Insulin-Like Growth Factor-1 and Receptor Contribute Genetic Susceptibility to Hypertension in a Han Chinese Population

Insulin-Like Growth Factor-1 and Receptor Contribute Genetic Susceptibility to Hypertension in a... Abstract BACKGROUND Insulin-like growth factor 1 (IGF-1) and IGF-1 receptor (IGF-1R) have been suggested to mediate the pathophysiological response to elevated blood pressure. This study aims to evaluate the association of IGF-1 and IGF-1R with hypertension. METHODS Overall, 2,012 hypertensive cases and 2,210 controls were included in a case–control study, and 10 tagging single nucleotide polymorphisms (tagSNPs) were selected. The association of these SNPs with hypertension was further evaluated in a follow-up analysis and in an adolescent population. RESULTS A case–control study indicated that rs1815009 and rs2654981 in IGF-1R were significantly associated with hypertension, with odds ratios of 0.89 (P = 0.009) and 1.19 (P = 0.034), respectively, after adjusting for covariates. Stratification analyses revealed significant associations with hypertension (P < 0.05) for rs35767 in normal weight and obese populations; for rs2229765 in individuals <55 years of age and in overweight and nondrinking populations; and for rs2002880 in overweight and drinking populations. In a follow-up study, rs13379905 in IGF-1R was associated with hypertension incidence (hazard ratio, HR = 1.24, P = 0.042). This association was more significant in individuals with a hypertensive family history (HR = 2.10, P = 0.001). The association of rs13379905 with prehypertension and hypertension was further replicated in adolescent males (P = 0.005). Significant associations with hypertension incidence (P < 0.05) were observed for rs6219 in individuals <55 years of age and among those with obesity and a hypertensive family history as well as rs2002880 in obese individuals. CONCLUSIONS Our findings suggest that IGF-1R may contribute to the genetic susceptibility to hypertension, with BMI, age, and family history of hypertension all potentially modulating the genetic effects of IGF-1 on hypertension. association study, blood pressure, IGF-1, IGF-1R, hypertension, polymorphism Elevated blood pressure (BP) has been recognized as a leading factor for the development and progression of cardiovascular diseases,1 which contributes significantly to disease and mortality around the world.2 Hypertension is involved in a set of highly heterogeneous disorders, and genetic determinants are believed to interact with environmental factors, such as sodium intake, alcohol, smoking, and overweight, in the etiology of this condition.3 The heritability of hypertension ranges from 30% to 60%.4 Recently, genome-wide association studies in a Han Chinese population revealed insulin-like growth factor 1 (IGF-1) as a new susceptibility gene for hypertension.5 IGF-1 is a downstream mediator of growth hormone (GH), which is important for growth and metabolism via the GH-IGF-1 axis. IGF-1 shares 50% homology and some receptors with insulin.6 Approximately 70% of circulating IGF-1 in the body is synthesized in the liver, while IGF-1Rs are hardly expressed in this organ or in adipose tissue.7 IGF-1 is a potent mitogen of vascular smooth muscle cells, mediating the contractile function of these cells by promoting proliferation and migration.8 Studies have illustrated that IGF-1 exerts a vasodilatory effect by combining with IGF-1R and thereby activating phosphatidylinositol 3-kinase (PI3K). In turn, PI3K phosphorylates endothelial nitric oxide synthase, resulting in the production of nitric oxide.9 IGF-1R is a member of the insulin tyrosine kinase class of cell-surface receptors6 and is the main biological target for both IGF-1, IGF-2, and insulin. These findings highlight the important role of IGF-1 in regulating endothelial dysfunction and in the relaxation of vascular smooth muscle. Animal research has revealed a site-specific insertional mutation in exon 3 of IGF-1 that contributes to a significant elevation of arterial pressure in homozygous mice.10 A systematic review reported a U-shaped association between IGF-1 levels and hypertension risk.11 The common variant −1411C>T (rs35767) in the IGF-1 upstream promoter P1 was reported to be significantly associated with a 27% lower risk of hypertension as well as lower systolic BP (SBP) and diastolic BP (DBP) in normotensive individuals.12 Previous studies have shown limited evidence of a relationship between IGF-1 and hypertension in the Han Chinese populations.5 In the present study, we evaluated the association of 10 tagging single nucleotide polymorphisms (tagSNPs) in IGF-1 and IGF-1R with hypertension in a case–control study. The positive associations of these variants with hypertension were further replicated in both a follow-up study and a study of adolescents. METHODS Study population Case–control study of adult subjects. A total of 4,222 participants aged 35 to 75 years were recruited from a rural population in 2009 (Yixing, Jiangsu). The study population included 2,012 hypertension cases and 2,210 normotensive subjects.13 Follow-up study of adult subjects. Ninety-four elderly matched controls for the case–control study were excluded. Thus, 2,116 healthy controls were followed for a median of 5.01 years (from May 2009 to January 2016). The endpoint events of incident hypertension were collected. Face-to-face and telephone interviews were performed to ascertain disease status and vital information. Hospital records and death certificates from the local public health department were reviewed and verified by a study-wide endpoint assessment committee at Yixing Hospital. The further replication stage consisted of the analysis of an adolescent population (Yixing, Jiangsu) using an epidemiological cluster sampling approach in 2014. A total of 3,787 children and adolescents aged 6–16 years were investigated. According to “The Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents”,14 normal BP was defined as both SBP and DBP <90th percentile; prehypertension was defined as either SBP or DBP between ≥the 90th and ≤95th percentile; and hypertension was defined as either SBP or DBP the >95th percentile. Height was recognized as a key covariate associated with BP levels.15 The Z-scores of SBP and DBP in children were calculated according to the method and data (www.cdc.gov/growthcharts/) provided by the US Centers for Disease Control and Prevention. BP was transformed into BP z-scores using age, gender, and height. Individuals with missing blood samples for genotyping (n = 215) or with the missing BP, age, gender, and/or height (n = 21) values were also excluded. Ultimately, 3,551 children, including 2,975 normotensive, 282 prehypertensive, and 294 hypertensive subjects were selected to replicate the association generated from the adult population. The study protocol was approved by the ethics committee of Nanjing Medical University, and informed consent was obtained from all subjects. Data collection All subjects completed a standard questionnaire (including demographic characteristics and medical history) and underwent physical examinations, including the determination of weight, height, and BP, by trained research staff. BP measurements were obtained 3 times with the participant in the sitting position after 5 minutes of rest, according to a standard protocol recommended by the American Heart Association.16 The participants were advised to avoid alcohol, cigarette smoking, coffee/tea, and exercise for at least 30 minutes before their BP measurements. A 5-ml venous blood sample was collected after at least 10 hours of fasting to measure plasma total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) levels, and glucose. The participants were asked about their smoking and drinking habits. Smoking was defined as at least 20 cigarettes per week for 3 months per year. Drinking was defined as at least 2 times per week for 6 months per year. SNP selection The IGF-1 (Gene ID: 3479) maps onto chromosome 12 at q23.2, spans 84.65 kb and consists of 7 exons. The IGF-1R (gene ID: 3480) is located on chromosome 15 at q26.3 and spans 315 kb. We searched for SNPs in IGF-1 and IGF-1R starting from the 2kb upstream to the 1kb downstream. We selected tagSNPs from the database of the Chinese Han population in Beijing, using the human reference genome (GRCh37, http://browser.1000genomes.org/Multi/Search/Results). All tagSNPs with minor allele frequency ≥0.05 and linkage disequilibrium r2 ≥0.8 were selected. Furthermore, we applied a functional candidate strategy to search for functional SNPs with roles in transcription, regulation, or splicing using an online SNP selection tool (SNPINFO, https://snpinfo.niehs.nih.gov/). Blood sampling and SNP genotyping Blood samples were collected into ethylenediamine tetraacetic acid-containing tubes. The DNA was extracted using a standard phenol-chloroform method17 and stored at −20 °C until required for batch genotyping. SNP genotyping was performed using TaqMan technology and an ABI 7900HT Fast Real-Time PCR System (Applied BioSystems, Foster City, CA). The primers and TaqMan-MGB probes were ordered from Applied BioSystems and Nanjing BioSteed BioTechnologies (Nanjing, China). The identification of individual genotypes was performed using Sequence Detection System 2.1 software (95% autoCaller confidence level).The PCR conditions were 50 °C for 2 minutes, 95 °C for 10 minutes, 95 °C for 15 seconds, and 60 °C for 1 minutes, with 45 cycles (except for rs2002880, which required 33 cycles). The agreement rate with the HapMap database genotypes was greater than 99.7%. Detection of serum IGF-1 and IGF-1R levels Serum IGF-1 and IGF-1R levels were measured for randomized samples, including 137 cases who did not receive hypotensive drugs and 159 healthy controls. The levels were measured using enzyme-linked immunosorbent assays (Catalogue No.CSB-E04580h, CSB-E13766h) from CUSABIO Laboratories. The values did not exhibit a normal distribution; thus, the IGF-1 (lnIGF-1) and IGF-1R levels were log-transformed. The IGF-1R levels did not display a normal distribution after transformation, so the nonparametric test was then used. Statistical analysis Unpaired Student’s t-test was used to test