The association of gene polymorphisms in SREBP and its interaction with nutritional status on blood pressure phenotypes among children: a cross-sectional study

Abstract

Introduction

Previous studies have confirmed that the SREBP polymorphisms are associated with dyslipidemia. However, no researchers investigated the association between SREBP polymorphisms and blood pressure phenotypes in children.

Methods

A convenient cluster sampling method was adopted to conduct field survey in three middle schools. A total of 872 children were included in this cross-sectional study final analysis. Matrix-supported laser release/ionization time-of-flight mass spectrometry was used for genotyping of SREBP polymorphism. The association between SREBP polymorphisms and blood pressure phenotypes was analyzed by multivariable linear regression and Logistic regression analysis. A Bonferroni-corrected threshold of P < 0.025 (SREBP1) or P < 0.0125 (SREBP2) was considered significant.

Results

After adjusting for age, sex, age squared and BMI, individuals with GA/AA genotype of SREBP1/rs11868035 had higher systolic blood pressure (SBP) (β = 7.34, P = 0.004) than GG genotype, and SREBP1/rs2297508 C allele carriers were positively associated with SBP (β = 7.19, P = 0.008). A significant interaction between SREBP2/rs2228314 and gender on the risk of High Blood Pressure (HBP) (P_interaction = 0.034) was found. In boys, HBP risk increased by 101% for each additional G allele (OR = 2.01, 95% CI 1.15–3.53, P = 0.015), but not in girls. Besides, we also identified an interaction between SREBP2/rs2267439 and nutritional status on the risk of HBP (P_interaction=0.023). The risk of HBP in SREBP2/rs2267439 CT/TT genotype carriers is higher than that in CC genotype carriers in the overweight/obese children (OR = 2.68, 95% CI 1.06–6.75, P = 0.036), but no in non-overweight/obese children.

Conclusions

SREBP polymorphisms were significantly associated with blood pressure in children. It provides possible clues for personalized prevention and intervention of HBP in children from the perspective of genetic susceptibility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-025-05038-3.

Introduction

Hypertension is the leading cause of premature death and cardiovascular disease, it is estimated that the global deaths of high systolic blood pressure in 2019 is approximately 10.8 million [1, 2]. According to the data of the China Health and Nutrition Survey (CHNS), the prevalence of High Blood Pressure (HBP) in children rose from 8.1% in 2011 to 11.5% in 2015 [3]. HBP in childhood can be tracked into adulthood, leading to a range of cardiovascular diseases and target organ damage [4–6]. It has also been found that a favorable change in blood pressure (BP) from childhood to adulthood is associated with a low risk of cardiovascular disease compared with those with persistently elevated blood pressure [7]. This also illustrates the importance of identifying HBP in childhood. Studies have demonstrated that 40–50% of blood pressure variations can be inherited and that the proportion of blood pressure related genetic variants is explained by no more than 2–6% [8–10]. The occurrence and development of HBP are influenced by genetic factors and environmental factors.

The Sterol regulatory element-binding protein (SREBP) genes include SREBP1 and SREBP2. SREBP1 and SREBP2 are co-expressed transcription factors that play a key role in regulating cholesterol and fatty acid metabolism [11]. Lipid metabolism is also closely related to the occurrence and development of cardiovascular diseases [12, 13]. Several studies have reported the association between SREBP gene polymorphisms and hypertension, dyslipidemia, obesity, and other cardiovascular metabolic diseases in adults. SREBP1 gene polymorphisms have been associated with an increased risk of type 2 diabetes and dyslipidemia [14–16]. SREBP1/rs2297508 may regulate the association between dietary cholesterol intake and serum low density lipoprotein (LDL)/ total cholesterol (TC) levels in Chinese preschoolers [17]. Previous study confirmed that SREBP2 gene polymorphisms was associated with TC levels, SBP and acute coronary syndrome in Mexican adults [18]. Therefore, this study further investigated the association between SRBEP gene polymorphisms and blood pressure in children.

