functioned as an informative classification feature.
METHODS: We pooled NHANES 2001-2018 data (adults ≥ 20 years). GI cancer history was screened using MCQ220 and site-confirmed using site-specific follow-up items for colon, rectal, stomach, esophageal, liver, and pancreatic cancers. HRR was calculated as hemoglobin (g/dL) divided by RDW (%). Survey-weighted logistic regression estimated odds ratios (ORs) and 95% CIs for prevalent GI cancer across HRR (per 0.1-unit increase and tertiles), with restricted cubic splines and subgroup interaction tests. In parallel, eight machine-learning (ML) algorithms were trained and evaluated (70/30 split) to assess predictive performance (AUC, calibration, and decision-curve analysis), with SHAP for interpretability.
RESULTS: Among 39,663 participants, 344 had site-confirmed GI cancer. Higher HRR was inversely associated with GI cancers in the fully adjusted model (per 0.1-unit increase: OR 0.86, 95% CI 0.77-0.95; P = 0.003). Participants in the highest HRR tertile had lower odds than the lowest tertile (OR 0.57, 95% CI 0.35-0.93; P = 0.026; P for trend = 0.028). Sex modified the association (P for interaction = 0.002), with an inverse association in men (OR 0.74) but not women. ML models showed similar discrimination (top AUC ≈ 0.82), and SHAP highlighted HRR as an important contributor alongside age.
CONCLUSIONS: Higher HRR was inversely associated with prevalent, site-confirmed GI cancers in NHANES. In machine-learning analyses, HRR functioned as an informative classification feature.