the differences in all the measured variables presented as the mean ± SD between cases and controls. Hardy–Weinberg equilibrium was evaluated using Fisher’s exact chi-square (χ2) test in the control groups. The qualitative variables and the allele and genotype frequency distributions between groups were compared using the chi-square (χ2) test. Additionally, a general linear model was applied to compare the BP levels (mean ± SD) of carriers of the considered genotypes among the cases and controls. Multiple unconditional logistic regression analysis was applied to estimate the risk of hypertension after adjusting for potential confounding factors. The odds ratio (OR) and 95% confidence intervals (CI) were used to test for association in the case–control study, and the hazard ratio (HR) and 95% CI by Cox’s proportional hazard regression analysis were used to estimate the risk of IGF-1, IGF-1R, and hypertension in the follow-up study. All of the statistical analyses were performed with SPSS version 15.0 (SPSS, Chicago, IL). A 2-tailed P <0.05 was defined as statistically significant. RESULTS Characteristics of the adult participants The demographic and clinical characteristics of participants are summarized in Supplementary Table S1. No significant differences in sex, HDL-C level, smoking, and drinking status were observed between the normotensive and hypertensive groups. The hypertension cases had significantly higher SBP, DBP, BMI, TC, TG, LDL-C, and glucose levels compared with controls (P < 0.05). Association analyses of case–control population All of the SNPs in the control individuals were in Hardy–Weinberg equilibrium, except for rs2002880 (P < 0.05). rs1815009 and rs2654981 of IGF-1R were statistically correlated with hypertension. After adjusting for age, gender, TC, TG, LDL-C, HDL-C, glucose, BMI, smoking, and drinking, the ORs (95% CIs) were 0.89 (0.81–0.97) and 0.83 (0.72–0.96) for the additive (TT vs. TC vs. CC) and dominant (TT vs. TC + CC) models of rs1815009, respectively, and 1.19 (1.01–1.41) for the recessive (AA + AG vs. GG) model of rs2654981 (Table 1). Further stratification analyses showed that (Table 2), the rs35767 G>A variant of IGF-1 was associated with hypertension in the normal weight and obese population, with ORs and 95% CIs of 0.83 (0.72–0.96) and 1.35 (1.02–1.77), respectively. Among individuals <55 years of ages, the additive model of rs1815009, the dominant model of rs2229765 and the recessive model of rs2654981 were associated with hypertension after adjustment for covariates, with P values of 0.021, 0.023, and 0.025, respectively. In the male and nonsmoking group, the rs1815009 was significantly associated with hypertension (P = 0.048, 0.018). Both the rs1815009 and rs2229765 were statistically associated with hypertension in overweight and nondrinking populations, as was rs2002880 in the drinking and overweight populations (P < 0.05). All of the postadjustment P values and ORs (95%CI) are listed in Supplementary Table S2. Table 1. Association analyses of 10 IGF-1 and IGF-1R SNPs with hypertension in a case–control study Gene  SNP  Group  MM/Mm/mm  Genotype OR (95% CI)a  Allele gene  Pc  Additive  Dominate  Recessive  Major/minor  OR (95% CI)/Pb  IGF-1  rs5742612  Case  1004/833/174  1.00 (0.91–1.11)  1.00 (0.88–1.13)  1.02 (0.81–1.27)  2,841/1,181  1.01 (0.92–1.11)  0.804    (A>G)  Control  1104/918/186  P = 0.972  P = 0.966  P = 0.877  3,126/1,290  P = 0.879      rs6218  Case  1124/766/121  0.99 (0.89–1.10)  0.99 (0.88–1.13)  0.95 (0.73–1.23)  3,014/1,008  1.00 (0.90–1.10)  0.645    (A>G)  Control  1234/838/135  P = 0.814  P = 0.932  P = 0.674  3,306/1,108  P = 0.966      rs35767  Case  882/923/205  0.96 (0.87–1.05)  0.97 (0.86–1.10)  0.87 (0.71–1.07)  2,687/1,333  0.95 (0.87–1.04)  0.834    (G>A)  Control  948/997/257  P = 0.347  P = 0.666  P = 0.191  2,893/1,511  P = 0.266      rs6214  Case  590/967/454  0.97(0.89–1.06)  0.92(0.80–1.06)  1.01(0.87–1.18)  2,147/1,875  0.98 (0.90–1.07)  0.555    (C>T)  Control  609/1109/480  P = 0.500  P = 0.239  P = 0.899  2,327/2,069  P = 0.682      rs6219  Case  1343/595/72  1.01(0.90–1.13)  0.99 (0.87–1.13)  1.13 (0.800–1.61)  3,281/739  1.01(0.91–1.13)  0.648    (C>T)  Control  1474/664/70  P = 0.914  P = 0.885  P = 0.480  3,612/804  P = 0.834    IGF-1R  rs1815009  Case  549/1002/458  0.89(0.81–0.97)  0.83 (0.72–0.96)  0.88 (0.75–1.02)  2,100/1,918  0.91 (0.83–0.99)  0.148    (T>C)  Control  530/1138/540  P = 0.009  P = 0.014  P = 0.079  2,198/2,218  P = 0.022      rs2229765  Case  785/944/281  0.92(0.84–1.01)  0.89 (0.78–1.02)  0.90 (0.75–1.07)  2,514/1,506  0.93 (0.85–1.01)  0.998    (G>A)  Control  812/1051/340  P = 0.070  P = 0.088  P = 0.234  2,675/1,731  P = 0.085      rs2654981  Case  656/959/395  1.07(0.98–1.17)  1.03 (0.90–1.18)  1.19 (1.01–1.41)  2,271/1,749  1.05 (0.96–1.14)  0.181    (T>C)  Control  723/1107/377  P = 0.151  P = 0.703  P = 0.034  2,533/1861  P = 0.287      rs13379905  Case  1768/234/9  0.90 (0.75–1.08)  0.91 (0.75–1.10)  0.62 (0.26–1.52)  3,770/252  0.93 (0.78–1.10)  0.222    (C>T)  Control  1917/274/14  P = 0.241  P = 0.308  P = 0.301  4,108/302  P = 0.378      rs2002880  Case  1712/256/42  1.11 (0.95–1.29)  1.09 (0.91–1.30)  1.49 (0.92–2.42)  3,680/340  1.10 (0.94–1.28)  0.000    (G>A)  Control  1895/279/32  P = 0.189  P = 0.346  P = 0.107  4,069/343  P = 0.251    Gene  SNP  Group  MM/Mm/mm  Genotype OR (95% CI)a  Allele gene  Pc  Additive  Dominate  Recessive  Major/minor  OR (95% CI)/Pb  IGF-1  rs5742612  Case  1004/833/174  1.00 (0.91–1.11)  1.00 (0.88–1.13)  1.02 (0.81–1.27)  2,841/1,181  1.01 (0.92–1.11)  0.804    (A>G)  Control  1104/918/186  P = 0.972  P = 0.966  P = 0.877  3,126/1,290  P = 0.879      rs6218  Case  1124/766/121  0.99 (0.89–1.10)  0.99 (0.88–1.13)  0.95 (0.73–1.23)  3,014/1,008  1.00 (0.90–1.10)  0.645    (A>G)  Control  1234/838/135  P = 0.814  P = 0.932  P = 0.674  3,306/1,108  P = 0.966      rs35767  Case  882/923/205  0.96 (0.87–1.05)  0.97 (0.86–1.10)  0.87 (0.71–1.07)  2,687/1,333  0.95 (0.87–1.04)  0.834    (G>A)  Control  948/997/257  P = 0.347  P = 0.666  P = 0.191  2,893/1,511  P = 0.266      rs6214  Case  590/967/454  0.97(0.89–1.06)  0.92(0.80–1.06)  1.01(0.87–1.18)  2,147/1,875  0.98 (0.90–1.07)  0.555    (C>T)  Control  609/1109/480  P = 0.500  P = 0.239  P = 0.899  2,327/2,069  P = 0.682      rs6219  Case  1343/595/72  1.01(0.90–1.13)  0.99 (0.87–1.13)  1.13 (0.800–1.61)  3,281/739  1.01(0.91–1.13)  0.648    (C>T)  Control  1474/664/70  P = 0.914  P = 0.885  P = 0.480  3,612/804  P = 0.834    IGF-1R  rs1815009  Case  549/1002/458  0.89(0.81–0.97)  0.83 (0.72–0.96)  0.88 (0.75–1.02)  2,100/1,918  0.91 (0.83–0.99)  0.148    (T>C)  Control  530/1138/540  P = 0.009  P = 0.014  P = 0.079  2,198/2,218  P = 0.022      rs2229765  Case  785/944/281  0.92(0.84–1.01)  0.89 (0.78–1.02)  0.90 (0.75–1.07)  2,514/1,506  0.93 (0.85–1.01)  0.998    (G>A)  Control  812/1051/340  P = 0.070  P = 0.088  P = 0.234  2,675/1,731  P = 0.085      rs2654981  Case  656/959/395  1.07(0.98–1.17)  1.03 (0.90–1.18)  1.19 (1.01–1.41)  2,271/1,749  1.05 (0.96–1.14)  0.181    (T>C)  Control  723/1107/377  P = 0.151  P = 0.703  P = 0.034  2,533/1861  P = 0.287      rs13379905  Case  1768/234/9  0.90 (0.75–1.08)  0.91 (0.75–1.10)  0.62 (0.26–1.52)  3,770/252  0.93 (0.78–1.10)  0.222    (C>T)  Control  1917/274/14  P = 0.241  P = 0.308  P = 0.301  4,108/302  P = 0.378      rs2002880  Case  1712/256/42  1.11 (0.95–1.29)  1.09 (0.91–1.30)  1.49 (0.92–2.42)  3,680/340  1.10 (0.94–1.28)  0.000    (G>A)  Control  1895/279/32  P = 0.189  P = 0.346  P = 0.107  4,069/343  P = 0.251    “M” is the major allele and “m” is the minor allele. Abbreviations: BMI, body mass index; CI, confidence interval; GLU, glucose; HDL-C, high-density lipoprotein cholesterol; HWE, Hardy–Weinberg equilibrium; IGF-1R, insulin-like growth factor 1 receptor; LDL-C, low-density lipoprotein cholesterol; OR, odds ratio; SNP, single nucleotide polymorphism; TC, total cholesterol; TG, triglycerides. aAdjusted for age, sex, TC, TG, HDL-C, LDL-C, GLU, BMI, drinking, and smoking. bP value of the χ2 test for comparison of allele frequencies between case and control groups. cP value of the χ2 test for HWE in controls. View Large Table 2. Stratification analyses by age, sex, smoking, drinking, and BMI for association between SNPs and hypertension SNP  Stratum  Group  MM/Mm/mm  Genotype OR (95% CI)  Additive  Dominate  Recessive  rs35767      GG/GA/AA          Normal weight  Case  382/372/75  0.83 (0.72–0.96)  0.83 (0.69–1.00)  0.70 (0.52–0.95)      Control  479/521/146  P = 0.011  P = 0.047  P = 0.023    Obesity  Case  147/160/46  1.35 (1.02–1.77)  1.46 (1.01–2.11)  1.48 (0.82–2.67)      Control  94/75/18  P = 0.035  P = 0.043  P = 0.194  rs1815009      TT/TC/CC          <55 years  Case  154/267/111  0.82 (0.70–0.97)  0.72 (0.56–0.93)  0.85 (0.65–1.12)      Control  195/463/199  P = 0.021  P = 0.012  P = 0.24    Male  Case  224/408/196  0.87 (0.76–1.00)  0.79 (0.63–0.99)  0.87 (0.70–1.10)      Control  199/453/232  P = 0.048  P = 0.041  P = 0.239    Nonsmoking  Case  429/762/339  0.88 (0.80–0.98)  0.82 (0.70–0.97)  0.87 (0.74–1.04)      Control  411/870/394  P = 0.018  P = 0.021  P = 0.128    Nondrinking  Case  437/805/345  0.89 (0.80–0.98)  0.86 (0.73–1.01)  0.85 (0.72–1.01)      Control  428/893/411  P = 0.024  P = 0.07  P = 0.061    Overweight  Case  216/399/162  0.81 (0.70–0.94)  0.77 (0.61–0.97)  0.75 (0.58–0.96)      Control  182/399/198  P = 0.006  P = 0.029  P = 0.020  rs2229765      GG/GA/AA          <55 years  Case  225/240/68  0.85 (0.72–1.01)  0.77 (0.61–0.96)  0.93 (0.67–1.30)      Control  308/425/122  P = 0.065  P = 0.023  P = 0.680    Nondrinking  Case  629/747/212  0.90 (0.81–0.99)  0.88 (0.76–1.02)  0.83 (0.68–1.02)      Control  644/814/269  P = 0.037  P = 0.099  P = 0.073    Overweight  Case  320/367/90  0.81 (0.70–0.95)  0.79 (0.64–0.98)  0.71 (0.52–0.96)      Control  282/378/118  P = 0.007  P = 0.031  P = 0.025  rs2654981      TT/TC/CC          <55 years  Case  166/255/112  1.15 (0.98–1.35)  1.08 (0.85–1.37)  1.39 (1.04–1.85)      Control  276/440/140  P = 0.097  P = 0.545  P = 0.025    Overweight  Case  237/387/153  1.17 (1.01–1.35)  1.18 (0.95–1.47)  1.30 (0.99–1.70)      Control  264/387/128  P = 0.041  P = 0.144  P = 