Abstract

   Background/Objectives: Alcoholic liver disease (ALD) is intricately
   linked to gut microbiota dysbiosis and metabolic disturbances along the
   gut–liver axis. Octenyl succinic anhydride (OSA) starch escapes
   digestion in the small intestine and ferments in the colon, modulating
   gut microbiota and metabolism. This study explored the protective
   effects of OSA starch against ALD and elucidated the underlying gut
   microbiota–metabolite interactions. Methods: A chronic ethanol-fed
   mouse model was conducted to evaluate the protective effects of OSA
   starch against ALD, and multi-omics analyses integrating 16S rRNA
   sequencing, PICRUSt2 functional predictions, and metabolomics were used
   to reveal potential mechanism. Results: OSA starch supplementation in
   ALD mice significantly reduced liver fat accumulation, lowered the
   liver index to 4.11%, and restored serum transaminase levels closer to
   normal. Multi-omics analyses revealed that OSA starch enriched
   beneficial gut bacteria such as Faecalibaculum rodentium and
   Bifidobacterium adolescentis. OSA starch also enhanced microbial
   metabolic functions, including pyruvate, butanoate, and propanoate
   metabolism. These shifts were accompanied by regulation of fecal and
   serum metabolites, including pyruvate, 2-hydroxybutanoic acid, and
   lactic acid. Structural equation modeling further confirmed that OSA
   starch ameliorates ALD via coordinated modulation of gut microbiota,
   microbial functions, metabolites, and serum markers. Conclusions: OSA
   starch protects against alcoholic liver injury by remodeling the
   gut–liver metabolic network, presenting a promising dietary strategy
   for ALD.

   Keywords: OSA starch, alcoholic liver disease, gut microbiota, host
   metabolism

1. Introduction

   Alcoholic liver disease (ALD) is a significant contributor to chronic
   liver injury and mortality worldwide, predominantly driven by prolonged
   excessive alcohol consumption [[32]1]. The progression of ALD involves
   hepatic steatosis, underpinned by complex interactions between the
   liver and the gut [[33]2,[34]3]. Growing evidence highlights the
   gut–liver axis as a central player in ALD pathogenesis, particularly
   through gut microbiota dysbiosis and increased intestinal permeability
   [[35]4]. These alterations facilitate the translocation of various
   microbial metabolites into the portal circulation, promoting hepatic
   steatosis and metabolic disruption. Moreover, shifts in gut-derived
   metabolites—such as short-chain fatty acids, amino acid derivatives,
   and bile acids—profoundly influence hepatic lipid metabolism, oxidative
   stress, and immune responses, all of which contribute to ALD
   progression [[36]5]. ALD is associated with gut bacterial products and
   their translocation to the liver, although the role of specific
   microbiota changes remains unclear [[37]6]. Therefore, nutritional
   interventions targeting the microbiome have emerged as a promising
   strategy for ALD prevention and treatment [[38]7,[39]8].

   Among nutritional interventions, resistant starches have emerged as
   promising prebiotic agents capable of reshaping gut microbial
   communities and their metabolic outputs [[40]9,[41]10]. Octenyl
   succinic anhydride (OSA) starch, as a modified resistant starch,
   escapes digestion in the small intestine and undergoes fermentation in
   the colon by commensal bacteria, leading to the production of
   short-chain fatty acids like butyrate, propionate, and acetate
   [[42]11,[43]12]. These microbial metabolites play essential roles in
   enhancing gut barrier integrity and regulating lipid and glucose
   metabolism. Additionally, resistant starch intake has been linked to
   favorable shifts in microbial functional pathways, such as those
   involved in energy metabolism, amino acid synthesis, and bile acid
   transformation, which collectively support host metabolic health
   [[44]13,[45]14]. OSA is a commonly used esterification agent that
   modifies starches, making the derivative amphiphilic and capable of
   acting as an emulsion stabilizer [[46]15]. Approved as a food additive,
   OSA-modified starch has undergone rapid global consumption due to its
   beneficial properties, including reducing fat absorption in the gut by
   encapsulating fat particles [[47]16]. Given that OSA starch has also
   been proven to have a highly similar ability to regulate the microbiota
   and SCFAs, this type of resistant starch may have the potential to
   prevent ALD. While resistant starch has shown efficacy in improving
   metabolic diseases like obesity and non-alcoholic fatty liver disease
   [[48]17,[49]18], OSA starch’s potential to mitigate alcohol-induced
   liver injury through modulation of gut microbiota and microbial
   metabolism remains underexplored.