Accumulating evidence indicates that overweight or obesity is closely associated with blood pressure in children [19]. A meta-analysis found that the blood pressure levels of children and adolescents worldwide showed a long-term upward trend from 2000 to 2015, and the prevalence of HBP was 9.7% [20]. Additionally, subjects who were overweight or obese from childhood into adulthood had a 2.7 times higher risk of hypertension than normal-weight children and a higher risk of cardiovascular disease [21]. There were also significant gender differences in HBP in children. Our previous study also showed that the prevalence of hypertension in boys and girls was 10.2% and 8.9%, respectively [22].

Thus, the purpose of this study is to further probe the association between SRBEP gene polymorphisms and BP phenotypes in children and explore the interaction of SREBP gene polymorphisms with gender and nutritional status on HBP in Chinese children.

Methods

Study populations

Using a convenient cluster sampling method, a questionnaire survey, blood sample collection, and measurement of height, weight, and blood pressure were conducted among students from 3 middle schools in Changsha City, Hunan Province from July to August 2019 [23]. About 1019 children participated in the survey, 7 subjects were excluded for invalid questionnaire (The overall response rate was 99.31%). We further excluded participants with missing data on height, weight, and blood pressure (n = 98) and missing data on venous blood samples (n = 42). A total of 872 children aged 10–15 years were enrolled and divided into HBP group (n = 83) and non-HBP group (n = 789). This cross-sectional study was approved by the Biomedical Ethics Committee of Hunan Normal University before implementation (approval number: 2019-88), and we obtained informed consent from the subjects and their parents or legal guardians.

Measurement

Measurements including height, weight, and blood pressure of the children were performed by qualified graduate students. Height and weight were measured by standard protocols. In this study, the Omron U30 electronic sphygmomanometer, equipped with a small cuff (17–22 cm), was used to measure the blood pressure of children. Although the equipment has not passed the specialized certification for pediatrics, it has been verified through a pre-experiment and comparison with a mercury sphygmomanometer (average deviation error ≤ ± 5 mmHg) [24]. With at least 5 min rest, an electronic sphygmomanometer was used to measure the blood pressure of the participants. Each participant was measured twice in a seated position with a time interval of no less than 5 min, and the mean value was used for the analysis. When the BP difference between the two measurements is more than 10 mmHg, the measurement of blood pressure was repeated, with average measurement of the last two times for analysis. The questionnaire was used to collect the demographic data.

HBP was defined as systolic blood pressure (SBP) and (or) diastolic blood pressure (DBP) in children and adolescents aged 7–17 years ≥ the 95th percentile for age, gender, and height according to the national blood pressure reference for Chinese Han children and adolescents [25].

BMI (kg/m²) was calculated by body weight (kg) divided by height² (m²). The nutritional status of the participants was defined by national overweight and obesity screening BMI reference for Chinese children, overweight was defined as BMI between 85th to 90th gender and age-specific cut-off points, and obesity as larger than the 90th gender and age specific cut-off points [24, 26].

Genetic polymorphisms selection and genotyping

For the SREBP1 and SREBP2 genes, the population studies on gene polymorphisms related to cardiometabolic health were searched, and the gene polymorphisms that positive results were selected (Reference see in Table S1). Based on the 1000 genome database (https://www.internationalgenome.org) to identify the Minor Allele Frequency (MAF) of the single nucleotide polymorphisms (SNPs) in East Asians and using the criterion of MAF > 0.1. In our study, the MAF range of SREBP gene variations was 0.133 to 0.497. Finally, two SNPs from the SREBP1 gene were selected (rs11868035, rs2297508) and four SNPs from the SREBP2 gene were selected (rs7287010, rs2228314, rs2267443, rs2267439).

Venous blood samples were collected from all participants. Peripheral DNA was extracted using the method of salt extraction. Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS, Agena) was utilized for the genotyping of SREBP1 and SREBP2 polymorphisms [23]. 1% of repeated DNA samples were randomly selected for genotyping and the results of genotyping between duplicate samples were 100% consistent. The call rates of the genotyping of all polymorphisms were above 98% (Table S1).

