Microalgae Oil Improves Hepatic Lipid Metabolism in A High-Fat Diet–Induced Mouse Model

Summary

Using a high-fat diet–induced mouse model of metabolic dysfunction–associated steatotic liver disease, this study evaluates the effects of docosahexaenoic acid (DHA)-rich microalgae oil supplementation and shows that the intervention improves hepatic lipid profiles and is associated with changes in gut microbiota composition.

Abstract

Metabolically, dysfunctional steatotic liver disease is a prevalent metabolic disorder associated with gut microbiota dysbiosis and hepatic lipid imbalance. In this study, a high-fat diet–induced mouse model was established to evaluate the effects of supplementation with DHA-rich microalgae oil. Mice (n = 4 per group) were fed a high-fat diet for 8 weeks and received daily oral administration of microalgae oil, probiotics, or the combination of DHA-rich microalgae oil and probiotics. Metabolic parameters, gut microbiota composition (16S rRNA sequencing), microbial functional pathways, and hepatic metabolomic profiles were assessed. The results showed that DHA-rich microalgae oil improved lipid homeostasis, as indicated by reduced serum LDL-c and hepatic triglyceride levels and increased high-density lipoprotein (HDL-c), and was associated with alleviation of liver injury and oxidative stress. Microbiome analysis revealed selective changes in gut microbial composition, including enrichment of Lactobacillus and Bifidobacterium and reduction of high-fat diet–associated taxa such as Clostridium and Ruminococcus. Functional profiling indicated alterations in microbial metabolic pathways, including the L-methionine salvage cycle and phenylethylamine degradation. Integrated microbiome–metabolome analysis further identified associations between microbial taxa and hepatic metabolites involved in fatty acid metabolism, bile acid turnover, and amino acid pathways. These findings indicate that DHA-rich microalgae oil supplementation is associated with improvements in hepatic lipid metabolism and gut microbiota composition in this model, without implying a direct causal mechanism.

Introduction

Metabolically-dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent global condition, impacting roughly 32.4% of the world's population¹. MASLD is defined by excessive fat in the liver, which contributes to various associated health problems. The human digestive tract contains a sophisticated and balanced microbial community, including bacteria, viruses, fungi, and parasites². Due to the interaction between this microbial community and the liver, probiotics are seen as a promising method to prevent and treat MASLD by modulating this critical connection³. Emerging research indicates that probiotics may positively impact MASLD by modulating the gut microbiome and reducing inflammation. Specifically, administering probiotics was shown to restore balance to the gut microbiota and reduce hepatic inflammation⁴. Furthermore, certain probiotics, especially those that could enhance bile acid metabolism, have been demonstrated to ease hepatic steatosis (fatty liver) and support overall metabolic health in individuals suffering from MASLD^5,6. Moreover, strong evidence indicated that patients with MASLD exhibit significant changes in the gut environment, a condition known as dysbiosis^7,8. A crucial finding was alpha diversity reduction, which means the total number and variety of microbial species is diminished. Furthermore, the gut microbiota of MASLD patients is often characterized by a dominance of the bacterial phylum Firmicutes^9,10,11. Individuals with MASLD commonly consume poor diets, typically featuring a high intake of saturated fats and a low consumption of essential micronutrients^12,13. These unhealthy eating patterns, frequently observed in urban populations, correlate with an elevated ratio of Firmicutes-to-Bacteroidetes when compared to people living in rural settings¹⁴. Furthermore, high-fat diets were directly implicated in causing more profound gut imbalance. Specifically, both research in animal models and studies on patients with advanced liver disease, MASLD, showed that high-fat intake was linked to gut virome alteration. These alterations included an increase in lytic phage activity, which ultimately worsened the existing gut dysbiosis^15,16.

