- Research article
- Open Access
- Open Peer Review
A routine biomarker-based risk prediction model for metabolic syndrome in urban Han Chinese population
- Wenchao Zhang†1,
- Qicai Chen†2,
- Zhongshang Yuan1,
- Jing Liu1,
- Zhaohui Du1,
- Fang Tang3,
- Hongying Jia4,
- Fuzhong Xue1 and
- Chengqi Zhang3Email author
© Zhang et al.; licensee BioMed Central. 2015
- Received: 10 August 2014
- Accepted: 15 January 2015
- Published: 31 January 2015
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.
- Metabolic Syndrome (MetS)
- Routine biomarkers
- Predictor model
- Risk matrix
Metabolic syndrome (MetS) is a disorder with co-occurrence of several known cardiovascular risk factors, including insulin resistance, obesity, atherogenic dyslipidemia and hypertension . With the economic development and the changing of people's lifestyle in china, the prevalence of MetS is increasing rapidly. Compared with Europeans and Americans, Asians are more likely to have MetS . Data from the China Health and Nutrition Survey conducted in 2009 suggested that the prevalence rate of MetS has reached up to 21.3% among the Chinese adults . Many studies indicated that incidence of MetS will increase the risk of type 2 diabetes , cardiovascular disease [5-9], renal damage [10,11], and so on. Therefore, prediction of MetS is very essential for early prevention of the above diseases.
Some risk scores based on cross-sectional studies were structured for screening undiagnosed MetS [12-14], which depended on questionnaire survey about participants’ lifestyle and medical histories. Although the area under the ROC curves for detecting the MetS in these studies were acceptable with a range from 72.4% to 80.1%, cross-sectional study could only provide temporal information of the subjects. Cohort study is more preferable for risk assessment. Hsiao and Yang conducted a two-year (from 2003 to 2005)  and a 5-year follow-up study (during 1997–2006)  respectively in Chinese population. Both of them confirmed that routine check-up biomarkers like serum cholesterol, triglycerides, blood glucose, measurement of body height and weight, blood pressure et al., could be served as effective predictors to MetS using multivariate logistic regression (MLR). However, MLR is not suitable for survival data, and it also limited the applying of the model in the first study due to relative short follow-up time and small sample. In the second study stepwise regression has ruled out many MetS related biomarkers from the model. Fortunately, many other studies [17-29] have found a number of MetS related biomarkers, which provide us a convenience to build the risk appraisal model of MetS. After studying 6Synthetic Latent Predictors from 11 MetS routine biomarkers in a MetS positive population, we develop a novel routine biomarker-based risk prediction model for MetS in urban Han Chinese population.
Subjects were selected from the urban adult citizen who came to the Center for Health Management of Shandong Provincial QianFoShan Hospital, and the Health Examination Center of Shandong Provincial Hospital to conduct medical examination from 2005 to 2010. Among 92,284 subjects (aged 18 to 82 years) completing all steps of physical examination, 16,557 subjects were diagnosed with MetS at their first check-up year according to the criteria of the Chinese Medical Association. Of 75,727 subjects without MetS at baseline, 1,565 (1,020 males and 545 females) completed a 5-year follow-up and were included in the cohort study design. The cumulative incidence rate was calculated for 1,565 subjects who were followed up.
Biomarkers selection and measurements
In the present study, eleven biomarkers were selected from routine health check-up data, including body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood-glucose (FBG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), hemoglobin (Hg), hematocrit (HCT), white blood cell count (WBC), lymphocyte (LC), neutrophile granulocyte (NGC). Among them, BMI, SBP, DBP, TG, HDL-C and FBG were selected based on the traditional definition of MetS. The others were included according to the available results of peer studies: Hb [17-19], HCT [17,18,28], WBC [20-27], LC [23,29], NGC [23,29]. All measurements were conducted in the Center for Health Management of Shandong Provincial QianFoShan Hospital and the Health Examination Center of Shandong Provincial Hospital following same and standard procedures. Both of the two institutions are nationally accredited. The whole study was approved by the Ethics Committee of School of Public Health, Shandong University, and written informed consent was obtained from all eligible participants.
