ArticleMetabolomics : Official journal of the Metabolomic Society2025
Self-organizing maps to aid prognostic and diagnostic biomarker identification in exploratory metabolomics of benign prostatic hyperplasia.
Article in Metabolomics : Official journal of the Metabolomic Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Plasma proteomic signatures predict incident benign prostatic hyperplasia: a prospective cohort study of 20 996 men.Journal of global health · 2026Article
- Disease Progression and Outcomes in Patients With Benign Prostatic Hyperplasia: Protocol for a Multicenter Retrospective Cohort Study.JMIR research protocols · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
introductionPopulation aging is increasing rapidly, representing a global trend that has launched many discussions regarding older adult's quality of life. Benign prostatic hyperplasia (BPH) is a commonly found condition among older men, affecting the lower urinary tract and quality of life. BPH diagnosis typically involves clinical evaluations which cannot accurately identify patients who will develop a clinically significant disease in the future. Metabolomics emerges as a promising approach to understand the metabolic pathways associated with BPH, offering potential aid in early diagnosis.
objectivesTo investigate the metabolic profile of BPH patients compared with a control group for diagnosing BPH at different stages.
methodA total of 62 individuals were selected and divided into two groups: 32 BPH (prostate volume > 40 mL) and 30 healthy individuals (prostate volume ≤ 40 mL and normal voiding patterns). Plasma samples were analyzed by LC-HRMS. Data were processed using unsupervised and supervised techniques. Self-organizing maps were further used to aid in the identification of potential biomarkers.
resultsSelf-organizing maps revealed three distinct groups which expanded upon the existing groups indicated by the medical team. Discriminant analysis models showed good predictive ability and accuracy, providing a classification perspective of patients and expanding diagnostic possibilities for different health states. Integration of chemical data with clinical variables revealed meaningful correlations. Our results also suggest the presence of discriminatory metabolites related to inflammation and oxidation processes, clarifying the metabolic basis of BPH.
conclusionSelf-organizing maps proved their effectiveness in classifying groups of samples from BPH patients, revealing potential biomarkers that went undetected by conventional data analysis methods. This approach underscores metabolomics as a relevant tool in identifying hidden patterns and supporting the development of more robust diagnostic and prognostic models.
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