Statistical analyses

Chi-square tests were conducted to test Hardy–Weinberg equilibrium of all polymorphisms. F-statistics (F_ST) was calculated with formula of F_ST = (P₁ − P₂) ² /[(P₁ + P₂) × (2 − (P₁ + P₂))] [27]. P₁ represents the effect allele frequency of a SNP obtained from the 1000 Genomes Project database of European ancestry and P₂ is the allele frequency of the SNP in the present study [27]. F_ST is an index reflecting the ancestral differences in the same gene polymorphism between the two populations. F_ST values ranging from 0 to 0.05 means the ancestral difference is small. F_ST of 0.05 to 0.15 (including 0.05, not 0.15) means the ancestral difference is moderate; F_ST of 0.15 to 0.25 (including 0.15, not 0.25) means ancestral differences is large; F_ST of > 0.25 means ancestral difference is very large [28]. Descriptive statistical analysis was performed to describe the general demographic characteristics, genotype and allele frequencies of the subjects. Using t/rank sum test to compare continuous variables, and chi-square tests for categorical variables. As previous genetic study, Akaike Information Criterion (AIC) were used to select the best genetic model [29], AIC is a index evaluating the model fitness, specically, lower AIC means better fit of the genetic model. The association between gene polymorphisms and blood pressure was analyzed by multiple linear regression and logistic regression analysis. The interaction effect between gene polymorphisms with gender and nutritional status on risk of HBP was analyzed by the multiplication interaction terms in Logistic regression analysis, and stratified analyses by gender and nutritional status of the association between polymorphisms with risk of HBP were performed. Considering two SNPs of SREBP1 and four SNPs of SREBP2 were selected in the present study, we adjusted multiple testing for Bonferroni correction (SREBP1: P < 0.05/2 = 0.025; SREBP2: P < 0.05/4 = 0.0125). Statistical analyses were performed using SPSS for Windows (version 22.0, SPSS Inc., Chicago, IL).

Results

General characteristics

The general characteristics of the study are presented in Table 1. A total of 872 children were included in this cross-sectional study, of which the prevalence of HBP was 9.5%, and the prevalence of HBP in boys and girls was 9.2% and 9.9%, respectively. The prevalence of HBP among overweight/obese children was significantly higher than that of non-overweight/obesity children (19.2% vs. 6.2%, P < 0.001). The mean BMI, SBP, and DBP in the HBP groups were significantly higher than those in the non-HBP group (P < 0.001). There was no significant difference between gender and age in HBP and non-HBP group (P > 0.05).

Table 1.

General characteristics of the present study

Variables	Non-HBP(n = 789)	HBP(n = 83)	Total(n = 872)	P
Gender, n (%)				0.721
Boys	387 (90.8%)	39 (9.2%)	426 (48.9%)
Girls	402 (90.1%)	44 (9.9%)	446 (51.1%)
Nutritional status, n (%)				< 0.001
Non-overweight/obesity	607 (93.8%)	40 (6.2%)	647 (74.3%)
Overweight/obesity	181 (80.8%)	43 (19.2%)	224 (25.7%)
Age (year)	13.31 ± 0.61	13.38 ± 0.35	13.33 ± 0.59	0.392
BMI (kg/m2)	19.96 ± 3.31	22.85 ± 4.34	20.23 ± 3.52	< 0.001
SBP (mmHg)	103.56 ± 10.21	126.25 ± 9.26	105.72 ± 12.11	< 0.001
DBP (mmHg)	65.12 ± 6.99	78.88 ± 7.21	66.43 ± 8.09	< 0.001

HBP high blood pressure, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure

P values < 0.05 were set in bold

Association between SREBP1 and SREBP2 gene polymorphisms and blood pressure levels

According to AIC criteria, the optimal genetic model of SREBP1/rs11868035, SREBP1/rs2297508, SREBP2/rs7287010, SREBP2/rs2267439 is the dominant genetic model, and the optimal model of SREBP2/rs2228314 is the additive genetic model. The optimal genetic model of SREBP2/rs2267443 is a recessive genetic model (Table S2). The results of other genetic models of the SREBP gene and BP phenotypes are shown in Tables S3–S5. Tables 2 and 3 show the associations between SREBP1 and SREBP2 polymorphisms with SBP and DBP.

