Skip to main content
NCBI home page
Environment & Health logoLink to Environment & Health
. 2026 Jan 2;4(5):965–977. doi: 10.1021/envhealth.5c00304

Foodborne Carbon Dots Trigger Appetite Suppression: Mechanistic Insights from the Microbiota–Gut–Brain Axis

Chunli Lei 1, Rui Liang 1, Bingxu Cheng 1, Xuesong Cao 1, Junyi Zhang 1, Chuanxi Wang 1,*, Zhenyu Wang 1
PMCID: PMC13185050  PMID: 42164875

Abstract

Foodborne carbon dots (FCDs) are an overlooked ingestible nanoscale food substance, and the potential ingestion risks associated with persistent dietary preferences need to be clearly articulated. In this study, the effects of FCDs and glucose CDs on feeding behavior in mice were elucidated from the perspectives of gut microbiome, metabolic flow monitoring, and metabolomics. The results demonstrated that persistent intake of FCDs (25 mg/kg/day) for 30 days significantly caused diminished appetite. Mechanistic findings suggested that FCDs were involved in the regulation of appetite reduction through the microbiota–gut–brain axis. FCDs induce perturbations in the gut microbiota, leading to increased levels of intestinal inflammation. In addition, the intake of FCDs was directly involved in microbial metabolism to generate SCFAs, activate intestinal GPR-41/43, promote the release of glucagon-like peptide, and further stimulate the hypothalamus to significantly downregulate the expression of AGRP and upregulate the expression of POMC, ultimately leading to the reduction of appetite. Moreover, intake of FCDs regulates intestinal fatty acid metabolism, amino acid metabolism, and other factors affecting the level of health of the organism. The present work highlights the impact of dietary intake of source FCDs on ingestion, providing unique perspectives on the potential health threats of FCDs.

Keywords: foodborne carbon dots, appetite, microbiota–gut–brain axis, metabolism

graphic file with name eh5c00304_0009.jpg

graphic file with name eh5c00304_0007.jpg

1. Introduction

Currently, virtually all research on the biosafety of nanoparticles in food has been focused on engineered nanoparticles employed as food additives, excluding endogenous nanoparticles in food. In particular, carbon dots (CDs) have been recognized in the field of food safety detection and are mainly centered on the development as well as the application of food additives. However, we cannot ignore the fact that food components are prone to carbonation at high temperatures and foodborne CDs (FCDs) are generated during cooking. , These FCDs are particularly prevalent in everyday human diets such as beverages, baked goods, and grilled meats. − Several studies have demonstrated that FCDs (>50 mg/L) accumulated intracellularly and exhibited significant cytotoxicity, inducing disturbances in cellular energy metabolism, reducing the normal physiological functions of various digestive enzymes, and even inducing disorders of lipid metabolism and glucose metabolism in organisms. − However, more evidence is needed to determine the response of organisms to sustained FCDs intake.

A limited number of studies have demonstrated that carbon materials alter the feeding behavior of the individual. − Plant-derived turmeric nanofibers were effective in suppressing appetite and reversing the differentiation of feeding neurons in the “gut–brain” axis. Liu et al. reported the blocking effect of nanocrystalline cellulose on ingestive behavior. Alternatively, cellulose nanofibrils reduced fat absorption in the jejunum and attenuated Western diet-induced fatty liver, while slightly decreasing body weight and affecting glucose homeostasis. Nanocrystalline barley β-glucan improved metabolic condition and regulated gut hormone levels, thereby further increasing plasma levels of glucagon-like peptide-1 (GLP-1) and suppressing appetite. Generally, carbon nanomaterials appeared to exert a positive effect on weight mitigation and appetite suppression. However, it is unclear whether the ingestion of FCDs modulates appetite. In particular, the precise details of the mechanisms underlying FCDs in appetite regulation have been elucidated.

The correlation between gut microbes and the metabolic health of the organism has been widely established. − These metabolites are involved in a variety of host processes, including gastropub regulation, blood pressure regulation, and neuroimmune and have been closely associated with a number of neurological disorders, including anorexia nervosa, Parkinson’s disease, and Alzheimer’s disease. − Remarkably, the microbe-gut–brain concept highlights the potential of the gut microbiota to exert control in the nervous system. , More specifically, short-chain fatty acids (SCFAs) are susceptible to dietary regulation as energy substrates linking dietary and gut microbes, but exogenous ingested substances have been shown to also participate in the metabolic processes of SCFAs, mediating neurological disorders through the gut–brain axis. , Furthermore, recent studies have documented that the consumption of CDs altered the original structural state of the gut microbiota. , Zhang et al. indicated that CDs exposure decreased the abundance of beneficial bacteria (Bacteroides, Coprococcus, etc.), among which Bacteroides could affect appetite and feeding behavior by directly influencing the sensory, appetite, and satiety regulatory systems of the ingested food. In particular, Bacteroides could influence the appetite and feeding behavior of the host by directly affecting the sensory, appetite, and satiety regulatory systems of ingested food. Thus, we hypothesized that the disturbance of gut microbes by FCDs may allow them to become disruptive to diet-induced metabolism and further influence digestive behavior via the gut–brain axis.

In this study, as the basic element in the most common foods, we used glucose as a raw material to synthesize FCDs and fed to mice. Then, we systematically elucidated the exact mechanism underlying the sequential uptake of FCDs for appetite suppression via microbiota–gut–brain by integrating microbial, isotopic metabolic flow analysis and metabolomics. This study will provide guidance on the health effects of sustained intake of foodborne FCDs on organisms and provide us with new insights into the potential risks of dietary preferences.

2. Materials and Methods

2.1. Preparation and Characteristic of FCDs

FCDs were synthesized as previously described and modified as appropriate. Briefly, 461 mg of d-glucose (U–13C, 98–99%) (Cambridge Isotope Laboratories, Inc., Andover, America) was dissolved in 30 mL of ultrapure water, reacted with Teflon liners in a stainless-steel reactor, and heated to 160 °C for 8 h. When cooled to room temperature, large particles were subsequently removed by filtration with a 0.22 μm membrane, followed by dialysis in ultrapure water on a dialysis bag (500 Da) (Solarbio, China) for 24 h. Finally, the resulting solution was freeze-dried into a solid, collected, and dried for long-term storage in the dark. Material-related characterization results for FCDs are shown in the Supporting Information (Text S1 and Figure S1).

