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 |
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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.