   In this study, the protective effects of OSA starch against ALD were
   systematically evaluated using a chronic alcohol-fed mouse model.
   Comprehensive assessments, including liver histopathology, serum
   biochemistry, fecal and serum metabolomics, and gut microbiota
   profiling via 16S rRNA gene sequencing, were conducted. Functional
   predictions and pathway analyses were further employed to elucidate
   microbial metabolic functions influenced by OSA starch. Structural
   equation modeling was applied to clarify the mechanistic pathways
   linking OSA starch, gut microbiota, microbial metabolites, and liver
   health. These findings underscore the potential of OSA starch as a
   dietary strategy for ALD.

2. Materials and Methods

2.1. Establishment of Alcoholic Liver Injury Model and Dietary OSA Starch
Intervention

   A total of 24 male C57BL/6J mice aged eight weeks were purchased from
   Gem Pharmatech Co., Ltd. (Nanjing, China) and maintained under specific
   pathogen-free conditions with controlled environment parameters. All
   animal procedures received ethical approval from the Animal Ethics
   Committee of Jiangnan University (JN. No 20240930c0641123), following
   the ARRIVE guidelines for animal research. Mice were randomly assigned
   to one of three experimental groups (n = 8 per group) using a random
   number generator to ensure unbiased group allocation: Control, Model,
   and OSA starch intervention groups. Both the Control and ALD Model
   groups were fed a standard chow containing native corn starch as the
   main carbohydrate source. The OSA starch group was provided with a diet
   where native starch was fully replaced by OSA-modified starch. The
   dietary formulations followed established protocols from a previous
   study [[50]19].

   To induce alcoholic liver damage, mice in the Model and OSA starch
   groups received daily oral gavage of ethanol. For the initial 27 days,
   42% (v/v) ethanol was administered, followed by a single dose of 50%
   (v/v) ethanol on day 28. Ethanol dosing began at 0.1 mL per 10 g body
   weight in the first week, and was progressively increased to 0.15 mL/10
   g from the third week onward. Control group mice were gavaged with an
   equivalent volume of sterile water throughout the experiment.

2.2. Serum Biochemical Assays

   Following an overnight fast, mice were anesthetized via intraperitoneal
   injection of 2–3% isoflurane. Blood was drawn, and liver tissues were
   promptly collected for further analysis. Serum was isolated by
   centrifugation at 3000× g for 10 min at 4 °C. Key biochemical markers,
   including total cholesterol (TC), triglycerides (TG), high-density
   lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol
   (LDL-C), alanine aminotransferase (ALT), and aspartate aminotransferase
   (AST), were quantified using a BS-350E automated biochemical analyzer
   (Myriad Biomedical, Shenzhen, China).

2.3. Liver Histology via Hematoxylin and Eosin Staining

   Liver specimens were fixed in 4% paraformaldehyde at room temperature
   for 24 h, dehydrated through a graded ethanol series, cleared with
   xylene, and embedded in paraffin. Paraffin blocks were sliced into 4 μm
   sections using a rotary microtome (Leica, IL, USA). The tissue sections
   were then stained with hematoxylin and eosin (H&E) and examined
   microscopically for histopathological evaluation.

2.4. Untargeted Metabolomics Profiling

   Metabolic profiling was conducted using a QTRAP 5500 mass spectrometer
   (AB SCIEX, Framingham, MA, USA) connected to an ultra-high-performance
   liquid chromatography (UHPLC) system. Both positive and negative
   ionization modes of electrospray ionization (ESI) were employed. For
   extraction, 50 μL/mg of each sample was combined with 400 μL of
   pre-cooled methanol containing internal standards. Fecal samples were
   processed with 2–3 stainless steel beads and homogenized using a tissue
   grinder (60 Hz, 15 s per cycle, 3–4 cycles) under chilled conditions.
   Samples were then subjected to 15 min of sonication in an ice-water
   bath and centrifuged at 10,000× g for 20 min at 4 °C.

