Next Article in Journal
Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Photos for Dietary Assessment
Next Article in Special Issue
Moving beyond Weight: A Narrative Review of the Dietary and Lifestyle Management for Reducing Cardiometabolic Risk in Polycystic Ovary Syndrome (PCOS)
Previous Article in Journal
Effect of a Diet-Induced Obesity on the Progeny Response in a Murine Model
Previous Article in Special Issue
Thermoneutral Housing Enables Studies of Vertical Transmission of Obesogenic Diet-Driven Metabolic Diseases
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 15
Issue 23
10.3390/nu15234971
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Combating Obesity: Harnessing the Synergy of Postbiotics and Prebiotics for Enhanced Lipid Excretion and Microbiota Regulation

by
Yueming Zhao
1,2,†,
Yaping Zheng
1,†,
Kui Xie
2,
Yanmei Hou
2,
Qingjing Liu
2,
Yujun Jiang
1,
Yu Zhang
1,* and
Chaoxin Man
1,*
1
Key Laboratory of Dairy Science, Ministry of Education, College of Food Science and Engineering, Northeast Agricultural University, Harbin 150030, China
2
Ausnutria Dairy (China) Co., Ltd., Changsha 410000, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed to the work equally.
Nutrients 2023, 15(23), 4971; https://doi.org/10.3390/nu15234971
Submission received: 30 October 2023 / Revised: 28 November 2023 / Accepted: 28 November 2023 / Published: 30 November 2023
(This article belongs to the Special Issue Specialized Diet, Obesity and Associated Metabolic Diseases (Volume II))
Browse Figures
Review Reports Versions Notes

Abstract

:
Obesity is a chronic metabolic disease that can be induced by a high-fat diet (HFD) and predisposes to a variety of complications. In recent years, various bioactive substances, such as probiotics, prebiotics, and postbiotics, have been widely discussed because of their good anti-lipid and anti-inflammatory activities. In this paper, soybean protein isolate was used as a substrate to prepare the postbiotic. Compound prebiotics (galactose oligosaccharides, fructose oligosaccharides, and lactitol) preparation Aunulife Postbiotics and Prebiotics Composition (AYS) is the research object. Weight loss and bowel movements in mice induced by a high-fat diet were studied. Moreover, qualitative and quantitative analyses of small-molecule metabolites in AYS were performed to identify the functional molecules in AYS. After 12 weeks of feeding, the weight gain of mice that were fed with high-dose AYS (group H) and low-dose AYS (group L) from 4 to 12 weeks was 6.72 g and 5.25 g (p < 0.05), both of which were significantly lower than that of the high-fat diet (group DM, control group) group (7.73 g) (p < 0.05). Serum biochemical analysis showed that TC, TG, and LDL-C levels were significantly lower in mice from the H and L groups (p < 0.05). In addition, the fecal lipid content of mice in the L group reached 5.89%, which was significantly higher than that of the DM group at 4.02% (p < 0.05). The study showed that AYS changed the structure of the intestinal microbiota in mice on a high-fat diet, resulting in a decrease in the relative abundance of Firmicutes and Muribaculaceae and an increase in the relative abundance of Bacteroidetes, Verrucomicrobia, and Lactobacillus. The metabolomics study results of AYS showed that carboxylic acids and derivatives, and organonitrogen compounds accounted for 51.51% of the AYS metabolites, among which pantothenate, stachyose, betaine, and citrate had the effect of preventing obesity in mice. In conclusion, the administration of prebiotics and postbiotic-rich AYS reduces weight gain and increases fecal lipid defecation in obese mice, potentially by regulating the intestinal microbiota of mice on a high-fat diet.

1. Introduction

Obesity, one of the major global health problems, causes an increased risk of diabetes, insulin resistance, cardiovascular disease, non-alcoholic fatty liver disease [1,2,3,4], and a range of chronic diseases. The prevalence of obesity is increasing, and obesity rates among children and adolescents have reached a historically high level. Although obesity can be controlled by increasing exercise and restricting diets, appropriate medication is needed for some patients. Studies have shown that taking phentermine, liraglutide, or orlistat for at least 12 months results in an overall weight loss of 2.9% to 6.8%. However, overdoses of these chemicals cause detrimental effects on the body [5]. Therefore, it is crucial to seek safe and effective methods of obesity prevention and treatment to maintain the metabolic balance of the body and to prevent and treat the occurrence of obesity symptoms.
In recent years, postbiotics have emerged as an effective tool for the treatment of obesity. Defined as “a preparation of inanimate microorganisms and/or their components that is beneficial to the host” by the International Scientific Association of Probiotics and Prebiotics (ISAPP) [6], postbiotics can be obtained by ultrasound, heating, solvent extraction, ultraviolet light, enzyme treatment, etc. It has probiotic functions such as immunomodulation, infection prevention, antioxidant, anti-inflammatory, blood pressure lowering, blood lipid lowering, and weight loss [7,8,9,10,11,12,13]. It was found that the gut microbiota’s release of the polyunsaturated fatty acid metabolite 10-hydroxy-cis-12-octadecenoic acid (HYA) could improve host resistance to high-fat diet-induced obesity [14]. In addition, Enterococcus faecalis and its metabolite, myristoleic acid, may reduce obesity by activating brown adipose tissue and forming beige fat. Therefore, postbiotics have the potential to regulate metabolism to improve obesity symptoms and are expected to be an effective alternative to other drug treatments to improve obesity.
Prebiotics also improve the energy and metabolic status of the body by increasing the abundance of beneficial microbiota in the intestine and having a hindering effect on the digestion and absorption of food [15]. The function and mechanism of well-known prebiotics such as galactose oligosaccharides, fructose oligosaccharides, human milk oligosaccharides (HMO), trans-oligo galactose (TOS), inulin, etc. are available elsewhere [16]. Among them, FOS is a class of naturally occurring oligosaccharides that are highly fermentable by the intestinal microbiota in the colon, thus promoting the growth of beneficial bacteria [17]. Studies have shown that oligofructose has a positive effect on blood lipids and that FOS can inhibit the accumulation of body fat induced by a high-fat diet and the absorption of dietary fat in the intestine [18]. In an animal experiment, a high-fat diet-induced mouse model was fed FOS for 6 weeks, which significantly reduced the Lees index and epididymal fat index in obese mice and restored the intestinal microbiota of the mice to a degree close to that of the normal group [19]. Another important prebiotic is GOS, a natural oligosaccharide that also has a positive effect on blood sugar and lipids in adults. GOS can improve host lipid homeostasis by promoting cholesterol catabolism and inhibiting obesity by accelerating the browning of white adipocytes and the thermogenesis of brown adipocytes [20]. Therefore, prebiotic supplementation can manage obesity-related comorbidities.
Even though the benefits of applying prebiotics or postbiotics are being investigated, there are few studies investigating the improvement of obesity in mice with postbiotic and prebiotic combinations and the underpinning mechanisms. In this study, the effect of a combination of postbiotic and prebiotic (AYS) on weight loss and defecation in high-fat diet-induced mice was investigated, and the effect of intestinal microbiota composition on obese mice was further analyzed.

2. Materials and Methods

2.1. Materials

Aunulife Postbiotics and Prebiotics Composition (AYS) was provided by Aunulife Biotechnology Co., Ltd. and stored at room temperature (Aunulife Biotechnology Co., Ltd., Changsha, China). Kits for TC (total cholesterol), TG (triglyceride), LDL-C (low-density lipid cholesterol), and HDL-C (high-density lipid cholesterol) were purchased from Shanghai Enzyme Link Biotechnology Co., Ltd. (Shanghai, China). Other reagents were obtained from Sino Pharma Co., Ltd. (Beijing, China).

