Red Blood Cell Distribution Width to Albumin Ratio for Predicting Type I Cardiorenal Syndrome in Patients with Acute Myocardial Infarction: A Retrospective Cohort Study.

Purpose: Red blood cell distribution width to albumin ratio (RAR) is a novel inflammatory biomarker that independently predicts adverse cardiovascular events and acute kidney injury. This study aimed to assess the predictive value of RAR for cardio-renal syndrome type I (CRS-I) risk in acute myocardial infarction (AMI) patients. Patients and methods: This study retrospectively enrolled 551 patients who were definitively diagnosed as AMI between October 2021 and October 2022 at the Affiliated Zhongda Hospital of Southeast University. Participants were divided into two and four groups based on the occurrence of CRS-I and the quartiles of RAR, respectively. Demographic data, laboratory findings, coronary angiography data, and drug utilization were compared among the groups. Logistic regression and receiver operating characteristic curve (ROC) analysis were performed to identify independent risk factors for CRS-I and evaluated the predictive value of RAR for CRS-I. Results: Among the cohort of 551 patients, 103 (18.7%) developed CRS-I. Patients with CRS-I exhibited significantly elevated RAR levels compared to those without the condition, and the incidence of CRS-I correlated with escalating RAR. Univariate and multivariate logistic regression analyses identified RAR as an independent risk factor for CRS-I. ROC curves analysis demonstrated that RAR alone predicted CRS-I with an area under the curve (AUC) of 0.683 (95% CI=0.642-0.741), which was superior to the traditional inflammatory marker C-reactive protein (CRP). Adding the variable RAR to the model for predicting the risk of CRS-I further improved the predictive value of the model from 0.808 (95% CI=0.781-0.834) to 0.825 (95% CI=0.799-0.850). Conclusion: RAR is an independent risk factor for CRS-I, and high levels of RAR are associated with an increased incidence of CRS-I in patients with AMI. RAR emerges as a valuable and readily accessible inflammatory biomarker that may play a pivotal role in risk stratification in clinical practice.

Effect of bariatric surgery on metabolism in diabetes and obesity comorbidity: Insight from recent research.

Obesity is a prevalent cause of diabetes mellitus (DM) and is a serious danger to human health. Type 2 DM (T2DM) mostly occurs along with obesity. Foodborne obesity-induced DM is caused by an excessive long-term diet and surplus energy. Bariatric surgery can improve the symptoms of T2DM in some obese patients. But different types of bariatric surgery may have different effects. There are some models built by researchers to discuss the surgical procedures' effects on metabolism in diabetes animal models and diabetes patients. It is high time to conclude all this effects and recommend procedures that can better improve metabolism.

Predicting cerebral white matter lesions based on the platelet-to-white blood cell ratio in hypertensive patients.

Hypertension is a common chronic disease affecting many people. White matter lesions (WMLs) are one of the imaging features of cerebrovascular disease. Predicting the possibility of developing syncretic WMLs in patients with hypertension may contribute to the early identification of serious clinical conditions. This study aims to build a model to identify patients who suffered from moderate-to-severe WMLs by using recognized WMLs risk factors including age and history of diabetes and a new factor named platelet-to-white blood cell ratio (PWR). A total of 237 patients were included in this study. The Affiliated ZhongDa Hospital of Southeast University Research Ethics Committee approved this study (Ethics No. 2019ZDSYLL189-P01). We developed a nomogram to predict the risk of syncretic WMLs in patients with hypertension using the above factors. Higher total scores on the nomogram indicated a higher risk of syncretic WMLs. This means older age, smaller PWR, and patients suffering from diabetes contributed to a greater chance for the patient to suffer from syncretic WMLs. We used a decision analysis curve(DCA) to determine the net benefit of the prediction model. The DCA we constructed showed that using our model to decide whether patients suffered from syncretic WMLs or not was better than assuming they all suffered from syncretic WMLs or all WMLs-free. As a result, the area under the curve of our model was 0.787. By integrating PWR, history of diabetes, and age, we could estimate integrated WMLs in hypertensive patients. This study provides a potential tool to identify cerebrovascular disease in patients with hypertension.