Ellagic Acid Prevents Obesity in High-Fat Diet-Fed Rats by Ameliorating Oxidative Stress via Modulation of the PPARG/STAT3/p-AKT1 Axis

Abstract

This study investigated the protective effects and underlying mechanisms of ellagic acid (EA) against high-fat diet (HFD)-induced obesity. Using network pharmacology, STAT3 and AKT1 were identified as pivotal regulatory targets. In vivo experiments demonstrated that EA intervention significantly reduced body weight inducement, improved lipid profiles, and attenuated intestinal oxidative stress and inflammation in HFD-fed rats. Furthermore, EA restored intestinal architecture and mitochondrial morphology. Mechanistically, EA markedly downregulated the expression of PPARG, STAT3 and p-AKT1 in both intestinal tissues and TNF-α-stimulated HaCaT cells. Collectively, these findings suggest that EA prevents HFD-induced obesity by alleviating intestinal oxidative stress and mitochondrial dysfunction through the modulation of the PPARG/STAT3/p-AKT1 signaling axis. This study provides novel evidence for EA as a potential natural bioactive compound for the management of metabolic disorders.

1. Introduction

According to the World Obesity Atlas 2023, approximately 38% of the global population is currently overweight or obese. This prevalence has led to a surge in obesity-related complications, including insulin resistance, type 2 diabetes, cardiovascular diseases, hepatic disorders, various cancers, and neurodegenerative diseases, posing a formidable challenge to global public health [1]. Investigations into the pathogenesis of metabolic disorders have revealed that obesity is frequently characterized by oxidative stress and chronic low-grade inflammation, underscoring a pivotal link between overnutrition and the activation of the immune system within major metabolic organs [2]. As the primary organ for nutrient absorption, the intestine demands substantial energy to sustain biological processes such as rapid epithelial turnover, ATPase-mediated transport, regulation of tight junction permeability, and synthesis of antimicrobial peptides [3]. Chronic high-fat diet (HFD) consumption induces mitochondrial dysfunction within the intestinal mucosa, disrupting redox homeostasis and leading to the excessive accumulation of superoxide anions (O²⁻), hydrogen peroxide (H₂O₂), and other reactive oxygen species (ROS). This state of oxidative stress not only triggers the activation of inflammasomes but also modulates the differentiation and recruitment of inflammatory cells, thereby instigating an immuno-inflammatory cascade [4]. Consequently, HFD-driven dysbiosis of the intestinal microenvironment and the subsequent induction of insulin resistance facilitate the onset and persistence of systemic low-grade inflammation, ultimately culminating in the development of obesity and associated metabolic syndromes [5,6].

Peroxisome proliferator-activated receptor gamma (PPARG), a vital nuclear receptor protein, is intricately involved in the regulation of lipid metabolism, inflammatory responses, and the maintenance of intestinal barrier integrity [7]. Emerging evidence suggests a close correlation between the expression of PPARG and Signal Transducer and Activator of Transcription 3 (STAT3) [8], a key component of the STAT signaling pathway that governs oxidative stress, immune function, and metabolic homeostasis across diverse tissues [9]. Recent studies have demonstrated that obesity and excessive fatty acid influx promote STAT3 activation and its mitochondrial translocation, subsequently leading to elevated production of ROS and pro-inflammatory cytokines such as interleukin-6 (IL-6) [10]. In obese states, the persistent activation of STAT3 interacts with the PI3K/AKT signaling pathway [11], thereby accelerating the progression of insulin resistance. Concurrently, activated AKT can upregulate PPARG expression, further exacerbating adipose accumulation [12]. Collectively, these findings highlight the pivotal roles of the PPARG/STAT3/AKT regulatory axis in sustaining mitochondrial function and metabolic stability within intestinal cells.

