Chengqi Zhang

Previous studies have reported that the potassium voltage-gated channel subfamily Q member 1 (KCNQ1) gene is associated with diabetes in both European and Asian population. This study aims to find a predictable single nucleotide polymorphism (SNP) to predict the risk of metabolic syndrome (MetS) through investigating the association of SNP in KCNQ1 gene with MetS in Han Chinese women of northern urban area. Six SNPs were selected and genotyped in 1381 unrelated women aged 21 and above, who have had physical check-up in Shandong Provincial Qianfoshan Hospital. Cox proportional model was conducted to access the association between SNPs and MetS. Sixty one women developed MetS between 2010 and 2015 during the 3055 person-year of follow-up. The cumulative incidence density was 19.964/1000 person-year. The SNP rs163182 was associated with MetS both in the additive genetic model (RR = 1.658, 95% CI: 1.144–2.402) and in the recessive genetic model (RR = 2.461, 95% CI: 1.347–4.496). It remained significant after adjustment. This relationship was also observed in MetS components (BMI and SBP). A novel association between rs163182 and MetS was found in this study, which can predict the occurrence of MetS among northern urban Han Chinese women. More investigations are needed to be done to assess the possible pathway in which KCNQ1 gene affects MetS.

Elevated levels of fibrinogen may contribute to a prothrombotic state. Cross-sectional studies suggest fibrinogen possibly linked with MetS/its components, while results of cohort studies remain controversial. Thus, this study was designed to identify the association of plasma fibrinogen with metabolic syndrome (MetS) and further to clarify the role of fibrinogen in the development of MetS. A large-scale prospective cohort study was conducted in routine health check-up population. 6209 participants free of MetS at baseline were included in the original cohort, with annually routine health check-up for incident MetS from 2005 to 2011. Then, 4 pre-MetS sub-cohorts, with overweight, hypertension, hyperglycemia and dyslipidemia at baseline respectively, were also created from the original cohort. Various strategies of Cox model analysis were performed for attempting to confirm the role of fibrinogen in the development of MetS. Total MetS incidence density was 75.58 per 1000 person-years. Cox regression analysis by adjusting for potential confounders as well as four MetS components showed a significant effect of fibrinogen on MetS just in female, with risk ratio (RR) (95 % CI) of 1.48 (1.02, 2.13) for Q4 vs. Q1. Further analysis in the 4 pre-MetS female sub-cohorts revealed this significant effect only in overweight sub-cohort, with RR (95 % CI) of 1.97 (1.20, 3.23), but no significant interaction of overweight with fibrinogen on MetS was revealed in original female cohort. Then, stratification analysis among the 4 sub-groups of fibrinogen quartiles showed that effects of overweight on MetS were different among the 4 sub-groups of fibrinogen quartiles, with RR of 2.98 for Q1, 4.40 for Q2, 3.93 for Q3, and 4.82 for Q4 respectively. Fibrinogen was associated with MetS just in overweight sub-cohort of female individuals, and fibrinogen might be a potential modifier in the pathway from overweight to MetS.

Introduction Research suggests that targeting high-risk, nonhypertensive patients for preventive intervention may delay the onset of hypertension. We aimed to develop a biomarker-based risk prediction model for assessing hypertension risk in an urban Han Chinese population. Methods We analyzed data from 26,496 people with hypertension to extract factors from 11 check-up biomarkers. Then, depending on a 5-year follow-up cohort, a Cox model for predicting hypertension development was built by using extracted factors as predictors. Finally, we created a hypertension synthetic predictor (HSP) by weighting each factor with its risk for hypertension to develop a risk assessment matrix. Results After factor analysis, 5 risk factors were extracted from data for both men and women. After a 5-year follow-up, the cohort of participants had an area under receiver operating characteristic curve (area under the curve [AUC]) with an odds ratio (OR) of 0.755 (95% confidence interval [CI], 0.746-0.763) for men and an OR of 0.801 (95% CI, 0.792-0.810) for women. After tenfold cross validation, the AUC was still high, with 0.755 (95% CI, 0.746-0.763) for men and 0.800 (95% CI, 0.791-0.810) for women. An HSP-based 5-year risk matrix provided a convenient tool for risk appraisal. Conclusion Hypertension could be explained by 5 factors in a population sample of Chinese urban Han. The HSP may be useful in predicting hypertension.

