Associations of E‐proteinoid 3 receptor genetic polymorphisms with salt sensitivity, longitudinal blood pressure changes, and hypertension incidence in Chinese adults

Abstract

The E‐proteinoid 3 receptor (PTGER3), a member of the prostaglandin E2 (PGE2) subtype receptor, belongs to the G‐protein‐coupled superfamily of receptors. Animal studies have demonstrated its involvement in salt sensitivity by regulating sodium reabsorption. This study aimed to investigate the association between genetic variants of PTGER3 and salt sensitivity, longitudinal blood pressure (BP) changes, and the incidence of hypertension in Chinese adults. A chronic salt intake intervention was conducted involving 514 adults from 124 families in the 2004 Baoji Salt‐Sensitivity Study Cohort in northern China. These participants followed a 3‐day regular baseline diet, followed by a 7‐day low‐salt diet (3.0 g/d) and a 7‐day high‐salt diet (18 g/d), and were subsequently followed for 14 years. The findings revealed a significant relationship between the single nucleotide polymorphism (SNP) rs17482751 of PTGER3 and diastolic blood pressure (DBP) response to high salt intervention. Additionally, SNPs rs11209733, rs3765894, and rs2268062 were significantly associated with longitudinal changes in systolic blood pressure (SBP), DBP, and mean arterial pressure (MAP) during the 14‐year follow‐up period. SNP rs6424414 was significantly associated with longitudinal changes in DBP over 14 years. Finally, SNP rs17482751 showed a significant correlation with the incidence of hypertension over 14 years. These results emphasize the significant role of PTGER3 gene polymorphism in salt sensitivity, longitudinal BP changes, and the development of hypertension in the Chinese population.

1. INTRODUCTION

Hypertension is one of the most prevalent independent risk factors for cardiovascular disease, making it a prominent global health concern. ¹ The development of hypertension is influenced by both genetic and environmental factors. ² , ³ Extensive epidemiological studies have consistently demonstrated a positive correlation between dietary salt intake and blood pressure (BP). ⁴ , ⁵ However, the individual response to sodium intake in relation to BP varies considerably. ⁶ , ⁷ The variations in salt sensitivity in relation to BP may be partly attributed to genetic susceptibility. ⁸ , ⁹ Therefore, investigating the relationship between genetic variation and the salt sensitivity of BP can not only contribute to understanding the underlying physiological pathways of hypertension but also facilitate the development of novel antihypertensive medications targeting specific genotypes.

The PTGER3 receptor, known as the E‐proteinoid 3 receptor, belongs to the G‐protein‐coupled superfamily of receptors and is classified as a subtype receptor of prostaglandin E2 (PGE2). ¹⁰ The human PTGER3 gene is located in the short arm of chromosome 1 (hchr 1p31), spanning a length of 80 kb and comprising 10 exons. ¹¹ Notably, PTGER3 exhibits a high expression in smooth muscle, adipose tissue, and kidney, ¹² thus playing a crucial role in various pathological processes, including the regulation of vascular tone, inflammation, and urine concentration and dilution. ¹³ , ¹⁴ Epidemiological studies have shown that the PTGER3 haplotype ATAAA (rs2206344, rs3765894, SNP_A‐4228934, rs2744918, rs2268062) is associated with the risk of hypertension in white Europeans. ¹⁵ In addition, the activation of PTGER3 in the systemic and renal vascular systems is linked to salt‐induced vasodilation damage and salt‐sensitive hypertension. ¹⁶ Animal studies have demonstrated that under a high‐salt diet, wild‐type mice exhibit heightened sympathetic nerve activation compared to PTGER3 knockout (PTGER3^−/−) mice, thereby stimulating immune cells involved in the development of salt‐sensitive hypertension. ¹⁷ However, no previous studies have explored the relationship between PTGER3 and salt sensitivity of BP in humans. Moreover, no studies have established an association between genetic variants in the PTGER3 and long‐term BP changes, or the development of hypertension.

Therefore, the objective of this study is to investigate the association between PTGER3 genetic variants and BP responses under strict sodium intervention. Additionally, we also aim to conduct a prospective analysis to investigate the relationship of PTGER3 genetic variants with longitudinal BP changes and the incidence of hypertension in our previously established Chinese cohort.