2.2. Animal Experimental Design and Treatment

All mice were approved by the Jiangsu Provincial Animal Society. Twenty-four SPF grade male C57BL/6J mice (5 weeks, weight: 18 ± 0.5 g) were selected from SPF biotechnology Co., Ltd. (Beijing, China). During domestication and experiment, all mice were raised in a barrier environment controlled for temperature (25 ± 0.5 °C), humidity (60 ± 5%), and a 12 h light–dark cycle. The mice feed was purchased from Jiangsu Synergy Pharmaceutical Bioengineering Co., Ltd. (XTC01WC-001, China). All mice were fed animal feed sterilized with 60 Co-γ irradiation and filtered water. The mice fed food and water were adapted to feeding for 7 days and then randomly divided into 4 groups: control group (Con), 1 mg/kg·bw FCDs-treated group (CDs-1), 10 mg/kg·bw FCDs-treated group (CDs-10), and 25 mg/kg·bw FCDs-treated group (CDs-25). After 4 weeks of exposure, the mice were anesthetized via intraperitoneal injection of sodium pentobarbital. Following confirmation of deep anesthesia through the absence of pedal reflex, cervical dislocation was performed to ensure humane euthanasia. Serum, feces, brain, and colon tissues were collected for follow-up analysis. All experimental procedures were conducted in accordance with the Guidelines for the Care and Use of Experimental Animals of Jiangnan University, and the animal ethics approval number was JN. No20240415c0400626[172]. All procedures were carried out strictly in accordance with the European Community guidelines (Directive 2010/63/EU) on the protection of animals used for scientific purposes.

2.4. Analysis of Hematoxylin and Eosin (H&E)

The colon and brain were immobilized with 4% paraformaldehyde and then paraffin-embedded. Sections of fixed tissue (5 μm) were labeled with hematoxylin and eosin (H&E). The morphological variation images were collected by confocal fluorescence microscopy (Nikon Corporation, Tokyo, Japan).

2.5. Biochemical Analyses

At the end of the 4-week experimental period, the mice were sacrificed, and serum was separated after blood was taken intravenously. The concentrations of serum and colon PYY (Mouse/Rat PYY ELISA Kit, Mskbio, Wuhan, China) and GLP-1 (GLP-1 (Active) ELISA Kit, Mskbio, Wuhan, China) were determined by ELISA kit assay protocol as described.

2.6. RNA Extraction and qPCR Analysis

Total RNA was extracted from colon and brain tissues using the TRNzol Universal Total RNA Extraction Reagent (KWBIO, Jiangsu, China), following the manufacturer’s protocol. The extracted RNA (5 μg) was reverse-transcribed into cDNA using a RevertAid First Strand cDNA Synthesis Kit (Sangon Biotech (Shanghai) Co., Ltd, China). The qPCR analysis was performed on an ABI 7500 Real Time PCR System (Applied Biosystems) with a 20 μL reaction mixture containing 10 μL of SYBR Green qPCR Master Mix (Selleck), 0.5 μL of each 10 μmol/L forward and reverse primer, 1 μL of cDNA (100 ng), and RNase/DNase-free ddH2O up to 20 μL. Data analysis was conducted employing the 2–ΔΔCt method to calculate relative gene expression changes compared to control samples. The primer sequences are shown in Table S1.

2.7. Analysis of Gut Microbiota

Fecal DNA was extracted from each group of frozen samples using the DNA LQ Kit (Magen, Guangdong, China). PCR amplification of the V3–V4 regions of the bacterial 16S-rRNA gene was performed using universal primer pairs (343F: 5′-TACGGRAGGCAGCAG-3′; 798R: 5′-AGGGTATC-TAATCCT-3′). The reverse primer included a unique barcode identifier, and both primers were ligated with an Illumina sequencing adapter. The 16S-rRNA gene sequences were processed by using the Illumina MiSeq system. The library construction, sequencing, and data analysis were performed by Shanghai Personal Biotechnology Co., Ltd. Based on the sequencing results, the α-diversity analysis including species richness estimators of Simpson, Shannon, and Chao1 indices and the β-diversity analysis including hierarchical clustering analysis were performed.

2.8. Isotope Tracing of SCFAs Metabolism in Mouse Intestine

20 mg of intestinal content was weighed, 1000 μL of 80% methanol was added, and the target metabolite was extracted by thorough shaking and grinding. 30 μL of supernatant solution was taken after centrifugation, 10 μL of internal standard solution was added, and then derivatization was carried out by adding 10 μL of derivatization reagent A (200 mmol/L 3-NPH in 75% aqueous methanol) and 10 μL of derivatization reagent B (96 mmol/L EDC-6% pyridine solution in methanol) at 30 °C for 30 min. After the reaction, the supernatant was centrifuged and analyzed. The supernatant was analyzed by using a Thermo Vanquish Flex (Thermo). The details are provided in the Supporting Information (Text S2, Table S2, and Figure S2).

2.9. Analysis of Intestinal Off-Target Metabolism

The samples were frozen in liquid nitrogen and ground with a pestle and mortar. 100 mg of sample was mixed with 1 mL of cold methanol/acetonitrile/H2O (2:2:1,v/v/v). The homogenate was sonicated at low temperatures (30 min/once, twice). The mixture was centrifuged for 20 min (14,000g, 4 °C). The supernatant was dried in a vacuum centrifuge. For LC-MS analysis, the samples were redissolved in 100 μL of acetonitrile/water (1:1, v/v) solvent. Analysis was performed using an UHPLC (1290 Infinity LC, Agilent Technologies) coupled to a quadrupole time-of-flight (AB Sciex TripleTOF 6600, Shanghai, China). The details are provided in the Supporting Information (Text S3).

2.10. Statistical Analysis

Data were presented as mean ± SEM. All significance analyses were conducted using one-way analysis of variance and Student’s t test. p-Values <0.05 were considered significant. All comparisons were performed using GraphPad Prism version 5.0.