   Approximately 300 μL of the supernatant was collected, evaporated to
   dryness under vacuum at low temperatures, and reconstituted in 100 μL
   of 20% methanol. The mixture was vortexed, sonicated, and centrifuged
   again before being transferred to autosampler vials for UHPLC–MS/MS
   analysis. Chromatographic separation was carried out using a BEH C18
   column (1.7 μm, 2.1 × 100 mm, Waters, Milford, MA, USA) with 0.1%
   formic acid in water as mobile phase A and acetonitrile as mobile phase
   B for positive mode. For negative ion mode, an HSS T3 column (1.8 μm,
   2.1 × 100 mm, Waters, Milford, MA, USA) was utilized. The column
   temperature was maintained at 50 °C. Data acquisition and initial
   analysis were performed using Analyst software 1.7.2 (AB SCIEX,
   Framingham, MA, USA). The identity of the metabolites was confirmed
   based on the MS/MS spectra, comparing the observed fragmentation
   patterns with available reference spectra in the database. This
   included matching retention times and the specific mass-to-charge
   values, as well as interpreting the fragmentation pathways for
   structural confirmation. Additionally, the metabolite analysis was
   further validated by comparing the results with data available on
   MetaboAnalyst 5.0, which provides comprehensive information on known
   metabolites and their fragmentation profiles [[51]20].

2.5. 16S rRNA Gene Sequencing and Microbial Functional Prediction

   Genomic DNA from fecal samples was extracted with the TIANamp Stool DNA
   Kit (DP328, TIANGEN, Beijing, China), following the manufacturer’s
   instructions. The quality and quantity of DNA were assessed via a
   NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA)
   and agarose gel electrophoresis. The V3–V4 regions of the bacterial 16S
   rRNA gene were amplified using specific primers with Illumina adapters.
   Purified PCR products were quantified and pooled in equimolar amounts.
   Libraries were prepared using the TruSeq DNA PCR-Free Library Prep Kit
   (Illumina, San Diego CA, USA), and sequencing was conducted on the
   Illumina NovaSeq 6000 platform (paired-end, 2 × 250 bp).

   Raw sequences underwent quality filtering with Trimmomatic to remove
   adapters and low-quality reads, and merged using FLASH. Operational
   taxonomic units (OTUs) were clustered at 97% sequence similarity via
   USEARCH. Taxonomic classification was performed against the SILVA
   database (version 138). Microbial functional potential was predicted
   using PICRUSt2 to infer KEGG Orthologs (KOs) based on 16S rRNA
   profiles. All sequencing and computational analyses were completed by
   Wekemo Tech Group Co., Ltd. (Shenzhen, China).

2.6. Statistical and Computational Analyses

   Data are expressed as mean ± standard deviation (SD). Statistical
   analyses were carried out using GraphPad Prism 7.0 (GraphPad Software
   Inc., San Diego, CA, USA). Differences between groups were evaluated by
   one-way analysis of variance (ANOVA) with Dunnett’s post hoc test. A
   p-value < 0.05 was considered statistically significant (* p < 0.05).
   Differential microbial taxa were identified using linear discriminant
   analysis effect size (LEfSe) on the Galaxy platform
   ([52]https://usegalaxy.org/).

3. Results and Discussion

3.1. OSA Starch Ameliorates Hepatic Injury and Lipid Dysregulation in ALD
Mice

   To evaluate the protective effects of OSA starch on ALD, a chronic
   alcohol feeding model in mice, together with a 28-day intervention with
   OSA starch, was established ([53]Figure 1A). Alcohol exposure
   significantly elevated the liver index, indicating hepatomegaly as a
   consequence of fat accumulation (p < 0.01, [54]Figure 1B) [[55]21]. OSA
   starch supplementation markedly reduced the liver index compared to the
   ALD Model group (p < 0.05), suggesting mitigation of alcohol-induced
   liver enlargement. This reduction corresponds with a significant
   decrease in serum alanine aminotransferase (ALT) and aspartate
   aminotransferase (AST) levels (p < 0.01 and p < 0.05, respectively;
   [56]Figure 1C,D), key biomarkers for liver injury [[57]22].
   Consistently, histological examination revealed pronounced steatosis
   and hepatocellular ballooning in the ALD Model group, whereas OSA
   starch intervention substantially alleviated these pathological
   features, maintaining liver architecture closer to that of controls
   ([58]Figure 1I). These outcomes align with prior studies indicating
   that dietary fibers can modulate the gut–liver axis, thereby
   alleviating alcohol-induced hepatic injury [[59]23].

