Capsaicin Improves The Hypoglycemic Effect of Metformin Benefitted From Gut Microbiota

doi:10.21203/rs.3.rs-882559/v1

Background: Capsaicin(CAP) possesses a hypoglycemic activity through remodulating gut microbiota as well as the first-line oral hypoglycemic drug, metformin(MET). However, CAP on the hypoglycemic effect of MET pertinent to gut microbiota remained poorly known.

Methods: The glucose and insulin tolerance of diabetic rats were monitored. The glycolipid metabolism were analyzed by detecting blood biochemical parameters. Liver pathological changes were observed by HE staining. The inflammatory cytokines and intestinal tight junction proteins were detected by RT-PCR and Western blot. 16S rRNA sequencing was employed to analyze gut microbiota.

Results: Compared with MET monotherapy, CAP and MET co-treatment could significantly reduce fasting blood glucose, improve glucose tolerance, lessen the degree of liver injury and inflammatory infiltration, reduce the expression of inflammatory cytokines and increase the expression of intestinal tight junction proteins in diabetic rats. Moreover, CAP and MET co-treatment altered gut microbiota profiles by regulating microbial abundances such as Akkermansia.

Conclusion: CAP showed significant hypoglycemic effect of MET benefitted from remodulating gut microbiota in diabetic rats.

Integrative & Complementary Medicine

Clinical Pharmacology

Capsaicin

Gut microbiota

Metformin

Hypoglycemic effect

Akkermansia

Type 2 diabetes mellitus (T2DM) is a chronic and complex metabolic disease, which has become one of the greatest threats to global health[1]. T2DM mainly involves in hyperglycemia, dyslipidemia, islet β-cell dysfunction and insulin resistance[1]. Metformin (MET), as a first-line oral hypoglycemic agent, is able to inhibit hepatic glucose production and intestinal glucose absorption in the proximal small intestine. Importantly, MET could restore gut microbiota dysbiosis associated with T2DM[15, 16]. Moreover, the interplay between MET and gut microbiota could lead to individual hypoglycemic discrimination of MET[17]. Therefore, it may be useful to improve hypoglycemic effect of MET by remodulating gut microbiota.

Diet is the most important factor in modulating gut microbiota. It indicates that spicy food preferred regional distribution is negatively correlated with diabetes prevalence in China [9]. As the main irritant component in pepper, dietary capsaicin (CAP) intake will result in diversity variations of gut microbiota, which has various beneficial metabolic properties, such as antioxidant, anti-inflammatory, anti-obesity and cardiovascular protection[10, 11]. CAP protects against high-fat diet (HFD)-induced metabolism disorder and low-grade inflammation through reshaping gut microbiota, such as increasing the abundance of Akkermansia muciniphila[13, 14]. Excitedly, it is recently reported that MET exerts hypoglycemic effect via increasing the abundance of A. muciniphila. Therefore, we hypothesized that long-term intake of CAP had better hypoglycemic effect of MET through remodulating gut microbiota.

In this study, CAP and MET co-treatment could significantly reduce fasting blood glucose and improve impaired glucose tolerance in diabetic rats, which showed stronger hypoglycemic effects than MET monotherapy. Combination therapy could lessen the degree of liver injury and inflammatory infiltration, reduce the expression of inflammatory cytokines and increase the expression of intestinal tight junction proteins in diabetic rats. The relative abundances of Akkermansia, as well as other gut beneficial bacterial, were significantly increased in diabetic rats, which was negatively correlated with fasting insulin (FINS), plasma lipopolysaccharide (LPS) and interleukin-1β (IL-1β). CAP showed significantly hypoglycemic effect of MET by remodulating gut microbiota in diabetic rats.

Animals and T2DM model

Forty-five male SD rats (140 ± 20 g, SPF grade) were purchased from Hunan SLYK Jingda experimental animal Co., Ltd. All animal experiments were approved by the laboratory animal management and ethics committee of Central South University. The rats were housed under the condition at constant temperature(24 ± 1℃) and humidity(55 ± 5%). After 7 days for adaptive feeding, SD rats were randomly divided into 5 groups: high-fat CAP group (CAP(H), n=12), high-fat control group (V(H), n=11), normal CAP group (CAP(N), n=10), normal vehicle group (V(N), n=12). CAP was administered intragastrically in soybean oil. CAP(H) and V(H) groups were fed with 34.5% high-fat diet with/without CAP (12.5 mg/kg/day), respectively, while CAP(N) and V(N) groups were fed with normal chow diet with CAP and soybean oil, respectively (Fig. 1). T2DM model was established by intraperitoneal injection of STZ (35 mg/kg) in high-fat fed rats (CAP(H) and V(H) group). Fasting blood glucose ≥11.1 mmol/L and random blood glucose ≥16.7 mmol/L were considered as a diabetic state.

