ФЕНОТИПИЧЕСКАЯ СТРАТИФИКАЦИЯ, МЕТАБОЛИЧЕСКИЕ МАРКЁРЫ И ПРОГНОСТИЧЕСКАЯ ЗНАЧИМОСТЬ РАННЕЙ ДИАГНОСТИКИ СИНДРОМА ПОЛИКИСТОЗНЫХ ЯИЧНИКОВ
Ключевые слова:
синдром поликистозных яичников; фенотипы; гиперандрогения; метаболические нарушения; инсулинорезистентностьАннотация
Dolzarblik. Tuxumdon polikistoz sindromi (TPS) reproduktiv yoshdagi ayollar orasida eng keng tarqalgan endokrin buzilishlardan biri bo‘lib, klinik va metabolik heterojenlik bilan tavsiflanadi. Fenotipik stratifikatsiya klinik prognozni aniqlashtirish va davolashga individual yondashuvni ta’minlaydi. Maqsad. TPSning turli fenotiplarida klinik, gormonal va metabolik xususiyatlarni baholash hamda kasallikni erta tashxislash uchun informativ markerlarni aniqlash.
Materiallar va usullar. TPS tashxisi qo‘yilgan 145 bemor Rotterdam mezonlariga (2003) ko‘ra to‘rtta fenotipga ajratildi va 22 ayol nazorat guruhiga kiritildi. Klinik, ultratovush, gormonal va metabolik ko‘rsatkichlar baholandi. Statistik tahlil regressiya modellarini, ROC-analiz va neyron tarmoqlar algoritmlarini o‘z ichiga oldi. Natijalar. Eng og‘ir profil A fenotipidagi bemorlarda aniqlanib, ular semirish, insulinga rezistentlik, dislipidemiya va yaqqol gipera ndrogenizm bilan xarakterlandi. D fenotipi esa minimal o‘zgarishlar bilan nazorat guruhiga yaqin edi. TPSni erta tashxislash uchun asosiy markerlar hayz siklining davomiyligi, testosteron darajasi, tana massasi indeksi, tuxumdon hajmi va antral follikulalar soni bo‘ldi. Xulosa. Fenotipik stratifikatsiya va diagnostik ahamiyatli markerlardan foydalanish TPSni erta tashxislashning aniqligini oshiradi va bemorlarni shaxsiylashtirilgan boshqarish strategiyalarini ishlab chiqishga imkon beradi.
Библиографические ссылки
Dewailly D, Lujan ME, Carmina E, et al. Definition and significance of polycystic ovarian morphology: a task force report from the Androgen Excess and Polycystic Ovary Syndrome Society. Hum Reprod Update. 2017;23(5):575–592.
Escobar-Morreale HF. Polycystic ovary syndrome: definition, aetiology, diagnosis and treatment. Nat Rev Endocrinol. 2018;14(5):270–284.
Kahal H, Kyrou I, Uthman OA, Randeva HS. Machine learning in the prediction of polycystic ovary syndrome: current state and future directions. Reprod Biomed Online. 2020;41(3):405–415.
Lizneva D, Suturina L, Walker W, Brakta S, Gavrilova-Jordan L, Azziz R. Phenotypes and prevalence of polycystic ovary syndrome. Fertil Steril. 2016;106(1):6–15.
Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Hum Reprod. 2004;19(1):41–47.
Teede HJ, Joham AE, Boyle J, et al. FIGO recommendations on polycystic ovary syndrome: An international expert consensus. Int J Gynaecol Obstet. 2023;163(2):323–341.
Teede HJ, Misso ML, Costello MF, et al. International evidence-based guideline for the assessment and management of polycystic ovary syndrome. Hum Reprod. 2018;33(9):1602–1618.