2.2. AYS Non-Targeted Metabolic Assay

2.2.1. Sample Pretreatment

A total of 80 g of AYS was dissolved in 200 μL of aqueous MP homogenate, vortexed for 60 s, and then added to 800 μL of methanol-acetonitrile solution. The vortex was continued for 60 s, and two repeated low-temperature sonications were performed for 30 min. The proteins were precipitated at −20 °C for 1 h, followed by centrifugation at 14,000 rpm at 4 °C for 20 min. The supernatant was freeze-dried and the sample was stored at −80 °C.

2.2.2. Chromatography-Mass Spectrometry Analysis

The metabolic composition and metabolite structure of AYS were determined. The samples (AYS) were separated on an Agilent 1290 Infinity LC ultra-high performance liquid chromatography system (UHPLC) HILIC column. Column temperature: 25 °C; flow rate: 0.3 mL/min; mobile phase composition A: water + 25 mM ammonium acetate + 25 mM ammonia; B: acetonitrile. The samples were placed in a 4 °C autosampler during the entire analysis. To avoid the effects caused by fluctuations in the instrument detection signal, the samples were analyzed continuously in random order. QC samples are inserted in the sample queue to monitor and evaluate the stability of the system and the reliability of the experimental data. The samples were separated by UHPLC and analyzed by mass spectrometry using an Agilent 6550 mass spectrometer. The ESI source conditions are as follows: Gas Tem: 250 °C, Drying Gas: 16 L/min, Nebulizer: 20 psig, Sheath Gas Tem: 400 °C, Sheath Gas Flow: 12 L/min; Vcap: 3000 V; Nozzle Voltage: 0 V. Fragment: 175 V, Mass Range: 50–1200, Acquisition Rate: 4 Hz, Cycle Time: 250 ms. After the samples were assayed, the metabolites were identified using an AB Triple TOF 6600 mass spectrometer, and primary and secondary spectra of QC samples were acquired. The data obtained were used for the structural identification of the metabolites using the self-constructed MetDDA and LipDDA methods, respectively.

2.3. Animal Experimental Design

Forty male C57BL/6J mice (6 weeks, 17 ± 2 g) were provided by the Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). The animal experiment was approved by the Laboratory Animal Welfare and Ethics Committee of Northeast Agricultural University (#NEAUEC-2023-04-14) and was conducted according to the Guide for the Care and Use of Laboratory Animals (China). Mice were housed in 20 cages (2 animals per cage, n = 8 per group) and kept at 23 ± 2 °C and 55 ± 5% relative humidity, maintaining a 12-h light-dark cycle. In order to prevent the two mice in each cage from chewing on each other’s feces and competing for food, we used partitions to divide a cage into two areas so that each mouse could be fed separately. Mice were acclimatized for one week, during which all mice were fed a chow diet with free access to water, ensuring that mice were fed the same initial value of feed and drinking water (purified water). To evaluate the effect of this product on the ability of mice to lower lipid and fecal lipid content after one week of acclimatization feeding, the animal experiments were randomly divided into five experimental groups (n = 8 per group): the N group, the DM group, the H group (high-dose AYS), the L group (low-dose AYS), and the C group. Groups H, L, and C were gavaged daily for eight weeks, and the specific gavage methods and doses are shown in Table 1. High-fat diet: 38% normal diet, 20% lard, 10% soybean oil, 10% sucrose, 10% maltodextrin, 10% yolk powder, 1.8% cholesterol, 0.2% bile salt; Energy: 4.7 kcal/g, protein: 16%, fat: 42%, carbohydrate: 48%, and remaining nutrients: 4%. The chow was purchased from Shenyang Maohua Biotechnology Co., Ltd., (Shenyang, China) and the high-fat feed was purchased from Beijing Keao Cooperative Feed Co. (Beijing, China). Body weight measurements were performed weekly, and changes in mice were recorded. The mice had free access to chow and water throughout the feeding period.
At the end of the feed at week 12, mice were fasted overnight without water for 12 h. Eye blood was collected from mice by centrifugation at 3000 rpm/min for 20 min, and the supernatant was collected and stored in a −80 °C refrigerator. The necks were broken and executed, and the small intestine and other tissues were quickly frozen and stored in a −80 °C freezer. Fresh feces were collected weekly from the mice for intestinal microbiota analysis.

2.4. Changes in Body Weight and Fecal Quality of Mice

The body weight of the mice was measured weekly, the quality of the feces was measured every two weeks, and the mice were recorded to observe the number of fecal pellets and fecal status. Each mouse was photographed once a week to record its mental status and coat color status.

2.5. Determination of Biochemical Indicators in Serum

Whole blood was obtained by ophthalmoscopic blood sampling, rested at room temperature for 2 h, centrifuged at 4 °C for 10 min at 3000 r/min, and the supernatant was aspirated. Lipid levels in mice are measured according to kit instructions for total cholesterol (TC), total triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and other lipid levels.

2.6. Determination of Fecal Lipid Content in Mice

Every two weeks, 9 g of mouse feces was selected from each group of mice, and three parallels were made. Five times the volume of extraction reagent (trichloromethane, methanol, and water = 2:2:1) was added, shook and mixed for 3 min, and centrifuged to obtain the lower chloroform layer. To extract adequately, after completing the first extraction, the upper aqueous phase was transferred to another glass test tube, adjusted to pH 1.5 or less by adding 0.2 mol/L HCl, acidified, and the lipid extraction steps were repeated. The first and second chloroform layers were then combined, blow-dried under nitrogen, and the mass determined. The formula is as follows:
X = A B   ✕   100 % X : faecal lipid content A : Lipid mass in faeces B : Faecelquality

2.7. Intestinal Microbiota Analysis

The feces of each group of fed mice were collected and weighed at 200 mg in EP tubes, and the genomic DNA of the content samples was extracted using a DNA extraction kit. The V3-V4 high variant region of bacterial 16S rRNA was then amplified using universal primers 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The amplified PCR products were tested by agarose gel electrophoresis and sequenced by an Illumina MiSeq sequencer to detect the composition and proportion of the major gut microbiota in each group of mouse samples.

2.8. Statistical Analysis

Each group of experiments was done in at least three parallels. GraphPad Prism 8.02 and Origin were used for plotting analysis, and SPSS 25.0 software was applied to perform a one-way ANOVA on all experimental data, with p < 0.05 representing significant differences.

3. Results

3.1. AYS Metabolite Analysis

Small molecule metabolites in AYS powder were analyzed by non-targeted metabolomic techniques. As shown in Table 2, though more than 15 categories of metabolites were identified in AYS, including carboxylic acids and derivatives, organonitrogen compounds were the most abundant metabolites identified in AYS, accounting for 51.51% of the total metabolites. Carboxylic acids and derivatives included Val-Val, Val-Asp, Val-Glu, Val-Tyr, Val-Pro, Val-Phe, Gly-Glu, Gly-Val, Gly-Arg, Thr-Glu, Thr-Leu, Ile-Glu, Ile-Met, Leu-Phe, Leu-Tyr, Pro-Thr, His-Phe, Arg-Ala, Ala-Lys, Tyr-Phe, Succinate, citrate, and betaine. Among them, betaine can reduce the methylation of the microsomal triglyceride transfer protein promoter by increasing genomic methylation in obese mice, which attenuates hepatic steatosis in mice [21]. Citrate prevents the deposition of lipids in the liver and adipose tissue in mice induced by a high-sugar, high-fat diet [22]. Organonitrogen compounds include Agmatine, Stachyose, Ile-Lys, Pantothenate, Maltotriose, Sucrose, Ribitol, Diacetyl, Maltotriose, and D-Mannose. Among them, hydrolase binding to berberine in vivo ameliorates disorders of glucolipid metabolism in mice with spontaneous type 2 diabetes. Pantothenate reduces obesity by increasing energy expenditure in C57BL/6J mice fed a high-fat diet-induced diet. Therefore, the non-targeted metabolomic analysis demonstrated that AYS metabolites contain components that reduce weight loss in mice induced by a high-fat diet.