Omega-3 Polyunsaturated Fatty Acids (PUFAs), commonly found in fish oil, could improve lipid metabolism and regulate the gut microbiota. Microalgae oil has emerged as a promising and sustainable source of omega-3 PUFAs. Specifically, Schizochytrium microalgae oil is a promising commercial source because it contains a high concentration of pure docosahexaenoic acid (DHA). Previous studies from this group reported that, in a high-fat diet (HFD)- induced obesity mouse model, the efficacy of microalgae oil in promoting weight loss was comparable to that of fish oil and the weight-loss medication Orlistat. Critically, microalgae oil exhibited these weight-loss effects earlier than either fish oil or Orlistat¹⁷. Previous findings also showed that microalgae oil exerted distinct and beneficial effects on the gut microbiota compared to commercial fish oil and Orlistat. Although microalgae oil did not markedly alter overall bacterial diversity, it was associated with an increased abundance of beneficial short-chain fatty acid (SCFA)-producing bacteria and a reduction in pathogenic taxa, including the obesity-associated genus Desulfovibrio. Furthermore, microalgae oil remarkably restored the gut microbiota's metabolic capacity, enhancing pathways related to amino acids, SCFAs, and bile acid metabolism, while crucially decreasing lipopolysaccharide (LPS) biosynthesis. These modulations were likely responsible for the improved lipid metabolism and enhanced colonic mucosal barrier, suggesting that microalgae oil was a superior dietary supplement for the alleviation of obesity¹⁷.

Despite progress in linking gut microbiota dysbiosis to the development of MASLD, the specific relationships between microbial composition, microbial metabolic functions, and hepatic lipid metabolism remain incompletely understood. In particular, the causal links between alterations in the microbiota and disease progression remain unclear, and whether dietary interventions, such as DHA-rich microalgae oil, are associated with coordinated changes in gut microbial communities and host metabolic profiles has not been systematically investigated. DHA-rich microalgae oil is a sustainable and efficient source of omega-3 fatty acids, with DHA-rich microalgae oil content comparable to fish-derived supplements. Previous studies have suggested that DHA-rich microalgae oil supplementation may improve hepatic steatosis and metabolic parameters^18,19. However, its associations with gut microbiota composition and microbial metabolic function in the context of MASLD remain unclear.

Therefore, in this study, the present study used a high-fat diet–induced mouse model to evaluate the effects of DHA-rich microalgae oil supplementation on metabolic parameters, gut microbiota composition, and hepatic metabolomic profiles. It was hypothesized that DHA-rich microalgae oil supplementation would be associated with improvements in hepatic lipid metabolism and concurrent alterations in gut microbiota composition and microbial metabolic pathways.

Protocol

Details of all animals, diets, reagents, kits, instruments, software, and service providers used in this protocol are provided in the Table of Materials. All animal studies were performed following the National Institutes of Health (NIH) Guidelines for the Care and Use of Laboratory Animals and received approval from the Experimental Animals Committee of Shandong Provincial Hospital (ethical approval no. 2022–007, approved on January 24, 2022).

CAUTION: Animal blood, fecal samples, and tissues should be handled as potentially biohazardous materials. All procedures involving live animals, biological samples, sharps, organic solvents, and chemical assay reagents should be performed in accordance with institutional biosafety, animal care, and chemical safety regulations. Animal carcasses, tissues, blood-contaminated materials, and fecal samples should be collected in designated biohazard waste containers and disposed of through the institutional animal facility or biosafety office. Needles, gavage needles, capillary tubes, and other sharps should be discarded immediately after use in approved sharps containers. Organic solvent waste, including methanol, acetonitrile, and formic acid-containing solutions, should be collected in labeled chemical waste containers and disposed of through the institutional hazardous chemical waste program. Waste from biochemical and oxidative stress assays should be collected and disposed of in accordance with the manufacturer’s safety data sheets and institutional chemical safety rules.

Experimental animals

Six-week-old male C57BL/6J mice (n = 4 per group) were maintained under specific pathogen-free (SPF) conditions with a 12 h light/dark cycle at 22 ± 2 °C and 50–60% relative humidity. Animals had ad libitum access to food and water, with a maximum of five mice housed per cage. The control mice were fed a standard chow diet consisting of 70% carbohydrates, 20% protein, and 10% fat (kcal). To establish the MASLD mouse model, mice were fed a high-fat diet for 8 weeks containing 20% carbohydrates, 20% protein, and 60% fat (kcal). All diets were stored at 4 °C and replenished twice per week. Mice were randomly assigned to experimental groups using a random number generator, and sample size (n = 4 per group) was determined based on previous studies using similar high-fat diet–induced mouse models and preliminary experiments. All treatments were administered at the same time each day to minimize circadian variation.