Definition of MetS
Chinese Medical Association Diabetes Branch criteria  were applied to define MetS in this paper. Subjects who had three or more of the following four signs were diagnosed with MetS: 1) overweight or obesity, BMI ≥25.0 Kg/M2; 2) hypertension, systolic/ diastolic ≥140 mmHg/90 mmHg or previous diagnosis; 3) dyslipidemia, fasting TG ≥1.7 mmol/L (110 mg/dl), or fasting high-density lipoprotein cholesterol (HDL-C) <0.9 mmol/L (35 mg/dl); 4) hyperglycemia, fasting blood-glucose (FBG) ≥6.1 mmol/L(110 mg/dl) or 2 h Post-meal glucose (PG) ≥7.8 mmol/L(140 mg/dl), or previous diagnosis.
Descriptive statistics were conducted for 16,557 subjects with MetS at baseline. Student's t test was used to detect the statistical significances for 11 biomarkers between males and females, and the χ2 test was conducted to detect the difference in the prevalence of the four basic components (obesity, hypertension, dyslipidemia and hyperglycemia) between males and females.
The prevalence of MetS in the study was 17.9% (16,557/92,284) (22.7% in males and 9.6% in females) at baseline. At the end of the follow-up period of 1,565 subjects, 348 incident MetS cases (286 males and 62 females) were diagnosed and the cumulative incidence rate was 22.2% (28% in males and 11.4% in females) (see Additional file 1: Table S1). The prevalence of four basic components (obesity, hypertension, hyperglycemia, and dyslipidemia) was significantly different between males and females (see Additional file 2: Table S2).
Distribution of age and the eleven biomarkers between male and female with baseline metabolic syndrome
Male (n = 13345)
Female (n = 3212)
Mean ± SD
Mean ± SD
49.00 ± 13.09
59.50 ± 12.49
body mass index (kg/m 2 )
28.29 ± 2.99
28.27 ± 3.10
systolic blood pressure (mmHg)
144.60 ± 17.28
150.40 ± 20.54
diastolic blood pressure (mmHg)
87.09 ± 12.00
81.76 ± 11.99
fasting blood-glucose (mmol/L)
6.43 ± 1.94
6.65 ± 2.09
2.87 ± 2.26
2.42 ± 1.53
high-density lipoprotein cholesterol (mmol/L)
1.18 ± 0.35
1.32 ± 0.35
157.70 ± 10.82
138.30 ± 11.88
46.27 ± 3.03
41.65 ± 3.18
white blood cell count (10 9 /L)
7.18 ± 1.67
6.90 ± 1.63
lymphocyte (10 9 /L)
2.25 ± 0.69
2.24 ± 0.68
neutrophile granulocyte (10 9 /L)
4.30 ± 1.28
4.13 ± 1.27
Factor loadings by principal component analysis with varimax rotation on 11 routine health check-up biomarkers in MetS patients
Males (n = 13345)
Females (n = 3212)
% Variance explained
The routine health check-up based biomarkers for predicting MetS
Currently, several potential routine health check-up based biomarkers, such as Hb [17-19], HCT [17,18,28], WBC [20-27], LC [23,29] and NGC [23,29], were identified for predicting MetS/its components. Correlation matrix between 11 biomarkers was illustrated in Additional file 3: Table S3, which Shows the necessity of EFA. In this paper, we extracted 6 independent synthetic latent predictors (SLPs) by EFA from 11 routine health check-up biomarkers (BMI, SBP, DBP, FBG, TG, HDL-C, Hb, HCT, WBC, LC, NGC), not only with their specific clinical significances, but eliminating the multicollinearity between them. Each SLPs reflected the specific pathogenesis of MetS, with IF contributed by WBC & LC & NGC, EPF by Hb & HCT, BPF by SBP & DBP, LMF by TG & HDL-C, OCF by BMI, and GMF by GMF (see Table 2). The cumulative Variances explained by the six SLPs were up to 81.55% and 79.65% for males and females respectively. Particularly, the IF and EPF were identified as the key factors for the variation of MetS with their contribution proportion of 22.25% &15.87% in males and 22.21%&16.21% in females respectively. Pathogenically, both of them were strong associated with insulin resistance [33-36] which was the ‘core’ for MetS [37-39]. EPF was contributed by Hb & HCT. Hb is a carrier and buffer of nitric oxide (NO), and various compounds of Hb with NO can affect Hb-oxygen affinity of the whole blood . Disturbed NO synthesis may exert an adverse effect on endothelial dysfunction through the L-arginine-NO pathway . Furthermore, endothelial dysfunction was reported to be associated with MetS [42,43]. HCT could change blood viscosity and peripheral resistance to blood flow, and further contribute to insulin resistance [44-46].