3.2. The Combination of Prebiotics and Postbiotics Reduces the Body Weight Gain in Mice

From the modeling period to the end of feeding, the body weight of each group of mice was recorded and analyzed (Figure 1). The initial body weight values of all five groups of mice were 17–19 g. There was no significant difference between the groups. After 12 weeks of continuous feeding, all five groups of mice showed an overall increase in body weight. From week 2 to week 4 of the experiment, the body weight of mice in the high-fat diet groups was higher than that of mice on a normal diet, reaching 22.40 ± 0.23 g in the DM group at week 4, 13.50% higher than the body weight of group N at week 4. From week 5 to week 12 of the test, the mice in the DM group continued to gain weight; however, the mice in groups C, H, and L had a slower rate of weight gain, and the mice gained less weight than those in the DM group. At the end of the twelfth week, the weight gain compared to the weight at the fourth week was 5.64 g, 6.72 g, and 5.25 g in the C, H, and L groups, respectively, which was lower than the 7.73 g in the HFD group (p < 0.05). Therefore, the administration of AYS prebiotics and postbiotics alleviates weight gain in mice, and the best results are achieved at low doses.

3.3. Effect on Fecal Lipid Content and Defecation of Mice

We further investigated the effect of AYS on the defecation of obese mice. As shown in Figure 2a, each group had normal bowel movements from the modeling period to the end of feeding. Mice fed with different doses of AYS promoted defecation compared to the DM group at 4–12 weeks. Low doses of AYS are more effective in promoting bowel movements. The number of pellets defecated by mice at the end of week 12 is shown in Figure 2b, which is consistent with the results of fecal quality in mice. Therefore, it indicates that gavage of AYS can promote defecation in mice.
As shown in Figure 2c, the lipid content in the feces of the drug-administered mice in group C (positive control) tended to be stable at 3–5%. The fecal lipid content in the DM group was higher than in the N group, and the fecal lipid content ratio in the AYS groups was significantly higher than in the DM group (p < 0.05). A significantly higher ratio (p < 0.05) of fecal lipid was observed in mice in the L group than in the H group after 6–12 weeks of feeding. This indicates that mice fed low-dose AYS can promote oil excretion.

3.4. Effects on Serum Levels of TC, TG, LDL-C, and HDL-C in Mice

Previous studies have shown that the increase of TC, TG, and LDL-C content and the decrease of HDL-C content in serum are related to the occurrence of nonalcoholic fatty liver disease, atherosclerosis [23], and cardiovascular disease symptoms [24]. To investigate whether AYS (prebiotics and postbiotics) regulate the metabolism of mice, TC, TG, LDL-C, and HDL-C in the serum of mice were measured and compared between different groups.
After ten weeks of high-fat diet feeding, the serum levels of TC, TG, LDL-C, and HDL-C in the DM group mice were significantly higher than those in the N group (p < 0.05, Figure 3). In contrast, TC, TG, and LDL-C levels significantly decreased and HDL-C levels significantly increased in the C, H, and L groups when compared with the DM group (p < 0.05). Mice in the L group showed the best-preventing effects among the experimental groups; serum TC (3.22 ± 0.13 mmoL/L), TG (1.26 ± 0.17 mmoL/L), and LDL-C (1.05 ± 0.05 mmoL/L) levels were significantly lower (p < 0.05) and HDL-C (2.73 ± 0.17 mmoL/L) levels were significantly higher (p < 0.05) when compared with the DM group. Similar to the results of Fu et al., which show that banana-resistant starch modulates the normalization of TC, TG, LDL-C, and HDL-C levels in the serum of obese rats induced by a high-fat diet [25], our results suggest that low doses of AYS can better maintain normal lipid levels in the body by improving serum levels of TG, TC, LDL-C, and HDL-C.

3.5. Effect on Intestinal Microbiota Composition

3.5.1. Alpha Diversity Analysis

A high-sugar and high-fat diet can alter the composition and diversity of the intestinal microbiota, influence the physiological functions of the host, and have a crucial impact on the human body. Long-term dietary intervention may be a safe and effective way to prevent and treat obesity. Alpha diversity reflects the number and diversity of microbial species in mouse feces and is commonly measured by the Chao1 index, the Shannon index, and the Simpson index [26]. The level of this index represents the richness and homogeneity of the microbial community in the sample. As shown in Figure 4, the Chao1 index, Shannon index, and Simpson index were significantly lower in the DM group compared to the N group (p < 0.05), indicating that a high-fat diet reduces the abundance and diversity of intestinal microbiota in mice. On the other side, the Chao1 index, Shannon index, and Simpson index were significantly higher in the C, H, and L groups compared with the DM group (p < 0.05), which indicates that the dietary intervention had a positive effect on maintaining intestinal microbiota diversity in mice. In addition, low-dose administration of AYS is the most efficient dose to restore the richness and diversity of the intestinal microbiota in high-fat mice.

3.5.2. Beta Diversity Analysis

Beta diversity analysis was used to investigate the effects of different interventions on the intestinal microbiota of mice in five groups: N, DM, C, H, and L. The results are shown in Figure 5. The beta diversity analysis mainly includes PCA, PCoA analysis, NMDS analysis, etc. Differences between individuals or groups can be observed using PCA. The closer the distance between two points on the graph, the more similar the species composition. Compared with the DM group, the three dietary intervention groups showed a higher aggregation of sample sites and were closer to the N group overall. The results suggest that feeding mice with low-dose AYS in the L group can restore the intestinal flora and improve the stability of the community structure of the intestinal microbiota in high-fat diet-fed mice.

3.5.3. Analysis of Intestinal Microbiota Composition

Composition of Intestinal Microbiota at Phylum Level

A long-term high-fat diet will cause a disorder of the internal environment of the body’s intestines and then affect the composition of the intestinal microbiota. An analysis of microbial composition at the phylum level was performed to analyze the differences among groups N, DM, C, H, and L at the phylum level. As shown in Figure 6a, the most abundant bacteria in all five groups are Firmicutes, Bacteroidota, Verrucomicrobia, Acidobacteriota, Proteobacteria, Gemmatimonadota, Patescibacteria, Cyanobacteria, and Deferribacteia. The relative abundance of Firmicutes increased and the relative abundance of Bacteroidetes decreased in the DM mice compared with the N group. The dietary intervention in the C, H, and L groups reversed this trend and converged in the N group, which is consistent with the findings of Dong et al. [27]. In addition, some reports demonstrate that obese mice have a higher Firmicutes/Bacteroidetes ratio than lean mice [28]. Verrucomicrobia is closely associated with the development of diabetes and obesity, and induction by a high-fat diet resulted in a decrease in the relative abundance of Verrucomicrobia in mice in the DM group, whereas dietary interventions in the C, H, and L groups increased the relative abundance of Verrucomicrobia. Therefore, the dietary interventions in the C, H, and L groups could restore the intestinal microbiota of mice, which is disrupted by a high-sugar and high-fat diet.

Composition of Intestinal Microbiota Based on Genus Level

Genus level-based microbial community composition analysis was performed to analyze the differences in the composition of the intestinal microbiota of mice in groups N, DM, C, H, and L at the genus level (Figure 6b). At the genus level, Akkermansia, Muribaculaceae_unclassified, Lactobacillus, Lachnospiraceae_NK4A136_group, Ruminococcaceae_UCG-014, Prevotellaceae_UCG-001, Coriobacteriaceae_UCG-002, Bifidobacterium, and Enterorhabdus are the most abundant genus in all groups. The relative abundance of Muribaculaceae, which has been reported to be associated with obesity [29], increased, and the relative abundance of Lactobacillus decreased in the DM group compared to the N group, whereas dietary interventions in the C, H, and L groups resulted in a convergence of the relative abundance of Muribaculaceae and Lactobacillus to the N group level. Akkermansia found in healthy human feces was associated with obesity [30]. Reducing the relative abundance of Akkermansia can accelerate the development of obesity [31]. Compared with the DM group, the dietary intervention in the C, H, and L groups increased the relative abundance of Akkermansia.

