Next Article in Journal
Air Pollution, Asthma and Diet: From Mechanisms to Prevention Strategies
Next Article in Special Issue
Physiological Regulation of Nutritional and Metabolic Biomarkers in Obesity: Implications for Precision Nutrition
Previous Article in Journal
Individually Recognized Functional Ingredients of Korean Health Functional Foods: Functional Classification, Regulatory Context, and Clinical Implication
Previous Article in Special Issue
Phenotype-Specific Mitochondrial Responses to Mediterranean Diet and Exercise in Elderly Obesity
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 18
Issue 4
10.3390/nu18040638
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

Structural Insights and Metabolic Profiles of Oxidized Green Coffee Extract, and Its Impact on Obesity and Gut Microbiota in High-Fat Diet-Fed Mice

by
Jun He
1,
Linxian Shan
1,
Lihui Yu
1,
Lijun Yu
1,
Xingjiao Jiang
1,
Yan Shen
1,
Zezhu Du
1,
Rongxian Yu
1,
Cunchao Zhao
1,
Xiaocui Du
2,
Haizhen Wang
3,
Ruijuan Yang
1,* and
Chongye Fang
1,*
1
College of Food Science and Technology, Yunnan Agricultural University, Kunming 650201, China
2
College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China
3
College of Veterinary Medicine, Yunnan Agricultural University, Kunming 650201, China
*
Authors to whom correspondence should be addressed.
Nutrients 2026, 18(4), 638; https://doi.org/10.3390/nu18040638
Submission received: 12 January 2026 / Revised: 6 February 2026 / Accepted: 12 February 2026 / Published: 15 February 2026
(This article belongs to the Special Issue Nutritional and Metabolic Biomarkers in Obesity)
Browse Figures
Versions Notes

Abstract

Background: Obesity is a severe chronic disease impacting health, closely linked to intestinal microbiota. Gut microbiome significantly contributes to obesity and metabolic issues. This study aims to explore the structural characterization of two coffee extracts and their effects on gut microbiota disturbances caused by a high-fat diet (HFD). Methods: Male C57BL/6J mice were divided into four groups—normal diet (ND), high-fat diet (HFD), HFD supplemented with unroasted coffee extract (UC), and HFD supplemented with oxidized green coffee extract (GCE). Results: Structural characterization revealed that both extracts are polymeric phenolic compounds rich in hydroxyl and carboxyl groups. Full-target metabolomic analysis revealed significant metabolic differences between the extracts, with 499 differential metabolites identified: a total of 247 metabolites were upregulated and 252 were downregulated in GCE compared to UC. Supplementation with GCE reduced body weight gain and adipose tissue accumulation, improved dyslipidemia and insulin sensitivity, and enhanced hepatic antioxidant capacity in high-fat model mice. Gut microbial analysis showed that GCE significantly (p < 0.05) increased the growth of beneficial bacteria such as Prevotella, Butyricimonas, and Parabacteroides. Conclusions: Oxidized green coffee extract has the effect of lowering lipids and increasing intestinal beneficial bacteria.

Graphical Abstract

1. Introduction

Adipose tissue is the primary fuel reserve of the body, playing a vital role in survival under conditions of limited food availability [1]. It regulates essential physiological functions in the body, including lipid metabolism, reproductive function, and insulin sensitivity [2]. However, excessive accumulation of adipose tissue leads to obesity, which is a chronic non-communicable disease with a dramatically increasing incidence worldwide, resulting in a severe global public health problem [3]. Obesity usually occurs when the body’s energy intake exceeds its energy expenditure and is influenced by genetic, physiological, and environmental factors [4]. Moreover, it triggers a range of underlying diseases and metabolic abnormalities, including insulin resistance, atherogenic dyslipidemia (characterized by high plasma triglycerides (TG) and low-plasma high-density lipoprotein-cholesterol (HDL-C) concentrations), non-alcoholic fatty liver disease (NAFLD), β-cell dysfunction, and type 2 diabetes [5]. Obesity is considered a severe metabolic disease with reduced life expectancy, impaired quality of life, and disability, including an increased risk of cardiovascular disease, osteoarthritis, and certain cancers [6].
Currently, several clinical drugs, including pioglitazone, metformin, and liraglutide, are used in the treatment of obesity, and have proven to reduce the incidence of type 2 diabetes and total cholesterol (TC) content. However, these drugs have obvious application limitations and cannot be widely used [7]. Furthermore, their administration may improve the metabolic profiles of obese and insulin-resistant patients [8]. Conversely, moderate intake of certain natural products reduces the risk of cardiovascular disease, death, and type 2 diabetes while slowing down the rate of weight gain compared with chemical drugs, as supported by biomarker-based evidence from human clinical studies [9]. Many herbal-based medications enriched with polyphenols can be utilized to control obesity, offering the advantages of a high safety margin and fewer adverse effects. Among nutraceuticals, mainly green coffee and green tea are well recognized as effective anti-obese agents [10]. Therefore, the identification of natural active substances to prevent and treat obesity complications can offer good market prospects with crucial public health and social significance [11].
Coffee is one of the world’s important commodities and a highly consumed beverage, followed by tea [12]. In the United States, 83% of adults consume some form of coffee [13]. Coffee originates from the genus coffee in the rubiaceae family, with more than 90 identified species. Raw coffee beans mainly comprise carbohydrates, lipids, proteins, chlorogenic acid (CGA), fatty acids, caffeine, trigonelline, and diterpenoids [14]. Such biologically active coffee compounds may prevent neurodegenerative diseases and depression [15], as well as modulate gut microbiome and Adipose tissue metabolism, thereby alleviating metabolic syndromes. During the roasting of the coffee beans, the carbohydrates, proteins, and CGA are reduced to produce numerous degradation products in vitro, with minimal changes in the constituent lipids, fatty acids, caffeine, and trigonelline content [16].
Coffee extracts are typically obtained through water extraction, organic solvent extraction, and ultrasound-assisted extraction [17]. In food production, methods such as Soxhlet extraction, traditional solid–liquid extraction, and liquid–liquid extraction are effective for extracting coffee compounds. However, these techniques pose challenges, including the increased risk of decomposing heat-sensitive components, the requirement for large solvent volumes, and extended extraction times [18]. However, the long extraction time of traditional cold coffee brewing dramatically reduces the production efficiency of this beverage. Zhai has established a new ultrasound-assisted cold-extraction (UAC) method, which combined ultrasound and cold-extraction coffee to produce good flavor and improve extraction efficiency. However, the content of ketones and phenols extracted by UAC is lower [19]. Green coffee is a novel food product since consumers usually use only roasted coffee. This product can be marketed as such or as an extract with potential health effects [20]. In this study, oxidation and cold extraction methods were utilized to mitigate the considerable loss of phenolic substances.
The pharmacological activities of coffee are primarily attributed to its major active compounds, particularly phenolic constituents and caffeine, which confer its antioxidant properties [21]. Green coffee extract is especially rich in CGA, a potent antioxidant known to increase metabolic rate, enhance fatty acid oxidation, and decrease hepatic triglyceride and total cholesterol levels [22]. In addition to CGA, polyphenols in coffee have properties that lower visceral adipose tissue accumulation [23]. Furthermore, several in vitro studies have suggested that coffee phenolics may inhibit α-amylase and α-glucosidase, enzymes involved in carbohydrate digestion, with effects comparable to those of the drug acarbose [24]. Supplementation of CGA to high-fat diet (HFD) mice was has been shown to reverse HFD-induced gut dysbiosis induced by the HFD, including significant inhibition of Ruminococcaceae, Lachnospiraceae, and Erysipelotrichaceae, while promoting the growth of the beneficial bacteria Bacteroidaceae, and Lactobacillaceae [25]. Collectively, evidence suggests that bioactive compounds in coffee, particularly CGA, exhibit anti-obesity potential by modulating lipid metabolism, energy intake, and gut microbiota composition [26,27]. Critically, the efficacy of these compounds is known to be influenced by the extraction and preparation processes [28], yet a direct comparison of how distinct preparation methods (like conventional vs. oxidative extraction) impact both the comprehensive metabolite profile and the subsequent anti-obesity efficacy mediated through gut microbiota remains unexplored. Different preparation process of CGA can improve the obesity caused by a HFD and increase gut microbial diversity, suggesting a potential emerging approach for obesity treatment.
To further explore this mechanism, we evaluated the impact of oxidized green coffee extract supplementation on obesity in mice fed a HFD, focusing on changes in serum biochemical indices, weight loss, adipose tissue reduction, and gut microbiome. Raw coffee beans were treated with gas oxidation to extract coffee compounds, based on principles similar to those used for extracting tea polyphenols [29]. These findings provide a scientific basis for the use of oxidized green coffee and extracts in the prevention of obesity and related complications, and support the further development and utilization of coffee extract as a functional food.

2. Materials and Methods

2.1. Materials and Reagents

The equipment and reagents required for the experiment are shown in Table 1 below.

