The relationship of high sensitivity C-reactive protein to percent body fat mass, body mass index, waist-to-hip ratio, and waist circumference in a Taiwanese population

This was a community-based cross-sectional study based on data from the Taichung Community Health Study. The detailed methodology has been described elsewhere [20‐25]. A total of 2,359 residents of Taichung City, Taiwan, aged 40 and over, participated in the study in October 2004. Hs-CRP levels were measured only for the first 1669 consecutive subjects. This study was approved by the Human Research Committee of China Medical University Hospital. Written informed consent was obtained from each participant.

Anthropometric measurements and blood samples were obtained from the complete physical examination. Weight and height were measured on an auto-anthropometer (super-view, HW-666), with the subjects shoeless and wearing light clothing. BMI was derived from the formula, weight (kg) ÷ (height)² (m²). With the participant standing, WC was measured midway between the superior iliac crest and the costal margin, and hip circumference at its maximum protrusion point of the buttocks around the pelvis, and then the WHR was calculated as a measure of regional fat distribution. %FM was assessed by a body composition analyzer (Tanita BC-418, Arlington Heights, Illinois, USA). The measurements were performed with the subjects stepping onto the measuring platform without shoes and after wiping the soles of their feet. The amount of body fat was expressed as a percentage of total weight. Previous studies have confirmed the validity of BIA in estimating body composition compared to dual energy X-ray absorptiometry (DEXA) [19, 26]. The means values for BIA and DEXA were very close [26]. BIA was a good predictor of DEXA-derived fat-free mass (r = 0.85-0.88) and was superior to BMI in measuring body fat [19]. The principle of BIA is that fat tissue exhibits greater resistance to the flow of electrical current than fat-free tissue because of differences in water content.

Blood pressure was measured by an electronic device (COLIN, VP-1000, Japan) three times after the subjects had rested for 20 minutes. The lowest systolic and diastolic blood pressure was recorded. Blood was drawn from an antecubital vein in the morning after a 12-hour overnight fasting and was sent for analysis within four hours of blood collection. Hs-CRP levels were measured by nephelometry, a latex particle-enhanced immunoassay (TBA-200FR, Tokyo, Japan). The interassay and intra-assay CVs were <2.0% and <1.9%, respectively. The lower detection limit of the assay was 0.1 mg/L. Biochemical markers such as fasting plasma glucose, HDL-C, and triglyceride were analyzed by a biochemical autoanalyzer (Beckman Coulter Synchron system, Lx-20, Fullerton, CA, USA) at the Clinical Laboratory Department of China Medical University Hospital. Fasting plasma glucose was measured in blood obtained by use of NAF TUBE. NAF TUBE contains 5 mg sodium fluoride to inhibit glucose metabolism and 4 mg potassium oxalate to chelate calcium and prevent coagulation. The interassay and intra-assay CVs for fasting plasma glucose were 4% and 4%, respectively. We measured cholesterol and triglyceride in serum mode. A SST tube was used. The SST™ tube refers to the Serum Separator Tube containing clot activator and serum separator gel. The silica particles that coat the walls of the BD Vacutainer^® SST™ tube are the clot activator. Initial activation occurs when blood enters the tube and contacts the particles on the tube wall. Triglyceride levels were determined using an enzymatic colorimetric method. The interassay and intra-assay CVs for triglyceride were 6.8% and 5%, respectively. The HDL-C level was measured by a direct HDL-C method and the interassay and intra-assay CVs were 4.5% and 4.5%, respectively. The low-density lipoprotein cholesterol (LDL-C) level was also measured using a direct LDL-C method, and interassay and intra-assay CVs were 4.5% and 3%, respectively. The serum insulin level was measured by a commercial enzyme-linked immunosorbent assay kit (Diagnostic Products, Los Angeles, CA). The interassay CV for insulin was 8.7% and the intra-assay CV was 3.4%. Insulin sensitivity was estimated with a Homeostasis Model Assessment (HOMA-IR) equation. The HOMA-IR equals fasting serum insulin (μU/ml) times fasting plasma glucose (mmol/l) divided by 22.5 [27]. The cut-offs defining abnormality of triglycerides, HDL-C, blood pressure, and fasting glucose were in accordance with the AHA/NHLBI statement that was used to define the four components of MetS [28]: elevated triglycerides (≥150 mg/dL), reduced HDL-C (< 40 mg/dL for men, <50 mg/dL for women), elevated blood pressure (BP ≥130/≥85 mmHg), and elevated fasting glucose (≥100 mg/dL).

Data regarding smoking, alcohol drinking, physical activity, betel nut chewing and family history of cardiovascular-related diseases were collected by questionnaire when the participants underwent a complete physical examination. Subjects who self-reported any of these characteristics (smoking, alcohol drinking, betel nut chewing and family history of cardiovascular-related diseases) were placed into groups based on the specific characteristic. Those in the non-smoking group had never smoked or had smoked less than 100 cigarettes during their lifetime, whereas those in smoking group smoked currently or had smoked more than or equal to 100 cigarettes during their lifetime. Individuals who self-reported alcohol drinking, betel nut chewing or exercise were classified into the group with this specific characteristic. Those whose parents or siblings had a specific cardiovascular-related disease were classified as having a family history of the disease.

Continuous variables were reported as means, whereas categorical variables were reported as numbers (percentage). The cutoff points for WC [28] were 90 (cm) for men and 80 (cm) for women, and for WHR [29] were 0.9 in men and 0.85 in women. The value of the cutoff point of BMI for defining overweight was 24 [30]. We defined the subjects in the upper quartile of the %FM and hs-CRP distributions as the high category group. To examine the relationship between obesity indicators and hs-CRP, crude odds ratios (ORs) were first used; then, multivariate logistic regression analysis was used to investigate the independent effect on the high level of hs-CRP, adjusting for age, smoking, alcohol drinking, and betel nut chewing. Finally, variables for abnormality of the MetS components other than obesity were entered into models. ORs and their 95% confidence intervals were calculated. The areas under curve (AUCs) for each receiver operating characteristics curve were calculated to compare the relative ability of obesity indicators to correctly classify subjects with a high level of hs-CRP, and the nonparametric method was used to test whether the AUCs of these four obesity indicators were different [31]. All analyses were conducted using SAS version 9.1 (SAS Institute Inc, Cary, NC). A two-sided significant level of p < 0.05 was reported.