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Association between tea consumption and risk of cancer: a prospective cohort study of 0.5 million Chinese adults.
Li, X, Yu, C, Guo, Y, Bian, Z, Shen, Z, Yang, L, Chen, Y, Wei, Y, Zhang, H, Qiu, Z, et al
European journal of epidemiology. 2019;(8):753-763
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Abstract
Current experimental and epidemiological studies provide inconsistent evidence toward the association between tea consumption and cancer incidence. We investigated whether tea consumption was associated with the incidence of all cancers and six leading types of cancer (lung cancer, stomach cancer, colorectal cancer, liver cancer, female breast cancer and cervix uteri cancer) among 455,981 participants aged 30-79 years in the prospective cohort China Kadoorie Biobank. Tea consumption was assessed at baseline (2004-2008) with an interviewer-administered questionnaire. Cancer cases were identified by linkage to the national health insurance system. Cox proportional hazard regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). In the present population, daily tea consumers were more likely to be current smokers and daily alcohol consumers. 22,652 incident cancers occurred during 10.1 years follow-up (5.04 cases/1000 person-years). When we restricted analyses to non-smokers and non-excessive alcohol consumers to minimize confounding, tea consumption was not associated with all cancers (daily consumers who added tea leaves > 4.0 g/day vs. less-than-weekly consumers: HR, 1.03; 95%CI, 0.93-1.13), lung cancer (HR, 1.08; CI, 0.84-1.40), colorectal cancer (HR, 1.08; CI, 0.81-1.45) and liver cancer (HR, 1.08; CI, 0.75-1.55), yet might be associated with increased risk of stomach cancer (HR, 1.46; CI, 1.07-1.99). In both less-than-daily and daily tea consumers, all cancer risk increased with the amount of tobacco smoked or alcohol consumed. Our findings suggest tea consumption may not provide preventive effect against cancer incidence.
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Prediction of the 20-year incidence of diabetes in older Chinese: Application of the competing risk method in a longitudinal study.
Liu, X, Fine, JP, Chen, Z, Liu, L, Li, X, Wang, A, Guo, J, Tao, L, Mahara, G, Tang, Z, et al
Medicine. 2016;(40):e5057
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Abstract
The competing risk method has become more acceptable for time-to-event data analysis because of its advantage over the standard Cox model in accounting for competing events in the risk set. This study aimed to construct a prediction model for diabetes using a subdistribution hazards model.We prospectively followed 1857 community residents who were aged ≥ 55 years, free of diabetes at baseline examination from August 1992 to December 2012. Diabetes was defined as a self-reported history of diabetes diagnosis, taking antidiabetic medicine, or having fasting plasma glucose (FPG) ≥ 7.0 mmol/L. A questionnaire was used to measure diabetes risk factors, including dietary habits, lifestyle, psychological factors, cognitive function, and physical condition. Gray test and a subdistribution hazards model were used to construct a prediction algorithm for 20-year risk of diabetes. Receiver operating characteristic (ROC) curves, bootstrap cross-validated Wolber concordance index (C-index) statistics, and calibration plots were used to assess model performance.During the 20-year follow-up period, 144 cases were documented for diabetes incidence with a median follow-up of 10.9 years (interquartile range: 8.0-15.3 years). The cumulative incidence function of 20-year diabetes incidence was 11.60% after adjusting for the competing risk of nondiabetes death. Gray test showed that body mass index, FPG, self-rated heath status, and physical activity were associated with the cumulative incidence function of diabetes after adjusting for age. Finally, 5 standard risk factors (poor self-rated health status [subdistribution hazard ratio (SHR) = 1.73, P = 0.005], less physical activity [SHR = 1.39, P = 0.047], 55-65 years old [SHR = 4.37, P < 0.001], overweight [SHR = 2.15, P < 0.001] or obesity [SHR = 1.96, P = 0.003], and impaired fasting glucose [IFG] [SHR = 1.99, P < 0.001]) were significantly associated with incident diabetes. Model performance was moderate to excellent, as indicated by its bootstrap cross-validated discrimination C-index (0.74, 95% CI: 0.70-0.79) and calibration plot.Poor self-rated health, physical inactivity, being 55 to 65 years of age, overweight/obesity, and IFG were significant predictors of incident diabetes. Early prevention with a goal of achieving optimal levels of all risk factors should become a key element of diabetes prevention.