Metabolic syndrome synthetic predictor and its application in MetS prediction
At the end of the follow-up period, the cumulative incidence rate reach up to 22.2% (28% in males and 11.4% in females) (see Additional file 1: Table S1). Currently, three cross-sectional design based risk scores [12-14] and two cohort design based predictive models [15,16] had been developed to predict MetS on different ethnicities. Although these predict tools obtain acceptable power with their AUC ranged from 0.724 to 0.827, their risk algorithm and visualization of risk assessment still had development potential for improving power, feasibility and practicability. In this paper we developed a routine health check-up cohort design based MetS synthetic predictor (MSP) for predict 5 year risk of MetS in the frame work of Cox regression model. The 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 was further used to construct the risk matrix with a series of risk warning indexes including average risk in population, AR & RAR for subjects, and the cut-off curve for predicted MetS (see Figure 2). This matrix provided a feasible and practical tool for visualization of risk assessment in the prediction of MetS. As an example, for a woman at a given age who receives health check-up, the risk matrices can provide her with AR (Figure 2B1) and RAR (Figure 2B2) compared with the average hazard within the same age group in females, this may urge her to intervene risk factors for reducing risk of MetS.
The risk distribution in urban Han Chinese population
The proportion of subjects with high-risk was higher in males than females before the age of 55, while it was in reverse after 55 (showed in Figure 3). Similar results have been obtained in the Korean population  with the demarcation point of 60 years old. In particular, the patterns of subjects with high-risk were quite different between males and females. The proportion of subjects with high-risk increased linearly with age in male population, while showed an S shaped curve in female population with the fastest growth period from 40 to 60 years old. This difference may be associated with women’s menopause. Various studies indicated that natural menopause was associated with increased central adiposity , blood pressure [49-55], total cholesterol, LDL cholesterol and triglyceride levels [50,56], which would further increase risk of MetS during the menopause transition years. The contribution of several metabolic components to the metabolic syndrome is different in males and females (see Additional file 2: Table S2).
We re-assessed the predictive ability using IF & EPF alone and four classical MetS components respectively. Additional file 4: Table S4 showed these results, as expected, our proposed 6 SLPs still have the best performance. Actually, in China, health check up was embedded in “physical examination package” and usually the biomarkers can be obtained together. The Chinese government request every Health Examination Center that at least all biomarkers used in this manuscript must be tested during the health examination process.
Study population was just employed urban residents, therefore the results may not extend to general population. In addition, five years follow-up was relatively short for predict long term risk of MetS.
In conclusion, 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 for visualization of risk assessment in the prediction of MetS.
This work was co-supported by grants from the National Nature Science Fund (No.81273082) of China and Science & Technology Development Projects of Shandong Province (2009GG20002035). The funding agencies were not involved in study design, analysis, and interpretation.
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