0.057  rs2002880      GG/GA/AA          Drinking  Case  362/48/12  1.27 (0.91–1.78)  1.13 (0.76–1.68)  5.12 (1.52–17.2)      Control  410/62/4  P = 0.165  P = 0.56  P = 0.008    Overweight  Case  664/94/20  1.23 (0.96–1.58)  1.17 (0.87–1.58)  2.51 (1.12–5.65)      Control  678/92/9  P = 0.103  P = 0.293  P = 0.026  SNP  Stratum  Group  MM/Mm/mm  Genotype OR (95% CI)  Additive  Dominate  Recessive  rs35767      GG/GA/AA          Normal weight  Case  382/372/75  0.83 (0.72–0.96)  0.83 (0.69–1.00)  0.70 (0.52–0.95)      Control  479/521/146  P = 0.011  P = 0.047  P = 0.023    Obesity  Case  147/160/46  1.35 (1.02–1.77)  1.46 (1.01–2.11)  1.48 (0.82–2.67)      Control  94/75/18  P = 0.035  P = 0.043  P = 0.194  rs1815009      TT/TC/CC          <55 years  Case  154/267/111  0.82 (0.70–0.97)  0.72 (0.56–0.93)  0.85 (0.65–1.12)      Control  195/463/199  P = 0.021  P = 0.012  P = 0.24    Male  Case  224/408/196  0.87 (0.76–1.00)  0.79 (0.63–0.99)  0.87 (0.70–1.10)      Control  199/453/232  P = 0.048  P = 0.041  P = 0.239    Nonsmoking  Case  429/762/339  0.88 (0.80–0.98)  0.82 (0.70–0.97)  0.87 (0.74–1.04)      Control  411/870/394  P = 0.018  P = 0.021  P = 0.128    Nondrinking  Case  437/805/345  0.89 (0.80–0.98)  0.86 (0.73–1.01)  0.85 (0.72–1.01)      Control  428/893/411  P = 0.024  P = 0.07  P = 0.061    Overweight  Case  216/399/162  0.81 (0.70–0.94)  0.77 (0.61–0.97)  0.75 (0.58–0.96)      Control  182/399/198  P = 0.006  P = 0.029  P = 0.020  rs2229765      GG/GA/AA          <55 years  Case  225/240/68  0.85 (0.72–1.01)  0.77 (0.61–0.96)  0.93 (0.67–1.30)      Control  308/425/122  P = 0.065  P = 0.023  P = 0.680    Nondrinking  Case  629/747/212  0.90 (0.81–0.99)  0.88 (0.76–1.02)  0.83 (0.68–1.02)      Control  644/814/269  P = 0.037  P = 0.099  P = 0.073    Overweight  Case  320/367/90  0.81 (0.70–0.95)  0.79 (0.64–0.98)  0.71 (0.52–0.96)      Control  282/378/118  P = 0.007  P = 0.031  P = 0.025  rs2654981      TT/TC/CC          <55 years  Case  166/255/112  1.15 (0.98–1.35)  1.08 (0.85–1.37)  1.39 (1.04–1.85)      Control  276/440/140  P = 0.097  P = 0.545  P = 0.025    Overweight  Case  237/387/153  1.17 (1.01–1.35)  1.18 (0.95–1.47)  1.30 (0.99–1.70)      Control  264/387/128  P = 0.041  P = 0.144  P = 0.057  rs2002880      GG/GA/AA          Drinking  Case  362/48/12  1.27 (0.91–1.78)  1.13 (0.76–1.68)  5.12 (1.52–17.2)      Control  410/62/4  P = 0.165  P = 0.56  P = 0.008    Overweight  Case  664/94/20  1.23 (0.96–1.58)  1.17 (0.87–1.58)  2.51 (1.12–5.65)      Control  678/92/9  P = 0.103  P = 0.293  P = 0.026  “M” is the major allele and “m” is the minor allele. Adjusted for age, sex, TC, TG, HDL-C, LDL-C, GLU, BMI, drinking, and smoking. Abbreviations: BMI, body mass index; CI, confidence interval; GLU, glucose; HDL-C, high-density lipoprotein cholesterol; IGF-1R, insulin-like growth factor 1 receptor; LDL-C, low-density lipoprotein cholesterol; OR, odds ratio; SNP, single nucleotide polymorphism; TC, total cholesterol; TG, triglycerides. View Large Association analyses of incident hypertension in the follow-up study In the follow-up study, 613 instances of hypertension were recorded, with an incidence density of 6,570 per 105 person-years. Cox’s proportional hazard regression analysis analyses showed that rs13379905 in IGF-1R was associated with an increased risk of hypertension (HR = 1.19, P = 0.088), with a HR of 1.24 (P = 0.042) after adjustment for age, sex, TC, TG, LDL-C, HDL-C, T2DM, BMI, smoking, and drinking (Table 3). Significant associations of rs13379905 with incident hypertension were observed in nondrinking (P = 0.009) individuals and among those with a family history of hypertension (P = 0.001) (Table 4). Table 3. Association between IGF-1 and IGF-1R genotypes and the risk of hypertension in a follow-up study Gene  SNP  Genotype  Hypertension  Follow-up year  Incidence density  HR (95% CI)  (/105 person-years)  Additive  Dominate  Recessive  IGF-1  rs5742612  AA  288  4,465  6,450  1.04 (0.92–1.18)  1.07 (0.91–1.26)  0.99 (0.75–1.32)      AG  272  3,776  7,203  P = 0.528  P = 0.396  P = 0.947      GG  53  751  7,057          rs6218  AA  328  4,953  6,622  1.00 (0.88–1.14)  1.01 (0.86–1.19)  0.96 (0.70–1.33)      AG  243  3,466  7,011  P = 0.999  P = 0.907  P = 0.815      GG  41  568  7,218          rs35767  GG  268  3,868  6,929  0.99 (0.88–1.12)  0.98 (0.83–1.15)  1.02 (0.79–1.31)      GA  272  4,064  6,693  P = 0.906  P = 0.798  P = 0.88      AA  71  1,052  6,749          rs6214  CC  153  2,467  6,202  1.03 (0.92–1.15)  1.10 (0.91–1.32)  1.00 (0.83–1.20)      CT  305  4,498  6,781  P = 0.573  P = 0.33  P = 0.955      TT  153  2,020  7,574          rs6219  CC  430  5,990  7,179  0.94 (0.81–1.10)  0.95 (0.79–1.13)  0.83 (0.49–1.41)      CT  168  2,733  6,147  P = 0.442  P = 0.531  P = 0.483      TT  14  2,67  5,243        IGF-1R  rs1815009  TT  142  2,144  6,623  0.96 (0.85–1.07)  0.93 (0.77–1.12)  0.96 (0.80–1.15)      TC  312  4,633  6,734  P = 0.448  P = 0.443  P = 0.637      CC  159  2,219  7,165          rs2229765  GG  231  3,303  6,994  0.97 (0.86–1.09)  0.96 (0.81–1.13)  0.95 (0.76–1.18)      GA  287  4,247  6,758  P = 0.565  P = 0.630  P = 0.646      AA  94  1,433  6,560          rs2654981  TT  209  2,993  6,983  1.05 (0.94–1.18)  1.06 (0.89–1.25)  1.09 (0.88–1.34)      TC  295  4,460  6,614  P = 0.376  P = 0.507  P = 0.424      CC  108  1,535  7,036          rs13379905  CC  520  7,814  6,655  1.24 (1.01–1.52)  1.26 (1.01–1.58)  1.32 (0.55–3.19)      CT  86  1,109  7,755  P = 0.042  P = 0.041  P = 0.538      TT  5  58  8,621          rs2002880  GG  533  7,705  6,918  0.95 (0.77–1.17)  0.93 (0.73–1.19)  1.03 (0.51–2.07)      GA  71  1,157  6,137  P = 0.630  P = 0.567  P = 0.941      AA  8  126  6,349        Gene  SNP  Genotype  Hypertension  Follow-up year  Incidence density  HR (95% CI)  (/105 person-years)  Additive  Dominate  Recessive  IGF-1  rs5742612  AA  288  4,465  6,450  1.04 (0.92–1.18)  1.07 (0.91–1.26)  0.99 (0.75–1.32)      AG  272  3,776  7,203  P = 0.528  P = 0.396  P = 0.947      GG  53  751  7,057          rs6218  AA  328  4,953  6,622  1.00 (0.88–1.14)  1.01 (0.86–1.19)  0.96 (0.70–1.33)      AG  243  3,466  7,011  P = 0.999  P = 0.907  P = 0.815      GG  41  568  7,218          rs35767  GG  268  3,868  6,929  0.99 (0.88–1.12)  0.98 (0.83–1.15)  1.02 (0.79–1.31)      GA  272  4,064  6,693  P = 0.906  P = 0.798  P = 0.88      AA  71  1,052  6,749          rs6214  CC  153  2,467  6,202  1.03 (0.92–1.15)  1.10 (0.91–1.32)  1.00 (0.83–1.20)      CT  305  4,498  6,781  P = 0.573  P = 0.33  P = 0.955      TT  153  2,020  7,574          rs6219  CC  430  5,990  7,179  0.94 (0.81–1.10)  0.95 (0.79–1.13)  0.83 (0.49–1.41)      CT  168  2,733  6,147  P = 0.442  P = 0.531  P = 0.483      TT  14  2,67  5,243        IGF-1R  rs1815009  TT  142  2,144  6,623  0.96 (0.85–1.07)  0.93 (0.77–1.12)  0.96 (0.80–1.15)      TC  312  4,633  6,734  P = 0.448  P = 0.443  P = 0.637      CC  159  2,219  7,165          rs2229765  GG  231  3,303  6,994  0.97 (0.86–1.09)  0.96 (0.81–1.13)  0.95 (0.76–1.18)      GA  287  4,247  6,758  P = 0.565  P = 0.630  P = 0.646      AA  94  1,433  6,560          rs2654981  TT  209  2,993  6,983  1.05 (0.94–1.18)  1.06 (0.89–1.25)  1.09 (0.88–1.34)      TC  295  4,460  6,614  P = 0.376  P = 0.507  P = 0.424      CC  108  1,535  7,036          rs13379905  CC  520  7,814  6,655  1.24 (1.01–1.52)  1.26 (1.01–1.58)  1.32 (0.55–3.19)      CT  86  1,109  7,755  P = 0.042  P = 0.041  P = 0.538      TT  5  58  8,621          rs2002880  GG  533  7,705  6,918  0.95 (0.77–1.17)  0.93 (0.73–1.19)  1.03 (0.51–2.07)      GA  71  1,157  6,137  P = 0.630  P = 0.567  P = 0.941      AA  8  126  6,349        Follow-up year, i.e., person-year reflects the total number of years that a subject was followed. Incidence density, reflects the number of new cases per population at risk in a given time period. Adjusted for age, sex, TC, TG, HDL-C, LDL-C, BMI, diabetes, drinking, and smoking. Abbreviations: BMI, body mass index; CI, confidence interval; GLU, glucose; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; IGF-1R, insulin-like growth factor 1 receptor; LDL-C, low-density lipoprotein cholesterol; SNP, single nucleotide polymorphism; TC, total cholesterol; TG, triglycerides. View Large Table 4. Stratification analyses of association between IGF-1 and IGF-1R genotypes and hypertension incidence of follow-up study Gene  SNP  Stratum  MM/Mm/mm  Additive  Dominant  HR (95% CI)  P  HR (95% CI)  P  IGF-1  rs6219  <55 years  136/43/4  0.74 (0.55–1.01)  0.054  0.71 (0.51–0.99)  0.045      Obesity  50/18/68  0.55 (0.31–0.95)  0.032  0.57 (0.32–1.03)  0.063      Family history  92/27/1  0.67 (0.45–0.99)  0.046  0.66 (0.43–1.03)  0.064  IGF-1R  rs13379905  Nondrinking  387/64/5  1.36 (1.08–1.71)  0.009  1.42 (1.10–1.84)  0.008      Family history  95/22/2  2.10 (1.38–3.19)  0.001  2.13 (1.33–3.41)  0.002    rs2002880  Obesity  60/8/0  0.43 (0.19–0.98)  0.044  0.43 (0.19–0.98)  0.045  Gene  SNP  Stratum  MM/Mm/mm  Additive  Dominant  HR (95% CI)  P  HR (95% CI)  P  IGF-1  rs6219  <55 years  136/43/4  0.74 (0.55–1.01)  0.054  0.71 (0.51–0.99)  0.045      Obesity  50/18/68  0.55 (0.31–0.95)  0.032  0.57 (0.32–1.03)  0.063      Family history  92/27/1  0.67 (0.45–0.99)  0.046  0.66 (0.43–1.03)  0.064  IGF-1R  rs13379905  Nondrinking  387/64/5  1.36 (1.08–1.71)  0.009  1.42 (1.10–1.84)  0.008      Family history  95/22/2  2.10 (1.38–3.19)  0.001  2.13 (1.33–3.41)  0.002    rs2002880  Obesity  60/8/0  0.43 (0.19–0.98)  0.044  0.43 (0.19–0.98)  0.045  “M” is the major allele and “m” is the minor allele. Adjusted for age, sex, TC, TG, HDL-C, LDL-C, BMI, diabetes, drinking, and smoking. Abbreviations: BMI, body mass index; CI, confidence interval; GLU, glucose; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; IGF-1R, insulin-like growth factor 1 receptor; LDL-C, low-density lipoprotein cholesterol; SNP, single nucleotide polymorphism; TC, total cholesterol; TG, triglycerides. View Large Moreover, among individuals <55 years of age with obesity and a family history of hypertension, the AG or GG genotypes of rs6219 (IGF-1) conferred a decreased risk of hypertension (P < 0.05). The HRs for these groups (95% CIs) were 0.71 (0.51–0.99), 0.55 (0.31–0.95), and 0.67 (0.45–0.99), respectively. Among