The association between plasma C-peptide concentration and the risk of prostate cancer: a nested case-control study within a Japanese population-based prospective study

Biomarkers of glucose homeostasis and inflammation with risk of prostate cancer: A case-cohort study

BACKGROUND

Few prospective studies have examined biomarkers of glucose homeostasis or inflammation with prostate cancer risk by tumor stage or grade.

METHODS

We conducted a case-cohort study to examine associations of pre-diagnosis hemoglobin A1c (HbA1c), c-peptide, and c-reactive protein (CRP) with prostate cancer risk overall and stratified by tumor stage and grade. The study included 390 non-aggressive (T1-2, N0, M0 and Gleason score <8) and 313 aggressive cases (T3-4, or N1, or M1, or Gleason score 8-10) diagnosed after blood draw (1998-2001) and up to 2013, and a random sub-cohort of 1,303 cancer-free men at blood draw in the Cancer Prevention Study-II Nutrition Cohort. Prentice-weighted Cox proportional hazards regression models were used to estimate hazards ratios (HRs) and 95% confidence intervals (CIs).

RESULTS

In the multivariable-adjusted model without body mass index (BMI), HbA1c was inversely associated with non-aggressive prostate cancer (HR per unit increase: 0.89, 95% CI: 0.80-1.00, P=0.04). Analyses stratified by tumor stage and grade separately showed that HbA1c was inversely associated with low-grade prostate cancer (HR per unit increase: 0.89, 95% CI: 0.80-1.00) and positively associated with high-grade prostate cancer (HR per unit increase: 1.15, 95% CI: 1.01-1.30). C-peptide and CRP were not associated with prostate cancer overall or by stage or grade.

CONCLUSIONS

The present study suggests that associations of hyperglycemia with prostate cancer may differ by tumor grade and stage.

IMPACT

Future studies need to examine prostate cancer by tumor stage and grade, and to better understand the role of hyperglycemia in prostate cancer progression.

Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study

The adverse drug reaction (ADR) of traditional Chinese medicine injection (TCMI) has become one of the major concerns of public health in China. There are significant advantages for developing methods to improve the use of TCMI in routine clinical practice. The method of predicting TCMI-induced ADR was illustrated using a nested case-control study in 123 cases and 123 controls. The partial least squares regression (PLSR) models, which mapped the influence of basic characteristics and routine examinations to ADR, were established to predict the risk of ADR. The software was devised to provide an easy-to-use tool for clinic application. The effectiveness of the method was evaluated through its application to new patients with 95.7% accuracy of cases and 91.3% accuracy of controls. By using the method, the patients at high-risk could be conveniently, efficiently and economically recognized without any extra financial burden for additional examination. This study provides a novel insight into individualized management of the patients who will use TCMI.