Correlation Between the Prognostic Nutritional Index and Breast Cancer in U.S. Adults: NHANES 2001-2018.

Abstract

Background: Previous studies have reported that both inflammation and nutrition may affect breast cancer development, but there has been no comprehensive analysis of the influence of the immune nutritional indicator Prognostic Nutritional Index on breast cancer. The Prognostic Nutritional Index (PNI), integrating serum albumin and lymphocyte count, serves as a dual biomarker reflecting systemic nutritional status and antitumor immune competence. Mechanistically, hypoalbuminemia signifies malnutrition and cancer-associated chronic inflammation, while lymphocytopenia indicates impaired immune surveillance facilitating tumor evasion. Clinically validated across gastrointestinal and breast malignancies, low PNI correlates with therapeutic resistance and reduced survival, attributable to compromised tissue repair and antitumor immunity. Despite its cost-effectiveness and calculability from routine blood tests, PNI's potential as an accessible risk stratification tool remains.

Methods: We selected 18,709 eligible participants from the National Health and Nutrition Examination Survey (NHANES) conducted from 2001-2018. Statistical methods such as weighted multivariate logistic regression and subgroup analysis were used to analyze the associations between the PNI and breast cancer incidence. In addition, the PNI thresholds for breast cancer incidence were determined via a two-stage linear regression model. Finally, a machine learning algorithm (XGBoost) was applied to verify the effect of the PNI on the incidence of breast cancer. The Prognostic Nutritional Index (PNI), derived from serum albumin (ALB, g/L) and peripheral blood lymphocyte count (×10⁹/L) via the formula PNI = ALB + 5 × lymphocyte count, was evaluated using weighted multivariable logistic regression to assess its dose-response relationship with the outcome. To this end, PNI was modeled both as a continuous variable (per 1-unit increase) and using gender-specific tertiles (T1: <46.8; T2: 46.8-52.4; T3: >52.4).

Results: In this study, the Prognostic Nutritional Index (PNI) demonstrated a significant inverse association with breast cancer risk. The mean PNI value was 52.5 (±8.9) in the overall population, with significantly lower values observed in breast cancer patients compared to controls (p < 0.001). A consistent dose-response relationship was identified, wherein each unit increase in PNI corresponded to a 4% reduction in breast cancer risk (fully adjusted OR = 0.96; 95% CI: 0.94-0.98). This linear association was further confirmed by restricted cubic splines (RCS) analysis (P-overall <0.001; P-non-linear > 0.05). Moreover, when PNI was categorized into tertiles, the highest tertile was associated with a substantially lower risk of breast cancer compared to the lowest tertile (OR = 0.58; 95% CI: 0.41-0.81; p < 0.001). A two-stage linear regression model identified a PNI threshold of 58.0 for breast cancer incidence. Importantly, the relevance of PNI was corroborated by machine learning approaches; XGBoost algorithm identified PNI as one of the top five predictive variables for breast cancer. In conclusion, these findings indicate that lower PNI levels are significantly associated with increased breast cancer risk, highlighting its potential role as an auxiliary indicator for risk stratification. However, further prospective studies are warranted to validate its clinical utility.

Conclusion: Our study suggests that the PNI is negatively and linearly correlated with the incidence of breast cancer. A lower Prognostic Nutritional Index (PNI) is associated with an increased risk of breast cancer.