the obese population, rs2002880 G>A in IGF-1R conferred a decreased risk of hypertension (HR = 0.43, P = 0.044) (Table 4). All of the subgroup results are listed in Supplementary Table S3. Replication of the associations of rs1815009 and rs13379905 with hypertensions in adolescents The demographic information for 3,551 adolescents is listed in Supplementary Table S4. The characteristics of sex, age, TC, HDL-C, and LDL-C were not significantly different between the groups, whereas z-SBP, z-DBP, TG, and BMI did exhibit significantly differences. The association of rs13379905 (TT vs. CC + CT) with prehypertension was replicated (OR = 3.84, P = 0.007) (Supplementary Table S5). Particularly, the association of rs13379905 with prehypertension/hypertension was statistically significant in males, with an additive OR of 1.80 (P = 0.005) following adjustment for age, sex, BMI, TC, TG, LDL, and HDL. The association of rs1815009 and hypertensions was not replicated (Supplementary Table S6). Further quantitative trait analysis of the SBP and DBP Z-scores indicated that those for SBP (1.04 ± 0.97, 1.09 ± 1.03, 1.89 ± 0.66) increased linearly for the CC, CT, TT rs13379905 genotypes in males, with a P value of 0.042 after adjusting for covariates (Figure 1). The detailed data are listed in Supplementary Table S7. Figure 1. View largeDownload slide Comparison of SBP Z-scores among the rs13379905 genotypes in adolescent males with an average age of 10.86 ± 2.94 years. The SBP Z-score (1.04 ± 0.97, 1.09 ± 1.03, 1.89 ± 0.66) increased linearly for the CC, CT, and TT genotypes of rs13379905 in males (P = 0.042). Abbreviation: SBP, systolic blood pressure. Figure 1. View largeDownload slide Comparison of SBP Z-scores among the rs13379905 genotypes in adolescent males with an average age of 10.86 ± 2.94 years. The SBP Z-score (1.04 ± 0.97, 1.09 ± 1.03, 1.89 ± 0.66) increased linearly for the CC, CT, and TT genotypes of rs13379905 in males (P = 0.042). Abbreviation: SBP, systolic blood pressure. Quantitative trait analysis of IGF-1 and IGF-1R The IGF-1 and IGF-1R levels showed no difference between the hypertension and control groups. The quantitative trait analysis results for IGF-1 and IGF-1R levels are shown in Supplementary Table S8. The IGF-1R concentration (median and interquartile range) in the subjects with the CC genotype of rs2002880 [0.70 (0.42, 1.18) (ng/ml)] were significantly different from those in subjects with the CT genotype [1.18 (0.83, 1.52) (ng/ml)], with P of 0.011 (Figure 2). Figure 2. View largeDownload slide Serum IGF-1R levels by rs2002880 genotypes in hypertension cases. The dots represent individual IGF-1R levels. The longest line represents the median, and the whiskers extend to 1.5-fold the interquartile range. The IGF-1R concentrations (median and interquartile range) in the subjects with rs2002880 CC genotype [0.70 (0.42, 1.18) (ng/ml)] were significantly different than in those with the CT genotype [1.18 (0.83, 1.52) (ng/ml)], P = 0.011. Abbreviations: IGF-1R, insulin-like growth factor 1 receptor. Figure 2. View largeDownload slide Serum IGF-1R levels by rs2002880 genotypes in hypertension cases. The dots represent individual IGF-1R levels. The longest line represents the median, and the whiskers extend to 1.5-fold the interquartile range. The IGF-1R concentrations (median and interquartile range) in the subjects with rs2002880 CC genotype [0.70 (0.42, 1.18) (ng/ml)] were significantly different than in those with the CT genotype [1.18 (0.83, 1.52) (ng/ml)], P = 0.011. Abbreviations: IGF-1R, insulin-like growth factor 1 receptor. DISCUSSION The GH-IGF-1 signal pathway has been reported to play an important role in the maintenance and development of cardiovascular disorders and to be involved in the pathophysiology of hypertension.18 We observed the significant associations between rs1815009 and rs2654981 (recessive model) and hypertension in the case–control study as well as a significant association of rs13379905 with incident hypertension in a follow-up population. rs1815009 and rs13379905 in IGF-1R were examined in an adolescent population, and the association of rs13379905 with prehypertension/hypertension was further verified. Furthermore, stratified analysis showed that the rs35767 and rs6219 SNPs in IGF-1 were associated with hypertension among individuals with different BMIs, ages, and family histories. These results jointly indicated that genetic variations in the IGF-1 signaling pathway might contribute to hypertension susceptibility. Previous studies have reported that BMI is strongly associated with hypertension in northern Chinese adults.19 Remarkably, we observed a negative association between the rs35767 G>A IGF-1 variant and hypertension in the normal weight group, while a positive association was observed in the obese population. These results suggested that BMI might modulate the genetic effects of IGF-1 on hypertension. D’Aloisio20 showed that the TT genotypic variant of rs6219 was associated with a 10% increase in the estimated mean IGF-1 levels relative to the reference genotypes among African Americans. Our analysis revealed that rs6219 might not play a causal role in IGF-1 levels but that, as with incident hypertension, the AG and GG rs6219 genotypes confer a decreased risk of hypertension in individuals who are <55 years of age, are obese or have a family history of hypertension. Several studies have showed that low circulating IGF-1 levels are associated with increased risk for stroke21 and hypertension.22 Our results indicated that hypertension cases had slightly higher IGF-1 concentrations compared with normotensives, although the difference was not very strong. This result may be due to the ethnicity of the examined study groups or inadequate statistical power. Rietveld et al.23 found that only homozygous carriers of the common allele (IGF-1 192 bp) exhibited a significant decline in serum IGF-1 levels with age, whereas heterozygous individuals and noncarriers did not. IGF-1R plays an important role in the 2 well-known signaling pathways namely, the PI3K-AKT and MAPK pathways.24,25 In addition, IGF-1R can modify calcium-dependent signaling pathways.26 Because the above pathways are involved in hypertension, it is possible that IGF-1R contributes to genetic susceptibility to hypertension. We observed that rs1815009 T>C, rs2654981 T>C, and rs13379905 C>T were significantly associated with hypertension, suggesting a putative role of IGF-1R in conferring susceptibility to the examined hypertension-related phenotypes. The rs1815009 variant was predicted to produce motif changes in smad4, a key effector in the transforming growth factor (TGF)-α signaling pathway.27 Our previous studies suggested that TGFBR2 variants increase the risk of hypertension and alter BP homeostasis.13 HapMap was used to identify nearby SNPs in linkage disequilibrium with the rs2654981 variant, revealing that the IGF-1R locus is not linked to any variant in a nearby locus, although a long-distance effect cannot be ruled out (Supplementary Figure 1). It is plausible that rs26548981 in the 3’UTR, a binding-site for miRNAs, may affect mRNA stability and translation.28 Moreover, bioinformatics analysis showed that the rs13379905 C>T variation may affect the transcription factor binding sites for NFATc2, which plays an important role in pathological cardiac remodeling and heart failure via the activation of NFAT transcription factors.29 Our data suggest that the CT and TT rs13379905 genotypes exacerbate the occurrence and development of hypertension compared with the CC genotype, and the association of these alleles with prehypertension/hypertension was further replicated in adolescent males. Family history is an important, nonmodifiable risk factor for hypertension, and the heritability of hypertension ranges from 25% in pedigree studies to 65% in twin studies.30 The association between rs13379905 and hypertension in individuals with a family history was stronger than in those without a family history. Additionally, no significant correlation between IGF-1R levels and hypertension was detected. Similarly, an animal study showed no significant differences in the IGF-1R mRNA and protein levels in the nucleus tractus solitarii between WKY and spontaneously hypertensive rats.31 There are several limitations in our study. First, no significant difference in the serum IGF-1 and IGF-1R were observed between hypertension cases and controls, despite the fact that strict standards were used to select representative cases and controls. Second, potential biases in case–control studies can lead to distorted results in epidemiological association studies. Regardless of the above limitations, this study found significant associations between the rs1815009, rs2654981, and rs13379905 polymorphisms in IGF-1R and hypertension in a relatively large population. Future functional studies on these results are warranted. In conclusion, our findings suggest that IGF-1R genetic polymorphisms are significantly associated with hypertension, and that BMI, age, and family history may play important modulating roles in the genetic effects of IGF-1 on hypertension susceptibility. SUPPLEMENTARY MATERIAL Supplementary materials are available at American Journal of Hypertension online. DISCLOSURE The authors declared no conflict of interest. ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (Grant No.81541071 and No.81573232), Anhui Provincial Natural Science Foundation (1308085MH135), Science & Technology Program of Wuxi (No. ZD1011 and CSEW1N1112), and the Priority Academic Program for the Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. REFERENCES 1. Messerli FH, Williams B, Ritz E. 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Insulin-Like Growth Factor-1 and Receptor Contribute Genetic Susceptibility to Hypertension in a Han Chinese Population

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Oxford University Press
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© American Journal of Hypertension, Ltd 2017. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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0895-7061
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1941-7225
D.O.I.