3. Results and Discussion

3.1. Changes in Body Growth Parameters during the Experiment

Mice were randomized into 4 treatments, oral delivery of sterile water and FCDs solutions (1, 10, and 25 mg/L) for 4 weeks (Figure a). The changes were recorded in the weekly body weights of mice throughout the experiment. Compared with Con, the body weights of mice in all treatment groups commenced to show a decreasing trend after FCDs gavage and exhibited a concentration correlation, with mice in the CDs-25-treated group displaying significant reductions by 2.6% (Figure b). In addition, the average body weight rate of the mice yielded consistent results, reducing with the increasing FCDs ingestion concentration (Figure c). Notably, the feed intake showed a gradual decrease over time after the intake of FCDs, especially after the treatment with high concentrations of FCDs, which showed a significant reduction in feed intake that began to appear in the third week and became more pronounced in the fourth week (Figure d). Among them, the daily intake of mice decreased by only 1.5% after 1 week of gavage in the CDs-25 group. With the extension of time, the food intake decreased significantly by 5.4% after 4 weeks for CDs-25 treatment, and the mice’s appetite was obviously reduced (Figure d). We further analyzed changes in the total caloric intake in mice, and the results showed that there was no significant change (Figure e). Therefore, the significant reduction in body weight observed in mice after 4 weeks of exposure to CDs-25 and no measurable difference in caloric intake is primarily attributed to the mutual regulation between digestive processes and associated hormones within the body. First, a smaller food intake may suffice to cover the calorie requirements of the organism of mice. CDs may more rapidly trigger satiety signals (such as reduced gastric distension despite adequate caloric intake), leading to diminished appetite. Second, CDs might more swiftly elevate blood glucose or stimulate the release of gut hormones, thereby suppressing appetite-related hormones and reducing hunger sensations. When long-term caloric intake remains stable, this effectively conveys a “sufficient energy” signal to the brain, thereby inhibiting appetite. Previous studies have reported that ingestion of graphene oxide has a hindering effect on nutrient absorption, leading to developmental delays in mice. This also suggested that carbon nanomaterials potentially exhibit detrimental effects on growth and development for mice.

1.

1

Open in a new tab

FCDs affect the growth and ingestion of mice. (a) Animal experimental schedule. (b) Change in mice body weight (n = 6). (c) Change in mice weight growth rate (%) (n = 6). (d) Mice food take (n = 3). (e) Total caloric intake (n = 3). The total caloric intake = caloric intake from diets + caloric intake from FCDs.

3.2. FCDs Ingestion Induced Gut Oxidative Stress and Inflammation

The intestinal tract is an important digestive and absorptive system for the organism’s intake of exogenous substances, and the implications of FCDs on the health status of the intestine after they arrive via ingestion need to be recognized. Healthy gut shapes were tightly aligned and the gut wall was in perfect condition (Figure a). The colonic tissues of mice after CDs-25 gavage showed significant infiltration of inflammatory cells, indicating that the ingestion of CDs-25 caused colonic tissue damage (Figure a). However, the changes in the brain tissue of mice were not obvious (Figure S3). Further analysis of oxidative stress levels in mice colon tissues revealed that SOD (35.3%), CAT (31.2%), and GSSG (57.9%) levels were significantly elevated in the CDs-25-treated group compared to the Con group (Figure b,c,f). Although MDA and GSH levels did not show significant changes, both presented a decreasing phenomenon (Figure d,e). In addition, the GSH: GSSG ratio also demonstrated a significant decrease (Figure g), which was commonly interpreted as evidence of a redox imbalance in the body and is associated with a variety of metabolic dysfunctions, and the ingestion of CDs-25 undoubtedly caused impairment to the gut environment. Overall, these results revealed that oxidative stress was induced by CDs-25 treatment, resulting in some degree of gut damage. Moreover, CDs-25 exposure caused an increase in the levels of proinflammatory factors IL-1β and IL-6 by 54.8 and 62.0%, respectively, suggesting that FCDs induced increased levels of intestinal inflammation. (Figure h,j). In contrast, levels of the anti-inflammatory factor IL-10 did not show a significant decrease (Figure i). A recent study reviewed that CDs increased intestinal permeability and led to a robust tendency toward intestinal inflammation. Typically, intestinal inflammation and oxidative stress can cause apoptosis of intestinal epithelial cells and damage to the intestinal mucus layer, thereby affecting the microbiota that colonizes the mucus layer that in turn provokes a systemic reaction.

2.

2

Open in a new tab

Effect of FCDs ingestion on the intestine. (a) H&E staining (from left to right: 10×, scale bar: 200 μm; 20×, scale bar: 100 μm; 40×, scale bar: 50 μm) (n = 3). Levels of gut oxidative stress, including (b) SOD, (c) CAT, (d) MDA, (e) GSH, (f) GSSH, and (g) GSH: GSSG (n = 6). Levels of gut inflammatory factors, including (h) IL-1β, (i) IL-10, and (j) IL-6 (n = 6).

3.3. FCDs Modified the Gut Microbiota

Comparison with Con, CDs-25 intake resulted in increased gut microbiota α diversity indices, including Chao 1, Shannon, and Simpson indices (Figures a–c and S4). In addition, PCoA also showed significant differences after 4 weeks between them (Figures d and S5). The weekly course of gut microbiota changes became more pronounced over time at the phylum level (Figure S6). The weekly change in the gut microbial sparing curve with exposure time after FCDs ingestion showed a consistent trend (Figure S7). At the phylum level, a significant increase in the abundance of Firmicutes_A and Campylobacterota was observed after 4 weeks of CDs-25 exposure, whereas the CDs-25 group exhibited a significant decrease in the abundance of Firmicutes_D and Bacteroidota compared to the Con group (Figure e). At the genus level, significant differences were observed between the Con and CDs-25 groups (Figure f). The abundance of Faecalibaculum, Ligilactobacillus, and Dubosiella was significantly increased, but the abundance of Bifidobacterium, Cryptobacteroides, and Desulfovibrio_R was significantly decreased (Figure f). Previous studies have reported that acetate consumption is the main driver of butyrate production by Faecalibaculum. The increase in Faecalibaculum undoubtedly enhanced the utilization of carbon source material in the intestine. As a major metabolic end product of carbohydrate fermentation, lactate participates in several important metabolic processes in the body, including amino acid metabolism, fatty acid metabolism, and purine metabolism. , To further characterize the specific bacterial taxa altered after CDs-25 ingestion, specific changes were compared using LefSe analysis (Figures g,h and S8). Differential species after CDs-25 ingestion in the first week were mainly focused on o_Oscilospirales and o_TANB77 (Figure g). In contrast, the most pronounced perturbation of the abundance of CDs-25 was recorded after 4 weeks for p_Firmicutes_A (Figure h). Furthermore, the KEGG pathway analysis of gut microbial metabolites after FCDs ingestion suggested that microbial changes are closely related to alterations in the basal metabolism of the organism (Figure S9). Accordingly, as a core microbiota of SCFAs-producing microorganisms in the intestinal tract, it was highly feasible that CDs-25 could be utilized for their uptake and production of relevant metabolites involved in intestinal physiological activities.