2.2. Preparation of Coffee Extract Sample

To prepare the untreated coffee extract (UC), green coffee beans were ground using a grinder and passed through a 20-mesh sieve. The resulting powder was mixed with drinking water at a sample-to-liquid ratio of 1:10 and subjected to cold extraction at 4 °C for 12 h. The extract was then vacuum freeze-dried to obtain UC.
For the oxidized green coffee extract (GCE), raw coffee beans were ground and passed through a 20-mesh sieve and mixed with water at the same sample-to-liquid ratio of 1:10. The mixture was subjected to gas oxidation for 12 h in the presence of 0.4% baking soda. The gas flow rate was maintained at 0.1 L/min per liter of reaction mixture, and the oxidation process was conducted at 25 ± 2 °C for 12 h under ambient pressure with continuous stirring. The oxidized extract was then freeze-dried and stored for later use. Compared to UC, GCE exhibited a bamboo green color. The preparation process is illustrated in Figure 1.

2.3. Determination of the Structural Characterization

2.3.1. Scanning Electron Microscopy Analysis

A high-resolution field emission scanning electron microscope (SEM) was used to analyze the morphology of the coffee samples using a ZEISS ULTRA PLUS field emission scanning electron microscope (Carl Zeiss AG, Oberkochen, Germany).

2.3.2. Ultraviolet–Visible–near Infrared Spectroscopy Analysis

Aqueous solutions were analyzed using a Hitachi UH4150 UV/VIS/NIR spectrophotometer (Hitachi High-Tech Corporation, Tokyo, Japan), recording the full absorption spectra. The different coffee extracts were prepared at a concentration of 1 mg/mL and analyzed using a UV–visible spectrophotometer, recording the full absorption spectra over the wavelength range of 200–800 nm.

2.3.3. X-Ray Diffraction Analysis

The crystal pattern of the powder was studied using XRD using a Rigaku Ultima IV X-ray diffractometer (Rigaku Corporation, Tokyo, Japan). Electricity of 40 kV and 30 mA was used for the analysis. Flow X-ray diffractometer (TRTracer 100, Shimadzu, Kyoto, Japan) collected data between 5 and 90° at a rate of 50 °/min to determine the profiles of different coffee extracts.

2.3.4. Fourier Transform Infrared Spectroscopy Analysis

Fourier transform infrared (FT-IR) spectra were acquired using a SHIMADZU IRTracer-100 Fourier transform infrared spectrometer (Shimadzu Corporation, Kyoto, Japan) that detects chemical bonds in molecules by generating infrared absorption spectra of solids, liquids, or gases. First, 1 mg of dried sample was combined with 500 mg of KBr (Merck, Darmstadt, Germany, for spectroscopy) and fully ground in an agate mortar. The mixture was then scanned using the spectrometer, with a scanning wavenumber range of 4000–400cm−1.

2.4. The LC-MS Analysis of the Green Coffee Extract

The LC-MS grade acetonitrile (ACN) was supplied by Fisher Scientific (Loughborough, UK), and formic acid was obtained from TCI (Shanghai, China). One gram each of UC and GCE was placed in 2 mL centrifuge tubes. To each tube, 600 µL of methanol containing 2-chloro-L-phenylalanine (4 ppm) was added, and the mixture was homogenized using a tissue grinder at 55 Hz for 60 s, followed by ultrasonic treatment at room temperature for 15 min. After centrifugation at 12,000× g at 4 °C for 10 min, the supernatants were collected and filtered through 0.22 μm membranes. The filtrates were transferred to test flasks for LC-MS analysis. Liquid chromatography analysis was performed on a Vanquish UHPLC system (Thermo Fisher Scientific, Waltham, MA, USA) using an ACQUITY UPLC® HSS T3 column (2.1 × 100 mm, 1.8 µm; Waters, Milford, MA, USA) maintained at 40 °C. Samples were injected at a flow rate of 0.3 mL/min using a 12 min linear gradient. The eluent and solvent gradients were set as described in a previous study [30]. Mass spectrometric detection of metabolites was carried out using a Q Exactive mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with an ESI ion source. Simultaneous MS1 and MS/MS acquisition was performed in full MS-ddMS2 mode (data-dependent MS/MS), following the methodology.

2.5. Animal Experimental Design

An obesity mouse model induced by HFD was used to evaluate the biofunctional activities of oxidized GCE. A total of 32 SPF healthy male C57BL/6 mice (7 weeks old) were purchased from Skibbes Bio-technology Co., Ltd. (Changsha, China). The sample size was determined by a priori power analysis using G*Power 3.1 software (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany). Based on a pilot study from our laboratory, an effect size of d = 1.2 was estimated for the primary outcome (body weight gain). With a significance level of α = 0.05 and a desired statistical power of 1 − β = 0.80, the analysis indicated that a minimum of 7 mice per group was required. To account for potential dropout and individual variability, 8 mice per group (total N = 32) were used. Mice were housed in a specific pathogen-free chamber, PVC transparent plastic box group raising, maintained at a temperature of 24 ± 1 °C with 50–60% relative humidity and a 12 h light/dark cycle. After 7 days of acclimatization, a complete randomization procedure was employed to assign mice to the four experimental groups. Briefly, each mouse was assigned a unique identification number. Using a computer-generated random number sequence (Microsoft Excel RAND function), the mice were then allocated into the following four groups n = 8 each: normal control group, HFD group, UC group, and GCE group; the intervention experiment was conducted for 16 weeks. The allocation was performed by an independent researcher not involved in the subsequent interventions or outcome assessments to ensure allocation concealment. Each group of mice received a specific dose of the solution for intervention via gavage on a daily basis, as specified in Table 2. During the rest of the time, the mice were permitted to eat and drink freely. The details of the feed composition can be found in Appendix A Table A1 and Table A2. Body weight was measured weekly throughout the 16-week intervention period using an electronic balance (precision ± 0.1 g). Mice were weighed at the same time of day prior to the daily gavage to minimize diurnal variation. Bedding was replaced every 48 h, and all experimental procedures were carried out in strict adherence to the ethical guidelines for animal research. All animal experiments reported in the current study were approved by the Animal Management and Use Committee of Yunan Agriculture University [No. APYNAU202502001] and adhered to the National Institutes of Health Guide for the Care and Use of Laboratory Animals.

2.6. Insulin Tolerance Measurements

For the intraperitoneal insulin tolerance test (IPITT), the mice were intraperitoneally injected with insulin at a dose of 0.75 U/kg body weight. At 30, 60, 90, and 120 min after injection, a small amount of blood was collected using a blood sampling needle from the dorsal tail vein located 0.5 cm from the tip of the mouse tail. The glucose content was measured using a glucose meter.

2.7. Tissue Sample Collection

After a 12 h fast, following anesthesia with ether at the end of the 16-week trial, after blood collection from the cardiac venous plexus, mice were placed in an environment filled with gradually increasing carbon dioxide concentration (CO2) and then sacrificed by cervical dislocation. The abdominal epididymal adipose tissue were immediately dissected, washed with normal saline, dried with filter paper, weighed, and the organ index was calculated.
Organ index (%) = Organ weight/Mouse weight × 100

2.8. Determination of Biochemical Indicators

After blood collection, serum was separated by centrifugation at 3000× g for 15 min at 4 °C. Serum levels of triglycerides (TG), glutathione peroxidase (GSH-Px), malondialdehyde (MDA), and superoxide dismutase (SOD) were measured using commercial assay kits according to the manufacturer’s instructions. For liver tissue, a 10% (w/v) homogenate was prepared in saline and centrifuged at 4000× g for 15 min; the supernatant was used for the determination of the same biochemical indices.

2.9. Gut Microbiome Analysis

The gut microbiome of the mice in the four groups was analyzed and compared using 16S rRNA gene sequencing. Genomic DNA was extracted from the cecal contents of all mice (n = 8 per group, total N = 32) using a Qiagen DNA kit (Hilden, Germany) according to the manufacturer’s instructions. DNA quality was assessed by 1.2% agarose gel electrophoresis, and concentration/purity were determined using a NanoDrop2000 (Thermo Scientific, Waltham, MA, USA).
The V3–V4 hypervariable region of the 16S rRNA gene was amplified using the universal primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). Amplification products were quantified using the Quant-iT PicoGreen dsDNA assay kit on a microplate reader (BioTek, Winooski, VT, USA, FLx800), and libraries were prepared using the Illumina TruSeq Nano DNA LT Library Prep kit for paired-end sequencing (Personal, Shanghai, China).
Following sequencing, raw data from all initial samples (N = 32) were processed jointly. A standardized bioinformatics quality control (QC) pipeline was applied, including filtering based on sequence read depth, quality scores, and removal of potential contaminants. This rigorous QC step is standard practice to ensure the reliability of downstream ecological analyses. Consequently, all subsequent analyses (alpha-diversity, beta-diversity, and taxonomic composition) are based on a final high-quality dataset comprising n = 7–8 biologically independent samples per group. Alpha-diversity indices, principal coordinate analysis (PCoA), non-metric multidimensional scaling (NMDS), and linear discriminant analysis (LDA) were used to evaluate the impacts of GCE on the intestinal microbiome.