LEfSe Analysis of Mice Gut Microbiota

LEfSe analysis is able to look for biomarkers that are statistically different between groups, with different colors indicating species in different subgroups. LEfSe analysis was used to analyze the main changes in the intestinal microbiota of mice after the induction of a high-fat diet, as shown in Figure 7. As seen in the figure, the relative abundance of Akkermansia, Verrucomicrobiae, Clostridioides, and Peptostreptococcaceae was significantly increased in group L and significantly different from the control group. Studies have shown that an increase in Akkermansia is strongly associated with metabolic diseases such as obesity and type 2 diabetes. Taken together, the results suggest that the dietary intervention in the C, H, and L groups can regulate the intestinal microbiota and alleviate the dysbiosis of the intestinal microbiota caused by long-term intake of a high-fat diet in mice by changing the relative abundance of Akkermansia, Muribaculaceae, and Lactobacillus.

4. Discussion

Obesity is a phenomenon in which the body deviates from normal energy homeostasis due to the abnormal accumulation of fat [32]. At present, obesity is widely prevalent throughout the world, and a high-fat diet and a sedentary lifestyle are the main causes of metabolic diseases such as obesity, associated with a decrease in microbial diversity. Recent studies have shown that regulating intestinal microbiota disorders through dietary interventions to protect the intestinal barrier can improve obesity and other metabolic diseases [33]. Therefore, it is necessary to prevent or improve the occurrence of obesity through dietary interventions. In this study, a high-fat diet was used to construct an obese mouse model, which provides a theoretical basis for the treatment of obesity based on postbiotics and probiotics and provides ideas for the application of AYS to products with therapeutic effects on obesity.
The introduction of metabolomics technology provides new methods with which to trace the origin and authenticity of agricultural products, reveal changes in nutrient composition during growth and storage, study the mechanism of nutrient function, and provide new strategies for the optimal adjustment of the dietary structure. Metabolomics is a technique for the comprehensive qualitative and quantitative analysis of all endogenous small-molecule metabolites in an organism under specific conditions [34]. The raw data collected by mass spectrometry is used to identify metabolites by database search and molecular network technology to produce more accurate results. In this study, the metabolites of AYS were identified, and AYS was found to contain succinate, citrate, betaine, agmatine, stachyose, ile-Lys, pantothenate, maltotriose, ribitol, dicetyl, D-mannose, and dipeptide metabolites. Among other things, succinate is produced by intestinal microorganisms and depletion of succinate levels in the body is associated with the regulation of obesity, diabetes, and other symptoms. Studies have shown that oral administration of Odoribacter lanes depletes succinate levels in mice and reduces levels of triglycerides and uric acid, thereby improving the symptoms of obesity [35]. Stachyose is an oligosaccharide that regulates the composition of the intestinal microbiota of animals. Studies have shown that supplementation with non-digestive stachyose enhances the absorption of soy isoflavones and thus prevents weight gain and fat accumulation in HFD-induced C57/BL6 mice [36]. In addition, AYS metabolites contain betaine, a natural compound found in foods that has a potential role in regulating lipid metabolism. Research showed that betaine supplementation could promote hepatic lipid metabolism in mice by increasing metabolic exercise in obese mice [37]. In addition, increasing the maternal intake of betaine during lactation resulted in reduced obesity and an increased relative abundance of Akkermansia in the intestine of mouse offspring throughout adulthood [38]. This is consistent with the findings that feeding AYS increased the relative abundance of Akkermansia in the intestinal microbiota of HFD-induced mice. However, we need to continue to determine what substances in AYS play a major role in obesity in mice. Thus, AYS metabolites contain components that can reduce weight gain due to HFD in mice.
The main characteristics of high-fat diet-induced obesity in mice are the increase in body weight, the increase in blood lipid and cholesterol levels, specifically in serum TG, TC, and LDL levels, and the decrease in HDL levels [39]. In this study, mice were fed a high-fat diet, and the results showed that after the AYS diet intervention, the body weight of mice in the high-dose and low-dose AYS groups was lower than that of the DM group, in which the blood lipid levels (TC, TG, and LDL-C) were increased and HDL-C levels were decreased, indicating that the mice were successfully modeled. After different doses of AYS gavage, the serum levels of TG, TC, and LDL in mice showed different trends of decrease, and HDL levels showed a trend of increase; the effect was more significant in the low-dose group. It was found that GOS fed to hypercholesterolemic female rats significantly reduced serum TC, TG, and LDL-C levels in rats, which is consistent with the results of this experimental study [40]. Therefore, AYS can improve the symptoms of obesity by regulating blood lipid and cholesterol levels.
With an increasing standard of living, diet and health issues are widely discussed. In recent years, it has been found that a high-fat diet induces abnormal bowel function in obese individuals [41]. Reasonable dietary interventions can effectively regulate the structure of the intestinal microbiota and achieve an effective improvement in abnormal bowel function. In the present study, the lipid content in the feces of the low-dose AYS group was significantly higher than that of the DM group, and in addition, the number and weight of defecations were significantly increased in the low-dose AYS group. Wu et al. showed that feeding HFD-induced mice with monoclonal antibody blockade of long fragment neurotensin increased their fecal lipid content, similar to the results of the present study [42]. In addition, it was shown that feeding the outer bran fraction of rice increased the fecal count, weight, and lipid content of rats to prevent lipid accumulation [43]. Thus, AYS promotes the excretion of feces and its lipid content in HFD mice.
The intestinal microbiota plays a key role in the regulation of host metabolism. The long-term intake of a high-fat diet can cause disturbances in the organism’s intestinal environment and reduce the diversity of the intestinal microbiota. In this experiment, the alpha diversity index of mice in the DM group decreased, whereas the high-dose and low-dose AYS groups increased the alpha diversity of mouse intestinal microbiota, indicating that AYS can alter the relative abundance and diversity of intestinal microbiota in mice on a high-fat diet. At the gate level, the high-dose and low-dose AYS groups fed AYS improved the dysbiosis of intestinal microbiota in mice induced by a high-fat diet by decreasing the relative abundance of Firmicutes and Verrucomicrobia and increasing the relative abundance of Bacteroidetes. It was found that the intervention of prebiotic GSF in combination with cellular components of Lactobacillus kefir reduced energy intake in mice by modulating the composition of their intestinal microbiota, which is consistent with the results of the present study [44]. At the genus level, the relative abundance of Muribaculaceae increased and that of Lactobacillus and Akkermansia decreased in the DM group, whereas dietary intervention in the high-dose and low-dose AYS groups reversed this trend. Muribaculaceae is one of the dominant bacteria in the intestinal microbiota that breaks down carbohydrates. Research showed that the proliferation of Muribaculaceae was promoted by feeding cooked red beans [45], and in turn, the relative abundance of Muribaculaceae in the DM group was higher than in the N group, similar to the results obtained in the present experiment. Akkermansia is a Gram-negative anaerobic bacterium that shows a negative association with diseases such as obesity [46]. Akkermansia was less abundant in the obese population compared to the healthy population, and the obese population showed a significant reduction in blood glucose parameters and, thus, obesity after Akkermansia supplementation [47]. Therefore, AYS can improve the symptoms of obesity by regulating the imbalance of the gut microbiota caused by HFD. The study showed that the effects of AYS on body weight, blood lipid level, defecation ability, and intestinal microbiota were more pronounced in the low-dose group. It is possible that the low dose of AYS can be more fully utilized by the mice for its efficacy because it is more helpful for their digestion and absorption. The high dose of AYS may be excreted because the dose is too much for the mice to be fully absorbed and utilized, so the low dose of AYS is more effective in mice. However, this study is still in its early stages, and the molecular mechanism of the combined intervention of prebiotics and postbiotics in the clinical prevention or treatment of lipid lowering and related metabolic disorders needs to be further investigated.

