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Qi H, Xie YY, Yang XJ, Xia J, Liu K, Zhang FX, Peng WJ, Wen FY, Li BX, Zhang BW, Yao XY, Li BY, Meng HD, Shi ZM, Wang Y, Zhang L. Susceptibility gene identification and risk evaluation model construction by transcriptome-wide association analysis for salt sensitivity of blood pressure. BMC Genomics 2024; 25:612. [PMID: 38890564 PMCID: PMC11184770 DOI: 10.1186/s12864-024-10409-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 05/13/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Salt sensitivity of blood pressure (SSBP) is an intermediate phenotype of hypertension and is a predictor of long-term cardiovascular events and death. However, the genetic structures of SSBP are uncertain, and it is difficult to precisely diagnose SSBP in population. So, we aimed to identify genes related to susceptibility to the SSBP, construct a risk evaluation model, and explore the potential functions of these genes. METHODS AND RESULTS A genome-wide association study of the systemic epidemiology of salt sensitivity (EpiSS) cohort was performed to obtain summary statistics for SSBP. Then, we conducted a transcriptome-wide association study (TWAS) of 12 tissues using FUSION software to predict the genes associated with SSBP and verified the genes with an mRNA microarray. The potential roles of the genes were explored. Risk evaluation models of SSBP were constructed based on the serial P value thresholds of polygenetic risk scores (PRSs), polygenic transcriptome risk scores (PTRSs) and their combinations of the identified genes and genetic variants from the TWAS. The TWAS revealed that 2605 genes were significantly associated with SSBP. Among these genes, 69 were differentially expressed according to the microarray analysis. The functional analysis showed that the genes identified in the TWAS were enriched in metabolic process pathways. The PRSs were correlated with PTRSs in the heart atrial appendage, adrenal gland, EBV-transformed lymphocytes, pituitary, artery coronary, artery tibial and whole blood. Multiple logistic regression models revealed that a PRS of P < 0.05 had the best predictive ability compared with other PRSs and PTRSs. The combinations of PRSs and PTRSs did not significantly increase the prediction accuracy of SSBP in the training and validation datasets. CONCLUSIONS Several known and novel susceptibility genes for SSBP were identified via multitissue TWAS analysis. The risk evaluation model constructed with the PRS of susceptibility genes showed better diagnostic performance than the transcript levels, which could be applied to screen for SSBP high-risk individuals.
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Affiliation(s)
- Han Qi
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Capital Medical University, Beijing, 100088, China
| | - Yun-Yi Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Xiao-Jun Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Juan Xia
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Kuo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Feng-Xu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Wen-Juan Peng
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Fu-Yuan Wen
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bing-Xiao Li
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bo-Wen Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Xin-Yue Yao
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bo-Ya Li
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Hong-Dao Meng
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Zu-Min Shi
- Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Yang Wang
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China.
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Zhang F, Xie Y, Yang X, Peng W, Qi H, Li B, Wen F, Li P, Sun Y, Zhang L. Association of Serum Metabolites and Salt Sensitivity of Blood Pressure in Chinese Population: The EpiSS Study. Nutrients 2023; 15:nu15030690. [PMID: 36771399 PMCID: PMC9921558 DOI: 10.3390/nu15030690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/26/2022] [Accepted: 12/27/2022] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND To identify novel metabolites associated with salt sensitivity of blood pressure (SSBP) in Chinese Han population. METHODS A case-control study was conducted with 25 salt sensitive (SS) and 26 salt resistant (SR) participants, which was selected from the Systems Epidemiology Study on Salt Sensitivity of Blood Pressure (EpiSS) study. The modified Sullivan's acute oral saline load and diuresis shrinkage test (MSAOSL-DST) was conducted to identify SS. Untargeted, ultra-high performance liquid chromatograph-high resolution mass