Table 2.

Association of SREBP gene polymorphisms with SBP

Gene	SNP	Models	N	Unstandardized β (mmHg)	SE	P
SREBP1	rs11868035	Model 1	859	5.75	2.85	0.044
		Model 2	859	7.34	2.56	0.004
	rs2297508	Model 1	856	5.82	3.06	0.057
		Model 2	856	7.19	2.72	0.008
SREBP2	rs7287010	Model 1	860	1.49	1.25	0.232
		Model 2	860	1.87	1.12	0.095
	rs2228314	Model 1	864	0.94	0.74	0.205
		Model 2	864	0.65	0.66	0.325
	rs2267443	Model 1	871	− 1.63	1.40	0.246
		Model 2	871	− 1.86	1.26	0.142
	rs2267439	Model 1	862	0.23	0.94	0.802
		Model 2	862	0.04	0.84	0.958

SBP systolic blood pressure, BMI body mass index

Model 1 we add gender, age and age² as covariates, Model 2 with gender, age, age² and BMI

P values < 0.05 were set in bold

Table 3.

Association of SREBP gene polymorphisms with DBP

Gene	SNP	Models	N	Unstandardized β (mmHg)	SE	P
SREBP1	rs11868035	Model 1	859	1.60	1.96	0.415
		Model 2	859	2.16	1.91	0.260
	rs2297508	Model 1	856	1.48	2.10	0.481
		Model 2	856	1.96	2.04	0.336
SREBP2	rs7287010	Model 1	860	− 0.34	0.85	0.686
		Model 2	860	− 0.22	0.83	0.789
	rs2228314	Model 1	864	0.08	0.50	0.867
		Model 2	864	− 0.01	0.49	0.990
	rs2267443	Model 1	871	− 0.34	0.96	0.721
		Model 2	871	− 0.41	0.93	0.657
	rs2267439	Model 1	862	− 0.69	0.63	0.277
		Model 2	862	− 0.72	0.62	0.245

DBP diastolic blood pressure, BMI body mass index

Model 1 we add gender, age and age² as covariates, Model 2 with gender, age, age² and BMI

In the dominant genetic model, we found that SREBP1/rs11868035 was significantly association with SBP after adjustment for gender, age, and age squared (β = 5.75, SE = 2.86, P = 0.044). However, SREBP1/rs11868035 were not significantly associated with SBP after correction for multiple comparison (P < 0.05/2 = 0.025). After further adjustment for BMI, SREBP1/rs11868035 was still associated with SBP, and GA/AA genotype carriers had higher SBP than GG genotype carriers (β = 7.34, SE = 2.56, P = 0.004). In the dominant genetic model, with adjustment of gender, age, age squared and BMI, a significant association between SREBP1/rs2297508 and SBP was found, and CG/CC genotype carriers had higher SBP than GG genotype carriers (β = 7.19, SE = 2.72, P = 0.008). No significant association between SREBP1 and other SREBP2 gene polymorphisms and SBP (P > 0.05) was observed. No significant association between SREBP1 and SREBP2 gene polymorphisms and DBP was found(P > 0.05).

Association between SREBP1 and SREBP2 gene polymorphisms with risk of HBP

After adjusting for gender, age, and age squared, and further adjusting for BMI, no significant association was found between SREBP1 and SREBP2 gene polymorphisms and risk of HBP (all P > 0.05) (Table 4).

Table 4.