2.10. Statistical Analysis

All quantitative data are presented as mean ± standard error of the mean (S.E.M.). Statistical analyses were performed using GraphPad Prism version 9.0 (GraphPad Software, San Diego, CA, USA). For comparisons between groups, data from two groups were analyzed by an unpaired Student’s t-test, while data from multiple groups were analyzed by one-way analysis of variance (ANOVA) followed by Tukey’s HSD post hoc test. The area under the blood glucose curve (AUC) was calculated using the trapezoidal method. The threshold for statistical significance was set at p < 0.05. Gut microbiota data were analyzed using the QIIME 2 pipeline (version 2023.9). Inter-group differences in alpha-diversity indices (Shannon, Simpson, and Chao1) were assessed using the Kruskal–Wallis test. Beta-diversity was analyzed based on Bray–Curtis distance matrices and visualized via principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS). The significance of differences in microbial community structure between groups was evaluated using permutational multivariate analysis of variance (PERMANOVA). The significance threshold was also set at p < 0.05.

3. Results

3.1. Structural Characterization of the Coffee Extracts

The SEM map of the two kinds of coffee extract powder is shown in the figures. The UC surface is slightly folded and dendritic at 1000× and 5000× magnification (Figure 2A,B). In contrast to the apparent morphology of UC, the GCE surface showed many folds and a honeycomb-like structure (Figure 2C,D). Overall, the apparent morphology of the two coffee extracts varied greatly, indicating that the preparation process will affect the apparent morphology of the final coffee extract. As shown in Figure 2E the UC and CGE exhibited similar patterns in the scanned wavelength map under the UV–visible absorption spectra, with maximum absorption at 344 nm and 360 nm. As shown in Figure 2F the spectra of the two coffee extracts showed broad “bread” patterns, and no apparent sharp diffraction peaks were observed, indicating that both were amorphous polymers. As shown in Figure 2G, the FTIR absorption peaks of the two coffee extract samples were approximately 3400, 2920, 1640, 1390, 1270, 1120, and 1050 cm−1, suggesting similarities in the structure. As shown in Table 3, the content of GCA, flavonoids, and total sugar extracted by oxidation contents decreased in GCE. However, the total phenolic content increased, and the caffeine content remained relatively unchanged.

3.2. Impact of Oxidized GCE on Metabolic Profiles of Mice

Spectra of coffee extract samples from different treatment methods were acquired using UHPLC–HRMS under positive- and negative-ion modes. From the total ion current chromatograms of one sample (TIC, Appendix B, Figure A1 and Figure A2), rich information on metabolites can be observed in both ion modes, which indicates that it could be used for data analysis. Principal component analysis (PCoA) was used to analyze the differences between UC and oxidized GCE in terms of the overall distribution trends of identified metabolites. In the PCoA plot, samples with greater similarity are aggregated, while those that differ more are dispersed on the plot (Figure 3A,B). A clear difference in metabolites was evident between the UC and oxidized GCE as assessed in both positive- and negative-ion modes using LC-MS, indicating that oxidation could significantly change the metabolites of coffee.
Significantly differential metabolites were screened based on a variable importance in projection (VIP) value > 1 and p < 0.05. The volcano maps in Figure 3C,D,F show the differential expression of metabolites in the UC and oxidized CGE samples. A total of 499 differential metabolites were identified, including 247 upregulated metabolites and 252 downregulated metabolites. Based on the VIP, fold change, and P values, the differential metabolites from the UC and oxidized GCE samples were identified as amino acids, fatty acids, phenolic acids, and nucleic acids. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis showed that the coffee extract mainly affected flavonoid biosynthesis, fat cells, protein digestion and absorption, and amino acid metabolic pathways (Figure 3E,G).

Classification of Differential Metabolites

To elucidate the chemical nature of the metabolic shift, the 499 differential metabolites were systematically classified into major super-classes and classes according to the Human Metabolome Database (HMDB) taxonomy (Appendix B, Figure A3). The oxidation process induced the most pronounced changes in metabolites belonging to the super-classes of lipids and lipid-like molecules (27.45% of differential metabolites) and phenylpropanoids and polyketides (10.82%). Significant alterations were also observed in Organoheterocyclic compounds (13.83%), organic acids and derivatives (13.23%), and benzenoids (11.22%). Notably, within these broader categories, classes of known bioactivity such as alkaloids and derivatives (5.81%) and lignans, neolignans and related compounds (0.89%) were specifically enriched. This detailed classification confirms that oxidative extraction extensively remodels diverse chemical sectors of the coffee extract, particularly those rich in phenolic and lipidic structures.

3.3. Regulatory Effects of Coffee Extract Interventions on Blood Glucose, Blood Lipid Levels, and Liver Antioxidant in HFD-Fed Mice

As shown in Figure 4A, the HFD-Fed mice gained more weight than the normal control group. The HFD-Fed mice supplemented with UC and oxidized GCE exhibited significant reductions in body weight after week 6 compared to the mice in the HFD group. Compared with those of the normal control group, the blood glucose insulin (Figure 4B,C) levels of the HFDl group were significantly higher at 0, 30, 60, 90, and 120 min (p < 0.001). However, oxidized green coffee extract supplementation significantly reduced the degree of insulin resistance compared with that of to the HFD group (p < 0.05), and the treated coffee extract significantly reduced blood glucose and improved insulin sensitivity to a greater degree than the untreated coffee (p < 0.01). As shown in Figure 4D, the epididymal fat organ index was highest in the HFD group, significantly decreasing with coffee supplementation (p < 0.001). As shown in Figure 4E, UC significantly reduced the TG content of the mice compared to that of the HFD group. Therefore, the oxidized GCE had a better effect than UC in improving liver injury in mice and reducing serum TG accumulation. As shown in Figure 4F, the HFD group had significantly increased ALT levels compared to the normal control group (p < 0.02). However, UC and oxidized GCE supplementation significantly reduced ALT levels, with a more significant effect observed for the latter (p < 0.04). MDA is an indicator of liver peroxidation, while SOD and GSH-Px reflect the antioxidant capacity of the liver. As shown in Figure 4G, supplementation with UC and oxidized GCE significantly reduced the MDA levels in the mouse liver. As seen in Figure 4H,I, coffee supplementation significantly increased the liver antioxidant capacity (p < 0.01). Thus, UC and oxidized GCE increased the antioxidant capacity in mice. Similar to the above results, oxidized GCE enhanced mouse hepatic SOD activity compared to UC.

3.4. Effects of Coffee Extract Interventions on Intestinal Microbiota in HFD-Fed Mice

As shown in Figure 5A–D, the Chao 1, Observed_species, and Faith_pd indices demonstrated a sharp decrease in the diversity and richness of gut microbes in the HFD group compared to the normal control group. Analysis of genus-level richness specifically showed a significant decrease in the HFD group, consistent with the known effect of high-fat diets in reducing microbial diversity. However, supplementation with UC and oxidized GCE enhanced gut microbiota diversity in HFD-fed mice to a certain degree. Notably, while the GCE group exhibited the lowest numerical richness (Figure 5J), its Shannon, Simpson, and Chao1 indices (Figure 5A–E) were significantly higher than those of the HFD group and restored to levels similar to the ND and UC groups. This suggests that GCE supplementation induced a selective restructuring rather than a simple loss, suppressing specific HFD-enriched taxa while promoting a more balanced consortium of beneficial bacteria.
The PCoA plots and NMDS analyses showed that the HFD significantly changed the population characteristics of mouse gut microbes. Furthermore, UC and oxidized GCE reshaped the gut microbiota and increased the diversity under the HFD condition. However, supplementation with UC and oxidized GCE enhanced gut microbiota diversity and richness in HFD-fed mice to a certain degree with some overlap between groups, as indicated in the Venn diagram (Figure 5F).
At the phylum level, Firmicutes was dominant (70%), followed by Bacteroidetes and Proteobacteria (Figure 5I,J). At the genus level, HFD caused a sharp decrease in the relative abundance of Alobaculum and a significant increase in the relative abundances of Oscilospira, Desulfovibrio, and Akkermansia to the normal control group. Supplementation with oxidized GCE mitigated this trend, agreeing with the PCoA and NMDS plot results. The heat map showed a higher relative abundance of Adlercreutzia, Coprococcus, Dorea, Dehalobacterium, Lactobacillus, Lactococcus, Roseburia, Pseudomonas, Anaerotruncus, Clostridium, Blautia, Coprobacilus, Holdemania, Akkermansia, and Streptococcus in the HFD group compared to the normal control group. This confirmed that oxidized GCE supplementation could reduce this trend. Compared with the effects of UC, the oxidized GCE significantly increased the relative abundances of the beneficial bacteria, including Bacteroides, Odoribacter, and Parabacteroides (Figure 5L).
To further explore the specific microorganisms regulated by the green coffee oxidized extract, LDA of effect size (LEfSe) analysis was performed with the LDA threshold set to 4. A higher LDA score indicates greater significance in the comparison and vice versa. LEfSe analysis of gut microbial colonies indicated that the relative abundance differences among the four groups was 126 species, including seven phyla, seven orders, one family, and ten genera. Seven species contributed to the normal control group, nine to the HFD group, five to the UC group, and 20 to the GCE group.

