Unveiling lipoprotein subfractions signature in high-FNPO PCOS: implications for PCOM diagnosis and risk assessment using advanced machine learning models.

Abstract

Background: Polycystic ovary syndrome (PCOS) is a common reproductive and metabolic disorder in the reproductive-age women. The international evidence-based guideline for the assessment and management of PCOS 2023 now suggests raising the follicle number per ovary (FNPO) threshold from 12 to 20 to define its key feature, polycystic ovarian morphology (PCOM). However, understanding of low- and high-FNPO PCOS cases defined in this cutoff is very limited. Given that the measures of lipoprotein subfractions are the biomarkers of several common diseases, this study aims to explore clinical characteristics and lipoprotein subfractions in low- and high-FNPO PCOS, and develop a diagnostic model.

Methods: A total of 1918 women including 792 low- and 182 high-FNPO PCOS cases, met the international evidence-based guideline 2023, and 944 controls were collected for clinical data analysis. Plasma samples of 66 low-FNPO and 24 high-FNPO PCOS cases and 22 controls matched with BMI and age were utilized for the measurement of 112 lipoprotein subfractions by nuclear magnetic resonance spectroscopy. Partial least squares discriminant analysis (PLS-DA) and logistic regression analysis were used to identify key lipoprotein subfractions. Ten machine learning algorithms and recursive feature elimination with logistic regression were used to construct the effective model to predict PCOM based on the new guideline. Models were validated with bootstrap resampling.

Results: High-FNPO PCOS cases presented worse lipid parameters compared with low-FNPO cases and controls. Based on the results of PLS-DA and logistic regression analysis, seven key lipoprotein subfractions were selected, including V2TG, V3TG, V4TG, V2CH, V3CH, V3PL, and V4PL. The addition of them into the anti-Müllerian hormone (AMH) models for predicting high-FNPO PCOS resulted in a significantly improved model performance (AUC increased from 0.750 to 0.874). Even if the only V3TG was added into the AMH model, the AUC increased to 0.807.

Conclusions: Lipid metabolism, particularly seven key lipoprotein subfractions, has been identified as a major risk factor for high-FNPO PCOS cases. Among these, V3TG subfraction warrants special attention, both from the perspective of disease risk and precision diagnosis. Due to the lack of effective external validation at this stage, validation of larger sample sizes is necessary before generalizing the application.