Study population
A population-based, cross-sectional survey of the Chinese Physiological Constant and Health Condition (CPCHC) was conducted between 2008 and 2010. Representative samples of the general Chinese population, aged 18 years and older, from the Hei Longjiang Province and Inner Mongolian Autonomous Region in mainland China were determined according to a random, multistage cluster sampling scheme, allowing for good prevalence estimates of the Chinese population. Two urban and two rural areas were selected from each province. Written informed consent was obtained from each participant prior to data collection. The protocol was approved by the Institutional Review Board of the Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences. Trained medical personnel collected information on risk factors via questionnaires (e.g. demographic, socioeconomic, and health-related information), and obtaining anthropometric measurements and blood samples for biochemical assessments.
Inclusions and exclusions
The 2008-2010 CPCHC samples included 29,639 apparently healthy participants. Those who suffered from systemic disease involving diabetes mellitus, hypertension or other cardiovascular, renal, gastro-intestinal, pulmonary disease or cancer were excluded. Moreover, participants taking any medication known to affect carbohydrate and lipid metabolism were also excluded. A schematic of the screening process is presented in Figure 1. Of the total number of participants, 44.3% (n = 13,140) were selected randomly to complete blood testing. Of 8,475 adults, aged ≥18 years, 287 (3.4%) had missing data on blood pressure (BP) and/or laboratory tests. Therefore, the final sample size of disease-free healthy adults was 8,188 (3,595 men and 4,593 women), of which 5,788 were Han Chinese, 936 were Korean-Chinese, 1,237 were Mongolian-Chinese, and 227 were of other ethnicity. Their age distribution was as follows: 3,214 were aged 18-39 years, 1,762 were aged 40-49 years, 1,580 were aged 50-59 years, and 1,632 were aged > 60 years.
Data collection and anthropometry
Epidemiological data were collected on all subjects via a standard questionnaire, which included demographic characteristics (i.e. age, gender, and ethnicity), socioeconomic data (i.e. educational level, marital status, and occupation), past history, and lifestyle risk factors. Smoking status was classified as non-smokers, current smokers (i.e. daily smoking regardless of the amount and type), and ex-smokers. Alcohol drinking status was defined as non-drinkers, current drinkers (frequent consumption of alcohol regardless of the amount and type), and ex-drinkers.
Body weight was measured to the nearest 0.1 kg on a calibrated beam scale and height was measured barefoot in triplicate using a wall-mounted stadiometer to the nearest 0.1 cm. Body mass index (BMI; an index of overall obesity) was calculated as body weight (in kilograms) divided by height (in meters squared). BMI was categorized according to the World Health Organization criteria, where a BMI of < 25 kg/m2 is considered normal, a BMI between 25 and 29 kg/m2 is considered overweight, and a BMI ≥30 kg/m2 is considered obese [14]. Waist circumference (WC; a surrogate marker for central adiposity) was measured midway between the lower rib margin and the iliac crest at the end of a gentle expiration.
BP was measured following a resting period of at least 10 min using an electronic sphygmomanometer (OMRON, HEM-7000). The participant's arm was placed at the level of the heart, and BP was measured three times. The averages of the three measurements were used. If a subject was hypertensive, then a review was performed by doctors to exclude secondary hypertension.
Laboratory measurements
All procedures were performed following a 12-h overnight fast. Blood was drawn from the antecubital vein of the right arm. Serum γ-glutamyltransferase (GGT; a sensitive marker of alcohol intake and hepatic inflammation) and uric acid (UA; a marker of inflammation and metabolic syndrome), both of which play important roles in the development of cardiovascular events, were assayed with an Olympus AU2700 Automatic Biochemical Analyzer and Olympus agent (Olympus, Tokyo, Japan). Fasting blood glucose and lipid profiles, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were also assessed. The biochemical laboratories participating in the survey followed the same internal quality control program that was standardized by the Peking Union Medical College Hospital.
Diagnosis and classification of diabetes and hypertension
Diagnosis of PreHTN was based on the criteria in the JNC-7 report [3]. Specifically, PreHTN was defined as a SBP of 120-139 mmHg and/or a DBP of 80-89 mmHg, whereas normotension was defined as a SBP < 120 mmHg and a DBP < 80 mmHg, and hypertension was defined as a SBP ≥140 mmHg and/or a DBP ≥90 mmHg.
Diagnosis of PreDM was based on the criteria of the American Diabetes Association [7]. Pre-DM was defined as fasting blood glucose (FBG) levels from 100 mg/dl (5.6 mmol/L) to 125 mg/dl (6.9 mmol/L)], which indicated IFG, and/or 2-h postprandial blood glucose (PBG) levels from 140 mg/dl (7.8 mmol/L) to 199 mg/dl (11.1 mmol/L) following a 75 g oral glucose load, which indicated IGT. Individuals with FBG < 5.6 mmol/L were considered normal, whereas individuals with FBG ≥7.0 or 2-h PBG ≥11.1 were diagnosed with diabetes. Fasting was defined as no caloric intake for at least 8 h.
Statistical analysis
Data were entered and documented on EpiData 3.1 software (The EpiData Association, Odense, Denmark). Datasets were transferred into an SPSS compatible format. Data are presented as counts and percentages ± standard errors (SE) for categorical variables, and means ± standard deviation (SD) for continuous variables with a normal distribution. Comparisons between groups were made using an analysis of covariance. Medians and interquartile ranges of GGT and TG were calculated due to their abnormal distributions, and comparisons between these groups were made using the Wilcoxon rank sum test. Prevalence (%) indicates the percentage of healthy Chinese men and women with a condition at the time of data collection, and means indicate the average value of a characteristic in healthy Chinese adults. Correlation analyses were performed using either the Pearson or Spearman correlations. Statistical analyses were performed on the statistical software, SPSS version 13.0 (SPSS Inc., Chicago, Illinois, USA). All tests for statistical significance were two-tailed, and considered significant when P < 0.05.