Association of SREBP gene polymorphisms with HBP

Gene	SNPs	Genotype	N	Model 1		Model 2
				OR (95%CI)	P	OR (95%CI)	P
SREBP 1	rs11868035	GG/GA+AA	17/842	1.79 (0.23–13.70)	0.576	2.64 (0.32–21.53)	0.365
	rs2297508	GG/CG+CC	15/841	1.56 (0.20–12.06)	0.669	2.37 (0.28–19.98)	0.428
SREBP 2	rs7287010	TT/TC+CC	102/758	1.03 (0.51–2.08)	0.926	1.12 (0.54–2.32)	0.754
	rs2228314	GG/CG/CC	572/264/28	1.33 (0.90–1.98)	0.150	1.31 (0.87–1.97)	0.204
	rs2267443	GG+GA /AA	749/77	0.92 (0.41–2.07)	0.837	0.85 (0.37–1.98)	0.706
	rs2267439	CC/CT+TT	213/649	1.19 (0.69–2.06)	0.527	1.15 (0.65–2.02)	0.631

HBP high blood pressure, BMI body mass index

Model 1 we add gender, age and age² as covariates, Model 2 with gender, age, age² and BMI

Interaction between SREBP gene polymorphisms and gender on risk of HBP

Associations of SREBP1 and SREBP2 gene polymorphisms with risk of HBP stratified by gender are shown in Fig. 1.

Fig. 1.

Adjusted odds ratio and 95% confidence interval of high blood pressure stratified by Sterol regulatory element-binding protein (SREBP) gene polymorphisms and gender. (P and P_interaction was adjusted for age, age² and body mass index; P values < 0.05 were set in bold)

A significant interaction between SREBP2/rs2228314 and gender on the risk of HBP (P_interaction=0.034), and interaction plots is shown in Fig. S1. In boys, this gene polymorphism was associated with a risk of HBP, in the additive genetic model, each C allele of rs2228314 was associated with a 2.01-fold risk of HBP (OR = 2.01, 95% CI 1.15–3.53, P = 0.015), but no significant association was examined in girls. Nevertheless, SREBP2/rs2228314 was not correlated with HBP in boys after correction for multiple comparison (P < 0.05/4 = 0.0125). No significant interactions were observed for the SREBP1 gene and rs7287010, rs2267443, and rs2267439 polymorphisms of SREBP2 with gender on risk of HBP.

Interaction between SREBP gene polymorphisms and nutritional status on HBP

As shown in Fig. 2, associations between SREBP1 and SREBP2 gene polymorphisms and HBP stratified according to nutritional status are illustrated.

Fig. 2.

Adjusted odds ratio and 95% confidence interval of high blood pressure stratified by Sterol regulatory element-binding protein (SREBP) gene polymorphisms and nutritional status. (P and P_interaction was adjusted for age, age² and gender; P values < 0.05 were set in bold)

A significant interaction between SREBP2/rs2267439 and nutritional status on risk of HBP was found (P_interaction=0.023), and interaction plots is shown in Fig. S2. In the overweight/obesity individuals, CC genotype carriers had a significantly higher risk of HBP than CT/TT genotype carriers (OR = 2.68, 95% CI 1.06–6.75, P = 0.036), which no significant association was show in non-overweight/obesity children. After multiple comparison corrections, this association was not significant (P < 0.05/4 = 0.0125). No significant interactions were observed for the SREBP1 gene and rs7287010, rs2228314, and rs2267443 polymorphisms of SREBP2 with nutritional status on the risk of HBP.

