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Journal of Atherosclerosis and Thrombosis logoLink to Journal of Atherosclerosis and Thrombosis
. 2017 Dec 1;24(12):1267–1281. doi: 10.5551/jat.41517

Genetic Variants of RAMP2 and CLR are Associated with Stroke

Teruhide Koyama 1,✉, Nagato Kuriyama 1, Etsuko Ozaki 1, Daisuke Matsui 1, Isao Watanabe 1, Wakiko Takeshita 1, Komei Iwai 1, Yoshiyuki Watanabe 1, Masahiro Nakatochi 2, Chisato Shimanoe 3, Keitaro Tanaka 3, Isao Oze 4, Hidemi Ito 4, Hirokazu Uemura 5, Sakurako Katsuura-Kamano 5, Rie Ibusuki 6, Ippei Shimoshikiryo 6, Naoyuki Takashima 7, Aya Kadota 7,8, Sayo Kawai 9, Tae Sasakabe 9, Rieko Okada 9, Asahi Hishida 9, Mariko Naito 9, Kiyonori Kuriki 10, Kaori Endoh 10, Norihiro Furusyo 11, Hiroaki Ikezaki 11, Sadao Suzuki 12, Akihiro Hosono 12, Haruo Mikami 13, Yohko Nakamura 13, Michiaki Kubo 14, Kenji Wakai 9
PMCID: PMC5742372  PMID: 28904253

Abstract

Aim: Stroke is associated closely with vascular homeostasis, and several complex processes and interacting pathways, which involve various genetic and environmental factors, contribute to the risk of stroke. Although adrenomedullin (ADM) has a number of physiological and vasoprotective functions, there are few studies of the ADM receptor system in humans. The ADM receptor comprises a calcitonin-receptor-like receptor (CLR) and receptor activity-modifying proteins (RAMPs). We analyzed single nucleotide polymorphisms (SNPs) in the RAMP2 and CLR genes to determine their association with stroke in the light of gene-environment interactions.

Methods: Using cross-sectional data from the Japan Multi-Institutional Collaborative Cohort Study in the baseline surveys, 14,087 participants from 12 research areas were genotyped. We conducted a hypothesis-based association between stroke prevalence and SNPs in the RAMP2 and CLR genes based on data abstracted from two SNPs in RAMP2 and 369 SNPs in CLR. We selected five SNPs from among the CLR variants (rs77035639, rs3815524, rs75380157, rs574603859, and rs147565266) and one RAMP2 SNP (rs753152), which were associated with stroke, for analysis.

Results: Five of the SNPs (rs77035639, rs3815524, rs75380157, rs147565266, and rs753152) showed no significant association with obesity, ischemic heart disease, hypertension, dyslipidemia, and diabetes. In the logistic regression analysis, rs574603859 had a lower odds ratio (0.238; 95% confidence interval, 0.076–0.745, adjusted for age, sex, and research area) and the other SNPs had higher odds ratios for association with stroke.

Conclusions: This was the first study to investigate the relationships between ADM receptor genes (RAMP2 and CLR) and stroke in the light of gene-environment interactions in human.

Keywords: Adrenomedullin, Receptor activity-modifying protein 2, Calcitonin-receptor-like receptor, Stroke

Introduction

The vascular system plays a crucial role in organ homeostasis, being essential for organ and tissue construction, the supply of oxygen and nutrients, and mobilization of inflammatory cells to regions of injury1, 2). Current and novel therapeutic approaches aimed at improving vascular function provide real benefits with respect to reducing cerebrovascular disease3). In addition, the vascular system can be considered the largest system in the body, given its length and area, and via its active secretion of bioactive molecules, plays a central role in vascular homeostasis4–6). Revealing the mechanisms underlying the functional integrity of the vascular system could lead to novel approaches to therapy and preventive medicine.

Strokes are associated closely with vascular homeostasis, and disruption of vascular function can also cause a stroke. A stroke is the clinical culmination of several complex processes and interacting pathways that involve various genetic and environmental factors7). Genetic contributions to strokes may result from common variants with small effect sizes, rare variants with large effect sizes, or their combination8). However, environmental risk factors are associated with the pathogenesis of stroke, and considerable evidence suggests that geneenvironment interactions are important9).

Adrenomedullin (ADM) is a vasoactive peptide first identified in human pheochromocytoma10). Although ADM is secreted by various organs and tissues, it is produced mainly by vascular endothelial cells and serves a number of physiological functions11–14). The ADM receptor is a seven transmembrane domain G protein-coupled receptor, called the calcitonin-receptor-like receptor (CLR)15, 16). The specificity of CLR for ADM is thought to be regulated by receptor activity-modifying proteins (RAMPs), which are membrane proteins having a single membrane-spanning domain. Analysis of genetically engineered knockout mice revealed that the ADM signal is indispensable for CLR and RAMP2 function17–20). By contrast, there have been few studies of the ADM receptor system in humans.

The present study aimed to analyze RAMP2 and CLR single nucleotide polymorphisms (SNPs) and evaluate their association with stroke in the light of gene-environment interactions. This analysis was conducted as a cross-sectional study using a large-scale pooled analysis of the Japanese general population.

