Subjects
A total of 200 adult Saudi subjects (94 men; 106 women), aged 23–65 years were randomly selected from the database of Biomarker Screening in Riyadh Project (RIYADH COHORT) collected by Center for Biomarkers in Chronic Diseases (CBCD) in Riyadh, KSA. All participants provided written and informed consent prior to inclusion. Ethical approval was granted by the Ethics Committee of the College of Science Research Center, King Saud University, Riyadh, Kingdom of Saudi Arabia (KSA). Participants completed a questionnaire their general health status, demographic information, and past medical history. Anthropometric and biochemical data from the database was utilized to assess the status of full MetS and its five components as present/absent (dichotomous data) according to the criteria set in the National Cholesterol Education Programme Adult Treatment Panel III (NCEP-ATP III) where MetS was present when at least three out of the five following components are present: Waist circumference of > 88 cm; fasting glucose > 5.6 mmol/L; HDL-cholesterol < 1.30 mmol/L; triglycerides > 1.7 mmol/L; systolic blood pressure > 130 mmHg and/or diastolic blood pressure > 85 mmHg 10.
The sample size was calculated based on an earlier study by Yeung et al. 7 where serum Zinc-alpha2-Glycoprotein in MetS patients was reported with an effect size of > 0.5. Thus, a total sample size of 176 subjects (88 per group) was required to detect the effect size of 0.5 with 95% power using 5% significance level. Based on this, 100 MetS and 100 non-MetS subjects were selected from the database by random selection based on the RAND function in the Microsoft excel. Prior to the final selection, the subjects with reported chronic conditions like liver, kidney, heart failures and pregnant women were removed from the selection and the process was repeated to get the final count of 100 in each group.
Inclusion and exclusion criteria
Subjects on anti-hyperglycemic treatment; pregnant or lactating women; with known renal, hepatic, pulmonary, cardiac, etc., complications were excluded from this study.
Anthropometry and Blood Collection
Anthropometry included height (cm), weight (kg), waist and hip circumference (cm), and mean systolic and diastolic blood pressure (mmHg, average of two reading). Body mass index (BMI) was calculated as weight in kilograms divided by height in square meters. Waist-hip ratio (WHR) was calculated as the quotient between waist and hip circumferences. They were asked to fast 10 hours or overnight before blood withdrawal. Fasting blood samples (> 10 h) were collected and transferred immediately to a non-heparinized tube for centrifugation. The collected serum was transferred to pre-labeled new tubes, kept on ice, and delivered to the CBCD in King Saud University, Riyadh, KSA, for immediate storage at -20°C.
Blood Chemistry
Fasting blood glucose and lipid profile (triglycerides, total and HDL-cholesterol) were assessed using routine chemical methods (Konelab analyzer, Espoo Finland). Hypertriglyceridemia was defined as circulating triglycerides ≥ 1.7mmol/l was considered abnormal level 11. Low HDL-cholesterol was defined as < 1.03 mmol/l and total cholesterol HDL ratio > 3.5. Low-HDL level for women was set at < 1.3 mmol/l 12,13.
Inflammatory markers and ZAG
Serum TNF- α and IL-6 level were determined using MILLIPLEX MAP human adipokine magnetic bead panel 2 kit obtained from Millipore Corporation. Serum human CRP level were determined using ELISA kits obtained from R&D System, USA. Serum ZAG level were determined using ELISA kits using BioVendor-Laboratory Medicine.
Data analysis
Data were analyzed using SPSS (version 22 Chicago, IL, USA). Continuous data were presented as mean ± standard deviation (SD) for normal variables and non-Gaussian variables were presented in median (1st and 3rd ) percentiles. Categorical data were presented as frequencies and percentages (%). All continuous variables were checked for normality using Kolmogorov-Smirnov test. Non-Gaussian variables were log-transformed prior to parametric analysis. Independent T-test and Mann Whitney U were used to compare mean differences in Gaussian and Non-Gaussian variables. Correlations between variables were done using Pearson’s and spearman correlation analysis. Stepwise regression analysis was performed for dependent predicators of ZAG (ug/ml) and area under the curve (AUC) was calculated using receiver operating characteristic (ROC) curve to determine viability of ZAG as a biomarker for MetS and its components. An AUC of 0.9 to 1 is considered excellent, 0.8 to 0.9 is considered good, 0.7 to 0.8 is considered fair, 0.6 to 0.7 is considered poor, and 0.5 to 0.6 is considered very poor. A p-value < 0.05 was considered statistically significant.