Figure 1.

   [60]Figure 1
   [61]Open in a new tab

   OSA starch ameliorates hepatic injury and lipid dysregulation in AL
   mice. (A) Schematic representation of the experimental protocol. (B)
   Liver index. (C,D) Serum ALT and AST levels. (E,F) Serum TC and TG
   levels. (G,H) Serum HDL-C and LDL-C levels. (I) Representative liver
   histology with H&E staining. Data are presented as mean ± SD (n = 8).
   Statistical significance was determined by one-way ANOVA with Dunnett’s
   post hoc test: ns, not significant p > 0.05, * p < 0.05, ** p < 0.01.

   In parallel, alcohol exposure disrupted lipid metabolism, as reflected
   by elevated total cholesterol (TC), triglycerides (TG), and low-density
   lipoprotein cholesterol (LDL-C), alongside decreased high-density
   lipoprotein cholesterol (HDL-C) ([62]Figure 1E–H) [[63]24,[64]25]. OSA
   starch supplementation significantly lowered TG levels (p < 0.05) and
   restored HDL-C levels (p < 0.01), both of which are critical in
   mitigating the progression of steatosis and cardiovascular risk.
   Interestingly, OSA starch did not significantly alter TC or LDL-C
   levels compared to the ALD Model group. Together, these findings
   demonstrate that OSA starch exerts benefits on liver morphology,
   function, and lipid metabolism in ALD.

3.2. OSA Starch Intervention Reshapes Gut Microbiota Composition in ALD Mice

   To investigate the regulatory effects of OSA starch on gut microbial
   dysbiosis induced by alcohol exposure, alpha diversity and microbial
   community structure in ALD mice were assessed [[65]26]. Shannon indices
   ([66]Figure 2A) revealed that chronic alcohol consumption increased
   species richness, reflecting an unstable gut microbial species
   [[67]27]. Interestingly, OSA starch supplementation attenuated the
   ethanol-induced increase in microbial species richness, indicating a
   selective regulation of the microbial composition. Principal component
   analysis (PCA) based on Bray–Curtis distance ([68]Figure 2B)
   demonstrated a clear segregation of microbial profiles, with the OSA
   starch group distinctly clustering away from the ALD model, indicating
   a pronounced shift in microbial structure attributable to the dietary
   intervention.

Figure 2.

   [69]Figure 2
   [70]Open in a new tab

   OSA starch intervention reshapes gut microbiota composition in ALD
   mice. (A) Alpha diversity indices—Shannon. (B) PCA based on Bray–Curtis
   dissimilarity reveals distinct clustering. (C) Gut microbiota
   composition at the phylum level. (D) Gut microbiota composition at the
   species level.

   At the taxonomic level, phylum-level analysis ([71]Figure 2C) showed
   that alcohol exposure disrupted the balance between Firmicutes and
   Bacteroidota. OSA starch partially restored this ratio, primarily by
   reducing the relative abundance of Proteobacteria, a phylum often
   associated with barrier dysfunction [[72]28]. At the phylum level,
   alcohol exposure increased the relative abundance of Proteobacteria
   while decreasing Firmicutes, consistent with dysbiosis patterns
   commonly observed in liver injury ([73]Figure 2C) [[74]29].
   Species-level profiling ([75]Figure 2D) revealed that OSA starch
   increased beneficial microbes such as Faecalibaculum rodentium and
   Bifidobacterium adolescentis, while reducing the abundance of
   potentially pathogenic taxa like Muribaculum gordoncarteri. These
   compositional changes are consistent with the known prebiotic effects
   of resistant starches, which modulate the gut microbiota to enhance
   host metabolic and immune health [[76]9,[77]30]. Collectively, these
   results suggest that OSA starch ameliorates alcohol-induced gut
   dysbiosis through microbiome modulation.

