Novel model predicts diastolic cardiac dysfunction in type 2 diabetes.

OBJECTIVE: Diabetes mellitus complicated with heart failure has high mortality and morbidity, but no reliable diagnoses and treatments are available. This study aimed to develop and verify a new model nomogram based on clinical parameters to predict diastolic cardiac dysfunction in patients with Type 2 diabetes mellitus (T2DM). METHODS: 3030 patients with T2DM underwent Doppler echocardiography at the First Affiliated Hospital of Shenzhen University between January 2014 and December 2021. The patients were divided into the training dataset (n = 1701) and the verification dataset (n = 1329). In this study, a predictive diastolic cardiac dysfunction nomogram is developed using multivariable logical regression analysis, which contains the candidates selected in a minor absolute shrinkage and selection operator regression model. Discrimination in the prediction model was assessed using the area under the receiver operating characteristic curve (AUC-ROC). The calibration curve was applied to evaluate the calibration of the alignment nomogram, and the clinical decision curve was used to determine the clinical practicability of the alignment map. The verification dataset was used to evaluate the prediction model's performance. RESULTS: A multivariable model that included age, body mass index (BMI), triglyceride (TG), creatine phosphokinase isoenzyme (CK-MB), serum sodium (Na), and urinary albumin/creatinine ratio (UACR) was presented as the nomogram. We obtained the model for estimating diastolic cardiac dysfunction in patients with T2DM. The AUC-ROC of the training dataset in our model was 0.8307, with 95% CI of 0.8109-0.8505. Similar to the results obtained with the training dataset, the AUC-ROC of the verification dataset in our model was 0.8083, with 95% CI of 0.7843-0.8324, thus demonstrating robust. The function of the predictive model was as follows: Diastolic Dysfunction = -4.41303 + 0.14100*Age(year)+0.10491*BMI (kg/m2) +0.12902*TG (mmol/L) +0.03970*CK-MB (ng/mL) -0.03988*Na(mmol/L) +0.65395 * (UACR > 30 mg/g) + 1.10837 * (UACR > 300 mg/g). The calibration plot diagram of predicted probabilities against observed DCM rates indicated excellent concordance. Decision curve analysis demonstrated that the novel nomogram was clinically useful. CONCLUSION: Diastolic cardiac dysfunction in patients with T2DM can be predicted by clinical parameters. Our prediction model may represent an effective tool for large-scale epidemiological study of diastolic cardiac dysfunction in T2DM patients and provide a reliable method for early screening of T2DM patients with cardiac complications.KEY MESSAGESThis study used clinical parameters to predict diastolic cardiac dysfunction in patients with T2DM. This study established a nomogram for predicting diastolic cardiac dysfunction by multivariate logical regression analysis. Our predictive model can be used as an effective tool for large-scale epidemiological study of diastolic cardiac dysfunction in patients with T2DM and provides a reliable method for early screening of cardiac complications in patients with T2DM.

Methylation of CpG islands of p16 associated with progression of primary gastric carcinomas.

Inactivation of p16 by methylation of CpG islands is a frequent early event in gastric carcinogenesis. The positive relationship between p16 methylation and the clinical characteristics of gastric carcinomas (GC) has not been reported to date. In the present study, a DHPLC assay to quantify p16 methylation was established (detection limit by fluorescence detector: 1:255 (Methlyated vs Unmethylated)). The proportion of methylated p16 in the representative samples was confirmed and standardized by clone sequencing. Then, the DHPLC and two regular methylation-specific PCR (MSP) assays were used to detect p16 methylation in 82 paired, resected GCs and their adjacent normal tissues. Results showed that the average proportion of methylated p16 in GCs was significantly higher than that in their adjacent samples (12.90 vs 0.63%; t-test P=0.005). A much higher proportion of methylated p16 was detected in GCs with metastases (local or distant) than without metastases (14.76 vs 2.61%; t-test P=0.014). A proportional relationship was observed between clinical stages and positive rates of p16 methylation in GCs and/or adjacent tissues: 27.3, 37.5, and 58.8% (by DHPLC) for stage-I, -II, -III-IV of GCs, respectively (two-sided Fisher's exact test P=0.016). To confirm the data obtained by DHPLC, two MSP primer sets (p16-M and p16-M2) were also used to analyze p16 methylation in the same set of samples simultaneously. Data of MSP assay using the primer set p16-M2, but not p16-M, correlated with that of DHPLC. These results imply that the primer set p16-M2 might be more suitable than p16-M to detect p16 methylation in gastric tissues. In conclusion, the present data indicates that p16 methylation correlates with progression of GCs significantly.