Diabetes mellitus is a metabolic disorder with a complex etiology in which glycemic dynamics are disturbed and the body is unable to maintain the process of glucose homeostasis through the pancreas. Persistent symptoms of high blood glucose or low blood glucose may lead to diabetic complications, such as neuropathy, nephropathy, retinopathy, and cardiovascular diseases. Glycemic variability which can represent the presence of excessive glycemic excursions is an indicator for evaluating glucose homoeostasis. Limiting glycemic variability has gradually become an emerging therapeutic target in improve diabetes metabolism and prevent associated complications. This article reviews the progress of research on the various quantifiable parameters of glycemic variability and their relationships with vascular lesions and mechanisms.

Background: Predicting hypoglycemia while maintaining a low false alarm rate is a challenge for the wide adoption of continuous glucose monitoring (CGM) devices in diabetes management. One small study suggested that a deep learning model based on the long short-term memory (LSTM) network had better performance in hypoglycemia prediction than traditional machine learning algorithms in European patients with type 1 diabetes. However, given that many well-recognized deep learning models perform poorly outside the training setting, it remains unclear whether the LSTM model could be generalized to different populations or patients with other diabetes subtypes. Objective: The aim of this study was to validate LSTM hypoglycemia prediction models in more diverse populations and across a wide spectrum of patients with different subtypes of diabetes. Methods: We assembled two large data sets of patients with type 1 and type 2 diabetes. The primary data set including CGM data from 192 Chinese patients with diabetes was used to develop the LSTM, support vector machine (SVM), and random forest (RF) models for hypoglycemia prediction with a prediction horizon of 30 minutes. Hypoglycemia was categorized into mild (glucose=54-70 mg/dL) and severe (glucose<54 mg/dL) levels. The validation data set of 427 patients of European-American ancestry in the United States was used to validate the models and examine their generalizations. The predictive performance of the models was evaluated according to the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: For the difficult-to-predict mild hypoglycemia events, the LSTM model consistently achieved AUC values greater than 97% in the primary data set, with a less than 3% AUC reduction in the validation data set, indicating that the model was robust and generalizable across populations. AUC values above 93% were also achieved when the LSTM model was applied to both type 1 and type 2 diabetes in the validation data set, further strengthening the generalizability of the model. Under different satisfactory levels of sensitivity for mild and severe hypoglycemia prediction, the LSTM model achieved higher specificity than the SVM and RF models, thereby reducing false alarms. Conclusions: Our results demonstrate that the LSTM model is robust for hypoglycemia prediction and is generalizable across populations or diabetes subtypes. Given its additional advantage of false-alarm reduction, the LSTM model is a strong candidate to be widely implemented in future CGM devices for hypoglycemia prediction.

Insulin resistance (IR) is the major mechanism in the pathogenesis of type 2 diabetes mellitus (T2DM). Early identification of IR is of great significance for preventing the onset of T2DM and delaying the progression of the disease. Previous studies have shown that triglyceride-glucose (TyG) index can be used as an effective surrogate marker for IR. There is a significant correlation between TyG index and T2DM and its common complications. In addition, the predictive efficacy of TyG index is better than that of other IR surrogate indicators. TyG index may not only become an important marker to identify people at high risk of T2DM and its complications, but is also expected to become a strong predictor of the prognosis of these diseases. However, there are still some challenges in the widespread application of TyG index in clinical practice. In the future, more high-quality studies are needed to clarify the assessment methods of TyG index for the prognosis of T2DM and its complications. Further investigations of the relationship between TyG index and T2DM and its complications will be expected to provide new ideas and methods for the prevention and treatment of T2DM and its complications.

Background. The aging population is increasingly susceptible to cardiovascular disease (CVD). Visit-to-visit variability in glucose and lipid levels both contributed to CVD risk independent of their mean values. However, whether variability in the triglyceride-glucose (TyG) index is a risk factor for CVD remains unknown. Research Design and Methods. In this retrospective study of electronic health records, 27,520 participants aged over 60 years were enrolled. The visit-to-visit variability of TyG index was calculated from annual health examination data and defined as average real variability (ARV), standard deviation (SD), or the coefficient of variability (CV). CVD events were identified from the chronic disease registry or follow-up database and included myocardial infarction, angina, coronary, and stroke. Multivariate Cox regression was used to examine the correlation between TyG variability and incident CVD. Results. Over a median follow-up of 6.2 years, there were 2,178 CVD events. When participants were divided into four quartiles according to their TyG variability, after adjusting for established CVD risk factors, subjects in the top quartile had (HR = 1.18, 95% CI 1.05–1.34, ) significantly higher CVD risk than those in the bottom quartile. The association remained significant in overweight individuals or those without diabetes ( and , respectively). Conclusions. High variability in TyG was significantly associated with elevated CVD risk in the elderly, independent of average TyG and other risk factors. Close monitoring variability in TyG might be informative to identify old individuals at high risk of CVD.