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PURPOSE: Diabetes mellitus (DM) is associated with elevated cancer risk and poor survival outcome in malignancies. The objective of this study was to evaluate the prognostic value of preexisting DM in chronic lymphocytic leukemia (CLL). MATERIALS AND METHODS: Six hundred and thirty-three subjects with newly-diagnosed CLL between 2007 and 2016 were recruited. Propensity score-matched method was performed to balance baseline characteristics and eliminate possible bias. Univariate and multivariate Cox regression analyses screened the independent risk indicators for time-to-first-treatment (TTFT) and cancer-specific survival (CSS) of CLL. Receiver operator characteristic curves and the corresponding areas under the curve assessed the predictive accuracy of CLL-International Prognostic Index (IPI) together with DM. RESULTS: The results showed that 111 patients had pre-existing DM. In the propensity-matched cohort, DM was correlated with inferior TTFT and CSS in CLL patients, and it was an independent prognostic factor for both CSS and TTFT. Pre-diabetics also shared undesirable prognostic outcome compared with patients with no diabetic tendency, and a positive association between longer diabetic duration and poorer prognosis of CLL was identified. DM as one additional point to CLL-IPI had larger area under the curve compared with CLL-IPI alone in CSS prediction and could improve the prognostic capacity of CLL-IPI. CONCLUSION: Pre-existing DM was found to be a valuable prognostic predictor and could help predict life expectancy and build refined prognostication models for CLL.

Original publication




Journal article


Cancer Res Treat

Publication Date





189 - 206


Chronic lymphocytic leukemia, Diabetes mellitus, Prognosis, Aged, Aged, 80 and over, Area Under Curve, Biomarkers, Diabetes Complications, Diabetes Mellitus, Female, Follow-Up Studies, Glycated Hemoglobin, Humans, Kaplan-Meier Estimate, Leukemia, Lymphocytic, Chronic, B-Cell, Male, Middle Aged, Neoplasm Staging, Prognosis, Propensity Score, Proportional Hazards Models