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Aims: To determine whether cumulative blood pressure (BP) could predict stroke in individuals with type 2 diabetes (T2D)., Methods: BP levels at baseline and the initial three visits were obtained from individuals participating in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial who had not experienced a stroke. Cumulative elevations in BP were assessed by adding the weighted mean BP values at various time intervals. The association of cumulative BP with stroke was evaluated by a multivariate-adjusted Cox proportional hazard model analysis., Results: Overall, 8282 participants were included (62.10% males and 37.90% females; mean age, 62.73 years). With a median follow-up period of 6.36 years, 324 (3.91%) and 305 (3.68%) patients had any and nonfatal stroke events, respectively. Only baseline systolic BP (SBP) independently predicted any stroke after adjustment for potential confounders, whereas cumulative SBP and pulse pressure independently predicted elevated stroke events. A strong dose-response relationship between cumulative BP and stroke was identified, and conventional risk factors combined with cumulative SBP improved prediction efficiency., Conclusion: Cumulative SBP independently predicts stroke in individuals with T2D and provides an incremental predictive value for stroke compared with baseline BP assessments., Trial Registration: URL: http://www., Clinicaltrials: gov. Unique identifier: NCT00000620)., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Shi Tai reports financial support was provided by National Natural Science Foundation of China. Liyao Fu reports article publishing charges was provided by Natural Science Foundation of Hunan Province. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In recent years, healthcare data from various sources such as clinical institutions, patients, and pharmaceutical industries have become increasingly abundant. However, due to the complex healthcare system and data privacy concerns, aggregating and utilizing these data in a centralized manner can be challenging. Federated learning (FL) has emerged as a promising solution for distributed training in edge computing scenarios, utilizing on-device user data while reducing server costs. In traditional FL, a central server trains a global model sampled client data randomly, and the server combines the collected model from different clients into one global model. However, for not independent and identically distributed (non-i.i.d.) datasets, randomly selecting users to train server is not an optimal choice and can lead to poor model training performance. To address this limitation, we propose the Federated Multi-Center Clustering algorithm (FedMCC) to enhance the robustness and accuracy for all clients. FedMCC leverages the Model-Agnostic Meta-Learning (MAML) algorithm, focusing on training a robust base model during the initial training phase and better capturing features from different users. Subsequently, clustering methods are used to ensure that features among users within each cluster are similar, approximating an i.i.d. training process in each round, resulting in more effective training of the global model. We validate the effectiveness and generalizability of FedMCC through extensive experiments on public healthcare datasets. The results demonstrate that FedMCC achieves improved performance and accuracy for all clients while maintaining data privacy and security, showcasing its potential for various healthcare applications., Competing Interests: Declaration of Competing Interest All authors disclosed no relevant relationships., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) clinically reduce atherosclerosis and lower blood pressure. However, their impact on endothelial dysfunction in type 2 diabetes (T2D) remains unclear. In this study, we investigated the protective effect and underlying mechanism of the SGLT2 inhibitor dapagliflozin in diabetes., Methods: Vascular reactivity was measured to assess the vasoprotective effect of dapagliflozin in a mouse model of high glucose (HG)-induced T2D. Pulse wave velocity was measured to quantify arterial stiffness. Protein expression was assessed by western blotting and immunofluorescence, oxidative stress was evaluated using dihydroethidium, nitric oxide was evaluated using the Griess reaction, and cellular senescence was assessed based on senescence-associated beta-galactosidase (SA-β-gal) activity and the expression of senescence markers. Furthermore, the endothelial nitric oxide synthase (eNOS) acetylation status was determined and eNOS interactions with SIRT1 were evaluated by coimmunoprecipitation assays., Results: Dapagliflozin protected against impaired endothelium-dependent vasorelaxation and improved arterial stiffness in the mouse model of T2D; mouse aortas had significantly reduced levels of senescence activity and senescence-associated inflammatory factors. HG-induced increases in senescence activity, protein marker levels, and oxidative stress in vitro were all ameliorated by dapagliflozin. The decreases in eNOS phosphorylation and nitric oxide (NO) production in senescent endothelial cells were restored by dapagliflozin. SIRT1 expression was reduced in HG-induced senescent endothelial cells, and dapagliflozin restored SIRT1 expression. SIRT1 inhibition diminished the antisenescence effects of dapagliflozin. Coimmunoprecipitation showed that SIRT1 was physically associated with eNOS, suggesting that the effects of dapagliflozin are dependent on SIRT1 activation., Conclusion: These findings indicate that dapagliflozin protects against endothelial cell senescence by regulating SIRT1 signaling in diabetic mice., Competing Interests: The authors declare that they have no competing interests., (© 2023 The Authors.)

Cardiometabolic disease is a common clinical syndrome with exact causal relationship between the aberrant of glucose/lipid metabolism and cardiovascular disfunction, but its pathogenesis is unclear. Cyclic guanosine monophosphate-adenosine monophosphate synthase (cGAS)-stimulator of interferon gene (STING) signaling pathway regulates the activation of innate immunity by sensing intracellular double stranded DNA. Metabolic risk factors drive the activation of cGAS-STING pathway through mitochondrial DNA, nuclear DNA and endoplasmic reticulum stress. In addition, the activation of the cGAS-STING pathway triggers chronic sterile inflammation, excessive activation of autophagy, senescence and apoptosis in related cells of cardiovascular system. These changes induced by cGAS-STING pathway might be implicated in the onset and deterioration of cardiometabolic disease. Therefore, the targeting intervention of cGAS-STING signaling pathway may emerge as a novel treatment for cardiometabolic disease.

Recent studies indicate that absence of the consideration of risk attitudes of decisionmakers in the risk matrix establishment process has become a major limitation. In order to evaluate risk in a more comprehensive manner, an approach to establish risk matrices that integrates risk attitudes based on utility theory is proposed. There are three main steps within this approach: (1) describing risk attitudes of decisionmakers by utility functions, (2) bridging the gap between utility functions and the risk matrix by utility indifference curves, and (3) discretizing utility indifference curves. A complete risk matrix establishment process based on practical investigations is introduced. This process utilizes decisionmakers' answers to questionnaires to formulate required boundary values for risk matrix establishment and utility functions that effectively quantify their respective risk attitudes., (© 2015 Society for Risk Analysis.)