Gut microbiota-derived 4-hydroxyphenylacetic acid (4-HPAA) inhibits weight gain and is negatively associated with childhood obesity

Introduction

In recent years, childhood obesity has increased rapidly and become a serious global public health problem. The prevalence of childhood obesity rose dramatically from 4% in 1975 to 18% in 2016 (1). Beyond raising the risk of metabolic diseases such as diabetes, childhood obesity causes more profound damage compared to adult obesity, impacting development, behavior, and mental health. Due to the unique physiological background of children, the pathological mechanisms of childhood obesity are distinct. However, these mechanisms are not yet fully understood.

Childhood obesity is closely linked to the development of obesity in adulthood. The composition of the gut microbiome in children is influenced by various factors, including maternal prenatal conditions, mode of delivery, and feeding practices. Similar to adults, dysbiosis of the gut microbiome and its metabolites—such as short-chain fatty acids (SCFAs), bile acids (BAs), indoles, and their derivatives—plays a significant role in the development and progression of childhood obesity (2). SCFAs promote the secretion of appetite-suppressing peptides, such as peptide YY (PYY) and glucagon-like peptide-1 (GLP-1), by activating specific G protein-coupled receptors (GPR41 and GPR43) on the surface of intestinal L-cells (3,4). Studies have identified six gut microbial taxa associated with childhood obesity: Bacteroides [odds ratio (OR) =0.9], Sensitive Clostridia (OR =0.9), Marvinbryantia (OR =0.9), Oscillospira (OR =0.1), Romboutsia (OR =0.9), and Turicibacter (OR =0.9) (5). Aromatic amino acids (AAAs), including tryptophan, phenylalanine and tyrosine, providing a rich source for gut microbiota to generate a number of aromatic metabolites. Tyr is metabolized into several compounds, including 4-HPAA, by metabolic pathway of AAA aminotransferase-mediated transamination. 4-hydroxyphenylacetic acid (4-HPAA) is a major colonic microbiota-derived metabolite, which is easily absorbed by the body (6). In a prior investigation, five Bacteroidetes and two Firmicutes were metabolized to the 4-HPAA and have demonstrated inhibitory effects on obesity (7). Bäckhed et al. (8) found that the gut microbiome can regulate fat storage, with an increase in the abundance of Firmicutes and a decrease in Bacteroidetes observed in the gut of obese individuals. Gut microbial metabolites can cross the intestinal barrier and influence the host’s physiological functions. Several gut microbial metabolites associated with obesity have been identified, including SCFAs, secondary BAs, glutamate, indoles, and trimethylamine N-oxide (TMAO). The gut microbiota and its metabolites may influence the development of obesity through multiple pathways, including the regulation of host energy metabolism and nutrient absorption, modulation of immune function and inflammatory response, and effects on hormone and neurotransmitter production (9). The gut microbial taxa associated with obesity also differ between males and females. In males, higher abundances of Parabacteroides helcogenes and Campylobacter canadensis are closely associated with higher body mass index (BMI), fat mass, and waist circumference. In females, higher abundances of three Prevotella species (Prevotella micans, Prevotella brevis, and Prevotella saccharolytica) are significantly correlated with higher BMI, fat mass, and waist circumference (10). However, the differences in gut microbiota and metabolites between children and adults with obesity have not yet been fully elucidated.

In this study, a comparative analysis of the gut microbiota between obese and healthy-weight children was conducted. The gut microbiome of obese children showed an increased abundance of taxa that facilitate the production of SCFAs (e.g., acetate, propionate, butyrate), while taxa associated with the generation of metabolites like 4-HPAA, valeric acid, and lactic acid were reduced. Traceability analysis revealed that the Bacillota phylum is responsible for the production of acetic acid, the Eubacteriaceae family produces 4-HPAA, and the Bacteroides genus generates valeric acid and lactic acid. Furthermore, treatment with 4-HPAA significantly reduced weight gain and improved glucose intolerance induced by a high-fat diet (HFD). We present this article in accordance with the ARRIVE reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-158/rc).