Microalgae oil was extracted and purified by the Shandong Academy of Agricultural Sciences, with a purity of approximately 99%. The microalgae oil was stored at −30 °C protected from light. Before administration, the oil was prepared with an emulsifier and thoroughly mixed to ensure homogeneity. Specifically, DHA-rich microalgae oil was emulsified in sterile 0.5% carboxymethylcellulose sodium containing 0.5% Tween-80 in normal saline. The microalgae oil and emulsifier vehicle were mixed at a ratio of 1:9 (v/v) and vortexed for 2 min immediately before gavage to obtain a uniform suspension. Mice in the microalgae oil-treated group received the preparation once daily by oral gavage using a sterile feeding needle at a dose of 10 µL/g body weight. The gavage volume was adjusted daily according to body weight to ensure dose consistency. Mice in the control group received an equivalent volume of sterile normal saline under the same conditions. The saline used was 0.9% sodium chloride injection.

Probiotic treatment consisted of a composite formulation containing Lactobacillus plantarum (DY-1), Lactobacillus acidophilus (KDB-03), Lactobacillus casei (KDB-LC), and Bacillus coagulans (GIM 1.645). The four strains were mixed at an equal viable-cell ratio of 1:1:1:1. Each strain contributed 2.5 × 10⁷ CFU/mL to the final formulation, yielding a total bacterial concentration of 1 × 10⁸ CFU/mL. The probiotic mixture was synthesized by the Shandong Academy of Agricultural Sciences and is not commercially available. Probiotic treatment consisted of a composite probiotic fermentation broth containing Lactobacillus plantarum DY-1, Lactobacillus acidophilus KDB-03, Lactobacillus casei KDB-LC, and Bacillus coagulans GIM 1.645. The bacterial concentration of the formulation was 1 × 10⁸ CFU/mL. The probiotic fermentation broth was stored at 4 °C before use. Mice in the probiotic-treated group received the probiotic formulation once daily by oral gavage at a dose of 0.01 mL/g body weight throughout the experimental period.

Individual strains were cultured under appropriate conditions and harvested at the mid-log growth phase (OD₆₀₀ ≈ 0.6), then combined at the indicated ratio. Bacterial cultures were centrifuged to remove the supernatant, and the pellets were resuspended in sterile phosphate-buffered saline (PBS). The freshly prepared bacterial suspension was kept on ice prior to administration to preserve viability. The final bacterial concentration was adjusted to 1 × 10⁸ CFU/mL before administration. Mice in the probiotic group received the formulation by oral gavage at a dose of 0.01 mL/g body weight once daily throughout the experimental period. No antibiotic pretreatment was applied prior to probiotic administration. To reduce experimental bias, outcome assessment was performed by blinded investigators where applicable. MASLD was confirmed through blood biochemical analysis.

Blood analysis

At the end of the experimental period, mice were fasted overnight (12 h) with free access to water before glucose measurement and terminal blood collection. Mice were deeply anesthetized with isoflurane using an anesthesia vaporizer until loss of pedal reflex was confirmed. Terminal blood samples were collected by retro-orbital bleeding. After blood collection, mice were euthanized by cervical dislocation while under deep anesthesia, and death was confirmed by cessation of respiration and heartbeat. Blood was allowed to clot at room temperature and then centrifuged at 3,000 × g for 10 min to obtain serum. Serum samples were aliquoted and stored at −80 °C until analysis. Serum glucose, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) were measured using an automatic biochemical analyzer, following the manufacturer's instructions. These parameters were quantified using enzymatic colorimetric assays with standard reagent kits integrated into the analyzer system. All biochemical parameters were normalized to the serum volume and expressed in the units specified by the manufacturer.

Oxidative stress assays

Oxidative stress biomarkers were measured using kits, including those for malondialdehyde, superoxide dismutase, and catalase. Assays were conducted following the manufacturer’s instructions. Briefly, liver tissues were homogenized in ice-cold buffer and centrifuged to obtain supernatants for analysis. Malondialdehyde (MDA) levels were determined using a thiobarbituric acid reactive substances (TBARS) assay; superoxide dismutase (SOD) activity was measured by its ability to inhibit superoxide-mediated reactions; and catalase (CAT) activity was quantified by monitoring the decomposition rate of hydrogen peroxide. Measurements were performed using a microplate reader, and values were calculated using standard curves or the formulae provided in the kits. Oxidative stress markers were normalized to liver tissue weight and expressed according to the manufacturer’s specifications.