3.

3

Open in a new tab

FCDs remodeled the gut microbial composition of mice. α-Diversity analyzed by (a) Shannon, (b) Chao 1, and (c) Simpson’s index (n = 3). (d) β-Diversity analyzed by principal coordinate analysis (PCoA) of the Bray–Curtis distance (n = 3). (e) Abundance of gut microbiota at the phylum level for each group (top 10) (n = 3). (f) Abundance of gut microbiota at the genus level for each group (top 30) (n = 3). Taxonomic branching plots from LEfSe analyses after (g) 1 week and (h) 4 weeks of FCDs exposure (n = 3).

3.4. FCDs Changed Gut SCFAs Content

Metabolic flow mass spectrometry techniques well validated this argument and revealed the equivalent detection of 9 SCFAs in acetic, propionic, butyric, isobutyric, valeric, and isovaleric acids (Figure a–i). Compared with the Con group, the content of SCFAs in the feces of mice fed with FCDs increased (Figure a–i). Specifically, the content of acetic acid in the CDs-25 group increased significantly by 57.0% compared with the Con group (Figure a). Although the content of butyric acid did not show significance, it was still 62.0% higher than that in the feces of mice not exposed to FCDs (Figure d). Furthermore, although we attempted to use the 13C isotope metabolic flow to track its specific activities, we did not detect 13C signals in these SCFAs. The most likely reason lies in the low limit of the detection signals, and the availability of FCDs cannot be denied. A recent report precisely identified the specific metabolic pathway by which Escherichia coli directly utilizes carbon nanomaterials as a carbon source and generates SCFAs. This indicated that FCDs would undoubtedly be directly degraded as dietary fiber after intake, participating in the in vivo fermentation process of SCFAs in the body, thereby stimulating the body to produce more SCFAs and further participating in physiological metabolic processes. The heat map analysis results of the correlation between SCFAs and species at the microbial genus level in the Mantel test showed a strong positive correlation, indicating that the changes in intestinal microbiota dominated the increase in the level of SCFAs in the intestinal tract of mice (Figure j). In summary, these findings implied that CDs-25 were directly utilized by gut microorganisms for physiological activities and generation of SCFAs after CDs-25 ingestion and further participated immediately in the regulation of feeding behaviors. SCFAs represent important regulators of energy metabolism and could affect appetite and energy intake through multiple mechanisms. Among them, acetic acid, the main metabolite of SCFAs, is an important signaling molecule in the regulation of energy metabolism. A considerable number of studies have confirmed that SCFAs can stimulate the production of satiety hormone. It has been found that endocrine hormones such as GLP-1, PYY, and leptin, which are produced by combining SCFAs with GPCRs, can improve excess appetite by increasing satiety. A previous study intraperitoneally injected three molecules of SCFAs (6 mmol/kg) into fasted mice and found that they could inhibit food intake for a short period of time (0.5–1 h) through activation of vagal afferent nerves, with anorexia intensity of butyric acid > propionic acid > acetic acid. Nevertheless, excessive SCFAs often cause irreversible damage to the body as well, such as reduced intestinal barrier function due to elevated intestinal trans fatty acids, leaky gut, and adverse effects on the digestive system. , Furthermore, excess SCFAs produced during the fermentation of intestinal flora may directly induce the development of autism. These seem to be interconnected with FCDs induced increased levels of intestinal inflammation.

4.

4

Open in a new tab

Quantitative detection of SCFAs in mouse feces after FCDs exposure (n = 3), including: (a) acetic acid, (b) propionic acid, (c) isobutyric acid, (d) butyric acid, (e) valeric acid, (f) isovaleric acid, (g) isocaproic acid, (h) caproic acid, and (i) 2-methylbutyric acid. (j) Analysis of the Mantel test correlation heat map between SCFAs and species at the microbial genus level.

3.5. FCDs Modified the Gut Metabolic Profile of Mice

Nontargeted metabolomics was used to assess the changes in gut metabolic profiles after CDs-25 exposure. Obvious segregation trends were observed between the Con group and the CDs-25 group for plots of PCA scores in positive ion mode and negative ion mode, suggesting that metabolic profiles and physiological states differed significantly between the two cohorts and that CDs-25 could induce metabolic disturbances in mice (Figure a,b). Volcano plots of differential metabolites revealed differential microbial metabolism after FCDs exposure (Figure c). To identify differences between groups and to determine potential biomarkers, pattern recognition analysis was performed using OPLS-DA (Figure d,e). The results showed a clear separation between the Con and CDs-25 groups, providing a clear distinction. Metabolites with VIP > 1 and p < 0.05 were then identified based on MS/MS spectra of the metabolites compared with online databases such as HMDB and METLIN. Ultimately, 29 potential biomarkers were identified and detailed information on the metabolites is shown in Table S3. Metabolite set enrichment overview is illustrated in Figure f. In order to clearly reveal the differences in the expression of potential metabolic biomarkers, a heat map analysis was constructed (Figure g). Linoleic acid, 13-keto-9z,11e-octadecadienoic acid, linolenic acid, and leukotriene d4 were significantly downregulated in the CDs-25 group compared with the Con group (Figure g). In contrast, metabolites including N-acetyl-d-glucosamine 6-phosphate, indole-3-pyruvic acid, and norleucine were significantly upregulated (Figure g). Twenty-nine perturbed metabolite levels were significantly modulated after CDs-25 intervention, with downregulation of 8 metabolites and upregulation of 21 of them. To further visualize the effects of FCDs exposure on metabolic pathways in mice, a pathway analysis of differential metabolites was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The results demonstrated that the effects of CDs-25 on metabolic pathways mainly involved five pathways, namely, fatty acid metabolism, purine and porphyrin metabolism (Figure h). And the metabolic regulatory network mapping of potential biomarkers was constructed based on the above results. The results indicated that sustained intake of CDs-25 caused potential metabolic perturbations and was directly associated with fluctuations in the SCFAs content. Previous studies have confirmed that gut microbes produce SCFAs mainly through the glycolytic pathway. , Acetic acid is the SCFAs with the highest concentration in the organism and is central to the carbohydrate and lipid metabolic pathways. In addition, some researches showed using radioisotope analysis that the main pathway for bacterial production of acetic acid is the Wood-Ljungdahl pathway; propionic acid is a central metabolite in the metabolism of odd-chain fatty acids, which is usually produced by the CO2 fixation pathway; butyric acid is formed by the condensation of acetyl coenzyme A by several specialized anaerobic bacteria. , SCFAs were directly or indirectly involved in the metabolic physiological activities of the organism as the major carbon flux from CDs-25 uptake through the intestinal flora to the host.

