Using machine learning to analyze the lipid profile of culture medium and predict the efficacy of assisted reproductive technologies

DOI

https://dx.doi.org/10.18565/aig.2024.280

Drapkina Yu.S., Makarova N.P., Chagovets V.V., Vasiliev R.A., Amelin V.V., Kalinina E.A.
1) Academician V.I. Kulakov National Medical Research Centre of Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, Moscow, Russia; 2) Laboratory of Applied Artificial Intelligence Z-union, Moscow, Russia

Relevance: Determining the lipid profile of embryo culture medium is a modern and promising noninvasive method for predicting the effectiveness of assisted reproductive technology (ART) programs. The use of machine learning for the identification of the most significant lipid groups in the culture medium allows the processing of non-linear relationships within the data and the extraction of the most informative data from the input parameters.
Objective: To develop a method for predicting ART outcomes based on analyzing the lipid profile of embryo culture medium on day 5 post-fertilization using gradient boosting (GB), and to identify the lipids that contribute most to this prediction.
Materials and methods: Sixty couples seeking ART for infertility treatment were included in the study. Patients with tubal-peritoneal factor infertility underwent ovarian stimulation, following a protocol that included a gonadotropin-releasing hormone antagonist. On the day of embryo transfer, culture medium was collected, followed by cryopreservation of the medium samples. The lipid profile of the samples was determined using liquid chromatography-mass spectrometry (LC-MS). The data obtained were analyzed using GB.
Results: A GB model was developed to predict ART outcomes based on lipid profiles of the culture medium. The model achieved 79% accuracy (f1 score: 0.81) in identifying the lipid profiles associated with embryos that resulted in pregnancy. Among the lipids identified, triacylglycerols were found to contribute the most to determining embryo implantation potential.
Conclusion: Analyzing LC-MS data using GB allows for the identification of different classes of lipids in the embryo culture medium, which can serve as a noninvasive approach to assess embryo quality and implantation potential. This can also facilitate the development of a predictive testing system to determine the effectiveness of ART programs. Additionally, this information enables a more detailed investigation of the mechanisms of gamete damage in patients with various extragenital diseases, and can assist in developing methods for the selective transfer of the most promising embryos.

Authors' contributions: Drapkina Yu.S. – data analysis and interpretation, drafting of the manuscript; Makarova N.P., Kalinina E.A. – study conception and design, drafting of the manuscript; Chagovets V.V. – laboratory phase of the study, editing of the manuscript; Vasiliev R.A., Amelin V.V. – development of a mathematical model using gradient boosting.
Conflicts of interest: The authors have no conflicts of interest to declare.
Funding: There was no funding for this study.
Ethical Approval: The study was reviewed and approved by the Research Ethics Committee of the V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia.
Patient Consent for Publication: All patients provided informed consent for the publication of their data.
Authors' Data Sharing Statement: The data supporting the findings of this study are available upon request from the corresponding author after approval from the principal investigator.
For citation: Drapkina Yu.S., Makarova N.P., Chagovets V.V., Vasiliev R.A., Amelin V.V., Kalinina E.A. Using machine learning to analyze the lipid profile of culture medium and predict the efficacy of assisted reproductive technologies.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2025; (2): 91-99 (in Russian)
https://dx.doi.org/10.18565/aig.2024.280

machine learning

lipid profile

culture medium

ART programs

efficiency prediction

Full text (in Russian)

