Jing Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jing Liu has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Chengqi Zhang
Shandong Provincial Qianfoshan Hospital, Shandong University, 16766 Jingshi Rd, Jinan, 250014, China
1 shared publication - Fang Tang
Health Management Center, Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China.
1 shared publication - Fuzhong Xue
Department of Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, China
1 shared publication - Hongying Jia
Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
1 shared publication - Qicai Chen
Department of Prevention and Health Care, Dongying Shengli Oilfield Central Hospital, Dongying, 257000 China
1 shared publication - Wenchao Zhang
Shandong Provincial Qianfoshan Hospital, Shandong University, 16766 Jingshi Rd, Jinan, 250014, China
1 shared publication
Latest Publications
- Open Access
- 31 January 2015
- Published by Springer Nature in BMC Public Health
- Vol. 15 (1) , 64
A routine biomarker-based risk prediction model for metabolic syndrome in urban Han Chinese population
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.
- 31 January 2015
- Published by Springer Nature in BMC Public Health
- Vol. 15 (1) , 64
Open AccessA routine biomarker-based risk prediction model for metabolic syndrome in urban Han Chinese population
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.