3.5. Correlation Between Dominant Intestinal Bacteria and Obesity-Related Parameters

As shown in Figure 6, Spearman correlation analysis shows the relationship between dominant intestinal bacteria and obesity-related parameters. Ruminococcus, Adlercreutzia, Lactobacillus, and Akkermansia were positively correlated with weight gain, TG, and epididymal adipose tissue, suggesting that controlling the increase in these bacteria might be beneficial for weight management. Conversely, Acinetobacter, Prevotella, Butyricimonas, and Olsenella were negatively correlated with weight gain, highlighting their potential use in controlling weight gain control. Additionally, Butyricimonas, Cetobacterium, Parabacteroides, and Oscillospira were negatively correlated with TG and Epididymal adipose tissue.

4. Discussion

Conventional extraction methods, such as water, organic solvent, and Soxhlet extraction, are commonly used but are limited by long processing times, high costs, and reliance on non-volatile solvents [31]. Modern techniques, including ultrasonic, microwave-assisted, and enzyme-assisted extraction, provide improvements yet often yield lower phenolic compound content [32]. Oxidative extraction, however, emerges as a superior alternative, offering cost efficiency, faster processing, environmental friendliness, and higher phenolic yields, addressing the shortcomings of both traditional and modern methods. Studies indicate that the phenolic content in coffee extracts depends on several factors, including the solvent, method, extraction technology, coffee’s origin, and raw materials quality [33]. Pimpley and Murthy investigated comprehensive methods used in green technology, such as enzymatic hydrolysis, steam, microwave, and ultrasound-assisted phenolic extraction. Their results showed that the combined effect of enzyme and steam-assisted extraction significantly improved total polyphenols, flavonoids, phenolic substances, and antioxidant capacity [34]. Low-temperature vacuum extraction has been shown to yield phenols with relatively high chlorogenic acid content. Ethanol and water were used as extraction solvents, and the main extract obtained was chlorogenic acid. Furthermore, ultrasonic and enzyme-assisted extraction was used to mainly extract chlorogenic acid, caffeic acid, and xylenic acid [35]. In this study, the oxidative extraction method was used, producing a more diverse range of substances, including chlorogenic acid, caffeine, flavonoids, and total phenols. Compared to UC, the flavone and chlorogenic acid content decreased, and the total phenol content in GCE increased. This may be attributed to the fact that some substances may convert into total phenolics, decreasing the maximum absorption wavelength. A honeycomb appearance accompanied the rough structure in GCE, changing the color from ginger to bamboo green. In conclusion, the coffee extract prepared using the oxidation method can cause changes in its structure and bioactive components, providing early foundation for exploring its effects on obese mice raised on a high-fat diet.
Long-term consumption of a HFD can lead to glucose and lipid metabolism disorders in multiple organs, such as the liver, causing NAFLD and substantial accumulation of subcutaneous and visceral adipose tissue [36]. Phenolic compounds can be used as prebiotics to alter Firmicutes/Bacteroidetes ratio, reduce harmful bacteria, and enrich bacteria that produce short-chain fatty acids, thereby protecting and regulating the intestinal barrier and immune system [37]. The combination of CGA and caffeine has been shown to attenuate adipose tissue and reduce the serum TG content by regulating adipose tissue metabolism and inhibiting adipocyte differentiation in 3T3-L1 cells [38]. In the present study, both coffee extracts reduced body weight and the accumulation of epididymal adipose tissue in mice fed a HFD compared to the HFD group. The improved glucose metabolism homeostasis and insulin sensitivity observed in mice supplemented with the green coffee extract are consistent with findings from previous studies on CGA-treated animals [39]. The GCE inhibited weight gain, reduced adipose tissue accumulation, improved glucose homeostasis, and reduced sensitivity to insulin resistance compared to supplementation with UC (p < 0.001). The coffee husk (pulp) is rich in phytochemicals, which can prevent NAFLD and reduce the accumulation of ALT and AST levels in the serum [40]. In this study, compared with those of the HFD group, oxidized GCE significantly reduced ALT levels (p < 0.05) and alleviated fatty liver lesions formation in mice to a certain extent. Supplementation with UC reduced serum TG content in mice. The oxidized green coffee extract (GCE) significantly reduced the content of TG in the serum (p < 0.05); this is consistent with the bioactivity of oxidized phenolic compounds. GCE reduced TG accumulation in serum, improved insulin sensitivity, and increased liver antioxidant effect more effectively than UC. The superior efficacy of GCE can now be mechanistically linked to a fundamental qualitative transformation of its chemical profile. As revealed by our detailed metabolomic classification (Appendix B, Figure A3), oxidation induced significant enrichment of differential metabolites in key bioactive super-classes, particularly lipids and lipid-like molecules and phenylpropanoids and polyketides, and within specific classes like alkaloids and derivatives and lignans and neolignans. This shift suggests the generation of novel or structurally modified compounds, such as more complex oxidized phenolic polymers and altered lipid species, which likely possess enhanced prebiotic, metabolic, and antioxidant properties compared to their precursors in UC. Therefore, the bioactivity of GCE stems from this transformed chemical landscape rather than merely the downregulation of specific metabolites.
An imbalance and dysfunction of the intestinal microbial ecology often accompanies the development of obesity. Intestinal microbiota disorder is one of the causative factors of obesity, mainly characterized by abnormal tissue structure and function [41]. A previous study showed that dietary supplementation with a fermented aqueous extract of Eucommia ulmoides leaves significantly reduced body weight, lipid accumulation, and TC, TG, and LDL-C levels in hyperlipidemic rats while increasing HDL-C levels [42]. Green coffee extract contains CGA, a phenolic compound with antioxidant property. CGA increases lipid metabolism, decreases TG and cholesterol levels, and increases plasma adiponectin levels [43]. Compared with the HFD-fed mice, the TG and TC contents in the liver were reduced in mice that received fecal transplantation of the bacterial genus Faecalibacterium [44]. Coffee extract gavage can significantly reduce the weight of the body and organs. In addition, it has an ameliorating effect on serum biochemical parameters, including reductions in TC, triacylglycerol, LDL cholesterol, very low-density lipoprotein cholesterol, glucose, and insulin levels. Moreover, in homeostasis model assessments, an improvement in insulin resistance and enhancement of HDL cholesterol levels were observed compared to the HFD-fed group [45]. In the current study, 16 weeks of HFD feeding significantly decreased the diversity and richness of gut microbes in mice. In contrast, supplementation with both UC and oxidized GCE reversed these changes. In addition, both treatments reduced the relative abundance of Firmicutes and increased the relative abundance of Bacteroidetes. Faecalibacterium is the most important bacterial indicator of a healthy gut and is a dominant member of the Clostridium sub-group, accounting for 5% of the total healthy human gut microbiota [46]. Caffeoylquinic acid (CQA) supplementation effectively enhanced energy expenditure by activating adipose browning, thus ameliorating obesity-related metabolic dysfunctions in HFD-induced obese mice. Furthermore, 16S rRNA gene amplicon sequencing revealed that CQA treatment remodeled gut microbiota to promote its anti-obesity effects [47]. The gut microbiota diversity of patients with obesity is relatively low, with a negative correlation between the relative abundance of Faecalibacterium and body weight [48]. Oscillospira is a bacterial genus widely found in the intestines of animals and humans. It has been frequently identified in high-throughput sequencing data but has yet to be successfully cultured. Nevertheless, there is a strong correlation between changes in the relative abundance of Oscillospira and obesity, emaciation, and human health [49]. Polysaccharides can effectively prevent HFD-induced obesity by regulating intestinal dysbiosis, and their use as prebiotics has attracted widespread attention in recent years. Polysaccharides can also increase the diversity of intestinal bacteria and the abundance of beneficial bacteria such as Akkermansia, Lactobacillus, and Bacteroides [50]. The changes in the relative abundance of intestinal microbiota in HFD-fed mice fed with dietary tannic acid, including Firmicutes, Bacteroidetes, Bacteroides, Alistipes, and Odoribacter, suggested its potential role in obesity prevention and control [51]. A synthetic Ganoderma meroterpene derivative was reported to prevent obesity-associated atherosclerosis by increasing Parabacteroides abundance in the gut and enhancing branched-chain amino acid catabolism [52].
LEfSe analysis further identified the specific bacterial taxa that were most significantly altered by the interventions. The dominant bacteria causing changes in the gut microbiota were Bacteroidia, Firmicutes, Bacteroidaceae, Alistipes, Rikenellaceae, Oscillospira, and Ruminococcus (Figure 5L,M). These findings indicate that specific bacteria such as Bacteroides, Odoribacter, Parabacteroides, Mucispirillum, Oscillospira, and Ruminococcus play a crucial role in regulating gut microbiota ecology and have the potential to serve as biomarkers for obesity prevention and management. This is supported by our correlation analysis (Figure 6), which showed that Parabacteroides and Butyricimonas were strongly negatively correlated with adiposity and serum TG levels. The enrichment of Parabacteroides by GCE is of particular interest, as recent studies have identified this genus as a microbial biomarker for metabolic health and have shown that Parabacteroides species can alleviate obesity and metabolic dysfunctions through the production of succinate and secondary bile acids [53]. Additionally, Bacteroides reduces weight gain, hyperglycemia, and hepatic steatosis in obese and HFD-fed mice [54]. This is consistent with our present findings that UC and GCE increased the relative abundances of Oscillospira, Parabacteroides, and Alistipes in the intestines of HFD-fed mice. Moreover, compared with those of the UC group, the relative abundances of Bacteroides, Odoribacter, and Parabacteroides were significantly increased in the GCE group. Overall, these results suggest certain differences in UC and oxidized GCE, thereby alleviating obesity and dyslipidemia by regulating different pathways such as linoleic acid metabolism, protein digestion and absorption, and tryptophan metabolism via the regulation of intestinal microbiota richness. Bifidobacterium, Akkermansia, and Bifidobacterium were negatively correlated with body weight, TC, TG, and TNF-α [55], while Clostridium exhibited a positive correlation with obesity [56]. The results of this study were similar to those of previous studies, wherein Butyricimonas, Cetobacterium, Parabacteroides, and Oscillospira were negatively correlated with TG and epididymal adipose tissue. These correlation results collectively support the notion that GCE modulates specific gut bacteria, confirming the extract‘s potential health benefits in weight loss, lipid reduction, and alleviation of metabolic syndrome. In summary, Acinetobacter, Prevotella, Butyricimonas, Parabacteroides, and Olsenella may control weight gain, with Parabacteroides and Butyricimon as demonstrating the most significant effect.”
In summary, both UC and GCE are effective natural anti-obesity extracts. Although GCE did not significantly surpass UC in all macroscopic physiological indicators within the parameters of this study, its unique chemical composition profile (as revealed by metabolomics) and its precise promotion of specific beneficial bacteria (such as Bacteroides, Odoribacter, and Parabacteroides), alongside its distinct mode of microbiota restructuring, reveal its potential as a differentiated functional ingredient with a potentially optimized mechanism of action. The pathogenesis of obesity is complex and unclear; however, significant changes in certain gut microbes (Faecalibacterium, Oscillospira, and Ruminococcus) can be observed and are related to indicators of obesity. These changes may reflect the ability of oxidized GCE to induce biological and metabolite conversions that contribute to the alleviation of obesity and its associated complications. Considering oxidized GCE as a potential functional food, its sensory quality and nutritional characteristics are also key drivers of consumer preference. Therefore, it is necessary to optimize the process of preparing oxidized GCE to confer a unique flavor and texture along with its original color. Taken together, these findings suggest that green coffee oxidized extract has the potential to be developed as a novel coffee extract for obesity prevention.
This study has several limitations that should be noted. First, the anti-obesity effects were evaluated at a single dose, and a dose–response relationship was not established. Second, although correlations between specific gut microbiota changes and improved metabolic parameters were observed, the direct causal mechanisms remain to be elucidated. Third, the bioactive compounds in the complex GCE mixture responsible for the observed effects were not individually identified or validated. Future studies incorporating dose-ranging designs, mechanistic investigations (e.g., using germ-free models or metabolomics), and compound isolation are needed to confirm and extend these findings.