Methods

Study Participants

In the present study, we evaluated participant data collected during the Japan Multi-Institutional Collaborative Cohort (J-MICC) Study from the baseline surveys using the cross-sectional data. That cohort study evaluated the general Japanese population in 12 research areas, using genetic and clinical data to detect and confirm gene-environment interactions related to lifestyle-associated diseases21). The study participants were 35–69 years old, and were enrolled after responding to study announcements in their specific research areas, attending health check-up examinations that were commissioned by their local governments, visiting local health check-up centers, or visiting a cancer hospital. A total of 14,539 participants were selected. We analyzed the data while minimizing the number of deleted participants. Each parameter was separated in the analysis because of missing data.

The J-MICC study participants included citizens, health check examiners, and first-visit patients to a cancer hospital. All participants in this study gave written informed consent. The study protocol was approved by the Ethics Committees at Aichi Cancer Center, the Nagoya University Graduate School of Medicine, and other institutions participating in the J-MICC study. The present study was conducted according to the principles expressed in the World Medical Association Declaration of Helsinki.

Lifestyle and Blood Biochemistry Data

In the present study, we evaluated the lifestyle and medical information obtained through self-administered questionnaires (alcohol consumption status, smoking habits, and physical exercise). The body mass index (BMI) was calculated as weight (kg) divided by the square of height (m2). Obesity was defined as a BMI ≥ 25.0 kg/m2. Alcohol consumption of each type of beverage was determined by the average number of drinks per day, and then converted into the Japanese sake unit, ‘gou’ (180 ml), which is equivalent to 23 g of ethanol (0, 0.1–22.9, 23.0–45.9, or ≥ 46.0 g ethanol/day). Regular physical activity was defined as three times a week and lasting over 30 minutes. Anamnesis and medication history were also assessed using a questionnaire. Information on stroke (n = 248) and ischemic heart disease (n = 403) was available from the self-administered questionnaires. Hypertension was defined as a systolic/diastolic blood pressure ≥ 140/90 mm Hg and/or current use of medication for hypertension. Dyslipidemia was defined as non-high density lipoprotein-C (HDL-C) ≥ 170 mg dl−1 and/or HDL-C < 40 mg dl−1 and/or current use of medication for dyslipidemia. Diabetes was defined as a glycated hemoglobin (HbA1c) level ≥ 6.5% and/or current use of medication for diabetes. The participants who have the absence of laboratory data and/or insufficient data were excluded in each analytic criterion.

In addition, blood chemistry data (serum levels of triglycerides, total cholesterol, HDL-C, non-HDL-C, creatinine, and HbA1c) and anthropometric data were obtained from health check-ups performed in the research areas. The estimated glomerular filtration rate (eGFR) was calculated using the following equation: eGFR (mL/min/1.73 m2) = 194 × creatinine−1.094 × age−0.287 (for men) and eGFR (mL/min/1.73 m2) = 194 × creatinine−1.094 × age−0.287 × 0.739 (for women)22). Each blood sample was centrifuged and the plasma was separated and stored at −80°C until analysis. Laboratories in each research area analyzed the serum samples.

Genotyping and Quality Control Filtering

In the study, buffy coat fractions and DNA were prepared from blood samples and stored at −80°C at the central J-MICC study office. DNA was extracted from all buffy coat fractions using a BioRobot M48 Workstation (Qiagen Group, Tokyo, Japan) at the central study office. For the samples from two areas (Fukuoka and Kyushu-KOPS), DNA was extracted locally from samples of whole blood, using an automatic nucleic acid isolation system (NA-3000, Kurabo, Co., Ltd, Osaka, Japan). The 14,539 study participants from the 12 areas of the J-MICC study were genotyped at RIKEN Center for Integrative Medicine using a Human-OmniExpressExome-8 v1.2 BeadChip array (Illumina Inc., San Diego, CA, USA). Twenty-six samples with inconsistent sex information between the questionnaire and the estimate from the genotyping results were excluded. The identity-by-descent method implemented in the PLINK 1.9 software23, 24) identified 388 closely related pairs (pi-hat > 0.1875) and one sample of each pair was excluded. Principal component analysis (PCA)25, 26) with the 1000 Genomes reference panel (phase 3)27) detected 34 subjects whose estimated ancestries were outside of the Japanese population28). These 34 samples were excluded. All the remaining 14,091 samples met a sample-wise genotype call rate criterion (≥ 0.99). SNPs with a genotype call rate < 0.98 and/or a Hardy-Weinberg equilibrium exact test P-value < 1 × 10−6 were removed, resulting in 873,254 autosomal variants. Among these, 298,644 variants with a low minor allele frequency (MAF) < 0.01 were excluded. This quality control filtering resulted in 14,091 participants and 570,162 SNPs. After genotyping, data from four participants who withdrew from follow-up were excluded from further analysis, resulting in 14,087 participants were included in the analyses.

Genotype Imputation

Genotype imputation was performed using SHA-PIT29) and Minimac330) software based on the 1000 Genomes Project cosmopolitan reference panel (phase 3)27). After the genotype imputation, variants with an R2 value < 0.3 were excluded, resulting in 12,617,547 variants. We identified variants in the RAMP2 and CLR loci, which identified two and 369 SNPs, respectively.

