Endocrine characteristics, body mass index and metabolic syndrome in women with polycystic ovary syndrome

The importance of inflammation markers in polycystic ovary syndrome

OBJECTIVE

This study aimed to examine inflammation markers in patients with polycystic ovary syndrome (PCOS) and to compare them with healthy women.

METHODS

This prospective study was conducted by examining patients who applied to the Near East University Gynecology and Obstetrics Outpatient Clinic between January 2019 and January 2020. A total of 110 PCOS patients with 135 control groups were compared in terms of metabolism, hormonal factors, and inflammation markers.

RESULTS

The neutrophil count, neutrophil-lymphocyte ratio (NLR), platelet, platelet-lymphocyte ratio (PLR), platelecrit (PCT), erythrocyte cell distribution width, platelet distribution width, mean platelet volume, and C-reactive protein (CRP) values were found to be statistically significantly higher in patients with PCOS. There was a positive correlation between inflammation markers and serum androgens. Also, a positive correlation was observed between inflammation markers and cardiovascular risk parameters. In receiver operating characteristic curve analysis, the most valuable parameter in distinguishing PCOS patients from healthy controls was serum CRP levels [areas under the curve (AUC)=0.928, 95%CI 0.894-0.963, p<0.001, 92.6% sensitivity, and 82.7% specificity].

CONCLUSIONS

Serum CRP, neutrophil count, and PCT and NLR levels are valuable markers that show the inflammatory process in PCOS patients.

Evaluation of Novel Obesity and Lipid-Related Indices as Indicators for the Diagnosis of Metabolic Syndrome and Premetabolic Syndrome in Chinese Women with Polycystic Ovary Syndrome

OBJECTIVES

According to the International Diabetes Federation (IDF) criteria, previous studies in Chinese women with polycystic ovary syndrome (PCOS) reported a low prevalence of metabolic syndrome (MS); however, the same population predisposed to developing pre-MS. Early identification and treatment of individuals with MS and pre-MS are imperative to prevent their adverse consequences. Moreover, fasting plasma glucose (FPG) was not accurate in detecting pathoglycemia in women with PCOS as they have shown characteristically postprandial abnormalities in the carbohydrate metabolism. Therefore, we aimed to compare the discriminative performance of various indices for identifying MS and pre-MS/MS (pre-MS and MS) using the updated Chinese Diabetes Society (uCDS) criteria in Chinese women with PCOS.

METHODS

1083 Chinese women with PCOS were included in this study. We measured and evaluated 8 indices in all individuals. Based on the uCDS criteria for MS, patients who had no less than two components of MS but did not meet the criteria for the diagnosis of MS were considered as having pre-MS. Receiver operating characteristic (ROC) curves and the area under ROC curves (AUCs) levels were used to assess the accuracy of each index in detecting MS and pre-MS/MS.

RESULTS

Among the 8 indices assessed, the lipid accumulation product (LAP) provided the highest AUCs for detecting MS and pre-MS/MS, followed by CVAI, WTI, VAI, TyG, TG/HDL, WC, and BMI. The optimal cutoff points determined for LAP were 45.13 (sensitivity 88.0%, specificity 88.4%, and Youden index 0.764) for MS and 28.01 (sensitivity 87.5%, specificity 80.7%, and Youden index 0.681) for pre-MS/MS, respectively.

CONCLUSIONS

uCDS criteria are reasonably more suitable for detecting MS and pre-MS in Chinese women with PCOS. Based on this criterion, LAP is the best index for the diagnosis of MS and pre-MS/MS in Chinese women with PCOS, out of the 8 obesity and lipid-related indices assessed.

The Relationship of Anti-Mullerian Hormone in Polycystic Ovary Syndrome Patients with Different Subgroups

PURPOSE

To explore the value of anti-Mullerian hormone (AMH) in patients with polycystic ovary syndrome (PCOS) with different phenotypes and ages, and to identify the relationship between hyperandrogenism (HA) and polycystic ovary morphology (PCOM), in a Chinese cohort.

METHODS

A total of 2262 women (1631 with PCOS and 631 controls) were enrolled. The serum AMH and total testosterone (TT) were analyzed, the AMH levels of each subgroup were compared, and the value of each phenotype and age group of patients with PCOS was evaluated.

RESULTS

The level of AMH in women with PCOS (mean±SD, 8.63±4.73 ng/mL) was higher than that in controls (5.57±3.31 ng/mL) (P<0.01). The level of AMH in the PCOM subgroup (11.19±6.4 ng/mL) was significantly higher than that in the HA subgroup (8.58±4.74 ng/mL) (P<0.01), and both were higher than that in controls (P<0.01). AMH was higher in PCOS patients than in controls, but the same values were found in subgroups of PCOS patients under 30 years old.

CONCLUSION

AMH changed in different subgroups of PCOS, which was the possible reason why AMH was not a diagnostic indicator. However, AMH could help to differentiate between clinical subgroups, as it was strongly related with PCOM but not with HA. AMH changed substantially with age, but was stable in PCOS patients under 30 years old.

AMH Is a Good Predictor of Metabolic Risk in Women with PCOS: A Cross-Sectional Study

OBJECTIVE

The relationship between metabolic risk and ovarian function is ambiguous. This retrospective study analyzed the medical records of 461 PCOS patients collected between January 2019 and June 2020 to investigate the relationship between serum anti-Müllerian hormone (AMH) and parameters of metabolic risk in the population with polycystic ovary syndrome (PCOS).

