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Assessment and Establishment of Correlation between Reactive Oxidation Species, Citric Acid, and Fructose Level in Infertile Male Individuals: A Machine-Learning Approach

BACKGROUND: Biochemical complexity of seminal plasma and obesity has an important role in male infertility (MI); so far, it has not been possible to provide evidence of clinical significance for all of them. AIMS: Our goal here is to evaluate the correlation between biochemical markers with semen pa...

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Detalles Bibliográficos
Autores principales: Shemshaki, Golnaz, Murthy, Ashitha S. Niranjana, Malini, Suttur S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279050/
https://www.ncbi.nlm.nih.gov/pubmed/34316227
http://dx.doi.org/10.4103/jhrs.jhrs_26_21
Descripción
Sumario:BACKGROUND: Biochemical complexity of seminal plasma and obesity has an important role in male infertility (MI); so far, it has not been possible to provide evidence of clinical significance for all of them. AIMS: Our goal here is to evaluate the correlation between biochemical markers with semen parameters, which might play a role in MI. STUDY SETTING AND DESIGN: We enlisted 100 infertile men as patients and 50 fertile men as controls to evaluate the sperm parameters and biochemical markers in ascertaining MI. MATERIALS AND METHODS: Semen analyses, seminal fructose, citric acid, and reactive oxidation species (ROS) were measured in 100 patients and 50 controls. STATISTICAL ANALYSIS: Descriptive statistics, an independent t-test, Pearson correlation, and machine-learning approaches were used to integrate the various biochemical and seminal parameters measured to quantify the inter-relatedness between these measurements. RESULTS: Pearson correlation results showed a significant positive correlation between body mass index (BMI) and fructose levels. Citric acid had a positive correlation with sperm count, morphology, motility, and volume but displayed a negative correlation with BMI and basal metabolic rate (BMR). However, BMI and BMR had a positive correlation with ROS. Sperm count, morphology, and motility were negative correlations with ROS. The machine-learning approach detected that pH was the most critical parameter with an inverse effect on citric acid, and BMI and motility were the most critical parameter for ROS. CONCLUSION: We recommend that evaluation of biochemical markers of seminal fluid may benefit in understanding the etiology of MI based on the functionality of accessory glands and ROS levels.