5. Conclusions

This study investigated the impacts of two preparation processes on the structural characteristics, non-volatile metabolites, hypolipidemic activity, and modulation of intestinal microbiota in coffee extracts. Structural characterization revealed that coffee extracts were polymeric phenolic compounds rich in hydroxyl and carboxyl groups but exhibited significant differences in their apparent morphology. Non-targeted metabolomics revealed that a clear difference in metabolites was evident between the untreated and green coffee oxidized extract as assessed in both positive- and negative-ion modes, indicating that oxidation can significantly change the metabolites of coffee. A total of four hundred ninety-nine differential metabolites were identified, with 247 metabolites being upregulated and 252 metabolites downregulated. Regarding biofunctional activities, UC interventions effectively controlled weight gain, improved lipid levels, and modulated the intestinal microbiota in HFD-fed mice. Specifically, GCE was associated with increased microbiota (Bacteroidetesand, Oscillospira) linked with hypolipidemic activity. Thus, this study offers a scientific basis for the application of coffee extract as dietary supplements in obesity intervention. Future studies can explore the specific biological mechanisms underlying the specific functional activities of coffee extract prepared using different processes.

Author Contributions

Conceptualization, J.H. and C.F.; methodology, C.Z.; validation, L.Y. (Lijun Yu), Y.S. and Z.D.; formal analysis, R.Y. (Rongxian Yu); investigation, L.S. and X.J.; data curation, L.Y. (Lihui Yu); writing—original draft preparation, J.H.; writing—review and editing, R.Y. (Ruijuan Yang); project administration, H.W. and X.D.; funding acquisition, C.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Yunnan Internstion Joint Laboratory of Green Health Food (China & Thailand) (202203AP140011) We again thank the above fund for their support.

Institutional Review Board Statement

All animal experiments reported in the current study were approved by the Animal Management and Use Committee of Yunan Agriculture University [No. APYNAU202502001] (approved date: February 8th, 2024) and adhered to the National Institutes of Health Guide for the Care and Use of Laboratory Animals.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to privacy considerations. However, interested researchers can request access to the data presented in this study by contacting the corresponding author. We will review and process data requests while ensuring privacy protection. All data presented in this study are available on request from the corresponding authors. The data are not be uploaded to a publicly accessible database.

Acknowledgments

We sincerely thank the Yunnan Provincial Government for supporting the “Yunnan interatioal joint laboratory of green health food (China & Thailand)” project. Special thanks go to the College of Animal Science and Technology at Yunnan Agricultural University for providing the poultry experiment facilities for this research.

Conflicts of Interest

The authors declare no conflicts of interest.

Correction Statement

This article has been republished with a minor correction to the Institutional Review Board Statement and Informed Consent Statement. This change does not affect the scientific content of the article.

Abbreviations

The following abbreviations are used in this manuscript:
ASTAspartate transaminase
AUCArea under the curve
CGAChlorogenic acid
CQACaffeoylquinic acid
HFDHigh-fat diet
KEGGKyoto Encyclopedia of Genes and Genomes
LDALinear discriminant analysis
NAFLDNon-alcoholic fatty liver disease
NMDSNon-metrological multidimensional scaling
PCAPrincipal component analysis
PCoAPrincipal coordinate component analysis
SEMStandard error of the mean
TGTriglycerides
ESIElectro spray ionization;
VIPVariable importance in projection.
TCTotal cholesterol

Appendix A

Table A1. Components of high-fat feed.
Table A1. Components of high-fat feed.
Ingredientgmkcal
Casein, 30 Mesh 200200800
L-Cystine312
Corn Starch00
Maltodextrin 10125500
Sucrose68.8275.2
Cellulose, BW200500
Soybean Oil25225
Lard 2452205
Mineral Mix S10026100
DiCalcium Phosphate130
Calcium Carbonate5.50
Potassium Citrate, 1 H2O16.50
Vitamin Mix V100011040
Choline Bitartrate20
FD&C Blue Dye #10.050
Table A2. Components of low-fat feed.
Table A2. Components of low-fat feed.
Ingredientgmkcal
Casein, 30 Mesh 200200800
L-Cystine312
Corn Starch506.22024.8
Maltodextrin 10125500
Sucrose68.8275.2
Cellulose, BW200500
Soybean Oil25225
Lard 2018
Mineral Mix S10026100
DiCalcium Phosphate130
Calcium Carbonate5.50
Potassium Citrate, 1 H2O16.50
Vitamin Mix V100011040
Choline Bitartrate20
FD & C Yellow Dye # 50.040
FD&C Blue Dye #10.010

Appendix B

Nutrients 18 00638 g0a1
Figure A1. TLC plot in positive-ion mode.
Figure A1. TLC plot in positive-ion mode.
Nutrients 18 00638 g0a1
Nutrients 18 00638 g0a2
Figure A2. TLC plot in negative-ion mode.
Figure A2. TLC plot in negative-ion mode.
Nutrients 18 00638 g0a2
Nutrients 18 00638 g0a3
Figure A3. Classification of differential metabolites.
Figure A3. Classification of differential metabolites.
Nutrients 18 00638 g0a3