3.3. OSA Starch Modulates Key Gut Microbial Taxa and Their Functional
Profiles in ALD Mice

   Comprehensive analysis of gut microbiota alterations induced by OSA
   starch intervention in ALD mice revealed significant shifts in
   bacterial composition and diversity. LEfSe analysis identified distinct
   microbial biomarkers across experimental groups ([78]Figure 3A). The
   ALD Model group exhibited an enrichment of potentially pathogenic
   species such as Haemophilus parainfluenzae, Veillonella atypica, and
   Muribaculum gordoncarteri, which have been implicated in gut barrier
   dysfunction and inflammation [[79]31,[80]32]. Conversely, OSA starch
   supplementation selectively enriched beneficial taxa, including
   Faecalibaculum rodentium, Bacteroides acidifaciens, and Bifidobacterium
   adolescentis. These species are recognized as producers of short-chain
   fatty acids, particularly butyrate, known to reinforce intestinal
   integrity and exert anti-inflammatory effects, potentially contributing
   to the alleviation of ALD pathology [[81]23,[82]33,[83]34].

Figure 3.

   [84]Figure 3
   [85]Open in a new tab

   OSA starch modulates key gut microbial taxa and their functional
   profiles in ALD mice. (A) LEfSe cladogram and LDA score of differential
   species. (B) Random forest classification error rates for overall and
   individual groups. (C) Top microbial features ranked by mean decrease
   accuracy contributing to group discrimination. (D) Co-occurrence
   network of differential gut microbes colored by group prevalence. (E)
   Predicted KEGG metabolic pathways (Level 3) of gut microbiota across
   groups.

   Random forest classification further validated the discriminative
   microbial features between groups, achieving low classification error
   rates ([86]Figure 3B). The most influential taxa contributing to group
   separation, as indicated by the mean decrease in accuracy, included
   Faecalibaculum rodentium, Haemophilus parainfluenzae, and
   Bifidobacterium adolescentis ([87]Figure 3C). These key species are
   involved in metabolic pathways essential for maintaining host lipid
   homeostasis and modulating immune responses [[88]35,[89]36].
   Co-occurrence network analysis ([90]Figure 3D) demonstrated that
   Bifidobacterium adolescentis and Faecalibaculum rodentium formed
   central nodes within the microbial ecosystem of OSA starch-treated
   mice, suggesting that OSA starch fosters a more interconnected
   microbial community structure.

   Functional predictions based on PICRUSt2 and KEGG pathway analysis
   revealed that OSA starch intervention notably influenced microbial
   metabolic potential ([91]Figure 3E). Specifically, pathways associated
   with propanoate metabolism, glycolysis/gluconeogenesis, and galactose
   metabolism were reduced in the OSA starch group, which linked to
   metabolic disturbances in ALD [[92]37]. In contrast, pathways such as
   glyoxylate and dicarboxylate metabolism were relatively enhanced,
   indicating a metabolic shift towards improved energy efficiency and
   substrate utilization. These microbial functional adaptations align
   with the biochemical improvements observed in liver function and lipid
   profiles, supporting the role of OSA starch in mitigating ALD
   progression through targeted modulation of the gut microbiome and its
   metabolism.

3.4. Key Gut Microbial Metabolic Pathways Modulated by OSA Starch in ALD Mice

   Functional pathway analysis based on PICRUSt2 prediction revealed that
   OSA starch notably regulated gut microbial metabolic functions centered
   on carbon metabolism, including the pyruvate, glyoxylate, and
   dicarboxylate, butanoate, and propanoate metabolism pathways
   ([93]Supplementary Figure S1). In the pyruvate metabolism pathway,
   several critical enzymes responsible for converting pyruvate into
   acetyl-CoA, lactate, and other intermediates were significantly altered
   ([94]Supplementary Figure S1A). Hepatic pyruvate metabolism is critical
   and complementary for maintenance of antioxidant capacity [[95]38].
   Additionally, modulation of glyoxylate and dicarboxylate metabolism
   suggests enhanced microbial capacity for the glyoxylate cycle
   ([96]Supplementary Figure S1B), which is pivotal in maintaining energy
   efficiency under metabolic stress conditions commonly associated with
   liver diseases [[97]39].

