SynthesisFrontiers in physiology2026
A bibliometric and text-mining analysis of lipidomics and metabolomics in human disease.
Synthesis in Frontiers in physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: 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. Methods: 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. Results: 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. Discussion: 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.