The study included 78 Japanese outpatients with abdominal obesity (waist circumference, >85 cm for men and >90 cm for women) who consulted for medical care in the Metabolic Syndrome Clinic, Department of Cardioangiology, Kitasato University Hospital. All subjects provided informed consent before participating in this study, and anonymity was maintained by tracing the patients through their clinical history number.
The project was approved by the Scientific and Ethical Committee of the Kitasato University School of Medicine, Japan. BMI was calculated as weight divided by height squared. Systolic and diastolic blood pressures were measured after a rest of at least 15 min with a sphygmomanometer while subjects were in a sitting position. HOMA-IR, which was used as a measure of insulin resistance, was calculated as fasting plasma insulin (μU/mL) × glucose (mg/dL)/405 . All subjects were free from chronic inflammation, immune disease, acute coronary syndrome, renal and/or liver dysfunction, malignancy, or immune diseases.
Definitions of MetS and pre-MetS
Metabolic scores were calculated using MetS components according to the MetS criteria proposed by the Japanese Society of Internal Medicine . The score consisted of 4 independent components, including abdominal obesity, which was defined as a waist circumference of ≥85 cm in men or ≥90 cm in women; high triglyceride and/or low HDL-cholesterol levels; hypertension; and elevated fasting glucose levels. The diagnosis of hypertension was made on the basis of blood pressure levels measured at the study visit (≥130/85 mmHg) or a prior diagnosis of hypertension and current treatment with antihypertensive medications. DM and/or IFG was considered present if the subject had a history of diabetes or had a fasting glucose level of 110 mg/dL or greater. Participants who had low metabolic scores (1 or 2) were designated as the pre-MetS subjects, whereas the patients who had high metabolic scores (3 or 4) were defined as MetS subjects. There were 64 patients diagnosed as having MetS and the remaining 14 patients were designated as pre-MetS, which was defined as having only 1 component of the MetS criteria.
Serum Sample Collection
After an overnight fast, blood serum samples were collected by venipuncture from 78 patients with abdominal obesity and from 14 healthy donors. The age range of the healthy donors matched that of the patients. For the IL-18 biomarker study examining the differences in IL-18 levels between MetS patients and subjects with pre-MetS conditions, 36 patients out of the 64 patients with MetS were excluded because they were taking metabolic-mimetic agents (statins, anti-diabetic drugs, and/or colestimide).
Measurement of Clinical Biomarkers
Biochemical markers such as triglycerides, LDL cholesterol, HDL cholesterol, insulin, plasma glucose, HbA1c, uric acid, gamma-glutamyl transpeptidase (γ-GTP), CRP, and BNP were measured.
In order to examine the effects of lifestyle modification in the IL-18 biomarker study, 64 patients with MetS were recruited to lose weight in order to improve their cardiovascular risk factors. We studied 57 patients with MetS (average BMI, 31.9) who successfully lost at least 5% of their initial weight through lifestyle modification. All subjects were instructed to maintain a standard mild energy-restricted diet and to engage in walking for at least 30 min a day, 5 days a week. Serum IL-18 and adiponectin levels were measured in these subjects before and after lifestyle modification, which was maintained for 8-34 weeks.
Measurement of Adipocytokines
Circulating levels of human IL-18 and adiponectin were determined by an enzyme-linked immunosorbent assay (ELISA) using the human IL-18 ELISA Kit (MBL, Co., Ltd., Nagoya, Japan) and the CircuLex™ human adiponectin ELISA Kit (CycLex Co., Ltd., Nagano, Japan), respectively. Samples were processed according to the manufacturer's instructions [35, 50].
Continuous data are summarized as either mean (SD) or median and quartiles, and categorical data are expressed as percentages. The data were compared by an unpaired t-test or Mann-Whitney U-tests, where appropriate. Differences in the proportions of variables were determined by a chi-squared analysis. In order to evaluate the relationship between IL-18 and selected variables, we calculated Spearman correlation coefficients between the circulating levels of fasting IL-18 and the following variables: (1) conventional risk factors for CVD (i.e., LDL cholesterol, HDL cholesterol, triglycerides, HbA1c, the presence of hypertension, and current smoking status), and history of coronary artery disease; (2) measures of adiposity and insulin resistance (i.e., BMI, waist circumference, fasting blood glucose levels, insulin levels, and HOMA-IR); and (3) metabolic risk scores (i.e., abdominal obesity, hypertension, high triglycerides and/or low HDL cholesterol, and glucose intolerance or diabetes) and MetS-related co-morbid conditions (i.e., hyper-uric acidemia, fatty liver disease, chronic kidney disease, and sleep apnea syndrome).
In order to evaluate the association of IL-18 with MetS, we constructed multivariable logistic regression models in order to assess whether the circulating IL-18 levels were independently associated with MetS. We calculated the odds ratio for the presence of MetS in each IL-18 level quartile, and the participants with IL-18 levels in the lowest IL-18 quartile were considered as the reference group. Adjustments were performed for age and sex and for age, sex, and β-blocker use. Two-sided P-values of < 0.05 were considered significant.