Intermittent Fasting Improves Glucose Metabolism Disorders Induced by High-fat Diet through Modulation of Gut Microbiota and Serum Metabolism in Middle-aged Mice

doi:10.21203/rs.3.rs-4958956/v1

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Research Article

Intermittent Fasting Improves Glucose Metabolism Disorders Induced by High-fat Diet through Modulation of Gut Microbiota and Serum Metabolism in Middle-aged Mice

https://doi.org/10.21203/rs.3.rs-4958956/v1

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Background

Intermittent fasting (IF) has received wide attention as an effective diet strategy. Existing studies shown that IF is a promising approach for weight control, improving insulin sensitivity and reducing type 2 diabetes (T2D) prevalence.

Methods

Twenty-eight 8-month-old male C57BL/6J mice were randomly divided into a normal control group (NC), a high-fat diet group (HF) and an IF group. Body weight (BW) and food intake were monitored weekly. After 20 weeks the intraperitoneal glucose tolerance test (IPGTT), oral glucose tolerance test (OGTT), and intraperitoneal insulin tolerance test (IPITT) were performed weekly in sequence. Fresh faeces were collected to examine changes in gut microbiota, and untargeted metabolite profiling was conducted on serum samples.

Results

IF significantly reduced weight gain in middle-aged mice fed a high-fat diet, reduced fat mass and liver weight, and improved glucose tolerance and insulin sensitivity. 16S rRNA gene sequencing revealed that IF significantly reduced the Firmicutes/Bacteroidetes (F/B) ratio by increased Muribaculaceae, Bacteroides, Parabacteroides, and decreased Bilophila, Colidextribacter, Oscillibacter. Spearman's correlation analysis indicated that these bacteria were strongly correlated with obesity-related parameters and serum metabolites such as capryloylglycine, N-acetylglycine, 4-ethyl-6-[(3E)-2-ethyl-3-hexen-1-yl]-6-methyl-1,2-dioxan-3-yl acetic acid, etc.

Conclusion

IF improves glucose metabolism, regulates gut microbiota, and alters serum metabolites. This provides a new pathway for trials testing diabetes prevention in middle-aged and elderly patients.

Intermittent fasting

glucose metabolism disorders

gut microbiota

serum metabonomics

middle age mice

The prevalence of type 2 diabete (T2D) continues to increase worldwide. Based on the report of International Diabetes Federation, 463 million people affected by diabetes in 2019, and 19.3% among those aged 65 years or elder (https://www.diabetesatlas.org). Senile diabetes and its complications cause disability and high mortality, resulting in a heavy economic and care burden for society and families.

Senile diabetes refers to mainly type 2 diabetes, which can be divided into presenile diabetes and postsenile new-onset diabetes. It has been well established that a long-lasting, prodromal stage exists before clinical diagnosis of type 2 diabetes, which consists of progressive elevation of plasma glucose (PG) within anondiabetic range, weight gain, and insulin resistance (IR). Glucose dysregulation precedes diagnosis of diabetes at least for 20 years ^[1]. So, for both presenile diabetes and postsenile new-onset diabetes, the main pathophysiological mechanism of diabetes may be initiated in middle age. Therefore, taking intervention measures in middle age are of great significance to prevent the occurrence of diabetes in elderly individuals.

A key strategy to improve insulin resistance and help prevent the onset and progression of the above maladies is weight loss^[2]. Dietary interventions are considered essential in the treatment and prevention of T2D. IF is an eating pattern where the individual alternates between periods of eating and a defined phase of prolonged fasting. Currently, major IF diets include alternate-day fasting, modified alternate day fasting, periodic fasting, time-restricted fasting, and religion-related fasting^[3]. Recent studies have shown that IF can reduce weight, inflammation, and antioxidant stress and improve insulin sensitivity^[4]. But the potential of IF to delay or correct the abnormal glucose metabolism resulting from a high-fat diet in middle-aged and elderly mice has not been definitively established.

Interestingly, recent studies have shown that periodic changes in the eating-fasting rhythm can improve the diversity and function of dysregulated gut microbiota^[5], and gut microbiota and associated metabolites are involved in the pathophysiological processes of obesity, insulin resistance and T2D^[6]. Thus, the gut microbiota and related metabolites may play an important role in the effect of IF on glucose metabolism.

In this study, a modern diet was simulated in order to compare and analyze the effects of IF on glucose metabolism, gut microbiota and serum metabolites in middle-aged mice fed a high-fat diet. Furthermore, we analyzed the correlation between glucose metabolism and gut microbiota and serum metabolites.

2.1 Animals and experimental design

Twenty-eight specific pathogen-free male C57/BL6J mice (8 months old, weighing 29–37 g) were obtained from Beijing Vitong Lihua Co., Ltd. All mice were housed at 25 ± 2°C and 60 ± 5% relative humidity with a 12h light/dark cycle and had ad libitum access to a normal-chow diet and water. After 7 days of acclimation, the mice were randomly divided into three group. The normal chow diet group (NC, n = 5) were fed a normal chow diet (provided by the Experimental Animal Center of Zhengzhou University), and the high-fat diet group (HF, n = 15) and intermittent fasting group (IF, n = 8) were fed a high-fat diet (H10060, HFK Bioscience, Beijing, China). The compositions and energy densities of the above diets were listed in Table S1. The IF group mice fasted for 3 days every other day (Monday, Wednesday and Friday). All mice drank water freely and body weight and food intake were measured on every Monday. The intraperitoneal glucose tolerance test (IPGTT), oral glucose tolerance test (OGTT) and intraperitoneal insulin tolerance test (IPITT) were performed at the 24th, 25th and 26th week, respectively. At the end of animal experiments, fresh fecal samples were collected, and stored at − 80°C. All mice were sacrificed under isoflurane anesthesia and the blood samples were gained from the eyeballs of mice and separated by centrifugation (4,000 rpm, 10 min), and the organs were collected and weighed. The process workflow is schematically represented in Fig. 1. All procedures in this study were approved by the Ethics Committee of the Experimental Animals of Zhengzhou University (approval number: ZZU-LAC20210930[07]), and the protocol strictly aligned with the ethical guidelines indicated by this governing body.