2. MATERIALS AND METHODS

2.1. Study participants

We utilized the data from the Baoji Salt‐Sensitive Study cohort, which comprised 514 adults from 124 families residing in seven villages within Baoji City, Shaanxi Province, China. The detailed study design has been published elsewhere. ¹⁸ , ¹⁹ , ²⁰ , ²¹ , ²² , ²³ , ²⁴ This cohort was established in 2004, with 514 participants meeting the eligibility criteria at baseline. We enrolled Han Chinese individuals aged 18–60 years, with systolic blood pressure (SBP) within the range of 130–160 mm Hg and diastolic blood pressure (DBP) within the range of 85–100 mm Hg, who were not using antihypertensive medication, as probands in this cohort. In addition, we recruited their parents, siblings in two‐generation families, spouses, and descendants of three‐generation families. Individuals with secondary hypertension, severe cardiovascular disease or diabetes, liver and kidney dysfunction, alcoholism, or pregnancy were excluded from the study. Out of these participants, 333 nonparent individuals underwent a dietary intervention at baseline to investigate the associations between potential genetic polymorphisms and BP response to salt intake, while 181 participants, who were the parents of the probands, were excluded due to safety concerns. Details of the dietary intervention have been reported. ²² , ²³ , ²⁴ , ²⁵ Briefly, the intervention involved a 3‐day usual diet, followed by a 7‐day low‐salt diet (51.3 mmol of sodium per day), and subsequently a 7‐day high‐salt diet (307.8 mmol of sodium per day). The study kitchen provided the participants with the designated food, while potassium intake remained constant throughout the intervention period. All subjects were given detailed dietary instructions to avoid eating table salt, cooking salt, and high‐salt foods during the 21‐day study period. To ensure participants adhered to the intervention plan, they were asked to eat three meals a day, all of which were unsalted, in the study kitchen under the supervision of the researchers throughout the study period. As required by the study protocol, the researchers added prepackaged salt to each subject's diet.

To further investigate the relationship between potential genetic polymorphisms and longitudinal BP changes, as well as the incidence of hypertension, the cohort was subsequently followed up in 2009, 2012, and 2018. Out of the initial 514 participants at baseline, 102 individuals were lost to follow‐up in 2009, 56 individuals in 2012, and 59 individuals in 2018. Information on hypertension and the use of antihypertensive medication was collected using a standardized questionnaire.

The Ethics Committee of the First Affiliated Hospital of Xi'an Jiaotong University approved the research protocol (code: 2015‐128). All participants provided written informed consent for the Intervention period and follow‐up periods. This study follows the principles of the Declaration of Helsinki. (ClinicalTrials.gov. Registration number: NCT02734472).

2.2. BP measurement and definition of BP response to dietary intervention

BP was measured in a seated position using a standard mercury sphygmomanometer. As mentioned before, ¹⁹ , ²⁰ BP was measured by trained and certified observers measured BP during the 3‐day baseline observation period and on days 5, 6, and 7 of each 7‐day intervention period. Hypertension was defined as SBP ≥140 mm Hg, DBP ≥90 mm Hg, or the use of antihypertensive drugs. ²⁶ The mean arterial pressure (MAP) was calculated as DBP + [1/3 × (SBP‐DBP)]. The pulse pressure (PP) was calculated as SBP–DBP.

BP changes from the low‐salt intervention to the high‐salt intervention may offer a more reliable measure of salt‐sensitivity as the participants' salt intake was controlled during both phases. However, identifying genetic factors that determine the BP response to a low salt diet compared to a regular diet would have more direct implications for clinical practice and public health. Therefore, following the approach described in previous studies, ¹⁹ , ²¹ the BP responses were defined as follows: BP response to high salt = BP on the high‐salt diet – BP on the low‐salt diet; and BP response to low salt = BP on the low‐salt diet – baseline BP.

