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Background <p>Insulin resistance (IR) is increasingly linked with poor cancer prognosis, but its impact on mortality among breast cancer patients—particularly regarding different causes of death—remains incompletely understood. The estimated glucose disposal rate (eGDR) is a surrogate index of insulin sensitivity. This study intended to investigate the links of eGDR with all-cause mortality (ACM) and breast cancer-specific mortality (BCSM) in US breast cancer survivors.</p> Methods <p>Data were sourced from the 2007–2016 National Health and Nutrition Examination Survey(NHANES). A cross-sectional design was used to assess the link between eGDR and breast cancer prevalence, while a cohort design was adopted for mortality follow-up. Additionally, weighted logistic regression was leveraged to estimate the link between eGDR and breast cancer prevalence. Cox models were employed to evaluate the relationship between eGDR and ACM. Restricted cubic splines were utilized to model nonlinear links, and competing risk models were adopted to analyze BCSM.</p> Results <p>Among 323 women with breast cancer, 73 deaths occurred during follow-up (median 6.2 years). After multiple confounders were controlled, a binary analysis using optimal cutoff values confirmed that higher eGDR was independently linked with lower ACM risk (HR: 0.40; 95% CI: 0.18–0.89; <i>P</i> = 0.025). This association was nonlinear, and the protective effect remained across multiple subgroups. In competing risk analysis, a potential association was observed between higher eGDR and lower BCSM risk (HR: 0.13; 95% CI: 0.02–0.99; <i>P</i> = 0.048), with no marked association observed for cardiovascular mortality.</p> Conclusion <p>In US breast cancer survivors, higher eGDR was independently linked with lower ACM risk. This association may be partly driven by a reduction in BCSM, but this specific finding requires validation in larger studies due to considerable statistical uncertainty.</p>

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Association of estimated glucose disposal rate with all-cause and breast cancer-specific mortality in US breast cancer survivors: a population-based study

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  • 发表日期：

    2025年11月27日

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  • Jinhao Li,
  • Jiasheng Liu,
  • Bingliang Cai,
  • Chuansheng Yang

摘要

Background

Insulin resistance (IR) is increasingly linked with poor cancer prognosis, but its impact on mortality among breast cancer patients—particularly regarding different causes of death—remains incompletely understood. The estimated glucose disposal rate (eGDR) is a surrogate index of insulin sensitivity. This study intended to investigate the links of eGDR with all-cause mortality (ACM) and breast cancer-specific mortality (BCSM) in US breast cancer survivors.

Methods

Data were sourced from the 2007–2016 National Health and Nutrition Examination Survey(NHANES). A cross-sectional design was used to assess the link between eGDR and breast cancer prevalence, while a cohort design was adopted for mortality follow-up. Additionally, weighted logistic regression was leveraged to estimate the link between eGDR and breast cancer prevalence. Cox models were employed to evaluate the relationship between eGDR and ACM. Restricted cubic splines were utilized to model nonlinear links, and competing risk models were adopted to analyze BCSM.

Results

Among 323 women with breast cancer, 73 deaths occurred during follow-up (median 6.2 years). After multiple confounders were controlled, a binary analysis using optimal cutoff values confirmed that higher eGDR was independently linked with lower ACM risk (HR: 0.40; 95% CI: 0.18–0.89; P = 0.025). This association was nonlinear, and the protective effect remained across multiple subgroups. In competing risk analysis, a potential association was observed between higher eGDR and lower BCSM risk (HR: 0.13; 95% CI: 0.02–0.99; P = 0.048), with no marked association observed for cardiovascular mortality.

Conclusion

In US breast cancer survivors, higher eGDR was independently linked with lower ACM risk. This association may be partly driven by a reduction in BCSM, but this specific finding requires validation in larger studies due to considerable statistical uncertainty.