Changes and Significance of Central Carbon Metabolism before and after Metformin Treatment of Type 2 Diabetes Based on Mass Spectrometry.

BACKGROUND: An imbalance in energy metabolism serves as a causal factor for type 2 diabetes (T2D). Although metformin has been known to ameliorate the overall energy metabolism imbalance, but the direct correlation between metformin and central carbon metabolism (CCM) has not been thoroughly investigated. In this study, we employed a high-performance ion chromatography-tandem mass spectrometry (HPIC-MS/MS) technique to examine the alterations and significance of CCM both before and after metformin treatment for T2D. METHODS: We recruited 29 participants, comprising 10 individuals recently diagnosed with T2D (T2D group). Among these, 10 patients underwent a 4-6-week treatment with metformin (MET group). Additionally, we included 9 healthy subjects (CON group). Employing HPIC-MS/MS, we quantitatively analyzed 56 metabolites across 18 biologically relevant metabolic pathways associated with CCM. Univariate and multivariate statistical analyses were utilized to identify differential metabolites. Subsequently, correlation analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted on the identified differential metabolites. RESULTS: We identified seven distinct metabolites in individuals with T2D (p < 0.05). Notably, cyclic 3',5'-Adenosine MonoPhosphate (AMP), Glucose 6-phosphate, L-lactic acid, Maleic acid, and Malic acid exhibited a reversal to normal levels following metformin treatment. Furthermore, Malic acid demonstrated a positive correlation with L-lactic acid (r = 0.94, p < 0.05), as did succinic acid with malic acid (r = 0.81, p < 0.05), L-lactic acid with succinic acid (r = 0.78, p < 0.05), and L-lactic acid with glucose-6-phosphate (r = 0.72, p < 0.05). These metabolites were notably enriched in pyruvate metabolism (p = 0.005), tricarboxylic acid cycle (TCA) (p = 0.007), propanoate metabolism (p = 0.007), and glycolysis or gluconeogenesis (p = 0.009), respectively. CONCLUSIONS: We employed HPIC-MS/MS to uncover alterations in CCM among individuals recently diagnosed with T2D before and after metformin treatment. The findings suggest that metformin may ameliorate the energy metabolism imbalance in T2D by reducing intermediates within the CCM pathway.

Reference intervals for cardiometabolic risk factors in China: a national multicenter cross-sectional study on an adult population sample.

Background: The reference intervals (RIs) of adult blood lipid parameters currently used in China are not derived from the results of research in local populations and have not been adjusted for age and sex. In this study, we aimed to determine accurate RIs for blood lipid parameters and blood glucose (GluG) for Chinese adults using a national multicenter study. Methods: A total of 11,333 adults between 18 and 90 years of age were recruited in seven representative regions in China between June 2020 and December 2020. Hospitals participating in the study were regrouped into two geographical regions, southern China (Changsha, Chengdu, Hangzhou, and Nanning) and northern China (Beijing, Shenyang, and Ningxia), according to their geographical and administrative location. All samples were freshly collected and measured collectively in one laboratory on the Mindray full Automatic biochemical analyzer chemistry BS2000 analytical systems. Outliers were removed using the Tukey test. Three-level nested analysis of variance and scatter plot were used to explore the variations in sex, age, and region. Percentile curves of each indicator were plotted using the least mean square (LMS) method. The lower limit (2.5th percentile) and the upper limit (97.5th percentile) of the RI were determined by using nonparametric statistical methods. We also calculated the 90% confidence interval (CI) for the lower and upper limits. Results: A total of 8,283 participants were enrolled in the final analysis, with 3,593 (43.4%) men and 4,690 (56.6%) women. Regionality was observed in three analytes [small dense low density lipoprotein cholesterol (sd-LDLC), GluG, and apolipoprotein A1 (ApoA1)]. In northern China, the sd-LDLC and GluG levels in Shenyang were significantly higher than those in Ningxia and Beijing (P<0.05). In southern China, the sd-LDLC and GluG levels in Nanning were significantly higher than those in the three other cities (P<0.05), whereas the sd-LDLC and GluG levels in Chengdu were significantly lower than those in the three other cities (P<0.05). The level of ApoA1 in Chengdu was significantly higher than that in the three other cities. The homocysteine (HCY) level in male participants was clearly higher than that in female participants [ratio of standard deviation (SDR)sex =0.56], whereas the levels of high density lipoprotein cholesterol (HDLC) (SDRsex =0.40) and ApoA1 (SDRsex =0.27) in males were lower. The GluG and HCY level increased gradually with age. In females aged 45-55 years, there was an interesting change in scatter charts, where triglyceride (TG) and total cholesterol (TC) increased rapidly. We also found that for the age group of >55 years, the levels of TG and TC in females gradually surpassed those in males. Conclusions: The findings of this study may help establish age- and sex-specific reference values for the blood lipids of Chinese adults and serve as a valuable guide for the screening, diagnosis, treatment, prevention, and monitoring of cardiovascular disease (CVD).