Experiment design

The experimental animals were randomly divided into 8 groups: CAP combined with MET group (CAP-MET(H), n=6), CAP monotherapy group (CAP(H), n=6), MET monotherapy group (MET(H), n=6), model control group (MODEL, n=5), CAP combined with MET normal group (CAP-MET(N), n=5), CAP monotherapy normal group (CAP(N), n=5), CAP monotherapy normal group (MET(N), n=6), normal control group (CON, n=6), respectively. MET was administered in 0.9% normal saline by gavage (200 mg/kg/day). The rats were fed for 8 weeks. Body weight (BW) and fasting body weight (FBW) were measured weekly.

Oral glucose tolerance test and Intraperitoneal insulin tolerance test

After fasting for 16 hours, SD rats were administered with glucose solution (0.9% normal saline) by gavage at the dose of 2 g/kg. Blood glucose was detected at 0, 15, 30, 60, 120 and 180 minutes after glucose loading. In 8 weeks, the rats were fasted for 4 hours and injected with intraperitoneal insulin solution at the dose of 0.6 IU/kg. Blood samples were collected from the tail vein to detect blood glucose by Roche dynamic blood glucose analyzer.

Blood glucose and insulin resistance index

Random blood glucose (RBG) and fasting blood glucose (FBG) were measured once a week. The insulin resistance index was calculated according to the formula HOMA-IR = fasting blood glucose (FBG, mmol/L) × fasting insulin (fins, μIU/ml)/22.5.

Samples collection

Fresh fecal samples were collected and stored in liquid nitrogen tank. Rats were sacrificed under anesthesia with a combination of Zoletil and Rompun (1:1, v/v). The blood samples were placed at room temperature for 30 minutes, then centrifuged at 3000 r/min, 4 ℃ for 20 min. The serum and plasma samples were collected and stored in -80 ℃ refrigerator. The liver, epididymal fat, jejunum and ileum tissue were quickly excised and weighed, and then stored in liquid nitrogen tank.

Hematoxylin eosin staining

A piece of liver tissue was placed in 4% paraformaldehyde fixative and stored at room temperature. Hematoxylin eosin staining of liver tissues were completed by Wuhan Seville Biotechnology Co., Ltd.

Detection of biochemical indexes

Blood biochemical indexes, including low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglyceride (TG) and alanine aminotransferase (ALT), were detected by automatic biochemical analyzer in Wuhan Seville Biotechnology Co., Ltd. Serum insulin (FINS), plasma lipopolysaccharide (LPS) and inflammatory cytokines (TNF-α, IL-1β and IL-6) levels were detected by ELISA kits.

RNA extraction and real-time fluorescence quantitative analysis

Total RNA was extracted using Trizol Reagent. 500 ng of total RNA was reverse-transcribed using the PrimeScript RT Master Mix to produce cDNA. SYBR Green qPCR SuperMix was used to perform qPCR. Real-time PCR was carried out in LightCycler 480 using the forward and reverse primers shown in Table S1. PCR programming was set as pre-denaturation at 95 ℃ for 30s, denaturation at 95 ℃ for 5S, and annealing at 60 ℃ for 30s in 45 cycles. Data were calculated by 2^−ΔΔCTmethod with β-actin as internal reference.

Western blotting

Tissue samples were crushed and incubated in RIPA lysis buffer to produce lysate. Protein concentrations were determined using a BCA kit (Beyotime Institute of Biotechnology, Jiangsu, China). Total protein (20 ng per sample) was separated using SDS-PAGE on a 10% polyacrylamide gel and then electrophoretically transferred onto a polyvinylidene difluoride membrane. The membranes were cut and incubated with the following specific primary antibodies, rabbit anti-mouse Occludin (1:1000, Abcam, USA) and rabbit anti-mouse β-actin (1:10000, Proteintech, USA). Bands were detected using an ECL chemiluminescence system after treated with secondary antibodies.

16S rRNA sequencing

The 16S rRNA sequencing was completed by Beijing Boao Jingdian Biotechnology Co., Ltd. The effective tags were selected from the original data, which were analyzed by OTU cluster analysis. The α-diversity was evaluated by calculating the ACE, Chao1, Shannon and Simpson indexes. According to the distance matrix, the β-diversity was evaluated and compared among different groups by principal component analysis (PCA) and principal coordinate analysis (PCoA: binary jaccard distance algorithm). The marker species were selected by LefSe analysis, while the linear discriminant analysis (LDA) value of significant difference was set as 4.0. KEGG database and PICRUSt software were used to predict the function of fecal microbiota.

