The authors have read the journal’s policy and have the following conflicts: the authors TN and MI are employees of Toyota Motor Co., Ltd. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
Conceived and designed the experiments: MN Y. Yasuda Y. Yoshida RK TN MI YK MA NH TK HO MH S. Kato MY S. Maruyama S. Matsuo HH. Performed the experiments: Y. Yasuda Y. Yoshida TN S. Maruyama. Analyzed the data: MN YU S. Kawai RK MA HH. Wrote the paper: MN HH.
Although many single nucleotide polymorphisms (SNPs) have been identified to be associated with metabolic syndrome (MetS), there was only a slight improvement in the ability to predict future MetS by the simply addition of SNPs to clinical risk markers. To improve the ability to predict future MetS, combinational effects, such as SNP—SNP interaction, SNP—environment interaction, and SNP—clinical parameter (SNP × CP) interaction should be also considered. We performed a case-control study to explore novel SNP × CP interactions as risk markers for MetS based on health check-up data of Japanese male employees. We selected 99 SNPs that were previously reported to be associated with MetS and components of MetS; subsequently, we genotyped these SNPs from 360 cases and 1983 control subjects. First, we performed logistic regression analyses to assess the association of each SNP with MetS. Of these SNPs, five SNPs were significantly associated with MetS (
Metabolic syndrome (MetS) is characterized by a clustering of metabolic abnormalities, including central obesity, insulin resistance, dyslipidemia, and hypertension; moreover, it has been identified as a common precursor to the development of cardiovascular disease (CVD) [
To explore risk markers for predicting MetS development, many studies have been performed utilizing health check-up data among different groups of people, such as company employees [
In some studies, several combinations of clinical risk markers have also been explored based on health check-up data. We have previously shown that the combination of the γ-glutamyl transpeptidase (γ-GTP) level and WBC count was the most significant combinatorial risk marker associated with MetS based on the health check-up data of company employees [
It is also known that genetic factors contribute to the development of MetS and MetS components (e.g., central obesity, insulin resistance, dyslipidemia, and hypertension). Recently, many single nucleotide polymorphisms (SNPs) that are associated with MetS and MetS components have been identified through candidate gene studies [
In this study, we performed a case-control study to explore novel SNP × CP interactions as risk markers for MetS based on health check-up data of Japanese male employees. We selected 99 candidate SNPs that were previously reported to be associated with MetS, MetS components, and coronary atherosclerosis. Subsequently, we screened SNPs that were significantly associated with MetS and explored SNP × CP interactions for association with MetS development. The explored interaction effect demonstrated in this study is expected to be utilized as a risk marker for MetS development. By combining conventional CP and SNP data, we can estimate the risk of future MetS development.
This study is case-control study for MetS, and part of an ongoing cohort, prospective observational study of MetS and chronic kidney disease (CKD). This original study has been following 33776 participants who underwent annual health check-ups for Toyota Motor Co., Ltd in both 2001 and 2009. Of these volunteers, 360 case subjects and 1983 control subjects who satisfied the definitions of cases or controls and attended health check-ups in 2011 or 2012 were randomly enrolled. Health check-up data were collected in 2001 and 2009, and DNA samples were obtained from case and control subjects in 2011 or 2012. This study was performed according to the guidelines of the Declaration of Helsinki. The study protocol was approved by Human Genome, Gene Analysis Research Ethics Committee of Nagoya University School of Medicine, and all participants provided written informed consent.
This study is a case-control study, and subjects who satisfied the definitions of cases or controls and attended health check-ups in 2011 or 2012 were randomly enrolled for this study. Health check-up data were collected in 2001 and 2009, and case / control groups were defined post hoc in 2009, while individuals meeting the criteria for MetS in 2001 were excluded. We used the criteria proposed by the Examination Committee of Criteria for the Metabolic Syndrome in Japan [
The health examinations performed in 2001 and 2009 included physical measurements and serum biochemical measurements. Physical measurements of height, weight, and BMI were measured in the fasting state. Waist circumference was only measured in 2009. SBP and DBP were measured in the sitting position. Blood samples were obtained from subjects who had fasted for serum biochemical measurements. After the subject had rested for 10 min in the sitting position, 14 mL of blood was collected from the antecubital vein into tubes containing ethylenediaminetetraacetic acid (EDTA). After blood samples were sent to a clinical laboratory testing company, biochemical measurements were determined according to standard laboratory procedures. The study included the biochemical measurements of the following: (1) lipids: total cholesterol, triglyceride, and HDL-cholesterol; (2) carbohydrates: FBS; (3) hematology: red blood cell (RBC) count, white blood cell (WBC) count, hemoglobin (Hb), and platelet (PLT) count; (4) non-protein nitrogenous compounds: uric acid (UA); and (5) serum enzymes: aspartate aminotransferase (AST), alanine aminotransferase (ALT), and γ-glutamyl transpeptidase (γ-GTP).