2.2 Histological analysis

The formalin fixed epididymal white adipose tissue (eWAT) and livers were paraffin-embedded, cut into 5–7 µm sections, and stained with hematoxylin and eosin (H&E, Sigma-Aldrich, St. louis, MO, USA) or Oil-Red O. Stained slides were viewed under a Zeiss Observer (Carl Zeiss, Oberkochen, Germany) at 200 × magnification, and images were taken by an Olympus digital camera (Nikon DS-L1, Tokyo, Japan). Finally, the images were analyzed with the Adiposoft software (CIMA, University of Navarra, Spain) to measure the size of adipocytes.

2.3 Intraperitoneal glucose tolerance test (IPGTT)

Before testing, mice were fasted for 16 h. On the day of experiments, after determination of basal blood glucose levels using an automatic glucometer (Nova Biomedical Corporation, MA, USA), mice were intraperitoneally injected with glucose (2 g/kg body weight). Blood glucose levels at 0, 15, 30, 60 and 120 min after injection were then measured using an automatic glucometer.

2.4 Oral glucose tolerance test (OGTT)

Before testing, mice were fasted for 16 h. On the day of the experiment, following determination of basal blood glucose levels using an automatic glucometer (Nova Biomedical Corporation, MA, USA), mice were orally gavaged with glucose (2 g/kg body weight). Blood glucose levels at 0, 15, 30, 60 and 120 min after injection were then measured as described above.

2.5 Intraperitoneal insulin tolerance test (IPITT)

Before testing, mice were fasted for 4 h. After determination of basal blood glucose levels using an automatic glucometer (Nova Biomedical Corporation, MA, USA), mice were intraperitoneally injected with insulin (0.5 U/kg body weight). Blood glucose levels at 0, 15, 30, 60 and 120 min after injection were then measured as described earlier.

2.6 Measurement of fasting plasma glucose, insulin, and HOMA-IR

Fasting glucose, insulin was measured as described. Briefly, mice were fasted for 10–12 hours prior to euthanasia and blood was collected by transthoracic cardiac puncture. Subsequently, glucose, insulin, triglycerides were measured in the plasma. Glucose was measured as above and insulin was measured using a mouse insulin ELISA kit (Wuhan Huamei Biotechnology Co., Ltd., Wuhan, China). The extent of insulin resistance was quantified by the homeostatic model assessment (HOMA-IR) using the matched fasting glucose and insulin concentrations. Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) was used for estimating insulin sensitivity from fasting blood insulin (FI) and fasting blood glucose (FG) with the following formula: HOMA-IR = FI × FG/22.5, where FI is measured in µU/mL and FG is measured in mmol/L^[7].

2.7 Gut microbiota analysis

Total bacterial DNA was extracted from fecal samples with a MagPure Soil DNA LQ Kit (Magen, Guangdong, China) following the manufacturer’s instructions. DNA concentration and integrity were measured with a NanoDrop 2000 (Thermo Fisher Scientific, USA) and 1% agarose gel electrophoresis. The purified DNA was used as a template for PCR amplification of the V3-V4 regions of the bacterial 16S rRNA genes with the primers 343F (5’-TACGGRAGGCAGCAG-3’) and 798R (5’-AGGGTATCTAATCCT-3’) by thermocycler PCR system (GeneAmp® 9700, ABI, USA). Amplicons were purified with Agencourt AMPure XP beads (Beckman Coulter, Inc., USA.) and quantified using a Qubit dsDNA Assay Kit (Thermo Fisher Scientific Inc.,USA). High-throughput 16S rRNA gene amplicon sequencing was performed on an Illumina NovaSeq 6000 platform (Illumina Inc., San Diego, USA) with 250 bp paired-end reads at OE Biotech Company (Shanghai, China). After quality filtering, trimming and merging by FLASH software^[8] and QIIME software (version 1.8.0)^[9], The DADA2 method in the QIIME2 software was used to denoise and obtain the amplicon sequence variants (ASVs), which is a 100% similar sequence, and then the feature table (collectively referred to as ASV/ASV, etc.) was obtained. The main process is as follows: (1) the filtered double-ended sequence was imported by QIIME tools import; (2) using the QIIME DADA2 denoise paid command, the imported double-ended sequence was constructed based on the DADA2 method; (3) the QIIME tools export was used to convert the feature table into a format that can be viewed directly^[10].

The alpha diversity analysis, including the Chao1 index and Shannon index were estimated using QIIME software (version 1.8.0) to compare the richness and average among different groups. Beta diversity analysis was performed to investigate the structural variation of microbial communities across samples using the unweighted UniFrac distance metric and visualized via principal coordinate analysis (PCoA). The linear discriminant analysis effect size (LEfSe) was used to compare differentially abundance taxa across groups^[11].

2.8 Serum metabolomic analysis

The metabolites in serum were detected by a Q Exactive quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) coupled with a Dionex Ultimate 3000 nano-ultra performance liquid chromatography (UPLC) system (UHPLC-Q-TOF MS). The supernatant was injected for analysis. Files for raw MS data were processed by Compound Discoverer 3.1 (Thermo Fisher Scientific). To further improve the accuracy of feature identification in targeted MA, only features with at least one data-dependent MS2 scan for the preferred adduct ion identified by mzCloud (https://www.mzcloud.org/) were calculated^[12].

All multivariate analyses were conducted with SIMCA14.1. PCA and OPLS-DA models were carried out to visualize the metabolic alterations among experimental groups. A permutation test (n = 200) was performed to validate the model and avoid overfitting. Differential metabolites contributing to the separation were identified using VIP values, fold change values and the corresponding p values. In general, metabolites

with VIP > 1 were considered relevant for interpreting the discrimination, a fold change value ≥ 1.5 or ≤ 0.8 was the cutoff for up- or downregulation in concentration, and a P value < 0.05 was considered to indicate a significant difference.