16S rRNA sequencing and microbiome analysis

Fecal samples were collected at the end of the experimental period directly from individual mice under sterile conditions, immediately frozen in liquid nitrogen, and stored at −80 °C until DNA extraction. Microbial DNA was extracted using a commercial kit according to the manufacturer’s protocol. The V3–V4 region of the bacterial 16S rRNA gene was amplified using primers 341F/806R. PCR amplification was performed in a 25 µL reaction system containing template DNA, 2x PCR master mix, and 0.2 µM of each primer. The amplification program consisted of initial denaturation at 95 °C for 3 min, followed by 25 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s, with a final extension at 72 °C for 10 min. Amplicon libraries were constructed and sequenced on an Illumina MiSeq platform using a paired-end 2 × 250 bp strategy by a commercial sequencing service provider. Library quality was assessed prior to sequencing, and pooled libraries were loaded at the manufacturer’s recommended cluster density. Low-quality reads with ambiguous bases, insufficient length, or an average quality score below Q20 were removed before downstream analysis.

Raw sequencing reads were processed using QIIME 2 version 2019.4²⁰. Briefly, paired-end reads were demultiplexed, quality-filtered, denoised, merged, and chimera-filtered using DADA2 with default parameters unless otherwise specified. Non-singleton amplicon sequence variants (ASVs) were retained for downstream analysis. Taxonomic assignment was performed using the Greengenes database version 13_8 implemented in QIIME2²¹. Given the inherent resolution limitations of 16S rRNA gene sequencing, particularly when using the Greengenes database, taxonomic annotations were interpreted exclusively at the genus level. Species-level classification was not considered reliable and therefore was not used in any downstream analyses, statistical comparisons, or biological interpretations. All microbial features were collapsed to the genus level prior to visualization and analysis.

Alpha-diversity metrics (Chao1, Observed species, Shannon, Simpson, Faith’s PD, and Pielou’s evenness) were used. Statistical comparisons of alpha diversity between groups were performed using appropriate non-parametric tests (e.g., Kruskal–Wallis test). The Jaccard distance and Bray-Curtis dissimilarity were used as beta diversity metrics. Principal Coordinate Analysis (PCoA) plots, non-metric multidimensional scaling (NMDS), and linear discriminant analysis effect size (LEfSe) were used to illustrate changes in microbiome communities, with an LDA score threshold of 2.0. Permutational multivariate analysis of variance (PERMANOVA) was used to assess statistical significance in beta diversity. Data visualization and statistical analyses were performed using the GenesCloud platform (https://www.genescloud.cn).

Microbial functional profiles were inferred from 16S rRNA sequencing data rather than directly measured by shotgun metagenomic or transcriptomic sequencing. Based on the normalized pathway or functional group abundance table, predicted functional units were mapped to commonly used databases, including KEGG, MetaCyc, and COG. KEGG pathway abundance was summarized according to its hierarchical functional categories, with level 2 pathway classifications used for downstream comparison. The average abundance of each pathway category was calculated using R software.

Untargeted metabolomics analysis of liver samples

Liver tissues were rapidly excised immediately after death confirmation, immediately frozen in liquid nitrogen, and stored at −80 °C prior to analysis. Approximately 50 mg of frozen liver tissue was homogenized in ice-cold methanol:water (4:1, v/v) supplemented with internal standards. The tissue-to-extraction solvent ratio was 1:10 (w/v). Samples were vortexed for 1 min, sonicated on ice for 10 min, and then incubated at −20 °C for 30 min to improve protein precipitation. The homogenates were thoroughly mixed, subjected to sonication on ice, and centrifuged at 12,000 × g for 15 min at 4 °C. The supernatants were carefully collected, evaporated under a mild nitrogen flow, and reconstituted in 50% methanol for subsequent analysis.

Quality control (QC) samples were generated by pooling equal volumes from each sample and were analyzed intermittently throughout the run to evaluate analytical stability and reproducibility. Untargeted metabolomics analysis was performed using an ultra-high-performance liquid chromatography system coupled to high-resolution mass spectrometry, operated by a commercial service provider. Metabolites were separated on a reverse-phase C18 column using a binary solvent system consisting of water with 0.1% formic acid and acetonitrile with 0.1% formic acid. The injection volume was 2 µL, the column temperature was maintained at 40 °C, and the flow rate was set at 0.30 mL/min. A gradient elution program was used to achieve broad metabolite separation. Mass spectrometric detection was conducted in both positive and negative electrospray ionization modes across an m/z range of 70–1,000 to achieve broad metabolite coverage. The ion spray voltage was set to 3.5 kV in positive mode and −2.5 kV in negative mode. The capillary temperature was maintained at 320 °C, and data were acquired in full-scan MS mode with data-dependent MS/MS acquisition for metabolite annotation. Raw data were processed using dedicated metabolomics software (MassLynx v4.1) for feature detection, alignment, and normalization. Metabolite identification was performed based on accurate mass, retention time, and MS/MS fragmentation patterns by comparison with public databases, including the Human Metabolome Database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) (v2023), where applicable.