References

  1. Wibmer, A.G.; Becher, T.; Eljalby, M.; Crane, A.; Andrieu, P.C.; Jiang, C.S.; Vaughan, R.; Schöder, H.; Cohen, P. Brown adipose tissue is associated with healthier body fat distribution and metabolic benefits independent of regional adiposity. Cell Rep. Med. 2021, 2, 100332. [Google Scholar] [CrossRef] [Scilit]
  2. Funcke, J.-B.; Scherer, P.E. Beyond adiponectin and leptin: Adipose tissue-derived mediators of inter-organ communication. J. Lipid Res. 2019, 60, 1648–1697. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  3. Stefan, N.; Häring, H.-U.; Hu, F.B.; Schulze, M.B. Metabolically healthy obesity: Epidemiology, mechanisms, and clinical implications. Lancet Diabetes Endocrinol. 2013, 1, 152–162. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  4. Lustig, R.H.; Collier, D.; Kassotis, C.; Roepke, T.A.; Kim, M.J.; Blanc, E.; Barouki, R.; Bansal, A.; Cave, M.C.; Chatterjee, S.; et al. Obesity I: Overview and molecular and biochemical mechanisms. Biochem. Pharmacol. 2022, 199, 115012. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  5. Klein, S.; Gastaldelli, A.; Yki-Järvinen, H.; Scherer, P.E. Why does obesity cause diabetes? Cell Metab. 2022, 34, 11–20. [Google Scholar] [CrossRef] [Scilit]
  6. Blüher, M. Metabolically healthy obesity. Endocr. Rev. 2020, 41, bnaa004. [Google Scholar] [CrossRef] [Scilit]
  7. Srivastava, G.; Apovian, C.M. Current pharmacotherapy for obesity. Nat. Rev. Endocrinol. 2018, 14, 12–24. [Google Scholar] [CrossRef] [Scilit]
  8. Mongioì, L.M.; La Vignera, S.; Cannarella, R.; Cimino, L.; Compagnone, M.; Condorelli, R.A.; Calogero, A.E. The role of resveratrol administration in human obesity. Int. J. Mol. Sci. 2021, 22, 4362. [Google Scholar] [CrossRef] [Scilit]
  9. Kalt, W.; Cassidy, A.; Howard, L.R.; Krikorian, R.; Stull, A.J.; Tremblay, F.; Zamora-Ros, R. Recent research on the health benefits of blueberries and their anthocyanins. Adv. Nutr. Int. Rev. J. 2020, 11, 224–236. [Google Scholar] [CrossRef] [Scilit]
  10. Sirotkin, A.; Kolesarova, A. The anti-obesity and health-promoting effects of tea and coffee. Physiol. Res. 2021, 70, 161–168. [Google Scholar] [CrossRef] [Scilit]
  11. Zhang, L.; Song, J.; Kong, L.; Yuan, T.; Li, W.; Zhang, W.; Hou, B.; Lu, Y.; Du, G. The strategies and techniques of drug discovery from natural products. Pharmacol. Ther. 2020, 216, 107686. [Google Scholar] [CrossRef] [Scilit]
  12. Angeloni, G.; Guerrini, L.; Masella, P.; Bellumori, M.; Daluiso, S.; Parenti, A.; Innocenti, M. What kind of coffee do you drink? An investigation on effects of eight different extraction methods. Food Res. Int. 2019, 116, 1327–1335. [Google Scholar] [CrossRef] [Scilit]
  13. Derossi, A.; Ricci, I.; Caporizzi, R.; Fiore, A.; Severini, C. How grinding level and brewing method (espresso, american, turkish) could affect the antioxidant activity and bioactive compounds in a coffee cup. J. Sci. Food Agric. 2018, 98, 3198–3207. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  14. Hall, R.D.; Trevisan, F.; de Vos, R.C. Coffee berry and green bean chemistry—Opportunities for improving cup quality and crop circularity. Food Res. Int. 2022, 151, 110825. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  15. Dong, W.; Chen, Q.; Wei, C.; Hu, R.; Long, Y.; Zong, Y.; Chu, Z. Comparison of the effect of extraction methods on the quality of green coffee oil from Arabica coffee beans: Lipid yield, fatty acid composition, bioactive components, and antioxidant activity. Ultrason. Sonochemistry 2021, 74, 105578. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  16. Grzelczyk, J.; Fiurasek, P.; Kakkar, A.; Budryn, G. Evaluation of the thermal stability of bioactive compounds in coffee beans and their fractions modified in the roasting process. Food Chem. 2022, 387, 132888. [Google Scholar] [CrossRef] [Scilit]
  17. Zamanipoor, M.H.; Yakufu, B.; Tse, E.; Rezaeimotlagh, A.; Hook, J.M.; Bucknall, M.P.; Thomas, D.S.; Trujillo, F.J. Brewing coffee?—Ultra-sonication has clear beneficial effects on the extraction of key volatile aroma components and triglycerides. Ultrason. Sonochemistry 2020, 60, 104796. [Google Scholar] [CrossRef] [Scilit]
  18. Heleno, S.A.; Diz, P.; Prieto, M.A.; Barros, L.; Rodrigues, A.; Barreiro, M.F.; Ferreira, I.C. Optimization of ultrasound-assisted extraction to obtain mycosterols from Agaricus bisporus L. by response surface methodology and comparison with conventional soxhlet extraction. Food Chem. 2016, 197, 1054–1063. [Google Scholar] [CrossRef] [Scilit]
  19. Zhai, X.; Yang, M.; Zhang, J.; Zhang, L.; Tian, Y.; Li, C.; Bao, L.; Ma, C.; El-Aty, A.M.A. Feasibility of ultrasound-assisted extraction for accelerated cold brew coffee processing: Characterization and comparison with conventional brewing methods. Front. Nutr. 2022, 9, 849811. [Google Scholar] [CrossRef] [Scilit]
  20. Klingel, T.; Kremer, J.I.; Gottstein, V.; de Rezende, T.R.; Schwarz, S.; Lachenmeier, D.W. A review of coffee by-products including leaf, flower, cherry, husk, silver skin, and spent grounds as novel foods within the European Union. Foods 2020, 9, 665. [Google Scholar] [CrossRef] [Scilit]
  21. Ali, A.M.A.; Yagi, S.; Qahtan, A.A.; Alatar, A.A.; Angeloni, S.; Maggi, F.; Caprioli, G.; Abdel-Salam, E.M.; Sinan, K.I.; Zengin, G. Evaluation of the chemical constituents, antioxidant and enzyme inhibitory activities of six yemeni green coffee beans varieties. Food Biosci. 2022, 46, 101552. [Google Scholar] [CrossRef] [Scilit]
  22. Cho, A.-S.; Jeon, S.-M.; Kim, M.-J.; Yeo, J.; Seo, K.-I.; Choi, M.-S.; Lee, M.-K. Chlorogenic acid exhibits anti-obesity property and improves lipid metabolism in high-fat diet-induced-obese mice. Food Chem. Toxicol. 2010, 48, 937–943. [Google Scholar] [CrossRef] [Scilit]
  23. Ilmiawati, C.; Fitri, F.; Rofinda, Z.D.; Reza, M. Green coffee extract modifies body weight, serum lipids and TNF-α in high-fat diet-induced obese rats. BMC Res. Notes 2020, 13, 208. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  24. Wu, H.; Barrow, C.; Dunshea, F.R.; Suleria, H.A. Phenolic bioaccessibility, antioxidant, and antidiabetic effects of indigenous fermented coffee beans after simulated gastrointestinal digestion and colonic fermentation. Food Biosci. 2023, 54, 102920. [Google Scholar] [CrossRef] [Scilit]
  25. Bhandarkar, N.S.; Brown, L.; Panchal, S.K. Chlorogenic acid attenuates high-carbohydrate, high-fat diet–induced cardiovascular, liver, and metabolic changes in rats. Nutr. Res. 2019, 62, 78–88. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  26. Uner, B.; Celebi, M.S.M. Anti-obesity effects of chlorogenic acid and caffeine- lipid nanoparticles through PPAR-γ/C/EBP-ɑ pathways. Int. J. Obes. 2023, 47, 1108–1119. [Google Scholar] [CrossRef] [Scilit]
  27. Wang, Z.; Lam, K.-L.; Hu, J.; Ge, S.; Zhou, A.; Zhou, A.; Zheng, B.; Zeng, S.; Lin, S. Chlorogenic acid alleviates obesity and modulates gut microbiota in high-fat-fed mice. Food Sci. Nutr. 2019, 7, 579–588. [Google Scholar] [CrossRef] [Scilit]
  28. Gligor, O.; Clichici, S.; Moldovan, R.; Muntean, D.; Vlase, A.-M.; Nadăș, G.C.; Matei, I.A.; Filip, G.A.; Vlase, L.; Crișan, G. The effect of extraction methods on phytochemicals and biological activities of green coffee beans extracts. Plants 2023, 12, 712. [Google Scholar] [CrossRef] [Scilit]
  29. Zhang, F.; Wang, Y.; Wang, M.; Tan, C.; Huang, S.; Mou, H.; Wu, K.; Peng, L.; Fang, Z.; Tian, Y.; et al. Structural characteristics and nonvolatile metabolites of theabrownins and their impact on intestinal microbiota in high-fat-diet-fed mice. Food Chem. 2024, 463, 141317. [Google Scholar] [CrossRef] [Scilit]
  30. Zelena, E.; Dunn, W.B.; Broadhurst, D.; Francis-McIntyre, S.; Carroll, K.M.; Begley, P.; O’Hagan, S.; Knowles, J.D.; Halsall, A.; HUSERMET Consortium; et al. Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal. Chem. 2009, 81, 1357–1364. [Google Scholar] [CrossRef] [Scilit]
  31. Azmir, J.; Zaidul, I.S.M.; Rahman, M.M.; Sharif, K.M.; Mohamed, A.; Sahena, F.; Jahurul, M.H.A.; Ghafoor, K.; Norulaini, N.A.N.; Omar, A.K.M. Techniques for extraction of bioactive compounds from plant materials: A review. J. Food Eng. 2013, 117, 426–436. [Google Scholar] [CrossRef] [Scilit]
  32. Ameer, K.; Shahbaz, H.M.; Kwon, J.-H. Green extraction methods for polyphenols from plant matrices and their byproducts: A review. Compr. Rev. Food Sci. Food Saf. 2017, 16, 295–315. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  33. Dias, É.C.P.P.; Macedo, G.A.; Camargo, G.A.; Macedo, J.A.; de Matuoka e Chiocchetti, G. Effects of extraction processes on recovery, the phenolic profile, and antiglycation activity from green coffee residues (Coffea arabica and Coffea canephora Pierre). ACS Sustain. Chem. Eng. 2024, 12, 13464–13474. [Google Scholar] [CrossRef] [Scilit]
  34. Hu, S.; Gil-Ramírez, A.; Martín-Trueba, M.; Benítez, V.; Aguilera, Y.; Martín-Cabrejas, M.A. Valorization of coffee pulp as bioactive food ingredient by sustainable extraction methodologies. Curr. Res. Food Sci. 2023, 6, 100475. [Google Scholar] [CrossRef] [Scilit]
  35. Lapčíková, B.; Lapčík, L.; Barták, P.; Valenta, T.; Dokládalová, K. Effect of extraction methods on aroma profile, antioxidant activity and sensory acceptability of specialty coffee brews. Foods 2023, 12, 4125. [Google Scholar] [CrossRef] [Scilit]
  36. Brown, O.I.; Drozd, M.; McGowan, H.; Giannoudi, M.; Conning-Rowland, M.; Gierula, J.; Straw, S.; Wheatcroft, S.B.; Bridge, K.; Roberts, L.D.; et al. Relationship among diabetes, obesity, and cardiovascular disease phenotypes: A UK biobank cohort study. Diabetes Care 2023, 46, 1531–1540. [Google Scholar] [CrossRef] [Scilit]
  37. Obrenovich, M.; Li, Y.; Tayahi, M.; Reddy, V.P. Polyphenols and small phenolic acids as cellular metabolic regulators. Curr. Issues Mol. Biol. 2022, 44, 4152–4166. [Google Scholar] [CrossRef] [Scilit]
  38. Kong, L.; Xu, M.; Qiu, Y.; Liao, M.; Zhang, Q.; Yang, L.; Zheng, G. Chlorogenic acid and caffeine combination attenuates adipogenesis by regulating fat metabolism and inhibiting adipocyte differentiation in 3T3-L1 cells. J. Food Biochem. 2021, 45, e13795. [Google Scholar] [CrossRef] [Scilit]
  39. Ma, Y.; Gao, M.; Liu, D. Chlorogenic acid improves high fat diet-induced hepatic steatosis and insulin resistance in mice. Pharm. Res. 2015, 32, 1200–1209. [Google Scholar] [CrossRef] [Scilit]
  40. Fan, Z.; Wang, C.; Yang, T.; Gao, T.; Wang, D.; Zhao, X.; Guo, X.; Li, D. Coffee peel extracts ameliorate non-alcoholic fatty liver diseaseviaa fibroblast growth factor 21–adiponectin signaling pathway. Food Funct. 2022, 13, 7251–7259. [Google Scholar] [CrossRef] [Scilit]