The health-promoting properties of natural bioactive compounds have become a focal point of current nutritional research. Ellagic acid (EA), a natural polyphenol abundant in fruits such as pomegranates, blueberries, and strawberries [13], possesses a unique molecular structure that confers potent free radical scavenging and antioxidant activities [14]. These properties underpin its diverse pharmacological effects, including anti-inflammatory and anti-cancer activities [15]. In the present study, we utilized bioinformatics to identify core targets linking EA, mitochondrial dysfunction, and obesity. We subsequently validated the efficacy of EA in attenuating intestinal oxidative stress, mitochondrial impairment, and the inflammatory milieu in a HFD rat model. Integrated with in vitro assays, our results suggest that the protective effects of EA are mediated, at least in part, by the modulation of PPARG, STAT3, and p-AKT1 expression. This research provides novel evidence supporting the anti-obesity potential of natural dietary compounds and offers new insights into their molecular mechanisms.

2. Materials and Methods

2.1. Experimental Animals and Cell Culture

Forty specific-pathogen-free male Sprague Dawley rats, weighing 180–200 g (SCXK2020-0001, Experimental Animal Center of Guizhou Medical University, Guiyang, China), were housed in the experimental animal facility of Guizhou Medical University. The animals were maintained under a controlled environment with a 12-h light/dark cycle, a temperature of 22 ± 1 °C, and a relative humidity of 55 ± 5%. The standard control diet consisted of crude protein, crude fat, crude fiber, crude ash, moisture, and essential trace elements and amino acids. The HFD was formulated with a 78.8% basal diet supplemented with 1% cholesterol, 10% egg yolk powder, 10% lard, and 0.2% bile salts, all provided by the Animal Facility of Guizhou Medical University. The human immortalized keratinocyte cell line (HaCaT, GDC0106) was obtained from the Wuhan Cell Bank (Wuhan, China). All experimental procedures were approved by the Ethics Committee of Guizhou Medical University (Approval No. 2100228).

2.2. Reagents and Antibodies

Ellagic acid (purity ≥ 98%, R016669) (Roan Chemical Technology, Shanghai, China); the BCA protein assay kit (PC0020), the Tumor Necrosis Factor-alpha (TNF-α) ELISA kit (SEKR-0009), and the Interleukin-6 (IL-6) ELISA kit (SEKR-0005) (Solarbio, Beijing, China); total superoxide dismutase (SOD, A001-1-2) and malondialdehyde (MDA, A003-1-2) assay kits (Jiancheng, Nanjing, China); RIPA lysis buffer (ab288006) (Abcam, Cambridge, UK); 5% BSA blocking buffer and PBS buffer (Solarbio, Beijing, China); 1% osmium tetroxide (Ted Pella Inc., Redding, CA, USA), uranyl acetate (SPI, West Chester, PA, USA), and lead citrate (Sigma-Aldrich, St. Louis, MO, USA); tissue fixative (G1101) (Servicebio, Wuhan, China); antibodies against PPARG (1:2000, K113849P), STAT3 (1:1000, K000233M), AKT1 (1:1000, K000186M), p-AKT1 (1:1000, K012659RR), β-actin (1:2000, K200058M), and GAPDH (1:5000, K200057M), the SDS-PAGE gel preparation kit (Solarbio, Beijing, China); Anti-Human TNF-alpha (Polyclonal Rabbit, PHC3013, PeproTech, Cranbury, NJ, USA); the Cell Counting Kit-8 (Solarbio, Beijing, China); the ROS assay kit (Yeasen Biotechnology, Shanghai, China); DMEM high-glucose medium, fetal bovine serum (FBS), and trypsin-EDTA (Thermo Fisher Scientific, Waltham, MA, USA) were all acquired.

2.3. Instrumentation

A low-speed benchtop centrifuge (TDL-5000bR, Linjia, Shanghai, China) and an automated biochemical analyzer (Lx-20, Beckman Coulter, Brea, CA, USA) were utilized. Microscopic observations were performed using fluorescence and optical microscopes (Olympus, Tokyo, Japan). The absorbance was measured using a microplate reader (Multiskan GO, Thermo Fisher Scientific, Waltham, MA, USA). Ultrastructural analysis was conducted via transmission electron microscopy (HT7800, Hitachi, Tokyo, Japan) following sample preparation with an ultramicrotome (UC7, Leica Microsystems, Wetzlar, Germany). Protein separation and visualization were carried out using a mini-vertical electrophoresis system and a gel imaging and analysis system (Universal Hood 2, Bio-Rad Laboratories, Hercules, CA, USA).

