A bibliometric and text-mining analysis of lipidomics and metabolomics in human disease

Lipidomics and metabolomics have become key approaches for understanding and diagnosing human diseases, including type 2 diabetes, Alzheimer's disease, cancer, and kidney dysfunction. This study provides a comprehensive overview of the evolution of these disciplines through a bibliometric and text-mining analysis of scientific production from 2004 to 2024, based on data from Scopus and validated through a multi-database comparative approach.

A total of 9,628 articles were harmonized and analyzed using Bibliometrix, Scimago Graphica, OpenRefine, and custom R scripts to identify the most productive journals, authors, countries, and institutions, and to map thematic structures and keyword dynamics. Beyond traditional bibliometric indicators, our integrative approach combined quantitative trends with semantic and conceptual mapping to trace the methodological and translational evolution of the field. To ensure robustness and generalizability, equivalent searches were conducted in the Web of Science Core Collection and PubMed, and cross-database validation assessed concordance in journal and country rankings, as well as temporal and thematic trends.

The field shows rapid expansion, with an annual growth rate of 32.6%. The United States and China lead global output, followed by major European contributors. Core topics include Alzheimer's disease, obesity, and breast cancer, while emerging areas focus on artificial intelligence, multi-omics integration, and Mendelian randomization. Analytical methodologies such as liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance, together with metabolic diseases, remain central to the field. In contrast, niche themes such as microbiota-COVID-19 interactions and oxidative stress-cancer associations represent emerging interdisciplinary bridges.

Overall, lipidomics and metabolomics are evolving toward integrative and computational frameworks with strong diagnostic potential, underscoring the need for validated biomarkers, standardized data pipelines, and open repositories to enable clinical translation.