5.

5

Open in a new tab

FCDs modified gut microbial metabolism in mice. Plots of PCA scores in (a) positive ion mode and (b) negative ion mode (n = 3). (c) Volcano plot of differential metabolites (n = 3). OPLS-DA score plots in (d) positive and (e) negative ion mode (n = 3). (f) Metabolite sets enrichment overview (n = 3). (g) Heat map of differential metabolites (n = 3). (h) Correlation network of perturbed metabolic pathways in response to CDs (n = 3).

3.6. Mechanistic Analysis of FCDs for Appetite Suppression

Subsequently, we examined the appetite-related changes in gut hormones and serum hormones after the gavage of CDs-25. The levels of the intestinal hormone PYY in the colon tissues and serum of mice fed with CDs-25 increased by 66.33 and 22.36% respectively, compared with the control group (Figure a,c). Similarly, GLP-1 showed similar results (Figure b,d). Elevated levels of the gut hormone PYY, an anorexigenic hormone, were directly demonstrated to suppress appetite in the brain, inhibit gut peristalsis, increase gut transit rate, and reduce energy acquisition from the diet. In addition, raised levels of the gut hormone GLP-1 promote systemic effects via the hypothalamus, increasing glucose-dependent insulin secretion and satiety, thereby decreasing appetite. , Therefore, to further elucidate the mechanism of the effect of CDs-25 on appetite, the variation of genes key to the action of gut hormones was further investigated. As predicted, sustained gavage of CDs-25 significantly altered the expression of appetite-regulating transcription factors, including intestinal GPR-41 and GPR-43, which were significantly upregulated by 45.3 and 56.2%, respectively (Figure e,f). Moreover, the expression of AGRP, a hypothalamic stimulatory factor critical for appetite regulation, was significantly downregulated by 29.6% and POMC significantly upregulated by 80.8% after treatment with CDs-25 (Figure g,h). As primary neurons that integrate hormonal and nutrient signals to maintain energy balance, the balance of both was disrupted after the intake of CDs-25, resulting in the phenomenon of appetite suppression and reduced food intake by the organism. Previous studies have demonstrated that the combination of SCFAs with GPR-41 and GPR-43 could promote the secretion of intestinal hormones, participating in gene regulation, intestinal barrier function regulation, immune regulation, oxidative stress, etc. , The conjugation of SCFAs with GPCRs produces endocrine hormones, such as GLP-1 and PYY, and these intestinal signals can be transmitted to the nucleus of the solitary tract via the vague nerve to, on the one hand, inhibit the activity of appetite-promoting AgRP neurons, and, on the other hand, activate the activity of appetite-suppressing POMC neurons, leading to appetite suppression. , Alternatively, the metabolite of SCFAs, acetic acid, could act on the hypothalamus directly through the blood–brain barrier and increase the expression of appetite-suppressing POMC, also causing a decrease in appetite. ,, Heat map analysis of the association between changes in gut microbiota genus levels and gut metabolism confirmed that changes in gut fatty acid metabolism and amino acid metabolism levels are strongly correlated with fluctuations in gut microbiota genus levels, indicating that FCDs intake regulates gut microbiota changes, which in turn alters the process of gut metabolism (Figure i). Furthermore, the correlation analysis also indicated a strong relevance of appetite regulation-related gut hormone levels and brain neuron expression to gut microbial fatty acid metabolism and amino acid metabolism (Figure j). These results demonstrated the precise mechanism of FCDs in regulating appetite through the “microbiota–gut metabolism-brain” axis. A recent study reported that gut microbiota metabolites influence host metabolism through multiple pathways, including direct interactions with the gastrointestinal tract and peripheral tissues, which then affect the enteric nervous system and directly induce vagal nerve signals to influence central appetite pathways, ultimately altering host appetite. It has been proposed that changes in ingestion behavior play an important regulatory role in body metabolism, and fatty acids in the body’s circulation are responsible for many aspects of homeostasis and are strongly linked to obesity and type II diabetes. Following the results of our work, FCDs ingestion regulated feeding behavior through the “microbiota–gut–brain” axis, eventually manifested as a decrease in the brain’s appetite for ingestion.

6.

6

Open in a new tab

FCDs for appetite suppression. Colon and serum levels of (a, c) PYY and (b, d) GLP-1 (n = 6). The relative levels of (e) GPR-41 and (f) GPR-43 mRNA in the colon (n = 6). The relative levels of (g) AgRP and (h) POMC mRNA in the brain (n = 6). (i) The correlation analysis of gut microbiota at the genus level to gut fatty acid metabolism and amino acid metabolism. (j) The correlation analysis of appetite regulation-related gut hormone levels and brain neuron expression to gut microbial fatty acid metabolism and amino acid metabolism.

4. Conclusion

The present study provided an intensive investigation into the potential effects and mechanisms of FCDs after ingestion in relation to appetite loss in digestive behavior by integrating microbiome, isotopic metabolism, and nontargeted metabolomics analyses. The results indicated that FCDs could regulate the composition of gut flora, and FCDs would be utilized by gut microorganisms as a carbon source to metabolize SCFAs after ingestion and further regulate gut metabolism, thus reducing the expression of factors related to appetite regulation. Mechanistic studies have shown that FCDs-induced appetite loss was associated with perturbation of the microbiota–gut–brain axis. FCDs induce alterations in the gut microbiota and promote intestinal inflammation. In addition, ingested FCDs transformed to generate SCFAs stimulate intestinal GPR-41/43 and downregulate the expression of the pro-appetite factor AGRP via the gut–brain axis, ultimately suppressing appetite. Moreover, FCDs intake activated physiological activities that regulate fatty acid metabolism, amino acid metabolism, etc. in the intestinal environment. However, other direct evidence of the effect of dietary intake of FCDs remains insufficient. Future studies will conduct the fecal microbiota transplantation (FMT) model to demonstrate the role of the microbiota and gut–brain axis to be highly research-worthy. These efforts will provide strong evidence to elucidate the risk of the ingestion of dietary-derived FCDs. Overall, this study revealed that the persistent intake of FCDs can lead to the onset of hyperphagia and has some negative effects on the health of the organism, providing insights into the potential intake risks of the human diet and theoretical support for potential threats to dietary preferences.