2.3. Blood and urine biochemical analyses

Peripheral venous blood samples were collected from fasting participants on the final day of each intervention period. The samples were immediately centrifuged at 3000×g for 10 min and stored at −80°C for subsequent analysis. To ensure compliance with the dietary intervention, 24‐h urine samples were obtained at baseline and on the last day of each intervention. Measurements of total cholesterol, triglycerides, high‐density lipoprotein, fasting blood glucose levels, urinary sodium, and urinary potassium concentrations were performed using an automated biochemical analyzer (Hitachi, Tokyo, Japan). The details of these tests have been described previously. ¹⁸ , ¹⁹

2.4. SNP selection and genotyping

The screening process for nine single nucleotide polymorphisms (SNPs) of the PTGER3 gene was conducted using the National Center for Biotechnology Information database (http://www.ncbi.nlm.nih.gov/projects/SNP) and the Genomic Variation Server database (http://gvs.gs.washington.edu/GVS147/). The selected SNPs include rs7543182, rs58239962, rs17482751, rs516647, rs6424414, rs11209733, rs3765894, rs2268062, and rs1005747. These SNPs were chosen based on specific criteria, namely, adherence of the SNPs at the tag site to Hardy–Weinberg equilibrium (HWE) with a p‐value ≥ .05, a minor allele frequency (MAF) ≥ 0.05, and a linkage disequilibrium coefficient R ² ≥ 0.8. All genotyping experiments were performed by CapitalBio (CapitalBio Corp, Beijing, China) as previously mentioned. ²¹ , ²² , ²³ , ²⁴

2.5. Statistical analyses

For quality control purposes, we employed PLINK software (version 1.9) to assess the mendelian consistency of the SNP genotype data. Additionally, we utilized Haploview software (version 4.1) to estimate pairwise linkage disequilibrium between the SNPs. To investigate associations between the phenotypes and each selected SNP, we conducted association analyses using mixed‐effects regression models. For each SNP analysis, three genetic models (additive, dominant, and recessive) were tested using the lme function in the nlme R package within the PLINK software.

For analyses of the incidence of hypertension, we excluded 51 participants with hypertension at baseline. Using a generalized linear mixed model, we assessed the additive association between each SNP and hypertension incidence. Multivariable analyses were conducted on glmer function in lme4 R package. In addition, those association analyses were all adjusted for baseline age, gender, BMI, fasting glucose, total cholesterol, triglycerides, and creatinine as fixed effects and familial correlation as a random effect. Bonferroni correction was used to adjust for multiple testing. p < 0.05 was considered as statistically significant.

3. RESULTS

3.1. Baseline characteristics and BP responses to salt interventions

As shown in Table 1, BP correlated with salt intake, decreasing on a low‐salt diet and increasing on a high‐salt diet. Additionally, levels of urinary sodium excretion significantly decreased on the low‐salt diet compared to the baseline diet and significantly increased on the high‐salt diet (p < 0.05, Table S1). These findings suggest compliance with the dietary intervention.

TABLE 1.

Baseline characteristics and BP response to salt intervention.