Statistical Analyses

We compared the associations between various genotypes and stroke, after combining the heterozygous and minor homozygous alleles because of the small number of minor homozygotes. We selected SNPs from the RAMP2 and CLR genes that exhibited a statistically significant association with stroke. Continuous variables are expressed as mean ± standard deviation (SD) and categorical data are expressed as sums and percentages. Inter-group comparisons were performed using Welch's t-tests for continuous variables, and the chi-square or Fisher's exact tests for categorical variables (sex, alcohol consumption, regular physical activity, smoking, stroke, obesity, ischemic heart disease, hypertension, dyslipidemia, and diabetes). The chisquare test was performed to examine the Hardy–Weinberg equilibrium for each locus studied. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using logistic regression analyses, in which stroke was defined as the dependent variable, and age, sex, research area, alcohol intake, current smoking, regular physical activity, obesity, hypertension, diabetes, dyslipidemia, and ischemic heart disease, were included as independent variables. The haplotype frequency and linkage disequilibrium of the SNPs were estimated using Haploview31). All statistical tests were two-tailed, and differences with a p-value < 0.05 were considered statistically significant. SPSS software (version 18.0, SPSS, Japan, Inc.) was used for all statistical analyses.

Results

Among these 14,087 participants, the mean age of the included 6337 men was 55.4 years, compared to 54.3 years for the 7750 women.

We identified two and 369 SNPs among the genetic variants of RAMP2 and CLR, respectively (Supplementary Table 1, Chromosomal locations were described based on hg19/GRCh37 coordinates). Supplementary Fig. 1 shows the linkage disequilibrium analyses of 13 CLR SNPs associated with stroke identified using the chi-square test. The position of the studied SNPs in CLR is shown. Pair-wise SNP R-squared D' linkage values (multiplied by 100) are also shown. We then selected five SNPs from among the CLR variants (rs77035639, rs3815524, rs75380157, rs574603859, and rs147565266) to avoid similar haplotypes. Similarly, RAMP2 SNP (rs753152), which is associated with stroke, was selected for analysis. The distributions of genotypes and alleles of the evaluated SNPs are summarized in Supplementary Table 2. Supplementary Fig. 2 shows exons (shown as boxes) 1–4 for RAMP2, and exons 1–15 for CLR. For analysis, we compared the associations between genotypes and stroke, after combining the heterozygous and minor homozygous alleles because of the small number of minor homozygotes alleles.

Supplementary Table 1. Genetic variants of the RAMP2 and CLR loci.

Gene chromosome Position
RAMP2 17 40913366 188216783 188225844 188236012 188252411 188269685 188287434 188298742
40913505 188216807 188225970 188236025 188252561 188269709 188287456 188298787
CLR 2 188206953 188217247 188226414 188236053 188252608 188270337 188288165 188300228
188207245 188217379 188226520 188236279 188253796 188270560 188288259 188300873
188207585 188217500 188226900 188236354 188253902 188270687 188288266 188301149
188207611 188217501 188227008 188236378 188254046 188270864 188288315 188301544
188208012 188217706 188227300 188236458 188254173 188270914 188289056 188302053
188208120 188217738 188227302 188236819 188254581 188270925 188289079 188302587
188208130 188218342 188227302 188237045 188255091 188271085 188289172 188302634
188208290 188218458 188227613 188237868 188255237 188271819 188289174 188302770
188208736 188218683 188227754 188238101 188255432 188272092 188289448 188303700
188209158 188218937 188227921 188238103 188255549 188272460 188289795 188303845
188209159 188218946 188228516 188238334 188255912 188272951 188289849 188303979
188209179 188219052 188228911 188239574 188256158 188273829 188289964 188304095
188209709 188219186 188229007 188239647 188256237 188275146 188289971 188304213
188210214 188219325 188229335 188239809 188256685 188275930 188290419 188304446
188210256 188219447 188229622 188240559 188256910 188275982 188290490 188304891
188210257 188219468 188229739 188241519 188257985 188276272 188290717 188305046
188210415 188219975 188229820 188241522 188258904 188276515 188290969 188305110
188210586 188220301 188230146 188241953 188259620 188276584 188290969 188305797
188210673 188220317 188230333 188242861 188260401 188276906 188291105 188306215
188210960 188220384 188230588 188243363 188260747 188276987 188291382 188306229
188211005 188220446 188230692 188243503 188260912 188277123 188291723 188306543
188211112 188220865 188230976 188243620 188260913 188277455 188291869 188306768
188211296 188221302 188231072 188243633 188260921 188277682 188292242 188306985
188211443 188221350 188231216 188243658 188261266 188278035 188292458 188307038
188211568 188221547 188231433 188243684 188261890 188278203 188293391 188307334
188211568 188221648 188231645 188243940 188261922 188278226 188293438 188307429
188211610 188221793 188231887 188244430 188262362 188278525 188293545 188307608
188211789 188221911 188232223 188245890 188262468 188278822 188293614 188307747
188212371 188221943 188232805 188246023 188263003 188279122 188293635 188307962
188212423 188222351 188233502 188247648 188263325 188279606 188293921 188308056
188213235 188222428 188233524 188247843 188264246 188280382 188294598 188308240
188213336 188222469 188233714 188248440 188264602 188280870 188294980 188308604
188213538 188222560 188234194 188248594 188264833 188280896 188295534 188308640
188213819 188222561 188234520 188248663 188265242 188282330 188295611 188308853
188214239 188222581 188234678 188248727 188265543 188282703 188296132 188309875
188214694 188222928 188234844 188249420 188266531 188283002 188296488 188310118
188214823 188222946 188234928 188250234 188266951 188283061 188297133 188310305
188214924 188223256 188235033 188250476 188267147 188283123 188297160 188311515
188215045 188223889 188235100 188250718 188267147 188284000 188297214 188311900
188215156 188224057 188235162 188250860 188267193 188284127 188297348 188311992
188215209 188224322 188235258 188251427 188267470 188284824 188297349
188215241 188224457 188235340 188251432 188268001 188285455 188297388
188215292 188224928 188235611 188251535 188268541 188285710 188298159
188215299 188225030 188235956 188251702 188268931 188286217 188298197
188216078 188225240 188236000 188251972 188269235 188286516 188298341
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Supplementary Fig. 1.