Discussion

The current study has observed for the first time the association of the SREBP1 gene polymorphism with blood pressure in children. In the dominant genetic model, we revealed that SREBP1/rs11868035 GA/AA genotype carriers had higher SBP than GG genotype carriers. Previous studies have focused on the association between SREBP1 gene and the risk of dyslipidemia, obesity, and diabetes. Grarup et al. found that SREBP1/rs11868035 T allele carriers had a higher risk of type 2 diabetes in the Danish population (OR = 1.19, 95% CI 1.07–1.33) [16]. Giovanni et al. also demonstrated that the A allele of SREBP1/rs11868035 increased the risk of severe steatosis in patients with non-alcoholic fatty liver disease (OR = 1.62, 95% CI 1.40–2.48) [30]. Moreover, our study also indicated that the C allele of the SREBP1/rs2297508 was associated with higher SBP in children. Previous studies reported that the CC genotype of SREBP1/rs2297508 might be a risk genotype for high low-density lipoprotein cholesterol levels [14]. Gilberto et al. found that SREBP1/rs2297508 was associated with the risk of acute coronary syndrome (OR = 1.50, 95% CI 1.05–1.28) [18]. Patients with dyslipidemia have a significantly increased risk of cardiovascular disease [31]. Associations of blood lipid indicators with both SBP and DBP have also been found in children [32]. Furthermore, rs2297508 is a synonymous mutation and rs11868035 is an intron mutation. Previous studies have found that SREBP1/rs2297508 does not change the amino acid sequence, but can predict hyperlipoproteinemia associated with Highly Active Anti-Retroviral Therapy [33]. Previous studies have found that SREBF1/11868035 can regulate changes in C-reactive protein, E-selectin, and Intercellular adhesion molecule (ICAM1). These molecules have also been demonstrated to be markers of subclinical atherosclerosis, hypertension, and cardiovascular disease risk [30, 34–36]. Experimental studies have noted that mouse liver SREBP-1c affects the susceptibility to atherosclerosis by controlling the size of newborn very low-density lipoproteins (VLDL) particles, and SREBP-1c activation promotes the secretion of large VLDL particles [37–39]. The above studies also suggest that the effect of SREBP1 gene polymorphisms on hypertension and cardiovascular disease risk may be mediated through the regulation of lipoprotein metabolism. Since we didn’t test the C-reactive protein, E-selectin, and ICAM1 level in the present study, it also limited us to explore the potential mediation effects of these molecules on the associations of SREBP1 gene polymorphisms and BP phenotypes, and future studies could further explore this mediation effect. Furthermore, the current study revealed a significant interacting effect between gender and SREBP2/rs2228314 on risk of HBP. In boys, C allele carriers had a significantly higher risk of HBP than G allele carriers (OR = 2.01, 95% CI 1.15–3.53), which was not found in girls. However, a study conducted in Finnish men confirmed no difference in the prevalence of hypertension among carriers of different genotypes of SREBP2/rs2228314. Reports of SREBP2/rs2228314 and cardiovascular disease are controversial [40]. In 2003, Robinet et al. found that variants in this gene were associated with early atherosclerosis in French men [40]. Besides, rs2228314 was also associated with an increased risk of premature myocardial infarction in American men (OR = 1.63, 95% CI 1.26–2.12) [41]. However, in the study of Chen et al., rs2228314 was not associated with the risk of premature coronary artery disease in Chinese men and women [42]. This also indicated that the association between the SREBP2 gene and BP phenotypes may vary regarding ethnicity, and gender. Goulding et al.’s study used data of the National Health and Nutrition Examination Survey from 2011 to 2018 showed that the prevalence of HBP in boys was higher than that in girls (prevalence difference: 4.6%; 95% CI 2.7%~6.4%) [43]. Previous study also showed that the prevalence of HBP in Chinese boys was higher than that in girls (10.2% vs. 8.9%) [22]. A cohort study also detected a positive correlation between testosterone/estradiol ratio and HBP, while estradiol was negatively correlated with HBP in Chinese children [44]. Reckelhoff’s study found that castration reduced the incidence of hypertension in spontaneously hypertensive rats and that long-term testosterone treatment resulted in irreversible increases in blood pressure levels in castrated male rats, normotensive female rats, and ovariectomized female hypertensive rats [45]. This suggests that sex hormones play a key role in sex differences in hypertension. In addition, studies have confirmed that androgen can activate SCAP gene transcription through androgen receptors, stimulate SREBP2 to increase the expression level of cholesterol producing genes, and thus increase total cholesterol-content [46]. Therefore, we speculate that this interaction may be related to the regulation of SREBP2 expression by sex hormones. Experimental validation is needed to test these speculative. Furthermore, these sex-specific interactions require replication in independent cohorts.