	Probands	Siblings	Spouses	Offspring	Parents
No. of participants	99	167	18	49	181
Age (years)	41.8 ± 8.4	39.8 ± 7.4	47.4 ± 6.1	23.3 ± 6.9	66.1 ± 8.3
Male (%)	69.7	49.1	26.3	49.0	48.4
Body mass index (kg/m2)	23.0 ± 2.8	22.2 ± 2.9	23.1 ± 4.7	20.1 ± 2.7	20.4 ± 2.6
BP at baseline (mm Hg)
SBP	120.9 ± 12.5 a	107.6 ± 11.1	108.6 ± 12.2	102.7 ± 10.7	123.2 ± 21.3
DBP	79.0 ± 8.3 a	70.1 ± 8.1	70.6 ± 6.9	63.4 ± 8.9	70.5 ± 10.5
MAP	93.0 ± 9.0 a	82.6 ± 8.7	83.3 ± 7.9	76.5 ± 9.2	88.0 ± 13.1
BP response to low‐salt diet (mm Hg)
SBP	111.7 ± 10.0 a , b	103.4 ± 9.1 b	102.5 ± 7.7 b	100.3 ± 9.4 b	–
DBP	72.8 ± 9.3 a , b	66.4 ± 7.7 b	67.1 ± 5.8 b	60.7 ± 8.3 b	–
MAP	85.7 ± 9.0 a , b	78.7 ± 7.6 b	78.9 ± 5.4 b	73.9 ± 8.3 b	–
SBP change	−8.65 ± 9.52 a	−3.90 ± 5.41	−6.15 ± 7.88	−2.38 ± 4.79	–
DBP change	−6.00 ± 6.71 a	−3.64 ± 4.83	−3.48 ± 6.36	−2.70 ± 5.21	–
MAP change	−6.88 ± 7.07 a	−3.73 ± 4.55	−4.37 ± 6.52	−2.59 ± 4.56	–
BP response to high‐salt diet (mm Hg)
SBP	118.9 ± 11.2 a , c	108.5 ± 11.1 c	108.4 ± 10.9 c	102.0 ± 10.0 c	–
DBP	76.2 ± 8.1 a , c	68.7 ± 9.3 c	68.6 ± 7.5	60.9 ± 8.3	–
MAP	90.4 ± 8.5 a , c	82.0 ± 9.5 c	81.9 ± 8.0 c	74.6 ± 8.4	–
SBP change	7.16 ± 7.40 a	5.09 ± 6.50	5.93 ± 7.90	1.72 ± 4.07	–
DBP change	3.49 ± 7.33 a	2.29 ± 5.73	1.51 ± 4.69	0.22 ± 4.52	–
MAP change	4.71 ± 6.86 a	3.22 ± 5.60	2.98 ± 5.61	0.72 ± 3.79	–

Note: Normally distributed variables are expressed as mean ± SD.

Abbreviations: BP, blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; SBP, systolic blood pressure.

^a p < .05 versus the siblings, spouses, or offspring.

^b p < .05 versus the baseline.

^c p < .05 versus the low‐salt period.

3.2. PTGER3 and BP response to salt intervention

The genomic position, MAF, HWE test, region and alleles of all SNPs in this study are shown in Table 2. With the exception of rs516647, which was excluded due to deviation from HWE, remaining SNPs were included in the follow‐up analysis. The association between SNPs and BP responses to dietary salt intake is shown in Table 3. The SNP rs17482751 was significantly associated with DBP response to the high‐salt intervention.

TABLE 2.

Information on genotyped SNPs of PTGER3.

SNP	Position	Region	Alleles a	MAF	p‐value b , c
rs7543182	70874290	Intronic	A/C	0.2427	0.532
rs58239962	70888503	Intronic	C/T	0.211	1
rs17482751	70910089	Intronic	A/C	0.3324	1
rs516647	70978466	Intronic	G/A	0.3293	<0.001
rs6424414	70989787	Intronic	C/T	0.4598	0.067
rs11209733	71006067	Intronic	G/C	0.2865	0.710
rs3765894	71015115	Intronic	G/A	0.2816	0.853
rs2268062	71019616	Intronic	C/T	0.2832	1
rs1005747	71045832	Intronic	A/G	0.2429	0.219

Abbreviations: MAF, minor allele frequency; SNP, single nucleotide polymorphism.

^a Alleles are presented as major: minor allele.

^b p values of Hardy–Weinberg equilibrium test.

^c Parents only (parental generation).

TABLE 3.

PTGER3 SNPs associated with BP response to dietary intervention.

