Evidence map›Paper›PMID 40962908›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2025

Metabolomic profiling and machine learning-based biomarker identification for oligoasthenozoospermia.

Jinli Li, Tangzhen Zhao, Mengmeng Ma, Pengcheng Kong, Yuping Fan, Xiaoming Teng, Yi Guo

Abstract read
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In one paragraph

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. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jinli Li *Center for Reproductive Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 2699 West Gao Ke Road, Shanghai, 201204, China.
Tangzhen Zhao *School of Physics and Astronomy & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China.
Mengmeng MaCenter for Reproductive Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 2699 West Gao Ke Road, Shanghai, 201204, China.
Pengcheng KongCenter for Reproductive Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 2699 West Gao Ke Road, Shanghai, 201204, China.
Yuping FanCenter for Reproductive Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 2699 West Gao Ke Road, Shanghai, 201204, China.
Xiaoming TengCenter for Reproductive Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 2699 West Gao Ke Road, Shanghai, 201204, China. tengxiaoming@51mch.com.
Yi GuoCenter for Reproductive Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 2699 West Gao Ke Road, Shanghai, 201204, China. gy_guoyi@tongji.edu.cn.

Funding

National Natural Science Foundation of China 82271632
6 · The paper itself

Abstract

INTRODUCTION AND

objectivesOligoasthenozoospermia, characterized by a low sperm count and impaired progressive motility, significantly contributes to male infertility. This study examines the metabolic disparities between individuals with oligoasthenozoospermia (n = 30) and healthy controls (n = 30) utilizing ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS).

methodsA total of 1,331 metabolites were identified in positive ion mode and 870 in negative ion mode, with differential analysis indicating 211 significantly different metabolites between the two groups. Pathway analysis identified key metabolic pathways, including the pentose phosphate pathway, TCA cycle, glycerophospholipid metabolism, and fatty acid metabolism. Subsequently, various machine learning models, including Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) were employed to assess the predictive capability of the identified metabolites, with 1-palmitoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine and [6]-gingerol demonstrating the highest predictive performance.

resultsThe diagnostic model, developed using LR, attained high sensitivity (0.93), specificity (1), and accuracy (0.97), with an AUC of 0.998 in the training set and 0.963 in the test set.

conclusionThese findings offer critical insights into the metabolic changes associated with oligoasthenozoospermia and establish a dependable diagnostic framework for differentiating it from controls.

Indexed as

AsthenozoospermiaBiomarkersMachine LearningMetabolomicsOligospermiaAdultCase-Control StudiesChromatography, High Pressure LiquidHumansMaleSupport Vector MachineBiomarkersBiomarkerMachine learningMetabolomicsOligoasthenozoospermiaSeminal plasma

Identifiers

PMID40962908

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.