5. Conclusions

In this study, a non-targeted metabolomics approach was used to analyze the derived AYS metabolites. By constructing a high-fat-induced mouse model, the body weight of mice in the L group was significantly reduced after 10 weeks of feeding, indicating that the administration of low-dose AYS could effectively control obesity in mice and significantly increase the fecal lipid content. Second, the administration of low doses of AYS resulted in a significant decrease in TC, TG, and LDL-C levels and a significant increase in HDL-C levels in mice. In addition, the administration of low-dose AYS interventions could improve the intestinal microbiota of high-sugar, high-fat diet-induced mice by altering the relative abundance of Firmicutes, Bacteroidota, and Verrucomicrobia to a certain extent. This research has important implications for AYS’s ability to improve obesity symptoms and for future research on related products.

Author Contributions

Conceptualization, Y.Z. (Yueming Zhao) and Y.Z. (Yaping Zheng); methodology, Y.Z. (Yueming Zhao); writing—original draft, Y.Z. (Yaping Zheng); data curation, K.X.; writing—review and editing, Y.H. and Q.L.; visualization, Y.Z. (Yueming Zhao); supervision, Y.Z. (Yu Zhang); project administration, Y.J.; funding acquisition, C.M. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by Ausnutria Hyproca Nutrition Co., Ltd. (Item No.AN-2022-03).

Institutional Review Board Statement

The animal experiment was approved by the Laboratory Animal Welfare and Ethics Committee of Northeast Agricultural University(#NEAUEC-2023-04-14) and was conducted according to the Guide for the Care and Use of Laboratory Animals (China).

Informed Consent Statement

Not applicable.

Data Availability Statement

Data in the project are still being collected, but all data used in the study is available by contacting the authors.