2.4. Screening of Core Target Genes for EA in Mitochondrial Dysfunction and Obesity

2.4.1. Identification of EA-Related Molecular Targets

The chemical structure of EA was retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Potential molecular targets of EA were predicted using multiple platforms, including TCMSP (https://tcmsp-e.com/tcmsp.php), TargetNet (http://targetnet.scbdd.com/), CTD (https://ctdbase.org), ETCM2 (http://www.tcmip.cn/ETCM2/front/#/), STITCH (http://stitch-db.org/), and SwissTargetPrediction (http://www.swisstargetprediction.ch/). The identified protein targets were standardized to their corresponding gene symbols using the UniProtKB database (https://www.uniprot.org/), and redundant entries were removed.

2.4.2. Identification of Targets Associated with Mitochondrial Dysfunction and Obesity

Targets related to “obesity” and “mitochondrial dysfunction” were identified by searching the GeneCards database (Version 5.26) (https://www.genecards.org/). Consistent with the methodology described by Wu H. et al. [16], only targets with a relevance score > 5 were included for further analysis.

2.4.3. Determination of Intersecting Targets

The candidate genes obtained from Section 2.4.1 and Section 2.4.2 were integrated, and the intersecting genes (common targets) were identified and visualized using the “VennDiagram” package in R software (version 4.5.1).

2.4.4. Identification of Hub Targets

The identified target genes were imported into the STRING database (Version: 12.0) (https://string-db.org/) for analysis, and the protein–protein interaction (PPI) network was visualized using Cytoscape software (version 3.10.3). Candidate hub genes were screened based on the top 10 scores from four topological algorithms (MCC, MNC, Degree, and Stress) within the cytoHubba plugin. The intersection of these results was determined using the “VennDiagram” package to identify the final hub targets of EA associated with mitochondrial dysfunction and obesity.

2.4.5. GO and KEGG Enrichment Analysis

Genetic ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the identified targets using the STRING database (Version: 12.0) (https://string-db.org/). The results were visualized using the “ggplot2” package in R software. Additionally, Sankey bubble charts were generated using the “ggalluvial” and “patchwork” packages to represent the enrichment profiles.

2.4.6. Molecular Docking Validation

The 3D crystal structures of the key hub proteins were retrieved from the RCSB PDB database (https://www.rcsb.org/), and the SDF structure of EA was obtained from the PubChem database. Molecular docking simulations between the hub proteins and EA were performed using AutoDock Vina (version 1.5.7). A docking energy less than 5 kcal/mol was considered to indicate the binding potential [17]. The docking results were visualized and analyzed using PyMOL (Version: 3.1.6.1) software.

2.5. Animal Grouping and Experimental Intervention

Following a one-week acclimatization period, the rats were randomly assigned into four groups (n = 10 per group): the control group, control + EA group, obesity group, and obesity + EA group. The control and obesity groups were fed a standard chow diet and an HFD, respectively, and received equal volumes of distilled water via intragastric gavage. The control + EA and obesity + EA groups were fed standard chow and HFD, respectively, and were administered EA (50 mg/kg body weight) via intragastric gavage. The dosage for the intragastric administration of ellagic acid (EA) was determined based on the established protocols described by Khan et al. [18] and ALTamimi et al. [19]. Body weights were systematically recorded throughout the study. Following the 12-week intervention, the naso-anal length of each rat was measured. The animals were then euthanized via an intraperitoneal injection of sodium pentobarbital (0.5 mL/100 g). Subsequently, blood samples (for detecting blood lipid levels and other indicators), abdominal adipose tissues (harvested to calculate the obesity index and evaluate the degree of adiposity), and intestinal segments were collected for further analysis. Given that both the obesity index and Lee’s index serve as robust morphometric parameters for assessing the onset of obesity in rodent models [20,21], these indices were calculated using the following formulas:

$$\mathrm{obesity} \mathrm{index} = {\displaystyle \frac{\mathrm{visceral} \mathrm{fat} \left(\mathrm{g}\right) }{ \mathrm{body} \mathrm{weight} \left(\mathrm{g}\right) }}\times 100.00\%,$$

