Mogroside Alleviates Diabetes Mellitus and Modulates Intestinal Microflora in Type 2 Diabetic Mice

Abstract

Mogroside, the main component of Siraitia grosvenorii (Swingle) C. Jeffrey (Cucurbitaceae) is a natural product with hypoglycemic and intestinal microbiota regulating properties. However, whether the alteration of intestinal microbiota is associated with the antidiabetic effect of mogroside remains poorly understood. This study investigated the mechanism underlying the hypoglycemic effect of mogroside in regulating intestinal flora and attenuating metabolic endotoxemia. Kunming mice with type 2 diabetes mellitus (T2DM) induced by high-fat diet and intraperitoneal injection of streptozotocin were randomly divided into model, pioglitazone (2.57 mg/kg) and mogroside (200, 100, and 50 mg/kg) groups. After 28 d of administration, molecular changes related to glucose metabolism and metabolic endotoxemia in mice were evaluated. The levels of insulin receptor substrate-1 (IRS-1), cluster of differentiation 14 (CD14) and toll-like receptor 4 (TLR4) mRNAs were measured, and the composition of intestinal microflora was determined by 16s ribosomal DNA (rDNA) sequencing. The results showed that mogroside treatment significantly improved hepatic glucose metabolism in T2DM mice. More importantly, mogroside treatment considerably reduced plasma endotoxin (inhibition rate 65.93%, high-dose group) and inflammatory factor levels, with a concomitant decrease in CD14 and TLR4 mRNA levels. Moreover, mogroside treatment reduced the relative abundance of Firmicutes and Proteobacteria (the inhibition rate of Proteobacteria was 85.17% in the low-dose group) and increased the relative abundance of Bacteroidetes (growth rate up to 40.57%, high-dose group) in the intestines of diabetic mice. This study reveals that mogroside can relieve T2DM, regulating intestinal flora and improving intestinal mucosal barrier, indicating that mogroside can be a potential therapeutic agent or intestinal microbiota regulator in the treatment of T2DM.

INTRODUCTION

Diabetes mellitus (DM) is a metabolic disease characterized by high blood glucose levels, and its prevalence has been increasing in recent years. According to the latest report of the International Diabetes Federation (IDF), the prevalence of diabetes in people aged 20–79 years is estimated to be 10.5% (536.6 million people) in 2021. It is expected to rise to 12.2% (783.2 million people) in 2045.¹⁾ There is growing evidence that the development of type-2 diabetes mellitus (T2DM) is closely related to changes in the composition of intestinal microflora.²⁾ Under physiological conditions, the intestinal microflora maintains dynamic equilibrium. In contrast, in diabetic patients, dynamic equilibrium is disrupted,³⁾ together with the disruption of the intestinal mucosal barrier, increasing intestinal microbial products entering the bloodstream and inducing metabolic endotoxemia.⁴⁾ An altered intestinal barrier is linked to the development of metabolic endotoxemia.⁵⁾ It has been reported that improving the composition of the intestinal microflora has a positive effect on the treatment of diabetes,^6,7) suggesting that targeting the intestinal microflora may be an effective approach for treating diabetes.

Siraitia grosvenorii (Swingle) C. Jeffrey (Cucurbitaceae) (SG), also known as Monk fruit or Luohan Guo, is a local medicinal herb in the Guangxi Zhuang Autonomous Region of China and a popular sweet food. SG has a diverse range of pharmacological properties, including lubrication of the intestine, cathartic action, antioxidant action, hypolipidemic, hypoglycemic, antibacterial, and immune-enhancing potential.⁸⁾ Mogroside is a central active component of SG, which was shown to stimulate insulin (INS) secretion from β-cells,⁹⁾ reduce blood glucose and lipid levels in T2DM rats,¹⁰⁾ and inhibit the secretion of inflammatory factors, such as interleukin (IL)-1β, IL-6, and tumor necrosis factor-α (TNF-α).¹¹⁾ Several studies have reported that mogroside V has probiotic potential and can regulate the composition of the gut microbiota.¹²⁾ Thus far, the mechanism underlying the hypoglycemic effect of mogroside is poorly understood. Further studies are required to determine whether mogroside regulates the composition of intestinal microflora, leading to hypoglycemic activity. Therefore, this study investigated the underlying mechanism of the hypoglycemic activity caused by mogroside from the perspective of intestinal microflora and metabolic endotoxemia.

MATERIALS AND METHODS

Animals

In this research, we obtained 74 male Kunming mice (8-week-old) weighing 16–20 g from Guangxi Medical University Laboratory Animal Center (Animal Lot Number: 45000300000114). Mice were housed in a pathogen-free laboratory with a 12 h light/dark cycle, 20–25 °C temperatures, 55 ± 10% humidity, and free access to water and food. The experimental procedures followed the guidelines of the Animal Welfare Council of China. The study was reviewed and approved by Guangxi University of Chinese Medicine Institutional Animal Ethical and Welfare Committee (Ethics No: DW20190530-59).

Drugs

Mogroside, a component of Luo Han Guo, was purchased from Guilin Layn Co., Ltd. (Guilin, China). The mogroside was consistent with the China National Standard GB1886.77, the chemical structure of the components and HPLC chromatograms are shown in Supplementary Fig. S1 (with >47.75% of mogroside V and 7.7% of 11-O-mogroside V, food additives). Streptozotocin (STZ; Sigma-Aldrich, St. Louis, MO, U.S.A.; Lot No. 506H021) and pioglitazone hydrochloride tablets (Sichuan Dikang Technology Pharmaceutical Co., Ltd., Lot No. 141206).

Reagents

The PyroGene TM Recombinant Factor C Endotoxin Detection System (Lot No. 0000475024) was purchased from Lonza (Basel, Switzerland); Mouse Tumor Necrosis Factor-α enzyme linked immunosorbent assay (ELISA) Kit (Lot No. 2411139508), Mouse Interleukin-6 ELISA Kit (Lot No. 1821177730), Wuhan Boster Bioengineering (Wuhan, China); Mouse Monocyte Chemotactic Protein 1 (MCP-1/CCL2/MCAF) ELISA Kit (Lot No. K11012463), Mouse Insulin ELISA Kit (Lot: K25012462), Wuhan Huamei Biotech Co., Ltd. (Wuhan, China); SV Total RNA Isolation System (Lot: 0000157171), Promega (Madison, WI, U.S.A.); SuperQuick RT cDNA first strand synthesis kit (Lot: 00031501), 2 × GC-rich PCR MasterMix (Lot: 00071503); Pyruvate Kinase (PK) Test Kit (Lot: 200512), Hexokinase (HK) Test Kit (Lot No. 200512), Liver/Muscle Glycogen Assay Kit (Lot No. 200511), both purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China); The primers for PCR experiments were synthesized by Bioengineering (Shanghai) Co., Ltd. (Shanghai, China).

