Evaluation of the correlation between fetuin-B levels and essential hypertension: a cross-sectional study

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Abstract

Background

Fetuin-B, a cytokine that regulates lipid metabolism, has recently been linked to cardiovascular diseases such as coronary heart disease. In this study, we discussed the relationship between fetuin-B and essential hypertension.

Method

A bioinformatics analysis of fetuin-B was performed. A total of 206 with essential hypertension and 180 age- and-sex-matched healthy subjects were enrolled. Plasma fetuin-B, endothelin 1 (ET-1), nitric oxide (NO), and adiponectin (ADI) levels were measured using ELISA kits.

Results

Bioinformatics analysis has revealed that fetuin-B plays an important role in pathways such as lipid metabolism. Compared with healthy subjects, serum fetuin-B levels in patients with essential hypertension were significantly increased. Correlation analysis showed that the serum fetuin-B level was positively correlated with systolic blood pressure (SBP), diastolic blood pressure, body mass index, fat percentage in vivo, waist–hip ratio, intima–media thickness, low-density lipoprotein cholesterol (LDL-C), glutamyltranspeptidase, alanine transaminase, albumin, fasting blood glucose (FBG), glycated hemoglobin, and ET-1 in the overall study subjects (all P < 0.05) and negatively correlated with HDL-C, ADI, and NO (all P < 0.05). Multivariate linear regression analysis showed that SBP, FBG, LDL-C, ADI, and ET-1 were independent factors affecting serum fetuin-B. A binary logistic regression analysis showed that fetuin-B was an independent risk factor for primary hypertension (odds ratio: 1.060, 95% CI: 1.034–1.086, P < 0.001). Receiver operating characteristic curve analysis was used to evaluate the predictive value of fetuin-B for primary hypertension, and the optimal cutoff point was 83.14 μg/mL (sensitivity 77.4%, specificity 63.3%) (area under the curve) = 0.7738, 95% CI 0.7276–0.8200, P < 0.001).

Conclusion

Elevated fetuin-B levels are associated with an increased risk of essential hypertension.

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Most cited references 36

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

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The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

Damian Szklarczyk, Annika Gable, Katerina C. Nastou … (2020)

Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.

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The global epidemiology of hypertension

Katherine T. Mills, Andrei D. Stefanescu, Jiang Ping He (2020)

Hypertension is the leading cause of cardiovascular disease and premature death worldwide. Owing to widespread use of antihypertensive medications, global mean blood pressure (BP) has remained constant or decreased slightly over the past four decades. By contrast, the prevalence of hypertension has increased, especially in low and middle-income countries (LMICs). Estimates suggest that in 2010, 31.1% of adults (1.39 billion) worldwide had hypertension. The prevalence of hypertension among adults was higher in LMICs (31.5%, 1.04 billion people) than in high-income countries (HICs; 28.5%, 349 million people). Variations in the levels of risk factors for hypertension, such as high sodium intake, low potassium intake, obesity, alcohol consumption, physical inactivity and unhealthy diet, may explain some of the regional heterogeneity in hypertension prevalence. Despite the increasing prevalence, the proportions of hypertension awareness, treatment and BP control are low, particularly in LMICs, and few comprehensive assessments of the economic impact of hypertension exist. Future studies are warranted to test implementation strategies for hypertension prevention and control, especially in low-income populations, and to accurately assess the prevalence and financial burden of hypertension worldwide.

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Author and article information

Journal

Journal ID (nlm-ta): Endocr Connect

Journal ID (iso-abbrev): Endocr Connect

Journal ID (hwp): EC

Title: Endocrine Connections

Publisher: Bioscientifica Ltd (Bristol )

ISSN (Electronic): 2049-3614

Publication date (Electronic): 09 October 2023

Publication date (Electronic preprint): 09 October 2023

Publication date Collection: 01 December 2023

Volume: 12

Issue: 12

Electronic Location Identifier: e230172

Affiliations

[1 ]Department of General Practice , The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China

[2 ]Department of Oncology. The Second Affiliated Hospital of Chongqing Medical University , Chongqing Medical University, Chongqing, China

[3 ]Institute of Life Sciences , Chongqing Medical University, Chongqing, China

Author notes

Correspondence should be addressed to D Ke: kedazhi@ 123456hospital.cqmu.edu.cn

^*(T Gao and R Liu contributed equally to this work)

Author information

Tao Gao http://orcid.org/0000-0003-0682-6623

Dazhi Ke http://orcid.org/0000-0003-0280-1234

Article

Publisher ID: EC-23-0172

DOI: 10.1530/EC-23-0172

PMC ID: 10692697

PubMed ID: 37811770

SO-VID: 1e35d0a3-bc50-403e-a23d-994db2b56816

License:

This work is licensed under a Creative Commons Attribution 4.0 International License.

History

Date received : 03 May 2023

Date accepted : 09 October 2023

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