10.1093/ajh/hpx195
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Abstract

Abstract BACKGROUND Insulin-like growth factor 1 (IGF-1) and IGF-1 receptor (IGF-1R) have been suggested to mediate the pathophysiological response to elevated blood pressure. This study aims to evaluate the association of IGF-1 and IGF-1R with hypertension. METHODS Overall, 2,012 hypertensive cases and 2,210 controls were included in a case–control study, and 10 tagging single nucleotide polymorphisms (tagSNPs) were selected. The association of these SNPs with hypertension was further evaluated in a follow-up analysis and in an adolescent population. RESULTS A case–control study indicated that rs1815009 and rs2654981 in IGF-1R were significantly associated with hypertension, with odds ratios of 0.89 (P = 0.009) and 1.19 (P = 0.034), respectively, after adjusting for covariates. Stratification analyses revealed significant associations with hypertension (P < 0.05) for rs35767 in normal weight and obese populations; for rs2229765 in individuals <55 years of age and in overweight and nondrinking populations; and for rs2002880 in overweight and drinking populations. In a follow-up study, rs13379905 in IGF-1R was associated with hypertension incidence (hazard ratio, HR = 1.24, P = 0.042). This association was more significant in individuals with a hypertensive family history (HR = 2.10, P = 0.001). The association of rs13379905 with prehypertension and hypertension was further replicated in adolescent males (P = 0.005). Significant associations with hypertension incidence (P < 0.05) were observed for rs6219 in individuals <55 years of age and among those with obesity and a hypertensive family history as well as rs2002880 in obese individuals. CONCLUSIONS Our findings suggest that IGF-1R may contribute to the genetic susceptibility to hypertension, with BMI, age, and family history of hypertension all potentially modulating the genetic effects of IGF-1 on hypertension. association study, blood pressure, IGF-1, IGF-1R, hypertension, polymorphism Elevated blood pressure (BP) has been recognized as a leading factor for the development and progression of cardiovascular diseases,1 which contributes significantly to disease and mortality around the world.2 Hypertension is involved in a set of highly heterogeneous disorders, and genetic determinants are believed to interact with environmental factors, such as sodium intake, alcohol, smoking, and overweight, in the etiology of this condition.3 The heritability of hypertension ranges from 30% to 60%.4 Recently, genome-wide association studies in a Han Chinese population revealed insulin-like growth factor 1 (IGF-1) as a new susceptibility gene for hypertension.5 IGF-1 is a downstream mediator of growth hormone (GH), which is important for growth and metabolism via the GH-IGF-1 axis. IGF-1 shares 50% homology and some receptors with insulin.6 Approximately 70% of circulating IGF-1 in the body is synthesized in the liver, while IGF-1Rs are hardly expressed in this organ or in adipose tissue.7 IGF-1 is a potent mitogen of vascular smooth muscle cells, mediating the contractile function of these cells by promoting proliferation and migration.8 Studies have illustrated that IGF-1 exerts a vasodilatory effect by combining with IGF-1R and thereby activating phosphatidylinositol 3-kinase (PI3K). In turn, PI3K phosphorylates endothelial nitric oxide synthase, resulting in the production of nitric oxide.9 IGF-1R is a member of the insulin tyrosine kinase class of cell-surface receptors6 and is the main biological target for both IGF-1, IGF-2, and insulin. These findings highlight the important role of IGF-1 in regulating endothelial dysfunction and in the relaxation of vascular smooth muscle. Animal research has revealed a site-specific insertional mutation in exon 3 of IGF-1 that contributes to a significant elevation of arterial pressure in homozygous mice.10 A systematic review reported a U-shaped association between IGF-1 levels and hypertension risk.11 The common variant −1411C>T (rs35767) in the IGF-1 upstream promoter P1 was reported to be significantly associated with a 27% lower risk of hypertension as well as lower systolic BP (SBP) and diastolic BP (DBP) in normotensive individuals.12 Previous studies have shown limited evidence of a relationship between IGF-1 and hypertension in the Han Chinese populations.5 In the present study, we evaluated the association of 10 tagging single nucleotide polymorphisms (tagSNPs) in IGF-1 and IGF-1R with hypertension in a case–control study. The positive associations of these variants with hypertension were further replicated in both a follow-up study and a study of adolescents. METHODS Study population Case–control study of adult subjects. A total of 4,222 participants aged 35 to 75 years were recruited from a rural population in 2009 (Yixing, Jiangsu). The study population included 2,012 hypertension cases and 2,210 normotensive subjects.13 Follow-up study of adult subjects. Ninety-four elderly matched controls for the case–control study were excluded. Thus, 2,116 healthy controls were followed for a median of 5.01 years (from May 2009 to January 2016). The endpoint events of incident hypertension were collected. Face-to-face and telephone interviews were performed to ascertain disease status and vital information. Hospital records and death certificates from the local public health department were reviewed and verified by a study-wide endpoint assessment committee at Yixing Hospital. The further replication stage consisted of the analysis of an adolescent population (Yixing, Jiangsu) using an epidemiological cluster sampling approach in 2014. A total of 3,787 children and adolescents aged 6–16 years were investigated. According to “The Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents”,14 normal BP was defined as both SBP and DBP <90th percentile; prehypertension was defined as either SBP or DBP between ≥the 90th and ≤95th percentile; and hypertension was defined as either SBP or DBP the >95th percentile. Height was recognized as a key covariate associated with BP levels.15 The Z-scores of SBP and DBP in children were calculated according to the method and data (www.cdc.gov/growthcharts/) provided by the US Centers for Disease Control and Prevention. BP was transformed into BP z-scores using age, gender, and height. Individuals with missing blood samples for genotyping (n = 215) or with the missing BP, age, gender, and/or height (n = 21) values were also excluded. Ultimately, 3,551 children, including 2,975 normotensive, 282 prehypertensive, and 294 hypertensive subjects were selected to replicate the association generated from the adult population. The study protocol was approved by the ethics committee of Nanjing Medical University, and informed consent was obtained from all subjects. Data collection All subjects completed a standard questionnaire (including demographic characteristics and medical history) and underwent physical examinations, including the determination of weight, height, and BP, by trained research staff. BP measurements were obtained 3 times with the participant in the sitting position after 5 minutes of rest, according to a standard protocol recommended by the American Heart Association.16 The participants were advised to avoid alcohol, cigarette smoking, coffee/tea, and exercise for at least 30 minutes before their BP measurements. A 5-ml venous blood sample was collected after at least 10 hours of fasting to measure plasma total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) levels, and glucose. The participants were asked about their smoking and drinking habits. Smoking was defined as at least 20 cigarettes per week for 3 months per year. Drinking was defined as at least 2 times per week for 6 months per year. SNP selection The IGF-1 (Gene ID: 3479) maps onto chromosome 12 at q23.2, spans 84.65 kb and consists of 7 exons. The IGF-1R (gene ID: 3480) is located on chromosome 15 at q26.3 and spans 315 kb. We searched for SNPs in IGF-1 and IGF-1R starting from the 2kb upstream to the 1kb downstream. We selected tagSNPs from the database of the Chinese Han population in Beijing, using the human reference genome (GRCh37, http://browser.1000genomes.org/Multi/Search/Results). All tagSNPs with minor allele frequency ≥0.05 and linkage disequilibrium r2 ≥0.8 were selected. Furthermore, we applied a functional candidate strategy to search for functional SNPs with roles in transcription, regulation, or splicing using an online SNP selection tool (SNPINFO, https://snpinfo.niehs.nih.gov/). Blood sampling and SNP genotyping Blood samples were collected into ethylenediamine tetraacetic acid-containing tubes. The DNA was extracted using a standard phenol-chloroform method17 and stored at −20 °C until required for batch genotyping. SNP genotyping was performed using TaqMan technology and an ABI 7900HT Fast Real-Time PCR System (Applied BioSystems, Foster City, CA). The primers and TaqMan-MGB probes were ordered from Applied BioSystems and Nanjing BioSteed BioTechnologies (Nanjing, China). The identification of individual genotypes was performed using Sequence Detection System 2.1 software (95% autoCaller confidence level).The PCR conditions were 50 °C for 2 minutes, 95 °C for 10 minutes, 95 °C for 15 seconds, and 60 °C for 1 minutes, with 45 cycles (except for rs2002880, which required 33 cycles). The agreement rate with the HapMap database genotypes was greater than 99.7%. Detection of serum IGF-1 and IGF-1R levels Serum IGF-1 and IGF-1R levels were measured for randomized samples, including 137 cases who did not receive hypotensive drugs and 159 healthy controls. The levels were measured using enzyme-linked immunosorbent assays (Catalogue No.CSB-E04580h, CSB-E13766h) from CUSABIO Laboratories. The values did not exhibit a normal distribution; thus, the IGF-1 (lnIGF-1) and IGF-1R levels were log-transformed. The IGF-1R levels did not display a normal distribution after transformation, so the nonparametric test was then used. Statistical analysis Unpaired Student’s t-test was used to test the differences in