Experimental Design

Type 2 diabetic mouse model was established using a high-fat diet combined with low-dose STZ injection in accordance with previously reported methods.^13,14) STZ can destroy islet β cells and induce insulin resistance in conjunction with high-fat diet. It is a internationally recognized, reliable and widely used type 2 diabetic model. Briefly, 64 mice in the model group were fed a high-fat diet (basal diet, 74.5%; cholesterol, 1%; egg yolk powder, 5%; sucrose, 10%; lard, 9%; sodium cholate, 0.5%), and 10 mice in the normal group (NOR) were fed a basal-feeding chow for 28 d. The sample size was decided according to previously reported methods and corrected with a 20% attrition rate.^15,16) STZ at a dose of 100 mg/kg was injected intraperitoneally with citrate buffer (0.1 mol/L) on day 29. Fasting blood glucose (FBG) level was measured 7 d after STZ injection, and 50 mice with FBG levels greater than 11.1 mmol/L were included in the study, 14 mice that did not meet this criterion were euthanized with 200 mg/kg pentobarbital sodium. Diabetic mice were randomly divided into five groups of 10 mice each, model group (MOD), pioglitazone group (PIO, 2.57 mg/kg), high-dose mogroside group (MGH, 200 mg/kg), medium-dose mogroside group (MGM, 100 mg/kg), and low-dose mogroside group (MGL, 50 mg/kg). All treatments were administered daily by oral gavage for 28 d, and the NOR and MOD groups were fed the same volume of purified water. Mice in the MOD and treatment groups were fed high-fat chow. The animals' body weight and general well-being were observed daily. If body weight decreased below 25% of original weight, hunched posture, loss of self-feeding, or persistent recumbency was observed, the mice were sacrificed by overdose anesthesia to avoid further suffering. Hence, 1 mouse in the MOD group was euthanized before the end of experiment. At the end of the administration, the mice were anesthetized for sample collection. Samples were labeled with consecutive numbers by a researcher who had no role in conducting the assays. Researchers involved in the assays were unaware of the group allocations.

FBG and Oral Glucose Tolerance Measurement

The FBG levels of mice were measured using a Blood Glucose Meter (Sanno, China) before the start of treatment and on the 7th, 14th, 21st, and 28th days after fasting overnight. On the 24th day of treatment, we conducted an oral glucose tolerance test. A glucose dose of 1.5 g/kg was administered to all groups, 30 min after the FBG test. Postprandial blood glucose values were measured by tail vein sampling at 30, 60, and 120 min after glucose administration.

ELISA

After 28 d of treatment, all mice were fasted for 12 h and anesthetized with 50 mg/kg of pentobarbital sodium. Blood samples were collected from the retro-orbital plexus, then centrifuged at 1000 × g for 15 min at 4 °C to separate and collect serum. Serum TNF-α, IL-6, MCP-1, and INS levels were determined using ELISA according to the manufacturer’s protocol. The homeostasis model assessment for insulin resistance (HOMA-IR) index was calculated using the following formula.

  

Determination of Hepatic HK and PK Activity

The livers were excised, snap-frozen in liquid nitrogen, and stored in a freezer at −80 °C. To determine HK and PK activity, liver samples were homogenized after adding saline at a ratio of 1 : 9 and centrifuged at 4 °C for 10 min at 1000 × g. Supernatants were extracted following centrifugation, and the kinase activities of HK and PK were measured according to the manufacturer’s instructions. HK activity was determined by the total glucose phosphorylation capacity of whole tissue lysates and reported as U/mg Lysate protein, where one unit (U) corresponds to the enzyme activity, resulting in the phosphorylation of 1 µmol of glucose per minute per gram of histone at 37 °C and pH 7.6. PK activity was determined by the total transferase activity of the tissue lysate and reported as U/mg Lysate protein, where one unit (U) corresponded to the enzyme activity, resulting in the conversion of 1 µmol of phosphoenolpyruvate to pyruvic acid per minute per gram of lysate protein at 37 °C and pH 7.6.

Hepatic Glycogen Content Determination

Frozen livers were obtained from −80 °C refrigerators, added to lysis buffer at a ratio of 1 : 3, and boiled in a water bath to obtain the glycogen hydrolysate. After cooling, the mixture was diluted with ddH₂O to obtain a 1% (w/v) glycogen assay solution, and hepatic glycogen content was determined colorimetrically according to the manufacturer’s protocol.

Assay of Plasma Endotoxin Content

To obtain plasma samples, ten blood samples from each group were randomly collected in lipopolysaccharide (LPS)-free Eppendorf tubes containing sodium heparin anticoagulant and centrifuged at 1000 × g for 15 min at 4 °C. Plasma LPS levels were measured using recombinant Factor C, according to the manufacturer’s instructions. The concentration of endotoxin in the samples was calculated based on the fluorescence intensity (excitation 380/emission 440) measured using a TECAN infinite M200PRO (Tecan, Grödig, Austria).

Real-Time-Quantitative PCR

Total RNA was extracted from seven randomly selected liver samples. cDNA was obtained by reverse transcription and the abundance of the cluster of differentiation 14 (CD14), toll-like receptor 4 (TLR4), and insulin receptor substrate-1 (IRS-1) mRNA was detected using a Light Cycler 96 Real-Time PCR System (Roche, Switzerland). The PCR program was as follows: initial denaturation at 95 °C for 120 s followed by 40 cycles of denaturation at 95 °C for 30 s, annealing at 60 °C for 30 s, and extension at 72 °C for 30 s. The final extension step was performed at 72 °C for 2 min. The mRNAs were quantified using the 2^-ΔΔCT method, with β-actin serving as an internal control to normalize the relative abundance of the genes. The primer sequences are listed in Table 1.