Multivariate and pathway analysis

Processed metabolomics data were subjected to multivariate statistical analyses, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), to visualize metabolic differences among experimental groups. Differential metabolites were identified based on a combination of variable importance in projection (VIP) scores (>1.0) and statistical significance (p < 0.05). Identified metabolites were further mapped to metabolic pathways using KEGG pathway enrichment analysis to elucidate biological processes associated with DHA-rich microalgae oil supplementation.

Statistical analysis

The data were presented as mean ± SEM and analyzed using one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparisons test. Statistical significance was defined as p < 0.05. Enrichment analysis of liver metabolites was performed using R (version 4.4.1). Pairwise associations between microbial genera and liver metabolites were analyzed using Spearman rank correlations. Only significant correlations (false discovery rate, FDR < 0.05) with |ρ| ≥ 0.6 were included in the integrative network analyses. Microbiome and metabolomics data processing were conducted as described above, including the use of the GenesCloud platform and quality control procedures.

Results

DHA-rich microalgae oil improves lipid metabolism, liver injury, and hepatic oxidative stress in HFD-induced MASLD mice

To evaluate the effect of dietary supplements on glucose and lipid homeostasis, biochemical parameters were assessed across five groups: regular chow (RC), high-fat diet (HFD), DHA-rich microalgae oil-supplemented, probiotic-supplemented, and combined DHA-rich microalgae oil + probiotic-supplemented mice. Because the primary objective of this study was to eva...

