Introduction. The continuum and pregnancy outcomes in adolescent girls and women of reproductive age, differences between them are partly predetermined by pregestational factors, in particular body mass characteristics. Today, the key role is assigned to the age at menarche, which indicates the achievement of mature reproductive function.Aim. To identify and compare the relationship between the age at menarche combined with the pregestational body mass index (BMI) and the adverse reproductive outcome (ARO) variant in women of reproductive age and adolescent girls.Materials and methods. At total of 967 women with ARO were enrolled in the prospective cohort multicenter study. The patients were divided into cohorts based on their age groups (adolescent girls (n = 182) or women of reproductive age (n = 785)) and the ARO variants. Four groups of women were identified: women with non-developing pregnancy (NDP) (n = 244), women with extra-uterine pregnancy (EP) (n = 115), women with spontaneous miscarriage (SM) (n = 299), and women with preterm birth (PB) (n = 309).Results and discussion. It was found that a later ARO corresponds to a higher BMI: a more probable BMI for SM is over 23, for NDP is 23 and less; for PB is over 25, for SM is 25 and less. No threshold BMI limit distinguishing between EP and NDP was identified. The BMI for adolescent girls is generally significantly lower than the BMI for women of reproductive age; it is significantly higher in EP as compared with women of reproductive age and comparable in SM. A trend towards a higher BMI in SM compared to NDP both in women of reproductive age with BMI over 24 and in adolescent girls with BMI over 20 but with different threshold limits is shown. The threshold BMI limit distinguishing PB from SM in reproductive age is 25 and higher, and in adolescent girls it does not reach 23.Conclusion. In clinical practice, it is recommended to use navigators for predicting ARO variants based on pregestational BMI and/or age at menarche, taking into account the age group of patients, which are obtained using classification trees.
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