Table 1. The Primer Sequence List for qRT-PCR

Primers	Forward/Reverse	Sequence
CD14	Forward	5′-CTCTGTCCTTAAAGCGGCTTAC-3′
	Reverse	5′-GTTGCGGAGGTTCAAGATGTT-3′
TLR4	Forward	5′-ATGGCATGGCTTACACCACC -3′
	Reverse	5′-GAGGCCAATTTTGTCTCCACA-3′
IRS-1	Forward	5′-CGATGGCTTCTCAGACGTG-3′
	Reverse	5′-CAGCCCGCTTGTTGATGTTG-3′
β-Actin	Forward	5′-TGGCACCCAGCACAATGAA-3′
	Reverse	5′-CTAAGTCATAGTCCGCCTAGAAGCA-3′

16s Ribosomal DNA (rDNA) Analysis of Intestinal Microflora

The fecal pellet from the distal colon was collected 2 h after drug administration on day 28, placed in sterile EP tubes, snap-frozen in liquid nitrogen, and then stored in a freezer at −80 °C. 16s rDNA sequencing of the intestinal microflora was performed by the Shanghai Meiji Biological Company (Shanghai, China). First, fecal pellets from the same group were randomly pooled in pairs, genomic DNA was extracted from the fecal pools of mice, and the integrity of the extracted DNA was checked by 1% agarose gel electrophoresis. The extracted DNA was PCR-amplified, and the fluorescence intensity was used to determine the quantity of PCR products. MiSeq libraries were constructed and MiSeq sequencing was performed. Finally, principal co-ordinates analysis (PCoA), similarities and differences of 16S microbiome, cluster analysis of species similarity, phylogenetic tree analysis, redundancy analysis (RDA), and phylum-level analysis were performed.

Hematoxylin–Eosin (H&E) Staining of Small Intestinal Sections

The intestinal tract was collected and fixed with 4% paraformaldehyde for 24 h. Paraffin-embedded tissue blocks were prepared and cut into 4 µm thick sections. The sections were mounted and rehydrated before being stained with hematoxylin for 2 min and incubated in saturated Li₂CO₃ for 20 s to make the hematoxylin stain blue. Sections were then stained with eosin for 2 min, dehydrated with ethanol, and then xylene before being sealed with coverslips using neutral gum. Images of the intestinal mucosa were captured, and histopathological analysis was performed under a microscope (Leica Microsystems, Wetzlar, Germany).

Statistical Analysis

Data are described as the mean ± standard error of mean (S.E.M.). Statistical software SPSS 22 was used for data analysis. One-way ANOVA was used for statistical comparisons. Post-hoc comparisons were performed with LSD tests for samples with equal variances, or Dunnett’s T3 test for samples with unequal variances. p < 0.05 was considered statistically significant.

RESULTS

Treatment of Mogroside Reduced Blood Glucose in Diabetic Mice

Blood glucose levels in diabetic mice after treatment with different doses of mogroside are shown in Fig. 1B. Our results showed that the FBG level in the MOD group was higher than that in the NOR group (p < 0.001). After 21 d of treatment, the blood glucose level of the MGH group were significantly lower than that of the MOD group (p < 0.05). After 28 d of treatment, the blood glucose level of the MGH and MGM groups were significantly lower than that of the MOD group (p < 0.01).

Fig. 1. Mogroside Reduced Blood Glucose Levels in Diabetic Mice

A: Schematic graph of experimental design. B: FBG levels. C: Blood glucose curve in oral glucose tolerance test. D: Areas under the glucose curve. All data are presented as mean ± S.E.M. FBG, oral glucose tolerance and AUC in MOD group n = 9, and n = 10 in the rest of the groups. NOR: normal group; MOD: model group; PIO: pioglitazone group; MGH: mogroside 200 mg/kg group; MGM: mogroside 100 mg/kg group; MGL: mogroside 50 mg/kg group; FBG: fasting blood glucose, AUC: area under the glucose curve. ^# p < 0.05, ^## p < 0.01, ^### p < 0.001 versus normal group; * p < 0.05, ** p < 0.01 versus model group.

We assessed the oral glucose tolerance in diabetic mice by administering glucose orally and calculating it using the area under the glucose curve (AUC). Overall, the blood glucose levels peaked 30 min after glucose administration and then gradually decreased. The blood glucose curve exhibited large fluctuations in the MOD group, indicating impaired blood glucose regulation. In contrast, the blood glucose curves were more stable and had fewer fluctuations in the MGH and MGM groups, indicating that mogroside at 200 mg/kg and 100 mg/kg might alleviate the disorder of blood glucose in diabetic mice (Figs. 1C, D).

Treatment of Mogroside Improved Glucose Metabolism in Diabetic Mice

INS levels in the serum of mice were detected using ELISA (Fig. 2A). Serum INS levels in the MOD group were significantly lower than those in the NOR group (p < 0.01). However, the INS levels in the MGH and MGL groups was significantly higher than that in the MOD group after treatment (p < 0.05). Moreover, treatment with MGH and MGM significantly reduced the insulin resistance index (p < 0.001, Fig. 2B). The relative expression of hepatic IRS-1 mRNA was determined using RT-quantitative (q)PCR. IRS-1 mRNA in the MOD group was significantly lower than that in the NOR group (p < 0.05, Fig. 2C). After treatment with MGH and MGM, the IRS-1 mRNA were increased (p < 0.01, compared with the MOD group).

Fig. 2. Efficacy of Mogroside Treatment to Improve Glucose Metabolism in Diabetic Mice

A: Serum INS level. B: HOMA-IR. C: Detection of hepatic IRS-1 mRNA abundance by RT-qPCR. D, E: Hepatic HK, PK level. F: Hepatic glycogen content. All data are presented as mean ± S.E.M. Except for hepatic IRS-1 mRNA in each group n = 7, INS, HOMA-IR, hepatic HK, PK level, and hepatic glycogen content in MOD group n = 9, and n = 10 in the rest of the groups. NOR: normal group; MOD: model group; PIO: pioglitazone group; MGH: mogroside 200 mg/kg group; MGM: mogroside 100 mg/kg group; MGL: mogroside 50 mg/kg group; INS: insulin; HOMA-IR: homeostasis model assessment for insulin resistance; HK: hexokinase; PK: pyruvate kinase. ^# p < 0.05, ^## p < 0.01, ^### p < 0.001 versus normal group; * p < 0.05, ** p < 0.01, *** p < 0.001 versus model group.

