Methods

In the Tanno-Sobetsu Study, a study with a population-based cohort design, a total of 617 Japanese subjects aged older than 30 years (male/female: 260/357, mean age: 66 ± 13 years) were recruited from residents of 2 rural towns, Tanno and Sobetsu, in Hokkaido, the northernmost island of Japan, in 2011. Subjects who were being treated with any medications were excluded, and subjects who were not on any medication (n = 265, male/female: 98/167) were enrolled in the present study. This study conformed to the principles outlined in the Declaration of Helsinki and was performed with the approval of the Ethical Committee of Sapporo Medical University. Written informed consent was received from all the study subjects.

Medical checkups were performed between 06:00 and 09:00 hours after an overnight fast. After measuring anthropometric parameters, blood pressure was measured twice consecutively on the upper arm using an automated sphygmomanometer (HEM-907; Omron Co., Kyoto, Japan) with subjects in a seated resting position, and average blood pressure was used for analysis. Body mass index (BMI) was calculated as body weight (in kilograms) divided by the square of body height (in meters). Peripheral venous blood samples were obtained from study subjects after physical examination for complete blood count and biochemical analyses. Samples of the serum and plasma were analyzed immediately or stored at −80°C until biochemical analyses.

The concentration of FABP4 was measured using a commercially available enzyme-linked immunosorbent assay kit for FABP4 (Biovendor R&D, Modrice, Czech Republic). The accuracy, precision, and reproducibility of the kit have been described previously. The intraassay and interassay coefficients of variation in the kit were <5%. PCSK9 level was measured using a commercially available enzyme-linked immunosorbent assay kit for PCSK9 (R&D Systems, Minneapolis, Minnesota). Plasma glucose was determined by the glucose oxidase method. Fasting plasma insulin was measured by a chemiluminescent enzyme immunoassay method. Hemoglobin A1c (HbA1c) was determined by a latex coagulation method and was expressed in National Glycohemoglobin Standardization Program Scale. Creatinine, blood urea nitrogen (BUN), uric acid, aspartate transaminase, alanine aminotransferase (ALT), γ-glutamyl transpeptidase, and lipid profiles, including total cholesterol, high-density lipoprotein cholesterol, and triglycerides, were determined by enzymatic methods. LDL cholesterol level was calculated by the Friedewald equation. Brain natriuretic peptide (BNP) was measured using an assay kit (Shionogi & Co., Osaka, Japan). High-sensitivity C-reactive protein (hsCRP) was measured by a nephelometry method. Homeostasis model assessment of insulin resistance (HOMA-R), an index of insulin resistance, was calculated by the previously reported formula: HOMA-R = insulin (μU/ml) × glucose (mg/dl)/405. As an index of renal function, estimated glomerular filtration rate (eGFR) was calculated by an equation for Japanese: eGFR (ml/min/1.73 m ² ) = 194 × Cr ^(−1.094) × age ^(−0.287) × 0.739 (if female).

Numeric variables are expressed as means ± SD for normal distributions or medians (interquartile ranges) for skewed variables. The distribution of each parameter was tested for its normality using the Shapiro–Wilk W test, and nonnormally distributed parameters were logarithmically transformed for regression analyses. Comparison between 2 groups was done with the Mann–Whitney U test. The correlation between 2 variables was evaluated using Pearson’s correlation coefficient. Multivariate regression analysis was performed to identify independent determinants of FABP4 and PCSK9 using the variables with a significant and nonconfounding correlation in simple regression analysis as independent predictors, showing the t-ratio calculated as the ratio of regression coefficient and standard error of regression coefficient and the percentage of variance in the object variables that the selected independent predictors explained (R ² ). Several models for independent determinants of FABP4 were prepared using all or different combinations of parameters as independent variables for calculation of both regression coefficients and Akaike Information Criterion. Among the candidate models, the best fit model using Akaike Information Criterion for each dependent variable was selected. A p value of <0.05 was considered statistically significant. All data were analyzed using JMP 9 for Macintosh (SAS Institute, Cary, North Carolina).

Results

Characteristics of the 265 recruited subjects with no medication (male/female: 98/167) are presented in Table 1 . The number of current smokers was 53 (20.0%). The number of subjects with suffering hypertension and diabetes mellitus were 95 (35.8%), and 5 (1.9%), respectively. Male subjects were significantly older than female subjects, and they had significantly larger BMI and waist circumference and had higher levels of triglycerides, glucose, HOMA-R, HbA1c, BUN, creatinine, uric acid, aspartate transaminase, ALT, γGTP, and hsCRP and lower levels of pulse rate, total cholesterol, LDL cholesterol, high-density lipoprotein cholesterol, BNP, and FABP4 than did female subjects. No significant difference in systolic and diastolic blood pressures, insulin, eGFR, or PCSK9 was found between male and female subjects.

