Prevalence of metabolic syndrome among ethnic groups in China

The Expansion Investigation of Human Physiology Constant was a nationwide cross-sectional, random and multistage clustering sampled survey in China that is part of the National Constitution and Health Database in 2008–2011 [16]. Our previous studies based on the National Constitution and Health Database had shown several factors associated with cardiovascular disease [14, 17]. This study was expanded to six provinces and eight ethnic groups according to the geographical, ethnical and economic characteristics from the previous studies. All study personnel was trained with standardised working manuals and surveys before they conducted this study. Sample processing, testing and quality control were conducted by certified personnel in a central laboratory (Department of Laboratory Medicine in Peking Union Medical College Hospital) to ensure the consistency and stability of the measurements.

Participants aged 8–86 years were recruited from six provinces (Inner Mongolian, Heilongjiang, Ningxia, Hunan, Yunnan, Sichuan) using a random, multistage cluster-sampling method as shown in Fig. 1. Eight ethnic groups, including Han, Mongolian, Korean, Hui, Miao, Li, Tibetan and Tujia, were enrolled in this study. Inclusion criteria were absence of self-reported systemic diseases, such as CAD, renal disease, autoimmune disease, hypersensitivity, gastrointestinal disease, pulmonary disease, and cancer. Considering different customs of ethnic groups, we included participants aged 8–86. Exclusion criteria included significant abnormalities on physical examination, and having a fever, acute illness, or hospitalization within 15 days. This study was approved by the Ethics Committee of Institute of Basic Medical Sciences Chinese Academy of Medical Sciences (No·005–2008). Written consents were obtained from all participants.

Participants were required to complete a demographic questionnaire and a health behaviour questionnaire, which included smoking, alcohol consumption and physical activity. Trained personnel assisted the participants in completing the questionnaires.

All participants were asked to avoid smoking and heavy physical activity for at least 2 h before the physical examinations, which included resting blood pressure, height, weight, and waist circumference (WC). Two blood pressure measurements were taken using OMRON HEM-7000 electronic sphygmomanometer (OMRON HealthCare, Kyoto, Japan) after the participants had rested in a sitting position for at least 5 min.

Participants were required to fast for 8 to 12 h before blood sampling. Fasting blood glucose (GLU), total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL cholesterol), and low-density lipoprotein cholesterol (LDL cholesterol) were analysed using a Hitachi 7020 chemistry analyser (Hitachi, Tokyo, Japan). Body mass index (BMI) was a person’s weight in kilograms divided by the square of height in meters.

The modified ATP III criteria were applied in the diagnosis of MetS, which requires the presence of at least three out of five factors [15]: (i) Abdominal density as defined by waist circumference ≥ 90 cm in men and ≥ 80 cm in women; (ii) triglycerides ≥1·7 mmol/L; (iii) HDL cholesterol < 1·03 mmol/L in men and < 1·29 mmol/L in women; (iv) systolic blood pressure (SBP) ≥130 mmHg or diastolic blood pressure (DBP) ≥ 85 mmHg; (v) GLU ≥5·6 mmol/L as impaired fasting glucose (IFG).

Results were analysed using SPSS version 22·0 (IBM SPSS Statistics, Armonk, NY, USA) in 2018. Descriptive statistics were expressed as frequency (percentage) for categorical data or mean ± SD or median (interquartile range) for continuous variables. Missing data were less than 3% for all included variables. Continuous variables were compared among groups using one-way ANOVA and Kruskal-Wallis tests as appropriate. Categorical variables were compared among groups using Chi-square test. Logistic regression was used to identify the predictors for MetS. Gender, age, ethnicities, exercises, smoking status, drinking status were included in the model. Age groups and gender were entered into the first adjusted model. Other predictor variables were entered stepwise if P < 0·05 and removed if P > 0·10. Exercise was added as a covariate in the second adjusted model and ethnicities, drinking status, smoking status were applied in the third adjusted model. Odds ratios (ORs) and 95% confidential interval (95% CI) were estimated. A two-tailed P < 0·05 was considered statistically significant. Data from the Sixth National Population Census of the People’s Republic of China provided by the National Statistics Bureau of China were used as the standard population. Age-specific prevalence and age-standardized prevalence were estimated from the standard population.

The age, gender and ethnic-specific crude and standardised prevalence of MetS are summarised in Table 1. The prevalence of MetS in males (13·5%) was slightly higher than in females (12·6%) (P = 0·28). The prevalence of MetS was substantially higher above age 25 (24·7%) compared with age ≤ 25 (2·3%) (P < 0·001). There was a significant difference in MetS prevalence among ethnic groups (P < 0·001). Korean Chinese had the highest prevalence of 35·42, followed by Hui, Han, Miao, Tujia, Li, Mongolian and Tibetan (Table 1, Fig. 3).

Figure 4 shows the prevalence of components of MetS in different ethnic groups. The highest prevalence of abdominal obesity, hyperlipidaemia, decreased HDL cholesterol, elevated blood pressure and IFG were found in Han, Li, Hui, Li and Korean ethnicities, respectively; while the lowest prevalence of the components of MetS appeared in Li and Tibetan ethnicities (Fig. 4). In all ethnic groups, the prevalence of decreased HDL cholesterol and elevated blood pressure were the two highest among the five components, while IFG was less prevalent, especially in Miao, Mongolian, Tibetan, Hui and Tujia ethnicities.

Predictors for MetS are summarized in Table 2. Age, gender, smoking, alcohol consumption and ethnic groups were all significant risk factors for MetS in the crude model (p < 0·001, p = 0·027, p < 0·001, p < 0·001, p < 0·001, respectively). Compared to females, males had an increased risk of MetS [OR (95% CI): 1·087 (1·009–1·171), p = 0·027]. However, maleness [1·028 (0·930–1·137), p = 0·059] and smoking [0·908 (0·806–1·022), p = 0·110] were not significant factors for MetS after multivariable adjustment. Compared to participants aged 8 to 12 years, the OR increased with age, and was highest in participants aged 60 to 86 years [34·117 (24·673–47·177), p < 0·001]. Relative to Tibetans, all other ethnicities were associated with increased risk of MetS. Korean Chinese had the highest adjusted risk for MetS [5·989 (4·249–8·442), p < 0.001], followed by Hui [4·020 (2·859–5·653), p < 0.001], Han [2·975 (2·194–4·034), p < 0.001], Li [2·096 (1·487–2·954), p < 0·001], Miao [1·961 (1·262–3·048), p = 0·003], Mongolian [1·835 (1·272–2·648), p = 0·001] and Tujia [1·753 (1·166–2·637), p = 0·007] ethnicities. MetS was not significantly associated with exercise in both crude and adjusted models (P = 0·140, P = 0·710, P = 0·710, P = 0·905, respectively).

There were some limitations in this study. China is a large country, so 24,796 participants represent only a small fraction of the total population. We were unable to study minorities in very remote parts of China. Different ethnic groups have different religious beliefs and customs, and these may account for some of the differences.