Bibliometrics and visual analysis of metformin and gut microbiota from 2012 to 2022: A systematic review.

BACKGROUND: Metformin is an old drug used for the treatment of type 2 diabetes mellitus and can play a variety of roles by regulating the gut microbiota. The number of research articles on metformin in the gut microbiota has increased annually; however, no bibliometric tools have been used to analyze the research status and hot trends in this field. This study presents a bibliometric analysis of publications on metformin and gut microbiota. METHODS: We searched the Web of Science core collection database on June 8, 2023, for papers related to metformin and gut microbiota from 2012 to 2022. We used Microsoft Excel 2021, VOSviewer1.6.19, CiteSpace 6.2.4, and R software package "bibliometrix" 4.0.0 to analyze the countries, institutions, authors, journals, citations, and keywords of the included publications. RESULTS: We included 517 papers, and the trend in publications increased over the last 11 years. The 517 articles were from 57 countries, including 991 institutions and 3316 authors, and were published in 259 journals. China led all countries (233 papers) and the most influential institution was the Chinese Academy of Sciences (16 papers). PLOS ONE (19 papers) was the most popular journal, and Nature (1598 citations) was the most cited journal. Li and Kim were the 2 most published authors (six papers each), and Cani (272 co-citations) was the most co-cited author. "Metabolites," "aging," and "intestinal barrier" were emerging topics in this field. CONCLUSIONS: This bibliometric study comprehensively summarizes the research trends and progress of metformin and gut microbiota, and provides new research topics and trends for studying the effects of metformin on gut microbiota in different diseases.

Berberine influences multiple diseases by modifying gut microbiota.

Berberine (BBR) is an isoquinoline alkaloid that is widely distributed in the plant kingdom and is commonly found in Coptis chinensis Franch. It has low bioavailability, but it can interact with gut microbiota and affect a variety of diseases. The effects of BBR in diabetes, hyperlipidemia, atherosclerosis, liver diseases, intestinal diseases, mental disorders, autoimmune diseases, and other diseases are all thought to be related to gut microbiota. This review systematically and comprehensively summarize these interactions and their effects, and describes the changes of gut microbiota after the intervention of different doses of berberine and its potential clinical consequences, in order to provide a basis for the rational application of BBR in the future clinical treatment.

Correlation between gut bacteria Phascolarctobacterium and exogenous metabolite α-linolenic acid in T2DM: a case-control study.