In addition, the current study showed an interacting effect between SREBP2/rs2267439 and nutritional status on the risk of HBP in children. In overweight/obese individuals, TT/TC genotype carriers had a significantly higher risk of HBP than CC genotype carriers, which was not observed in non-overweight/obese children. Gilberto et al. found that carriers of the C allele of SREBP2/rs2267439 had lower SBP [18]. The prevalence of hypertension varies among children with different nutritional status and obesity is closely related to blood pressure levels and may share a common genetic background [47, 48]. A recent meta-analysis in Africa identified that the prevalence of hypertension was significantly higher in overweight/obese children (18.5%) than in the underweight/normal group (1.0%) [49]. The CHNS data from 1991 to 2015 demonstrated that general obesity (OR = 2.69, 95% CI 2.10–3.44) and central obesity (OR = 1.49, 95% CI 1.21–1.83) were positively correlated with hypertension in children aged 7–17 years [50]. This might be due to overweight or obese participants being more likely to have lipid metabolism disorders and insulin resistance compared with participants with normal BMI, which leads to an increased risk of cardiovascular diseases such as hypertension [51]. SREBP2, one of the SREBP proteins, plays an important role in regulating cholesterol metabolism and maintaining cholesterol homeostasis [52, 53]. Imbalance of sterol homeostasis can lead to some metabolic diseases, such as obesity [54]. The association between SREBP2 gene polymorphisms and obesity indicators has also been reported in the relevant literature, such as Liu et al. investigated that there was a gene-gene interaction between SNPs in SREBP2, INSIG2, and SCAP genes affecting the risk of obesity [55]. Tang et al. reported that dyslipidemia (OR = 3.05, 95% CI 2.36–3.90), overweight (OR = 1.70, 95% CI 1.39–2.09), and obesity (OR = 2.60, 95% CI 1.84–3.66) increased the risk of hypertension, and there was an interaction between overweight or obesity and dyslipidemia on the risk of hypertension, but no interaction between low weight and dyslipidemia on risk of hypertension was found [51]. Therefore, being overweight or obese may amplify the genetic susceptibility of carriers of specific genotypes of SREBP2 gene polymorphisms to HBP. There is growing evidence that childhood blood pressure levels are important predictors of hypertension and other related cardiometabolic risk factors in adulthood. To the best of our knowledge, no interaction effect between nutritional status and SREBP2 gene polymorphisms on the risk of HBP was reported before. The interaction of SREBP2/rs2267439 with nutritional status found in our study also provides possible clues to the pathogenesis of blood pressure, children carrying risk alleles of SREBP polymorphisms who also have elevated BMI may benefit from earlier and more intensive weight management interventions, as their genetic background could amplify BMI’s adverse effects on blood pressure. However, the association between the SREBP gene and HBP was not significant in boys and overweight/obese populations after correcting for multiple testing. The findings should be interpreted with caution, and larger sample size cohort studies and functional studies are needed to verify these results and the Long Short-Term Memory (LSTM) networks might be attempted to be applied to longitudinal pediatric blood pressure data to model dynamic gene-environment interactions over time [56].

The strength of the study was that we first explore the relationship between SREBP gene polymorphisms and BP phenotypes in children, and provide a basis for subsequent functional studies on the pathogenesis of hypertension and these genetic variations may serve as early biomarkers to help identify children at high risk of hypertension. Exploring the genetic mechanism of HBP in children is of potential public health significance for the intervention and control of HBP in children and the early prevention and treatment of hypertension and cardiovascular disease in adulthood such as combining genetic testing (for SREBP gene variants) with routine BMI monitoring could improve early detection of children at highest risk for hypertension.

