Colorectal cancer remains a major health burden worldwide and is closely related to type 2 diabetes. this study aimed to develop and validate a colorectal cancer risk prediction model to identify high-risk individuals with type 2 diabetes. Records of 930 patients with type 2 diabetes were reviewed and data were collected from 1 November 2013 to 31 December 2019. Clinical and demographic parameters were analyzed using univariable and multivariable logistic regression analysis. The nomogram to assess the risk of colorectal cancer was constructed and validated by bootstrap resampling. Predictors in the prediction nomogram included age, sex, other blood-glucose-lowering drugs and thiazolidinediones. The nomogram demonstrated moderate discrimination in estimating the risk of colorectal cancer, with Hosmer-Lemeshow test P = 0.837, an unadjusted C-index of 0.713 (95% CI 0.670-0.757) and a bootstrap-corrected C index of 0.708. In addition, the decision curve analysis demonstrated that the nomogram would be clinically useful. We have developed a nomogram that can predict the risk of colorectal cancer in patients with type 2 diabetes. The nomogram showed favorable calibration and discrimination values, which may help clinicians in making recommendations about colorectal cancer screening for patients with type 2 diabetes. Colorectal cancer is one of the most common and aggressive clinical gastrointestinal cancer that causes a serious threat to human life and health 1 , accounting for approximately seven hundred thousand annual deaths worldwide 2. Despite the rapid development of diagnostic and treatment methods, the 5-year survival rate for colorectal cancer is ≈ 50% overall 3 , although this rate for colorectal cancer diagnosed in the early stages is > 90% 4. The reason for this abysmal prognosis is that the vast majority of colorectal cancer patients are diagnosed at an advanced stage 5. Therefore, early diagnosis of colorectal cancer is particularly important. Unfortunately, huge resources have been invested in the prevention and early diagnosis of colorectal cancer, but there is a limitation on the effective and early diagnosis of colorectal cancer 6. Screening can reduce the incidence and mortality of colorectal cancer by 30% and 50%, respectively 7. Current screening methods for colorectal cancer mainly include flexible sigmoidoscopy, colonoscopy, fecal occult blood testing, double-contrast barium enema, stool DNA testing, and computed tomographic colonography 8. However, these examinations are invasive or time-consuming or expensive, and it is not feasible to screen the general population for colorectal cancer. To improve the early diagnosis of this disease, we need to broaden our understanding of it. International screening guidelines recommend that screening for colorectal cancer starts at 50 years of age for the average risk group and 50-75 years of age as the target age group for colorectal cancer screening 9,10. It is worth mentioning that diabetes is one of the indicators of the average risk group evaluation in...
Objective: The prevalence of diabetes mellitus (DM), impaired glucose tolerance (IGT) and impaired fasting glucose (IFG) were hypothesised to be different among different tumor patients. This study aimed to study the association between the prevalence of DM, IGT and IFG and liver cancer, colorectal cancer, breast cancer, cervical cancer, nasopharyngeal cancer and benign tumor. Methods: A hospital based retrospective study was conducted on 4610 patients admitted to the Internal Medical Department of the Affiliated Tumor Hospital of Guangxi Medical University, China. Logistic regression was used to examine the association between gender, age group, ethnicity , cancer types or benign tumors and prevalence of DM, IFG, IGT. Results: Among 4610 patients, there were 1000 liver cancer patients, 373 breast cancer patients, 415 nasopharyngeal cancer patients, 230 cervical cancer patients, 405 colorectal cancer patients, and 2187 benign tumor patients. The prevalence of DM and IGT in liver cancer patients was 14.7% and 22.1%, respectively. The prevalence of DM and IGT was 13.8% and 20%, respectively, in colorectal cancer patients, significantly higher than that of benign cancers. After adjusting for gender, age group, and ethnicity, the prevalence of DM and IGT in liver cancers patients was 1.29 times (CI :1.12-1.66) and 1.49 times (CI :1.20-1.86) higher than that of benign tumors, respectively. Conclusion: There was a high prevalence of DM and IGT in liver cancer patients.
Purpose: Digestive carcinomas remain a major health burden worldwide and are closely related to type 2 diabetes. The aim of this study was to develop and validate a digestive carcinoma risk prediction model to identify high-risk individuals among those with type 2 diabetes. Patients and Methods: The prediction model was developed in a primary cohort that consisted of 655 patients with type 2 diabetes. Data were collected from November 2013 to December 2018. Clinical parameters and demographic characteristics were analyzed by logistic regression to develop a model to predict the risk of digestive carcinomas; then, a nomogram was constructed. The performance of the nomogram was assessed with respect to calibration, discrimination, and clinical usefulness. The results were internally validated by a bootstrapping procedure. The independent validation cohort consisted of 275 patients from January 2019 to December 2019. Results: Predictors in the prediction nomogram included sex, age, insulin use, and body mass index. The model showed good discrimination (C-index 0.747 [95% CI, 0.718-0.791]) and calibration (Hosmer-Lemeshow test P=0.541). The nomogram showed similar discrimination in the validation cohort (C-index 0.706 [95% CI, 0.682-0.755]) and good calibration (Hosmer-Lemeshow test P=0.418). Decision curve analysis demonstrated that the nomogram would be clinically useful. Conclusion: We developed a low-cost and low-risk model based on clinical and demographic parameters to help identify patients with type 2 diabetes who might benefit from digestive cancer screening.
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