Supplementary Fig. 1.

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The linkage disequilibrium analyses of 13 CLR (calcitonin-receptor-like receptor) SNPs associated with stroke.

The haplotype structure and the position of the studied single nucleotide polymorphisms in the CLR gene exhibited a statistically significant association with stroke.

Supplementary Table 2. Allele and genotype frequencies of the RAMP2 and CLR genes in the participants.

SNP Allele frequency Genotype frequency n P for Hardy-Weinberg equilibrium
rs753152 (T/G)
    TT T = 0.965 T/T = 0.933 13137
    TG T/G = 0.065 921 0.003
    GG G = 0.035 G/G = 0.002 29
rs77035639 (A/G)
    AA A = 0.983 A/A = 0.965 13604
    AG A/G = 0.034 476 0.177
    GG G = 0.017 G/G = 0.001 7
rs3815524 (G/C)
    GG G = 0.940 G/G = 0.885 12464
    GC G/C = 0.110 1552 0.003
    CC C = 0.060 C/C = 0.005 71
rs75380157 (A/T)
    AA A = 0.959 A/A = 0.921 12978
    AT A/T = 0.076 1073 0.006
    TT T = 0.041 T/T = 0.003 36
rs574603859 (A/T)
    AA A = 0.975 A/A = 0.952 13405
    AT A/T = 0.047 664 0.001
    TT T = 0.025 T/T = 0.001 18
rs147565266 (T/A)
    TT T = 0.998 T/T = 0.996 14024
    TA T/A = 0.004 63 0.790
    AA A = 0.002 A/A = 0.000 0
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Supplementary Fig. 2.

Supplementary Fig. 2.

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Organization of the RAMP2 (receptor activity-modifying protein 2) and CLR (calcitonin-receptor-like receptor) genes and locations of the SNPs used in the present study. Closed boxes indicate exons and lines represent introns.

Table 1 shows the distribution of stroke for each SNP. The major homozygotes had significantly higher incidences of stroke compared with the heterozygotes and minor homozygotes, except for rs574603859. SNP rs574603859 showed an inverse ratio between major homozygotes and the other genotypes. Table 2 summarizes the baseline characteristics of the participants divided into two groups, classified by major homozygous alleles versus heterozygous and minor homozygous alleles. None of the SNPs showed a constant tendency for these characteristics. Table 3 shows the distribution of obesity, ischemic heart disease, hypertension, dyslipidemia, and diabetes for each SNP. SNP rs574603859 was associated with a higher incidence of obesity in the heterozygotes and minor homozygotes. SNP rs753152 was associated with a higher incidence of diabetes in the heterozygotes and minor homozygotes. The other four SNPs showed no significant association with these diseases.

Table 1. Genotype and allele distributions in stroke.

SNPs Chromosome: position Genotype
 p value
Major Homo Hetero+Minor Homo
rs753152 (T/G) chr17: 40913505 control (n) 12139       881       0.003
(%) 93.2%       6.8%      
Stroke (n) 219       29      
(%) 88.30%       11.7%      
rs77035639 (A/G) chr2: 188220301 control (n) 12579       441       0.020
(%) 96.6%       3.4%      
Stroke (n) 232       16      
(%) 93.5%       6.5%      
rs3815524 (G/C) chr2: 188224322 control (n) 11519       1501       0.041
(%) 88.5%       11.5%      
Stroke (n) 209       39      
(%) 84.3%       15.7%      
rs75380157 (A/T) chr2: 188271085 control (n) 12005       1015       0.002
(%) 92.2%       7.8%      
Stroke (n) 215       33      
(%) 86.7%       13.3%      
rs574603859 (A/T) chr2: 188301544 control (n) 12375       645       0.003
(%) 95.0%       5.0%      
Stroke (n) 245       3      
(%) 98.8%       1.2%      
rs147565266 (T/A) chr2: 188311515 control (n) 12967       53       0.022
(%) 99.6%       0.4%      
Stroke (n) 244       4      
(%) 98.4%       1.6%      
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Homo, homozygote; Hetero, heterozygote.

Table 2. Characteristics of study participants for each single nucleotide polymorphism (SNP).