Supplementary Material

eh5c00304_si_001.pdf (1.1MB, pdf)

Acknowledgments

This research was supported by the National Natural Science Foundation of China (42421005 and 42322705), the Funded by Basic Research Program of Jiangsu (BK20230044), and the Fundamental Research Funds for the Central Universities (JUSRP622019).

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/envhealth.5c00304.

  • Characterization of FCDs (Text S1 and Figure S1); LC-MS analysis (Text S2); LC-MS/MS analysis (Text S3); spectral peaks of LC-MS analysis for the determination of SCFAs in mouse feces (Figure S2); H&E staining of mice brain (Figure S3); weekly changes in gut microbial of α-diversity (Figure S4); weekly changes in gut microbial of β-diversity (Figure S5); weekly changes in abundance of gut microbial at the phylum level (Figure S6); weekly change in gut microbial sparing curve with exposure time after FCDs ingestion (Figure S7); taxonomic branching plots from LEfSe analyses (Figure S8); KEGG pathway analysis of gut microbial metabolites after FCDs ingestion (Figure S9); paired primers for the RT-qPCR (Table S1); chromatographic gradient setting (Table S2); and potential biomarkers were screened and preliminarily identified in feces (Table S3) (PDF)

The authors declare no competing financial interest.

References

  1. Zhang T. R., Li L., Chunta S., Wu W., Chen Z. J., Lu Y.. Enhanced oral bioavailability from food protein nanoparticles: A mini review. J. Controlled Release. 2023;354:146–154. doi: 10.1016/j.jconrel.2022.12.043. [DOI] [PubMed] [Google Scholar]
  2. Liu C., Mei Y., Lei Q., Ma X., Nan X., Zhu Y., Liao J., Xu Y., Luo Y., Zhang H., Yang M., Lin X., Huang Q.. Fluorescent carbon dots based on food wastes: Applications in food safety detection. Chem. Eng. J. 2024;499:156434. doi: 10.1016/j.cej.2024.156434. [DOI] [Google Scholar]
  3. Luo X. L., Han Y., Chen X. M., Tang W. Z., Yue T. L., Li Z. H.. Carbon dots derived fluorescent nanosensors as versatile tools for food quality and safety assessment: A review. Trends Food Sci. Technol. 2020;95:149–161. doi: 10.1016/j.tifs.2019.11.017. [DOI] [Google Scholar]
  4. Chen J., Guo Y. X., Zhang X. L., Liu J. H., Gong P., Su Z. Q., Fan L. H., Li G. L.. Emerging Nanoparticles in Food: Sources, Application, and Safety. J. Agric. Food Chem. 2023;71(8):3564–3582. doi: 10.1021/acs.jafc.2c06740. [DOI] [PubMed] [Google Scholar]
  5. Zhang B. Y., Yu Z. T., Zhao X. Y., He T. Y., Fan X. P., Zhu R. J., Feng Y. J., Lu W. H., Qi D. P., Ma X., Gu N.. Foodborne Carbon Dots Aggravate High-Fat-Diet-Induced Glucose Homeostasis Imbalance by Disrupting the Gut-Liver Axis. ACS Appl. Mater. Interfaces. 2024;16(10):12263–12276. doi: 10.1021/acsami.3c17656. [DOI] [PubMed] [Google Scholar]
  6. Wang H. T., Xie Y. S., Liu S., Cong S., Song Y. K., Xu X. B., Tan M. Q.. Presence of Fluorescent Carbon Nanoparticles in Baked Lamb: Their Properties and Potential Application for Sensors. J. Agric. Food Chem. 2017;65(34):7553–7559. doi: 10.1021/acs.jafc.7b02913. [DOI] [PubMed] [Google Scholar]
  7. Huang T., Peng S. Y., Tu Z. C., Chao Y., Zhan S. N.. The carbon dots in toasted shrimp: Structural and biological characteristics. Food Biosci. 2024;58:103814. doi: 10.1016/j.fbio.2024.103814. [DOI] [Google Scholar]
  8. Song X. Y., Song Y. K., Guo Z. X., Tan M. Q.. Influence of protein coronas between carbon nanoparticles extracted from roasted chicken and pepsin on the digestion of soy protein isolate. Food Chem. 2022;385:132714. doi: 10.1016/j.foodchem.2022.132714. [DOI] [PubMed] [Google Scholar]
  9. Liu K. J., Song Y. K., Tan M. Q.. Toxicity Alleviation of Carbon Dots from Roast Beef after the Formation of Protein Coronas with Human Serum Albumin. J. Agric. Food Chem. 2020;68(36):9789–9795. doi: 10.1021/acs.jafc.0c03499. [DOI] [PubMed] [Google Scholar]
  10. Moradi M., Molaei R., Kousheh S. A., Guimaraes J. T., McClements D. J.. Carbon dots synthesized from microorganisms and food by-products: active and smart food packaging applications. Crit. Rev. Food Sci. Nutr. 2023;63(14):1943–1959. doi: 10.1080/10408398.2021.2015283. [DOI] [PubMed] [Google Scholar]
  11. Rui X., Fu K., Wang H., Pan T. G., Wang W. J.. Formation Mechanisms of Protein Coronas on Food-Related Nanoparticles: Their Impact on Digestive System and Bioactive Compound Delivery. Foods. 2025;14(3):512. doi: 10.3390/foods14030512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Zhang B. Y., Yu L. D., Zhu R. J., Wei X. J., Fan X. P., Hu H. L., Yang D. Q., Du H. N., Zhao M. M., Li L., Oh Y. R., Feng Y. J., Gu N.. Malting barley carbon dots-mediated oxidative stress promotes insulin resistance in mice via NF-κB pathway and MAPK cascade. J. Nanobiotechnol. 2022;20(1):331. doi: 10.1186/s12951-022-01543-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Wang J. L., Zhang T. Y., Gu R. L., Ke Y. Y., Zhang S. Q., Su X. Q., Pan X. P., He Q. X., Li G. L., Zhang Z. X., Zhang L. Y., Li J., Wu W. J., Chen C. X.. Development and Evaluation of Reconstructed Nanovesicles from Turmeric for Multifaceted Obesity Intervention. ACS Nano. 2024;18(34):23117–23135. doi: 10.1021/acsnano.4c05309. [DOI] [PubMed] [Google Scholar]
  14. Liu L., Kong F.. The behavior of nanocellulose in gastrointestinal tract and its influence on food digestion. J. Food Eng. 2021;292:110346. doi: 10.1016/j.jfoodeng.2020.110346. [DOI] [Google Scholar]