all the measured variables presented as the mean ± SD between cases and controls. Hardy–Weinberg equilibrium was evaluated using Fisher’s exact chi-square (χ2) test in the control groups. The qualitative variables and the allele and genotype frequency distributions between groups were compared using the chi-square (χ2) test. Additionally, a general linear model was applied to compare the BP levels (mean ± SD) of carriers of the considered genotypes among the cases and controls. Multiple unconditional logistic regression analysis was applied to estimate the risk of hypertension after adjusting for potential confounding factors. The odds ratio (OR) and 95% confidence intervals (CI) were used to test for association in the case–control study, and the hazard ratio (HR) and 95% CI by Cox’s proportional hazard regression analysis were used to estimate the risk of IGF-1, IGF-1R, and hypertension in the follow-up study. All of the statistical analyses were performed with SPSS version 15.0 (SPSS, Chicago, IL). A 2-tailed P <0.05 was defined as statistically significant. RESULTS Characteristics of the adult participants The demographic and clinical characteristics of participants are summarized in Supplementary Table S1. No significant differences in sex, HDL-C level, smoking, and drinking status were observed between the normotensive and hypertensive groups. The hypertension cases had significantly higher SBP, DBP, BMI, TC, TG, LDL-C, and glucose levels compared with controls (P < 0.05). Association analyses of case–control population All of the SNPs in the control individuals were in Hardy–Weinberg equilibrium, except for rs2002880 (P < 0.05). rs1815009 and rs2654981 of IGF-1R were statistically correlated with hypertension. After adjusting for age, gender, TC, TG, LDL-C, HDL-C, glucose, BMI, smoking, and drinking, the ORs (95% CIs) were 0.89 (0.81–0.97) and 0.83 (0.72–0.96) for the additive (TT vs. TC vs. CC) and dominant (TT vs. TC + CC) models of rs1815009, respectively, and 1.19 (1.01–1.41) for the recessive (AA + AG vs. GG) model of rs2654981 (Table 1). Further stratification analyses showed that (Table 2), the rs35767 G>A variant of IGF-1 was associated with hypertension in the normal weight and obese population, with ORs and 95% CIs of 0.83 (0.72–0.96) and 1.35 (1.02–1.77), respectively. Among individuals <55 years of ages, the additive model of rs1815009, the dominant model of rs2229765 and the recessive model of rs2654981 were associated with hypertension after adjustment for covariates, with P values of 0.021, 0.023, and 0.025, respectively. In the male and nonsmoking group, the rs1815009 was significantly associated with hypertension (P = 0.048, 0.018). Both the rs1815009 and rs2229765 were statistically associated with hypertension in overweight and nondrinking populations, as was rs2002880 in the drinking and overweight populations (P < 0.05). All of the postadjustment P values and ORs (95%CI) are listed in Supplementary Table S2. Table 1. Association analyses of 10 IGF-1 and IGF-1R SNPs with hypertension in a case–control study Gene  SNP  Group  MM/Mm/mm  Genotype OR (95% CI)a  Allele gene  Pc  Additive  Dominate  Recessive  Major/minor  OR (95% CI)/Pb  IGF-1  rs5742612  Case  1004/833/174  1.00 (0.91–1.11)  1.00 (0.88–1.13)  1.02 (0.81–1.27)  2,841/1,181  1.01 (0.92–1.11)  0.804    (A>G)  Control  1104/918/186  P = 0.972  P = 0.966  P = 0.877  3,126/1,290  P = 0.879      rs6218  Case  1124/766/121  0.99 (0.89–1.10)  0.99 (0.88–1.13)  0.95 (0.73–1.23)  3,014/1,008  1.00 (0.90–1.10)  0.645    (A>G)  Control  1234/838/135  P = 0.814  P = 0.932  P = 0.674  3,306/1,108  P = 0.966      rs35767  Case  882/923/205  0.96 (0.87–1.05)  0.97 (0.86–1.10)  0.87 (0.71–1.07)  2,687/1,333  0.95 (0.87–1.04)  0.834    (G>A)  Control  948/997/257  P = 0.347  P = 0.666  P = 0.191  2,893/1,511  P = 0.266      rs6214  Case  590/967/454  0.97(0.89–1.06)  0.92(0.80–1.06)  1.01(0.87–1.18)  2,147/1,875  0.98 (0.90–1.07)  0.555    (C>T)  Control  609/1109/480  P = 0.500  P = 0.239  P = 0.899  2,327/2,069  P = 0.682      rs6219  Case  1343/595/72  1.01(0.90–1.13)  0.99 (0.87–1.13)  1.13 (0.800–1.61)  3,281/739  1.01(0.91–1.13)  0.648    (C>T)  Control  1474/664/70  P = 0.914  P = 0.885  P = 0.480  3,612/804  P = 0.834    IGF-1R  rs1815009  Case  549/1002/458  0.89(0.81–0.97)  0.83 (0.72–0.96)  0.88 (0.75–1.02)  2,100/1,918  0.91 (0.83–0.99)  0.148    (T>C)  Control  530/1138/540  P = 0.009  P = 0.014  P = 0.079  2,198/2,218  P = 0.022      rs2229765  Case  785/944/281  0.92(0.84–1.01)  0.89 (0.78–1.02)  0.90 (0.75–1.07)  2,514/1,506  0.93 (0.85–1.01)  0.998    (G>A)  Control  812/1051/340  P = 0.070  P = 0.088  P = 0.234  2,675/1,731  P = 0.085      rs2654981  Case  656/959/395  1.07(0.98–1.17)  1.03 (0.90–1.18)  1.19 (1.01–1.41)  2,271/1,749  1.05 (0.96–1.14)  0.181    (T>C)  Control  723/1107/377  P = 0.151  P = 0.703  P = 0.034  2,533/1861  P = 0.287      rs13379905  Case  1768/234/9  0.90 (0.75–1.08)  0.91 (0.75–1.10)  0.62 (0.26–1.52)  3,770/252  0.93 (0.78–1.10)  0.222    (C>T)  Control  1917/274/14  P = 0.241  P = 0.308  P = 0.301  4,108/302  P = 0.378      rs2002880  Case  1712/256/42  1.11 (0.95–1.29)  1.09 (0.91–1.30)  1.49 (0.92–2.42)  3,680/340  1.10 (0.94–1.28)  0.000    (G>A)  Control  1895/279/32  P = 0.189  P = 0.346  P = 0.107  4,069/343  P = 0.251    Gene  SNP  Group  MM/Mm/mm  Genotype OR (95% CI)a  Allele gene  Pc  Additive  Dominate  Recessive  Major/minor  OR (95% CI)/Pb  IGF-1  rs5742612  Case  1004/833/174  1.00 (0.91–1.11)  1.00 (0.88–1.13)  1.02 (0.81–1.27)  2,841/1,181  1.01 (0.92–1.11)  0.804    (A>G)  Control  1104/918/186  P = 0.972  P = 0.966  P = 0.877  3,126/1,290  P = 0.879      rs6218  Case  1124/766/121  0.99 (0.89–1.10)  0.99 (0.88–1.13)  0.95 (0.73–1.23)  3,014/1,008  1.00 (0.90–1.10)  0.645    (A>G)  Control  1234/838/135  P = 0.814  P = 0.932  P = 0.674  3,306/1,108  P = 0.966      rs35767  Case  882/923/205  0.96 (0.87–1.05)  0.97 (0.86–1.10)  0.87 (0.71–1.07)  2,687/1,333  0.95 (0.87–1.04)  0.834    (G>A)  Control  948/997/257  P = 0.347  P = 0.666  P = 0.191  2,893/1,511  P = 0.266      rs6214  Case  590/967/454  0.97(0.89–1.06)  0.92(0.80–1.06)  1.01(0.87–1.18)  2,147/1,875  0.98 (0.90–1.07)  0.555    (C>T)  Control  609/1109/480  P = 0.500  P = 0.239  P = 0.899  2,327/2,069  P = 0.682      rs6219  Case  1343/595/72  1.01(0.90–1.13)  0.99 (0.87–1.13)  1.13 (0.800–1.61)  3,281/739  1.01(0.91–1.13)  0.648    (C>T)  Control  1474/664/70  P = 0.914  P = 0.885  P = 0.480  3,612/804  P = 0.834    IGF-1R  rs1815009  Case  549/1002/458  0.89(0.81–0.97)  0.83 (0.72–0.96)  0.88 (0.75–1.02)  2,100/1,918  0.91 (0.83–0.99)  0.148    (T>C)  Control  530/1138/540  P = 0.009  P = 0.014  P = 0.079  2,198/2,218  P = 0.022      rs2229765  Case  785/944/281  0.92(0.84–1.01)  0.89 (0.78–1.02)  0.90 (0.75–1.07)  2,514/1,506  0.93 (0.85–1.01)  0.998    (G>A)  Control  812/1051/340  P = 0.070  P = 0.088  P = 0.234  2,675/1,731  P = 0.085      rs2654981  Case  656/959/395  1.07(0.98–1.17)  1.03 (0.90–1.18)  1.19 (1.01–1.41)  2,271/1,749  1.05 (0.96–1.14)  0.181    (T>C)  Control  723/1107/377  P = 0.151  P = 0.703  P = 0.034  2,533/1861  P = 0.287      rs13379905  Case  1768/234/9  0.90 (0.75–1.08)  0.91 (0.75–1.10)  0.62 (0.26–1.52)  3,770/252  0.93 (0.78–1.10)  0.222    (C>T)  Control  1917/274/14  P = 0.241  P = 0.308  P = 0.301  4,108/302  P = 0.378      rs2002880  Case  1712/256/42  1.11 (0.95–1.29)  1.09 (0.91–1.30)  1.49 (0.92–2.42)  3,680/340  1.10 (0.94–1.28)  0.000    (G>A)  Control  1895/279/32  P = 0.189  P = 0.346  P = 0.107  4,069/343  P = 0.251    “M” is the major allele and “m” is the minor allele. Abbreviations: BMI, body mass index; CI, confidence interval; GLU, glucose; HDL-C, high-density lipoprotein cholesterol; HWE, Hardy–Weinberg equilibrium; IGF-1R, insulin-like growth factor 1 receptor; LDL-C, low-density lipoprotein cholesterol; OR, odds ratio; SNP, single nucleotide polymorphism; TC, total cholesterol; TG, triglycerides. aAdjusted for age, sex, TC, TG, HDL-C, LDL-C, GLU, BMI, drinking, and smoking. bP value of the χ2 test for comparison of allele frequencies between case and control groups. cP value of the χ2 test for HWE in controls. View Large Table 2. Stratification analyses by age, sex, smoking, drinking, and BMI for association between SNPs and hypertension SNP  Stratum  Group  MM/Mm/mm  Genotype OR (95% CI)  Additive  Dominate  Recessive  rs35767      GG/GA/AA          Normal weight  Case  382/372/75  0.83 (0.72–0.96)  0.83 (0.69–1.00)  0.70 (0.52–0.95)      Control  479/521/146  P = 0.011  P = 0.047  P = 0.023    Obesity  Case  147/160/46  1.35 (1.02–1.77)  1.46 (1.01–2.11)  1.48 (0.82–2.67)      Control  94/75/18  P = 0.035  P = 0.043  P = 0.194  rs1815009      TT/TC/CC          <55 years  Case  154/267/111  0.82 (0.70–0.97)  0.72 (0.56–0.93)  0.85 (0.65–1.12)      Control  195/463/199  P = 0.021  P = 0.012  P = 0.24    Male  Case  224/408/196  0.87 (0.76–1.00)  0.79 (0.63–0.99)  0.87 (0.70–1.10)      Control  199/453/232  P = 0.048  P = 0.041  P = 0.239    Nonsmoking  Case  429/762/339  0.88 (0.80–0.98)  0.82 (0.70–0.97)  0.87 (0.74–1.04)      Control  411/870/394  P = 0.018  P = 0.021  P = 0.128    Nondrinking  Case  437/805/345  0.89 (0.80–0.98)  0.86 (0.73–1.01)  0.85 (0.72–1.01)      Control  428/893/411  P = 0.024  P = 0.07  P = 0.061    Overweight  Case  216/399/162  0.81 (0.70–0.94)  0.77 (0.61–0.97)  0.75 (0.58–0.96)      Control  182/399/198  P = 0.006  P = 0.029  P = 0.020  rs2229765      GG/GA/AA          <55 years  Case  225/240/68  0.85 (0.72–1.01)  0.77 (0.61–0.96)  0.93 (0.67–1.30)      Control  308/425/122  P = 0.065  P = 0.023  P = 0.680    Nondrinking  Case  629/747/212  0.90 (0.81–0.99)  0.88 (0.76–1.02)  0.83 (0.68–1.02)      Control  644/814/269  P = 0.037  P = 0.099  P = 0.073    Overweight  Case  320/367/90  0.81 (0.70–0.95)  0.79 (0.64–0.98)  0.71 (0.52–0.96)      Control  282/378/118  P = 0.007  P = 0.031  P = 0.025  rs2654981      TT/TC/CC          <55 years  Case  166/255/112  1.15 (0.98–1.35)  1.08 (0.85–1.37)  1.39 (1.04–1.85)      Control  276/440/140  P = 0.097  P = 0.545  P = 0.025    Overweight  Case  237/387/153  