spectrometer (UPLC-HRMS) was conducted and orthogonal partial least squares-discriminate analysis (OPLS-DA) and multivariable logistic regression model were used to screen the metabolites related to SS, mixed linear regressions models were used to examined the association of SSBP with metabolites during saline load period and diuresis shrinkage period. Receiver operating characteristic (ROC) curve analysis was performed. The area under the curve's (AUC) sensitivity and specificity were calculated to identified metabolites biomarkers for SS. RESULTS There were 39 differentially expressed metabolites (DE-metabolites) between SS and SR. Thirty-five and four of DE-metabolites were inversely or positively associated with SS, respectively. Four biochemical pathways demonstrated significant enrichment for identified metabolites. In single-metabolite analyses, L-Glutamine displayed the best diagnostic performance (AUC = 0.88, 95% CI: 0.78-0.97). In multi-metabolites analyses, L-Glutamine + Cholesterol ester 22:5n6 combination showed the best diagnostic performance (AUC = 0.96, 95% CI: 0.91-1.00). Adjusted for traditional risk factors, L-Glutamine and Cholesterol ester 22:5n6 explained an additional 38.3% of SS susceptibility. CONCLUSIONS This study provide potential evidence for clarifying the mechanism of SS and provide novel biological insights into salt sensitive hypertension.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Ling Zhang
- Correspondence: ; Tel./Fax: +86-010-83911777
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Polina I, Spicer MJ, Domondon M, Schibalski RS, Sarsenova E, Sultanova RF, Ilatovskaya DV. Inhibition of neprilysin with sacubitril without RAS blockage aggravates renal disease in Dahl SS rats. Ren Fail 2021; 43:315-324. [PMID: 33541194 PMCID: PMC8901277 DOI: 10.1080/0886022x.2021.1879856] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Salt-sensitive (SS) hypertension is accompanied with severe cardiorenal complications. In this condition, elevated blood pressure (BP) resulting from salt retention is associated with counterintuitively lower levels of atrial natriuretic peptide (ANP). In plasma, ANP is degraded by the neprilysin; therefore, pharmacological inhibition of this metalloprotease (i.e., with sacubitril) can be employed to increase ANP level. We have shown earlier that sacubitril in combination with valsartan (75 μg/day each) had beneficial effects on renal function in Dahl SS rats. The goal of this study was to evaluate the effects of a higher dose of sacubitril on renal damage in this model. To induce hypertension, male Dahl SS rats were fed a 4% NaCl diet (HS) for 21 days, and were administered sacubitril (125 μg/day) or vehicle via s.c. osmotic pumps. At the end of the HS challenge, both groups exhibited similar outcomes for GFR, heart weight, plasma electrolytes, BUN, and creatinine. Sacubitril exacerbated kidney hypertrophy, but did not affect levels of renal fibrosis. We also observed aggravated glomerular lesions and increased formation of protein casts in the sacubitril-treated animals compared to controls. Thus, in Dahl SS rats, administration of sacubitril without renin-angiotensin-system blockage had adverse effects on renal disease progression, particularly in regards to glomerular damage and protein cast formation. We can speculate that while ANP levels are increased because of neprilysin inhibition, there are off-target effects of sacubitril, which are detrimental to renal function in the SS hypertensive state.
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Affiliation(s)
- Iuliia Polina
- Department of Medicine, Division of Nephrology, Medical University of South Carolina, Charleston, SC, USA
| | - Morgan J Spicer
- Department of Medicine, Division of Nephrology, Medical University of South Carolina, Charleston, SC, USA
| | - Mark Domondon
- Department of Medicine, Division of Nephrology, Medical University of South Carolina, Charleston, SC, USA
| | - Ryan S Schibalski
- Department of Medicine, Division of Nephrology, Medical University of South Carolina, Charleston, SC, USA
| | - Elizaveta Sarsenova
- Department of Medicine, Division of Nephrology, Medical University of South Carolina, Charleston, SC, USA.,Saint-Petersburg State Chemical Pharmaceutical University, St. Petersburg, Russia
| | - Regina F Sultanova
- Department of Medicine, Division of Nephrology, Medical University of South Carolina, Charleston, SC, USA.,Saint-Petersburg State Chemical Pharmaceutical University, St. Petersburg, Russia
| | - Daria V Ilatovskaya
- Department of Medicine, Division of Nephrology, Medical University of South Carolina, Charleston, SC, USA
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