Conflicts of Interest

The authors declare no conflict of interest. Ausnutria Hyproca Nutrition Co., Ltd. provided the investigational product only and had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Amer Diabet, A. 8. Obesity management for the treatment of type 2 diabetes: Standards of medical care in diabetes-2021. Diabetes Care 2021, 44, S100–S110. [Google Scholar] [CrossRef]
  2. Fahed, G.; Aoun, L.; Zerdan, M.B.; Allam, S.; Zerdan, M.B.; Bouferraa, Y.; Assi, H.I. Metabolic Syndrome: Updates on pathophysiology and management in 2021. Int. J. Mol. Sci. 2022, 23, 786. [Google Scholar] [CrossRef]
  3. Rao, G. Identification of obesity and cardiovascular risk in ethnically and racially diverse populations: A scientific statement from the american heart association. Circulation 2015, 132, E130. [Google Scholar] [CrossRef]
  4. Younossi, Z.M.; Corey, K.E.; Lim, J.K. AGA clinical practice update on lifestyle modification using diet and exercise to achieve weight loss in the management of nonalcoholic fatty liver disease: Expert review. Gastroenterology 2021, 160, 912–918. [Google Scholar] [CrossRef] [PubMed]
  5. Tak, Y.J.; Lee, S.Y. Long-term efficacy and safety of anti-obesity treatment: Where do we stand? Curr. Obes. Rep. 2021, 10, 14–30. [Google Scholar] [CrossRef]
  6. Salminen, S.; Collado, M.C.; Endo, A.; Hill, C.; Lebeer, S.; Quigley, E.M.M.; Sanders, M.E.; Shamir, R.; Swann, J.R.; Szajewska, H.; et al. The international scientific association of probiotics and prebiotics (ISAPP) consensus statement on the definition and scope of postbiotics. Nat. Rev. Gastroenterol. Hepatol. 2021, 18, 649–667. [Google Scholar] [CrossRef] [PubMed]
  7. Makino, S.; Sato, A.; Goto, A.; Nakamura, M.; Ogawa, M.; Chiba, Y.; Hemmi, J.; Kano, H.; Takeda, K.; Okumura, K.; et al. Enhanced natural killer cell activation by exopolysaccharides derived from yogurt fermented with Lactobacillus delbrueckii ssp. bulgaricus OLL1073R-1. J. Dairy Sci. 2016, 99, 915–923. [Google Scholar] [CrossRef]
  8. Rigo-Adrover, M.D.; Knipping, K.; Garssen, J.; van Limpt, K.; Knol, J.; Franch, A.; Castell, M.; Rodríguez-Lagunas, M.J.; Pérez-Cano, F.J. Prevention of rotavirus diarrhea in suckling rats by a specific fermented milk concentrate with prebiotic mixture. Nutrients 2019, 11, 189. [Google Scholar] [CrossRef] [PubMed]
  9. Amaretti, A.; di Nunzio, M.; Pompei, A.; Raimondi, S.; Rossi, M.; Bordoni, A. Antioxidant properties of potentially probiotic bacteria: In vitro and in vivo activities. Appl. Microbiol. Biotechnol. 2013, 97, 809–817. [Google Scholar] [CrossRef]
  10. Sokol, H.; Pigneur, B.; Watterlot, L.; Lakhdari, O.; Bermudez-Humaran, L.G.; Gratadoux, J.J.; Blugeon, S.; Bridonneau, C.; Furet, J.P.; Corthier, G.; et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of grohn disease patients. Proc. Natl. Acad. Sci. USA 2008, 105, 16731–16736. [Google Scholar] [CrossRef]
  11. Sawada, H.; Furushiro, M.; Hirai, K.; Motoike, M.; Watanabe, T.; Yokokura, T. Purification and characterization of an antihypertensive compound from Lact. Casei. Agric. Biol. Chem. 1990, 54, 3211–3219. [Google Scholar] [CrossRef]
  12. Nakamura, F.; Ishida, Y.; Sawada, D.; Ashida, N.; Sugawara, T.; Sakai, M.; Goto, T.; Kawada, T.; Fujiwara, S. Fragmented lactic acid bacterial cells activate peroxisome proliferator-activated receptors and ameliorate dyslipidemia in obese mice. J. Agric. Food Chem. 2016, 64, 2549–2559. [Google Scholar] [CrossRef] [PubMed]
  13. Quan, L.H.; Zhang, C.H.; Dong, M.; Jiang, J.; Xu, H.D.; Yan, C.L.; Liu, X.; Zhou, H.; Zhang, H.; Chen, L.; et al. Myristoleic acid produced by enterococci reduces obesity through brown adipose tissue activation. Gut 2020, 69, 1239–1247. [Google Scholar] [CrossRef] [PubMed]
  14. Miyamoto, J.; Igarashi, M.; Watanabe, K.; Karaki, S.; Mukouyama, H.; Kishino, S.; Li, X.; Ichimura, A.; Irie, J.; Sugimoto, Y.; et al. Gut microbiota confers host resistance to obesity by metabolizing dietary polyunsaturated fatty acids. Nat. Commun. 2019, 10, 4007. [Google Scholar] [CrossRef] [PubMed]
  15. Marchesi, J.R.; Adams, D.H.; Fava, F.; Hermes, G.D.A.; Hirschfield, G.M.; Hold, G.; Quraishi, M.N.; Kinross, J.; Smidt, H.; Tuohy, K.M.; et al. The gut microbiota and host health: A new clinical frontier. Gut 2016, 65, 330–339. [Google Scholar] [CrossRef] [PubMed]
  16. Davani-Davari, D.; Negahdaripour, M.; Karimzadeh, I.; Seifan, M.; Mohkam, M.; Masoumi, S.J.; Berenjian, A.; Ghasemi, Y. Prebiotics: Definition, types, sources, mechanisms, and clinical applications. Foods 2019, 8, 92. [Google Scholar] [CrossRef] [PubMed]
  17. McIntyre, A.; Young, G.P.; Taranto, T.; Gibson, P.R.; Ward, P.B. Different fibers have different regional effects on luminal contents of rat colon. Gastroenterology 1991, 101, 1274–1281. [Google Scholar] [CrossRef] [PubMed]
  18. Nakamura, Y.; Natsume, M.; Yasuda, A.; Ishizaka, M.; Kawahata, K.; Koga, J. Fructooligosaccharides suppress high-fat diet-induced fat accumulation in C57BL/6J mice. Biofactors 2017, 43, 145–151. [Google Scholar] [CrossRef]
  19. Li, Y.; Wang, G.; Hao, Z.; Huang, J.; Gao, C.; Huang, J.; Li, H. Dietary fructooligosaccharides prevent obesity and regulate the gut microbiota in C57BL/6 mice. Food Sci. 2022, 43, 150–157. [Google Scholar]
  20. Kong, S.; Huang, X.J.; Cao, H.; Bai, Y.; Che, Q.S.; Nie, H.; Su, Z.Q. Anti-obesity effects of galacto-oligosaccharides in obese rats. Eur. J. Pharmacol. 2022, 917, 174728. [Google Scholar] [CrossRef]
  21. Hattori, H.; Moriyama, A.; Ohno, T.; Shibata, T.; Iwahashi, H.; Mitsunaga, T. Molecular networking-based lipid profiling and multi-omics approaches reveal new contributions of functional vanilloids to gut microbiota and lipometabolism changes. Food Chem. Mol. Sci. 2022, 5, 100123. [Google Scholar] [CrossRef] [PubMed]
  22. Branco, J.R.; Esteves, A.M.; Imbroisi, R.I.; Demaria, T.M.; Lisboa, P.C.; Lopes, B.P.; Moura, E.G.; Zancan, P.; Sola-Penna, M. Citrate enrichment in a western diet reduces weight gain via browning of adipose tissues without resolving diet-induced insulin resistance in mice. Food Funct. 2022, 13, 10947–10955. [Google Scholar] [CrossRef] [PubMed]
  23. Li, X.J.; Shen, Y.B.; Wu, G.C.; Qi, X.G.; Zhang, H.; Wang, L.; Qian, H.F. Determination of key active components in different edible oils affecting lipid accumulation and reactive oxygen species production in HepG2 cells. J. Agric. Food Chem. 2018, 66, 11943–11956. [Google Scholar] [CrossRef] [PubMed]
  24. Dyrbus, K.; Osadnik, T.; Desperak, P.; Desperak, A.; Gasior, M.; Banach, M. Evaluation of dyslipidaemia and the impact of hypolipidemic therapy on prognosis in high and very high risk patients through the Hyperlipidaemia Therapy in tERtiary Cardiological cEnTer (TERCET) Registry. Pharmacol. Res. 2018, 132, 204–210. [Google Scholar] [CrossRef] [PubMed]
  25. Fu, J.F.; Wang, Y.T.; Tan, S.M.; Wang, J. Effects of banana resistant starch on the biochemical indexes and intestinal flora of obese rats induced by a high-fat diet and their correlation analysis. Front. Bioeng. Biotechnol. 2021, 9, 575724. [Google Scholar] [CrossRef] [PubMed]
  26. Campbell, C.L.; Yu, R.Q.; Li, F.Z.; Zhou, Q.; Chen, D.Z.; Qi, C.; Yin, Y.; Sun, J. Modulation of fat metabolism and gut microbiota by resveratrol on high-fat diet-induced obese mice. Diabetes Metab. Syndr. Obes. -Targets Ther. 2019, 12, 97–107. [Google Scholar] [CrossRef] [PubMed]
  27. Dong, J.L.; Zhu, Y.Y.; Ma, Y.L.; Xiang, Q.S.; Shen, R.L.; Liu, Y.Q. Oat products modulate the gut microbiota and produce anti-obesity effects in obese rats. J. Funct. Foods 2016, 25, 408–420. [Google Scholar] [CrossRef]
  28. Jiang, P.R.; Zheng, W.Y.; Sun, X.N.; Jiang, G.P.; Wu, S.; Xu, Y.X. Sulfated polysaccharides from undaria pinnatifida improved high fat diet-induced metabolic syndrome, gut microbiota dysbiosis and inflammation in BALB/c mice. Int. J. Biol. Macromol. 2021, 167, 1587–1597. [Google Scholar] [CrossRef]