Genotype rs753152
 p value
Major Homo
 Hetero+Minor Homo
n mean ± SD (%) n mean ± SD (%)
Sex (male) 5890 44.8% 447 47.1% 0.188
Age (year) 13137 54.8 ± 9.4 950 55.0 ± 9.5 0.462
BMI (kg/m2) 10578 23.2 ± 3.4 752 23.1 ± 3.5 0.809
Systolic blood pressure (mmHg) 10514 128 ± 20.2 747 128 ± 19.1 0.528
Diastolic blood pressure (mmHg) 10513 78.2 ± 12.3 747 77.9 ± 11.7 0.441
Triglyceride (mg/dl) 10861 128 ± 96.5 792 130 ± 94.0 0.585
Total cholesterol (mg/dl) 9947 211 ± 34.7 749 211 ± 36.0 0.821
nonHDL-C (mg/dl) 9946 148 ± 35.1 749 149 ± 36.1 0.781
HDL-C (mg/dl) 10863 62.7 ± 16.3 792 62.3 ± 15.8 0.458
Hemoglobin A1C (%) 8057 5.55 ± 0.73 581 5.61 ± 0.74 0.055
eGFR (mL/min/1.73 m2) 10509 78.8 ± 15.1 774 78.3 ± 14.9 0.374
Alcohol drinking
    0 g/d 5912 45.8% 433 46.6%
    0.1–22.9 g/d 4224 32.7% 305 32.8%
    23–45.9 g/d 1412 10.9% 101 10.9% 0.870
    46.0+ g/d 1359 10.5% 90 9.7%
Smoking 2471 18.8% 171 18.0% 0.574
Regular physical activity 3830 29.2% 289 30.5% 0.417
Genotype rs77035639
 p value
Major Homo
 Hetero+Minor Homo
n mean ± SD (%) n mean ± SD (%)
Sex (male) 6114 44.9% 223 46.2% 0.609
Age (year) 13604 54.8 ± 9.4 483 54.5 ± 9.3 0.587
BMI (kg/m2) 10937 23.2 ± 3.4 393 23.3 ± 3.2 0.562
Systolic blood pressure (mmHg) 10874 128 ± 20.1 387 128 ± 20.4 0.605
Diastolic blood pressure (mmHg) 10873 78.2 ± 12.2 387 77.6 ± 12.3 0.318
Triglyceride (mg/dl) 11246 128 ± 96.3 407 129 ± 96.0 0.868
Total cholesterol (mg/dl) 10326 211 ± 34.7 370 211 ± 36.1 0.966
nonHDL-C (mg/dl) 10325 148 ± 35.1 370 149 ± 36.5 0.879
HDL-C (mg/dl) 11248 62.7 ± 16.3 407 62.5 ± 15.6 0.795
Hemoglobin A1C (%) 8347 5.55 ± 0.73 291 5.63 ± 0.91 0.171
eGFR (mL/min/1.73 m2) 10899 78.7 ± 15.1 384 78.2 ± 13.8 0.527
Alcohol drinking
    0 g/d 6136 45.9% 209 44.5%
    0.1–22.9 g/d 4376 32.7% 153 32.6%
    23–45.9 g/d 1458 10.9% 55 11.7% 0.856
    46.0+ g/d 1396 10.4% 53 11.3%
Smoking 2548 18.7% 94 19.5% 0.682
Regular physical activity 3985 29.3% 134 27.7% 0.475
Genotype rs3815524
 p value
Major Homo
 Hetero+Minor Homo
n mean ± SD (%) n mean ± SD (%)
Sex (male) 5613 45.0% 724 44.6% 0.750
Age (year) 12464 54.8 ± 9.4 1623 54.7 ± 9.4 0.816
BMI (kg/m2) 10010 23.2 ± 3.4 1320 23.2 ± 3.3 0.950
Systolic blood pressure (mmHg) 9949 128 ± 20.1 1312 128 ± 20.1 0.868
Diastolic blood pressure (mmHg) 9948 78.3 ± 12.2 1312 77.9 ± 12.1 0.374
Triglyceride (mg/dl) 10288 128 ± 96.3 1365 130 ± 96.3 0.447
Total cholesterol (mg/dl) 9426 211 ± 34.8 1270 212 ± 34.2 0.703
nonHDL-C (mg/dl) 9425 148 ± 35.2 1270 149 ± 35.0 0.382
HDL-C (mg/dl) 10289 62.7 ± 16.3 1366 62.3 ± 15.8 0.372
Hemoglobin A1C (%) 7659 5.56 ± 0.74 979 5.55 ± 0.66 0.684
eGFR (mL/min/1.73 m2) 9966 78.8 ± 15.2 1317 78.3 ± 14.1 0.237
Alcohol drinking
    0 g/d 5615 45.8% 730 46.1%
    0.1–22.9 g/d 4007 32.7% 522 32.9%
    23–45.9 g/d 1336 10.9% 177 11.2% 0.848
    46.0+ g/d 1293 10.6% 156 9.8%
Smoking 2345 18.8% 297 18.3% 0.635
Regular physical activity 3655 29.4% 464 28.6% 0.542