  41. Sankararaman, S.; Noriega, K.; Velayuthan, S.; Sferra, T.; Martindale, R. Gut microbiome and its impact on obesity and obesity-related disorders. Curr. Gastroenterol. Rep. 2023, 25, 31–44. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  42. Duan, Y.; Guo, F.; Li, C.; Xiang, D.; Gong, M.; Yi, H.; Chen, L.; Yan, L.; Zhang, D.; Dai, L.; et al. Aqueous extract of fermented Eucommia ulmoides leaves alleviates hyperlipidemia by maintaining gut homeostasis and modulating metabolism in high-fat diet fed rats. Phytomedicine 2024, 128, 155291. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  43. Asbaghi, O.; Sadeghian, M.; Nasiri, M.; Khodadost, M.; Shokri, A.; Panahande, B.; Pirouzi, A.; Sadeghi, O. The effects of green coffee extract supplementation on glycemic indices and lipid profile in adults: A systematic review and dose-response meta-analysis of clinical trials. Nutr. J. 2020, 19, 71. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  44. Munukka, E.; Rintala, A.; Toivonen, R.; Nylund, M.; Yang, B.; Takanen, A.; Hänninen, A.; Vuopio, J.; Huovinen, P.; Jalkanen, S.; et al. Faecalibacterium prausnitzii treatment improves hepatic health and reduces adipose tissue inflammation in high-fat fed mice. ISME J. 2017, 11, 1667–1679. [Google Scholar] [CrossRef] [Scilit]
  45. Seliem, E.M.; Azab, M.E.; Ismail, R.S.; Nafeaa, A.A.; Alotaibi, B.S.; Negm, W.A. Green coffee bean extract normalize obesity-induced alterations of metabolic parameters in rats by upregulating adiponectin and GLUT4 levels and reducing RBP-4 and HOMA-IR. Life 2022, 12, 693. [Google Scholar] [CrossRef] [Scilit]
  46. Xuan, W.; Ou, Y.; Chen, W.; Huang, L.; Wen, C.; Huang, G.; Tang, W.; Zeng, D.; Huang, S.; Xiao, L.; et al. Faecalibacterium prausnitzii improves lipid metabolism disorder and insulin resistance in type 2 diabetic mice. Br. J. Biomed. Sci. 2023, 80, 10794. [Google Scholar] [CrossRef] [Scilit]
  47. Liu, Y.; Zhong, X.; Lin, S.; Xu, H.; Liang, X.; Wang, Y.; Xu, J.; Wang, K.; Guo, X.; Wang, J.; et al. Limosilactobacillus reuteri and caffeoylquinic acid synergistically promote adipose browning and ameliorate obesity-associated disorders. Microbiome 2022, 10, 226. [Google Scholar] [CrossRef] [Scilit]
  48. Navab-Moghadam, F.; Sedighi, M.; Khamseh, M.E.; Alaei-Shahmiri, F.; Talebi, M.; Razavi, S.; Amirmozafari, N. The association of type II diabetes with gut microbiota composition. Microb. Pathog. 2017, 110, 630–636. [Google Scholar] [CrossRef] [Scilit]
  49. Yang, J.; Li, Y.; Wen, Z.; Liu, W.; Meng, L.; Huang, H. Oscillospira—A candidate for the next-generation probiotics. Gut Microbes 2021, 13, 1987783. [Google Scholar] [CrossRef] [Scilit]
  50. Zhu, M.; Ouyang, J.; Zhou, F.; Zhao, C.; Zhu, W.; Liu, C.; Huang, P.; Li, J.; Tang, J.; Zhang, Z.; et al. Polysaccharides from fu brick tea ameliorate obesity by modulating gut microbiota and gut microbiota-related short chain fatty acid and amino acid metabolism. J. Nutr. Biochem. 2023, 118, 109356. [Google Scholar] [CrossRef] [Scilit]
  51. Fang, J.; Zeng, L.; He, Y.; Liu, X.; Zhang, T.; Wang, Q. Effects of dietary tannic acid on obesity and gut microbiota in c57bl/6j mice fed with high-fat diet. Foods 2022, 11, 3325. [Google Scholar] [CrossRef] [Scilit]
  52. Qiao, S.; Liu, C.; Sun, L.; Wang, T.; Dai, H.; Wang, K.; Bao, L.; Li, H.; Wang, W.; Liu, S.-J.; et al. Gut Parabacteroides merdae protects against cardiovascular damage by enhancing branched-chain amino acid catabolism. Nat. Metab. 2022, 4, 1271–1286. [Google Scholar] [CrossRef] [Scilit]
  53. Hu, J.; Guo, P.; Mao, R.; Ren, Z.; Wen, J.; Yang, Q.; Yan, T.; Yu, J.; Zhang, T.; Liu, Y. Gut microbiota signature of obese adults across different classifications. Diabetes Metab. Syndr. Obes. Targets Ther. 2022, 15, 3933–3947. [Google Scholar] [CrossRef] [Scilit]
  54. Wang, K.; Liao, M.; Zhou, N.; Bao, L.; Ma, K.; Zheng, Z.; Wang, Y.; Liu, C.; Wang, W.; Wang, J.; et al. Parabacteroides distasonis alleviates obesity and metabolic dysfunctions via production of succinate and secondary bile acids. Cell Rep. 2019, 26, 222–235.e5. [Google Scholar] [CrossRef] [Scilit]
  55. Wei, B.; Peng, Z.; Zheng, W.; Yang, S.; Wu, M.; Liu, K.; Xiao, M.; Huang, T.; Xie, M.; Xiong, T. Probiotic-fermented tomato alleviates high-fat diet-induced obesity in mice: Insights from microbiome and metabolomics. Food Chem. 2024, 436, 137719. [Google Scholar] [CrossRef] [Scilit]
  56. Cai, Y.Y.; Huang, F.Q.; Lao, X.; Lu, Y.; Gao, X.; Alolga, R.N.; Yin, K.; Zhou, X.; Wang, Y.; Liu, B.; et al. Integrated metagenomics identifies a crucial role for trimethylamine-producing Lachnoclostridium in promoting atherosclerosis. NPJ Biofilms Microbiomes 2022, 8, 11. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Nutrients 18 00638 g001
Figure 1. Coffee extract preparation process.
Figure 1. Coffee extract preparation process.
Nutrients 18 00638 g001
Nutrients 18 00638 g002
Figure 2. Structural features of coffee extract (A,B); UV–visible spectral scans of two coffee extracts (C) X-ray diffraction patterns of two coffee extracts (D); Fourier transform infrared of two coffee extracts (E); X-ray diffraction analysis of two coffee extracts (F); Fourier transform infrared spectroscopy analysis of two coffee extracts (G).
Figure 2. Structural features of coffee extract (A,B); UV–visible spectral scans of two coffee extracts (C) X-ray diffraction patterns of two coffee extracts (D); Fourier transform infrared of two coffee extracts (E); X-ray diffraction analysis of two coffee extracts (F); Fourier transform infrared spectroscopy analysis of two coffee extracts (G).
Nutrients 18 00638 g002
Nutrients 18 00638 g003
Figure 3. Principal component maps of the green coffee extract (GCE) and untreated coffee (UC) in positive- (A) and negative- (B) ion mode of LC-MS analysis. (C) Overall differential metabolites. Volcano maps of the differential metabolites between treated and untreated coffee determined in positive- (D) and negative- (F) ion mode by LC-MS and the corresponding KEGG pathways (E,G).
Figure 3. Principal component maps of the green coffee extract (GCE) and untreated coffee (UC) in positive- (A) and negative- (B) ion mode of LC-MS analysis. (C) Overall differential metabolites. Volcano maps of the differential metabolites between treated and untreated coffee determined in positive- (D) and negative- (F) ion mode by LC-MS and the corresponding KEGG pathways (E,G).
Nutrients 18 00638 g003
Nutrients 18 00638 g004
Figure 4. Effects of coffee extract supplementation on metabolic parameters and hepatic status in HFD-fed mice. (A) Body weight; (B) blood glucose levels during the intraperitoneal insulin tolerance test (IPITT); (C) area under the curve (AUC) for the IPITT; (D) epididymal adipose tissue organ index; (E) serum triglyceride (TG) levels; (F) serum alanine aminotransferase (ALT) activity; (G) hepatic malondialdehyde (MDA) content; (H) hepatic reduced glutathione (GSH) content; (I) hepatic superoxide dismutase (SOD) activity. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.
Figure 4. Effects of coffee extract supplementation on metabolic parameters and hepatic status in HFD-fed mice. (A) Body weight; (B) blood glucose levels during the intraperitoneal insulin tolerance test (IPITT); (C) area under the curve (AUC) for the IPITT; (D) epididymal adipose tissue organ index; (E) serum triglyceride (TG) levels; (F) serum alanine aminotransferase (ALT) activity; (G) hepatic malondialdehyde (MDA) content; (H) hepatic reduced glutathione (GSH) content; (I) hepatic superoxide dismutase (SOD) activity. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.
Nutrients 18 00638 g004
Nutrients 18 00638 g005
Figure 5. Modulation of green coffee extract (GCE) on the gut microbiota’s richness, diversity, composition, and characteristics in HFD-induced obese mice. (A–E) Alpha-diversity indices, (F) Venn diagram, (G) PCoA plot, and (H) NMDS of gut microbiota. (I) Relative abundance of gut microbiota at the phylum and genus levels. (J) Relative abundance of gut microbiota at the genus level. (K) Heat map of the relative abundance of gut microbiota at the genus level. (L) Taxonomic cladogram and (M) histogram of LDA scores based on LEfSe analysis (LDA > 4 were considered to indicate differential characteristic taxa). Data are expressed as mean ± SEM (n = 3). * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 5. Modulation of green coffee extract (GCE) on the gut microbiota’s richness, diversity, composition, and characteristics in HFD-induced obese mice. (A–E) Alpha-diversity indices, (F) Venn diagram, (G) PCoA plot, and (H) NMDS of gut microbiota. (I) Relative abundance of gut microbiota at the phylum and genus levels. (J) Relative abundance of gut microbiota at the genus level. (K) Heat map of the relative abundance of gut microbiota at the genus level. (L) Taxonomic cladogram and (M) histogram of LDA scores based on LEfSe analysis (LDA > 4 were considered to indicate differential characteristic taxa). Data are expressed as mean ± SEM (n = 3). * p < 0.05, ** p < 0.01, *** p < 0.001.
Nutrients 18 00638 g005
Nutrients 18 00638 g006
Figure 6. Spearman correlation analysis between dominant intestinal bacteria and obesity-related parameters.
Figure 6. Spearman correlation analysis between dominant intestinal bacteria and obesity-related parameters.
Nutrients 18 00638 g006
Table 1. Reagent, material, equipment.
Table 1. Reagent, material, equipment.
Reagent/Material/EquipmentSupplier
Green coffee beans (Arabica-Cartier)Baoshan Galaozhuang Agricultural and Sideline Products Development Co., Ltd. (Baoshan, China)
Edible baking sodaHubei Yucheng E-commerce Co., Ltd. (Xianning, China)
Sodium anhydrous carbonateTianjin Zhiyuan Chemical Reagent Co., Ltd. (Tianjin, China)
Potassium and persulfateShanghai Maclin Biochemical Technology Co., Ltd. (Shanghai, China)
High-fat diet and low-fat diet (Normal control group of high-fat diet)Research Diets, Inc. (New Brunswick, NJ, USA)
G3 independent ventilation cage IVCSuzhou Suhang Science and Technology Equipment Co., Ltd. (Suzhou, China)
Corn cob bedding, wood chipsKunming Triassic Biotechnology Co., Ltd. (Kunming, China)
TC, TG, HDL-C, LDL-C, MDA, GSH, SOD kitNanjing Jiancheng Biology Co., Ltd. (Nanjing, China)
Formic acidSigma-Aldrich (Shanghai, China)
Ammonium formateSigma-Aldrich (Shanghai, China)
DA5001 Silent oil-free air compressorJiangsu Dailuo Medical Technology Co., Ltd. (Suzhou, China)
Atomize and FD-1A-50 vacuum freeze-dryerShanghai Bilang Instrument Manufacturing Co., Ltd. (Shanghai, China)
TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MDA, malondialdehyde; GSH, reduced glutathione; SOD, superoxide dismutase.
Table 2. Experimental design and intervention details for the animal study.
Table 2. Experimental design and intervention details for the animal study.
GroupRaising PlanTreatment SolutionTreatment Volume
NDLow-fat feedphysiological saline1 g/kg
HFDHigh-fat feedphysiological saline
HFD+UCHigh-fat feeduntreated coffee extract
HFD+GCEHigh-fat feedoxidized green coffee extract
Table 3. Results of relative content of major components in two coffee extracts.
Table 3. Results of relative content of major components in two coffee extracts.
IngredientsUCGCE
Chlorogenic acid (mg/g)122.03 ± 0.8781.83 ± 2.02
Caffeine (mg/g)41.84 ± 0.2841.20 ± 0.08
Total phenols (mg/g)331.54 ± 0.48341.37 ± 1.06
Flavonoid (mg/g)63.10 ± 0.8640.46 ± 1.18
Total sugar (mg/g)294.85 ± 0.17277.07 ± 3.30
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