METHODS

A total of 461 PCOS patients aged 20-40 years were included and stratified into four groups according to the AMH level. The association between AMH and the parameters related to metabolic risk in these groups was compared, and the discrepancies were further explored. Binary logistic regression was performed to examine the risk factors of HOMA-IR. The values of AMH that best predicted the risk of HOMA-IR were also analyzed by ROC curves.

RESULTS

AMH was negatively associated with HOMA-IR (odds ratio (OR) -0.279, 95% confidence interval (CI) -0.36 to -0.20), fasting insulin (OR -0.282, 95% CI -0.36 to -0.20), 1-hour postprandial insulin (OR -0.184, 95% CI -0.28 to -0.11), 2-hour postprandial insulin (-0.180, 95%CI -0.28 to -0.11), 3-hour postprandial insulin (OR -0.198, 95% CI -0.30 to -0.13), waist-hip ratio (OR -0.235, 95% CI -0.31 to -0.14), and body mass index (OR -0.350, 95% CI -0.43 to -0.27). There was no statistically significant relationship between blood pressure, serum glucose profile, or lipid levels and AMH. Binary logistic regression showed that AMH protected against the occurrence of PCOS patients (OR: 0.835, 0.776, and 0.898). For the prediction of HOMA-IR, AMH had an AUC-ROC of 0.704 (95% CI 0.652-0.755) with a cutoff value of 7.81 mmol/L, a sensitivity of 70.3%, and a specificity of 70.1%.

CONCLUSIONS

Higher AMH levels were significantly associated with a lower insulin profile and might be a useful predictor for HOMA-IR in PCOS patients.

How to Screen and Prevent Metabolic Syndrome in Patients of PCOS Early: Implications From Metabolomics

BACKGROUND

Polycystic ovary syndrome (PCOS) is a complex reproductive endocrine disorder. And metabolic syndrome (MS) is an important bridge for PCOS patients to develop other diseases, such as diabetes and coronary heart disease. Our aim was to study the potential metabolic characteristics of PCOS-MS and identify sensitive biomarkers so as to provide targets for clinical screening, diagnosis, and treatment.

METHODS

In this study, 44 PCOS patients with MS, 34 PCOS patients without MS, and 32 healthy controls were studied. Plasma samples of subjects were tested by ultraperformance liquid chromatography (UPLC) system combined with LTQ-orbi-trap mass spectrometry. The changes of metabolic characteristics from PCOS to PCOS-MS were systematically analyzed. Correlations between differential metabolites and clinical characteristics of PCOS-MS were assessed. Differential metabolites with high correlation were further evaluated by the receiver operating characteristic (ROC) curve to identify their sensitivity as screening indicators.

RESULTS

There were significant differences in general characteristics, reproductive hormone, and metabolic parameters in the PCOS-MS group when compared with the PCOS group and healthy controls. We found 40 differential metabolites which were involved in 23 pathways when compared with the PCOS group. The metabolic network further reflected the metabolic environment, including the interaction between metabolic pathways, modules, enzymes, reactions, and metabolites. In the correlation analysis, there were 11 differential metabolites whose correlation coefficient with clinical parameters was greater than 0.4, which were expected to be taken as biomarkers for clinical diagnosis. Besides, these 11 differential metabolites were assessed by ROC, and the areas under curve (AUCs) were all greater than 0.7, with a good sensitivity. Furthermore, combinational metabolic biomarkers, such as glutamic acid + leucine + phenylalanine and carnitine C 4: 0 + carnitine C18:1 + carnitine C5:0 were expected to be sensitive combinational biomarkers in clinical practice.

CONCLUSION

Our study provides a new insight to understand the pathogenesis mechanism, and the discriminating metabolites may help screen high-risk of MS in patients with PCOS and provide sensitive biomarkers for clinical diagnosis.

Correlation Between Recovery of Menstrual Cycle and Improvement of Glucose and Lipid Metabolism in Patients with Sleeve Gastrectomy in a Small Chinese Cohort Sample

OBJECTIVE

To analyze the status of insulin resistance and hyperlipidemia in the regulation of menstrual cycle in patients undergoing sleeve gastrectomy (LSG).

MATERIALS AND METHODS

Retrospective analysis of a typical metabolic syndrome population: 65 cases of sexually mature women underwent weight reduction surgery from January 2015 to June 2018, and for 35 of these cases we collected all clinical data including age, menstrual status, body mass index (BMI), waist-to-hip ratio (WHR), blood lipid level, homeostasis model assessment in insulin resistance (HOMA-IR) and free androgen index (FAI). Sixteen of these patients had menstrual disorder that was characterized by oligomenorrhea and their menstrual cycle was >2 months or <24 days before operation, while their menstrual cycle recovered approximately 1 month to one year after sleeve gastrectomy.

RESULTS

The age of our 35 patients with metabolic syndrome was 15-40 years; 19 cases were in the menstrual regularity group and 16 cases in the menstrual disorder group. On analysis of metabolic factors in the two groups, only BMI and FAI distribution in the two groups were statistically different, while lipid levels and insulin resistance levels for abnormal menstrual cycle were not statistically significant. The analysis of blood lipid components showed that high triglyceride levels and high cholesterol levels increased the occurrence of menstrual disorders.

CONCLUSION

BMI can be used as one of the important metabolic indicators of menstrual disorders in patients with metabolic syndrome. Excess androgen can increase the occurrence of menstrual disorders, leading to polycystic ovarian syndrome (PCOS). Although insulin resistance is universal in patients with metabolic syndrome, it is not a specific factor that affects menstrual cycle. Hyperlipidemia, especially triglyceride composition, may be one of the driving factors of menstrual disorder.