(1)

$$\mathrm{Lee}’\mathrm{s} \mathrm{index}={\displaystyle \frac{\sqrt[3]{\mathrm{body} \mathrm{weight} \left(\mathrm{g}\right)}}{\mathrm{nose}\text{-}\mathrm{to}\text{-}\mathrm{anus} \mathrm{length} \left(\mathrm{cm}\right)}}\times 1000.$$

(2)

2.6. Measurement of ROS and Inflammatory Cytokines

Intestinal tissues were mechanically fragmented and homogenized in normal saline at a ratio of 1:9 (w/v) to prepare a 10% tissue homogenate, which was then collected after centrifugation. For the in vitro analysis, HaCaT cells were seeded in 6-well plates, rinsed twice with PBS, and harvested using a cell scraper. After centrifugation at 3000 rpm for 10 min, the cell pellet was lysed using repeated freeze–thaw cycles. The concentrations of SOD, MDA, TNF-α, and IL-6 in both the intestinal tissues and HaCaT cells were quantified using commercial kits, according to the manufacturer’s instructions [22].

2.7. Histopathological Examination

Intestinal segments were fixed in tissue fixative and underwent standardized procedures, including dehydration, paraffin embedding, sectioning, and hematoxylin and eosin (H&E) staining. Morphological changes were examined under an optical microscope.

2.8. Transmission Electron Microscopy (TEM) Analysis

Small intestinal tissues were harvested and immediately fixed in glutaraldehyde fixative at room temperature, protected from light for 2 h, and then transferred to 4 °C for long-term preservation. Following three washes with PBS, the samples were post-fixed in 1% osmium tetroxide, dehydrated through a graded ethanol series, and subsequently embedded in epoxy resin. Ultrathin sections were prepared and double-stained with uranyl acetate and lead citrate. The mitochondrial ultrastructure of the intestinal epithelium was then visualized and photographed using a TEM [23].

2.9. Cell Viability Assay (CCK-8)

Cells were seeded into 96-well plates at a density of 1 × 10⁵ cells/well and cultured for 24 h until reaching approximately 80% confluence. Subsequently, the cells were treated with EA (80 μmol/L) and TNF-α (10 ng/mL) for 72 h. The concentration of EA was determined based on our preliminary dose-response assays, while the concentration of TNF-α was selected according to previously established protocols [24,25]. Following the intervention, cells were washed once with PBS. A mixture of 100 μL complete medium and 10 μL CCK-8 reagent was added to each well, followed by an additional 2 h incubation at 37 °C. The absorbance was subsequently measured at 450 nm using a microplate reader. Consistent with prior reports, the concentration of EA utilized in this study exhibited no cytotoxic effects on the target cells [26].

2.10. Detection of Intracellular ROS

The cells were assigned to three groups: the control, TNF-α, and TNF-α + EA groups. HaCaT cells were seeded 24 h prior to detection. At ~80% confluence, cells were incubated with 10 μM DCFH-DA probe (diluted 1:1000 in serum-free medium) at a volume sufficient to cover the cell layer. The plates were incubated in the dark at 37 °C for 30 min. Subsequently, the cells were washed 1–2 times with serum-free medium to remove the extracellular DCFH-DA. After three final washes with PBS, intracellular ROS-associated green fluorescence was visualized and captured using a fluorescence microscope [27].

2.11. Western Blot Analysis

Intestinal tissues were lysed using RIPA buffer, and the total protein concentration was quantified using a BCA protein assay kit, according to the manufacturer’s instructions. Protein samples were separated by SDS-PAGE (using stacking and resolving gels) and subsequently transferred onto PVDF membranes. The membranes were then blocked with 5% BSA for 1 h at room temperature to minimize non-specific binding. The membranes were incubated with specific primary antibodies overnight at 4 °C, followed by incubation with appropriate secondary antibodies for 1 h at room temperature. Protein bands were visualized using enhanced chemiluminescence reagents and captured using a gel imaging system [28].

