Predicting chemotherapy-induced menopause using baseline and post-chemotherapy anti-Mullerian hormone levels: Results of a pilot study.
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ABSTRACT: Background
Chemotherapy can cause premature menopause which may result in adverse effects such as fertility loss, osteoporosis, cardiovascular disease and menopausal symptoms. It is thus very important that women are provided with accurate information regarding their risk of premature menopause as a consequence of proposed chemotherapy. Unfortunately, at present there are no reliable tools which can be applied in clinical practice to estimate the risk of premature menopause in women undergoing chemotherapy, beyond age of the patient and form of chemotherapy utilized.Aim
This was a pilot study to determine whether AMH levels pre and during chemotherapy are able to predict for chemotherapy induced menopause, and to assess quality of life and menopausal symptoms.Methods and results
Premenopausal women between 18 to 45 who were planned to undergo gonadotoxic chemotherapy with curative intent for either breast cancer or haematologic malignancy were recruited from a single centre. AMH, FSH, LH and oestradiol levels were recorded prior to commencement of therapy, during and following completion of chemotherapy. Menstrual status, menopausal symptoms and quality of life data were collected at baseline and during follow-up. Twenty two women were recruited. The baseline AMH was higher in women who regained menses post-chemotherapy (median 23.1 vs 9.9 pM (P = .06). Menopausal symptoms were significantly higher at 1 year post diagnosis than at baseline however quality of life was similar.Conclusion
AMH may be useful for predicting chemotherapy induced menopause. Further research is still required to determine the place of such testing for patient counselling and management.
SUBMITTER: Martin HL
PROVIDER: S-EPMC8222560 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAnti-Müllerian hormone (AMH) is an important determinant of ovarian reserve in fertility workups in many clinical settings. Thus, we investigated the age dependent decline in AMH specific to the Egyptian population and sought to establish an age dependent reference interval parametrically.MethodsSerum samples were collected from 841 apparently healthy women. AMH was measured using an electro-chemiluminescent technique. Box-Cox power transformation was used to make the AMH distribution Gaussian for parametric derivation of reference intervals.ResultsPower of 0.4 was found optimal for Gaussian transformation of AMH reference values. We demonstrate the strong negative relation between circulating AMH and female age with Spearman's correlation coefficient of rS = -0.528. Age-specific reference interval was determined for every 5 years of age from 16 to 49, and nomogram was constructed by smoothing the lines connecting adjacent lower and upper reference limits.ConclusionThe age-specific reference intervals and the age-AMH nomogram could be valuable in the clinical practice of in reproductive medicine. To our knowledge, this is the first study to confirm AMH levels in Egyptian females. We were able to explore age-related AMH levels specific to Egyptian females in the fertile age group and to treat skewed AMH data in a multi-step scheme using power transformation. Thus, a more accurate nomogram was constructed accommodating a profile delineated for a wide age range and a rescaled AMH axis improving its usability.
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Project description:BackgroundPreviously, we reported a model for assessing ovarian reserves using 4 predictors: anti-Müllerian hormone (AMH) level, antral follicle count (AFC), follicle-stimulating hormone (FSH) level, and female age. This model is referred as the AAFA (anti-Müllerian hormone level-antral follicle count-follicle-stimulating hormone level-age) model.ObjectiveThis study aims to explore the possibility of establishing a model for predicting ovarian reserves using only 3 factors: AMH level, FSH level, and age. The proposed model is referred to as the AFA (anti-Müllerian hormone level-follicle-stimulating hormone level-age) model.MethodsOocytes from ovarian cycles stimulated by gonadotropin-releasing hormone antagonist were collected retrospectively at our reproductive center. Poor ovarian response (<5 oocytes retrieved) was defined as an outcome variable. The AFA model was built using a multivariable logistic regression analysis on data from 2017; data from 2018 were used to validate the performance of AFA model. Measurements of the area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predicative value were used to evaluate the performance of the model. To rank the ovarian reserves of the whole population, we ranked the subgroups according to the predicted probability of poor ovarian response and further divided the 60 subgroups into 4 clusters, A-D, according to cut-off values consistent with the AAFA model.ResultsThe AUCs of the AFA and AAFA models were similar for the same validation set, with values of 0.853 (95% CI 0.841-0.865) and 0.850 (95% CI 0.838-0.862), respectively. We further ranked the ovarian reserves according to their predicted probability of poor ovarian response, which was calculated using our AFA model. The actual incidences of poor ovarian response in groups from A-D in the AFA model were 0.037 (95% CI 0.029-0.046), 0.128 (95% CI 0.099-0.165), 0.294 (95% CI 0.250-0.341), and 0.624 (95% CI 0.577-0.669), respectively. The order of ovarian reserve from adequate to poor followed the order from A to D. The clinical pregnancy rate, live-birth rate, and specific differences in groups A-D were similar when predicted using the AFA and AAFA models.ConclusionsThis AFA model for assessing the true ovarian reserve was more convenient, cost-effective, and objective than our original AAFA model.
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