   Moreover, the butanoate and propanoate metabolism pathways were
   distinctly influenced, as evidenced by the differential regulation of
   enzymes involved in the synthesis of key short-chain fatty acids, such
   as butyrate and propionate ([98]Supplementary Figure S1C,D).
   Short-chain fatty acids are critical mediators of gut–liver axis
   health, with butyrate known to strengthen intestinal barrier integrity
   and propionate contributing to lipid metabolism regulation
   [[99]40,[100]41]. The attenuation of propanoate metabolism alongside
   the remodeling of butanoate pathways may help restore short-chain fatty
   acid homeostasis disrupted in ALD. This metabolic reconfiguration in
   the gut microbiota highlights a potential mechanism by which OSA starch
   exerts its protective effects against hepatic injury via microbial
   metabolic modulation.

3.5. OSA Starch Reshapes Fecal Metabolome and Modulates Key Metabolic
Pathways in ALD Mice

   Untargeted fecal metabolomics revealed a distinct metabolic
   reprogramming following OSA starch intervention in ALD mice. PCA
   demonstrated clear separation between the Control, Model, and OSA
   starch groups, reflecting the overall impact of OSA starch on metabolic
   profiles ([101]Figure 4A). The volcano plots identified pyruvate
   accumulation as a hallmark of ALD progression, with the Model group
   showing significant decreases compared to Control ([102]Figure 4B).
   Conversely, OSA starch supplementation not only sustained elevated
   pyruvate and 2-hydroxy-butanoic acid levels but also substantially
   reduced the abundance of lactic acid, L-glutamic acid, beta-leucine,
   and creatine—metabolites linked to disrupted amino acid and energy
   metabolism ([103]Figure 4C). These metabolic shifts suggest that OSA
   starch may redirect pyruvate flux away from lactate production and
   abnormal amino acid turnover, promoting more efficient energy
   homeostasis. Further analysis using Upset plots identified metabolites
   converging on four pivotal metabolic pathways: pyruvate metabolism,
   propanoate metabolism, butanoate metabolism, and glyoxylate and
   dicarboxylate metabolism ([104]Figure 4D). These pathways are central
   to maintaining carbon flow between glycolysis, the TCA cycle, and
   short-chain fatty acid production [[105]42]. The heatmap clustering
   emphasized the normalized metabolic landscape in the OSA starch group
   relative to the Model ([106]Figure 4E).

Figure 4.

   [107]Figure 4
   [108]Open in a new tab

   OSA starch reshapes the fecal metabolome in ALD mice. (A) PCA score
   plot of fecal metabolome showing group separation. (B) Volcano plot of
   differential metabolites between Control and Model groups. (C) Volcano
   plot of differential metabolites between OSA starch and Model groups.
   (D) Upset plot indicating metabolite overlap among four key pathways
   (pyruvate, propanoate, butanoate, glyoxylate/dicarboxylate metabolism).
   (E) Heatmap of representative differential metabolites across groups.

   A detailed metabolite–pathway interaction network ([109]Figure 5A)
   mapped the relationships between these four key pathways and their
   representative metabolites. Pyruvate metabolism emerged as a central
   hub, directly connected to citrulline, ornithine, creatine, lactic
   acid, and beta-leucine, among others. This centrality underscores
   pyruvate’s critical role as a metabolic crossroad linking amino acid
   biosynthesis and energy production [[110]43]. Additionally, propanoate,
   butanoate, and glyoxylate metabolism were interconnected through shared
   metabolites such as glutamine, glyceric acid, and malic acid,
   highlighting the intricate metabolic crosstalk restored by OSA starch
   intervention. KEGG enrichment analysis further revealed significant
   regulation of pathways involved in arginine and proline metabolism,
   pyruvate metabolism, histidine metabolism, and glyoxylate pathways
   ([111]Figure 5B), which are also relevant to hepatic and intestinal
   health [[112]44]. Complementarily, SMPDB enrichment confirmed
   involvement of beta-alanine, purine, and arachidonic acid metabolism
   ([113]Figure 5C), suggesting broader impacts on amino acid turnover and
   inflammatory responses. Collectively, these findings suggest that OSA
   starch exerts hepatoprotective effects by orchestrating the gut-derived
   metabolic networks.