ESHRE Add-ons working group; Lundin K., Bentzen J.G., Bozdag G., Ebner T., Harper J., Le Clef N. et al. Good practice recommendations on add-ons in reproductive medicine. Hum. Reprod. 2023; 38(11): 2062-104. https://dx.doi.org/10.1093/humrep/dead184.
Armstrong S., Bhide P., Jordan V., Pacey A., Marjoribanks J., Farquhar C. Time-lapse systems for embryo incubation and assessment in assisted reproduction. Cochrane Database Syst. Rev. 2019; 5(5): CD011320. https://dx.doi.org/10.1002/14651858.CD011320.pub4.
ESHRE PGT Consortium Steering Committee; Carvalho F., Coonen E., Goossens V., Kokkali G., Rubio C., Meijer-Hoogeveen M. et al. ESHRE PGT Consortium good practice recommendations for the organisation of PGT. Hum. Reprod. Open. 2020; 2020(3): hoaa021. https://dx.doi.org/10.1093/hropen/hoaa021.
Кулакова Е.В., Михайлов И.А., Макарова Н.П., Драпкина Ю.С., Калинина Е.А., Назаренко Т.А., Трофимов Д.Ю. Клинико-экономический анализ эффективности преимплантационного генетического тестирования у пациентов с различными формами бесплодия в программах вспомогательных репродуктивных технологий. Гинекология. 2022; 24(3): 181-5. [Kulakova E.V., Mikhailov I.A., Makarova N.P., Drapkina Ju.S., Kalinina E.A., Nazarenko T.A., Trofimov D.Iu. Clinical and economic analysis of the effectiveness of pre-implantation genetic testing in patients with various types of infertility in assisted reproductive technology programs. Gynecology. 2022; 24(3): 181-5. (in Russian)]. https://dx.doi.org/10.26442/20795696.2022.3.201708.
Долгушина Н.В., Коротченко О.Е., Бейк Е.П., Абдурахманова Н.Ф., Ильина Е.О., Кулакова Е.В. Клинико-экономический анализ эффективности преимплантационного генетического скрининга у пациенток позднего репродуктивного возраста. Акушерство и гинекология. 2017; 11: 56-61. [Dolgushina N.V., Korotchenko O.E., Beik E.P., Abdurakhmanova N.F., Ilyina E.O., Kulakova E.V. Clinical and cost-effectiveness analysis of preimplantation genetic screening in patients of late reproductive age. Obstetrics and Gynegology. 2017; (11): 56-61 (in Russian)]. https://dx.doi.org/10.18565/aig.2017.11.56-61.
Timofeeva A., Drapkina Y., Fedorov I., Chagovets V., Makarova N., Shamina M. et al. Small noncoding RNA signatures for determining the developmental potential of an embryo at the morula stage. Int. J. Mol. Sci. 2020; 21(24): 9399. https://dx.doi.org/10.3390/ijms21249399.
Eldarov C., Gamisonia A., Chagovets V., Ibragimova L., Yarigina S., Smolnikova V. et al. LC-MS analysis revealed the significantly different metabolic profiles in spent culture media of human embryos with distinct morphology, karyotype and implantation outcomes. Int. J. Mol. Sci. 2022; 23(5): 2706. https://dx.doi.org/10.3390/ijms23052706.
Комедина В.И., Юренева С.В., Чаговец В.В., Стародубцева Н.Л. Особенности липидного состава сыворотки крови женщин в период менопаузального перехода. Акушерство и гинекология. 2022; 6: 90-7. [Komedina V.I., Yureneva S.V., Chagovets V.V., Starodubtseva N.L. Changes in serum lipid profile during the menopausal transition. Obstetrics and Gynecology. 2022; (6): 90-7 (in Russian)]. https://dx.doi.org/10.18565/aig.2022.6.90-97.
Бурдули А.Г., Кициловская Н.А., Сухова Ю.В., Ведихина И.А., Иванец Т.Ю., Чаговец В.В., Стародубцева Н.Л., Франкевич В.Е. Фолликулярная жидкость и исходы программ вспомогательных репродуктивных технологий (обзор литературы). Гинекология. 2019; 21(6): 36-40. [Burduli A.G., Kitsilovskaya N.A., Sukhova Yu.V., Vedikhina I.A., Ivanets T.Yu., Chagovets V.V., Starodubtseva N.L., Frankevich V.E. Follicular fluid and assisted reproductive technology programs outcomes (literature review). Gynecology. 2019; 21(6): 36-40. (in Russian)]. https://dx.doi.org/10.26442/20795696.2019.6.190663.
Tokareva A.O., Chagovets V.V., Kononikhin A.S., Starodubtseva N.L., Nikolaev E.N., Frankevich V.E. Comparison of the effectiveness of variable selection method for creating a diagnostic panel of biomarkers for mass spectrometric lipidome analysis. J. Mass Spectrom. 2021; 56(3): e4702. https://dx.doi.org/10.1002/jms.4702.
Фортыгина Ю.А., Макарова Н.П., Драпкина Ю.С., Новоселова А.В., Гамисония А.М., Чаговец В.В., Франкевич В.Е., Калинина Е.А. Сравнительный анализ липидного профиля крови и фолликулярной жидкости женщин, проходящих лечение бесплодия методами вспомогательных репродуктивных технологий. Акушерство и гинекология. 2024; 4: 93-102. [Fortygina Yu.A., Makarova N.P., Drapkina Yu.S., Novoselova A.V., Gamisonia A.M., Chagovets V.V., Frankevich V.E., Kalinina E.A. Comparative analysis of blood and follicular fluid lipid profiles in women undergoing infertility treatment with assisted reproductive technologies. Obstetrics and Gynecology. 2024; (4): 93-102 (in Russian)]. https://dx.doi.org/10.18565/aig.2024.62.
Ding Y., Jiang Y., Zhu M., Zhu Q., He Y., Lu Y. et al. Follicular fluid lipidomic profiling reveals potential biomarkers of polycystic ovary syndrome: A pilot study. Front. Endocrinol. (Lausanne). 2022; 13: 960274. https://dx.doi.org/10.3389/fendo.2022.960274.
