VOLATILOMICS AS AN EMERGING STRATEGY TO DETERMINE POTENTIAL BIOMARKERS OF FEMALE INFERTILITY: A PILOT STUDY
2IPCB, Instituto Politecnico de Castelo Branco, Castelo Branco
3Centro de Estudos Florestais, Instituto Superior de Agronomia, Lisboa
4Assisted Reproduction Laboratory, Academic Hospital of Cova da Beira, Covilha
5Gynaecology and Obstetrics Department, Sao Jose do Rio Preto School of Medicine, Sao Jose do Rio Preto
6C4 - Cloud Computing Competence Centre, University of Beira Interior, Covilh
7UCIBIO - Applied Molecular Biosciences Unit, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica
8Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica
9Laboratorio de Farmaco-Toxicologia, UBIMedical, University of Beira Interior, Covilha
Problem statement:
Infertility has become a prominent public health issue worldwide, challenging the future of modern reproductive medicine. Polycystic ovary syndrome (PCOS), endometriosis, and premature ovarian failure (POF) are some of the clinical conditions that lead to female infertility. Follicular fluid (FF) is the biological matrix that has the most contact with the oocyte and can, therefore, be used as a predictor of its quality. Volatilomics has recently emerged as a non-invasive, straightforward, affordable, and simple method for characterizing various diseases and determining the effectiveness of their current therapies. The aim of this study is to determine the volatomic pattern of follicular fluid from patients with PCOS, endometriosis, and POF, and to find potential biomarkers of these clinical conditions.
Methods:
Headspace solid-phase microextraction and subsequent GC-MS analysis was used to analyze the volatile organic compounds (VOCs) present in 52 FF samples from infertile women, corresponding to 15 patients with PCOS, 8 with endometriosis, 12 with POF, and 17 controls.
Results:
A total of 136 VOCs were identified. Due to their prevalence in all samples, 37 compounds were considered, and the multivariate statistical analysis revealed significant changes in the levels of certain metabolites according to each clinical condition. The analysis of biochemical profiles revealed compromised metabolic pathways, as well as the presence of compounds strongly linked with infertility.
Conclusion:
The findings point to specific metabolite patterns as potential biomarkers for the studied diseases. These open the door for further research into the relevant metabolomic pathways to enhance infertility knowledge and diagnostic tools. The high-throughput methodologies employed suggest the possibility of using metabolite identification as a facilitator for determining potential infertility biomarkers. Our findings may also benefit the exploration of the associated metabolomic pathways and the improvement of clinical diagnostic tools.
