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A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
A 2004-2025 Bibliometric Study of Genetic Variation and Multiomics Biomarkers in Sepsis Based on 940 Publications
Qiuyan Yang1, Siqi Yang2, Yanyan Li2
1Department of Emergency, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China, hospital-nsmc.com.cn.
Abstract:
Sepsis is a highly heterogeneous syndrome, and conventional clinical indicators and single biomarkers often fail to capture its biological complexity or support precise risk stratification. To clarify the development of research in this area, this bibliometric study analyzed the literature on the clinical translation of genetic variation and multiomics biomarkers in sepsis. Publications were retrieved from PubMed, Web of Science Core Collection, and Scopus for the period from 2004 to 2025, and 940 eligible records were analyzed using Bibliometrix, VOSviewer, and CiteSpace. The results showed rapid expansion of the field, with annual output increasing from 1 publication in 2004 to 185 in 2025. China and the United States emerged as the leading contributors, while major institutions and journals reflected strong interdisciplinary collaboration across critical care, molecular biology, and translational medicine. The knowledge structure of the field has evolved from early emphasis on polymorphisms, susceptibility, and conventional inflammatory biomarkers toward metabolomics, transcriptomics, genomics, precision medicine, machine learning, and Mendelian randomization. At the same time, clinically relevant themes such as mortality, septic shock, acute kidney injury, and neonatal sepsis have remained central. These findings map a research trajectory from exploratory biomarker discovery toward translational frameworks for sepsis endotyping, prognostic evaluation, and risk stratification, but they should not be interpreted as evidence that the identified biomarkers or models are already clinically implemented. Future progress will depend on multicenter prospective validation, standardized analytic pipelines, broader population representation, and clinically deployable models that can translate molecular heterogeneity into real-world decision-making.
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