		SBP response		DBP response		MAP response		PP response
SNP	Allele	β	P	β	P	β	P	β	P
Low‐salt intervention
rs7543182	A	−0.005	0.952	0.092	0.304	0.059	0.513	−0.104		0.247
rs58239962	C	−0.152	0.125	−0.153	0.122	−0.166	0.093	−0.044		0.656
rs17482751	A	−0.102	0.216	−0.001	0.988	−0.044	0.591	−0.136		0.098
rs6424414	C	−0.004	0.960	−0.020	0.790	−0.015	0.844	0.016		0.833
rs11209733	G	0.016	0.863	0.055	0.543	0.043	0.634	−0.036		0.687
rs3765894	G	0.013	0.883	0.043	0.631	0.034	0.704	−0.027		0.762
rs2268062	C	0.016	0.861	0.052	0.563	0.042	0.647	−0.033		0.712
rs1005747	A	0.112	0.227	0.109	0.238	0.120	0.195	0.036		0.694
High‐salt intervention
rs7543182	A	0.016	0.864	0.063	0.487	0.049	0.587	−0.056		0.542
rs58239962	C	0.142	0.159	0.081	0.412	0.110	0.266	0.094		0.348
rs17482751	A	0.060	0.473	0.203	0.043	0.131	0.113	−0.111		0.185
rs6424414	C	0.006	0.934	−0.080	0.286	−0.053	0.484	0.108		0.158
rs11209733	G	−0.007	0.940	−0.105	0.244	−0.075	0.406	0.121		0.189
rs3765894	G	−0.018	0.848	−0.114	0.209	−0.085	0.348	0.117		0.205
rs2268062	C	−0.004	0.966	−0.107	0.237	−0.076	0.406	0.128		0.167
rs1005747	A	−0.080	0.395	−0.162	0.081	−0.143	0.126	0.090		0.339

Note: For associations those were not significant under any model, β and P values for an additive model are listed. All genetic models are based on the minor allele of each SNP. Statistically values are presented in bold.

Abbreviations: BP, blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; PP, pulse pressure; SBP, systolic blood pressure; SNP, single nucleotide polymorphism.

3.3. Association analyses for longitudinal BP changes and incidence of hypertension

The characteristics of the cohort at baseline (2004) and during the follow‐up periods (2009, 2012, and 2018) are shown in Table 4. After 14 years of follow‐up, the SBP, DBP, and MAP of the participants showed an increase of 21.2, 7.9, and 12.3 mm Hg, respectively. Additionally, 160 participants, accounting for 53.9% of the cohort, developed hypertension.

TABLE 4.

Characteristics of cohort during baseline and follow‐up.

Characteristics	Baseline in 2004	Follow‐up in 2009	Follow‐up in 2012	Follow‐up in 2018
Sex (M/F)	267/247	208/204	185/171	155/142
Age (years)	48.6 ± 19.8	53.3 ± 14.2	56.6 ± 19.0	62.3 ± 12.1
Body mass index (kg/m2)	22.2 ± 3.1	22.4 ± 3.3	23.6 ± 3.5	24.6 ± 3.7
SBP (mm Hg)	115.2 ± 17.6	120.0 ± 17.3	129.6 ± 18.7	136.4 ± 17.4
DBP (mm Hg)	71.3 ± 10.0	75.8 ± 10.4	77.9 ± 10.9	79.2 ± 11.2
MAP (mm Hg)	86.0 ± 11.5	90.5 ± 11.7	95.1 ± 11.9	98.3 ± 12.0
HR (times/min)	71.9 ± 7.6	72.1 ± 8.0	69.3 ± 8.3	75.5 ± 12.3
Fasting glucose (mg/dL)	86.9 (80.9–94.4)	91.5 (86.0–99.1)	92.6 (86.7–100.8)	90.8 (84.8–97.5)
Total cholesterol (mg/dL)	155.5(138.5–177.6)	157.7 ± 29.0	162.4 (145.7–186.4)	178.7 ± 35.3
Triglycerides (mg/dL)	112.7 (82.9–158.5)	129.3 (94.5–175.5)	119.0 (87.0–167.4)	126.5 (91.6–183.7)
HDL (mg/dL)	47.4 ± 11.2	50.9 ± 11.6	49.9 (42.7–58.6)	50.0 (43.2–61.6)
Hypertension at baseline (n, %)	51 (9.9)	–	–	–
Hypertension incidence (n, %) a	–	77 (18.9)	103 (28.9)	160 (53.9)
Use of antihypertensive drugs
ACEI/ARB (n, %)	–	164 (39.7)	94 (26.3)	54 (18.3)
β‐blockers (n, %)	–	6 (1.4)	0	13 (4.3)
CCB (n, %)	–	28 (6.8)	89 (25.0)	132 (44.3)
Diuretics (n, %)	–	40 (9.6)	98 (27.6)	72 (24.3)
Others	–	175 (42.5)	75 (21.1)	26 (8.7)

Note: Normal distribution variables are represented by mean ± SD. Variables with non‐normal distribution are represented as median (quartile range). Categorical variables are represented by n %.