Núñez Calonge R., Guijarro J.A., Andrés C., Cortés S., Saladino M., Caballero P. et al. Relationships between lipids levels in blood plasma, follicular fluid and seminal plasma with ovarian response and sperm concentration regardless of age and body mass index. Rev. Int. Androl. 2022; 20(3): 178-88. https://dx.doi.org/10.1016/j.androl.2021.02.004.
Shehadeh A., Bruck-Haimson R., Saidemberg D., Zacharia A., Herzberg S., Ben-Meir A. et al. A shift in follicular fluid from triacylglycerols to membrane lipids is associated with positive pregnancy outcome. FASEB J. 2019; 33(9): 10291-9. https://dx.doi.org/10.1096/fj.201900318RR.
Zarezadeh R., Mehdizadeh A., Leroy J.L.M.R., Nouri M., Fayezi S., Darabi M. Action mechanisms of n-3 polyunsaturated fatty acids on the oocyte maturation and developmental competence: Potential advantages and disadvantages. J. Cell. Physiol. 2019; 234(2): 1016-29. https://dx.doi.org/10.1002/jcp.27101.
Jamro E.L., Bloom M.S., Browne R.W., Kim K., Greenwood E.A., Fujimoto V.Y. Preconception serum lipids and lipophilic micronutrient levels are associated with live birth rates after IVF. Reprod. Biomed. Online. 2019; 39(4): 665-73. https://dx.doi.org/10.1016/j.rbmo.2019.06.004.
Guan S.Y., Liu Y.Y., Guo Y., Shen X.X., Liu Y., Jin H.X. Potential biomarkers for clinical outcomes of IVF cycles in women with/without PCOS: Searching with metabolomics. Front. Endocrinol. (Lausanne). 2022; 13: 982200. https://dx.doi.org/10.3389/fendo.2022.982200.
Wang Q.Q., Yu S.C., Qi X., Hu Y.H., Zheng W.J., Shi J.X. et al. [Overview of logistic regression model analysis and application]. Zhonghua Yu Fang Yi Xue Za Zhi. 2019; 53(9): 955-60. (in Chinese). https://dx.doi.org/10.3760/cma.j.issn.0253-9624.2019.09.018.
Uddin S., Khan A., Hossain M.E., Moni M.A. Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak. 2019; 19(1): 281. https://dx.doi.org/10.1186/s12911-019-1004-8.
Драпкина Ю.С., Макарова Н.П., Васильев Р.А., Амелин В.В., Франкевич В.Е., Калинина Е.А. Изучение аналитической обработки клинико-анамнестических и эмбриологических данных пациентов в программе вспомогательных репродуктивных технологий различными методами машинного обучения. Акушерство и гинекология. 2024; 3: 96-107. [Drapkina Yu.S., Makarova N.P., Vasilev R.A., Amelin V.V., Frankevich V.E., Kalinina E.A. Application of various machine learning techniques to the analysis of clinical, anamnestic, and embryological data of patients undergoing assisted reproductive technologies. Obstetrics and Gynecology. 2024; (3): 96-107 (in Russian)]. https://dx.doi.org/10.18565/aig.2023.281.
Mirabi P., Chaichi M.J., Esmaeilzadeh S., Ali Jorsaraei S.G., Bijani A., Ehsani M. et al. The role of fatty acids on ICSI outcomes: a prospective cohort study. Lipids Health Dis. 2017; 16(1): 18. https://dx.doi.org/10.1186/s12944-016-0396-z.
Kennedy T. Interactions of eicosanoids and other factors in blastocyst implantation. In: Hilier K., ed. Eicosanoids and Reproduction. MTP Press Limited; Vancouver, BC, Canada; 2012; 73.
Harden S.L., Zhou J., Gharanei S., Diniz-da-Costa M., Lucas E.S., Cui L. et al. Exometabolomic analysis of decidualizing human endometrial stromal and perivascular cells. Front. Cell Dev. Biol. 2021; 9: 626619. https://dx.doi.org/10.3389/fcell.2021.626619.
Guan S.Y., Liu Y.Y., Guo Y., Shen X.X., Liu Y., Jin H.X. Potential biomarkers for clinical outcomes of IVF cycles in women with/without PCOS: Searching with metabolomics. Front. Endocrinol. (Lausanne). 2022; 13: 982200. https://dx.doi.org/10.3389/fendo.2022.982200.
Jia C., Xu H., Xu Y., Xu Y., Shi Q. Serum metabolomics analysis of patients with polycystic ovary syndrome by mass spectrometry. Mol. Reprod. Dev. 2019; 86(3): 292-7. https://dx.doi.org/10.1002/mrd.23104.
Li F., Jiang C., Larsen M.C., Bushkofsky J., Krausz K.W., Wang T. et al. Lipidomics reveals a link between CYP1B1 and SCD1 in promoting obesity. J. Proteome Res. 2014; 13(5): 2679-87. https://dx.doi.org/10.1021/pr500145n.
Han M.S., Lim Y.M., Quan W., Kim J.R., Chung K.W., Kang M. et al. Lysophosphatidylcholine as an effector of fatty acid-induced insulin resistance. J. Lipid. Res. 2011; 52(6): 1234-46. https://dx.doi.org/10.1194/jlr.M014787.
Драпкина Ю.С., Макарова Н.П., Васильев Р.А., Амелин В.В., Калинина Е.А. Сравнение прогностических моделей, построенных с помощью разных методов машинного обучения, на примере прогнозирования результатов лечения бесплодия методом вспомогательных репродуктивных технологий. Акушерство и гинекология. 2024; 2: 97-105. [Drapkina Yu.S., Makarova N.P., Vasiliev R.A., Amelin V.V., Kalinina E.A. Comparison of predictive models built with different machine learning techniques using the example of predicting the outcome of assisted reproductive technologies. Obstetrics and Gynecology. 2024; (2): 97-105 (in Russian)]. https://dx.doi.org/10.18565/aig.2023.263.