Background: The relationship between gut microbiota and metabolites play an important role in the occurrence and development of type 2 diabetes mellitus (T2DM). However, the interaction between intestinal flora abundance and metabolites is still unclear. The purpose of this study was to investigate the correlation of the interaction network between intestinal flora and fecal metabolites in regulating the occurrence of T2DM. Methods: This a case-control study. T2DM patients with different glucose levels and healthy people were divided into case group and normal controls (NC) group. Fasting plasma and fecal samples were collected from the subjects. Ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS) untargeted fecal metabolomics was used to detect small molecular metabolites within 1,500 Da in two groups. The diversity and richness of intestinal flora were analyzed by the 16SrRNA third-generation full-length sequencing technique and the correlation between intestinal microflora and different metabolites was evaluated. Results: A total of 30 patients with T2DM and 21 NC were included for analysis, glycated hemoglobin (HbAlc) (P<0.001), fasting blood glucose (FBG) (P<0.001), total triglycerides (TG) (P=0.002), and fasting serum insulin (FINS) (P=0.026) were significantly higher in the T2DM group compared with the NC group. The fecal metabolomics profiles of the T2DM group and NC group were significantly different, and 355 different metabolites were identified among the two. Compared with the NC group, the levels of ornithine (P=0.04), L-lysine (P=0.03), glutamate (P=0.01), alpha-linolenic acid (P=0.004), traumatin (P=0.05), and erucic acid (P=0.004) in the T2DM group decreased significantly, while PC[18:3(6Z,9Z,12Z)/24:1(15Z)] (P<0.001) levels increased. Compared with the NC group, the richness of Megamonas and Escherichia increased in T2DM patients, while that of Bacteroidota and Phascolarctobacterium were lower. Pearson correlation analysis revealed associations between gut microbiota and faecal metabolites, and Phascolarctobacterium was positively correlated with alpha-linolenic acid (r=0.72, P<0.001). Conclusions: There may be a mutual regulatory network between intestinal bacteria and fecal metabolites in T2DM. The increased abundance of Phascolarctobacterium may increase alpha-linolenic acid uptake, and alpha-linolenic acid may also increase the abundance of intestinal Phascolarctobacterium in vivo after metabolic transformation. The combination of the two may play an important role in the treatment of diabetes.

Mechanism research on the interaction regulation of Escherichia and IFN-γ for the occurrence of T2DM.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a major social and public health problem which may be induced by intestinal flora imbalance through inflammatory response, and the specific mechanism remains unclear. In this study, we aim to explore the interaction network of intestinal flora and cell inflammation in T2DM. METHODS: This a case-control study. Patients with T2DM was the case group and healthy people as control. The differences of cytokine expression levels between patients with T2DM and healthy controls were assessed by using flow cytometry. The diversity and abundance of intestinal flora were evaluated by using 16S rRNA three-generation full-length sequencing technology. RESULTS: A total of 29 patients with T2DM and 28 healthy controls were included for analysis. Compared with the healthy control group, the expression levels of plasma cytokine interleukin-2 (IL-2) (P=0.0000006), IL-6 (P=0.000193), tumor necrosis factor α (TNF-α) (P=0.016), interferon-γ (IFN-γ) (P=0.000036) and interleukin-17 (IL-17) (P=0.004) were significantly up-regulated in T2DM patients, and the abundance of Megamonas_funiformis (P=0.0016) and Escherichia (P=0.049) in the intestine were significantly increased. In contrast, the abundance of Bacteroides_stercoris (P=0.0068), Bacteroides_uniformis (P=0.033), and Phascolarctobacterium_faecium (P=0.033) were decreased in T2DM patients. Further, differentially expressed Escherichia had a positive correlation with IFN-γ (r=0.73) by Pearson correlation analysis. CONCLUSIONS: The interaction network between the intestinal bacteria Escherichia and the cytokine IFN-γ may drive inflammation in visceral adipose tissue (VAT), indicating insulin signal transduction can be inhibited in adipocytes to induce insulin resistance.

Programmed Cell Death Ligand 1-Transfected Mouse Bone Marrow Mesenchymal Stem Cells as Targeted Therapy for Rheumatoid Arthritis.

Programmed cell death 1 ligand (PD-L1) and its receptor (PD-1) are key molecules for immunoregulation and immunotherapy. PD-L1 binding PD-1 is an effective way to regulate T or B cell immunity in autoimmune diseases such as rheumatoid arthritis (RA). In our study, we overexpressed PD-L1 by constructing a recombinant of PD-L1-lentiviral vector, which was subsequently used to transfect mouse bone marrow mesenchymal stem cells (MBMMSCs) and significantly suppressed the development of collagen-induced arthritis (CIA) in DBA/1j mice. In addition, PD-L1-transfected MBMMSCs (PD-L1-MBMMSCs) ameliorated joint damage, reduced proinflammatory cytokine expression, and inhibited T and B cell activation. Furthermore, PD-L1-MBMMSCs decreased the number of dendritic cells and increased the numbers of regulatory T cells and regulatory B cells in joints of CIA mice. In conclusion, our results provided a potential therapeutic strategy for RA treatment with PD-L1-MBMMSC-targeted therapy.

