Prevalence and treatment of high cardiovascular disease risk in Inner Mongolia, China.

Early identification of individuals with high risk is crucial to preventing cardiovascular disease (CVD). We aimed to determine the prevalence of high CVD risk in Inner Mongolia and to analyze the treatment of major risk factors among individuals with high CVD risk. We selected 70,380 participants aged 35-75 years in Inner Mongolia between 2015 and 2017 using multistage stratified sampling. All participants completed a questionnaire and their blood pressure, blood glucose and lipid levels, height, weight and waist circumference were measured. Participants without a history of CVD were defined as high CVD risk if the predicted 10-year risk for CVD exceeded 10%. We assessed rates of high CVD risk and the prevalence and treatment of major risk factors among individuals with high CVD risk. After excluding participants with previous CVD, 68,083 participants remained. The overall prevalence of high CVD risk was 24.96%. The age- and sex-standardized rate of high CVD risk was 22.92%. Among high-risk participants, the prevalence of risk factors was hypertension (91.9%), dyslipidemia (54.1%), obesity (34.6%), diabetes (27.6%), and smoking (24.5%); clustering of these risk factors was common. The percentage of high-risk individuals taking antihypertensive drugs was 45.94% in those with hypertension; 27.99% of those with diabetes took hypoglycemic drugs and only 5.01% of those with dyslipidemia took lipid-lowering drugs. Control rates of hypertension, diabetes, and dyslipidemia were 1.20%, 4.43%, and 2.78%, respectively. Therefore, the prevalence of high CVD risk was elevated in Inner Mongolia, and treatment and control rates were low.

Body Weight Is a Valid Predictor of the Long-Term Prognosis of Cervical Cancer.

Objective: To identify and validate effective clinical predictors for the long-term prognosis of patients with cervical cancer. Methods: Cervical cancer patients were retrieved from the TCGA database, and patients' clinical data were collected and analyzed for the predictive value of long-term prognosis. In the other branch of the study, patients with cervical cancer and admitted to our hospital between January 1, 2016, and December 31, 2016, were retrieved and followed up for prognosis analysis. Results: In the database patient cohort of our study, 607 cases with cervical cancer were analyzed. Aneuploidy score (p = 0.012), Buffa hypoxia score (p = 0.013), histologic grade (p = 0.01), fraction genome altered >0.4 (p < 0.001), weight > 60 kg (p < 0.001), height > 160 cm (p = 0.047), BMI <18.5 (p = 0.023), Winter hypoxia score (p = 0.002), and adjuvant postoperative radiotherapy were good predictors for disease-free survival (DFS), while aneuploidy score (p = 0.001), MSI sensor score > 0.5 (p = 0.035), person neoplasm status (p < 0.001), race (p = 0.006), Ragnum hypoxia score (p = 0.012), weight (p < 0.001), height (p < 0.001), and BMI < 18.5 (p = 0.04) were good predictors for overall survival (OS). In the admitted patient cohort, age over 60 years old at the time of diagnosis was the only clinical factor influencing the long-term DFS (p = 0.004). TNM stage above III (p = 0.004), body weight > 70 kg (p < 0.001), and complicated with other cancer (p < 0.001) were clinical factor influencing the long-term OS. Conclusions: Clinical factors, especially common to both cohorts, could be used to show the long-term prognosis of cervical cancer.

Clinical Characteristics in the Prediction of Posttreatment Survival of Patients with Ovarian Cancer.

Objective: To determine the efficacy of clinical characteristics in the prediction of prognosis in patients with ovarian cancer. Methods: Clinical data were collected from 3 datasets from TCGA database, including 1680 cases of ovarian serous cystadenocarcinoma, and were analyzed. Patients with ovarian cancer admitted to our hospital in 2016 were retrieved and followed up for prognosis analysis. Results: From the datasets, for patients > 75 years old at the time of diagnosis, histologic grade and mutation count were good predictors for disease-free survival, while for patients > 50 years old at the time of diagnosis, histologic grade, race, fraction genome altered, and mutation count were good predictors for overall survival. In the patients (n = 38) retrieved from our hospital, the longest dimension of lesion (cm) and body weight at admission were good predictors for overall survival. Conclusions: Those clinical factors, together with the two predictive equations, could be used to comprehensively predict the long-term prognosis of patients with ovarian cancer.