Methods

Animal study

Specific pathogen free (SPF)-grade healthy male C57BL/6J mice (6–8 weeks old, weighing 21–25 g) were purchased from Changzhou Cavens Laboratory Animal Co. and housed under specific pathogen-free conditions in the SPF-grade transgenic animal center at the Institute of Translational Medicine, Nanjing Medical University. The temperature was maintained at 22±1 °C, and humidity at 45–55%. All animal experiments adhered to the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health (NIH Publications No. 85-23, revised 1996), and the animal study protocol was duly approved by the Institutional Animal Care and Use Committee at Affiliated Children’s Hospital of Jiangnan University (No. WXCH2025-02-077). A protocol was prepared before the study without registration.

Mice were randomly assigned to one of four groups: normal diet (ND), ND + 4-HPAA, HFD, and HFD + 4-HPAA. For the HFD challenge, 6–8-week-old male mice were fed a standard HFD containing 60 kcal% lard-derived fat (D12492, Research Diets). Control mice were fed a ND [SPF (Beijing) Biotechnology Co., Ltd., Beijing, China]. For the 4-HPAA intervention, mice received intraperitoneal injections of 20 (mg/kg) 4-HPAA (HY-N1902, MedChemExpress, Shanghai, China) three times a week for about 5 weeks. Equal-volume saline injections served as the control for 4-HPAA treatment (n=5 per group).

Throughout the experiment, body weight, food, and water consumption were measured every 2 days. After approximate 5 weeks (37 days) of treatment, an intraperitoneal glucose tolerance test (IPGTT) was performed. Mice were fasted overnight for 12 hours before testing, with access to drinking water. Baseline blood glucose concentrations were measured using a Yuwell glucometer (Yuwell, Zhenjiang, China). Mice were then administered 2 (g/kg) glucose (ST1227, Beyotime, Shanghai, China) in saline via intraperitoneal injection. Blood glucose levels were measured from tail vein samples at 0, 15, 30, 60, 90, and 120 minutes post-injection.

For the intraperitoneal insulin tolerance test (IPITT), mice were fasted for 4 hours before testing, with access to drinking water. Baseline blood glucose concentrations were measured, followed by intraperitoneal injection of 0.5 (U/kg) insulin (Beyotime) in saline. Blood glucose levels were then measured from tail vein samples at 0, 15, 30, 60, 90, and 120 minutes post-injection.

Dataset source and analysis

For this study, we utilized curated children’s microbiota data at the genus level from the GMrepo database (https://gmrepo.humangut.info/), a resource of curated gut microbiome metagenomes. Metadata of interest collected for the entire sample included age, sex, location, and BMI. Using the provided RESTful API, we obtained run IDs for obese children (run ID: ERR1090828) and normal-weight children (run ID: ERR1090809). These run IDs were then used to download the relative abundance of gut microbiota at the genus level. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

To investigate metabolic pathways, we used the MetOrigin database (https://metorigin.met-bioinformatics.cn/home/) to analyze the gut microbiota metabolites 4-HPAA, butyric acid, and propionic acid. MetOrigin identifies the origins of metabolites, whether from the host, bacteria, or both. It also provides a comprehensive list of bacteria potentially involved in specific metabolic reactions based on database searches. We employed the Sankey network to visualize the relationships between bacteria at different taxonomic levels and their associated metabolites in a given metabolic reaction. This approach allowed us to link bacteria with substrate and product metabolites, as well as the enzymes involved in these processes.

Statistical analysis

All data are presented as mean ± standard deviation. Statistical analyses were performed using GraphPad Prism 8.0 software. Significant differences were assessed by one-way analysis of variance (ANOVA) with Tukey’s post hoc test. Statistical parameters are provided in the figures and figure legends. Significance was set at P<0.05, denoted as *, P<0.05; **, P<0.01; and ***, P<0.001.