2.12. Statistical Analysis

Statistical analyses and data visualization were performed using SPSS Statistics (version 22.0), GraphPad Prism (version 10.1.2), and R software (version 4.5.1). All experimental data are expressed as the mean ± SD. When variances were homogenous, one-way ANOVA was employed. When differences existed between populations, the least significant difference (LSD) t-test was used to further compare intergroup variations. A p-value < 0.05 was considered to indicate a statistically significant difference.

3. Results

3.1. Identification of Overlapping Targets and Functional Enrichment Analysis of EA in Mitochondrial Dysfunction and Obesity

From multiple databases, 397 unique EA targets were identified after removing redundancies. These were intersected with 3351 mitochondrial dysfunction-related and 1594 obesity-related targets (relevance score > 5) from GeneCards, yielding 104 common targets (Figure 1A). GO enrichment analysis revealed that these targets were primarily associated with responses to oxygen-containing compounds (BP), localized in the mitochondria and endomembrane system (CC), and involved in coactivator or kinase binding (MF) (Figure 1B). KEGG analysis showed significant enrichment in metabolic and immune pathways, notably the AGE-RAGE, PI3K-Akt, and insulin resistance signaling axes (Figure 1C).

3.2. Hub Target Identification and Molecular Docking Validation

A PPI network of 104 intersection targets was constructed using Cytoscape (v3.10.3) and ranked by degree centrality (Figure 2A,B). By intersecting the top 10 genes from four topological algorithms (MCC, MNC, Degree, and Stress), seven hub targets were identified (Figure 2C). KEGG enrichment analysis revealed these targets were predominantly involved in immuno-metabolic pathways, including Inflammatory bowel disease, Osteoclast differentiation, the AGE-RAGE signaling pathway in diabetic complications, and Toll-like receptor signaling. Furthermore, pathways intimately associated with metabolic homeostasis, including Insulin resistance and Non-alcoholic fatty liver disease, were significantly represented (Figure 3A,B). Notably, PPARG, STAT3, and AKT1 emerged as pivotal regulators. Molecular docking further confirmed strong binding affinities between EA and these targets, with binding energies of −8.849, −7.894, and −6.381 kcal/mol, respectively—all well below the −5.0 kcal/mol threshold (Figure 3C).

3.3. Differences in Obesity Parameters, Lipid Profiles, ROS, and Inflammatory Cytokines

Following the 12-week intervention, the obesity group displayed significantly higher body weight, obesity index, and Lee’s index compared to all other groups. These parameters were markedly attenuated in the obesity + EA group, while no significant differences were observed between the control and control + EA groups (Figure 4A–C). Similarly, serum lipid analysis (

Table 1. Comparison of blood lipid levels among groups of rats (n = 10).

Group	TG (mmol/L)	TC (mmol/L)	LDL-C (mmol/L)	HDL-C (mmol/L)
Control	1.30 ± 0.08	1.58 ± 0.12	0.43 ± 0.03	0.77 ± 0.09
Control + EA	1.26 ± 0.07	1.43 ± 0.17	0.38 ± 0.04	0.74 ± 0.17
Obesity	1.63 ± 0.07 1	1.96 ± 0.09 1	0.55 ± 0.07 1	0.50 ± 0.07 1
Obesity + EA	1.31 ± 0.10 3	1.57 ± 0.19 3	0.43 ± 0.05 3	0.75 ± 0.15 2

¹ Compared with the Control group, p < 0.001; ² Compared with the Obesity group, p < 0.01; ³ Compared with the Obesity group, p < 0.001.

) showed that EA intervention significantly reversed HFD-induced elevations in TG, TC, and LDL-C, as well as the reduction in HDL-C. Notably, lipid profiles in the control + EA group remained comparable to those of the control group.

Regarding oxidative stress markers, the obesity group exhibited significantly reduced intestinal SOD activity and elevated MDA levels compared to other groups. EA intervention markedly reversed these changes and further augmented SOD levels in rats fed a standard diet, indicating an enhanced antioxidant defense (Figure 4D,E). Concurrently, HFD-induced elevations in intestinal TNF-α and IL-6 were significantly suppressed by EA treatment, effectively mitigating the pro-inflammatory microenvironment triggered by HFD (Figure 4F,G).