Statistical analysis

Statistical analyses and visualization were performed using SPSS software (version 21.0, IBM, USA.). All data are expressed as the mean ± standard error of mean (SEM). The significance of the differences among two groups was evaluated by two-tailed unpaired Student's t-tests. More than two groups were evaluated by one-way ANOVA followed by Tukey’s multiple comparison post-hoc tests. Differences among groups were considered significant at values of p < 0.05. Spearman’s correlation analysis were used to assess the correlation between gut microbiota and serum metabolites.

3.1 IF reduced body weight, food intake, fat mass and liver weight in HFD-fed mice.

To investigate whether IF can delay or prevent the increase in blood sugar caused by high-fat diet in middle-aged and elderly mice, the body weight, food intake, glucose and fat weight were measured. There were no significant differences in the baseline body weight and blood glucose levels of the mice in the three groups (S1, P > 0.05). After intervention, no significant differences were found between the NC and IF groups (P > 0.05), and compared with the NC group, body weight was significantly higher in the HF group (Fig. 1C). The average weekly food intake in the IF group was less than that in the HF diet group, but this difference was not significant (P > 0.05). Subcutaneous fat weight, visceral fat weight and liver weight in the HF group were significantly higher than those of the NC group. The IF group showed significant decreases in subcutaneous fat weight, visceral fat weight and liver weight compared to those reported for the HF group (Fig. 1C-F.). In addition, by staining Fat and liver with H&E, it was observed that IF inhibited the enlargement of adipocytes induced by highfat-diet feeding, and the size of adipocytes of the IF group was significantly reduced (Fig. 2E).

3.2 IF improved glucose tolerance and insulin sensitivity in HFD-fed mice.

After 22 weeks of intervention, IPGTT, OGTT and IPITT were performed every week, and fasting serum insulin levels were measured. The IPGTT results showed a greater degree of impaired glucose tolerance in the IF group compared with that in the NC group, but glucose tolerance improved in the IF group compared with the HF group (Fig. 2A-C). OGTT results showed that glucose tolerance improved in the IF group compared with that in HF group, and the difference between the IF and NC groups was not significant. HOMA-IR results showed that insulin sensitivity in IF group significantly decreased compared with NC group, and improved in the IF group compared with HF group (Fig. 2D).

3.3 IF alleviated gut microbiota dysbiosis in HFD-fed mice

To assess the effect of IF on the gut microbiota in HFD-fed mice, we performed 16S rRNA sequencing on fecal samples. Our results showed no significant difference in intrinsic biodiversity, shown by Chao 1, ACE, Shannon and Simpson indices ( Table 1 ). However, beta diversity with PCA analysis revealed a good separation among the three groups, suggesting significant differences in microboial populations after IF intervention (Fig. 3B.). Notbaly, The dominant phyla were Bacteroidota and Firmicutes in all groups.

Table 1

Effects of intermittent fasting on body weight, food intake and fat weight
Group	NC	HF	IF
Baseline body weight(g)	33.14 ± 2.65	32.40 ± 1.88	32.90 ± 2.46
Baseline glucose(mmol/L)	5.70 ± 0.50	5.91 ± 0.56	5.89 ± 0.48
Final body weight(g)	30.46 ± 2.61	52.26 ± 2.67^*	33.08 ± 2.30
Average cumulative food intake(g)	——	489.41 ± 6.63	411.13 ± 7.22
SF weight(g)	0.12 ± 0.13	3.02 ± 0.37^*	0.48 ± 0.34^#
VF weight(g)	0.72 ± 0.16	4.25 ± 0.66^*	1.64 ± 0.78^#
Liver weight(g)	1.42 ± 0.21	2.63 ± 0.47^*	1.36 ± 0.34^#
Results presented are means ± SEM of samples from each group.* p < 0.05 versus NC group, # p < 0.05 versus HF group.

Table 2

Effects of intermittent fasting on Alpha-diversity and beta-diversity analyses.
	Chao1	Shannon	Simpson	ACE
NC	256.898 ± 44.421	6.314 ± 0.210	0.971 ± 0.011	257.189 ± 45.488
HF	269.348 ± 38.893	6.542 ± 0.283	0.977 ± 0.008	267.737 ± 39.953
IF	262.411 ± 35.131	6.440 ± 0.376	0.975 ± 0.011	259.981 ± 33.759
F value	0.166	0.836	0.480	0.135
P value	0.848	0.449	0.627	0.875

At the genus level, Muribaculaceae, Lachnospiraceae_NK4A136_group, Bacteroides, and Bilophila were the most abundant bacteria in the three groups.

At the genus level, the relative abundances of Bilophila, Colidextribacter, Oscillibacter, and Mucispirillum in the HF group were significantly increased compared with those in the NC group (P < 0.05). The relative abundance of Muribaculaceae in the HF group was significantly decreased compared with those in the NC group. Compared with the HF group, Muribaculaceae, Bacteroides, Alistipes, Parabacteroides, and Rikenellaceae_RC9_gut_group were significantly increased in the IF group, and Bilophila, Colidextribacter, Oscillibacter, and Blautia were significantly decreased in the IF group.

The Firmicutes/Bacteroidetes (F/B) ratio has been suggested as an indicator of several pathological conditions. The F/B ratio of the HF group was significantly higher than that of the NC group (P < 0.05), while the IF group exhibited a lower F/B ratio than the HF group (P < 0.05). The relative abundance of Lactobacillus in the HF group was significantly decreased compared with that in the NC group. The IF group showed a slight increase in the relative abundances of Lactobacillus when compared to the HF group, which was, however, not statistically significant. The relative abundance of Escherichia-Shigella was significantly increased in the HF group (P < 0.05), while it was significantly decreased in the IF group compared to the HF group (P < 0.05).