Genotype rs75380157
 p value
Major Homo
 Hetero+Minor Homo
n mean ± SD (%) n mean ± SD (%)
Sex (male) 5843 45.5% 494 44.5% 0.777
Age (year) 12978 54.8 ± 9.4 1109 54.6 ± 9.4 0.629
BMI (kg/m2) 10439 23.2 ± 3.4 891 23.0 ± 3.2 0.280
Systolic blood pressure (mmHg) 10377 128 ± 20.1 884 128 ± 20.3 0.845
Diastolic blood pressure (mmHg) 10376 78.3 ± 12.2 884 77.9 ± 12.3 0.367
Triglyceride (mg/dl) 10734 128 ± 96.1 919 129 ± 98.9 0.663
Total cholesterol (mg/dl) 9829 211 ± 34.8 867 211 ± 34.4 0.888
nonHDL-C (mg/dl) 9829 148 ± 35.2 867 149 ± 34.7 0.930
HDL-C (mg/dl) 10735 62.7 ± 16.3 920 62.5 ± 15.6 0.723
Hemoglobin A1C (%) 7954 5.56 ± 0.74 684 5.56 ± 0.71 0.794
eGFR (mL/min/1.73 m2) 10393 78.8 ± 15.2 890 78.1 ± 14.0 0.190
Alcohol drinking
    0 g/d 5841 45.8% 504 46.5%
    0.1–22.9 g/d 4169 32.7% 360 33.2%
    23–45.9 g/d 1393 10.9% 120 11.1% 0.637
    46.0+ g/d 1348 10.6% 101 9.3%
Smoking 2424 18.7% 218 19.7% 0.424
Regular physical activity 3801 29.3% 318 28.7% 0.679
Genotype rs74603859
 p value
Major Homo
 Hetero+Minor Homo
n mean ± SD (%) n mean ± SD (%)
Sex (male) 6044 45.1% 293 43.0% 0.287
Age (year) 13405 54.8 ± 9.4 682 54.8 ± 9.2 0.889
BMI (kg/m2) 10778 23.1 ± 3.4 552 23.6 ± 3.5 0.001
Systolic blood pressure (mmHg) 10711 128 ± 20.1 550 130 ± 20.6 0.020
Diastolic blood pressure (mmHg) 10710 78.2 ± 12.2 550 79.0 ± 12.1 0.137
Triglyceride (mg/dl) 11082 128 ± 95.0 571 133 ± 118 0.192
Total cholesterol (mg/dl) 10183 211 ± 34.8 513 212 ± 34.4 0.871
nonHDL-C (mg/dl) 10182 148 ± 35.2 513 150 ± 34.8 0.393
HDL-C (mg/dl) 11084 62.7 ± 16.3 571 61.9 ± 15.9 0.213
Hemoglobin A1C (%) 8235 5.55 ± 0.72 403 5.59 ± 0.94 0.277
eGFR (mL/min/1.73 m2) 10732 78.7 ± 15.1 551 79.4 ± 14.7 0.302
Alcohol drinking
    0 g/d 6033 45.8% 312 46.7%
    0.1–22.9 g/d 4325 32.8% 204 30.5%
    23–45.9 g/d 1432 10.9% 81 12.1% 0.558
    46.0+ g/d 1378 10.5% 71 10.6%
Smoking 2523 18.8% 119 17.5% 0.392
Regular physical activity 3905 29.2% 214 31.4% 0.210
Genotype rs147565266
 p value
Major Homo
 Hetero+Minor Homo
n mean ± SD (%) n mean ± SD (%)
Sex (male) 6305 45.0% 32 50.8% 0.376
Age (year) 14024 54.8 ± 9.4 63 56.0 ± 8.3 0.312
BMI (kg/m2) 11277 23.2 ± 3.4 53 23.3 ± 3.2 0.739
Systolic blood pressure (mmHg) 11209 128 ± 20.1 52 133 ± 26.2 0.189
Diastolic blood pressure (mmHg) 11208 78.2 ± 12.2 52 79.6 ± 14.6 0.501
Triglyceride (mg/dl) 11599 128 ± 95.9 54 151 ± 161 0.293
Total cholesterol (mg/dl) 10644 211 ± 34.7 52 215 ± 36.6 0.446
nonHDL-C (mg/dl) 10643 148 ± 35.1 52 152 ± 39.1 0.488
HDL-C (mg/dl) 11601 62.7 ± 16.2 54 62.9 ± 21.2 0.950
Hemoglobin A1C (%) 8592 5.56 ± 0.73 46 5.63 ± 0.84 0.520
eGFR (mL/min/1.73 m2) 11230 78.7 ± 15.1 53 80.2 ± 14.2 0.482
Alcohol drinking
    0 g/d 6318 45.9% 27 43.5%
    0.1–22.9 g/d 4513 32.8% 16 25.8%
    23–45.9 g/d 1505 10.9% 8 12.9% 0.226
    46.0+ g/d 1438 10.4% 11 17.7%
Smoking 2632 18.8% 10 15.9% 0.631
Regular physical activity 4105 29.3% 14 22.2% 0.268
Open in a new tab