3.4. Intestinal Mitochondrial Dysfunction and Protein Expression Profiles

H&E staining confirmed intact intestinal architecture in the control and control + EA groups. Conversely, the obesity group displayed severe structural impairment, characterized by mucosal epithelial detachment, lamina propria exposure, and muscularis disorganization. EA supplementation significantly ameliorated these pathological alterations (Figure 5A). TEM observations showed that EA effectively mitigated HFD-induced mitochondrial fragmentation and cristae disarray, maintaining ultrastructural integrity comparable to the control groups (Figure 5B). Collectively, these results indicate that EA protects against HFD-induced intestinal injury and mitochondrial dysfunction.

Western blot analysis revealed that the protein expression levels of PPARG, STAT3, and p-AKT1 in the intestinal tissues of the obesity group differed significantly from those in the other three groups. Specifically, EA intervention significantly upregulated PPARG expression while effectively attenuating the levels of STAT3 and p-AKT1 (Figure 5C).

3.5. Results of In Vitro Antioxidant Assays

Consistent with the in vivo findings, TNF-α treatment significantly elevated the levels of ROS and MDA while reducing SOD activity in HaCaT cells (Figure 6A,B). These oxidative stress-related alterations were markedly reversed by EA intervention. Furthermore, Western blot analysis demonstrated that EA effectively restored the expression of PPARG and suppressed the aberrant activation of STAT3 and p-AKT1 induced by TNF-α (Figure 6C). Collectively, these results suggest that EA alleviates oxidative stress in HaCaT cells, likely through the modulation of the PPARG, STAT3, and p-AKT1 signaling axes.

4. Discussion

Over the past decades, extensive research has established that obesity is inextricably linked to oxidative stress and chronic low-grade inflammation across multiple tissues, including the intestine, adipose tissue, skeletal muscle, liver, and brain [2,29]. As the primary organ for nutrient absorption and the “first line of defense” against exogenous substances, intestinal homeostasis is paramount for systemic physiological health. EA, a natural polyphenolic compound, possesses potent antioxidant, anti-inflammatory, and anti-apoptotic properties that offer potential benefits for both intestinal and systemic homeostasis [30,31]. Our findings suggest that EA modulates the expression levels of PPARG, STAT3, and p-AKT1, thereby attenuating HFD-induced intestinal oxidative stress, mitigating mitochondrial impairment, and suppressing the inflammatory microenvironment, ultimately exerting a preventive effect against obesity.

In response to excessive dietary fat absorption, intestinal epithelial cells undergo metabolic adaptation characterized by augmented fatty acid β-oxidation (FAO). However, this intensified FAO often triggers an overwhelming production of ROS that surpasses the endogenous antioxidant capacity [32]. Furthermore, chronic HFD consumption causes intestinal barrier impairment, lipid dysmetabolism, and multi-organ immune cell infiltration, culminating in a persistent chronic inflammatory milieu marked by elevated levels of pro-inflammatory cytokines such as TNF-α and IL-6 [33], that serves as the pathological foundation for obesity and related metabolic syndromes. PPARG, a ligand-activated transcription factor belonging to the nuclear receptor superfamily, plays a pivotal role in maintaining homeostasis. It orchestrates lipid metabolism, mitochondrial biogenesis, and antioxidant defenses by regulating targets such as CD36, PGC-1α, and NRF1-2 [34]. Moreover, PPARG suppresses macrophage activation and the secretion of pro-inflammatory cytokines (e.g., TNF-α, IL-6, and IL-1β), thereby alleviating intestinal inflammation [35]. Evidence also suggests that PPARG activation modulates inflammatory responses by inhibiting the phosphorylation of STAT3 and the NF-κB/IκB axis [36]. As a central regulator of immune responses, STAT3 interacts with inflammatory mediators like IL-6, potentially creating a feedback loop that exacerbates systemic inflammation [37,38,39]. In the present study, EA intervention significantly restored intestinal PPARG expression and attenuated STAT3 activation in HFD-fed rats, leading to marked improvements in oxidative stress, lipid profiles, and inflammatory markers. Our findings align with previous reports indicating that EA upregulates PPARG to activate lipid metabolism-related genes and reduce body weight [40], while simultaneously decreasing cytokine levels by inhibiting IκB-NF-κB signaling [18]. Additionally, considering that STAT3 expression is intricately linked to ROS levels and its mitochondrial translocation regulates electron transport chain function [41], it is highly probable that EA’s therapeutic efficacy stems from a multi-faceted approach. Beyond direct transcriptional modulation of PPARG and STAT3, EA may restore fatty acid homeostasis by optimizing the redox balance and remodeling the intestinal inflammatory microenvironment.