Statistical analysis

Graphpad prism 7.0 software was used for analysis and mapping. The experimental data were expressed as mean ± SEM. T test and one-way ANOVA or two-way ANOVA combined with LSD multiple test were used for statistically analysis. Spearman correlation analysis was performed on R software. Welch's t test and Benjamin Hochberg multiple test were used for comparison between two groups. p<0.05 was considered statistically significant.

CAP and MET co-treatment reduced blood glucose levels

FBW showed significant differences between diabetic and normal groups (Fig. 2A), but not among diabetic groups. Combination therapy (p < 0.0001) and MET (p < 0.01) could control FBW (Fig. 2C). FBG in the treated groups was lower than model group (Fig. 2B), while FBG of CAP-MET(H) group (p < 0.0001) decreased by comparing with model group, as well as CAP(H) (p < 0.05) and MET(H) groups (p < 0.01) (Fig. 2D).

CAP and MET co-treatment improved glucose tolerance and insulin resistance

0w-AUC for OGTT test increased in model group (Figure S1). As shown in Fig. 3A, 8w-AUC (p < 0.001) of CAP-MET(H) group decreased by comparing with model group, as well as 8w-AUC (p = 0.052) of MET(H) group. 8w-AUC value of the combination therapy group was lower than CAP (p < 0.01) and MET monotherapy group (p < 0.05), respectively.

As shown in Fig. 3B, IPITT- AUC of each group (CAP-MET(H) (p < 0.01), CAP(H) (p < 0.05) and MET(H) (p < 0.05)) was lower than model group. The HOMA-IR values decreased in CAP-MET(H) (p = 0.001), CAP(H) (p < 0.05) and MET(H) groups (p = 0.004) (Fig. 3C). However, the FINS values showed no significant difference among each group (Fig. 3D).

CAP and MET co-treatment alleviated hyperlipidemic and liver injury

The contents of serum TG, TC and LDL-C in treatment groups showed a downward trend (Fig. 4A). CAP and MET co-treatment reduced the levels of serum TC (p = 0.063) and LDL-C (p < 0.05) in diabetic rats. The HE staining of liver tissue were presented in Fig. 4B. The pathological damage of liver was observed in model group with serious expansion of hepatic sinusoid and obvious increase of inflammatory infiltrating cells. The liver damage in treatment groups was alleviated, while the effects of combination therapy were much better than monotherapy.

CAP and MET co-treatment attenuated inflammation and improved intestinal integrity

The levels of plasma LPS, IL-1β, IL-6 and TNF-α in treatment groups were lower than model group. CAP and MET co-treatment could significantly decrease the contents of LPS (p < 0.001), IL-1β (p < 0.001) and TNF-α (p < 0.05). LPS level in CAP-MET(H) group were also much lower than CAP(H) group (p < 0.001) and MET(H) group (p < 0.05), respectively. Additionally, the gene expression of inflammation-related molecules in liver (Fig. 5B), fat (Fig. 5C) and intestinal tissues (Fig. 5D-E) was determined. In liver, the mRNA expression of TLR4 (p < 0.001), NF-κB (p < 0.05) and IL-1β (p < 0.01) in CAP-MET(H) group was lower than model group, while the combination therapy could decrease the expression of TLR4 (p < 0.05) and IL-1β (p < 0.05) by comparing with MET monotherapy. In epididymal fat, the mRNA expression of NF-κB treated with combination therapy (p < 0.01) and MET monotherapy (p < 0.05) was decreased. The mRNA expression of TNF-α was different between CAP-MET(H) group and MET(H) group (p < 0.05). In jejunum and ileum, compared with model group, the mRNA expression of TLR4 and NF-κB in CAP-MET(H) and MET(H) groups, TNF-α in CAP-MET(H) group were statistically significant, as well as the mRNA expression of tight junction proteins, Occludin (Fig. 6A and 6B, p < 0.01). The protein expression of Occludin in ileum of CAP-MET(H) group was higher than CAP(H) group (Fig. 6C, p < 0.05).

CAP and MET co-treatment modulated gut microbiota

The OUT amounts (Table 1) and microbial α-diversity (Table 2) in treatment groups were obviously increased, which were much higher in CAP-MET(H) group than MET(H) group (p = 0.090). According to PCA and PCoA analysis, β-diversity varied between diabetic and normal rats (Figure S2 A), while CAP-MET(H) group and model group could be distinctly clustered (Fig. 7A, Anosim: p = 0.002; Adonis: p = 0.003). As shown in Fig. 7B and 7C, β-diversity were significantly different between CAP-MET(H) group and MET(H) group (Anosim: p = 0.020;Adonis: p = 0.003) or CAP(H) group (Anosim: p = 0.003; Adonis: p = 0.003), which was not found in normal group (Figure S2 C, D).

Table 1

16S rRNA sequencing data
Group	OTU number
CAP-MET(H)	238.30 ± 7.44^##
CAP(H)	228.20 ± 6.58^#
MET(H)	222.20 ± 5.26^&,+
MODEL	202.60 ± 6.67^****
CAP-MET(N)	322.80 ± 4.03
CAP(N)	325.80 ± 7.74
MET(N)	300.50 ± 6.30^**
CON	323.80 ± 3.78
CAP-MET(H): capsaicin combined with metformin model group (n = 6), CAP(H): capsaicin monotherapy model group (n = 6), MET(H): metformin monotherapy model group (n = 6), MODEL: model control group (n = 5), CAP-MET(N): capsaicin combined with metformin normal group (n = 5), CAP(N): capsaicin monotherapy normal group (n = 5), MET(N): metformin monotherapy normal group (n = 6), CON: normal control group (n = 6). OTU: operational taxonomy unit. ^p < 0.01 vs. CON group, ^**p < 0.0001 vs. CON group, ^#p < 0.01 vs. MODEL group, ^##p < 0.01 vs. MODEL group, ^&p = 0.053 vs. MODEL group, ⁺p = 0.090 vs. CAP-MET(H) group.

Table 2

α diversity index
Group	ACE	Chao1	Simpson	Shannon
CAP-MET(H)	289.30 ± 4.45^####	286.80 ± 5.86^#	0.16 ± 0.01^###	2.62 ± 0.03^##
CAP(H)	265.70 ± 7.56⁺⁺	269.10 ± 8.43	0.16 ± 0.02^###	2.60 ± 0.11^##
MET(H)	256.40 ± 4.09⁺⁺⁺	259.70 ± 6.24⁺	0.18 ± 0.04^##	2.57 ± 0.19^##
MODEL	250.30 ± 4.75^****	252.10 ± 10.80^****	0.37 ± 0.06^**	1.82 ± 0.24^****
CAP-MET(N)	339.20 ± 3.95	341.90 ± 3.97	0.09 ± 0.01	3.54 ± 0.11
CAP(N)	341.10 ± 5.27	341.80 ± 6.31	0.05 ± 0.01^*	3.89 ± 0.18
MET(N)	328.60 ± 5.18	329.90 ± 5.55	0.10 ± 0.03	3.31 ± 0.20
CON	340.60 ± 2.52	341.80 ± 3.59	0.08 ± 0.01	3.55 ± 0.10
CAP-MET(H): capsaicin combined with metformin model group (n = 6), CAP(H): capsaicin monotherapy model group (n = 6), MET(H): metformin monotherapy model group (n = 6), MODEL: model control group (n = 5), CAP-MET(N): capsaicin combined with metformin normal group (n = 5), CAP(N): capsaicin monotherapy normal group (n = 5), MET(N): metformin monotherapy normal group (n = 6), CON: normal control group (n = 6).^p < 0.05 vs.* CON group, ^*p < 0.01 vs.* CON group, ^***p < 0.0001 vs.* CON group, ^#p < 0.05 vs. MODEL group, ^##p < 0.01 vs. MODEL group, ^###p < 0.001 vs. MODEL group, ^####p < 0.0001 vs. MODEL group, ⁺p < 0.05 vs. CAP-MET(H) group, ⁺⁺p < 0.01 vs. CAP-MET(H) group, ⁺⁺⁺p < 0.001 vs. CAP-MET(H) group.

CAP and MET co-treatment could increase microbial abundances such as Allobaculum, Akkermansia, Bacteroides, Lactobacillus, Faecalibaculum, Parabacteroides, Ruminiclostridium_9, Lachnospiraceae-NK4A136, Lachnospiraceae-UCG-006, Roseburia, Oscillibacter and Phascolarctobacterium, which were much higher than model group (p < 0.01), CAP(H) group (p < 0.01) and MET(H) group (p < 0.05) (Fig. 8), and increased the relative abundance of Cyanobacteria (p < 0.01), Verrucomicrobia (p < 0.001) (Figure S3), as well as Akkermansiaceae (p < 0.001) (Fig. 9A, Figure S4). LEfSe analysis indicated that Akkermansia could be the predominant biomarker of CAP-MET(H) group (Fig. 9B, C).

Actinobacteria, Clostridium_sensu_stricto_1, ratio of Firmicutes to Bacteroidetes, Clostridiaceae_1, Bifidobacteria and Agathobacter were positively correlated with T2DM indicators (p < 0.05), while Akkermansia, Parabacteroides, Parasutterella, Phascolarctobacterium, Oscillibacter, Ruminiclostridium_9, Lachnospiraceae_UCG-006, Bacteroides and Muribaculaceae were negatively associated with T2DM indicators (p < 0.05) (Fig. 10). The relative abundance of Akkermansia was negatively correlated with FINS, LPS, IL-1β and TNF-α (p < 0.05, Q < 0.05).

Functional microbial pathways

According to the prediction analysis of microbial function by PICRUSt, biosynthesis of amino acids, 2-oxocarboxylic acid metabolism, biosynthesis of antibiotics, biosynthesis of secondary metabolites, quorum sensing, C5-Branched dibasic acid metabolism, valine, leucine and isoleucine biosynthesis and nitrogen metabolism were enriched in model group (p < 0.01, Q < 0.01), while butanoate metabolism, pyruvate metabolism, citrate cycle (TCA cycle), flagellar assembly, glycolysis/gluconeogenesis, glycerolipid metabolism, amino sugar and nucleotide sugar metabolism were enriched in CON group (p < 0.01, Q < 0.01) (Fig. 11A). CAP and MET co-treatment enhanced the activity of fatty acid metabolism, tryptophan metabolism, lysine degradation, biosynthesis of unsaturated fatty acids, butanoate metabolism and citrate cycle and inhibited the activity of biosynthesis of amino acids, valine, leucine and isoleucine biosynthesis, nicotinate and nicotinamide metabolism and peptidoglycan biosynthesis in diabetic rats (Fig. 11B). Compared with CAP(H) and MET(H) groups, the enrichment in histidine metabolism pathway was significantly reduced in CAP-MET(H) group (p < 0.01) (Fig. 11C and D).

Gut microbiota play an important role in metabolic syndrome. The composition and function of gut microbiota in patients with T2DM are significantly different from healthy individuals [18, 19]. CAP can regulate gut microbiota, which has anti-obesity, anti-inflammatory, hypoglycemic and lipid-lowering effects. In the study, we found that CAP and/or MET can both improve the impaired glucose tolerance. The fasting blood glucose of rats treated with CAP and MET co-treatment reached the lowest level at the third week. In the mid-term (3–5 weeks), the combination therapy could significantly reduce the fasting blood glucose and improve the impaired glucose tolerance and insulin resistance. After 8 weeks, the combination therapy showed better control of blood glucose and improvement of impaired glucose tolerance than MET monotherapy.

Chronic inflammation is an important mechanism of diabetes and other chronic diseases [20]. T2DM is often accompanied by peripheral insulin resistance and low-grade systemic inflammation. High fat diet can increase the expression of inflammatory cytokines IL-6, IL-1β and TNF-α, which can reduce the insulin sensitivity and accelerate the occurrence of insulin resistance. CAP and MET co-treatment could improve the hyperlipidemia of diabetic rats, reduce the levels of serum TC and LDL-C and alleviate liver injury and inflammatory infiltration. The plasma IL-1β, IL-6 and TNF-α and the mRNA expressions of IL-1β and TNF-α in liver, epididymal fat and intestinal tissues were relatively increased. Compared with MET monotherapy, the results suggested that the combination therapy may have a better anti-inflammatory effects.

Gut microbiota in low-level inflammatory response involve bacterial LPS, SCFA and branched chain amino acid (BCAA) metabolism, which have effects on the pathogenesis and treatment of T2DM [21]. LPS, as a ligand of TLR4, can activate related inflammatory signaling pathways and induce tissue inflammatory response. The results showed that the CAP and MET co-treatment could significantly reduce the content of LPS in plasma and the mRNA expression of TLR4 and NF-κB in liver and intestine. Meanwhile, the combination treatment could relatively increase the expression of intestinal tight junction molecule, occludin, which was able to maintain the intestinal barrier homeostasis and reduce gut microbiota-derived LPS entering the systemic circulation.

The imbalance of gut microbiota can induce inflammatory reactions, change intestinal permeability and cause metabolic endotoxemia. In diabetic rats, the ratio of Firmicutes to Bacteroidetes and the relative abundances of Actinobacteria and Bifidobacteria were significantly increased [22]. CAP and MET co-treatment could significantly regulate the composition and function of gut microbiota, which could increase the α-diversity and alter β-diversity of gut microbiota. Evidences have proved that the abundances of pro-inflammatory microbials in T2DM patients are significantly increased [23–25]. CAP and MET co-treatment could increase the relative abundances of a variety of beneficial bacteria, such as Akkermansia, Allobaculum, Bacteroides, Faecalibaculum, Lactobacillus, Lachnospiraceae-UCG-006, Ruminiclostridium_9, Lachnospiraceae-NK4A136, Roseburia, Oscillibacter, Parabacteroides and Phascolarctobacterium, all of which were SCFA producing microbials. SCFA are a subset of fatty acids produced by gut microbiota during polysaccharide fermentation, which play an important role in glucose and lipid metabolism, intestinal barrier homeostasis and anti-inflammatory response [26]. In the correlation analysis, metabolic pathway analysis indicated the co-treatment could increase the enrichment of lysine degradation pathway and butanoate metabolism pathway, which were reported to be related to butyric acid synthesis [27]. Compared with MET monotherapy, combination therapy could reduce the proportion of some pathogenic bacteria, such as Blautia Escherichia Shigella and Enterococcus, which are correlated to the adverse gastrointestinal reactions of MET [28].

Alternation in gut microbial composition can influence the type and quantity of microbial metabolites. Amino acids are the key substrates in many metabolic pathways, while clinical data have shown that the serum amino acid level is higher in diabetic patients by comparing with healthy individuals [29]. CAP combined with MET could inhibit the biosynthesis of amino acids pathway and reduce the amino acid synthesis of gut microbiota. BCAA (such as valine, leucine and isoleucine) can increase the risk of diabetes [30, 31], which can promote liver lipid storage and lead to insulin resistance [32], while gut microbiota are involved in BCAA synthesis. CAP combined with MET can reduce the synthesis of valine, leucine and isoleucine. Moreover, the co-treatment could enhance the tryptophan metabolism function, which could be directly metabolized into indole derivatives by gut microbiota. These metabolites have important effects on regulating immune homeostasis and protecting the intestinal barriers [33]. The synthesis of unsaturated fatty acids may be related to the improvement of blood lipid, blood glucose and antioxidant levels in T2DM [34]. The metabolism of nicotinic acid and nicotinamide was also related to the occurrence of T2DM and inflammatory response [35, 36]. Our results showed that the above-mentioned related pathways could be regulated by CAP and MET co-treatment [37]. Compared with MET monotherapy, the combination therapy could significantly reduce the microbial metabolism of histidine, which was associated with T2DM. The gut microbiota of T2DM patients have a stronger histidine metabolism ability than healthy subjects. The histidine metabolites lead to decreased glucose tolerance and impaired insulin signal [38]. These results suggested gut-derived microbial metabolites played important role in therapeutical efficacy.

In the study, CAP and MET co-treatment showed better effects than MET monotherapy on blood glucose control, enhancing insulin sensitivity, improving impaired glucose tolerance and reducing inflammatory reactions. CAP and MET co-treatment could remodulate the profiles and metabolic function of gut microbiota, which may play an important role in therapeutic effects of T2DM. Our study provided experimental evidence for the treatment of T2MD by combination drugs with diet intervention.

AUC, area under the receiver operating characteristic curve; ALT, alanine aminotransferase; BCAA, branched-chain amino acid; CAP, capsaicin; ddH₂O, double distilled water; FBG, fasting blood glucose; FINS, fasting insulin; GCG, proglucagon; HE, hematoxylin-eosin; HFD, high-fat diet; HOMA, homeostasis model assessment; IPITT, intraperitoneal insulin tolerance test; IR, insulin resistance; LDL-C, low-density lipoprotein cholesterol; LPS, lipopolysaccharide; MET, metformin; OGTT, oral glucose tolerance test; PCR, polymerase chain reaction; RNA, ribonucleic acid; SCFA, short chain fatty acids; STZ, steptozotocin; T2DM, type 2 diabetes mellitus; TC, total cholesterol; TG, triglyceride.

Authors’ contributions

W. Z. and W.-H. H. conceived and designed the study. Z.-Q. K. and J.-L. H. performed the experiments, analyzed the data and wrote the manuscript. M.-Y. C. and N. Y. provided technical help and revised the manuscript.

Acknowledgements

None.

Competing interests

None of the authors has any conflict of interest regarding this study.

Availability of data and materials

Not applicable.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Fund

This work was supported by the National Natural Scientific Foundation of China (82074000, 81903784), the Hunan Provincial Natural Science Foundation of China (2020JJ4878), Science and Technology Benefiting People Plan of Zhengzhou (2020KJHM0028), Medical Science and Technology Research Plan of Henan Province (2018020762), the Scientific Research Project of Department of Education of Hunan Province (20K136), Sanming Project of Medicine in Shenzhen(SZSM201811057) and NHC Key Laboratory of Birth Defect for Research and Prevention (Hunan Provincial Maternal and Child Health Care Hospital, No. KF2020002).

Agus A, Planchais J, Sokol H. Gut microbiota regulation of tryptophan metabolism in health and disease. Cell Host Microbe. 2018;23(6):716–24.
Bauer PV, Duca FA, Waise TMZ, Rasmussen BA, Abraham MA, Dranse HJ, et al. Metformin alters upper small intestinal microbiota that impact a glucose-sglt1-sensing glucoregulatory pathway. Cell Metab. 2018;27(1):101–17.
Candela M, Biagi E, Soverini M, Consolandi C, Quercia S, Severgnini M, et al. Modulation of gut microbiota dysbioses in type 2 diabetic patients by macrobiotic Ma-Pi 2 diet. Br J Nutr. 2016;116(1):80–93.
Cani PD, Van Hul M, Lefort C, Depommier C, Rastelli M, Everard A. Microbial regulation of organismal energy homeostasis. Nat Metab. 2019;1(1):34–46.
Chen T, Ni Y, Ma X, Bao Y, Liu J, Huang F, et al. Branched-chain and aromatic amino acid profiles and diabetes risk in Chinese populations. Sci Rep. 2016;6:20594. doi:10.1038/srep20594.
Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271–81.
Domotor A, Szolcsanyi J, Mozsik G. Capsaicin and glucose absorption and utilization in healthy human subjects. Eur J Pharmacol. 2006;534(1–3):280–3.
Faber F, Baumler AJ. The impact of intestinal inflammation on the nutritional environment of the gut microbiota. Immunol Lett. 2014;162:48–53.
Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E, Sunagawa S, et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature. 2015;528(7581):262–6.
Gregor MF, Hotamisligil GS. Inflammatory mechanisms in obesity. Annu Rev Immunol. 2011;29:415–45.
Hui S, Huang L, Wang X, Zhu X, Zhou M, Chen M, et al. Capsaicin improves glucose homeostasis by enhancing glucagon-like peptide-1 secretion through the regulation of bile acid metabolism via the remodeling of the gut microbiota in male mice. FASEB J. 2020;34(6):8558–73.
Kang C, Wang B, Kaliannan K, Wang X, Lang H, Hui S, et al. Gut Microbiota Mediates the Protective Effects of Dietary Capsaicin against Chronic Low-Grade Inflammation and Associated Obesity Induced by High-Fat Diet. mBio 2017; 8 (3): doi:10.1128/mBio.00470-17.
Kim Y, Hwang SW, Kim S, Lee YS, Kim TY, Lee SH, et al. Dietary cellulose prevents gut inflammation by modulating lipid metabolism and gut microbiota. Gut Microbes. 2020;11(4):944–61.
Koh A, Manneras-Holm L, Yunn NO, Nilsson PM, Ryu SH, Molinaro A, et al. Microbial Imidazole Propionate Affects Responses to Metformin through p38gamma-Dependent Inhibitory AMPK Phosphorylation. Cell Metab. 2020;32(4):643–53.
Koh A, Molinaro A, Stahlman M, Khan MT, Schmidt C, Manneras-Holm L, et al. Microbially Produced Imidazole Propionate Impairs Insulin Signaling through mTORC1. Cell. 2018;175(4):947–61 e917.
Krych L, Nielsen DS, Hansen AK, Hansen CH. Gut microbial markers are associated with diabetes onset, regulatory imbalance, and IFN-gamma level in NOD mice. Gut Microbes. 2015;6(2):101–9.
Li K, Zhang L, Xue J, Yang X, Dong X, Sha L, et al. Dietary inulin alleviates diverse stages of type 2 diabetes mellitus via anti-inflammation and modulating gut microbiota in db/db mice. Food Funct. 2019;10(4):1915–27.
Ma Y, Bao Y, Wang S, Li T, Chang X, Yang G, et al. Anti-inflammation effects and potential mechanism of saikosaponins by regulating nicotinate and nicotinamide metabolism and arachidonic acid metabolism. Inflammation. 2016;39(4):1453–61.
Ouyang J, Isnard S, Lin J, Fombuena B, Marette A, Routy B, et al. Metformin effect on gut microbiota: insights for HIV-related inflammation. AIDS Res Ther. 2020;17(1):10. doi:10.1186/s12981-020-00267-2.
Panchal SK, Bliss E, Brown L. Capsaicin in metabolic syndrome. Nutrients 2018; 10 (5): doi:10.3390/nu10050630.
Pandey A, Chawla S, Guchhait P. Type-2 diabetes: Current understanding and future perspectives. IUBMB Life. 2015;67(7):506–13.
Pandurangan M, Kim DH. Therapeutic potential of cyanobacteria against streptozotocin-induced diabetic rats. 3 Biotech. 2016;6(1):94. doi:10.1007/s13205-016-0411-0.
Papatheodorou K, Banach M, Bekiari E, Rizzo M, Edmonds M. Complications of Diabetes 2017. J Diabetes Res 2018; 3086167. doi:10.1155/2018/3086167.
Patowary P, Pathak MP, Zaman K, Raju PS, Chattopadhyay P. Research progress of capsaicin responses to various pharmacological challenges. Biomed Pharmacother. 2017;96:1501–12.
Patterson E, Marques TM, O'Sullivan O, Fitzgerald P, Fitzgerald GF, Cotter PD, et al. Streptozotocin-induced type-1-diabetes disease onset in Sprague-Dawley rats is associated with an altered intestinal microbiota composition and decreased diversity. Microbiology. 2015;161(Pt 1):182–93.
Phillip JW. Branched-chain amino acids in disease. Science. 2019;6427:(363).
Rao Kondapally Seshasai S, Kaptoge S, Thompson A, Di Angelantonio E, Gao P, Sarwar N, et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med. 2011;364(9):829–41.
Routy B, Gopalakrishnan V, Daillere R, Zitvogel L, Wargo JA, Kroemer G. The gut microbiota influences anticancer immunosurveillance and general health. Nat Rev Clin Oncol. 2018;15(6):382–96.
Shin NR, Lee JC, Lee HY, Kim MS, Whon TW, Lee MS, et al. An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice. Gut. 2014;63(5):727–35.
Si X, Shang W, Zhou Z, Strappe P, Wang B, Bird A, et al. Gut microbiome-induced shift of acetate to butyrate positively manages dysbiosis in high fat diet. Mol Nutr Food Res 2018; 62 (3): doi:10.1002/mnfr.201700670.
Sircana A, Framarin L, Leone N, Berrutti M, Castellino F, Parente R, et al. Altered gut microbiota in type 2 diabetes: just a coincidence? Curr Diab Rep. 2018;18(10):98. doi:10.1007/s11892-018-1057-6.
Stumvoll M, Goldstein BJ, van Haeften TW. Type 2 diabetes: principles of pathogenesis and therapy. The Lancet. 2005;365(9467):1333–46.
Tilg H, Adolph TE, Gerner RR, Moschen AR. The intestinal microbiota in colorectal cancer. Cancer Cell. 2018;33(6):954–64.
Turpin W, Espin-Garcia O, Xu W, Silverberg MS, Kevans D, Smith MI, et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat Genet. 2016;48(11):1413–7.
Wu H, Esteve E, Tremaroli V, Khan MT, Caesar R, Manneras-Holm L, et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat Med. 2017;23(7):850–8.
Yu M, Jia HM, Zhou C, Yang Y, Sun LL, Zou ZM. Urinary and fecal metabonomics study of the protective effect of chaihu-shu-gan-san on antibiotic-induced gut microbiota dysbiosis in rats. Sci Rep. 2017;7:46551. doi:10.1038/srep46551.
Zhao XQ, Guo S, Lu YY, Hua Y, Zhang F, Yan H, et al. Lycium barbarum L. leaves ameliorate type 2 diabetes in rats by modulating metabolic profiles and gut microbiota composition. Biomed Pharmacother. 2020;121:109559. doi:10.1016/j.biopha.2019.109559.
Zhao Z, Li M, Li C, Wang T, Xu Y, Zhan Z, et al. Dietary preferences and diabetic risk in China: A large-scale nationwide Internet data-based study. J Diabetes. 2020;12(4):270–8.

Capsaicin Improves The Hypoglycemic Effect of Metformin Benefitted From Gut Microbiota

Status:

Version 1

Abstract

Figures

Introduction

Methods

Results

Discussion

Conclusions

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1