The role of circulating miRNAs in mechanism of action and prediction of therapeutic responses of metformin in polycystic ovarian syndrome.

OBJECTIVE: To study the involvement of microribonucleic acids (miRNAs) in the pathogenesis of chronic anovulation and mechanism of metformin treatment in polycystic ovary syndrome (PCOS). DESIGN: Case-control and prospective validation cohort study. SETTING: Tertiary university hospital. PATIENT(S): A total of 146 patients with PCOS and chronic anovulation and 20 non-PCOS controls were enrolled. Patients who resumed ovulation after metformin treatment (MET-OV) and remained anovulatory after metformin treatment (MET-AO) were assigned to MET-OV and MET-AO groups, respectively. INTERVENTION(S): All patients with PCOS received metformin treatment for 6 months. MAIN OUTCOME MEASURE(S): Baseline and chronological changes in the plasma levels of 14 miRNAs (miR-21, 93, 132, 193b, 221, 222, 223, 27a, 125b, 200b, 212, 320a, 429, and 483) selected by literature review, anthropometric data, and hormonal as well as metabolic profiles were measured. Predictive modeling based on baseline circulatory miRNA levels and clinical parameters was performed to predict ovulation recovery after metformin treatment. RESULT(S): No significant differences were observed in the baseline hormonal and metabolic profiles between the MET-OV and MET-AO groups. However, the expression of miR-27a, miR-93, and miR-222 was significantly higher in the MET-OV group than that for the MET-AO and control groups. After 6 months of metformin treatment, the levels of insulin, luteinizing hormone, and 6 circulating miRNAs (miR-21, 27a, 93, 221, 222, and 223) and homeostatic model assessment for insulin resistance decreased significantly in the MET-OV group, but remained unchanged in the MET-AO group. The area under curve, sensitivity, and specificity of the adjusted prediction model, based on miRNA levels and clinical parameters using logistic regression analysis for predicting ovulatory response after metformin treatment, were 0.807, 0.892, and 0.632, respectively. CONCLUSION(S): The present study demonstrated a distinct pattern of baseline expression and chronological changes in the levels of several circulatory miRNAs between the MET-OV and MET-AO groups, suggesting that aberrantly overexpressed diabetogenic miRNAs are involved in the pathophysiology of chronic anovulation in PCOS, and their down-regulation might contribute toward the therapeutic effects of metformin. This could provide new insights into the mechanism of action and applicability of individualized metformin therapy in women with PCOS.

A novel nomogram model for differentiating Kawasaki disease from sepsis.

Kawasaki disease (KD) is a form of systemic vasculitis that occurs in children under the age of 5 years old. Due to prolonged fever and elevated inflammatory markers that are found in both KD and sepsis, the treatment approach differs for each. We enrolled a total of 420 children (227 KD and 193 sepsis) in this study. Logistic regression and a nomogram model were used to analyze the laboratory markers. We randomly selected 247 children as the training modeling group and 173 as the validation group. After completing a logistic regression analysis, white blood cell (WBC), anemia, procalcitonin (PCT), C-reactive protein (CRP), albumin, and alanine transaminase (ALT) demonstrated a significant difference in differentiating KD from sepsis. The patients were scored according to the nomogram, and patients with scores greater than 175 were placed in the high-risk KD group. The area under the curve of the receiver operating characteristic curve (ROC curve) of the modeling group was 0.873, sensitivity was 0.893, and specificity was 0.746, and the ROC curve in the validation group was 0.831, sensitivity was 0.709, and specificity was 0.795. A novel nomogram prediction model may help clinicians differentiate KD from sepsis with high accuracy.