Correlation between alterations of gut microbiota and miR-122-5p expression in patients with type 2 diabetes mellitus.

BACKGROUND: To investigate the correlation between gut microbiota and circulating microRNAs (miRNAs) in patients with primary diagnosis of type 2 diabetes mellitus (T2DM) and to explore the possible mechanisms of miRNA-gut microbiota crosstalke network in the regulation of the insulin signaling pathway and glucose homeostasis in T2DM. METHODS: T2DM patients and normal controls were recruited. Fasting plasma and fecal samples were collected from the subjects, and their biochemical indexes including fasting blood glucose (FBG), glycated hemoglobin (HbAlc), cholesterol (TC), total triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and insulin were recorded. The variations in intestinal microbiota in the two groups were analyzed using 16S rRNA third-generation sequencing technology, and the differential expression of miRNAs between the groups was screened using miRNA high-throughput sequencing. The correlation and association between specifically changed intestinal microbiota and miRNA expressions were analyzed using a combination of bioinformatics analysis and statistical methods. Finally, 16S functional gene prediction analysis and target gene enrichment pathway analysis were carried out to predict relevant gut microbiota and miRNAs. RESULTS: Compared with normal controls, the biochemical indexes of HAlbc, FBG, TG, TC, LDL, HDL, and insulin were significantly different in T2DM patients (P<0.001, P<0.001, P=0.0125, P=0.98, P<0.001 P=0.022, and P=0.0013, respectively). The two groups also showed significantly different intestinal microbiota distribution and miRNA expression characteristics, including in the counts of Bacteriodes. uniformis and Phascolarctobacterium. Faecium (P=0.023, 0.031), which were negatively correlated (P=0.014, FC = -2.36) with the expression levels of serum miR-122-5p (r=-0.68, -0.60, P=0.01, 0.01). CONCLUSIONS: This study discovered specific gut microbiota and miRNA characteristics in patients with a primary diagnosis of T2DM. A negative correlation between miR-122-5p and the intestinal bacteria Bacteriodes. uniformis and Phascolarctobacterium. Faecium was also revealed, suggesting that the crosstalke between miRNA and gut microbiota may regulate the insulin secretion and signal transduction by controling key genes of glucose metabolism during the development of T2DM.

Insulin combined with Chinese medicine improves glycemic outcome through multiple pathways in patients with type 2 diabetes mellitus.

INTRODUCTION/AIMS: Insufficient insulin secretion or inefficient insulin response are responsible for the clinical outcome of type 2 diabetes mellitus. Administration of insulin alone is prone to cause secondary effects, resulting in an unsatisfactory outcome. Shen-Qi-Formula (SQF), a well-known Chinese medicinal formula, has been used for diabetic treatment for a long time. The present study was designed to investigate whether SQF in combination with insulin improved the clinical outcome of type 2 diabetes mellitus, and what mechanisms were possibly involved in the treatment. MATERIALS AND METHODS: A total of 219 patients were included in the study. Of these, 110 patients were treated with insulin monotherapy, and 109 with the combination therapy of SQF and insulin. Before and after 12-week treatment, the fasting blood glucose, postprandial blood glucose, ß-cell function, insulin resistance and blood lipids were measured. RESULTS: The 12 weeks of SQF treatment in combination with insulin significantly decreased the fasting and postprandial blood glucose levels. Insulin secretion was not increased after the treatment, but ß-cell function and insulin resistance were obviously improved. Furthermore, 12 weeks of treatment with SQF and insulin improved the levels of glucagon-like peptide-1, oxidative stress, blood lipids, coagulation function and bodyweight. CONCLUSION: The results from our study showed that the combination therapy of SQF and insulin significantly improved the clinical outcome of type 2 diabetes mellitus compared with insulin monotherapy. The mechanism of improvement was possibly involved in the multiple pathways.