Using public databases, such as PubMed and Online Mendelian Inheritance in Man, we selected 99 candidate SNPs that have been characterized and are associated with coronary atherosclerosis or vasospasm, obesity, hypertension, dyslipidemia, diabetes mellitus, hyperuricemia, or renal disease based on a comprehensive overview of vascular biology, coagulation and fibrinolysis cascades, platelet and leukocyte biology, as well as lipid and glucose metabolism and other metabolic factors (
All SNPs were genotyped using the DigiTag2 assay [
This study is a case-control study based on a prospective cohort data. The aim of this study is to identify interactions between SNPs and CPs from the 2001 data that successfully predicted MetS that was diagnosed in 2009. The explored interaction effect is expected to be utilized as a risk marker for MetS development. From the combined data of conventional CPs from 2001 and the SNPs, the risk of MetS development by 2009 could be estimated for each subject. To reduce false-positive interactions, we applied a two-step approach. We initially performed a screening analysis using the 98 SNPs. In this screening analysis, the cutoff
The Hardy-Weinberg equilibrium was assessed using the Fisher’s exact test. Simple comparison of characteristics between case and control groups was carried out using the Mann—Whitney U test, Fisher’s exact test, and Student’s
The characteristics of the study subjects in 2001 and 2009 are shown in Tables
Characteristic | Case (n = 360 males) | Control (n = 1983 males) | ||||
---|---|---|---|---|---|---|
In 2001 | In 2009 | In 2001 | In 2009 | In 2001 | In 2009 | |
Age (years) | 42 (37, 46) | 50 (45, 54) | 41 (33, 45) | 49 (41, 53) | 1.93 ×10–9 | 1.93 ×10–9 |
BMI (kg/m2) | 24.6 (23.1, 26.5) | - | 21.6 (20.0, 23.3) | - | 1.28 ×10–78 | - |
Waist circumference (cm) | - | 90.0 (87.0, 95.0) | - | 79.0 (74.0, 83.0) | - | 1.99 ×10–160 |
SBP (mmHg) | 123 (114, 130) | 133.5 (129, 138) | 114 (107, 122) | 116 (107, 123) | 4.84 ×10–34 | 2.64 ×10–113 |
DBP(mmHg) | 79 (72, 84) | 86 (80, 89) | 71 (65, 77) | 73 (66, 78) | 1.33 ×10–37 | 2.60 ×10–93 |
Total cholesterol (mg/dL) | 205 (183, 229) | 221 (196, 247) | 184 (163, 208) | 200 (180, 222) | 2.17 ×10–20 | 9.43 ×10–24 |
HDL-cholesterol (mg/dL) | 54 (45, 63) | 49 (42, 58) | 63 (54, 74) | 60 (51, 71) | 9.90 ×10–30 | 2.34 ×10–40 |
Triglyceride (mg/dL) | 130 (92, 195) | 185 (154, 238) | 76 (56, 107) | 81 (59, 114) | 2.00 ×10–65 | 3.74 ×10–128 |
FBS (mg/dL) | 95 (89, 102) | 101 (91, 115) | 90 (85, 96) | 90 (85, 96) | 9.79 ×10–22 | 7.72 ×10–52 |
RBC (×104/μL) | 491 (468, 513.3) | 489 (465.8, 513) | 476 (455, 499) | 468 (446, 491) | 4.86 ×10–14 | 2.70 ×10–22 |
WBC (×103/μL) | 7.0 (5.8, 8.2) | 6.4 (5.5, 7.7) | 6.0 (5.1, 7.2) | 5.5 (4.7, 6.6) | 2.42 ×10–18 | 1.59 ×10–24 |
Hb (g/dL) | 15.5 (15.0, 16.2) | 15.6 (14.9, 16.1) | 15.1 (14.6, 15.7) | 15 (14.3, 15.5) | 5.44 ×10–18 | 1.21 ×10–25 |