Discussion

In recent years, several studies have investigated changes in the microbiome linked to MASLD. However, most of these studies have focused on comparing healthy individuals with MASLD patients or examining different steatosis grades²⁴. Consequently, there is still limited evidence detailing the specific gut microbiota changes associated with liver fibrosis in MASLD, even though fibrosis is the most critical factor in predicting patient outcomes. In this study, we show that DHA-rich microalgae oil pr...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
2x PCR Master Mix	Takara Bio Inc., Japan	RR902A	Used for 16S rRNA PCR amplification
Acetonitrile	Merck, Germany	100030	Solvent used for UHPLC mobile phase
Anesthesia vaporizer	RWD Life Science	R580	Used to deliver isoflurane anesthesia to mice
Automatic biochemical analyser	Jian Cheng Biological Engineering Institute, Nanjing, China	A110-1 & A111-1	Measures serum parameters (glucose, TC, TG, LDL-c, HDL-c, AST, ALT) and liver lipid levels
Bacillus coagulans (GIM 1.645)	Guangdong Microbial Culture Collection Center, China	GIM 1.645	Component strain of probiotic formulation
Carboxymethylcellulose sodium (CMC-Na)	Sigma-Aldrich, USA	C4888	Used as emulsifier component for DHA-rich microalgae oil preparation
Catalase (CAT) assay kit	Nanjing Construction Ltd.	A007-1-1	Detects CAT activity (an indicator of antioxidant capacity) in liver tissue
DADA2 software package	DADA2 Development Team	Included in QIIME 2 v2019.4	Used for denoising and chimera filtering of sequencing reads
DHA-rich microalgae oil (Schizochytrium sp.-derived)	-	-	Rich in docosahexaenoic acid (DHA); administered daily by gavage at 10µL/g as the experimental intervention
Experimental animals (Male C57BL/6J mice)	Beijing Vital River Laboratory Animal Technology Co.	-	6 weeks old, n = 4 per group; used for establishing the MASLD model via HFD feeding
Formic acid	Sigma-Aldrich, USA	F0507	Mobile phase additive for metabolomics analysis
Greengenes database	Greengenes	Version 13_8	Used for microbial taxonomic assignment
High-Fat Diet (HFD)	Research Diets Inc., USA	D12492	Composed of 20% carbohydrates, 20% protein, 60% fat (kcal); fed for 8 weeks to induce MASLD
High-resolution mass spectrometer	Thermo Fisher Scientific, USA	Q Exactive Orbitrap	Used for metabolite detection in positive and negative ion modes
Human Metabolome Database (HMDB)	University of Alberta, Canada	2024 release	Public metabolite annotation database
Illumina MiSeq platform	Illumina, Inc., USA	MiSeq System	Used for paired-end 2 × 250 bp sequencing
Isoflurane	Merck, Germany	PHR2874	Used for inhalation anesthesia in mice
Kyoto Encyclopedia of Genes and Genomes (KEGG)	Kanehisa Laboratories, Japan	Version 2023	Database used for pathway enrichment and metabolite annotation
Lactobacillus acidophilus (KDB-03)	Shandong Academy of Agricultural Sciences, China	-	Component strain of probiotic formulation
Lactobacillus casei (KDB-LC)	Shandong Academy of Agricultural Sciences, China	-	Component strain of probiotic formulation
Lactobacillus plantarum (DY-1)	Shandong Academy of Agricultural Sciences, China	-	Component strain of probiotic formulation
Liquid nitrogen	Air Liquide	-	Used for rapid freezing and preservation of fecal and liver samples
Malondialdehyde (MDA) assay kit	Nanjing Construction Ltd.	A003-1	Detects MDA (a marker of lipid peroxidation/oxidative damage) in liver tissue
MassLynx software	Waters Corporation, USA	Version 4.1	Used for metabolomics raw data processing, feature extraction, and normalization
Methanol	Merck, Germany	106035	Solvent used for metabolite extraction
Microbiome data analysis platform	Genescloud	V1	Online platform: https://www.genescloud.cn/home; used for PCoA, NMDS, LEfSe and other microbiome visualization analyses
OMEGA Soil DNA Kit	Omega Bio-Tek, Norcross, GA, USA)	(M5635-02)	DNA Isolation
OTUs reference sequences	Genescloud		Referenced from McDonald et al. 2012; used for taxonomy assignment of amplicon sequence variants (ASVs)
Phosphate-buffered saline (PBS)	Gibco, Thermo Fisher Scientific, USA	10010023	Used for bacterial pellet resuspension
Primer 341F/806R	Tsingke Biotechnology Co., Ltd., China	-	Used for amplification of the 16S rRNA V3–V4 region. 341F: 5′-CCTACGGGNGGCWGCAG-3′; 806R: 5′-GGACTACHVGGGTATCTAAT-3′
Probiotics	-	-	Specific strains not explicitly mentioned; used as a comparative intervention (mainly improves glucose homeostasis)
QIIME 2 software	-	2019.4	Used for microbiome bioinformatics analysis (ASV processing, alpha/beta diversity calculation)
R software	-	4.4.1	Used for liver metabolite enrichment analysis and Spearman correlation analysis of microbial-metabolite associations
Refrigerated centrifuge	Eppendorf, Germany	5810R	Used for serum and metabolomics sample centrifugation
Reverse-phase C18 column	Waters Corporation, USA	186002350	Used for chromatographic separation of metabolites
Saline	Shijiazhuang No.4 Pharmaceutical	2211112304	Administered daily by gavage to the control group (vs. microalgae oil treatment)
Standard chow diet	Keao Xieli Feed Ltd., China	1016706714625204224	Composed of 70% carbohydrates, 20% protein, 10% fat (kcal); fed to the control (RC) group
Sterile feeding needle	Instech Laboratories, USA	FTP-20-38	Used for oral gavage administration
Superoxide dismutase (SOD) assay kit	Nanjing Construction Ltd.	A001-3	Detects SOD activity (an indicator of antioxidant capacity) in liver tissue
Tween-80	Sigma-Aldrich, USA	P4780	Used as emulsifier component for DHA-rich microalgae oil preparation
Ultrasonic homogenizer	Scientz Biotechnology, China	JY92-IIN	Used for liver tissue sonication during metabolite extraction
Untargeted Hepatic Metabolomics	Metabo-Profile Biotechnology (Shanghai) Co., Ltd.	Project Code: P035Y23A13_SFMU_MJWang_SH
Vortex mixer	Thermo Fisher Scientific, USA	LP Vortex Mixer	Used for sample homogenization and emulsification