Considering that this discriminant analysis did not distinguish the predominant taxon, LEfSe was used to generate a cladogram to identify the specific bacteria associated with IF. Several opportunistic pathogens, including o_Desulfovibrionales, f_Oscillospiraceae, g_Bilophila, p_Desulfobacterota, f_Lachnospiraceae, p_Firmicutes, f_Desulfovibrionaceae, and c_Clostridia, were all significantly overrepresented (all LDA scores (log10) > 4.2) in the HF group, whereas f_Rikenellaceae, f_Bacteroidaceae, g_Bacteroides, and g_Parabacteroides were the most abundant microbes in the IF group (LDA scores (log10) > 4.2) (Fig. 3E.). These results indicate that the observed alterations in the composition of gut microbiota were associated with IF.

3.4 IF altered serum metabolites in HFD-fed mice

To evaluate the effect of IF on metabolism in HFD-fed mice, we detected and analyzed mouse serum metabolites using a nontargeted metabolomics approach with UHPLC-Q-TOF MS (Fig. 4). Subsequently, using orthogonal partial least-squares-discriminant analysis (OPLS-DA), we found that the HF group separated completely from the NC group (R2Y (cum) = 0.988, Q2 (cum) = 0.953), demonstrating that metabolic disturbances exist in these two groups. The permutation test indicated that the analytical platform exhibited excellent stability and repeatability (R2 = 0.708, Q2 = -0.338) and can be utilized in subsequent metabolomics research.

Based on the differential screening strategy, 154 discriminating metabolites were found in the HF group compared with the NC group. In the HF group, 79 metabolites were significantly increased, such as prostaglandin D1, prostaglandin F2, tetradecanedioic acid, elaidic acid, stearic acid, deoxycholic acid, 3a,7a-dihydroxycholanoic acid, obeticholic acid, and varanic acid. Seventy-five metabolites were significantly decreased in the HF group, such as β-muricholic acid, linoelaidic acid, linoleic acid, C8-HSL, citric acid, docosapentaenoic acid, eicosapentaenoic acid, and N-acetylglycine.

Next, we performed KEGG pathway analysis to understand how those metabolites altered the metabolism pathways. Results showed that (1) the biosynthesis of unsaturated fatty acids, (2) linoleic acid metabolism, (3) Fc gamma R-mediated phagocytosis, and (4) the phospholipase D signaling pathway.

Similarly, an OPLSDA was performed to separate the IF group and the HF group. We found that the HF group separated completely from the NC group (R2Y (cum) = 0.967, Q2 (cum) = 0.435), demonstrating that metabolic disturbances exist in these two groups. The permutation test indicated that the analytical platform exhibited excellent stability and repeatability (R2 = 0.935, Q2 = -0.226) and can be utilized in subsequent metabolomics research.

There were 51 discriminating metabolites in the IF group compared with the HF group. In the IF group, 37 metabolites were significantly increased, such as capryloylglycine, C8-HSL, 3-oxo-C12-HSL, 3-hydroxybutyric acid, 3-tert-butyladipic acid, obeticholic acid, N-acetylglycine, and dihomogamma-linolenic acid. 14 metabolites were significantly decreased in the IF group, such as stearic acid, docosapentaenoic acid, and α-D-glucose.

KEGG pathway enrichment analysis indicated that these differentiall metabolites were related to (1) biosynthesis of unsaturated fatty acids, (2) linoleic acid metabolism, (3) synthesis and degradation of ketone bodies, and (4) the GnRH signaling pathway (Fig. 5.).

3.5 Correlation analysis between gut microbiota and serum metabolites.

To identify specific bacteria related to glucose metabolism, we examined Spearman’s correlations between genus level microbiota and other glucose metabolism-related indexes, such as body weight, blood glucose, fat mass, liver weight and HOMA-IR, in the NC and HF groups (Fig. 6.). Clostridia_UCG-014 and Dubosiella were negatively associated with body weight, blood glucose, fat mass and HOMA-IR, whereas Muribaculaceae was negatively linked to fat mass. Bilophila was positively correlated with body weight, blood glucose, liver weight and fat mass. Colidextribacter, Lachnoclostridium, and Tuzzerella were positively correlated with body weight, fat mass and HOMA-IR. Escherichia-Shigella was positively correlated with body weight and fat mass.

Furthermore, correlations between the abovementioned specific bacteria and the differential metabolites of the NC group and HF group were analyzed. 7(S), 17(S)-Dihydroxy-8(E), 10(Z), 13(Z), 15(E), 19(Z)-docosapentaenoic acid, (±)18-HEPE, 1-Palmitoyl-2-hydroxy-sn-glycero-3-PE, docosahexaenoic acid, prostaglandin A1 ethyl ester, β-muricholic acid, N-acetylglycine, 3-tert-butyladipic acid, etc., were positively correlated with Muribaculaceae and Dubosiella and negatively correlated with Bilophila and Lachnoclostridium. 3a,7a-Dihydroxycholanoic acid, brassylic acid, obeticholic acid, deoxycholic acid, etc., were positively correlated with Bilophila and negatively correlated with Muribaculaceae.

Similarly, we analyzed Spearman’s correlations between genus level microbiota and other glucose metabolism-related indexes of the HF group and IF group. We found that Muribaculaceae was negatively associated with body weight and fat mass, and Bacteroides and Parabacteroides were negatively associated with body weight, fat mass, liver weight and HOMA-IR. Escherichia-Shigella was positively correlated with fat mass. Colidextribacter, Bilophila, Intestinimonas, Oscillibacter, and Incertae_Sedis were positively correlated with fat mass, liver weight and HOMA-IR.

Then, the correlation between the abovementioned specific bacteria and the differential metabolites of the HF group and IF group was analyzed. N-Acetylglycine, obeticholic acid, thromboxane B2, and cetylbenzoate were positively correlated with Parabacteroides and negatively associated with Bilophila. Stearic acid and α-D-glucose were positively correlated with Bilophila and were negatively associated with Parabacteroides.