PLT (×104/μL) | 25.0 (21.9, 28.9) | 24.4 (21.0, 28.5) | 23.6 (20.7, 27.1) | 22.9 (20.0, 26.4) | 2.02 ×10–6 | 1.25 ×10–7 |
UA (mg/dL) | 6.3 (5.5, 7.2) | 6.3 (5.5, 7.3) | 5.8 (5.1, 6.5) | 5.8 (5.0, 6.5) | 1.92 ×10–14 | 3.11 ×10–16 |
AST (IU/L) | 23 (19, 28) | 24 (20, 31) | 20 (17, 24) | 20 (17, 23) | 3.56 ×10–13 | 1.58 ×10–34 |
ALT (IU/L) | 28 (20.8, 39) | 31 (22, 47) | 20 (15, 27) | 18 (14, 25) | 5.71 ×10–33 | 3.70 ×10–62 |
42.5 (28, 69) | 53 (35, 87) | 26 (18, 42) | 28 (20, 44) | 1.05 ×10–29 | 2.40 ×10–51 |
Values are medians (1st quartile, 3rd quartile). The abbreviations of the characteristics: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBS, fasting blood sugar; RBC, red blood cell; WBC, white blood cell; Hb, hemoglobin; PLT, platelet; UA, uric acid; AST, aspartate aminotransferase; ALT, alanine aminotransferase; γ-GTP, gamma-glutamyl transpeptidase.
Characteristic | Case (n = 360 males) | Control (n = 1983 males) | ||||
---|---|---|---|---|---|---|
In 2001 | In 2009 | In 2001 | In 2009 | In 2001 | In 2009 | |
MetS component | ||||||
Obesity, n (%) | 157 (43.6) | 360 (100) | 188 (9.5) | 239 (12.1) | 4.61 ×10–50 | < 1.00 ×10–200 |
Raised blood pressure, n (%) | 128 (35.6) | 326 (90.6) | 236 (11.9) | 264 (13.3) | 3.12 ×10–25 | 3.30 ×10–189 |
Raised FBS, n (%) | 33 (9.2) | 131 (36.4) | 45 (2.3) | 58 (2.9) | 5.35 ×10–9 | 7.60 ×10–71 |
Dyslipidemia, n (%) | 151 (41.9) | 306 (85.0) | 192 (9.7) | 197 (9.9) | 2.17 ×10–45 | 3.45 ×10–186 |
Number of MetS components | 1.3 ± 0.8 | 3.1 ± 0.3 | 0.3 ± 0.6 | 0.4 ± 0.7 | 4.10 ×10–130 | < 1.00 ×10–200 |
Number of MetS components excluding obesity | 0.9 ± 0.7 | 2.1 ± 0.3 | 0.2 ± 0.5 | 0.3 ± 0.5 | 5.10 ×10–85 | < 1.00 ×10–200 |
Categorical data are n values (%). The numbers of MetS components are mean ± SD.
We initially assessed the association between MetS diagnosed in 2009 and 98 genotyped SNPs. Five out of 98 SNPs were found to be significantly associated with MetS (
SNP | Chr | Position (GRCh37) | Near genes | Minor/major alleles | HWE |
N | MAF | Logistic regression analysis | |||
---|---|---|---|---|---|---|---|---|---|---|---|
Case | Control | Case | Control | OR (95%CI) | |||||||
rs2544390 | 2 | 170,204,846 | LRP2 | C/T | 0.231 | 360 | 1983 | 0.450 | 0.498 | 0.84 (0.71–0.98) | 0.027 |
rs1800592 | 4 | 141,493,961 | UCP1, TBC1D9 | G/A | 0.772 | 360 | 1982 | 0.456 | 0.502 | 0.83 (0.70–0.97) | 0.022 |
rs662799 | 11 | 116,663,707 | APOA5 | G/A | 0.515 | 360 | 1983 | 0.368 | 0.327 | 1.21 (1.03–1.43) | 0.023 |
rs7965413 | 12 | 6,234,889 | VWF | T/C | 0.770 | 360 | 1980 | 0.400 | 0.452 | 0.81 (0.69–0.96) | 0.012 |
rs1411766 | 13 | 110,252,160 | MYO16, IRS2 | T/C | 0.515 | 360 | 1982 | 0.131 | 0.102 | 1.31 (1.03–1.67) | 0.030 |
HWE, Hardy Weinberg equilibrium; MAF, minor allele frequency; OR, odds ratio; CI, confidence interval.
