Daily Endocrinology Research Analysis

Systemic metabolism ensures energy homeostasis through inter-organ crosstalk regulating thermogenic adipose tissue. Unlike the well-described inductive role of the sympathetic system, the inhibitory signal ensuring energy preservation remains poorly understood. Here, we show that, via the mechanosensor Piezo2, sensory neurons regulate morphological and physiological properties of brown and beige fat and prevent systemic hypermetabolism. Targeting runt-related transcription factor 3 (Runx3)/parvalbumin (PV) sensory neurons in independent genetic mouse models resulted in a systemic metabolic phenotype characterized by reduced body fat and increased insulin sensitivity and glucose tolerance. Deletion of Piezo2 in PV sensory neurons reproduced the phenotype, protected against high-fat-diet-induced obesity, and caused adipose tissue browning and beiging, likely driven by elevated norepinephrine levels. Finding that brown and beige fat are innervated by Runx3/PV sensory neurons expressing Piezo2 suggests a model in which mechanical signals, sensed by Piezo2 in sensory neurons, protect energy storage and prevent a systemic hypermetabolic phenotype.

Re-epithelialization constitutes a critical stage in the intricate process of wound healing, yet its mechanisms in the context of diabetic wounds remain elusive. In this study, the role of the mesenchymal-epithelial transition (MET) vis-à-vis the epithelial-mesenchymal transition (EMT) of keratinocytes in diabetic wound re-epithelialization is investigated. The findings reveal an impediment in the MET process, rather than EMT, which significantly compromised re-epithelialization in diabetic wounds. Furthermore, Desmoplakin (DSP) gene expression, encoding a key desmosome protein, is down-regulated in diabetic rats. This down-regulation coincided with aberrant hypo-demethylation of the DSP promoter. The inhibition of DSP expression is linked to reduced occupancy of Ten-eleven translocation 3 (TET3) at the DSP promoter, consequently suppressing TET3-dependent DNA demethylation. Additionally, a novel lncRNA termed DSP-AS1is identified, which is antisense to DSP. Notably, DSP-AS1 expression is down-regulated in diabetic skin wounds, and it interacted with TET3, a DNA demethylase. Notably, DSP-AS1 is found to form R-loops, triple-stranded DNA:RNA hybrids, at the DSP promoter, facilitating TET3 localization to the DSP promoter. Collectively, the findings suggest that reduced R-loop formation by DSP-AS1 impairs DSP gene transcription by repressing TET3-mediated DNA demethylation. This disruption of the orchestrated re-epithelialization process contributes to refractory diabetic wound healing.

BACKGROUND: Cardiovascular complications are major concerns for Chinese patients with type 2 diabetes. Accurately predicting these risks remains challenging due to limitations in traditional risk models. We aimed to develop a dynamic prediction model using machine learning and longitudinal trajectories of cardiovascular risk factors to improve prediction accuracy. METHODS: We included 16,378 patients from the Kailuan cohort, splitting them into training and testing datasets. Using baseline characteristics and changes over a four-year observation period, we developed the ML-CVD-C (Machine Learning Cardiovascular Disease in Chinese) score to predict 10-year cardiovascular risk, including cardiovascular death, nonfatal myocardial infarction, and stroke. We compared the discrimination and calibration of ML-CVD-C with models using only baseline variables (ML-CVD-C [base]), China-PAR (Prediction for ASCVD Risk in China), and PREVENT (Predict Risk of cardiovascular disease EVENTs). Risk stratification improvements were assessed through net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Transition analysis examined the changes in risk stratification over time. RESULTS: The ML-CVD-C score achieved a C-index of 0.80 (95% CI: 0.78-0.82) in the testing cohort, significantly outperforming the ML-CVD-C (base) score, China-PAR, and PREVENT, which had C-index values of 0.62-0.65. ML-CVD-C also provided more accurate cardiovascular risk estimates, though all models tended to overestimate the prevalence of high-risk cases. Stratification by the ML-CVD-C score showed substantial improvement, with NRI gains of 57.7%, 44.1%, and 47.3%, and IDI gains of 10.1%, 7.9%, and 8.4% compared to the other three scores. Both the trajectory and machine learning algorithm contributed significantly to the enhancement of model performance. Transition analysis revealed that participants who remained in the same risk category or were reclassified to a lower category exhibited 22% and 86% reductions in cardiovascular risk compared to those reclassified to a higher risk category during the observation period. CONCLUSIONS: The ML-CVD-C model, incorporating dynamic cardiovascular risk trajectories and a machine learning algorithm, significantly improves risk prediction accuracy for Chinese patients with diabetes. This model may serve as a valuable tool for more personalized cardiovascular risk management in type 2 diabetes.