Results

Integration analysis of gut microbiota associated with childhood obesity

The GMrepo database was utilized to analyze the gut microbiota of obese and normal-weight children. In the obesity group, the most prevalent gut microbiome was Dialister, comprising approximately 15.5% of the total microbiota (Figure 1A). The nine bacterial genera with the highest abundance in this group were Caldanaerobius, Blautia, Facklamia, Enterococcus, Lactobacillus, Bacillus, Bifidobacterium, Oceanobacillus, and Dialister (Figure 1B). In contrast, in the healthy group, Akkermansia was the most prevalent, accounting for about 49.1% of the gut microbiome (Figure 1C). The top nine genera in this group included Streptococcus, Sutterella, Alistipes, Dialister, Parabacteroides, Bifidobacterium, Prevotella, Bacteroides, and Akkermansia (Figure 1D).

Figure 1 Integration analysis of gut microbiota associated with childhood obesity. The curated children microbiota data at the genus level from the GMrepo database was used. (A) Proportion of gut microbiota in 7-year-old obese children. Run ID is ERR1090828. (B) The relative abundance of the gut microbiota in 7-year-old obese children. (C) Proportion of the gut microbiota in 4-year-old normal-weight children. Run ID is ERR1090809. (D) The relative abundance of the gut microbiota in 4-year-old girls.

To investigate the differences in gut microbiota between obese children and healthy controls, we conducted an integrative analysis using recent datasets from East Asia, South Asia, Europe, and North America. This analysis focused on identifying significant changes in bacterial strains across these regions. While no single bacterial strain was consistently elevated or reduced across all datasets, several trends emerged (Tables 1,2). Increased bacteria: Sutterellaceae (11,12); Fusobacteriaceae and Klebsiella (12,13); Lachnospiraceae (11,12); Prevotella (11,14,15); Streptococcaceae (11-13); Veillonellaceae (12-14). Decreased bacteria: Porphyromonadaceae, Rikenellaceae, and Erysipelotrichaceae (11,12); Eubacteriaceae (11,13,14); Akkermansia (12,16,17); Faecalibacterium (13,14,17); Odoribacter (11-13). These findings suggest distinct alterations in the gut microbiota associated with childhood obesity.

Integrative analysis of metabolite alterations in the gut microbiota of obese children

To determine the changes in gut microbiota metabolites associated with childhood obesity, we performed an integrative analysis of recent datasets. These datasets included studies from various regions, including East Asia, Europe, and North America. We analyzed the datasets for significant changes in bacterial metabolites. While not all datasets showed consistent alterations, we found the following trends (Tables 3,4): increased levels of acetic acid, propionic acid, and butyric acid in datasets (18,19). Decreased levels of valeric acid and lactic acid in datasets (15,22). Decreased levels of 4-HPAA in datasets (20,21). These findings highlight the distinct profiles of gut microbiota metabolites in childhood obesity.

The level of 4-HPAA is negatively associated with childhood obesity

Our analysis revealed that the abundance of gut microbiota Eubacteriaceae was down-regulated in obese children. Correspondingly, levels of 4-HPAA were also reduced. Using MetOrigin traceability, we identified that 4-HPAA can be produced by the host (human/animal), resident bacteria, and food. Multiple microbiota, including Pseudomonadota, Actinomycetota, Bacillota, Myxococcota, Cyanobacteriota, Verrucomicrobiota, Planctomycetota, Acidobacteriota, Deferribacterota, Nitrospirota, Ascomycota, Basidiomycota, and Chordata, can produce this metabolite (Figure 2A). Enrichment of metabolic pathways indicated that 4-HPAA is produced through tyrosine metabolism by these bacteria. The observed reduction in 4-HPAA in obese children may be linked to the decreased abundance of Eubacteriaceae in their gut.