Serum FABP4 level was positively correlated with age, BMI, waist circumference, systolic and diastolic blood pressures, pulse rate, and levels of total cholesterol, LDL cholesterol, triglycerides, glucose, insulin, HOMA-R, HbA1c, BUN, BNP, hsCRP, and PCSK9 and was negatively correlated with level of eGFR ( Table 2 ). Similar correlations between the parameters were observed when male and female subjects were separately analyzed. Multiple regression analysis showed that gender, waist circumference, pulse rate, total cholesterol, eGFR, and PCSK9 were independently correlated with FABP4 level, explaining a total of 53.5% of the variance in this measure ( Table 3 ).

As presented in Table 4 , PCSK9 level was positively correlated with BMI, waist circumference, systolic and diastolic blood pressures, and levels of total cholesterol, LDL cholesterol ( Figure 1 ), triglycerides, insulin, HOMA-R, HbA1c, ALT, γGTP, and FABP4 ( Figure 1 ). Similar correlations between PCSK9 level and the parameters except for age were found when male and female subjects were separately analyzed ( Table 4 ). Interestingly, a significant and gender-different correlation between PCSK9 level and age was found: negatively in male subjects ( r = −0.250, p = 0.013; Figure 1 ) and positively in female subjects ( r = 0.183, p = 0.018; Figure 1 ). After adjustment of age, gender, and LDL cholesterol level, PCSK9 was independently correlated with BMI, waist circumference, systolic and diastolic blood pressures, and levels of triglycerides, insulin, HOMA-R, HbA1c, γGTP, and FABP4 ( Table 4 ).

Table 4

Simple and adjusted multiple regression analyses for log PCSK9 (n = 265)

	Total (n = 265)		Male (n = 98)		Female (n = 167)		Adjustment ∗
	r	P	r	P	r	P	r	P
Age	0.001	0.984	-0.250	0.013	0.183	0.018	–	–
Body mass index	0.161	0.009	0.214	0.034	0.147	0.058	2.33	0.021
Waist circumference	0.140	0.023	0.222	0.028	0.115	0.140	2.09	0.038
Systolic blood pressure	0.131	0.033	0.135	0.186	0.133	0.086	2.04	0.042
Diastolic blood pressure	0.159	0.010	0.268	0.008	0.099	0.205	2.35	0.020
Pulse rate	0.006	0.925	-0.074	0.478	0.059	0.464	0.03	0.978
Total cholesterol	0.182	0.003	0.241	0.017	0.130	0.093	1.55	0.121
LDL cholesterol	0.151	0.014	0.194	0.057	0.108	0.163	–	–
HDL cholesterol	-0.040	0.516	-0.093	0.362	-0.046	0.551	-0.83	0.409
log Triglycerides	0.315	<0.001	0.387	<0.001	0.313	<0.001	5.26	<0.001
log Glucose	0.119	0.054	-0.020	0.843	0.238	0.002	2.09	0.037
log Insulin	0.207	0.001	0.204	0.044	0.225	0.003	2.13	0.034
log HOMA-R	0.212	0.001	0.186	0.067	0.256	0.001	3.34	0.001
HbA1c	0.158	0.010	0.037	0.716	0.487	0.001	2.40	0.017
Blood urea nitrogen	0.013	0.836	-0.032	0.753	0.069	0.378	0.27	0.785
Creatinine	-0.057	0.354	0.010	0.920	-0.070	0.371	-0.72	0.470
eGFR	-0.011	0.864	0.060	0.555	-0.050	0.524	0.13	0.893
Uric acid	0.084	0.172	0.157	0.123	0.103	0.184	1.67	0.097
log AST	0.047	0.442	0.106	0.299	0.034	0.659	0.75	0.454
log ALT	0.126	0.041	0.178	0.080	0.145	0.062	1.90	0.059
log γGTP	0.179	0.003	0.274	0.006	0.188	0.015	3.30	0.001
log BNP	-0.026	0.673	-0.163	0.108	0.063	0.421	-0.43	0.669
log hsCRP	0.064	0.310	0.033	0.757	0.108	0.171	1.07	0.284
log FABP4	0.198	0.001	0.208	0.040	0.178	0.021	2.66	0.008

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