Received 02.11.2024

Accepted 14.02.2025

Yulia S. Drapkina, PhD, Senior Researcher at the Department of IVF named after Prof. B.V. Leonov, Academician V.I. Kulakov National Medical Research Center
for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparin str., 4, yu_drapkina@oparina4.ru,
https://orcid.org/0000-0002-0545-1607
Natalya P. Makarova, Dr. Bio. Sci., Leading Researcher at the Department of IVF named after Prof. B.V. Leonov, Academician V.I. Kulakov National Medical Research
Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparin str., 4, np_makarova@oparina4.ru,
https://orcid.org/0000-0003-1396-7272
Vitaliy V. Chagovets, PhD, Head of the Laboratory for Metabolomics and Bioinformatics, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparin str., vvchagovets@gmail.com, https://orcid.org/0000-0002-5120-376X
Robert A. Vasiliev, Head of the Laboratory of Applied Artificial Intelligence Z-union, Vice-President of the Association of Laboratories for the Development of Artificial Intelligence, PhD student at the Moscow Institute of Physics and Technology (MIPT), Master of the Department of Applied Physics and Mathematics of the Moscow Institute of Physics and Technology, Master of Economics, Bachelor’s degree at the Research University «Moscow Institute of Electronic Technology».
Vladislav V. Amelin, Technical Director of the Laboratory of Applied Artificial Intelligence Z-union, Expert in machine learning. Master’s degree from Moscow State University (Faculty of Computational Mathematics and Cybernetics, Department of Mathematical Methods), Bachelor’s degree from the National Research University «Moscow Institute of Electronic Technology».
Elena A. Kalinina, Dr. Med. Sci., Professor, Head of the Department of IVF named after Prof. B.V. Leonov, Academician V.I. Kulakov National Medical Research Center
for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparin str., 4, e_kalinina@oparina4.ru,
https://orcid.org/0000-0002-8922-2878

Using machine learning to analyze the lipid profile of culture medium and predict the efficacy of assisted reproductive technologies

Drapkina Yu.S., Makarova N.P., Chagovets V.V., Vasiliev R.A., Amelin V.V., Kalinina E.A.

Keywords

References

About the Authors

Similar Articles