Homo, homozygote; Hetero, heterozygote.

Table 3. Genotype and allele distributions in obesity, ischemic heart disease, hypertension, dyslipidemia, and diabetes.

SNPs Genotype Obesity
 Ischemic heart disease
 Hypertension
 Dyslipidemia
 Diabetes
≥ 25 < 25 p value (−) (+) p value (−) (+) p value (−) (+) p value (−) (+) p value
rs753152 Major Homo 7823 2755 0.729 12006 374 0.774 6624 3889 0.432 6396 3676 0.846 7496 561 0.030
74.0% 26.0% 97.0% 3.0% 63.0% 37.0% 63.5% 36.5% 93.0% 7.0%
Hetero+ 561 191 883 29 482 265 476 278 526 55
Minor Homo 74.6% 25.4% 96.8% 3.2% 64.5% 35.5% 63.1% 36.9% 90.5% 9.5%
rs77035639 Major Homo 8097 2840 0.643 12447 388 0.678 6864 4009 0.832 6642 3810 0.414 7758 589 0.163
74.0% 26.0% 97.0% 3.0% 63.1% 36.9% 63.5% 36.5% 92.9% 7.1%
Hetero+ 287 106 442 15 242 145 230 144 264 27
Minor Homo 73.0% 27.0% 96.7% 3.3% 62.5% 37.5% 61.5% 38.5% 90.7% 9.3%
rs3815524 Major Homo 7423 2587 0.301 11399 351 0.388 6275 3673 0.878 6067 3473 0.498 7105 554 0.323
74.2% 25.8% 97.0% 3.0% 63.1% 36.9% 63.6% 36.4% 92.8% 7.2%
Hetero+ 961 359 1490 52 831 481 805 481 917 62
Minor Homo 72.8% 27.2% 96.6% 3.4% 63.3% 36.7% 62.6% 37.4% 93.7% 6.3%
rs75380157 Major Homo 7718 2721 0.632 11878 366 0.349 6541 3835 0.611 6316 3634 1.000 7381 573 0.395
73.9% 26.1% 97.0% 3.0% 63.0% 37.0% 63.5% 36.5% 92.8% 7.2%
Hetero+ 666 225 1011 37 565 319 556 320 641 43
Minor Homo 74.7% 25.3% 96.5% 3.5% 63.9% 36.1% 63.5% 36.5% 93.7% 6.3%
rs574603859 Major Homo 8001 2777 0.012 12255 385 0.803 6778 3932 0.085 6550 3759 0.575 7648 587 0.921
74.2% 25.8% 97.0% 3.0% 63.3% 36.7% 63.5% 36.5% 92.9% 7.1%
Hetero+ 383 169 634 18 328 222 322 195 374 29
Minor Homo 69.4% 30.6% 97.2% 2.8% 59.6% 40.4% 62.3% 37.7% 92.8% 7.2%
rs147565266 Major Homo 8345 2932 1.000 12833 402 1.000 7079 4129 0.111 6841 3933 0.570 7981 611 0.378
74.0% 26.0% 97.0% 3.0% 63.2% 36.8% 63.5% 36.5% 92.9% 7.1%
Hetero+ 39 14 56 1 27 25 31 21 41 5
Minor Homo 73.6% 26.4% 98.2% 1.8% 51.9% 48.1% 59.6% 40.4% 89.1% 10.9%
Open in a new tab

Homo, homozygote; Hetero, heterozygote.

To determine the relationship of the SNPs with stroke in consideration of environmental factors, a logistic regression analysis adjusted for age, sex, research area, alcohol intake, current smoking, regular physical activity, obesity, hypertension, diabetes, dyslipidemia, and ischemic heart disease was performed. For the logistic regression analysis, the major homozygous genotypes were used as the reference group and the heterozygous and minor homozygous genotypes were used as the exposed group in the dominant model. Table 4 shows model I adjusted for basic characteristics (age, sex, research area), model II adjusted for lifestyle, and model III adjusted for anamnesis. RAMP2 SNP rs753152 was associated with a significantly higher OR in model I (OR, 1.773; 95% CI, 1.194–2.634). The CLR SNPs were associated with a significantly higher OR in model I (OR, 1.448–3.735) in participants with stroke, excluding rs574603859. SNP rs574603859 had a lower OR in model I (OR, 0.238; 95% CI, 0.076–0.745) between major homozygotes and the others. The model II results were similar to those for model I. In model III, rs574603859 showed no significant OR. The lack of statistical significance when adjusting for anamnesis indicated that rs574603859 has no strong effect on the risk of stroke.

Table 4. Associations between the RAMP2 and CLR gene variants and stroke.

SNPs Model I
 Model II
 Model III
OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value
rs753152 1.773 1.194–2.634 0.005 1.784 1.200–2.652 0.004 1.767 1.059–2.947 0.029
rs77035639 2.015 1.199–3.385 0.008 2.084 1.239–3.506 0.006 2.25 1.177–4.302 0.014
rs3815524 1.448 1.023–2.051 0.037 1.482 1.046–2.099 0.027 1.908 1.259–2.891 0.002
rs75380157 1.845 1.270–2.683 0.001 1.877 1.290–2.731 0.001 2.115 1.338–3.342 0.001
rs574603859 0.238 0.076–0.745 0.014 0.245 0.078–0.768 0.016 0.276 0.068–1.126 0.073
rs147565266 3.735 1.325–10.52 0.013 3.997 1.412–11.31 0.009 5.316 1.775–15.92 0.002
Open in a new tab

Model I: adjusted for age, sex, research area

Model II: adjusted for age, sex, research area, alcohol intake, current smoking, regular physical activity, obesity