As we all know, T2D has become a global epidemic, and represents over 90% of cases of diabetes mellitus and is characterized by hyperglycaemia caused by insulin resistance. In recent years, studies have reported that intermittent fasting can enhance antioxidant capacity, inhibit inflammatory responses, and it has beneficial effects on glucose metabolism and health without relying on weight loss^[13]. However, there are no detailed information as to whether IF programs, would result in diabetes prevention, which should be studied further. In this study, we conducted intermittent fasting on high-fat diet mice during middle age, and found IF can reduce body weight, fat mass and liver mass, and we also found insulin resistance, improved glucose tolerance in elder mice fed a high fat diet. A previous study has shown that a decrease in insulin sensitivity and glucose regulation abnormalities may occur at least 10–20 years before the diagnosis of diabetes^[1]. Therefore, late-onset diabetes may originate from insulin resistance occurring during middle age. Insulin resistance is the most important pathophysiological mechanism for the development of type 2 diabetes, and aging and obesity both promote insulin resistance^[14]. Chronic inflammation, oxidative stress, lipid overload and other factors all contribute to insulin resistance and the development of diabetes^[15]. Our findings has shown that IF can reduce fat accumulation and alleviate the damage caused by high-fat diets on glucose metabolism in middle-age mice. Therefore, it suggest that IF may curb the development of insulin resistance and prevent late-onset diabetes.

Numerous studies indicates a close correlation between gut microbiota and energy metabolism^[6] as well as the development of diabetes^[16]. In a 2012 genome-wide association study, Qin et al. sequenced the gut microbiomes of 345 Chinese patients with T2D and found that these patients exhibited moderate dysbiosis, decreased abundance of butyrate-producing bacteria, and increased opportunistic pathogens compared to nondiabetic individuals^[17]. In prediabetic individuals, after excluding confounding factors such as medication use, both the abundance and functional potential for butyrate-producing bacteria were reduced^[18]. Based on these changes in gut microbiota among diabetic populations, two hypotheses can be proposed: first, alterations in gut microbiota characteristics may directly affect glucose metabolism; second, disturbances in glucose metabolism may impact the composition of gut microbiota. Furthermore, fecal transplantation studies have shown that transplanting gut microbiota from lean donors to obese patients with metabolic syndrome increases insulin sensitivity^[19]. Sanna et al. collected a large amount of data and used bidirectional Mendelian randomization analysis to evaluate causality. They found no differences in gut microbiota characteristics between groups when participants were grouped based on gene variations related to glucose metabolism parameters^[20]. These results suggest that gut microbiota characteristics influence the risk of developing T2D as oppose to the inverse (disturbances in glucose metabolism influence gut microbiota composition). Gut microbiota may induce secretion of glucagon-like peptide-1 (GLP-1) and peptide YY (PYY), which improve glucose metabolism and insulin secretion by binding to G protein-coupled receptors (GPCR41) and GPCR43 expressed on enteroendocrine L cells through their product short-chain fatty acids (SCFAs)^[21]. In this study, the OGTT results of the IF group showed no significant difference compared to the NC group, while the IPGTT results indicated reduced glucose tolerance compared to the NC group. Since IPGTT does not involve gastrointestinal absorption of glucose metabolism while OGTT requires it, these results suggest that gut microbiota and/or gut hormones may play an important role in glucose metabolism.

In this study, it was observed that the F/B ratio exhibited an increase in the HF group. Conversely, the application of IF resulted in a decrease in the F/B ratio compared to the HF group. Furthermore, an increase in the abundance of Bilophila, Colidextribacter, and Escherichia-Shigella was observed in the HF group, while Muribaculaceae and Lactobacillus exhibited a decrease. Similarly, the IF group displayed an increase in the abundance of Muribaculaceae, Bacteroides, and Parabacteroides, accompanied by a decrease in Bilophila and Colidextribacter. Moreover, our findings revealed a significant inverse relationship between the prevalence of Muribaculaceae, Clostridia_UCG-014, Dubosiella, Bacteroides, Parabacteroides, and glucose metabolism-associated indicators. Conversely, a notable positive correlation was observed between the prevalence of Bilophila, Lachnoclostridium, Escherichia-Shigella, and the aforementioned indicators. Previous research^{[22, 23]} has demonstrated a positive correlation between a higher F/B ratio in the gastrointestinal tract and obesity, a significant risk factor for the emergence of insulin resistance. The Muribaculaceae bacteria, classified within the Bacteroidales order, possess the ability to degrade intricate plant polysaccharides, generate short-chain fatty acids (SCFAs), and encode vitamin B ^[24]. Furthermore, numerous studies have identified a positive relationship between increased Muribaculaceae abundance and enhanced obesity and glucose metabolism.^[25–27]. Parabacteroides is a gram-negative bacterium that can decompose complex polysaccharides to produce SCFAs such as acetate, propionic acid and butyric acid^[28]. A research conducted in Mexico observed a reduction in Parabacteroides levels among individuals with obesity and metabolic syndrome^[29]. In the current investigation, it is suggested that Dubosiella, a bacterium known for its production of butyric acid, may have stimulated incretin secretion and enhanced glucose metabolism^[30]. Previous studies have demonstrated that can induce inflammation, resulting in impaired intestinal barrier function, abnormal bile acid metabolism, disrupted glucose metabolism, and liver steatosis^[31]. Lachnoclostridium is reported to be a characteristic bacterium in obese individuals with polycystic ovary syndrome^[32]. Escherichia-Shigella is a common opportunistic pathogen that has been found to increase significantly in the T2D population^[33]. Our findings are in alignment with these reports.

Several studies have delved into the impact of intermittent fasting on the composition of gut microbiota. Cignarella et al. have reported that intermittent fasting exhibits the potential to augment the prevalence of Bacteroides and lactic acid bacteria.^[34]; Li et al conducted a study wherein they observed a direct impact of intermittent fasting on the gut microbiota of mice. The results indicated an increase in the abundance of Firmicutes, ultimately leading to an improvement in glucose tolerance. ^[35]. We analyzed the 16S rRNA gene of gut samples from middle-aged mice on different diets: normal, high-fat, and intermittent fasting. No significant increase in microbial diversity was found in the IF group. However, there was a significant change in flora structure, with an increase in Bacteroides abundance and a decrease in Phaeophyta abundance. There was also a trend towards an increase in Lactobacillus, which aligns with Cignarella et al.'s study but not with Li et al.'s results. These differences could be attributed to models, individual variations, and sample collection.