Figure 2 The level of 4-HPAA is negatively associated with childhood obesity. (A-C) Sankey diagram of MetOrigin analysis of tyrosine metabolism (A), butanoate metabolism (B), and ethylbenzene degradation (C). 4-HPAA, 4-hydroxyphenylacetic acid.

We also found that butyric acid is derived from the host, bacteria, food, and drugs. Several microbial communities, such as Ascomycota, Bacillota, Pseudomonadota, Thermodesulfo bacteriota, Nitrospirota, Actinomycetota, Chloroflexota, and Chordata can produce butyric acid (Figure 2B). Enrichment of metabolic pathways showed that bacteria produce butyric acid through butanoate metabolism. Propionic acid, derived from the host, bacteria, food, and drugs, is produced by microbial communities including Pseudomonadota, Actinomycetota, Bacillota, and Euryarchaeota (Figure 2C). Pathway enrichment indicated that bacteria produce propionic acid through ethylbenzene degradation, propanoate metabolism, nicotinate and nicotinamide metabolism. The decrease in Bacillota in the intestines of obese children may lead to reduced production of butyric acid and propionic acid.

Additionally, literature (23) suggests that Faecalibacterium may produce valeric acid. Porphyromonadaceae and Rikenellaceae can produce lactic acid through glycolysis, gluconeogenesis, and pyruvate metabolism pathways, Bacillota can produce acetic acid through Wood-Ljungdahl pathway. These pathways promote monosaccharide absorption, regulate energy metabolism hormones and factors [such as fasting-induced adipose factor (Fiaf), adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK), and SCFAs], reduce leptin, enhance the intestinal barrier, and decrease lipopolysaccharides (LPS) release (24).

4-HPAA reduce the weight gain in mice fed an HFD

To investigate the role of 4-HPAA in obesity, 6- to 8-week-old mice were fed an HFD and received intraperitoneal injections of 4-HPAA (20 mg/kg) three times per week. After 5 weeks (37 days) of treatment, the HFD + 4-HPAA group exhibited significantly slower weight gain compared to the HFD group (P=0.046, Figure 3A,3B).

Figure 3 4-HPAA reduce the weight gain in mice fed an HFD. (A) Body weights of male C57 BL/6J mice (6–8 weeks old) were fed with ND or HFD for 5 weeks (37 days), the mice received intraperitoneal injections of saline or 4-HPAA (20 mg/kg) three times a week, n=5 per group. (B) Mean cumulative AUC of mice weight. (C,D) The IPGTT was performed and quantification, n=5 per group. (E,F) The IPITT was performed and quantification, n=5 per group. Data are presented as mean ± standard deviation. Statistical significance was determined using one-way ANOVA with Tukey’s post hoc test. ns, no significant; *, P<0.05. 4-HPAA, 4-hydroxyphenylacetic acid; ANOVA, analysis of variance; AUC, area under the curve; HFD, high-fat diet; IPGTT, intraperitoneal glucose tolerance test; IPITT, intraperitoneal insulin tolerance test; ND, normal diet.

The IPGTT was conducted to assess glucose metabolism and overall glucose homeostasis in mice. Blood glucose levels peaked 15 minutes after intraperitoneal glucose injection. Compared to mice in the HFD group, glucose levels in the ND + 4-HPAA group decreased more rapidly within 120 minutes (Figure 3C). Quantification also revealed that the area under the curve (AUC) for the HFD + 4-HPAA group was significantly lower compared to the HFD group (P=0.050, Figure 3D). Further, the IPITT was also performed. Blood glucose levels significantly decreased in all groups following insulin injection, reaching their lowest point at 30 minutes (Figure 3E). As expected, the HFD + 4-HPAA group exhibited a more rapid decline in blood glucose levels compared to the HFD group, achieving a lower glucose level post-injection (Figure 3E). Quantitative analysis also demonstrated a lower AUC in the HFD + 4-HPAA group (P=0.042, Figure 3F). Together, these results suggest that 4-HPAA can reduce weight gain and improve glucose intolerance in the context of an HFD.

