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. 2025 Aug 1;111(8):4972-4985.
doi: 10.1097/JS9.0000000000002543. Epub 2025 Jun 5.

Associations of the NPAR index with breast cancer incidence and mortality based on the National Health and Nutrition Examination Survey (NHANES) 2001-2018: a cross-sectional study

Yongcheng Su  1   2   3 , Beibei Xu  4 , Miaomiao Ma  2 , Wenqing Zhang  2 , Zhong Ouyang  3 , Tianhui Hu  1   2
Affiliations

Associations of the NPAR index with breast cancer incidence and mortality based on the National Health and Nutrition Examination Survey (NHANES) 2001-2018: a cross-sectional study

Yongcheng Su et al. Int J Surg. .

Abstract

Background: Breast cancer (BC) remains one of the most prevalent cancers affecting women globally, imposing significant health and economic burdens on both patients and society. This study aims to investigate the relationship between the neutrophil percentage-to-albumin ratio (NPAR) and BC risk and mortality.

Materials and methods: Clinical data from 13 540 participants in the NHANES database were analyzed, including 331 individuals with a documented history of BC. Survival analysis and advanced machine learning (ML) techniques were applied to assess the data.

Results: Higher NPAR levels were significantly associated with increased BC risk in the unadjusted model, with quartile comparisons revealing an odds ratio (OR) of 1.51 (95% CI: 0.99-2.29, P = 0.057). After adjustment, the OR increased to 1.70 (95% CI: 1.12-2.57, P < 0.05), indicating the robustness of this association. Elevated NPAR levels were also linked to higher all-cause mortality (ACM). Multivariate Cox regression models showed that a one-unit increase in NPAR was associated with adjusted hazard ratios of 1.09 (95% CI: 1.07-1.12) for overall mortality and 1.17 (95% CI: 1.13-1.22) for cardiovascular disease mortality, both with P values <0.001. Restricted cubic splines analysis revealed a linear correlation between NPAR and BC risk ( P for nonlinearity = 0.15), while a nonlinear relationship was observed for ACM ( P for nonlinearity < 0.01). Among nine ML models evaluated, the LightGBM model exhibited the best diagnostic performance, achieving an area under the receiver operating characteristic curve of 0.995, outperforming models such as CATBoost, Naive Bayes, logistic regression, random forest, K-nearest neighbors, support vector machine, decision tree, and XGBoost. After model selection, an online calculator was built for use in the clinic, and the web-service is available at https://fast.statsape.com/tool/detail?id=11 .

Conclusion: NPAR emerged as a crucial biomarker in BC risk assessment. This study suggests that NPAR may serve as a dual-purpose biomarker for both BC risk evaluation and prognostic assessment, potentially aiding in early screening and personalized treatment strategies.

Keywords: all-cause mortality; breast cancer; inflammatory markers; neutrophil percentage-to-albumin rate; risk assessment.

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