Model III: adjusted for age, sex, research area, alcohol intake, current smoking, regular physical activity, obesity, hypertension, diabetes, dyslipidemia, ischemic heart disease

Discussion

There is considerable evidence to suggest that the pathogenesis of stroke is affected by not only genetic factors, but also environment interactions9). Previous studies showed that a history of hypertension, dyslipidemia, diabetes, physical inactivity, diet, waist-to-hip ratio, current smoking, cardiac causes, and alcohol consumption were associated with risk of stroke32, 33). There was a J-shaped association between high amounts of alcohol and increased risk of both ischemic and hemorrhagic stroke34). Therefore, we defined age, sex, research area, alcohol consumption status, current smoking, regular physical activity, obesity, hypertension, diabetes, dyslipidemia, and ischemic heart disease as independent variables in the logistic regression analyses. To the best of our knowledge, this is the first study to investigate the relationships between RAMP2 and CLR and stroke in the light of gene-environment interactions in humans.

The pathogenesis of stroke is very complex and is associated closely with vascular dysfunction and disruption. Indeed, similar to chronic obstructive pulmonary disease, systemic inflammation and oxidative stress might play important roles in increasing the risk of stroke by promoting vascular dysfunction and platelet hyperactivity35). A review study showed that ADM has strong anti-oxidation and anti-inflammation activities14). Moreover, ADM acts via CLR/RAMP2 to prevent brain injury in both acute and chronic cerebral ischemia36), and exerts crucial vasoprotective effects following vascular injury37). The vascular ADM-CLR/RAMP2 system is critical in the regulation of vascular integrity, including the maintenance of vascular structure, and the regulation of angiogenesis and vasoprotection against vascular injury14, 19). Studying the ADM-CLR/RAMP2 system should reveal the mechanisms underlying the functional integrity of the vascular system, and could serve as the basis for novel approaches to therapy and preventive medicine.

RAMP expression is modulated by various agents in cell culture and in animal models of human disease38). For example, marked changes induced in the cardiovascular and renal systems provided evidence of an important role for dynamic RAMP regulation in those systems. Studies suggest that regulation of RAMPs might modulate the pathophysiology of conditions linked to RAMP-interacting G protein-coupled receptors. For example, human SNP studies described the relationship between CLR and essential hypertension and primary angle closure glaucoma39, 40). Polymorphisms in the ADM gene have also been reported to have a possible association with essential hypertension, dysglycemia, and adrenomedullin levels41–45). Genetic variants of the ADM-CLR/RAMP2 system might affect vascular homeostasis and cerebrovascular/cardiovascular disease. Several studies have revealed interactions of SNPs with stroke46–49). In these studies, the functional genetic polymorphisms were located in the promoters, which could cause differences in the plasma levels of the encoded target protein; were located in coding exons, leading to amino acid changes; or were located in an intron. Although we demonstrated that an intronlocated SNP is not functional, it could have other effects, such as influencing splicing or regulatory processes; for example, the binding of transcription factors to the gene. Furthermore, as a tag SNP, the polymorphism might be representative of many other variants, which could regulate the function of the receptor.

The limitations of our study include its cross-sectional design. However, case-control studies can be used to assess previously identified candidate regions and to determine target selections more precisely. In general, strokes can be divided into three subtypes: ischemic, lacunar, and hemorrhagic. In this study, we did not investigate the subtypes of stroke because we used a self-administered questionnaire to judge anamnesis. By contrast, a previous study reported that stroke and myocardial infarct seem sensitive enough to use self-administered questionnaire for judgment at baseline in Japanese cohort studies50). In a future (follow-up) survey, we plan to assess the participants by looking up the actual medical records; therefore, we expect these additional data will lead to further detailed analysis of genetic variants of ADM receptor genes in accordance with the stroke subtypes and cardiovascular disease. In addition, we only assessed Japanese participants in the present study, and further studies in other ethnic groups are needed to validate our findings.

Conclusions

In conclusion, the association of the RAMP2 and CLR genes with stroke in a Japanese cohort implicates these genes in the pathogenesis of stroke, although further investigation is required to confirm their associations. It will be interesting to determine whether the polymorphisms of RAMP2 and CLR are responsible for functional changes, and to reveal the underlying mechanism, given the potentially important role that ADM receptor genes play in stroke and/or vascular fragility.

Acknowledgements

The authors would like to thank Mr. Kyota Ashikawa, Ms. Tomomi Aoi, and the other members of the Laboratory for Genotyping Development, Center for Genomic Medicine, RIKEN, for support with the genotyping; and Mses. Yoko Mitsuda, Keiko Shibata, and Etsuko Kimura at the Department of Preventive Medicine, Nagoya University Graduate School of Medicine for their co- operation, technical assistance, and valuable comments.

Conflict of Interest

There are no conflicts of interest.

Sources of Funding

Funding was provided by Grant-in-Aid for Scientific Research on Priority Areas of Cancer (No. 17015018) and Grant-in-Aid for Scientific Research on Innovative Areas (No. 221S0001) from the Japanese Ministry of Education, Culture, Sports, Science and Technology. Japan Society for the Promotion of Science KAKENHI Grant Number 16H06277 supported this work.

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