Abbreviations: ACEI, Angiotensin‐converting enzyme inhibitors; ARB, angiotensin receptor blockers; CCB, calcium channel blockers; DBP, diastolic pressure; HDL, high‐density lipoprotein; HR, heart rate; MAP, mean arterial pressure; SBP, systolic blood pressure.

^a Participants with hypertension at baseline were excluded.

The relationship between a single SNP of the PTGER3 gene and BP changes after 5 years (2004–2009), 8 years (2004–2012), and 14 years (2004–2018) can be found in Table 5. SNP rs17482751 was significantly associated with longitudinal changes in SBP, MAP, and PP after 5 or 8 years of follow‐up. SNP rs6424414 was significantly associated with longitudinal changes in DBP after 8 or 14 years of follow‐up. SNPs rs11209733, rs3765894 and rs2268062 were significantly associated with longitudinal changes in SBP, DBP, and MAP after 14 years of follow‐up.

TABLE 5.

Association of PTER3 SNPs with longitudinal BP changes blood pressure from baseline to the follow‐ups.

SNP	BP (2004–2009)				BP (2004–2012)				BP (2004–2018)
	SBP change	DBP change	MAP change	PP change	SBP change	DBP change	MAP change	PP change	SBP change	DBP change	MAP change	PP change
rs7543182	0.295	0.710	0.458	0.245	0.449	0.272	0.302	0.878	0.871	0.898	0.996	0.756
rs58239962	0.825	0.661	0.886	0.509	0.074	0.271	0.120	0.129	0.651	0.430	0.484	0.982
rs17482751	0.007 a	0.385	0.029 a	0.009 a	0.010	0.389	0.049 a	0.006	0.090	0.668	0.270	0.059
rs6424414	0.304	0.119	0.155	0.860	0.379	0.018	0.066	0.512	0.615	0.021 a	0.215	0.510
rs11209733	0.100	0.087	0.069	0.356	0.340	0.149	0.181	0.886	0.018 a	0.026 a	0.012 a	0.258
rs3765894	0.135	0.065	0.068	0.550	0.609	0.156	0.272	0.674	0.033 a	0.015 a	0.012 a	0.441
rs2268062	0.138	0.093	0.086	0.474	0.571	0.190	0.289	0.793	0.036 a	0.020 a	0.014 a	0.425
rs1005747	0.386	0.314	0.307	0.691	0.792	0.995	0.897	0.724	0.326	0.292	0.261	0.640

Note: For associations that were not significant under any model, P values for an additive model are listed. All genetic models are based on the minor allele of each SNP.

Abbreviations: BP, blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; OR, odds ratio; PP, pulse pressure; SBP, systolic blood pressure; SNP, single nucleotide polymorphism.

^a Dominant model. Statistically values are presented in bold.

The relationship between the PTGER3 SNPs and hypertension incidence over the 5‐, 8‐, and 14‐year periods of follow‐up is shown in Table 6. The SNP rs7543182 was significantly associated with hypertension incidence during the 8‐year follow‐up period, and the SNP rs17482751 was significantly associated with hypertension incidence during the 14‐year follow‐up period.

TABLE 6.

Association of PTER3 individual SNPs with hypertension incidence over 14 years of follow‐up.