Limitations

However, there were limitations in the current study. Firstly, the cross-sectional nature of this research limits causal inference. Since both genotype and BP phenotype data were collected simultaneously, we cannot establish temporal relationships between genetic variants and disease outcomes. In the future, we need to establish prospective cohort studies or use Mendelian randomization analysis to confirm the potential causal relationship. Secondly, our findings may not generalize to populations with substantially different demographic structures because our research group consists of Asian children and no survey weighting was applied. Future research should consider using international child cohort data[such as BCAMS, UK Biobank, Generation R and Birth to Twenty Plus (South Africa)], to explore the associations between genetic polymorphisms and blood pressure in African and European children or conduct trans-ethnic meta-analyses combining results from multiple cohorts while testing for heterogeneity can clarify whether associations are consistent across groups or integrating multi-omics data (methylation, proteomics), which will help bridge the gap between genetic associations and biological mechanisms. The sample size of some genotypes (such as rs2297508 GG reference group) is relatively small (n = 15), and it is even smaller after stratification by gender or BMI, our study may underrepresent certain genotypes, which may lead to unstable OR estimation and requires careful interpretation [57]. It needs to be verified in larger samples or functional experiments in the future. Finally, the absence of detailed family history data may lead to overestimation of genetic effect sizes and limits our ability to assess inheritance patterns (e.g., maternal vs. paternal effects). The lack of lifestyle data (Such as smoking, sleep, diet and physical activities) introduces potential confounding. This may obscure true genetic effects or create spurious associations if lifestyle factors correlate with genotype. Future studies should integrate wearable activity monitoring, 24-h dietary recalls, and lipid markers to disentangle genetic effects from modifiable lifestyle influences. Reliance on single BP measurements may affect results, such as failing to explain the variation of day and night or the white-coat effect and possibly underestimating the true BP association. In the future, the association between 24-h ambulatory blood pressure and genetic variations will be further explored. In addition, incorporating lipid profiles, sex hormones, and inflammatory markers in future cohort studies would indeed strengthen the mechanistic understanding of our findings.

Conclusion

To sum up, this cross-sectional study found that the association of the SREBP1 gene polymorphisms with SBP in children and the interaction of the SREBP2 gene polymorphisms with gender and nutritional status could affect the risk of HBP in children. These findings imply that the SREBP gene polymorphisms may be related to blood pressure regulation, which provides some clues to reveal the pathogenesis of hypertension and provides possible ideas for future personalized prevention and intervention of HBP in children from the perspective of genetic susceptibility.

Electronic supplementary material

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Abbreviations

AIC

AIC Akaike information criterion

BP

Blood pressure

BMI

Body mass index

CHNS

China health and nutrition survey

DBP

Diastolic blood pressure

HBP

High blood pressure

ICAM1

Intercellular adhesion molecule

LDL

Low density lipoprotein

MAF

Minor allele frequency

SREBP

Sterol regulatory element-binding protein

SBP

Systolic blood pressure

SNPs

Single nucleotide polymorphisms

TC

Total cholesterol

VLDL

Very low-density lipoproteins

Author contributions

Conceptualization: Yuan Zeng, ZH Fan, YL Zhu, YD Yang; Development of methodology: Yuan Zeng, HJ Tian, YD Yang; Analysis and interpretation of data (e.g., statistical analysis, computational analysis): Yuan Zeng, MX Chen, Yue Gong, Bin Mao, WY Xiang; Writing, review, and/or revision of the manuscript: Yuan Zeng, ZH Fan, YL Zhu, XQ Hong, YD Yang.

Funding

This study was supported by the National Natural Science Foundation of China (81903336), Hunan Provincial Natural Science Foundation of China (2025JJ50564), a project supported by the Scientific Research Fund of Hunan Provincial Education Department (22B0038), Scientific Research Project of Hunan Provincial Health Commission (202112031516), National College Student Innovation and Entrepreneurship Training Program (S202410542075), the Hunan Provincial Natural Science Foundation of China (2019JJ50376), Key Project of Developmental Biology and Breeding from Hunan Province(2022XKQ0205), and Hunan Province key research and development project (2023SK2059).

Data availability

The data supporting this study’s findings are available upon reasonable request from corresponding author(yangyide2007@126.com). However, due to ethical guidelines and confidentiality agreements, raw participant data cannot be shared. Aggregated and anonymized data may be provided after obtaining approval from the relevant institutional and ethical review board.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval and consent to participate

Our research was conducted ethically in accordance with the World Medical Association Declaration of Helsinki. The study was approved by Hunan Normal University Ethics Committee (2019-88), and written informed consent was obtained from all participants and their parents or legal guardians in the present study.

Consent for publication

Not applicable.