Utility of body mass index and neck circumference to screen for metabolic syndrome in Thai people

This prospective cross-sectional study was a part of the wider Healthy Ageing Khon Kaen University (KKU) Campus Project. We included were KKU employees and their relatives who were aged ≥50 years old and resided in Khon Kaen province. Eligible participants were excluded if there was loss to follow up or incomplete data in medical records. The complete details of the study population are described elsewhere [15].

Metabolic syndrome (MetS) was identified using the index criteria from the International Diabetes Foundation (IDF) [8] that requires for South Asians (based on a Chinese, Malay and Asian-Indian population), Chinese, and Japanese “central obesity as waist circumference (WC) ≥90 cm in men and ≥80 cm in women plus any two of the following four factors: (1) hypertriglyceridemia: fasting plasma triglycerides ≥150mg/dL, (2), reduced HDL cholesterol: fasting HDL cholesterol <40 mg/dL in men and <50 mg/dL in women, or specific treatment for this abnormality, (3) raised blood pressure: blood pressure ≥130/85 mmHg or treatment for previously diagnosed hypertension, (4) a hyperglycemic fasting glucose level of ≥100 mg/dL, or treatment for previously diagnosed type 2 diabetes”.

We collected baseline data including age, sex, comorbid diseases, blood pressure as described previously [15]. “Blood pressure was measured after each participant had been lying for 10 min. Anthropometric measures were taken while subjects were lightly clothed and wore no shoes. Waist circumferences (WC) were taken midway between the inferior margin of the last rib and the iliac crest at the end of expiration. Neck circumference (NC) was measured with head erect and eyes facing forward, horizontally at the upper margin of the laryngeal prominence (Adam’s apple). Measurements of standing height were performed without shoes with a stadiometer. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). Fasting blood samples were collected to measure including blood sugar, uric acid, lipid profiles, and liver function profiles.”

We invited all KKU personnel and their relatives aged ≥50 years to participate as described previously [15]. The volunteer participants completed required written informed consent forms, and were scheduled for an appointment at a special outpatient clinic in Srinagarind Hospital, Khon Kaen, Thailand. We collected baseline descriptive data from consecutive participants from March 21st, 2012 to April 28th, 2015. Physicians conducted a systemic physical examination of participants including blood pressure, and collected blood samples for fasting blood sugar levels, uric acid levels, lipid profiles, and liver function profiles. A flow diagram of participants is shown in Figure 1.

Figure 1

Sample size calculations were based on the areas under receiver operating characteristic (ROC) curves (AUC) [29]. As described earlier, ROC curves were used to summarize the diagnostic accuracy of the tests. This method varied the sample size until a sufficiently small standard error (SE) of the area under the ROC curve was achieved. A web-based calculator (www.anaesthetist.com/mnm/stats/roc/#stderr) was used to determine the SE. Ultimately, a sample size of at least 500 participants of men and women was found to be adequate to conduct the trial at an AUC of 0.8 and SE of 0.02 [15].

Demographic data and anthropometric variables were defined with descriptive statistics. Univariate logistic regression was used to examine the magnitude of factors associated with MetS as unadjusted odds ratios (OR) and 95% confidence intervals (CI). Pearson’s correlation coefficients were calculated to quantify associations between anthropometric measures (WC, BMI, and NC). The ROC curves were used to summarize the overall accuracy of the anthropometric measures for MetS detection. Subsequently, optimal cut-off points were determined. The performance of the BMI and NC were indicated by the sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and likelihood ratios.

All of the data analyses were conducted by using STATA (version 10.0; StataCorp, College Station, TX, USA).

MetS was found in around 40% of participants. Participants with MetS were significantly older, had greater BMI, NC, uric acid and ALT levels than participants without MetS as shown in Table 1. Simple correlations between all anthropometric indices (WC, BMI, and NC) in both sexes are presented in Figure 2. They all showed positive linear correlations, which were statistically significant (P < 0.001). The BMI (r = 0.83 in women and 0.88 in men) was better correlated to WC than the NC in either sex (r = 0.62 in women and 0.72 in men).

Figure 2

Table 1

Baseline data of studied population

The overall performance of the BMI and the NC in both sexes using the AUCs are shown in Figures 3 and 4. In general, BMI was better than NC in predicting metabolic syndrome particularly in men; the AUC for predicting metabolic syndrome of the BMI was 0.88 (95%CI 0.84, 0.91) and the NC was 0.79 (95%CI 0.75, 0.84) in women, and for men, the AUC of the BMI was 0.91 (95%CI 0.87, 0.95) and the NC was 0.84 (95%CI 0.79, 0.90). The performance of the BMI and NC including the cutoff points for all variables corresponding to the criterion values with the best compromises are shown in Table 2.

Figure 3

Figure 4

Table 2

Performance of BMI and NC to predict metabolic syndrome by sex