  29. Cao, W.X.; Chin, Y.X.; Chen, X.; Mi, Y.; Xue, C.H.; Wang, Y.M.; Tang, Q.J. The role of gut microbiota in the resistance to obesity in mice fed a high fat diet. Int. J. Food Sci. Nutr. 2020, 71, 453–463. [Google Scholar] [CrossRef]
  30. Ottman, N.; Geerlings, S.Y.; Aalvink, S.; De Vos, W.M.; Belzer, C. Action and function of Akkermansia muciniphila in microbiome ecology, health and disease. Best Pract. Res. Clin. Gastroenterol. 2017, 31, 637–642. [Google Scholar] [CrossRef]
  31. Aron-Wisnewsky, J.; Vigliotti, C.; Witjes, J.; Le, P.; Holleboom, A.G.; Verheij, J.; Nieuwdorp, M.; Clément, K. Gut microbiota and human NAFLD: Disentangling microbial signatures from metabolic disorders. Nat. Rev. Gastroenterol. Hepatol. 2020, 17, 279–297. [Google Scholar] [CrossRef]
  32. Maruvada, P.; Leone, V.; Kaplan, L.M.; Chang, E.B. The human microbiome and obesity: Moving beyond associations. Cell Host Microbe 2017, 22, 589–599. [Google Scholar] [CrossRef] [PubMed]
  33. Liu, M.; Bi, R.; Sun, Y. Recent advances on the interaction between fruits or vegetables and intestinal flora and its effect on health. J. Chin. Inst. Food Sci. Technol. 2022, 22, 387–407. [Google Scholar]
  34. Canuto, G.A.B.; Da Costa, J.L.; Da Cruz, P.L.R.; De Souza, A.R.L.; Faccio, A.T.; Klassen, A.; Rodrigues, K.; Tavares, M. Metabolomics:definitions, state-of-the-art and representative applications. Quim. Nova 2018, 41, 75–91. [Google Scholar] [CrossRef]
  35. Huber-Ruano, I.; Calvo, E.; Mayneris-Perxachs, J.; Rodriguez-Pena, M.M.; Ceperuelo-Mallafre, V.; Cedo, L.; Fernandez-Veledo, S. Orally administered Odoribacter laneus improves glucose control and inflammatory profile in obese mice by depleting circulating succinate. Microbiome 2022, 10, 135. [Google Scholar] [CrossRef] [PubMed]
  36. Wu, Y.M.; Lu, Y.L.; Ren, D.Y.; Chen, X.F.; Yang, X.B. Non-digestive stachyose enhances bioavailability of isoflavones for improving hyperlipidemia and hyperglycemia in mice fed with high fat diet. J. Food Drug Anal. 2021, 29, 87–97. [Google Scholar] [CrossRef] [PubMed]
  37. Yu, J.; Laybutt, D.R.; Youngson, N.A.; Morris, M.J. Concurrent betaine administration enhances exercise-induced improvements to glucose handling in obese mice. Nutr. Metab. Cardiovasc. Dis. 2022, 32, 2439–2449. [Google Scholar] [CrossRef] [PubMed]
  38. Ribo, S.; Sanchez-Infantes, D.; Martinez-Guino, L.; Garcia-Mantrana, I.; Ramon-Krauel, M.; Tondo, M.; Arning, E.; Nofrarías, M.; Osorio-Conles, Ó.; Fernández-Pérez, A.; et al. Increasing breast milk betaine modulates Akkermansia abundance in mammalian neonates and improves long-term metabolic health. Sci. Transl. Med. 2021, 13, 587. [Google Scholar] [CrossRef]
  39. Nazli, A.; Yang, P.C.; Jury, J.; Howe, K.; Watson, J.L.; Soderholm, J.D.; Sherman, P.M.; Perdue, M.H.; McKay, D.M. Epithelia under metabolic stress perceive commensal bacteria as a threat. Am. J. Pathol. 2004, 164, 947–957. [Google Scholar] [CrossRef]
  40. Hashmi, A.; Naeem, N.; Farooq, Z.; Masood, S.; Iqbal, S.; Naseer, R. Effect of prebiotic galacto-oligosaccharides on serum lipid profile of hypercholesterolemics. Probiotics Antimicrob. Proteins 2016, 8, 19–30. [Google Scholar] [CrossRef]
  41. Brochard, C.; Venara, A.; Bodere, A.; Ropert, A.; Bouguen, G.; Siproudhis, L. Pathophysiology of fecal incontinence in obese patients: A prospective case-matched study of 201 patients. Neurogastroenterol. Motil. 2017, 29, e13051. [Google Scholar] [CrossRef] [PubMed]
  42. Wu, Z.R.; Stadler, N.; Abbaci, A.; Liu, J.; Boullier, A.; Marie, N.; Biondi, O.; Moldes, M.; Morichon, R.; Feve, B.; et al. Effect of monoclonal antibody blockade of long fragment neurotensin on weight loss, behavior, and metabolic traits after high-fat diet induced obesity. Front. Endocrinol. 2021, 12, 739287. [Google Scholar] [CrossRef] [PubMed]
  43. Ijiri, D.; Nojima, T.; Kawaguchi, M.; Yamauchi, Y.; Fujita, Y.; Ijiri, S.; Ohtsuka, A. Effects of feeding outer bran fraction of rice on lipid accumulation and fecal excretion in rats. Biosci. Biotechnol. Biochem. 2015, 79, 1337–1341. [Google Scholar] [CrossRef] [PubMed]
  44. Seo, K.H.; Lee, H.G.; Eor, J.Y.; Jeon, H.J.; Yokoyama, W.; Kim, H. Effects of kefir lactic acid bacteria-derived postbiotic components on high fat diet-induced gut microbiota and obesity. Food Res. Int. 2022, 157, 111445. [Google Scholar] [CrossRef] [PubMed]
  45. Zhao, Q.Y.; Liu, Z.Y.; Zhu, Y.Q.; Wang, H.; Dai, Z.J.; Yang, X.H.; Ren, X.; Xue, Y.; Shen, Q. Cooked adzuki bean reduces high-fat diet-induced body weight gain, ameliorates inflammation, and modulates intestinal homeostasis in mice. Front. Nutr. 2022, 9, 918696. [Google Scholar] [CrossRef] [PubMed]
  46. Belzer, C.; De Vos, W.M. Microbes inside-from diversity to function: The case of Akkermansia. Isme J. 2012, 6, 1449–1458. [Google Scholar] [CrossRef] [PubMed]
  47. Plovier, H.; Everard, A.; Druart, C.; Depommier, C.; Van Hul, M.; Geurts, L.; Chilloux, J.; Ottman, N.; Duparc, T.; Lichtenstein, L.; et al. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice. Nat. Med. 2017, 23, 107–113. [Google Scholar] [CrossRef]
Nutrients 15 04971 g001
Figure 1. (a) Change in body weight of mice and (b) body weight gain from a successful mouse model (week 4) to the end of feeding (week 12). Different letters denote significant differences between different groups of mice (p < 0.05). N: normal diet; DM: diet model; H: high-dose AYS; L: low-dose AYS; and C: control.
Figure 1. (a) Change in body weight of mice and (b) body weight gain from a successful mouse model (week 4) to the end of feeding (week 12). Different letters denote significant differences between different groups of mice (p < 0.05). N: normal diet; DM: diet model; H: high-dose AYS; L: low-dose AYS; and C: control.
Nutrients 15 04971 g001
Nutrients 15 04971 g002
Figure 2. (a) Changes in fecal quality of mice; (b) number of fecal pellets in mice from the start of feeding until the end of week 12; and (c) changes in fecal lipid content of mice from week 2 to the end of week 12 of feeding. Abbreviations for each group are shown in Figure 1. Different letters denote significant differences between different groups of mice (p < 0.05). N: normal diet; DM: diet model; H: high-dose AYS; L: low-dose AYS; and C: control.
Figure 2. (a) Changes in fecal quality of mice; (b) number of fecal pellets in mice from the start of feeding until the end of week 12; and (c) changes in fecal lipid content of mice from week 2 to the end of week 12 of feeding. Abbreviations for each group are shown in Figure 1. Different letters denote significant differences between different groups of mice (p < 0.05). N: normal diet; DM: diet model; H: high-dose AYS; L: low-dose AYS; and C: control.
Nutrients 15 04971 g002
Nutrients 15 04971 g003
Figure 3. (a) Changes in serum total cholesterol (TC) content in mice; (b) changes in serum total triglyceride (TG) content in mice; (c) changes in serum low-density lipoprotein cholesterol (LDL-C) levels in mice; (d) and changes in serum high-density lipoprotein cholesterol (HDL-C) content in mice. Different letters denote significant differences between different groups of mice (p < 0.05). N: normal diet; DM: diet model; H: high-dose AYS; L: low-dose AYS; and C: control.
Figure 3. (a) Changes in serum total cholesterol (TC) content in mice; (b) changes in serum total triglyceride (TG) content in mice; (c) changes in serum low-density lipoprotein cholesterol (LDL-C) levels in mice; (d) and changes in serum high-density lipoprotein cholesterol (HDL-C) content in mice. Different letters denote significant differences between different groups of mice (p < 0.05). N: normal diet; DM: diet model; H: high-dose AYS; L: low-dose AYS; and C: control.
Nutrients 15 04971 g003