To investigate the effect of mogroside on hepatic glucose metabolism in diabetic mice, hepatic HK, PK activities, and glycogen content were evaluated (Figs. 2D–F). Compared to the NOR group, the hepatic HK activity of the MOD group was significantly reduced (p < 0.001). After administration, hepatic HK activity was significantly higher in the MGH and MGL groups than that in the MOD group (p < 0.05). Similarly, hepatic PK activity in the MOD group was significantly reduced compared to that in the NOR group (p < 0.001). However, the hepatic PK activity was significantly higher in the MGH group than in the MOD group (p < 0.01). In addition, the hepatic glycogen content in the MOD group was significantly lower than that in the NOR group (p < 0.001); however, the hepatic glycogen content in the MGH and MGM groups was significantly higher than that in the MOD group after treatment (p < 0.001 and p < 0.01).

Treatment of Mogroside-Inhibited Metabolic Endotoxemia in Diabetic Mice

The plasma endotoxin levels were measured using the recombinant C-factor method (Fig. 3A). The results showed that plasma endotoxin levels were significantly higher in the MOD group than in the NOR group (p < 0.001). After treatment, plasma endotoxin levels were dramatically reduced in all three mogroside treated groups than in the MOD group (p < 0.05), the inhibition rate of endotoxin reached 65.93% in the MGH group.

Fig. 3. Mogroside Inhibited Metabolic Endotoxemia in Diabetic Mice

A: Plasma endotoxin content. B, C: Detection of hepatic CD14 and TLR4 mRNA abundance by RT-qPCR. D, F: Serum TNF-α, IL-6, MCP-1 levels. All data are presented as mean ± S.E.M. Except for hepatic CD14 and TLR4 mRNA in each group n = 7, LPS, serum TNF-α, IL-6, MCP-1 levels in MOD group n = 9, and n = 10 in the rest of the groups. NOR: normal group; MOD: model group; PIO: pioglitazone group; MGH: mogroside 200 mg/kg group; MGL: mogroside 50 mg/kg group. ^# p < 0.05, ^## p < 0.01, ^### p < 0.001 versus normal group; * p < 0.05, ** p < 0.01, *** p < 0.001 versus model group.

To determine whether CD14 and TLR4 were affected by mogroside treatment, we measured the relative expression of hepatic CD14 and TLR4 mRNAs using RT-qPCR. The relative expression of hepatic CD14 and TLR4 mRNAs in the MOD group (Figs. 3B, C) was significantly higher than that in the NOR group (p < 0.001 and p < 0.01, respectively). The relative expression of hepatic CD14 and TLR4 mRNAs in the MGH group was lower than that in the MOD group (p < 0.001).

Subsequently, the TNF-α, IL-6, and MCP-1 serum levels in diabetic mice were measured using ELISA. The results showed that serum TNF-α, IL-6, and MCP-1 levels in the MOD group were significantly higher than those in the NOR group (p < 0.001). The levels of serum TNF-α in the MGH and MGM groups were significantly lower than those in the MOD group (p < 0.01, Fig. 3D). In addition, the serum IL-6 levels in the MGH and MGL group were significantly lower than those in the MOD group (p < 0.01 and p < 0.05, Fig. 3E). Serum MCP-1 levels in diabetic mice after both MGH and MGM treatment were significantly lower than those in the MOD group (p < 0.05 and p < 0.001, Fig. 3F).

Treatment of Mogroside Improved the Composition of the Intestinal Microflora in Diabetic Mice

Dendrogram analysis of the intestinal microflora revealed similarities and differences in the composition of intestinal microflora across different treatment groups (Figs. 4A, B). The composition of the intestinal microflora in the NOR group was clearly distinguished from that of the other groups. The intestinal microflora composition of the MGL group samples and the MGH group samples were closer to that of the majority of the samples from the pioglitazone group. Moreover, the intestinal microflora compositions of the three groups were clearly distinguishable from that of the MOD group. To further understand the similarities and differences in the composition of intestinal microflora between the different treatment groups, we performed beta diversity analysis using PCoA plots (Figs. 4C, D). The results showed that the characteristics of the intestinal microflora composition were clearly different and more dispersed across the different groups. The overall composition of intestinal microflora in the NOR and MOD groups showed two distinct clusters. The characteristics of the flora in the mice treated with mogroside occurred between the MOD and NOR groups, suggesting that the microflora changed in mice after mogroside treatment.

Fig. 4. Characterization of the Intestinal Microflora Composition in Diabetic Mice Following Mogroside Treatment

A, B: Dendrogram illustrating similarities in intestinal microflora composition. C, D: PCoA plot showing the characteristics of intestinal microflora. All data are presented as mean ± S.E.M. (n = 5). NOR: normal group; MOD: model group; PIO: pioglitazone group; MGH: mogroside 200 mg/kg group; MGL: mogroside 50 mg/kg group. PCoA: principal co-ordinates analysis.

Next, we analyzed the differences in intestinal microflora composition across different treatment groups at the phylum and genus levels. At the phylum level, compared with the NOR group, the relative abundance of the phylum Firmicutes was significantly higher (p < 0.01, Fig. 5C), and the relative abundance of the phylum Bacteroidetes was significantly lower (p < 0.001, Fig. 5B) in the MOD group. After MGH and MGL treatment, the relative abundance of the phylum Firmicutes decreased (p < 0.05 and p < 0.01), and the relative abundance of the phylum Bacteroidetes increased significantly (p < 0.01). The relative abundance of Proteobalteria was increased in the MOD group compared to the NOR group, but unfortunately, this difference did not show significance. And the relative abundance of Proteobalteria was decreased after mogroside treatment than those in the MOD group, but still no significant differences were observed.

Fig. 5. Clade Level Analysis of the Intestinal Microflora in Diabetic Mice Following Mogroside Treatment

A: Microflora structure clade component map. B: Relative abundance of Bacteroidetes. C: Relative abundance of Firmicutes. D: Relative abundance of Proteobacteria. All data are presented as mean ± S.E.M. (n = 5). NOR: normal group; MOD: model group; PIO: pioglitazone group; MGH: mogroside 200 mg/kg group; MGL: mogroside 50 mg/kg group. ^## p < 0.01, ^### p < 0.001 versus normal group; * p < 0.05, ** p < 0.01, *** p < 0.001 versus model group.

Gut Microbiota Structure at Genus Level

A clustering analysis of species abundance similarity among the samples was performed, and the gut microbiota structure at genus level were displayed in Fig. 6A. The relative abundance of Bacteroidales_S24–7_group_norank in the MOD group was significantly lower than that in the NOR group (p < 0.001, Fig. 6B); however, it tended to increase following mogroside treatment compared to the MOD group. In addition, the relative abundance of Odoribacter tended to increase following mogroside treatment (Fig. 6E). The relative abundance of Bacteroides tended to decrease following mogroside treatment compared with that in the MOD group (Fig. 6C).

Fig. 6. Taxonomic Analysis at Genus Level in the Intestinal Microflora of Diabetic Mice Following Mogroside Treatment

A: Map of the microflora structure at the genus level. B: Relative abundance of Bacteroidales_S24–7_group_norank. C: Relative abundance of Bacteroides. D: Relative abundance of Lactobacillus. E: Relative abundance Odoribacter. All data are presented as mean ± S.E.M. (n = 5). NOR: normal group; MOD: model group; PIO: pioglitazone group; MGH: mogroside 200 mg/kg group; MGL: mogroside 50 mg/kg group. ^# p < 0.05, ^### p < 0.001 versus normal group; * p < 0.05 versus model group.

RDA was conducted to investigate the relationship between the changes in intestinal microflora, factors facilitating metabolic endotoxemia, and blood glucose (Fig. 7). Surprisingly, we found that the intestinal microecology of mice in the MOD group showed a highly positive correlation with the plasma endotoxin levels. In contrast, the intestinal microecology of mogroside-treated mice showed little correlation with endotoxins, with a much lower correlation coefficient than that of the MOD group. Moreover, the intestinal microecology of mogroside-treated mice was less correlated with serum TNF-α and MCP-1 levels, which was in contrast to mice in the MOD group.

Fig. 7. Redundancy Analysis

NOR: normal group; MOD: model group; PIO: pioglitazone group; MGH: mogroside 200 mg/kg group; MGL: mogroside 50 mg/kg group; RDA: redundancy analysis; INS: insulin; FBG: fasting blood glucose.

Treatment of Mogroside Ameliorated Intestinal Mucosal Injury in Diabetic Mice

H&E staining of the intestinal tissues revealed histological changes in the morphology of the intestinal tissues across the different treatment groups (Fig. 8). In the MOD group, the intestinal villi appeared disorganized with a rough surface, and the mucosal and submucosal layers were edematous (indicated by red arrows) and infiltrated by inflammatory cells (indicated by black arrows). In the MGH group, the morphology of the intestinal mucosa was significantly improved, the small intestinal villi appeared smooth, the mucosal and submucosal layers were less edematous, and the inflammatory cell infiltration was significantly reduced.

Fig. 8. Effect of Mogroside on the Structure of the Intestinal Mucosa in Diabetic Mice (H&E Staining, ×200)

A: Normal group; B: Model group; C: Pioglitazone group; D: Mogroside 200 mg/kg group; E: Mogroside 100 mg/kg group; F: Mogroside 50 mg/kg group. Red arrows indicate submucosal edema, while black arrows indicate infiltration of inflammatory cells.

DISCUSSION

Mogroside has been suggested to possess antidiabetic properties.¹⁷⁾ It is also popular among individuals with abnormal glucose tolerance as a natural sweetener that does not generate blood glucose. Our previous study found that mogroside ameliorated glucose metabolism disorders, elevated blood INS levels, and repaired pancreatic damage in rats with T2DM.¹⁸⁾ In the present study, we found that the administration of mogroside significantly increased blood INS levels and decreased blood glucose levels in type 2 diabetic mice. In agreement with our experimental results, investigators showed that mogroside-rich extract reduced FBG levels and increased insulin sensitivity in a dose-dependent manner in T2DM mice.¹⁹⁾ Mogroside treatment also reduced blood glucose levels, and alleviated pathological damage in the pancreas of gestational diabetic mice, the effect is thought to be mediated by AMP activated protein kinase (AMPK) activation and attenuation of pancreatic inflammatory response.²⁰⁾ We may suppose that mogroside may protect the INS secretion capacity of pancreatic through its anti-inflammatory effects.

Metabolic endotoxemia has been recognized as a potential risk for the onset and progression of T2DM.²¹⁾ A high-fat diets for more than four weeks elevated plasma LPS levels that are two to three times higher than the normal range result in metabolic endotoxemia.^22,23) Higher levels of LPS trigger the release of inflammatory factors, causing impairments in the tissues involved in glucose metabolism, leading to insulin resistance and diabetes.^24,25) In the present study, mogroside treatment reduced plasma LPS levels and increased hepatic HK activity, PK activity, and glycogen synthesis, indicating improved glucose metabolism in the liver. One of the key reasons for abnormal glucose metabolism in metabolic endotoxemia is LPS-induced tissue inflammatory response. CD14 receptors bind to LPS and transfer the bound LPS to the TLR4/MD-2 complex,²⁶⁾ activating pattern recognition receptors (such as classical Toll-like receptor-4),²⁷⁾ stimulating downstream inflammatory pathways (such as nuclear factor-kappaB (NF-κB)),²⁸⁾ and inducing the production of pro-inflammatory cytokines (such as TNF-α and IL-6),²⁹⁾ resulting in metabolic inflammation in tissues such as the liver and muscle^19,30) and insulin resistance.³¹⁾ Our study revealed that mogroside treatment reduced the expression of CD14 and TLR4 mRNA, and the serum levels of inflammatory factors, such as TNF-α, IL-6, and MCP-1, also decreased in parallel with the normalization of CD14 mRNA. These observations strongly suggest that the hypoglycemic effect of mogroside is involved in the inhibition of metabolic inflammation in the liver. Similar findings have been reported²⁵⁾ that glucoraphanin can attenuate obesity-associated inflammation and insulin resistance by reducing metabolic endotoxemia. Moreover, muramyl dipeptide was shown to reduce hepatic insulin resistance during obesity and low-level endotoxemia, increase hepatic insulin sensitivity, and attenuate abnormalities of glucose metabolism induced by intraperitoneal injection of LPS.³²⁾ These findings are consistent with our experimental results demonstrating mogroside-mediated reduction of metabolic endotoxemia in diabetic mice.