He, J.; Shan, L.; Yu, L.; Yu, L.; Jiang, X.; Shen, Y.; Du, Z.; Yu, R.; Zhao, C.; Du, X.; et al. Structural Insights and Metabolic Profiles of Oxidized Green Coffee Extract, and Its Impact on Obesity and Gut Microbiota in High-Fat Diet-Fed Mice. Nutrients 2026, 18, 638. https://doi.org/10.3390/nu18040638

AMA Style

He J, Shan L, Yu L, Yu L, Jiang X, Shen Y, Du Z, Yu R, Zhao C, Du X, et al. Structural Insights and Metabolic Profiles of Oxidized Green Coffee Extract, and Its Impact on Obesity and Gut Microbiota in High-Fat Diet-Fed Mice. Nutrients. 2026; 18(4):638. https://doi.org/10.3390/nu18040638

Chicago/Turabian Style

He, Jun, Linxian Shan, Lihui Yu, Lijun Yu, Xingjiao Jiang, Yan Shen, Zezhu Du, Rongxian Yu, Cunchao Zhao, Xiaocui Du, and et al. 2026. "Structural Insights and Metabolic Profiles of Oxidized Green Coffee Extract, and Its Impact on Obesity and Gut Microbiota in High-Fat Diet-Fed Mice" Nutrients 18, no. 4: 638. https://doi.org/10.3390/nu18040638

APA Style

He, J., Shan, L., Yu, L., Yu, L., Jiang, X., Shen, Y., Du, Z., Yu, R., Zhao, C., Du, X., Wang, H., Yang, R., & Fang, C. (2026). Structural Insights and Metabolic Profiles of Oxidized Green Coffee Extract, and Its Impact on Obesity and Gut Microbiota in High-Fat Diet-Fed Mice. Nutrients, 18(4), 638. https://doi.org/10.3390/nu18040638

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

Article Metrics

Back to TopTop