BackgroundThe profile of gut microbiota, serum metabolites, and lipids of type 1 diabetes (T1D) patients with different human leukocyte antigen (HLA) genotypes remains unknown. We aimed to explore gut microbiota, serum metabolites, and lipids signatures in individuals with T1D typed by HLA genotypes.MethodsWe did a cross-sectional study that included 73 T1D adult patients. Patients were categorized into two groups according to the HLA haplotypes they carried: those with any two of three susceptibility haplotypes (DR3, DR4, DR9) and without any of the protective haplotypes (DR8, DR11, DR12, DR15, DR16) were defined as high-risk HLA genotypes group (HR, n=30); those with just one or without susceptibility haplotypes as the non-high-risk HLA genotypes group (NHR, n=43). We characterized the gut microbiome profile with 16S rRNA gene amplicon sequencing and analyzed serum metabolites with liquid chromatography-mass spectrometry.ResultsStudy individuals were 32.5 (8.18) years old, and 60.3% were female. Compared to NHR, the gut microbiota of HR patients were characterized by elevated abundances of Prevotella copri and lowered abundances of Parabacteroides distasonis. Differential serum metabolites (hypoxanthine, inosine, and guanine) which increased in HR were involved in purine metabolism. Different lipids, phosphatidylcholines and phosphatidylethanolamines, decreased in HR group. Notably, Parabacteroides distasonis was negatively associated (p ≤ 0.01) with hypoxanthine involved in purine metabolic pathways.ConclusionsThe present findings enabled a better understanding of the changes in gut microbiome and serum metabolome in T1D patients with HLA risk genotypes. Alterations of the gut microbiota and serum metabolites may provide some information for distinguishing T1D patients with different HLA risk genotypes.

Background: The process of aging and metabolism are intricately linked, thus rendering the identification of reliable biomarkers related to metabolism crucial for delaying the aging process. However, research of reliable markers that reflect aging profiles based on machine learning is scarce. Methods: Serum samples were obtained from aged mice (18−month−old) and young mice (3−month−old). LC−MS was used to perform a comprehensive analysis of the serum metabolome and machine learning was used to screen potential aging−related biomarkers. Results: In total, aging mice were characterized by 54 different metabolites when compared to control mice with criteria: VIP ≥ 1, q−value＜0.05, and Fold−Change ≥ 1.2 or ≤0.83. These metabolites were mostly involved in fatty acid biosynthesis, cysteine and methionine metabolism, D−glutamine and D−glutamate metabolism, and the citrate cycle (TCA cycle). We merged the comprehensive analysis and four algorithms (LR, GNB, SVM, and RF) to screen aging−related biomarkers, leading to the recognition of oleic acid. In addition, five metabolites were identified as novel aging−related indicators, including oleic acid, citric acid, D−glutamine, trypophol, and L−methionine. Conclusions: Changes in the metabolism of fatty acids and conjugates, organic acids, and amino acids were identified as metabolic dysregulation related to aging. This study revealed the metabolic profile of aging and provided insights into novel potential therapeutic targets for delaying the effects of aging.

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)