The gut microbiota is involved in maintaining energy homeostasis and the induction of host immunity.³³⁾ Alterations in the gut microbiota composition are associated with the development of metabolic diseases.³⁴⁾ The gut microbiota also affects the function and integrity of the intestinal epithelial barrier,³⁵⁾ which is maintained by intestinal epithelial tight junction (TJ) proteins that form a physical barrier and connect adjacent epithelial cells.³⁶⁾ Increased intestinal permeability is responsible for pathogen invasion into the bloodstream.³⁷⁾ In contrast, TJ proteins ensure the integrity of the intestinal barrier and prevent the translocation of commensal and pathogenic bacteria across the gut epithelium.^38,39) Moreover, specific gut microbial profiles in diabetes are thought to be associated with increased intestinal permeability and subsequent endotoxemia.⁴⁰⁾ In contrast, LPS generated by gut microbiota can alter the assembly of TJ proteins (ZO-1 and occludin) in the intestinal epithelium, leading to increased intestinal permeability and allowing LPS translocation from the gastrointestinal lumen into the bloodstream, leading to endotoxemia^41,42) and overproduction of pro-inflammatory cytokines.⁴³⁾ Studies have shown that the gastrointestinal microbiota primarily consists of Bacteroidetes and Firmicutes,⁴⁴⁾ and in general, the abundance of Bacteroidetes is higher in non-diabetic mice than in obese diabetic mice in comparison to Firmicutes⁴⁵⁾; however, T2DM patients have a higher proportion of Firmicutes and a higher ratio of Firmicutes to Bacteroidetes ratio.⁴⁶⁾ The researchers found that fecal samples from T2DM mice fed Grifola frondosa polysaccharides significantly increased the abundance of Bacteroidetes but decreased the abundance of thick-walled bacteria Aspergillus spp.⁴⁷⁾ Using 16S rDNA sequencing of fecal samples, the researchers showed that Lactobacillus casei CCFM419 significantly increased the proportion of intestinal Bacteroidetes and decreased the proportion of Firmicutes in T2DM mice.⁴⁸⁾ Our experimental data show similar results. We found that mogroside treatment increased the relative abundance of intestinal Bacteroidetes and decreased that of Firmicutes and Proteobacteria, as detected by 16s rDNA high-throughput sequencing. Recent studies have also shown that rosmarinic acid sweeteners restore dysbiosis of intestinal microbiota in T2DM rats.¹⁷⁾ Notably, our study showed that mogroside did not induce a significant change in the abundance of Bacteroidales_S24–7_group_norank and Odoribacter in the intestine of diabetic mice, although it showed a trend of increased abundance of Bacteroidales. It has been shown that treatment with Auricularia auricula-judae (Bull.) polysaccharides increase the abundance of Lactobacillus and Bacteroides in T2DM mice, increase gut microbiota diversity, and optimize microbial composition and function.⁴⁹⁾ These data are contrary to our findings, in which we showed that the abundance of Bacteroides decreased rather than increased after mogroside treatment, and the abundance of Lactobacillus also showed a decreasing trend. Nevertheless, several reports have corroborated our findings. It is reported that a combination of oat β-glucan, anti-oat starch, and whole oat flour could alter the composition of the intestinal microbiota in high-fat diet-induced T2DM rats and reduce the levels of Bacteroides and Lactobacillus.⁵⁰⁾ These seemingly conflicting results suggest that Bacteroides and Lactobacillus are more sensitive to exogenous substances in the intestinal microflora; however, identifying their potential role in the development of diabetes warrants further in-depth investigation. Meanwhile, histopathological analysis of the small intestine concluded that mogroside treatment reduced intestinal mucosal damage in a mouse MOD of diabetes, indicating the effect of mogroside on improving the structure of the intestinal microflora.

Earlier studies have demonstrated that metabolic changes associated with hyperglycemia in developing diabetes are linked to the accumulation of senescent cells in various organs.⁵¹⁾ Cellular senescence, caused by telomere shortening or senescence-induced stress, refers to a state of irreversible cell cycle arrest.⁵²⁾ Senescent cells produce an excess of senescence-associated secretory phenotype (SASP), which contributes to tissue damage and aging.⁵³⁾ Recent research indicates that type 2 diabetes disrupts tissue biology in patients, leading to cellular senescence and activation of SASP, characterized by increased tissue inflammation and oxidative stress.⁵⁴⁾ Gut flora dysbiosis has been suggested as a determinant of SASP and chronic low-grade inflammation.⁵⁵⁾ Therefore, it is essential to investigate the impact of mogroside on gut flora, as this may lead to impaired tissue biology and SASP in diabetic animals in future studies.

Based on our findings, it is reasonable to assume that the therapeutic efficacy of mogroside in diabetes may be achieved by improving intestinal microflora dysbiosis and intestinal mucosal barrier function, reducing elevated endotoxin levels in the circulation, and reducing metabolic endotoxemia-associated insulin resistance. It is notable that, the hypoglycemic effect of mogroside in diabetic mice was not prominent until the dose reached 100 mg/kg, its human equivalent dose was about 560 mg (for a 70 kg human body weight), which suggests multiple dosing in a day is required for human. Hence, it is of considerable interest to enrich the active ingredients in mogrosides or to isolate higher active hypoglycemic ingredients from it. In this way, our findings will help guide the discovery of viable clinical candidates or gut microbiota modulators for the treatment of type 2 diabetes mellitus.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (81960728), Program for top-notch talents of Guangxi traditional Chinese medicine (2022C007), Inheritance and innovation team of Guangxi traditional Chinese medicine (2022B005), 2022 Guangxi University middle-aged and young teachers’ scientific research ability improvement project (2022KY0302), Youth innovation team of Guangxi University of Chinese Medicine (2015QT002), College students innovation and entrepreneurship training program (S202210600068) and Qihuang high-level talent team of Guangxi University of Chinese Medicine (202405).

Conflict of Interest

The authors declare no conflict of interest.

Supplementary Materials

This article contains supplementary materials.