HWE
OR and
OR value represents increased risk of MetS per minor allele copy in each SNP.
Based on the results of the screening analysis, we focused on the five SNPs listed in
Model term | Model 1 | Model 2 | Model 3 | |||
---|---|---|---|---|---|---|
OR (95%CI) | OR (95%CI) | OR (95%CI) | ||||
SNP | 0.78 (0.66–0.92) | 0.004 |
0.84 (0.70–1.02) | 0.076 | 0.82 (0.68–0.98) | 0.033 |
CP | 1.35 (1.20–1.52) | 1.17×10–6 |
1.26 (1.10–1.44) | 8.22×10–4 |
1.31 (1.15–1.49) | 4.14×10–5 |
Interaction | 1.33 (1.12–1.58) | 0.001 |
1.32 (1.08–1.60) | 0.006 |
1.35 (1.12–1.63) | 0.002 |
PLT, platelet; OR, odds ratio.
OR value for SNP term represents increased risk of MetS per minor allele T copy in rs7965413.
OR value for CP term represents increased risk of MetS per one standard deviation (SD) change in log10(PLT).
OR value for interaction term represents increased risk of MetS per one SD change in log10(PLT) × minor allele T copy in rs7965413.
*
†
Furthermore, we transformed PLT count into a dichotomous value based on the median value of platelet count across all subjects, which was equal to 23.8×104/μL and assessed an interaction effect between SNP rs7965413 and dichotomous PLT for MetS (
The horizontal dashed line indicates the null value (odds ratio (OR) = 1.0). OR represents risk of MetS development in the group with each genotype of rs7965413 and each dichotomous platelet count compared with the group with the CC genotype and PLT count of < 23.8×104/μL. *:
Finally, we assessed the association of rs7965413 with PLT count in each group of cases and controls. In the case group, the minor allele T of rs7965413 was significantly positively associated with PLT. In the control group, the minor allele T was significantly negatively associated with PLT (
Group | N | Linear regression analysis | Heterogeneity | ||
---|---|---|---|---|---|
P value | |||||
Case | 360 | 0.19 ± 0.08 | 0.013 |
90.2 | 0.001 |
Control | 1980 | -0.07 ± 0.03 | 0.021 |
PLT, platelet;
Multiple linear regression analysis was performed with adjustment for age.
*
We performed a case-control study of MetS based on the health check-up data of Japanese male employees and found significant associations of five SNPs with MetS, including
As shown in Tables
As shown in
As shown in
We found the association of rs7965413 with PLT count in both case and control groups. A GWAS recently showed that an SNP upstream of
It was also reported that variants in the
As shown in
Our study has some limitations. First, this is a case-control and exploratory study that does not establish a cause-and-effect relationship. Future studies are thus necessary to evaluate the predictive potential of SNP × CP interactions as risk markers in prospective cohorts. We assumed that the interaction is linear; that is, the per-allele effect of an SNP changes across the continuous spectrum of a CP. However, if the interaction effect is nonlinear or a threshold effect exists, in which case the association would only be present in one extreme of the CP distribution, this analysis is not suitable and other analytical methods should be applied.
In conclusion, our data demonstrate associations of five SNPs with MetS and of an interaction between SNP rs7965413 and platelet count for MetS. Our results reveal new insight into PLT count as a risk marker for MetS.
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The authors would like to thank Dr. Nao Nishida and Prof. Katsushi Tokunaga in the Department of Human Genetics, Graduate School of Medicine, University of Tokyo, for their technical assistance on genotyping. The authors also would like to thank Ms. Michiyo Hiraoka in Innovative Research Center for Preventative Medical Engineering for her technical support on genotyping, and Ms. Michiko Katagiri, Ms. Michiyo Tanaka, Ms. Mieko Torii, and Ms. Chika Nagata in Aichi Dietetic Association for their technical support.