Metabolically healthy obesity and unhealthy normal weight rural adults in Xinjiang: prevalence and the associated factors

This study was conducted in Xinjiang from 2009 to 2010. A multi-stage (prefecture-county-township-village) stratified cluster random sampling method was used to select the participants. First, according to the region, ethnic distribution, and economic development, we selected three representative regions namely Kashi, Yili, and Shihezi. We then randomly selected one county in each prefecture and one township in each county. That is, we used a stratified sampling method to select the corresponding villages in each township and further investigated the residents of the selected villages. We chose individuals who met the following inclusion criteria: (1) Uyghur, Kazakh or Han residents age ≥ 18 years and living in the local area for at least 6 months (2) were free of no serious illness, and (3) willingness to cooperate in completing the investigation. Additionally, we excluded pregnant women (n = 135). Consequently, this study included 13,525 participants who completed the questionnaire surveys, physical examinations, and biochemical tests. The overall response rate was 89.6%. All the participants signed an informed consent form, and all the experiment protocol for involving human data was in accordance to Declaration of Helsinki.

The measurements, which included height, weight, waist circumstance (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP), was conducted by uniformly trained investigators. A height and weight scale was used to measure height and weight after the participants took their shoes, hats, and heavy clothing off. The BMI was calculated as weight (kg) divided by the square of height (m). The WC was measured using an inelastic tape at the midpoint between the iliac crest and the lowest rib. The participants sat and rested for at least 5 min after which they had their SBP and DBP measured using a mercury sphygmomanometer. Each measurement required an interval of 30 s; the average value was obtained after three measurements. After 10 h of fasting, whole blood samples were collected from each participant. Fasting plasma glucose (FPG), TG, and HDL-C were tested using an automatic biochemical analyzer (Olympus AU 2700; Olympus Diagnostics, Hamburg, Germany) at the Laboratory Department of the First Affiliated Hospital of Shihezi University School of Medicine.

Each participant was interviewed face-to-face. The questionnaires included general demographic data (age, gender, ethnicity, and educational level), behavior and lifestyle (smoking, drinking), and eating habits (fresh milk, eggs, fresh meat, vegetables, and fruits). Smoking was defined as current smoking with more than 100 smoked cigarettes before the time of the interview [20]. Drinking was defined as drinking alcoholic beverages (beer, red wine, and white wine) at least twice a month and continuing to drink actively [21]. A semi-quantitative food frequency method was used to investigate the dietary intake. The intake of fresh milk, meat, eggs, and fruits and vegetables was measured in liters, kilograms, numbers, and a plate with a diameter of 16 cm, respectively.

According to the regulations of the Working Group on Obesity in China [22], the participants were defined as underweight (BMI < 18.5 kg/m²), normal weight (BMI 18.5–23.9 kg/m²), overweight (BMI 24.0–27.9 kg/m²), and obese (BMI ≥28.0 kg/m²). Abdominal obesity was defined as WC ≥85 cm and WC ≥80 cm among men and women, respectively. The MS components in the National Cholesterol Education Program Adult Treatment Panel III (2005) were used to define the metabolic status (because WC and BMI have collinearity, remove it) [23] Metabolic health and unhealth were defined as the following four metabolic abnormalities ≤1, and metabolic abnormalities > 1, respectively: (1) TG ≥1.70 mmol/L; (2) SBP ≥130 mmHg or DBP ≥85 mmHg, or have been diagnosed as hypertension; (3) FPG ≥5.6 mmol/L, or have been diagnosed as type 2 diabetes; (4) HDL-C < 1.04 mmol/L (men), HDL-C < 1.30 mmol/L (women).

A total of 13,525 participants with an average age of 45.04 ± 14.60 years were recruited. Among the participants, 51.6% (6973) were normal weight, 30.2% (4078) were overweight, and 14.4% (1945) were obese. The prevalence of MHO was 5.5%, accounting for 38.5% of obese population. The prevalence of MUNW was 15.5%, accounting for 30.1% of the normal weight population. In each weight group, the age and WC of the metabolically unhealthy participants were higher than those of the metabolically healthy participants (P <  0.001). The percentage of females in the metabolically healthy group among the obese individuals was higher than that in the metabolically unhealthy group (P = 0.036). There were statistical differences in the percentage of education level and consumption of fresh milk, vegetables, and fruits between the healthy and unhealthy metabolic groups, for both the normal weight and overweight individuals (P <  0.05) (Table 1).

Among the 1945 obese individuals, the prevalence of MHO in males (35.8%) was lower than that in females (40.5%) (P = 0.036). The prevalence of MHO in males and females gradually decreased with the increase in age (p trend< 0.001). There was no statistical difference in the prevalence of MHO between the three ethnic groups (P > 0.05) (Table 2).

There was no statistical difference in the prevalence of MUNW between males and females (P > 0.05) among the 6973 normal weight individuals. The prevalence of MUNW in males and females gradually increased (p trend< 0.001) with the increase in age. There was no statistical difference in the prevalence of MHO between the three ethnic groups (P > 0.05) (Table 3).

A multivariate logistic regression analysis was conducted among the obese individuals; the results showed that a metabolically healthy phenotype was positively associated with females (odds ratio [OR] = 1.42, 95% confidence interval [CI] = 1.13–1.77) and the consumption of vegetables ≥4 plates per week (OR = 1.85, 95%CI = 1.02–3.35). This was however, negatively associated with higher age and the consumption of red meat ≥2 kg per week (OR = 0.62, 95%CI = 0.45–0.85), and a larger WC. Further, the risk of MHO showed a significant downward trend as age increased. Compared with 18–24 years, the OR dropped from 0.56 in the 25–34 years group to 0.26 in the ≥65 years group (Table 4).

A multiple-factor analysis among the normal weight individuals indicated that a metabolically unhealthy phenotype was positively associated with higher age, the consumption of red meat ≥2 kg per week (OR = 1.25, 95%CI = 1.05–1.48), and a larger WC; however, this was inversely associated with the consumption of vegetables ≥4 plates per week (OR = 0.64, 95%CI = 0.48–0.85). The risk of MUNW obviously increased with the increase in age. Compared with the 18–24 years group, the OR increased from 1.32 in the 25–34 years group to 3.33 in the ≥65 years group (Table 5).