Further analyses were conducted on the metabolite data, revealing that the differential metabolites primarily pertained to the synthesis of unsaturated fatty acids and the metabolism of linoleic acid. Notably, a significant interaction between fatty acids and the endocrine system was observed. Hormonal regulation significantly impacts both the metabolic processes of fatty acids and the composition of tissue lipids. Multiple pivotal stages involved in fatty acid synthesis, encompassing desaturation, oxidation, conversion to linoleic acid, as well as uptake and subsequent entry into tissues, are under the control of one or more hormones.^[36]. Saturated fatty acids have traditionally been regarded as potential contributors to the development of insulin resistance and diabetes^[37]. Our investigation revealed an elevation in stearic acid levels within HF group. Stearic acid, classified as a long-chain saturated fatty acid, has been consistently associated with an augmented risk of T2DM in extensive cohort studies conducted among European and Asian populations^{[38, 39]}. The activation of Toll-like receptors by saturated fatty acids results in the activation of the nuclear factor κB (NF-κB) pathway, which subsequently induces the transcription of proinflammatory factors, including tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6). ^[40]. Our study found that IF greatly decreases stearic acid levels, supporting this finding.

This study discovered that the intake of IF resulted in a reduction in the levels of arachidonic acid, an omega-6 fatty acid, as well as docosahexaenoic acid (DHA), a twenty-two carbon six unsaturated fatty acid. Arachidonic acid possesses the ability to generate various compounds such as prostaglandins and thromboxanes through metabolic processes, thereby facilitating inflammation and contributing to the development of thrombosis and atherosclerosis.^{[41, 42]}. Docosahexaenoic acid (DHA) has the ability to interact with G protein-coupled receptors, impede the NF-κB pathway via downstream signaling pathways, and potentially mitigate hypoxia in adipocytes by upregulating vascular endothelial growth factor A^[40]. The rise in arachidonic acid in mice on a high-fat diet worsens inflammation, while the increase in DHA helps relieve it. IF's reduction of DHA may somewhat improve inflammation in these mice.

Numerous studies have demonstrated that the microbial fermentation of dietary fiber within the gut generates a substantial quantity of short-chain fatty acids (SCFAs), which play a pivotal role as potential mediators in the intricate interplay between microbiota and host cells.^[43]. Li et al. found that intermittent fasting can increase the levels of acetate and lactate^[44]. In this study, we also found that intermittent fasting upregulated the levels of 2,4-dihydroxyheptadec-16-ynyl acetate, 3-hydroxybutyric acid, and 4-ethyl-6-[(3E)-2-ethyl-3-hexen-1-yl]-6-methyl-1,2-dioxan-3-yl acetic acid, which is consistent with previous research. SCFAs play a key role in shaping and regulating immune systems locally and peripherally by triggering inflammatory pathways in response to host metabolism. Consequently, SCFAs assume a pivotal role in governing the functions of various systems, such as the gastrointestinal, nervous, endocrine, and hematological systems, thereby emerging as pivotal factors in the regulation of metabolic disorders and immunity.^[45]. Recent studies have suggested that SCFAs, particularly acetate, can impact adipose tissue metabolism by inhibiting lipolysis in adipocytes. This is achieved through a reduction in hormone-sensitive lipase phosphorylation mediated by GPR^{[46, 47]} .Obese patients typically experience chronic low-grade inflammation, primarily in their adipose tissue, as a characteristic response to insulin resistance^[48]. Co-culturing 3T3-L1 adipocytes with RAW264.7 macrophages and butyrate for 24 hours reduces TNF, monocyte chemoattractant protein 1, and IL-6 concentrations^[49]. Similarly, treating human adipose tissue explants with propionate decreases secretion of pro-inflammatory cytokines IL-4, IL-10, TNF, and several chemokines^[50], suggesting that SCFAs have anti-inflammatory effects on adipose tissue. Acetate affects metabolic pathways through FFAR2, while butyrate and propionate activate PPARγ and regulate the Angptl4 gene in intestinal cells. FFAR2 regulates lipid accumulation and inflammation in adipocytes, while PYY and GLP-1 regulate appetite. NDCs regulate glucose homeostasis, intestinal integrity, and hormones through a gut microbiota-dependent pathway involving GPCRs, NF-κB, and AMPK^[51].

We analyzed the relationship between metabolites and gut bacteria in the HF and IF groups. We found that N-acetylglycine is positively correlated with beneficial bacteria like Muribaculaceae and Parabacteroides. N-acetylglycine, a derivative of glycine, has been shown to reduce weight gain caused by smoking. Additionally, mice given N-acetylglycine without smoke exposure had less weight gain from a high-fat diet. Further validation is needed to understand the mechanisms behind the improved glucose tolerance, increased plasma albumin levels, decreased plasma triglycerides, and improved alanine aminotransferase activity associated with this supplementation^[52].

In conclusion, the findings of our research indicate that intermittent fasting in middle-aged mice fed a high-fat diet results in decreased weight gain, enhanced insulin sensitivity, and improved glucose tolerance. It also changes the gut microbiota and metabolites, potentially improving glucose metabolism. This study offers new insights into IF's effectiveness in preventing and treating age-related diabetes.

Our study did not include verification experiments with microbiota transplantation, but the beneficial metabolites identified through non-targeted metabolomics screening for glucose metabolism can be validated and their mechanisms can be investigated further, potentially leading to the discovery of new targets for improving glucose metabolism disorders.

