A prospective multicenter study on plasma metabolomics and machine learning for diagnosing premature ejaculation and predicting dapoxetine treatment response
Abstract
BACKGROUND: Premature ejaculation is a prevalent sexual dysfunction, yet the variable patient response to first-line dapoxetine treatment poses a major clinical challenge, highlighting the unmet need for biomarkers to guide diagnosis and therapy.
AIM: This study aimed to investigate the distinct plasma metabolic profile of primary premature ejaculation (PPE) patients, and to develop machine learning-based diagnostic and therapeutic response prediction models.
METHODS: A multicenter cohort comprising 69 patients with PPE and 51 healthy control (HC) subjects was enrolled. Plasma samples underwent untargeted metabolomic analysis. Differentially expressed metabolites were identified, and pathway enrichment analyses were conducted using Small Molecule Pathway Database and Kyoto Encyclopedia of Genes and Genomes. Three machine learning algorithms-Support Vector Machine, Random Forest, and Least Absolute Shrinkage and Selection Operator regression-were employed to screen biomarkers. Subsequently, targeted metabolomics analysis was used to quantify neurotransmitter levels.
OUTCOMES: The primary outcomes included the Premature Ejaculation Diagnostic Tool, the intravaginal ejaculation latency time, and the Clinical Global Impression of Change scale score after a 4-week observation period of on-demand dapoxetine treatment.
RESULTS: Multivariate analysis revealed clear separations in metabolic profiles between the PPE and HC groups, and between dapoxetine treatment (DT)-Response and…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 69 participants or records.
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