1.17 (1.01–1.35)  1.18 (0.95–1.47)  1.30 (0.99–1.70)      Control  264/387/128  P = 0.041  P = 0.144  P = 0.057  rs2002880      GG/GA/AA          Drinking  Case  362/48/12  1.27 (0.91–1.78)  1.13 (0.76–1.68)  5.12 (1.52–17.2)      Control  410/62/4  P = 0.165  P = 0.56  P = 0.008    Overweight  Case  664/94/20  1.23 (0.96–1.58)  1.17 (0.87–1.58)  2.51 (1.12–5.65)      Control  678/92/9  P = 0.103  P = 0.293  P = 0.026  SNP  Stratum  Group  MM/Mm/mm  Genotype OR (95% CI)  Additive  Dominate  Recessive  rs35767      GG/GA/AA          Normal weight  Case  382/372/75  0.83 (0.72–0.96)  0.83 (0.69–1.00)  0.70 (0.52–0.95)      Control  479/521/146  P = 0.011  P = 0.047  P = 0.023    Obesity  Case  147/160/46  1.35 (1.02–1.77)  1.46 (1.01–2.11)  1.48 (0.82–2.67)      Control  94/75/18  P = 0.035  P = 0.043  P = 0.194  rs1815009      TT/TC/CC          <55 years  Case  154/267/111  0.82 (0.70–0.97)  0.72 (0.56–0.93)  0.85 (0.65–1.12)      Control  195/463/199  P = 0.021  P = 0.012  P = 0.24    Male  Case  224/408/196  0.87 (0.76–1.00)  0.79 (0.63–0.99)  0.87 (0.70–1.10)      Control  199/453/232  P = 0.048  P = 0.041  P = 0.239    Nonsmoking  Case  429/762/339  0.88 (0.80–0.98)  0.82 (0.70–0.97)  0.87 (0.74–1.04)      Control  411/870/394  P = 0.018  P = 0.021  P = 0.128    Nondrinking  Case  437/805/345  0.89 (0.80–0.98)  0.86 (0.73–1.01)  0.85 (0.72–1.01)      Control  428/893/411  P = 0.024  P = 0.07  P = 0.061    Overweight  Case  216/399/162  0.81 (0.70–0.94)  0.77 (0.61–0.97)  0.75 (0.58–0.96)      Control  182/399/198  P = 0.006  P = 0.029  P = 0.020  rs2229765      GG/GA/AA          <55 years  Case  225/240/68  0.85 (0.72–1.01)  0.77 (0.61–0.96)  0.93 (0.67–1.30)      Control  308/425/122  P = 0.065  P = 0.023  P = 0.680    Nondrinking  Case  629/747/212  0.90 (0.81–0.99)  0.88 (0.76–1.02)  0.83 (0.68–1.02)      Control  644/814/269  P = 0.037  P = 0.099  P = 0.073    Overweight  Case  320/367/90  0.81 (0.70–0.95)  0.79 (0.64–0.98)  0.71 (0.52–0.96)      Control  282/378/118  P = 0.007  P = 0.031  P = 0.025  rs2654981      TT/TC/CC          <55 years  Case  166/255/112  1.15 (0.98–1.35)  1.08 (0.85–1.37)  1.39 (1.04–1.85)      Control  276/440/140  P = 0.097  P = 0.545  P = 0.025    Overweight  Case  237/387/153  1.17 (1.01–1.35)  1.18 (0.95–1.47)  1.30 (0.99–1.70)      Control  264/387/128  P = 0.041  P = 0.144  P = 0.057  rs2002880      GG/GA/AA          Drinking  Case  362/48/12  1.27 (0.91–1.78)  1.13 (0.76–1.68)  5.12 (1.52–17.2)      Control  410/62/4  P = 0.165  P = 0.56  P = 0.008    Overweight  Case  664/94/20  1.23 (0.96–1.58)  1.17 (0.87–1.58)  2.51 (1.12–5.65)      Control  678/92/9  P = 0.103  P = 0.293  P = 0.026  “M” is the major allele and “m” is the minor allele. Adjusted for age, sex, TC, TG, HDL-C, LDL-C, GLU, BMI, drinking, and smoking. Abbreviations: BMI, body mass index; CI, confidence interval; GLU, glucose; HDL-C, high-density lipoprotein cholesterol; IGF-1R, insulin-like growth factor 1 receptor; LDL-C, low-density lipoprotein cholesterol; OR, odds ratio; SNP, single nucleotide polymorphism; TC, total cholesterol; TG, triglycerides. View Large Association analyses of incident hypertension in the follow-up study In the follow-up study, 613 instances of hypertension were recorded, with an incidence density of 6,570 per 105 person-years. Cox’s proportional hazard regression analysis analyses showed that rs13379905 in IGF-1R was associated with an increased risk of hypertension (HR = 1.19, P = 0.088), with a HR of 1.24 (P = 0.042) after adjustment for age, sex, TC, TG, LDL-C, HDL-C, T2DM, BMI, smoking, and drinking (Table 3). Significant associations of rs13379905 with incident hypertension were observed in nondrinking (P = 0.009) individuals and among those with a family history of hypertension (P = 0.001) (Table 4). Table 3. Association between IGF-1 and IGF-1R genotypes and the risk of hypertension in a follow-up study Gene  SNP  Genotype  Hypertension  Follow-up year  Incidence density  HR (95% CI)  (/105 person-years)  Additive  Dominate  Recessive  IGF-1  rs5742612  AA  288  4,465  6,450  1.04 (0.92–1.18)  1.07 (0.91–1.26)  0.99 (0.75–1.32)      AG  272  3,776  7,203  P = 0.528  P = 0.396  P = 0.947      GG  53  751  7,057          rs6218  AA  328  4,953  6,622  1.00 (0.88–1.14)  1.01 (0.86–1.19)  0.96 (0.70–1.33)      AG  243  3,466  7,011  P = 0.999  P = 0.907  P = 0.815      GG  41  568  7,218          rs35767  GG  268  3,868  6,929  0.99 (0.88–1.12)  0.98 (0.83–1.15)  1.02 (0.79–1.31)      GA  272  4,064  6,693  P = 0.906  P = 0.798  P = 0.88      AA  71  1,052  6,749          rs6214  CC  153  2,467  6,202  1.03 (0.92–1.15)  1.10 (0.91–1.32)  1.00 (0.83–1.20)      CT  305  4,498  6,781  P = 0.573  P = 0.33  P = 0.955      TT  153  2,020  7,574          rs6219  CC  430  5,990  7,179  0.94 (0.81–1.10)  0.95 (0.79–1.13)  0.83 (0.49–1.41)      CT  168  2,733  6,147  P = 0.442  P = 0.531  P = 0.483      TT  14  2,67  5,243        IGF-1R  rs1815009  TT  142  2,144  6,623  0.96 (0.85–1.07)  0.93 (0.77–1.12)  0.96 (0.80–1.15)      TC  312  4,633  6,734  P = 0.448  P = 0.443  P = 0.637      CC  159  2,219  7,165          rs2229765  GG  231  3,303  6,994  0.97 (0.86–1.09)  0.96 (0.81–1.13)  0.95 (0.76–1.18)      GA  287  4,247  6,758  P = 0.565  P = 0.630  P = 0.646      AA  94  1,433  6,560          rs2654981  TT  209  2,993  6,983  1.05 (0.94–1.18)  1.06 (0.89–1.25)  1.09 (0.88–1.34)      TC  295  4,460  6,614  P = 0.376  P = 0.507  P = 0.424      CC  108  1,535  7,036          rs13379905  CC  520  7,814  6,655  1.24 (1.01–1.52)  1.26 (1.01–1.58)  1.32 (0.55–3.19)      CT  86  1,109  7,755  P = 0.042  P = 0.041  P = 0.538      TT  5  58  8,621          rs2002880  GG  533  7,705  6,918  0.95 (0.77–1.17)  0.93 (0.73–1.19)  1.03 (0.51–2.07)      GA  71  1,157  6,137  P = 0.630  P = 0.567  P = 0.941      AA  8  126  6,349        Gene  SNP  Genotype  Hypertension  Follow-up year  Incidence density  HR (95% CI)  (/105 person-years)  Additive  Dominate  Recessive  IGF-1  rs5742612  AA  288  4,465  6,450  1.04 (0.92–1.18)  1.07 (0.91–1.26)  0.99 (0.75–1.32)      AG  272  3,776  7,203  P = 0.528  P = 0.396  P = 0.947      GG  53  751  7,057          rs6218  AA  328  4,953  6,622  1.00 (0.88–1.14)  1.01 (0.86–1.19)  0.96 (0.70–1.33)      AG  243  3,466  7,011  P = 0.999  P = 0.907  P = 0.815      GG  41  568  7,218          rs35767  GG  268  3,868  6,929  0.99 (0.88–1.12)  0.98 (0.83–1.15)  1.02 (0.79–1.31)      GA  272  4,064  6,693  P = 0.906  P = 0.798  P = 0.88      AA  71  1,052  6,749          rs6214  CC  153  2,467  6,202  1.03 (0.92–1.15)  1.10 (0.91–1.32)  1.00 (0.83–1.20)      CT  305  4,498  6,781  P = 0.573  P = 0.33  P = 0.955      TT  153  2,020  7,574          rs6219  CC  430  5,990  7,179  0.94 (0.81–1.10)  0.95 (0.79–1.13)  0.83 (0.49–1.41)      CT  168  2,733  6,147  P = 0.442  P = 0.531  P = 0.483      TT  14  2,67  5,243        IGF-1R  rs1815009  TT  142  2,144  6,623  0.96 (0.85–1.07)  0.93 (0.77–1.12)  0.96 (0.80–1.15)      TC  312  4,633  6,734  P = 0.448  P = 0.443  P = 0.637      CC  159  2,219  7,165          rs2229765  GG  231  3,303  6,994  0.97 (0.86–1.09)  0.96 (0.81–1.13)  0.95 (0.76–1.18)      GA  287  4,247  6,758  P = 0.565  P = 0.630  P = 0.646      AA  94  1,433  6,560          rs2654981  TT  209  2,993  6,983  1.05 (0.94–1.18)  1.06 (0.89–1.25)  1.09 (0.88–1.34)      TC  295  4,460  6,614  P = 0.376  P = 0.507  P = 0.424      CC  108  1,535  7,036          rs13379905  CC  520  7,814  6,655  1.24 (1.01–1.52)  1.26 (1.01–1.58)  1.32 (0.55–3.19)      CT  86  1,109  7,755  P = 0.042  P = 0.041  P = 0.538      TT  5  58  8,621          rs2002880  GG  533  7,705  6,918  0.95 (0.77–1.17)  0.93 (0.73–1.19)  1.03 (0.51–2.07)      GA  71  1,157  6,137  P = 0.630  P = 0.567  P = 0.941      AA  8  126  6,349        Follow-up year, i.e., person-year reflects the total number of years that a subject was followed. Incidence density, reflects the number of new cases per population at risk in a given time period. Adjusted for age, sex, TC, TG, HDL-C, LDL-C, BMI, diabetes, drinking, and smoking. Abbreviations: BMI, body mass index; CI, confidence interval; GLU, glucose; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; IGF-1R, insulin-like growth factor 1 receptor; LDL-C, low-density lipoprotein cholesterol; SNP, single nucleotide polymorphism; TC, total cholesterol; TG, triglycerides. View Large Table 4. Stratification analyses of association between IGF-1 and IGF-1R genotypes and hypertension incidence of follow-up study Gene  SNP  Stratum  MM/Mm/mm  Additive  Dominant  HR (95% CI)  P  HR (95% CI)  P  IGF-1  rs6219  <55 years  136/43/4  0.74 (0.55–1.01)  0.054  0.71 (0.51–0.99)  0.045      Obesity  50/18/68  0.55 (0.31–0.95)  0.032  0.57 (0.32–1.03)  0.063      Family history  92/27/1  0.67 (0.45–0.99)  0.046  0.66 (0.43–1.03)  0.064  IGF-1R  rs13379905  Nondrinking  387/64/5  1.36 (1.08–1.71)  0.009  1.42 (1.10–1.84)  0.008      Family history  95/22/2  2.10 (1.38–3.19)  0.001  2.13 (1.33–3.41)  0.002    rs2002880  Obesity  60/8/0  0.43 (0.19–0.98)  0.044  0.43 (0.19–0.98)  0.045  Gene  SNP  Stratum  MM/Mm/mm  Additive  Dominant  HR (95% CI)  P  HR (95% CI)  P  IGF-1  rs6219  <55 years  136/43/4  0.74 (0.55–1.01)  0.054  0.71 (0.51–0.99)  0.045      Obesity  50/18/68  0.55 (0.31–0.95)  0.032  0.57 (0.32–1.03)  0.063      Family history  92/27/1  0.67 (0.45–0.99)  0.046  0.66 (0.43–1.03)  0.064  IGF-1R  rs13379905  Nondrinking  387/64/5  1.36 (1.08–1.71)  0.009  1.42 (1.10–1.84)  0.008      Family history  95/22/2  2.10 (1.38–3.19)  0.001  2.13 (1.33–3.41)  0.002    rs2002880  Obesity  60/8/0  0.43 (0.19–0.98)  0.044  0.43 (0.19–0.98)  0.045  “M” is the major allele and “m” is the minor allele. Adjusted for age, sex, TC, TG, HDL-C, LDL-C, BMI, diabetes, drinking, and smoking. Abbreviations: BMI, body mass index; CI, confidence interval; GLU, glucose; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; IGF-1R, insulin-like growth factor 1 receptor; LDL-C, low-density lipoprotein cholesterol; SNP, single nucleotide polymorphism; TC, total cholesterol; TG, triglycerides. View Large Moreover, among individuals <55 years of age with obesity and a family