Nutrients 15 04971 g004
Figure 4. (a) Changes in the Chao1 index of alpha diversity in mouse intestinal microbiota; (b) changes in the Shannon index of alpha diversity in mouse intestinal microbiota; and (c) changes in the Simpson index of alpha diversity in mouse intestinal microbiota. Different letters denote significant differences between different groups of mice (p < 0.05). N: normal diet; DM: diet model; H: high-dose AYS; L: low-dose AYS; and C: control.
Figure 4. (a) Changes in the Chao1 index of alpha diversity in mouse intestinal microbiota; (b) changes in the Shannon index of alpha diversity in mouse intestinal microbiota; and (c) changes in the Simpson index of alpha diversity in mouse intestinal microbiota. Different letters denote significant differences between different groups of mice (p < 0.05). N: normal diet; DM: diet model; H: high-dose AYS; L: low-dose AYS; and C: control.
Nutrients 15 04971 g004
Nutrients 15 04971 g005
Figure 5. (a) Principal component analysis; (b) principal coordinate analysis; and (c) MDS analysis. Note: A represents group N, B represents group DM, C represents group C, D represents group H, and E represents group L.
Figure 5. (a) Principal component analysis; (b) principal coordinate analysis; and (c) MDS analysis. Note: A represents group N, B represents group DM, C represents group C, D represents group H, and E represents group L.
Nutrients 15 04971 g005
Nutrients 15 04971 g006
Figure 6. (a) Differences in the composition of mouse intestinal microbiota at the phylum level, and (b) differences in the composition of mouse intestinal microbiota at the genus level. N: normal diet; DM: diet model; H: high-dose AYS; L: low-dose AYS; and C: control.
Figure 6. (a) Differences in the composition of mouse intestinal microbiota at the phylum level, and (b) differences in the composition of mouse intestinal microbiota at the genus level. N: normal diet; DM: diet model; H: high-dose AYS; L: low-dose AYS; and C: control.
Nutrients 15 04971 g006
Nutrients 15 04971 g007
Figure 7. LEfSe analysis of mice gut microbiota. A represents group N, B represents group DM, C represents group C, D represents group H, and E represents group L.
Figure 7. LEfSe analysis of mice gut microbiota. A represents group N, B represents group DM, C represents group C, D represents group H, and E represents group L.
Nutrients 15 04971 g007
Table 1. Treatment for experimental mice.
Table 1. Treatment for experimental mice.
GroupDietTreatment
NNormal diet0.2 mL PBS per day
DMHigh-fat diet0.2 mL PBS per day
HHigh-fat diet200 μL of AYS per day
LHigh-fat diet100 μL of AYS per day
CHigh-fat diet200 μL postbiotics of Yicuizhi dilute solution per day
Table 2. Composition of AYS metabolites.
Table 2. Composition of AYS metabolites.
No.CompoundsM/zRt (s)B2Class
1Val-Val217.1537224.884134,779.7Carboxylic acids and derivatives
2Val-Asp233.1115382.09489,946.17Carboxylic acids and derivatives
3Val-Glu247.1313402.4932501.2Carboxylic acids and derivatives
4Val-Tyr281.1486231.593173,349.6Carboxylic acids and derivatives
5Val-Pro215.1373261.452176,255.3Carboxylic acids and derivatives
6Val-Phe265.1537190.596424,802.5Carboxylic acids and derivatives
7Gly-Glu205.0854440.61128,407.88Carboxylic acids and derivatives
8Gly-Val175.1071390.34411,429.14Carboxylic acids and derivatives
9Gly-Arg232.1391448.6925,774.23Carboxylic acids and derivatives
10Thr-Glu231.0964306.56920,522.67Carboxylic acids and derivatives
11Thr-Leu233.1492250.772801,098.3Carboxylic acids and derivatives
12Ile-Glu261.1436359.03630,986.21Carboxylic acids and derivatives
13Ile-Met263.1417193.436280,259.7Carboxylic acids and derivatives
14Leu-Phe279.1695177.737412,315.3Carboxylic acids and derivatives
15Leu-Tyr295.1643212.834629,953.3Carboxylic acids and derivatives
16Pro-Thr199.1063119.8664,398.15Carboxylic acids and derivatives
17His-Phe285.1339148.378478,955.4Carboxylic acids and derivatives
18Arg-Ala246.154449.3833,634.96Carboxylic acids and derivatives
19Ala-Lys218.1479437.541166,954.3Carboxylic acids and derivatives
20Tyr-Phe311.137139.74526,125.42Carboxylic acids and derivatives
21Succinate117.0193432.63911,464.94Carboxylic acids and derivatives
22Citrate191.02553.862334,888.1Carboxylic acids and derivatives
23Betaine118.086274.1817,530,456Carboxylic acids and derivatives
24Agmatine131.1272450.1196,303.9Organonitrogen compounds
25Stachyose667.227488.158210,245.2Organooxygen compounds
26Ile-Lys260.1943354.88637,147.84Organooxygen compounds
27Pantothenate220.1162276.73124,141.14Organooxygen compounds
28Maltotriose503.1593504.9357323.386Organooxygen compounds
29Pantothenate218.1041277.019150,933Organooxygen compounds
30Sucrose341.1078488.11641,952.1Organooxygen compounds
31Ribitol133.051199.8331,769,454Organooxygen compounds
32Diacetyl87.04301177.737136,216.7Organooxygen compounds
33Maltotriose522.198494.36765,036.57Organooxygen compounds
34D-Mannose239.0775296.48878,534.97Organooxygen compounds
35Hypoxanthine137.0436258.532721.8929Imidazopyrimidines
36Adenine134.0478158.4262,083,855Imidazopyrimidines
37Hypoxanthine135.0314167.125862,657.7Imidazopyrimidines
38Uric acid167.0213329.31611,690.18Imidazopyrimidines
39Xanthine151.0262214.702242,308.8Imidazopyrimidines
40Cytosine112.0496196.825315,753.4Diazines
41Uracil113.0333159.34835,649.18Diazines
42Thymine125.035472.761549,390.5Diazines
43Uracil111.0196284.66819,261.86Diazines
44Orotate155.0098248.47270,828.3Diazines
45Nicotinate122.0245227.376533,340.51Pyridines and derivatives
46Pyridoxine170.0804132.01165,434.2Pyridines and derivatives
47Nicotinate124.0377232.26390,057.04Pyridines and derivatives
48Benzoic acid121.029315.86222,075.6Benzene and substituted derivatives
49Tyramine138.0904214.19425,018.14Benzene and substituted derivatives
50Hippuric acid178.0505193.264609,644Benzene and substituted derivatives
51Palmitic acid255.232849.6426,242,620Fatty Acyls
52Linoleic acid279.233936.7331,609,868Fatty Acyls
53Heptadecanoic acid269.248948.362181,756.1Fatty Acyls
54Riboflavin377.1541250.7723816.888Pteridines and derivatives
55Riboflavin435.1591262.953317.16Pteridines and derivatives
561,2-dioleoyl-sn-glycerol-3-phosphatidylcholine786.596895.83185,056.19Glycerophospholipids
57sn-Glycerol 3-phosphoethanolamine216.0617393.80359,884.73Glycerophospholipids
58Norharmane169.075472.333164,364.3Indoles and derivatives
59Indolelactic acid204.0666159.156112,7278Indoles and derivatives
60Ile-Pro229.1539272.911246,708.1Pyrimidine nucleosides
61Deoxythymidine 5′-phosphate (dTMP)321.0482411.01110,607.32Pyrimidine nucleotides
62Cholesteryl sulfate465.302326.694165,882.3Steroids and steroid derivatives
63Cholic acid426.3198226.8845626.847Steroids and steroid derivatives
64(S)-2-Hydroxyglutarate147.0304392.132111,556.1Hydroxy acids and derivatives
65Dopamine136.0745255.0829297.395Phenols
66Bestatin350.204200.34535,719.13Peptidomimetics
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhao, Y.; Zheng, Y.; Xie, K.; Hou, Y.; Liu, Q.; Jiang, Y.; Zhang, Y.; Man, C. Combating Obesity: Harnessing the Synergy of Postbiotics and Prebiotics for Enhanced Lipid Excretion and Microbiota Regulation. Nutrients 2023, 15, 4971. https://doi.org/10.3390/nu15234971

AMA Style

Zhao Y, Zheng Y, Xie K, Hou Y, Liu Q, Jiang Y, Zhang Y, Man C. Combating Obesity: Harnessing the Synergy of Postbiotics and Prebiotics for Enhanced Lipid Excretion and Microbiota Regulation. Nutrients. 2023; 15(23):4971. https://doi.org/10.3390/nu15234971

Chicago/Turabian Style

Zhao, Yueming, Yaping Zheng, Kui Xie, Yanmei Hou, Qingjing Liu, Yujun Jiang, Yu Zhang, and Chaoxin Man. 2023. "Combating Obesity: Harnessing the Synergy of Postbiotics and Prebiotics for Enhanced Lipid Excretion and Microbiota Regulation" Nutrients 15, no. 23: 4971. https://doi.org/10.3390/nu15234971

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop