Association between glucose levels and all-cause mortality in cancer survivors: findings from NHANES 1999-2018

Abstract

Background: Hyperglycemia is a rapidly increasing risk factor for cancer mortality worldwide. However, the dose‒response relationship between glucose levels and all-cause mortality in cancer survivors is still uncertain.

Methods: We enrolled 4,491 cancer survivors (weighted population 19,465,739) from the 1999-2019 National Health and Nutrition Examination Survey (NHANES). Cancer survivors were defined based on the question of whether they had ever been diagnosed with cancer by a doctor or a health professional. Hemoglobin A1c (HbA1c) was selected in this study as a stable marker of glucose level. Mortality was ascertained by linkage to National Death Index records until December 31, 2019. Cox proportional hazard, Kaplan‒Meier survival curves and Restricted cubic spline regression models were used to evaluate the associations between HbA1c and all-cause mortality risk in cancer survivors.

Results: In NHANES, after adjusting for confounders, HbA1c had an independent nonlinear association with increased all-cause mortality in cancer survivors (nonlinear P value < 0.05). The threshold value for HbA1c was 5.4%, and the HRs (95% CI) below and above the threshold value were 0.917 (0.856,0.983) and 1.026 (1.010,1.043), respectively. Similar associations were found between fasting glucose and all-cause mortality in cancer survivors, and the threshold value was 5.7 mmol/L.

Conclusions: HbA1c was nonlinearly associated with all-cause mortality in cancer survivors, and the critical value of HbA1c in decreased mortality was 5.4%, suggesting optimal glucose management in cancer survivors may be a key to preventing premature death in cancer survivors.

Keywords: All-cause mortality; Cancer survivors; Fasting glucose; HbA1c; NHANES.

Fig. 1

Kaplan‒Meier curve for all-cause mortality categorized by different subgroups of HbA1c (A) and association between HbA1c and all-cause mortality (B). Note: A restricted cubic spline model was calculated after adjusting for age, sex, race/ethnicity, BMI, education level, family income-poverty ratio, alcohol use, smoking status, ideal physical activity, HEI score, self-reported hypertension, hypercholesterolemia, CVD, years since first cancer diagnosis, use of prescription medications, and cancer sites