The relationship between mean arterial pressure and decreased glomerular filtration rate in rural areas of Northeast China

The study was approved by the Ethics Committee of China Medical University (Shenyang, China). Liaoning Province is located in Northeast China. A representative sample of people aged ≥35 years was selected, and a cross-sectional survey was conducted from January 2012 to August 2013 to describe the sex-specific prevalence and factors associated with decreased GFR in rural areas of Liaoning Province. The study adopted a multi-stage, stratified random cluster-sampling scheme. In the first stage, three counties (Dawa, Zhangwu, and Liaoyang County) were selected from the eastern, southern, and northern regions of Liaoning province. In the second stage, one town was randomly selected from each county (a total of three towns). In the third stage, six to eight rural villages near each town were randomly selected (a total of 26 rural villages). Exclusion criteria for individuals in the present study included pregnancy, malignant tumor, or mental disorder. All the eligible permanent residents aged ≥35 years from each village were invited to join the study (a total of 14 016 participants). Of those, 11 956 participants responded and completed the study: the response rate was 85.3%. All procedures were performed in accordance with standard ethical principles. Written consent was obtained from all participants after they had been informed of the objectives, benefits, medical procedures and the confidentiality of personal information. For illiterate participants, we obtained written informed consent from their proxies. In this report, we used data obtained at baseline, and only participants with a complete set of data for the variables analyzed in the study were included, making a final sample size of 11 345 (5253 men and 6092 women).

Data were collected during a single clinic visit by trained doctors and nurses using a standard questionnaire by face-to-face interview. Before the survey was performed, all eligible investigators were invited to attend training. The training content included the purpose of this study, how to administer the questionnaire, the standard methods of measurement, the study procedures, and the importance of standardization. After the training a strict test was carried out, and only those who scored perfectly on the test could become investigators. During data collection, our inspectors received further instructions and support. There was a central steering committee with a subcommittee for quality control.

Data on demographic characteristics, lifestyle risk factors, dietary habits, family income, family history of cardiovascular disease and medical history of hypertension were obtained by interview with a standardized questionnaire. Educational level was divided into primary school or below, middle school, and high school or above. Family income was classified as ≤5000, 5000–20 000 and >20 000 CNY/year.

According to the American Heart Association protocol, the investigators performed blood pressure (BP) measurement three times for each participant at 2-min intervals after at least 5 min of rest, using a standardized automatic electronic sphygmomanometer (HEM-907; Omron), which had already been validated according to the British Hypertension Society protocol [12]. The participants were advised to avoid caffeinated beverages and exercise for at least 30 min before the measurement. During the measurement, the participants were seated with the arm supported at the level of the heart. The average of three BP measures was calculated and used in all analyses.

Weight and height were measured to the nearest 0.1 kg and 0.1 cm respectively, with the participants in lightweight clothing and without shoes. Waist circumference (WC) was measured (to the nearest 0.1 cm) at the umbilicus using a non-elastic tape, with the participants standing, at the end of normal expiration. BMI was calculated as weight in kilograms divided by the square of the height in meters.

Fasting blood samples were collected from all participants in the morning after at least 12 h of fasting. Blood samples were obtained from an antecubital vein into Vacutainer tubes containing EDTA. Fasting plasma glucose (FPG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), serum creatinine (Scr) and other routine blood biochemical indexes were analyzed enzymatically on an autoanalyzer. All laboratory equipment was calibrated, and blinded duplicate samples were used.

According to the JNC-8 report [13], hypertension is defined as SBP ≥140 mmHg and/or DBP ≥90 mmHg and/or the use of antihypertensive medications. According to the World Health Organization (WHO) criteria, BMI was categorized into three groups: normal (BMI <25 kg/m2), overweight (25 ≤ BMI <30 kg/m2) and obesity (BMI ≥30 kg/m2) [14]. Abdominal obesity was defined as WC ≥88 cm for females and WC ≥102 cm for males [15]. Dyslipidemia was defined according to the National Cholesterol Education Program-Third Adult Treatment Panel (ATP III) criteria [16]. High TC was defined as TC ≥6.21 mmol/L (240 mg/dL). Low HDL-C was defined as HDL-C < 1.03 mmol/L (40 mg/dL). High LDL-C was defined as LDL-C ≥4.16 mmol/L (160 mg/dL). High TG was defined as TG ≥2.26 mmol/L (200 mg/dL). Dyslipidemia was defined as having at least one of the disorders mentioned above. Diabetes mellitus (DM) and anemia were both diagnosed according to the WHO criteria: DM was defined as FPG ≥7 mmol/L (126 mg/dL) and/or being on treatment for diabetes; anemia was defined as a hemoglobin level <13.0 g/dL (<130 g/L) for men and <12.0 g/dL (<120 g/L) for women [17, 18]. Hyperuricaemia was determined as uric acid >422 μmol/L for men and >363μmol/L for women [19]. Drinking and smoking status were divided into current drinkers/smokers and nondrinkers/smokers. Physical activity included occupational and leisure-time physical activity. A detailed description of the methods has been presented before [20]. Occupational and leisure-time physical activity were merged and regrouped into three categories: (1) low—participants who reported light levels of both occupational and leisure-time physical activity; (2) moderate—participants who reported moderate or high levels of either occupational or leisure-time physical activity; and (3) high—participants who reported a moderate or high level of both occupational and leisure-time physical activity.

The dietary pattern was assessed using recall of foods eaten in the previous year. First, the participants were questioned on their average consumption of several food items per week. The reported consumption was quantified approximately in terms of grams per week (vegetable consumption: rarely = 3, <1000 g = 2, 1000–2000g = 1, ≥2000 g = 0; meat consumption, including red meat, fish, and poultry: rarely = 0, <250 g = 1, 250–500 g = 2, ≥500 g = 3). Subsequently, a special diet score (vegetable consumption score plus meat consumption score) was calculated for each participant (range 0–6). Higher values of the diet score indicate higher meat consumption and lower vegetable consumption and greater adherence to a Westernized diet, while lower values indicate adherence to the Chinese diet. Similar methods for calculating diet score could be found in the ATTICA study [21].

Fasting blood samples were collected as described under “Data collection and measurement”. Serum creatinine (Scr) was measured enzymatically on an autoanalyzer. The GFR was estimated using an equation originating from the CKD Epidemiology Collaboration (CKD-EPI) equation [22], which is more appropriate than the Modification of Diet in Renal Disease (MDRD) Study group equation [23]. Decreased GFR was defined as estimated GFR (eGFR) <60 ml/min/1.73 m2.

Descriptive statistics were calculated for all the variables, including continuous variables (reported as mean values and standard deviations) and categorical variables (reported as numbers and percentages). Differences among them were evaluated using Student’s t-test, ANOVA, non-parametric tests or the χ2-test as appropriate. Multivariate logistic regression analyses were used to identify independent factors and associated comorbidities of decreased eGFR, with odds ratios (ORs) and corresponding 95 % confidence intervals (CIs) calculated. All the statistical analyses were performed using SPSS version 17.0 software, and P values <0.05 were considered to be statistically significant.