Objective: Metformin is the first pharmacological option for preventing and treating type 2 diabetes. However, the use of this drug in preventing cardiovascular disease associated with the development of newly diagnosed type 2 diabetes is unclear. We aimed to examine whether the metformin reduced the incidence of major cardiovascular events over a 20-year follow-up of patients with newly diagnosed type 2 diabetes. Research Design and Methods: We conducted a retrospective observational cohort study of 48,338 patients with type 2 diabetes from Anhui Provincial Hospital between January 1, 2000, and June 30, 2021. Data were collected for patients with newly diagnosed T2D (within 6 months) who did not have prior cardiovascular disease and took metformin at baseline were compared with those who did not using. The primary outcome was the first occurrence of major cardiovascular events including coronary heart disease (CHD), stroke, and hospitalization due to heart failure (HF). The multivariate Cox proportional hazards regression with inverse probability of treatment weighting. Propensity score matching (PSM) was conducted for reducing the biases of baseline characteristics. Results: After matching, newly diagnosed T2D patients who used metformin, compared with those who did not use, had significantly lower risk of HF (HR 0.51, 95% CI 0.28 to 0.93) and CHD (HR 0.37, 95% CI 0.13 to 1.09). No difference in the risk incident stroke (HR 0.89, 95% CI 0.68 to 1.17) was observed. In both the original and PSM groups, use of metformin can reduce the risk of CHD events from newly diagnosed T2D patients (adjusted HR 0.74; 95% CI 0.69-0.87; P = 0.029). Conclusions: In the present retrospective study, metformin use may be beneficial for preventing CHD and HF in newly diagnosed T2D patients. Further randomized controlled trials are needed to confirm these findings. Disclosure T.Yue: None. X.Wang: None. M.Zhao: None. J.Weng: None. S.Luo: None. X.Zheng: None.
Background: An increasing amount of research shows that the gut microbiota and metabolites play a role in type 1 diabetes mellitus (T1D). We aimed to use machine learning to explore gut microbiota, serum metabolites, and lipids signatures in T1D individuals. Methods: We evaluated 137 individuals in a cross-sectional cohort that included 38 T1D patients, 38 healthy controls, and 61 T1D patients for validation. After clinical examination and biospecimen collection, we characterized the gut microbiome profile with 16S rRNA gene amplicon sequencing and analyzed serum metabolites and lipids with liquid chromatography-mass spectrometry. All molecular data were analyzed using a combination of univariate, multivariate, and machine-learning approaches (Logistic Regression, Support Vector Machine, Gaussian Naive Bayes, and Random Forest). Results: Machine-learning approaches using microbiota composition did not accurately predict T1D status (model accuracy=0.7555), while the accuracy of model using metabolite composition was 0.9333. Based on bacterial species-level composition, Ruminococcus torques, Anaerostipes, Veillonella, Erysipelotrichaceae UCG-003, Blautia, and Coprococcus were coincident microbes which all increased in T1D. Increased 3-hydroxybutyric acid and 9-oxo-ode (AUC=0.70 and 0.67) were meaningful coincident metabolites in T1D. PC(36:4e)(rep) was the most significant lipid (coefficient index=3.11e-9, increased in T1D). We confirmed the biological relevance of the microbiome, metabolome, and lipidome features in the validation group. Ruminococcus torques was positively associated with 3-hydroxybutyric acid (p<0.01). Conclusions: By using machine-learning algorithms and multi-omics, we demonstrated that T1D patients are associated with altered microbiota, metabolites, and lipidomic signatures or functions. Machine-learning approaches have potential clinical applications in T1D diagnostics and treatment. Disclosure H.Tan: None. J.Yan: None. S.Luo: None. J.Weng: None. X.Zheng: None. Y.Shi: None. T.Yue: None. D.Zheng: None. C.Wang: None. Z.Liu: None. D.Yang: None. Y.Ding: None. W.Xu: None. Funding National Natural Science Foundation of China (82100822); Anhui Provincial Natural Science Foundation (2008085MH248, 2008085MH278); Guangdong Basic and Applied Basic Research Foundation (2019A1515010979); National Key R&D Program (2017YFC1309600)
Background: Type 1 diabetes (T1D) progression is affected by the level of circulating glutamic acid decarboxylase antibody (GADA) , a main manifestation of islet autoimmunity. The gut microbiome and serum metabolites may contribute to islet autoimmunity in patients with T1D. Methods: We used radiobinding assay to measure GADA titers and identify the GADA+ ones (n=49) in 1T1D patients. The gut microbiome was assessed by 16S rRNA gene sequencing, and the serum metabolites were analysed with GC/MS. Results: T1D patients with and without GADA exhibited compositional and functional changes in the gut microbiome. Profiles in patients with GADA were distinct, with a significant increase in the abundances of Alistipes, Ruminococcus, Prevotella, Dialister, and marked depletion of Bacteroides, Bifidobacterium, Roseburia. For the untargeted serum metabolites, compared with those of GADA-, there were 54 significantly different metabolites with tryptophan metabolism: phenylalanine, and tyrosine and tryptophan biosynthesis decreased in patients with GADA. In addition, patients with GADA had elevated concentrations of Indole-3-acetic-acid, a marker of intestinal inflammation and epithelial damage. Co-occurrence network analysis revealed that gut microbiome disturbances affected tryptophan metabolism, suggesting that disturbed gut microbiome may mediate T1D immunotypes. Tryptophan metabolism may mediate inflammation in the pathogenesis and pathophysiology of T1D. Conclusion: These findings suggest that metabolites produced by the patients with GADA gut microbiota could cause damage to intestinal and systemic homeostasis and could be targeted for preventing the development of T1D. Disclosure T.Yue: None. X.Zheng: None. Z.Liu: None. Y.Ding: None. J.W.Wei: None. W.Xu: None. J.Weng: None. S.Luo: None. Funding the National Key r&D Program (2017YFC1309600) the Anhui Provincial Natural science foundation (006212943003)
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.