Discussion

In this study, we collected and integrated data from multiple databases on gut microbiota and metabolites in obese children. Our analysis revealed a reduced proportion of Eubacteriaceae in the gut microbiota of obese children, accompanied by a decrease in its derived metabolite, 4-HPAA. Functional studies in animal models confirmed that 4-HPAA plays a role in reducing obesity induced by an HFD. Previous research has suggested a potential link between AAAs and obesity (25). In our study, the metabolite of AAA-4-HPAA-could be associated with obese children and reduced weight and improved glucose intolerance in HFD-induced mice. The study provides possible evidence for active molecules in the AAA metabolic pathway for the against obesity. It has been reported that seven strains from human colonic species produced amounts of phenylacetic acid (PAA) and 4-HPAA, including five Bacteroidetes (Bacteroides thetaiotaomicron, Bacteroides eggerthii, Bacteroides ovatus, Bacteroides fragilis, Parabacteroides distasonis) and two Firmicutes (Eubacterium hallii and Clostridium bartlettii), in line with our study (7). The study further suggests that Eubacteriaceae could be potential probiotic for anti-obesity therapy, warranting further investigation.

4-HPAA is a small molecule compound that is easily used by the body (6). A recent study reported that 4-HPAA could protect mice from HFD-induced hepatic steatosis (26). However, in this work, 4-HPAA needed to be used high chemical doses, which could be contributed to the short blood half-life of 4-HPAA (27). What is more, a recent study (28) has shown that 4-HPAA does not reduced weight gain when injected intraperitoneally into the mice, suggesting that blood-circulating metabolites have no effect for anti-obesity. These studies suggest that gut microbiota and related metabolites functions in intestine.

The composition of gut microbiota differs between obese children and obese adults. Specifically, the family Eubacteriaceae is reduced in obese children (11,13,14) but increased in obese adults (29,30). Comparing two studies (11,29) reveals several factors contributing to these differences: (I) age-related physiological changes: the studies involve different age groups—children aged 9 to 16 years and adults aged 20 to 79 years. As individuals age, changes in intestinal transit time, barrier function, and other physiological parameters can create distinct environments in the gut. These age-related changes may favor the growth of specific bacterial taxa, such as Eubacteriaceae. Additionally, the gut microbiota interacts with the host immune system, which evolves throughout childhood and adolescence, affecting microbial composition. (II) Geographical and environmental differences: the studies are conducted in different locations—children in Genoa, Italy, and adults in the Midwestern United States. Variations in environment, climate, diet, and the availability of specific nutrients and substrates can influence the growth of gut microbiota, including Eubacteriaceae. Dietary differences between the two age groups may contribute to the observed variations in Eubacteriaceae levels. These factors highlight the need to consider age, environmental context, and diagnostic criteria when comparing gut microbiota profiles between children and adults with obesity.

Moreover, the integrative analysis concerned that increased bacteria including Prevotella, Sutterellaceae, Lachnospiraceae, Veillonellaceae, Streptococcaceae, Fusobacteriaceae, and Klebsiella. In a prior investigation, a significant positive correlation was observed between Prevotella and obesity in children, and Prevotella reduced the relative abundance of the Akkermansia genus in mice and increased BAs and decreased SCFAs in HFD-induced mice (31). Interestingly, our study also revealed Prevotella was increased in obese children, and also found that Akkermansia genus was decreased in obese children. This may indicate an antagonistic phenomenon between Prevotella and Akkermansia. Recent studies have reported the family Lachnospiraceae was highly colonized in human and mice with obesity and produced long-chain fatty acids facilitating diet-induced obesity, in agreement with our findings (32). However, we lacked analysis of long-chain fatty acids in obese children, which will be further developed in the future. Some prospective studies have shown that a greater presence after bariatric surgery belonging to the families of Sutterellaceae, Veillonellaceae, Streptococcaceae, Fusobacteriaceae, Klebsiella, typically observed after massive weight loss in these patients (33). Nevertheless, in our study, we found that they were significantly increased in obese children, which could be partly explained because age-related gut microbiota changes (11,29). Although some studies shown that these bacteria can ferment amino acids and carbohydrates into metabolites such as propionate and butyrate which have been weight reduction (34), these levels of SCFAs were increased in our study indicating SCFAs could have a positive correlation with obesity in children not adults.