The PI3K/AKT signaling pathway plays a cardinal role in orchestrating glucose and lipid metabolic homeostasis as well as insulin sensitivity. Dysregulation of AKT signaling and subsequent insulin resistance are recognized as primary drivers of obesity. Under physiological conditions, activated AKT maintains metabolic equilibrium by phosphorylating diverse metabolic enzymes and nutrient transporters, such as glucose transporters (GLUTs) and sterol regulatory element-binding protein 1c (SREBP-1c) [42]. Recent evidence also identifies mitochondrial AKT1 as a regulator of oxidative phosphorylation, ROS production, and cell survival [43], noting that AKT1 is activated to initiate apoptosis in response to the loss of mitochondrial transmembrane electrochemical gradients [44]. In this study, the phosphorylation of AKT1 was significantly elevated in the intestinal tissues of HFD-fed rats but was markedly attenuated following EA intervention. These results suggest that HFD disrupts intestinal metabolic homeostasis, where excessive fatty acid influx induces compensatory AKT1 phosphorylation; however, this aberrant activation heightens the risk of insulin resistance. Chronic HFD-induced AKT hyperactivation facilitates the remodeling and accumulation of dietary fats while promoting de novo lipogenesis [45]. Notably, inhibiting AKT1-mediated imbalance of Foxa2 signaling in the intestinal epithelium has been shown to prevent obesity and insulin resistance [46]. Beyond its anti-inflammatory properties, EA acts as a potent antioxidant. Previous research demonstrated that EA supplementation significantly enhances systemic and hepatic insulin sensitivity, reduces hepatic oxidative stress, and inhibits the development of pro-inflammatory adipose tissue [47]. Thus, EA likely lowers intestinal AKT1 phosphorylation, thereby alleviating the synergistic impact of oxidative stress and inflammation. Given the established correlation between PPARG, STAT3, and AKT1 [48,49], AKT1 may modulate the expression of PPARG and STAT3 through its effects on lipid metabolism and inflammatory cascades. While the present study lacks an in-depth exploration of these regulatory mechanisms and multi-organ expression profiles (e.g., liver and adipose tissue), these aspects remain high-priority objectives for our future investigations.

5. Conclusions

In summary, this study integrates bioinformatics with experimental validation to identify PPARG, STAT3, and AKT1 as pivotal targets mediating the anti-obesity effects of EA. Our findings demonstrate that EA synergistically modulates these targets to alleviate intestinal oxidative stress and inflammation, restoring cellular homeostasis and preventing HFD-induced obesity. These results were further corroborated in vitro, where EA suppressed TNF-α-induced oxidative stress in HaCaT cells. Nevertheless, certain limitations of this study warrant acknowledgement: (1) this study focused on prevention in rat models, necessitating further clinical evidence for therapeutic efficacy in humans; (2) the systemic dose–response relationship of EA requires further elucidation; and (3) while HaCaT cells are a robust oxidative stress model [50,51,52], tissue-specific differences exist. Future research should prioritize investigating EA’s precise regulatory mechanisms under HFD conditions, its in vivo biotransformation, and conducting randomized controlled trials. Collectively, this research provides a scientific rationale for utilizing EA as a natural bioactive functional ingredient against obesity and associated metabolic syndromes.