SNP	Allele	Incident hypertension (2004–2009)		Incident hypertension (2004–2012)		Incident hypertension (2004–2018)
		OR (95%CI)	p value	OR (95%CI)	p value	OR (95%CI)	p value
rs7543182	A	0.146 (−0.276, 0.556)	0.490	−0.425 (−0.056, 0.801)	0.025	0.261 (−0.129, 0.662)	0.194
rs58239962	C	0.414 (−0.049, 0.873)	0.077	−0.069 (−0.491, 0.344)	0.745	0.116(−0.324, 0.559)	0.605
rs17482751	A	0.254 (−0.156, 0.664)	0.223	0.025(−0.349, 0.398)	0.895	−0.452 (−0.091, 0.821)	0.015
rs6424414	C	0.238 (−0.134, 0.678)	0.242	−0.212 (−0.546, 0.116)	0.207	−0.126(−0.465, 0.210)	0.462
rs11209733	G	−0.189 (−0.652, 0.257)	0.412	−0.062 (−0.451, 0.322)	0.753	−0.191(−0.591, 0.202)	0.342
rs3765894	G	−0.236 (−0.712, 0.224)	0.321	−0.100 (−0.503, 0.297)	0.624	−0.189(−0.603, 0.219)	0.367
rs2268062	C	−0.200 (−0.673, 0.259)	0.400	−0.080 (−0.483, 0.317)	0.694	−0.200(−0.615, 0.207)	0.338
rs1005747	A	−0.219 (−0.715, 0.255)	0.374	−0.124 (−0.541, 0.285)	0.557	−0.154(−0.460, 0.147)	0.317

Note: For associations that were not significant under any model, P values for an additive model are listed. All genetic models are based on the minor allele of each SNP. Statistically values are presented in bold.

Abbreviations: OR, odds ratio; SNP, single nucleotide polymorphism.

4. DISCUSSION

In our study, we discovered that genetic variations in PTGER3 are implicated in the salt sensitivity of BP under strict salt intervention in the Chinese population. Additionally, we have identified several novel SNPs in the PTGER3 gene that exhibit significant associations with longitudinal BP changes and the development of hypertension. These findings suggest that the PTGER3 gene plays a biologically significant role in BP regulation and can contribute to a better understanding of the genetic mechanisms underlying hypertension.

Several evidence has shown that PTGER3 mRNA is widely expressed in the medulla thick ascending limb (mTAL) and collecting duct. ²⁷ Jensen et al. ²⁸ conducted research on rat outer medulla and observed that under a high salt diet, the expression of PTGER3 mRNA doubled. Furthermore, multiple studies have highlighted the involvement of PTGER3 in renal sodium excretion. For instance, Hao et al. ²⁹ discovered that PTGER3 activation under high salt conditions inhibits the activity of Na⁺‐K⁺‐2Cl⁻ cotransporter in the mTAL and aquaporin‐2 in the collecting ducts, leading to increased urine output. Rytved et al. ³⁰ also reported that PTGER3 activation in the skin epithelium of frogs suppresses the stimulating effect of antidiuretic hormones on intraepithelial Na⁺ transport. Additionally, recent evidence has linked PTGER3 to the development of salt‐sensitive hypertension. In mice with impaired function of peroxisome proliferator‐activated receptor γ, PTGER3 activation caused salt‐induced dysvasodilation and salt‐sensitive hypertension. ¹⁶ Under continuous hypertensive stimulation, wild‐type mice experienced a significant SBP increase, accompanied by renal T cell infiltration and accumulation of isolevuglandin adduct in spleen dendritic cells. Conversely, in PTGER3 knockout mice, these phenomena were completely suppressed. ¹⁷ To our knowledge, this study represents the first investigation of the relationship between PTGER3 genetic variants and salt sensitivity in humans. The results demonstrated a significant association between PTGER3 SNP rs17482751 and the response of DBP to high salt intervention. However, the precise mechanism through which SNP rs17482751 is associated with salt sensitivity of BP remains unclear and warrants further study.