REFERENCES

• 1) Schmidt AM. Recent highlights of ATVB: diabetes mellitus. Arterioscler. Thromb. Vasc. Biol., 34, 954–958 (2014).
• 2) Navab-Moghadam F, Sedighi M, Khamseh ME, Alaei-Shahmiri F, Talebi M, Razavi S, Amirmozafari N. The association of type II diabetes with gut microbiota composition. Microb. Pathog., 110, 630–636 (2017).
• 3) Yang G, Wei J, Liu P, Zhang Q, Tian Y, Hou G, Meng L, Xin Y, Jiang X. Role of the gut microbiota in type 2 diabetes and related diseases. Metabolism, 117, 154712 (2021).
• 4) Thaiss CA, Levy M, Grosheva I, et al. Hyperglycemia drives intestinal barrier dysfunction and risk for enteric infection. Science, 359, 1376–1383 (2018).
• 5) Mohammad S, Thiemermann C. Role of metabolic endotoxemia in systemic inflammation and potential interventions. Front. Immunol., 11, 594150 (2021).
• 6) Sheng Y, Zheng S, Ma T, Zhang C, Ou X, He X, Xu W, Huang K. Mulberry leaf alleviates streptozotocin-induced diabetic rats by attenuating NEFA signaling and modulating intestinal microflora. Sci. Rep., 7, 12041 (2017).
• 7) Wang F, Zhao T, Wang W, Dai Q, Ma X. Will intestinal flora therapy become a new target in type-2 diabetes mellitus? A review based on 13 clinical trials. Nutr. Hosp., 39, 425–433 (2022).
• 8) Duan J, Zhu D, Zheng X, Ju Y, Wang F, Sun Y, Fan B. Siraitia grosvenorii (Swingle) C. Jeffrey: research progress of its active components, pharmacological effects, and extraction methods. Foods, 12, 1373 (2023).
• 9) Zhou Y, Zheng Y, Ebersole J, Huang C. Insulin secretion stimulating effects of mogroside V and fruit extract of Luo Han Kuo (Siraitia grosvenori Swingle) fruit extract. Yao Xue Xue Bao, 44, 1252–1257 (2009).
• 10) Zhang Y, Zhou G, Peng Y, Wang M, Li X. Anti-hyperglycemic and anti-hyperlipidemic effects of a special fraction of Luohanguo extract on obese T2DM rats. J. Ethnopharmacol., 247, 112273 (2020).
• 11) Cai X, He L, Zhou G, Li S, Liao X. Mogroside II_e ameliorates cardiomyopathy by suppressing cardiomyocyte apoptosis in a type 2 diabetic model. Front. Pharmacol., 12, 650193 (2021).
• 12) Xiao R, Liao W, Luo G, Qin Z, Han S, Lin Y. Modulation of gut microbiota composition and short-chain fatty acid synthesis by mogroside V in an in vitro incubation system. ACS Omega, 6, 25486–25496 (2021).
• 13) Gheibi S, Kashfi K, Ghasemi A. A practical guide for induction of type-2 diabetes in rat: incorporating a high-fat diet and streptozotocin. Biomed. Pharmacother., 95, 605–613 (2017).
• 14) Li H, Zhou J, Liu S, Chen X, Qin T, Huang G, Luo P, Hu Y, Xia X. Cinnamomum cassia Presl flavonoids prevent hyperglycemia-induced cognitive impairment via inhibiting of AGEs accumulation and oxidative stress. J. Funct. Foods, 100, 105374 (2023).
• 15) Charan J, Kantharia ND. How to calculate sample size in animal studies? J. Pharmacol. Pharmacother., 4, 303–306 (2013).
• 16) Arifin WN, Zahiruddin WM. Sample size calculation in animal studies using resource equation approach. Malays. J. Med. Sci., 24, 101–105 (2017).
• 17) Zhang Y, Peng Y, Zhao L, Zhou G, Li X. Regulating the gut microbiota and SCFAs in the faeces of T2DM rats should be one of antidiabetic mechanisms of mogrosides in the fruits of Siraitia grosvenorii. J. Ethnopharmacol., 274, 114033 (2021).
• 18) Li B, Xia X, Zhong S, Xu L, Yuan X. Effects of Momordica grosvenori saponins on type 2 diabetes rats induced by high-sucrose/high-fat diet and streptozotocin. Chin Anim Husb Vet Med, 47, 4148–4155 (2020).
• 19) Liu H, Qi X, Yu K, Lu A, Lin K, Zhu J, Zhang M, Sun Z. AMPK activation is involved in hypoglycemic and hypolipidemic activities of mogroside-rich extract from Siraitia grosvenorii (Swingle) fruits on high-fat diet/streptozotocin-induced diabetic mice. Food Funct., 10, 151–162 (2019).
• 20) Zou C, Zhang Q, Zhang S. Mogroside IIIE attenuates gestational diabetes mellitus through activating of AMPK signaling pathway in mice. J. Pharmacol. Sci., 138, 161–166 (2018).
• 21) Gomes JMG, Costa J de A, Alfenas R de CG. Metabolic endotoxemia and diabetes mellitus: a systematic review. Metabolism, 68, 133–144 (2017).
• 22) Cani PD, Amar J, Iglesias MA, et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes, 56, 1761–1772 (2007).
• 23) Rhee SH. Lipopolysaccharide: basic biochemistry, intracellular signaling, and physiological impacts in the gut. Intest. Res., 12, 90–95 (2014).
• 24) Tanti J-F, Ceppo F, Jager J, Berthou F. Implication of inflammatory signaling pathways in obesity-induced insulin resistance. Front. Endocrinol. (Lausanne), 3, 181 (2013).
• 25) Xu L, Nagata N, Ota T. Glucoraphanin: a broccoli sprout extract that ameliorates obesity-induced inflammation and insulin resistance. Adipocyte, 7, 218–225 (2018).
• 26) Kim SJ, Kim HM. Dynamic lipopolysaccharide transfer cascade to TLR4/MD2 complex via LBP and CD14. BMB Rep., 50, 55–57 (2017).
• 27) Płóciennikowska A, Hromada-Judycka A, Borzęcka K, Kwiatkowska K. Co-operation of TLR4 and raft proteins in LPS-induced pro-inflammatory signaling. Cell. Mol. Life Sci., 72, 557–581 (2015).
• 28) Yu H, Ha T, Liu L, Wang X, Gao M, Kelley J, Kao R, Williams D, Li C. Scavenger receptor A (SR-A) is required for LPS-induced TLR4 mediated NF-κB activation in macrophages. Biochim. Biophys. Acta Mol. Cell Res., 1823, 1192–1198 (2012).
• 29) Heumann D, Gallay P, Barras C, Zaech P, Ulevitch RJ, Tobias PS, Glauser MP, Baumgartner JD. Control of lipopolysaccharide (LPS) binding and LPS-induced tumor necrosis factor secretion in human peripheral blood monocytes. J. Immunol., 148, 3505–3512 (1992).
