Frontiers in oncology · 2026
Higher Framingham steatosis index is associated with prevalent breast cancer in women: cross-sectional evidence from NHANES 1999-2018 and an exploratory hospital-based dataset.
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Tang Shuling, Mo Yong, Su Tiansheng, Huang Guangxiang, Lu Jiachao, Bi Jianbin, Li Hui, Mo Ligen, Yan Jun
Аннотация
BACKGROUND: Metabolic dysfunction and hepatic steatosis-related phenotypes have increasingly been linked to extrahepatic malignancies, including breast cancer. The association between the Framingham Steatosis Index (FSI), a composite index incorporating age and multiple metabolic components, and prevalent breast cancer in women has not been well characterized.
METHODS: We conducted a cross-sectional analysis of female participants in the National Health and Nutrition Examination Survey (NHANES) 1999-2018. Multivariable logistic regression models were used to evaluate the association between FSI and prevalent breast cancer, with FSI analyzed as both a continuous variable and by quartiles. Restricted cubic spline analysis and generalized additive modeling were applied to assess potential non-linearity, and two-piecewise logistic regression was used to explore a possible threshold effect. Component-level and AUC analyses compared FSI with its individual components. An exploratory hospital-based supportive dataset was analyzed to assess whether the continuous association showed a similar direction in a different clinical setting.
RESULTS: Among 21,042 women in NHANES, 531 (2.5%) reported a history of breast cancer. In the fully adjusted model, each 1-unit increase in FSI was associated with higher odds of prevalent breast cancer (OR 1.10, 95% CI 1.05-1.16). After additional adjustment for age, the association was substantially attenuated and no longer statistically significant (OR 1.02, 95% CI 0.96-1.07). Higher FSI quartiles were also associated with greater odds of prevalent breast cancer. Restricted cubic spline analysis suggested a non-linear association. In component-level analyses, age showed the strongest association and discriminatory performance among the FSI components, whereas adding FSI to the base model only modestly increased the AUC. In the exploratory hospital-based dataset, FSI was positively associated with breast cancer case status when modeled continuously, but the quartile-based dose-response pattern was not reproduced after full adjustment.
CONCLUSIONS: In the primary NHANES analysis, higher FSI was associated with prevalent breast cancer status in women; however, this association was substantially attenuated after additional adjustment for age. FSI should therefore be interpreted as a composite marker of metabolic-hepatic burden rather than as an age-independent hepatic steatosis effect or causal biomarker.
METHODS: We conducted a cross-sectional analysis of female participants in the National Health and Nutrition Examination Survey (NHANES) 1999-2018. Multivariable logistic regression models were used to evaluate the association between FSI and prevalent breast cancer, with FSI analyzed as both a continuous variable and by quartiles. Restricted cubic spline analysis and generalized additive modeling were applied to assess potential non-linearity, and two-piecewise logistic regression was used to explore a possible threshold effect. Component-level and AUC analyses compared FSI with its individual components. An exploratory hospital-based supportive dataset was analyzed to assess whether the continuous association showed a similar direction in a different clinical setting.
RESULTS: Among 21,042 women in NHANES, 531 (2.5%) reported a history of breast cancer. In the fully adjusted model, each 1-unit increase in FSI was associated with higher odds of prevalent breast cancer (OR 1.10, 95% CI 1.05-1.16). After additional adjustment for age, the association was substantially attenuated and no longer statistically significant (OR 1.02, 95% CI 0.96-1.07). Higher FSI quartiles were also associated with greater odds of prevalent breast cancer. Restricted cubic spline analysis suggested a non-linear association. In component-level analyses, age showed the strongest association and discriminatory performance among the FSI components, whereas adding FSI to the base model only modestly increased the AUC. In the exploratory hospital-based dataset, FSI was positively associated with breast cancer case status when modeled continuously, but the quartile-based dose-response pattern was not reproduced after full adjustment.
CONCLUSIONS: In the primary NHANES analysis, higher FSI was associated with prevalent breast cancer status in women; however, this association was substantially attenuated after additional adjustment for age. FSI should therefore be interpreted as a composite marker of metabolic-hepatic burden rather than as an age-independent hepatic steatosis effect or causal biomarker.