Accession information for sequencing

All raw sequences from 16S rRNA gene-based analysis have been deposited at the public Genome Sequence Archive (GSA) server in China National Center for Bioinformation (https://www.cncb.ac.cn/?lang=en) under accession number subCRA021906.

Declaration of interest

The authors declare that there is no conflict of interest.

Author contribution statement

Sufang Chen, Zhi Sun and Ziru Li contributed to the literature search and study design. Ziru Li, Bingbing Yin, Duofei Wang and Sufang Chen carried out the experiments and acquired the raw data. Ziru Li, Huoxiang Zhou and Jiacun Wei analysed and visualized all data, and drafted the manuscript. Sufang Chen, Jiacun Wei and Huoxiang Zhou revised the manuscript. Sufang Chen and Zhi Sun supervised the project. All the authors have read and approved to the published version of the manuscript.

Author Contribution

Acknowledgements

This work was supported by the Key Research Project of the Education Department of Henan Province (20A320030), the Key Scientific and Technological Project of Henan Province (232102311235, 222102310348), and the Henan Provincial Medical Science and

Sagesaka H, Sato Y, Someya Y, et al. Type 2 Diabetes: When Does It Start?[J]. J Endocr Soc. 2018;2(5):476–84.
Hamman RF, Wing RR, Edelstein SL, et al. Effect of weight loss with lifestyle intervention on risk of diabetes[J]. Diabetes Care. 2006;29(9):2102–7.
Stratton MT, Tinsley GM, Alesi MG et al. Four Weeks of Time-Restricted Feeding Combined with Resistance Training Does Not Differentially Influence Measures of Body Composition, Muscle Performance, Resting Energy Expenditure, and Blood Biomarkers[J]. Nutrients, 2020, 12(4).
De Cabo R, Mattson MP. Effects of Intermittent Fasting on Health, Aging, and Disease[J]. N Engl J Med. 2019;381(26):2541–51.
Hu D, Xie Z, Ye Y, et al. The beneficial effects of intermittent fasting: an update on mechanism, and the role of circadian rhythm and gut microbiota[J]. Hepatobiliary Surg Nutr. 2020;9(5):597–602.
Canfora EE, Meex RCR, Venema K, et al. Gut microbial metabolites in obesity, NAFLD and T2DM[J]. Nat Rev Endocrinol. 2019;15(5):261–73.
Matthews DR, Hosker JP, Rudenski AS, et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man[J]. Diabetologia. 1985;28(7):412–9.
Reyon D, Tsai SQ, Khayter C, et al. FLASH assembly of TALENs for high-throughput genome editing[J]. Nat Biotechnol. 2012;30(5):460–5.
Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data[J]. Nat Methods. 2010;7(5):335–6.
Liu L, Zhao Z, Hou X, et al. Effect of sphincter of Oddi dysfunction on the abundance of biliary microbiota (biliary microecology) in patients with common bile duct stones[J]. Front Cell Infect Microbiol. 2022;12:1001441.
Segata N, Izard J, Waldron L, et al. Metagenomic biomarker discovery and explanation[J]. Genome Biol. 2011;12(6):R60.
Yin P, Xu G. Current state-of-the-art of nontargeted metabolomics based on liquid chromatography-mass spectrometry with special emphasis in clinical applications[J]. J Chromatogr A. 2014;1374:1–13.
Joaquim L, Faria A, Loureiro H et al. Benefits, mechanisms, and risks of intermittent fasting in metabolic syndrome and type 2 diabetes[J]. J Physiol Biochem, 2022.
Lee PG, Halter JB. The Pathophysiology of Hyperglycemia in Older Adults: Clinical Considerations[J]. Diabetes Care. 2017;40(4):444–52.
Roden M, Shulman GI. The integrative biology of type 2 diabetes[J]. Nature. 2019;576(7785):51–60.
Lau WL, Tran T, Rhee CM, et al. Diabetes and the Gut Microbiome[J]. Semin Nephrol. 2021;41(2):104–13.
Qin J, Li Y, Cai Z, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes[J]. Nature. 2012;490(7418):55–60.
Zhong H, Ren H, Lu Y, et al. Distinct gut metagenomics and metaproteomics signatures in prediabetics and treatment-naive type 2 diabetics[J]. EBioMedicine. 2019;47:373–83.
Kootte RS, Levin E, Salojarvi J, et al. Improvement of Insulin Sensitivity after Lean Donor Feces in Metabolic Syndrome Is Driven by Baseline Intestinal Microbiota Composition[J]. Cell Metab. 2017;26(4):611–9. e6.
Sanna S, Van Zuydam NR, Mahajan A, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases[J]. Nat Genet. 2019;51(4):600–5.
Fan Y, Pedersen O. Gut microbiota in human metabolic health and disease[J]. Nat Rev Microbiol. 2021;19(1):55–71.
Turnbaugh PJ, Ley RE, Mahowald MA, et al. An obesity-associated gut microbiome with increased capacity for energy harvest[J]. Nature. 2006;444(7122):1027–31.
Abenavoli L, Scarpellini E, Colica C et al. Gut Microbiota and Obesity: A Role for Probiotics[J]. Nutrients, 2019, 11(11).
Ormerod KL, Wood DL, Lachner N, et al. Genomic characterization of the uncultured Bacteroidales family S24-7 inhabiting the guts of homeothermic animals[J]. Microbiome. 2016;4(1):36.
Zhao Q, Hou D, Fu Y et al. Adzuki Bean Alleviates Obesity and Insulin Resistance Induced by a High-Fat Diet and Modulates Gut Microbiota in Mice[J]. Nutrients, 2021, 13(9).
Ye J, Zhao Y, Chen X, et al. Pu-erh tea ameliorates obesity and modulates gut microbiota in high fat diet fed mice[J]. Food Res Int. 2021;144:110360.
Fu S, Dang Y, Xu H et al. Aloe Vera-Fermented Beverage Ameliorates Obesity and Gut Dysbiosis in High-Fat-Diet Mice[J]. Foods, 2022, 11(22).