• 30) Vyberg M, Ravn V, Andersen B. Pattern of progression in liver injury following jejunoileal bypass for morbid obesity. Liver, 7, 271–276 (1987).
• 31) Grimble RF. Inflammatory status and insulin resistance. Curr. Opin. Clin. Nutr. Metab. Care, 5, 551–559 (2002).
• 32) Cavallari JF, Fullerton MD, Duggan BM, Foley KP, Denou E, Smith BK, Desjardins EM, Henriksbo BD, Kim KJ, Tuinema BR, Stearns JC, Prescott D, Rosenstiel P, Coombes BK, Steinberg GR, Schertzer JD. Muramyl dipeptide-based postbiotics mitigate obesity-induced insulin resistance via IRF4. Cell Metab., 25, 1063–1074.e3 (2017).
• 33) Clarke G, Stilling RM, Kennedy PJ, Stanton C, Cryan JF, Dinan TG. Minireview: gut microbiota: the neglected endocrine organ. Mol. Endocrinol., 28, 1221–1238 (2014).
• 34) Gomes AC, Hoffmann C, Mota JF. The human gut microbiota: metabolism and perspective in obesity. Gut Microbes, 9, 308–325 (2018).
• 35) Di Tommaso N, Gasbarrini A, Ponziani FR. Intestinal barrier in human health and disease. Int. J. Environ. Res. Public Health, 18, 12836 (2021).
• 36) Otani T, Furuse M. Tight junction structure and function revisited. Trends Cell Biol., 30, 805–817 (2020).
• 37) Paradis T, Bègue H, Basmaciyan L, Dalle F, Bon F. Tight junctions as a key for pathogens invasion in intestinal epithelial cells. Int. J. Mol. Sci., 22, 2506 (2021).
• 38) Goyer M, Loiselet A, Bon F, L’Ollivier C, Laue M, Holland G, Bonnin A, Dalle F. Intestinal cell tight junctions limit invasion of Candida albicans through active penetration and endocytosis in the early stages of the interaction of the fungus with the intestinal barrier. PLOS ONE, 11, e0149159 (2016).
• 39) Li L, Wang N, Jin Q, Wu Q, Liu Y, Wang Y. Protection of Tong-Qiao-Huo-Xue decoction against cerebral ischemic injury through reduction blood–brain barrier permeability. Chem. Pharm. Bull., 65, 1004–1010 (2017).
• 40) Serino M, Luche E, Gres S, et al. Metabolic adaptation to a high-fat diet is associated with a change in the gut microbiota. Gut, 61, 543–553 (2012).
• 41) Nusrat A, Turner JR, Madara JL. Molecular physiology and pathophysiology of tight junctions. IV. Regulation of tight junctions by extracellular stimuli: nutrients, cytokines, and immune cells. Am. J. Physiol. Gastrointest. Liver Physiol., 279, G851–G857 (2000).
• 42) Mohammad S, Thiemermann C. Role of metabolic endotoxemia in systemic inflammation and potential interventions. Front. Immunol., 11, 594150 (2021).
• 43) Takano M, Yokoyama K, Yayama K, Itoh N, Okamoto H. Endotoxin-induced enhancement of angiotensinogen synthesis in the liver: decreased response following repeated endotoxin exposure. Biol. Pharm. Bull., 16, 917–920 (1993).
• 44) Sankar SA, Lagier J-C, Pontarotti P, Raoult D, Fournier P-E. The human gut microbiome, a taxonomic conundrum. Syst. Appl. Microbiol., 38, 276–286 (2015).
• 45) Horie M, Miura T, Hirakata S, Hosoyama A, Sugino S, Umeno A, Murotomi K, Yoshida Y, Koike T. Comparative analysis of the intestinal flora in type 2 diabetes and nondiabetic mice. Exp. Anim., 66, 405–416 (2017).
• 46) Hung WC, Hung WW, Tsai HJ, Chang CC, Chiu YW, Hwang SJ, Kuo MC, Chen SC, Dai CY, Tsai YC. The association of targeted gut microbiota with body composition in type 2 diabetes mellitus. Int. J. Med. Sci., 18, 511–519 (2021).
• 47) Chen Y, Liu D, Wang D, Lai S, Zhong R, Liu Y, Yang C, Liu B, Sarker MR, Zhao C. Hypoglycemic activity and gut microbiota regulation of a novel polysaccharide from Grifola frondosa in type 2 diabetic mice. Food Chem. Toxicol., 126, 295–302 (2019).
• 48) Wang G, Li X, Zhao J, Zhang H, Chen W. Lactobacillus casei CCFM419 attenuates type 2 diabetes via a gut microbiota dependent mechanism. Food Funct., 8, 3155–3164 (2017).
• 49) Xu N, Zhou Y, Lu X, Chang Y. Auricularia auricula-judae (Bull.) polysaccharides improve type 2 diabetes in HFD/STZ-induced mice by regulating the AKT/AMPK signaling pathways and the gut microbiota. J. Food Sci., 86, 5479–5494 (2021).
• 50) Zhu Y, Dong L, Huang L, Shi Z, Dong J, Yao Y, Shen R. Effects of oat β-glucan, oat resistant starch, and the whole oat flour on insulin resistance, inflammation, and gut microbiota in high-fat-diet-induced type 2 diabetic rats. J. Funct. Foods, 69, 103939 (2020).
• 51) Murakami T, Inagaki N, Kondoh H. Cellular senescence in diabetes mellitus: distinct senotherapeutic strategies for adipose tissue and pancreatic β cells. Front. Endocrinol. (Lausanne), 13, 869414 (2022).
• 52) Herranz N, Gil J. Mechanisms and functions of cellular senescence. J. Clin. Invest., 128, 1238–1246 (2018).
• 53) Childs BG, Durik M, Baker DJ, van Deursen JM. Cellular senescence in aging and age-related disease: from mechanisms to therapy. Nat. Med., 21, 1424–1435 (2015).
• 54) Marino F, Scalise M, Salerno N, Salerno L, Molinaro C, Cappetta D, Torella M, Greco M, Foti D, Sasso FC, Mastroroberto P, De Angelis A, Ellison-Hughes GM, Sampaolesi M, Rota M, Rossi F, Urbanek K, Nadal-Ginard B, Torella D, Cianflone E. Diabetes-induced cellular senescence and senescence-associated secretory phenotype impair cardiac regeneration and function independently of age. Diabetes, 71, 1081–1098 (2022).
• 55) Fernandes R, Viana SD, Nunes S, Reis F. Diabetic gut microbiota dysbiosis as an inflammaging and immunosenescence condition that fosters progression of retinopathy and nephropathy. Biochim. Biophys. Acta Mol. Basis Dis., 1865, 1876–1897 (2019).