Our study is the first to explore the association of PTGER3 genetic variants with long‐term BP changes and the development of hypertension. In this study, we found that PTGER3 SNPs rs11209733, rs3765894, and rs2268062 was significantly associated with the longitudinal changes of SBP, DBP, and MAP over 14 years, and SNP rs6424414 was significantly associated with longitudinal DBP changes over 14 years. Furthermore, we found a significant correlation between SNP rs17482751 and the incidence of hypertension after the 14‐year follow‐up period. PTGER3 may influence BP through its interaction with proinflammatory cytokines such as TNF‐α and IL‐1β. ³¹ Multiple animal studies have highlighted the involvement of PTGER3 in the regulation of inflammatory response led to hypertension development through mechanisms such as vascular tone, kidney function, and neuroregulation. In the central system, PTGER3 mediates the sympathetic response by binding to central prostaglandin E2 (PGE2), the main prostaglandin metabolized by arachidonic acid, thus influencing BP regulation. ³² , ³³ Regarding peripheral blood vessels, Chen et al. ³⁴ discovered that PTGER3 activation not only elevated baseline BP, but also partially facilitated angiotensin II‐dependent hypertension. Additionally, Kraemer et al. ³⁵ found that synchronized signaling between angiotensin II receptor 1 and PTGER3 in peripheral arteries of mice promoted vasoconstriction. Further studies demonstrated that blocking PTGER3 in prehypertensive stages inhibited the development of hypertension in spontaneously hypertensive rats. ³⁶ All this evidence suggests that the activity of PTGER3 can regulate the release of inflammatory factors, thus affecting the stability of BP. However, this process is very complex and more research is needed to reveal its detailed mechanisms and interactions. In an epidemiological study involving European whites, PTGER3 haplotypes ATAAA (composed of rs2206344, rs3765894, SNP_A‐4228934, rs2744918, and rs2268062) were associated with the risk of hypertension. ¹⁵ No evidence indirectly suggests that PTGER3 genetic variants are associated with long‐term BP in humans. To our knowledge, no evidence has suggested an association between PTGER3 genetic variants and the development of hypertension in humans. Therefore, our study, based on a long‐term follow‐up cohort, provides valuable evidence supporting the influence of PTGER3 genetic variants on long‐term BP and the development of hypertension. These findings contribute to our understanding of hypertension pathogenesis from a genetic perspective and offer new insights for targeted hypertension treatments.

The study possesses several strengths. Firstly, our study employed a family‐based approach in a rural area of northwest China, which ensured relative genetic homogeneity among participants. Additionally, the participants in the rural area shared similar living and dietary habits, thereby reducing bias arising from population stratification. Secondly, we utilized the widely recognized dietary intervention protocol from The Genetic Epidemiology Network of Salt Sensitivity (GenSalt) study. ¹⁸ This rigorous chronic salt loading intervention allowed for a more objective assessment of the impact of salt diet on BP phenotype. Furthermore, the 24‐h excretion of urine sodium confirmed good compliance among the subjects. Lastly, BP measurements were taken nine times, including at baseline and during each intervention stage, significantly reducing measurement bias. However, our research does have certain limitations. Firstly, the study lacks a validation cohort, necessitating replication of these findings in other populations with different genetic backgrounds. Additionally, due to the limited number of genotypic SNPs in the PTGER3 gene, some lower‐frequency genetic variants may have been overlooked in this study. Moreover, though our study had a rigorous salt intervention process, we did not count participants' salt intake at baseline. However, our selected subjects are all from the same rural area in the northwest of China, and they have the same diet, so this difference can be greatly reduced. Finally, when screening participants, based on strict exclusion criteria, we excluded individuals with severe liver or kidney function abnormalities, cardiovascular disease and diabetes based on BP range and biochemical tests; however, it is possible that participants with these complications have emerged during subsequent follow‐ups. We did not take into account the effect of complications on longitudinal BP changes and hypertension incidence in the follow‐up population.

In conclusion, this study presents the first evidence of significant associations between PTGER3 gene polymorphisms and BP response to salt intervention. By conducting single‐marker analyses, this study directly supports the involvement of the PTGER3 gene in long‐term BP changes and the development of hypertension. These findings serve as a foundation for potential prevention strategies and future treatment targets for hypertension. Moreover, this research contributes to the growing knowledge of genomic mechanisms that regulate BP and the development of hypertension.

CONFLICT OF INTEREST STATEMENT

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supporting information

Supplementary Table 1. Effects of dietary intervention on urinary sodium and potassium excretions

Chang M‐K, Wu G‐J, Bao P, et al. Associations of E‐proteinoid 3 receptor genetic polymorphisms with salt sensitivity, longitudinal blood pressure changes, and hypertension incidence in Chinese adults. J Clin Hypertens. 2024;26:955–963. 10.1111/jch.14859

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.