Josef van Helden1,2, Osman Evliyaoglu3, Andreas Küberl1, Ralf Weiskirchen3. 1. Labor Mönchengladbach - MVZ Dr. Stein + Kollegen, Mönchengladbach, Germany. 2. Laboratory Diagnostic Center, RWTH University Hospital Aachen, Aachen, Germany. 3. Institute of Molecular Pathobiochemistry, Experimental Gene Therapy and Clinical Chemistry, RWTH University Hospital Aachen, Aachen, Germany.
Abstract
BACKGROUND: Different polycystic ovary syndrome (PCOS) phenotypes are correlated with different clinical severity levels. Insulin resistance correlates with higher severity. In a retrospective study, 130 patients with polycystic ovary syndrome were examined for insulin resistance. The aim of the study was to investigate relationships between glucose metabolism and different PCOS phenotypes and to identify biomarkers or combinations thereof to obtain information on the type of metabolic disorder or the severity of PCOS. METHODS: A total of 130 patients with PCOS were included in the study. Biometric data such as weight, height, cycle day and cycle length were compared with glucose metabolism parameters such as fasting glucose, insulin before and 60 and 120 minutes after 75 g glucose intake, intact proinsulin, C-peptide and ovarian function parameters including Anti-Müllerian hormone (AMH) and the soluble AMH receptor (sAMHR2). The parameters were correlated, and their diagnostic performance with respect to different expressions of PCOS was evaluated. RESULTS: The biomarkers of impaired glucose metabolism showed strong significant difference in HOMA Index, adiponectin, proinsulin and body mass index (BMI) and Insulin levels in 0-60-120 minutes of glucose tolerance test but also with parameters of ovarian function as AMH, AMH z-score sAMHR2, and sAMHR2/AMH ratio. A strong correlation between sAMHR2 and adiponectin (r = .818, P < .0001) was found indicating a relationship between the degree of glucose metabolic impairment and ovarian function. CONCLUSIONS: The parameters glucose, insulin, insulin 60 minutes after intake of 75 g glucose and adiponectin or sAMHR2 enable a biochemical classification of PCOS patients that correlates with morphological PCOS phenotypes. By determining biomarkers, it is possible to classify PCOS patients into subgroups that correlate with different PCOS phenotypes and the clinical severity.
BACKGROUND: Different polycystic ovary syndrome (PCOS) phenotypes are correlated with different clinical severity levels. Insulin resistance correlates with higher severity. In a retrospective study, 130 patients with polycystic ovary syndrome were examined for insulin resistance. The aim of the study was to investigate relationships between glucose metabolism and different PCOS phenotypes and to identify biomarkers or combinations thereof to obtain information on the type of metabolic disorder or the severity of PCOS. METHODS: A total of 130 patients with PCOS were included in the study. Biometric data such as weight, height, cycle day and cycle length were compared with glucose metabolism parameters such as fasting glucose, insulin before and 60 and 120 minutes after 75 g glucose intake, intact proinsulin, C-peptide and ovarian function parameters including Anti-Müllerian hormone (AMH) and the soluble AMH receptor (sAMHR2). The parameters were correlated, and their diagnostic performance with respect to different expressions of PCOS was evaluated. RESULTS: The biomarkers of impaired glucose metabolism showed strong significant difference in HOMA Index, adiponectin, proinsulin and body mass index (BMI) and Insulin levels in 0-60-120 minutes of glucose tolerance test but also with parameters of ovarian function as AMH, AMH z-score sAMHR2, and sAMHR2/AMH ratio. A strong correlation between sAMHR2 and adiponectin (r = .818, P < .0001) was found indicating a relationship between the degree of glucose metabolic impairment and ovarian function. CONCLUSIONS: The parameters glucose, insulin, insulin 60 minutes after intake of 75 g glucose and adiponectin or sAMHR2 enable a biochemical classification of PCOSpatients that correlates with morphological PCOS phenotypes. By determining biomarkers, it is possible to classify PCOSpatients into subgroups that correlate with different PCOS phenotypes and the clinical severity.