In obese children, fecal levels of acetic acid, propionic acid, and butyric acid are elevated (18,19). These changes are consistent with findings in obese adults, where increased levels of these SCFAs are also observed in plasma (35). The concentration of SCFAs in feces and plasma reflects the balance between their production in the large intestine and their absorption or utilization by the body. Normal-weight individuals may absorb and utilize these SCFAs more efficiently, resulting in lower concentrations in their feces and plasma. Given that SCFAs are absorbed through the intestinal wall into the bloodstream and excreted in feces, the trends in SCFA concentrations across blood and feces tend to be consistent. Therefore, SCFAs may serve as biomarkers for early diagnosis and screening of childhood obesity. They may also be useful for evaluating the effectiveness of interventions such as diet and exercise and could be associated with obesity-related complications like type 2 diabetes and cardiovascular disease. Monitoring SCFA levels might help predict the long-term prognosis of childhood obesity.

In contrast, valeric acid levels are reduced in the feces of both obese children (15,22) and adults (36), indicating a consistent trend. Additionally, lactic acid levels are reduced in the feces of obese children (15,22) but elevated in the plasma of obese adults compared to healthy individuals (37). This increase in plasma lactic acid may be linked to impaired aerobic metabolism and could serve as an indicator of the severity of metabolic syndrome. The inconsistency in lactic acid trends between plasma and feces in obese individuals suggests that plasma lactic acid levels might reflect obesity-related metabolic disorders such as insulin resistance and muscle dysfunction, while decreased fecal lactic acid could indicate an imbalance in gut microbiota affecting lactate metabolism.

Our study has some limitations, including the analysis of only a subset of data and the lack of further validation in a cohort of children with obesity. Microbial metabolites are frequently studied in animal models by incorporation into diet or drinking water. This method falls short as inconsistent oral intake, inconsistent gastrointestinal absorption, and further modification of the metabolite by gut microbes yield imprecise levels of drug delivery. In efforts to overcome this, the physiological impact of 4-HPAA is studied by intermittent exogenous administration in a non-physiologically relevant manner (intraperitoneal injection). Although this approach can effectively raise circulating levels, it does not mimic the natural delivery of gut microbial-derived small molecules through the portal circulation to the target organ. Future work will focus on addressing these limitations and conducting more comprehensive studies.

In summary, our study reveals for the first time that the gut microbiota metabolite 4-HPAA is inversely associated with childhood obesity, offering novel insights into the pathogenesis of this condition.

Conclusions

Our study analyzed the gut microbiota characteristics of obese children across multiple regions and suggests that the downregulation of 4-HPAA may be associated with the development of obese children.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the ARRIVE reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-158/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-158/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-158/prf

Funding: This study was supported by the Top Medical Expert Team of Wuxi Taihu Talent Plan (grant Nos. DJTD202106, GDTD202105, YXTD202101); Medical Key Discipline Program of Wuxi Health Commission (grant Nos. ZDXK2021007, CXTD2021005); Top Talent Support Program for Young and Middle-Aged People of Wuxi Health Committee (grant No. BJ2023090); Scientific Research Program of Wuxi Health Commission (grant Nos. Z202109, M202208, Q202162) and Wuxi Science and Technology Development Fund (grant Nos. N20202003, Y20222001).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-158/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All animal experiments adhered to the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health (NIH Publications No. 85-23, revised 1996), and the animal study protocol was duly approved by the Institutional Animal Care and Use Committee at Affiliated Children’s Hospital of Jiangnan University (No. WXCH2025-02-077).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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