  15. Chen Y., Lin Y., Nagy T., Kong F., Guo T.. Subchronic exposure to cellulose nanofibrils induces nutritional risk by non-specifically reducing the intestinal absorption. Carbohydr. Polym. 2020;229:115536. doi: 10.1016/j.carbpol.2019.115536. [DOI] [PubMed] [Google Scholar]
  16. Miyamoto J., Watanabe K., Taira S., Kasubuchi M., Li X., Irie J., Itoh H., Kimura I.. Barley β-glucan improves metabolic condition via short-chain fatty acids produced by gut microbial fermentation in high fat diet fed mice. PLoS One. 2018;13(4):e0196579. doi: 10.1371/journal.pone.0196579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fan Y., Pedersen O.. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 2021;19(1):55–71. doi: 10.1038/s41579-020-0433-9. [DOI] [PubMed] [Google Scholar]
  18. Postler T. S., Ghosh S.. Understanding the Holobiont: How Microbial Metabolites Affect Human Health and Shape the Immune System. Cell Metab. 2017;26(1):110–130. doi: 10.1016/j.cmet.2017.05.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Armet A. M., Deehan E. C., O’Sullivan A. F., Mota J. F., Field C. J., Prado C. M., Lucey A. J., Walter J.. Rethinking healthy eating in light of the gut microbiome. Cell Host Microbe. 2022;30(6):764–785. doi: 10.1016/j.chom.2022.04.016. [DOI] [PubMed] [Google Scholar]
  20. Colella M., Charitos I. A., Ballini A., Cafiero C., Topi S., Palmirotta R., Santacroce L.. Microbiota revolution: How gut microbes regulate our lives. World J. Gastroenterol. 2023;29(28):4368–4383. doi: 10.3748/wjg.v29.i28.4368. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Duan W.-X., Wang F., Liu J.-Y., Liu C.-F.. Relationship Between Short-chain Fatty Acids and Parkinson’s Disease: A Review from Pathology to Clinic. Neurosci. Bull. 2024;40(4):500–516. doi: 10.1007/s12264-023-01123-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Huang Y., Wang Y. F., Miao J., Zheng R. F., Li J. Y.. Short-chain fatty acids: Important components of the gut-brain axis against AD. Biomed. Pharmacother. 2024;175:116601. doi: 10.1016/j.biopha.2024.116601. [DOI] [PubMed] [Google Scholar]
  23. Loh J. S., Mak W. Q., Tan L. K. S., Ng C. X., Chan H. H., Yeow S. H., Foo J. B., Ong Y. S., How C. W., Khaw K. Y.. Microbiota–gut–brain axis and its therapeutic applications in neurodegenerative diseases. Signal Transduction Targeted Ther. 2024;9(1):37. doi: 10.1038/s41392-024-01743-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Mann E. R., Lam Y. K., Uhlig H. H.. Short-chain fatty acids: linking diet, the microbiome and immunity. Nat. Rev. Immunol. 2024;24(8):577–595. doi: 10.1038/s41577-024-01014-8. [DOI] [PubMed] [Google Scholar]
  25. Zhang B., Fan X., Du H., Zhao M., Zhang Z., Zhu R., He B., Zhang Y., Li X., Li J., Gu N.. Foodborne Carbon Dot Exposure Induces Insulin Resistance through Gut Microbiota Dysbiosis and Damaged Intestinal Mucus Layer. ACS Nano. 2023;17(6):6081–6094. doi: 10.1021/acsnano.3c01005. [DOI] [PubMed] [Google Scholar]
  26. Yu M., Bing Y., Chen D.. The effects of gut microbiota on appetite regulation and the underlying mechanisms. Gut Microbes. 2024;16(1):2414796. doi: 10.1080/19490976.2024.2414796. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Kalyanaraman B., Cheng G., Hardy M.. Gut microbiome, short-chain fatty acids, alpha-synuclein, neuroinflammation, and ROS/RNS: Relevance to Parkinson’s disease and therapeutic implications. Redox Biol. 2024;71:103092. doi: 10.1016/j.redox.2024.103092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Lei C., Tao M., Xu L., Yue L., Cao X., Cheng B., Wang C., Wang Z.. Different functional groups of carbon dots influence the formation of protein crowns and pepsin characteristic in vitro digestion. Food Chem. 2024;440:138224. doi: 10.1016/j.foodchem.2023.138224. [DOI] [PubMed] [Google Scholar]
  29. Zimmerman C. A., Knight Z. A.. Layers of signals that regulate appetite. Curr. Opin. Neurobiol. 2020;64:79–88. doi: 10.1016/j.conb.2020.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Bany Bakar R., Reimann F., Gribble F. M.. The intestine as an endocrine organ and the role of gut hormones in metabolic regulation. Nat. Rev. Gastroenterol. Hepatol. 2023;20:784–796. doi: 10.1038/s41575-023-00830-y. [DOI] [PubMed] [Google Scholar]
  31. Hu F., Yu L., Tu Y., Huang R., Yan X., Wang L., Wei Y., Wei Y., Wu Z.. A high-fiber diet mimics aging-related signatures of caloric restriction in mammals. Nat. Commun. 2025;16:5917. doi: 10.1038/s41467-025-61046-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hashemi E., Giesy J. P., Liang Z., Akhavan O., Tayefeh A. R., Joupari M. D., Sanati M. H., Shariati P., Shamsara M., Farmany A.. Impacts of graphene oxide contamination on a food web: Threats to somatic and reproductive health of organisms. Ecotoxicol. Environ. Saf. 2024;285:117032. doi: 10.1016/j.ecoenv.2024.117032. [DOI] [PubMed] [Google Scholar]
  33. Giustarini D., Colombo G., Garavaglia M. L., Astori E., Portinaro N. M., Reggiani F., Badalamenti S., Aloisi A. M., Santucci A., Rossi R., Milzani A., Dalle-Donne I.. Assessment of glutathione/glutathione disulphide ratio and S-glutathionylated proteins in human blood, solid tissues, and cultured cells. Free Radical Biol. Med. 2017;112:360–375. doi: 10.1016/j.freeradbiomed.2017.08.008. [DOI] [PubMed] [Google Scholar]
  34. Schwärzler J., Mayr L., Grabherr F., Tilg H., Adolph T. E.. Epithelial metabolism as a rheostat for intestinal inflammation and malignancy. Trends Cell Biol. 2024;34(11):913–927. doi: 10.1016/j.tcb.2024.01.004. [DOI] [PubMed] [Google Scholar]