Cui Y, Zhang L, Wang X et al. Roles of intestinal Parabacteroides in human health and diseases[J]. FEMS Microbiol Lett, 2022, 369(1).
Gallardo-Becerra L, Cornejo-Granados F, García-López R, et al. Metatranscriptomic analysis to define the Secrebiome, and 16S rRNA profiling of the gut microbiome in obesity and metabolic syndrome of Mexican children[J]. Microb Cell Fact. 2020;19(1):61.
Huang ZR, Zhao LY, Zhu FR et al. Anti-Diabetic Effects of Ethanol Extract from Sanghuangporous vaninii in High-Fat/Sucrose Diet and Streptozotocin-Induced Diabetic Mice by Modulating Gut Microbiota[J]. Foods, 2022, 11(7).
Natividad JM, Lamas B, Pham HP, et al. Bilophila wadsworthia aggravates high fat diet induced metabolic dysfunctions in mice[J]. Nat Commun. 2018;9(1):2802.
Zhou L, Ni Z, Yu J, et al. Correlation Between Fecal Metabolomics and Gut Microbiota in Obesity and Polycystic Ovary Syndrome[J]. Front Endocrinol (Lausanne). 2020;11:628.
Thingholm LB, Rühlemann MC, Koch M, et al. Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition[J]. Cell Host Microbe. 2019;26(2):252–e26410.
Cignarella F, Cantoni C, Ghezzi L, et al. Intermittent Fasting Confers Protection in CNS Autoimmunity by Altering the Gut Microbiota[J]. Cell Metab. 2018;27(6):1222–35. e6.
Li G, Xie C, Lu S, et al. Intermittent Fasting Promotes White Adipose Browning and Decreases Obesity by Shaping the Gut Microbiota[J]. Cell Metab. 2017;26(4):672–e6854.
Bhathena SJ. Relationship between fatty acids and the endocrine system[J]. Biofactors, 2000, 13(1–4): 35 – 9.
Eyre H, Kahn R, Robertson RM. Preventing cancer, cardiovascular disease, and diabetes: a common agenda for the American Cancer Society, the American Diabetes Association, and the American Heart Association[J]. Diabetes Care. 2004;27(7):1812–24.
Forouhi NG, Koulman A, Sharp SJ, et al. Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study[J]. Lancet Diabetes Endocrinol. 2014;2(10):810–8.
Lin JS, Dong HL, Chen GD et al. Erythrocyte Saturated Fatty Acids and Incident Type 2 Diabetes in Chinese Men and Women: A Prospective Cohort Study[J]. Nutrients, 2018, 10(10).
Kalupahana NS, Goonapienuwala BL, Moustaid-Moussa N. Omega-3 Fatty Acids and Adipose Tissue: Inflammation and Browning[J]. Annu Rev Nutr. 2020;40:25–49.
Simopoulos AP. The importance of the ratio of omega-6/omega-3 essential fatty acids[J]. Biomed Pharmacother. 2002;56(8):365–79.
Innes JK, Calder PC. Omega-6 fatty acids and inflammation[J]. Prostaglandins Leukot Essent Fat Acids. 2018;132:41–8.
Wong JM, De Souza R, Kendall CW, et al. Colonic health: fermentation and short chain fatty acids[J]. J Clin Gastroenterol. 2006;40(3):235–43.
Li G, Xie C, Lu S, et al. Intermittent Fasting Promotes White Adipose Browning and Decreases Obesity by Shaping the Gut Microbiota[J]. Cell Metabol. 2017;26(4):672–e6854.
Machiels K, Joossens M, Sabino J, et al. A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis[J]. Gut. 2014;63(8):1275–83.
Aberdein N, Schweizer M, Ball D. Sodium acetate decreases phosphorylation of hormone sensitive lipase in isoproterenol-stimulated 3T3-L1 mature adipocytes[J]. Adipocyte. 2014;3(2):121–5.
Jocken JW, E, González Hernández MA, Hoebers NTH, et al. Short-Chain Fatty Acids Differentially Affect Intracellular Lipolysis in a Human White Adipocyte Model[J]. Front Endocrinol (Lausanne). 2017;8:372.
Stinkens R, Goossens GH, Jocken JW, et al. Targeting fatty acid metabolism to improve glucose metabolism[J]. Obes Rev. 2015;16(9):715–57.
Ohira H, Fujioka Y, Katagiri C, et al. Butyrate attenuates inflammation and lipolysis generated by the interaction of adipocytes and macrophages[J]. J Atheroscler Thromb. 2013;20(5):425–42.
Al-Lahham S, Roelofsen H, Rezaee F, et al. Propionic acid affects immune status and metabolism in adipose tissue from overweight subjects[J]. Eur J Clin Invest. 2012;42(4):357–64.
Kumar J, Rani K, Datt C. Molecular link between dietary fibre, gut microbiota and health[J]. Mol Biol Rep. 2020;47(8):6229–37.
Fluhr L, Mor U, Kolodziejczyk AA, et al. Gut microbiota modulates weight gain in mice after discontinued smoke exposure[J]. Nature. 2021;600(7890):713–9.

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Intermittent Fasting Improves Glucose Metabolism Disorders Induced by High-fat Diet through Modulation of Gut Microbiota and Serum Metabolism in Middle-aged Mice

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials and methods

2.1 Animals and experimental design

2.2 Histological analysis

2.3 Intraperitoneal glucose tolerance test (IPGTT)

2.4 Oral glucose tolerance test (OGTT)

2.5 Intraperitoneal insulin tolerance test (IPITT)

2.6 Measurement of fasting plasma glucose, insulin, and HOMA-IR

2.7 Gut microbiota analysis

2.8 Serum metabolomic analysis

Statistical analysis

Results

3.3 IF alleviated gut microbiota dysbiosis in HFD-fed mice

3.4 IF altered serum metabolites in HFD-fed mice

Discussion

Declarations

Declaration of interest

Author contribution statement

Author Contribution

Acknowledgements

References

Additional Declarations

Supplementary Files

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