Objectives: To evaluate the use of texture‐based gray‐level co‐occurrence matrix (GLCM) features extracted from thyroid sonograms in building prediction models to determine the nature of thyroid nodules.Methods: A GLCM was used to extract the texture features of 155 sonograms of thyroid nodules (76 benign and 79 malignant). The GLCM features included energy, contrast, correlation, sum of squares, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy, information measures of correlation, and maximal correlation coefficient. The texture features extracted by the GLCM were used to build 6 different statistical models, including support vector machine, random tree, random forest, boost, logistic, and artificial neural network models. The models' performances were evaluated by 10‐fold cross‐validation combining a receiver operating characteristic curve, indices of accuracy, true‐positive rate, false‐positive rate, sensitivity, specificity, precision, recall, F‐measure, and area under the receiver operating characteristic curve. External validation was used to examine the stability of the model that showed the best performance.Results: The logistic model showed the best performance, according to 10‐fold cross‐validation, among the 6 models, with the highest area under the curve (0.84), accuracy (78.5%), true‐positive rate (0.785), sensitivity (0.789), specificity (0.785), precision (0.789), recall (0.785), and F‐measure (0.784), as well as the lowest false‐positive rate (0.215). The external validation results showed that the logistic model was stable.Conclusions: Gray‐level co‐occurrence matrix texture features extracted from sonograms of thyroid nodules coupled with a logistic model are useful for differentiating between benign and malignant thyroid nodules.

Many MetS related biomarkers had been discovered, which provided the possibility for building the MetS prediction model. In this paper we aimed to develop a novel routine biomarker-based risk prediction model for MetS in urban Han Chinese population. Exploring Factor analysis (EFA) was firstly conducted in MetS positive 13,345 males and 3,212 females respectively for extracting synthetic latent predictors (SLPs) from 11 routine biomarkers. Then, depending on the cohort with 5 years follow-up in 1,565 subjects (male 1,020 and female 545), a Cox model for predicting 5 years MetS was built by using SLPs as predictor; Area under the ROC curves (AUC) with 10 fold cross validation was used to evaluate its power. Absolute risk (AR) and relative absolute risk (RAR) were calculated to develop a risk matrix for visualization of risk assessment. Six SLPs were extracted by EFA from 11 routine health check-up biomarkers. Each of them reflected the specific pathogenesis of MetS, with inflammatory factor (IF) contributed by WBC & LC & NGC, erythrocyte parameter factor (EPF) by Hb & HCT, blood pressure factor (BPF) by SBP & DBP, lipid metabolism factor (LMF) by TG & HDL-C, obesity condition factor (OCF) by BMI, and glucose metabolism factor (GMF) by FBG with the total contribution of 81.55% and 79.65% for males and females respectively. The proposed metabolic syndrome synthetic predictor (MSP) based predict model demonstrated good performance for predicting 5 years MetS with the AUC of 0.802 (95% CI 0.776-0.826) in males and 0.902 (95% CI 0.874-0.925) in females respectively, even after 10 fold cross validation, AUC was still enough high with 0.796 (95% CI 0.770-0.821) in males and 0.897 (95% CI 0.868-0.921) in females. More importantly, the MSP based risk matrix with a series of risk warning index provided a feasible and practical tool for visualization of risk assessment in the prediction of MetS. MetS could be explained by six SLPs in Chinese urban Han population. The proposed MSP based predict model demonstrated good performance for predicting 5 years MetS, and the MetS-based matrix provided a feasible and practical tool.