  35. Van-Wehle T., Vital M.. Investigating the response of the butyrate production potential to major fibers in dietary intervention studies. npj Biofilms Microbiomes. 2024;10(1):63. doi: 10.1038/s41522-024-00533-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Liao C., Rolling T., Djukovic A., Fei T., Mishra V., Liu H., Lindberg C., Dai L., Zhai B., Peled J. U., van den Brink M. R. M., Hohl T. M., Xavier J. B.. Oral bacteria relative abundance in faeces increases due to gut microbiota depletion and is linked with patient outcomes. Nat. Microbiol. 2024;9(6):1555–1565. doi: 10.1038/s41564-024-01680-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Brooks G. A.. Lactate as a fulcrum of metabolism. Redox Biol. 2020;35:101454. doi: 10.1016/j.redox.2020.101454. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Rabinowitz J. D., Enerbäck S.. Lactate: the ugly duckling of energy metabolism. Nat. Metab. 2020;2(7):566–571. doi: 10.1038/s42255-020-0243-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Cui X., Wang X., Chang X., Chen C.. et al. A new capacity of gut microbiota: Fermentation of engineered inorganic carbon nanomaterials into endogenous organic metabolites. Proc. Natl. Acad. Sci. U. S. A. 2023;120(20):e2218739120. doi: 10.1073/pnas.2218739120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Chambers E. S., Morrison D. J., Frost G.. Control of appetite and energy intake by SCFA: what are the potential underlying mechanisms? Proc. Nutr. Soc. 2015;74(3):328–336. doi: 10.1017/S0029665114001657. [DOI] [PubMed] [Google Scholar]
  41. van der Hee B., Wells J. M.. Microbial Regulation of Host Physiology by Short-chain Fatty Acids. Trends Microbiol. 2021;29(8):700–712. doi: 10.1016/j.tim.2021.02.001. [DOI] [PubMed] [Google Scholar]
  42. Xu Y., Zhu Y., Li X., Sun B.. Dynamic balancing of intestinal short-chain fatty acids: The crucial role of bacterial metabolism. Trends Food Sci. Technol. 2020;100:118–130. doi: 10.1016/j.tifs.2020.02.026. [DOI] [Google Scholar]
  43. Zhang D., Jian Y. P., Zhang Y. N., Li Y., Gu L. T., Sun H. H., Liu M. D., Zhou H. L., Wang Y. S., Xu Z. X.. Short-chain fatty acids in diseases. Cell Commun. Signaling. 2023;21(1):212. doi: 10.1186/s12964-023-01219-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Serino M.. SCFAs - the thin microbial metabolic line between good and bad. Nat. Rev. Endocrinol. 2019;15(6):318–319. doi: 10.1038/s41574-019-0205-7. [DOI] [PubMed] [Google Scholar]
  45. Murugesan S., Nirmalkar K., Hoyo-Vadillo C., García-Espitia M., Ramírez-Sánchez D., García-Mena J.. Gut microbiome production of short-chain fatty acids and obesity in children. Eur. J. Clin. Microbiol. Infect. Dis. 2018;37(4):621–625. doi: 10.1007/s10096-017-3143-0. [DOI] [PubMed] [Google Scholar]
  46. Goswami C., Iwasaki Y., Yada T.. Short-chain fatty acids suppress food intake by activating vagal afferent neurons. J. Nutr. Biochem. 2018;57:130–135. doi: 10.1016/j.jnutbio.2018.03.009. [DOI] [PubMed] [Google Scholar]
  47. Kierans S. J., Taylor C. T.. Glycolysis: A multifaceted metabolic pathway and signaling hub. J. Biol. Chem. 2024;300(11):107906. doi: 10.1016/j.jbc.2024.107906. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Xie C., Qi C., Zhang J., Wang W., Meng X., Aikepaer A., Lin Y., Su C., Liu Y., Feng X., Gao H.. When short-chain fatty acids meet type 2 diabetes mellitus: Revealing mechanisms, envisioning therapies. Biochem. Pharmacol. 2025;233:116791. doi: 10.1016/j.bcp.2025.116791. [DOI] [PubMed] [Google Scholar]
  49. Miller T. L., Wolin M. J.. Pathways of acetate, propionate, and butyrate formation by the human fecal microbial flora. Appl. Environ. Microbiol. 1996;62(5):1589–1592. doi: 10.1128/aem.62.5.1589-1592.1996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Byrne C. S., Chambers E. S., Morrison D. J., Frost G.. The role of short chain fatty acids in appetite regulation and energy homeostasis. Int. J. Obes. 2015;39(9):1331–1338. doi: 10.1038/ijo.2015.84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Larraufie P., Martin-Gallausiaux C., Lapaque N., Dore J., Gribble F. M., Reimann F., Blottiere H. M.. SCFAs strongly stimulate PYY production in human enteroendocrine cells. Sci. Rep. 2018;8(1):74. doi: 10.1038/s41598-017-18259-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Ge X., Cheng L., Liu Y., Wu Z., Zhang X.. Regulation of the gut microbiota by diet and exercise: Improvements in cognition and emotion. Future Foods. 2023;8:100256. doi: 10.1016/j.fufo.2023.100256. [DOI] [Google Scholar]
  53. Lee D. H., Kim M. T., Han J. H.. GPR41 and GPR43: From development to metabolic regulation. Biomed. Pharmacother. 2024;175:116735. doi: 10.1016/j.biopha.2024.116735. [DOI] [PubMed] [Google Scholar]
  54. Wu S., Wu Q., Wang J., Li Y., Chen B., Zhu Z., Huang R., Chen M., Huang A., Xie Y., Jiao C., Ding Y.. Novel Selenium Peptides Obtained from Selenium-Enriched Cordyceps militaris Alleviate Neuroinflammation and Gut Microbiota Dysbacteriosis in LPS-Injured Mice. J. Agric. Food Chem. 2022;70(10):3194–3206. doi: 10.1021/acs.jafc.1c08393. [DOI] [PubMed] [Google Scholar]
  55. Chen X., Memory Kunda L. S., Li X., Wang N., Huang Y., Hao Y., He Q., Liao W., Chen J.. A Comprehensive Review of Beneficial Effects of Phytosterols on Glycolipid Metabolism and Related Mechanisms. J. Agric. Food Chem. 2025;73(7):3826–3841. doi: 10.1021/acs.jafc.4c10375. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

eh5c00304_si_001.pdf (1.1MB, pdf)

Articles from Environment & Health are provided here courtesy of American Chemical Society

RESOURCES