Bone morphogenetic protein-9 maybe an important factor which improves insulin resistance in PCOS.

BACKGROUND: Polycystic ovary syndrome (PCOS) is closely related to insulin resistance (IR). Bone morphogenetic protein-9 (BMP-9) plays an important role in maintaining glucose homeostasis, but an association between BMP-9 and PCOS has not been reported. Here, we report the changes in BMP-9 and the influence of this protein on IR in PCOS. METHODS: 57 PCOS patients were selected (among them 25 received interventional treatment with exenatide (EX) for 3 months, and 32 received no treatment). 22 normal control individuals and 30 IR patients were also recruited. We evaluated IR with the euglycaemic-hyperinsulinaemic clamp (EHC) technique. IR and the glucose metabolism rate were assessed by EHC and [3-3H]glucose tracer experiments. We determined the protein expression levels of BMP-9, p-AKT (protein kinase B) and androgen receptor in the ovaries and liver by Western blotting. RESULTS: We found that circulating BMP-9 levels were significantly decreased in PCOS with IR patients. Circulating BMP-9 levels and p-AKT levels were decreased in HFD and PCOS rats and increased after MF and EX treatment. The glucose infusion rate, glucose disappearance rate and suppression of hepatic glucose production decreased in the HFD and PCOS groups, the opposite results were found for HGP. AR protein expression levels increased in the HFD and PCOS groups and decreased in the MF and EX groups. CONCLUSIONS: Our study results suggest that BMP-9 is an independent factor that influences IR in PCOS patients. The decrease in BMP-9 levels in the liver and ovaries may be involved in IR through the PI3K/AKT signaling pathway in PCOS rats.

[Calcium pyrophosphate dihydrate crystal deposition disease: a clinicopathologic analysis of 20 cases].

OBJECTIVE: To investigate the clinicopathologic features of calcium pyrophosphate dihydrate crystal deposition disease (CPPD-CDD). METHODS: The clinical and pathologic profiles were retrospectively analysed in 20 cases of CPPD-CDD. RESULTS: CPPD-CDD was far more common in women, most frequently involving joints, especially the knees and presenting with various arthrisis. Abnormally calcified and the articular damages were characteristic features by imageing. Histologically, multifocal indigo granular calcinosis was seen in synovium and sometimes appeared as needle-shaped or rhomboid crystals, which characterized the CPPD. CONCLUSIONS: Though clinical symptoms of CPPD are quite variable, the definite diagnosis can be made by the abnormal calcification and joint damage radiographically and the indigo CPPD crystals histopathologically.

Diabetic retinopathy risk prediction in patients with type 2 diabetes mellitus using a nomogram model.

Background: This study aims to develop a diabetic retinopathy (DR) hazard nomogram for a Chinese population of patients with type 2 diabetes mellitus (T2DM). Methods: We constructed a nomogram model by including data from 213 patients with T2DM between January 2019 and May 2021 in the Affiliated Hospital of Zunyi Medical University. We used basic statistics and biochemical indicator tests to assess the risk of DR in patients with T2DM. The patient data were used to evaluate the DR risk using R software and a least absolute shrinkage and selection operator (LASSO) predictive model. Using multivariable Cox regression, we examined the risk factors of DR to reduce the LASSO penalty. The validation model, decision curve analysis, and C-index were tested on the calibration plot. The bootstrapping methodology was used to internally validate the accuracy of the nomogram. Results: The LASSO algorithm identified the following eight predictive variables from the 16 independent variables: disease duration, body mass index (BMI), fasting blood glucose (FPG), glycated hemoglobin (HbA1c), homeostatic model assessment-insulin resistance (HOMA-IR), triglyceride (TG), total cholesterol (TC), and vitamin D (VitD)-T3. The C-index was 0.848 (95% CI: 0.798-0.898), indicating the accuracy of the model. In the interval validation, high scores (0.816) are possible from an analysis of a DR nomogram's decision curve to predict DR. Conclusion: We developed a non-parametric technique to predict the risk of DR based on disease duration, BMI, FPG, HbA1c, HOMA-IR, TG, TC, and VitD.