history of hypertension, the AG or GG genotypes of rs6219 (IGF-1) conferred a decreased risk of hypertension (P < 0.05). The HRs for these groups (95% CIs) were 0.71 (0.51–0.99), 0.55 (0.31–0.95), and 0.67 (0.45–0.99), respectively. Among the obese population, rs2002880 G>A in IGF-1R conferred a decreased risk of hypertension (HR = 0.43, P = 0.044) (Table 4). All of the subgroup results are listed in Supplementary Table S3. Replication of the associations of rs1815009 and rs13379905 with hypertensions in adolescents The demographic information for 3,551 adolescents is listed in Supplementary Table S4. The characteristics of sex, age, TC, HDL-C, and LDL-C were not significantly different between the groups, whereas z-SBP, z-DBP, TG, and BMI did exhibit significantly differences. The association of rs13379905 (TT vs. CC + CT) with prehypertension was replicated (OR = 3.84, P = 0.007) (Supplementary Table S5). Particularly, the association of rs13379905 with prehypertension/hypertension was statistically significant in males, with an additive OR of 1.80 (P = 0.005) following adjustment for age, sex, BMI, TC, TG, LDL, and HDL. The association of rs1815009 and hypertensions was not replicated (Supplementary Table S6). Further quantitative trait analysis of the SBP and DBP Z-scores indicated that those for SBP (1.04 ± 0.97, 1.09 ± 1.03, 1.89 ± 0.66) increased linearly for the CC, CT, TT rs13379905 genotypes in males, with a P value of 0.042 after adjusting for covariates (Figure 1). The detailed data are listed in Supplementary Table S7. Figure 1. View largeDownload slide Comparison of SBP Z-scores among the rs13379905 genotypes in adolescent males with an average age of 10.86 ± 2.94 years. The SBP Z-score (1.04 ± 0.97, 1.09 ± 1.03, 1.89 ± 0.66) increased linearly for the CC, CT, and TT genotypes of rs13379905 in males (P = 0.042). Abbreviation: SBP, systolic blood pressure. Figure 1. View largeDownload slide Comparison of SBP Z-scores among the rs13379905 genotypes in adolescent males with an average age of 10.86 ± 2.94 years. The SBP Z-score (1.04 ± 0.97, 1.09 ± 1.03, 1.89 ± 0.66) increased linearly for the CC, CT, and TT genotypes of rs13379905 in males (P = 0.042). Abbreviation: SBP, systolic blood pressure. Quantitative trait analysis of IGF-1 and IGF-1R The IGF-1 and IGF-1R levels showed no difference between the hypertension and control groups. The quantitative trait analysis results for IGF-1 and IGF-1R levels are shown in Supplementary Table S8. The IGF-1R concentration (median and interquartile range) in the subjects with the CC genotype of rs2002880 [0.70 (0.42, 1.18) (ng/ml)] were significantly different from those in subjects with the CT genotype [1.18 (0.83, 1.52) (ng/ml)], with P of 0.011 (Figure 2). Figure 2. View largeDownload slide Serum IGF-1R levels by rs2002880 genotypes in hypertension cases. The dots represent individual IGF-1R levels. The longest line represents the median, and the whiskers extend to 1.5-fold the interquartile range. The IGF-1R concentrations (median and interquartile range) in the subjects with rs2002880 CC genotype [0.70 (0.42, 1.18) (ng/ml)] were significantly different than in those with the CT genotype [1.18 (0.83, 1.52) (ng/ml)], P = 0.011. Abbreviations: IGF-1R, insulin-like growth factor 1 receptor. Figure 2. View largeDownload slide Serum IGF-1R levels by rs2002880 genotypes in hypertension cases. The dots represent individual IGF-1R levels. The longest line represents the median, and the whiskers extend to 1.5-fold the interquartile range. The IGF-1R concentrations (median and interquartile range) in the subjects with rs2002880 CC genotype [0.70 (0.42, 1.18) (ng/ml)] were significantly different than in those with the CT genotype [1.18 (0.83, 1.52) (ng/ml)], P = 0.011. Abbreviations: IGF-1R, insulin-like growth factor 1 receptor. DISCUSSION The GH-IGF-1 signal pathway has been reported to play an important role in the maintenance and development of cardiovascular disorders and to be involved in the pathophysiology of hypertension.18 We observed the significant associations between rs1815009 and rs2654981 (recessive model) and hypertension in the case–control study as well as a significant association of rs13379905 with incident hypertension in a follow-up population. rs1815009 and rs13379905 in IGF-1R were examined in an adolescent population, and the association of rs13379905 with prehypertension/hypertension was further verified. Furthermore, stratified analysis showed that the rs35767 and rs6219 SNPs in IGF-1 were associated with hypertension among individuals with different BMIs, ages, and family histories. These results jointly indicated that genetic variations in the IGF-1 signaling pathway might contribute to hypertension susceptibility. Previous studies have reported that BMI is strongly associated with hypertension in northern Chinese adults.19 Remarkably, we observed a negative association between the rs35767 G>A IGF-1 variant and hypertension in the normal weight group, while a positive association was observed in the obese population. These results suggested that BMI might modulate the genetic effects of IGF-1 on hypertension. D’Aloisio20 showed that the TT genotypic variant of rs6219 was associated with a 10% increase in the estimated mean IGF-1 levels relative to the reference genotypes among African Americans. Our analysis revealed that rs6219 might not play a causal role in IGF-1 levels but that, as with incident hypertension, the AG and GG rs6219 genotypes confer a decreased risk of hypertension in individuals who are <55 years of age, are obese or have a family history of hypertension. Several studies have showed that low circulating IGF-1 levels are associated with increased risk for stroke21 and hypertension.22 Our results indicated that hypertension cases had slightly higher IGF-1 concentrations compared with normotensives, although the difference was not very strong. This result may be due to the ethnicity of the examined study groups or inadequate statistical power. Rietveld et al.23 found that only homozygous carriers of the common allele (IGF-1 192 bp) exhibited a significant decline in serum IGF-1 levels with age, whereas heterozygous individuals and noncarriers did not. IGF-1R plays an important role in the 2 well-known signaling pathways namely, the PI3K-AKT and MAPK pathways.24,25 In addition, IGF-1R can modify calcium-dependent signaling pathways.26 Because the above pathways are involved in hypertension, it is possible that IGF-1R contributes to genetic susceptibility to hypertension. We observed that rs1815009 T>C, rs2654981 T>C, and rs13379905 C>T were significantly associated with hypertension, suggesting a putative role of IGF-1R in conferring susceptibility to the examined hypertension-related phenotypes. The rs1815009 variant was predicted to produce motif changes in smad4, a key effector in the transforming growth factor (TGF)-α signaling pathway.27 Our previous studies suggested that TGFBR2 variants increase the risk of hypertension and alter BP homeostasis.13 HapMap was used to identify nearby SNPs in linkage disequilibrium with the rs2654981 variant, revealing that the IGF-1R locus is not linked to any variant in a nearby locus, although a long-distance effect cannot be ruled out (Supplementary Figure 1). It is plausible that rs26548981 in the 3’UTR, a binding-site for miRNAs, may affect mRNA stability and translation.28 Moreover, bioinformatics analysis showed that the rs13379905 C>T variation may affect the transcription factor binding sites for NFATc2, which plays an important role in pathological cardiac remodeling and heart failure via the activation of NFAT transcription factors.29 Our data suggest that the CT and TT rs13379905 genotypes exacerbate the occurrence and development of hypertension compared with the CC genotype, and the association of these alleles with prehypertension/hypertension was further replicated in adolescent males. Family history is an important, nonmodifiable risk factor for hypertension, and the heritability of hypertension ranges from 25% in pedigree studies to 65% in twin studies.30 The association between rs13379905 and hypertension in individuals with a family history was stronger than in those without a family history. Additionally, no significant correlation between IGF-1R levels and hypertension was detected. Similarly, an animal study showed no significant differences in the IGF-1R mRNA and protein levels in the nucleus tractus solitarii between WKY and spontaneously hypertensive rats.31 There are several limitations in our study. First, no significant difference in the serum IGF-1 and IGF-1R were observed between hypertension cases and controls, despite the fact that strict standards were used to select representative cases and controls. Second, potential biases in case–control studies can lead to distorted results in epidemiological association studies. Regardless of the above limitations, this study found significant associations between the rs1815009, rs2654981, and rs13379905 polymorphisms in IGF-1R and hypertension in a relatively large population. Future functional studies on these results are warranted. In conclusion, our findings suggest that IGF-1R genetic polymorphisms are significantly associated with hypertension, and that BMI, age, and family history may play important modulating roles in the genetic effects of IGF-1 on hypertension susceptibility. SUPPLEMENTARY MATERIAL Supplementary materials are available at American Journal of Hypertension online. DISCLOSURE The authors declared no conflict of interest. ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (Grant No.81541071 and No.81573232), Anhui Provincial Natural Science Foundation (1308085MH135), Science & Technology Program of Wuxi (No. ZD1011 and CSEW1N1112), and the Priority Academic Program for the Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. REFERENCES 1. Messerli FH, Williams B, Ritz E. 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American Journal of HypertensionOxford University Press

Published: Apr 1, 2018

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