Figure 5.

   [114]Figure 5
   [115]Open in a new tab

   OSA starch modulates key metabolic pathways in ALD mice. (A)
   Metabolite–Pathway network connecting key metabolites to their
   respective metabolic pathways. Blue represents metabolic pathways, and
   green represents metabolic metabolites. (B) KEGG pathway enrichment
   analysis of altered metabolites (p < 0.01). (C) SMPDB pathway
   enrichment analysis of altered metabolites (p < 0.01). Red represents
   altered metabolic pathways in KEGG Database and SMPDB database,
   respectively.

3.6. OSA Starch Improves Serum Bile Acid Profiles in ALD Mice

   Untargeted serum metabolomics revealed that OSA starch supplementation
   significantly remodeled the serum metabolic landscape in ALD mice. PCA
   illustrated distinct clustering of the Control, Model, and OSA starch
   groups, indicating that OSA starch markedly shifted the serum
   metabolome towards a healthier profile ([116]Figure 6A). Differential
   analysis between the OSA starch and Model groups identified several
   metabolites that were significantly altered, particularly those
   associated with amino acid metabolism and energy balance ([117]Figure
   6B). Key metabolites such as lactic acid, L-glutamic acid,
   beta-leucine, creatine, and propionyl-L-carnitine showed substantial
   reductions following OSA starch intervention, suggesting improved
   mitochondrial function and reduced metabolic stress [[118]45].

Figure 6.

   [119]Figure 6
   [120]Open in a new tab

   OSA starch improves serum metabolism profiles in ALD mice. (A) PCA
   based on untargeted serum metabolomics. (B) Volcano plot of
   differentially expressed serum metabolites between the Control and
   Model groups. (C) Volcano plot of differentially expressed serum
   metabolites between OSA starch and Model groups. (D) Heatmap showing
   the relative abundance of significantly altered metabolites.

   Heatmap visualization further highlighted that OSA starch restored the
   serum levels of these representative metabolites ([121]Figure 6D). The
   reduction in lactic acid and glutamic acid implies mitigation of
   anaerobic glycolysis and excitotoxicity, respectively, both of which
   are aggravated in liver disease [[122]46,[123]47]. Additionally, the
   decrease in beta-leucine and propionyl-L-carnitine may reflect a
   normalization of branched-chain amino acid metabolism and fatty acid
   oxidation, respectively, further indicating metabolic reprogramming
   favoring hepatic health [[124]48]. These findings are consistent with
   previous reports where targeting amino acid and lipid metabolism
   alleviated liver injury and improved metabolic resilience [[125]18].
   Overall, the serum metabolomic data support the hypothesis that OSA
   starch exerts protective effects against ALD by rebalancing energy
   production.

3.7. OSA Starch Modulates Gut Microbiota–Metabolite–Host Axis to Alleviate
ALD

   To further elucidate the mechanism underlying the beneficial effects of
   OSA starch on ALD, a comprehensive multi-layer network was constructed
   integrating gut microbiota, microbial functions, fecal and serum
   metabolites, and clinical serum indicators. The interaction network
   ([126]Figure 7A) revealed Faecalibaculum rodentium and Bifidobacterium
   adolescentis as central bacterial nodes connected to key metabolic
   pathways, including butanoate and propanoate metabolism, and to
   signature fecal metabolites such as pyruvate, lactic acid, and
   2-hydroxy-butanoic acid. These metabolites exhibited strong links with
   serum markers like ALT, AST, and TG, suggesting that gut microbial
   modulation impacts host metabolic health. Additionally, microbial
   functional pathways including pyruvate and propanoate metabolism were
   identified as pivotal nodes linking gut microbiota alterations to
   metabolic outputs, reinforcing the microbiota–metabolite–host axis
   hypothesis.

Figure 7.

   [127]Figure 7
   [128]Open in a new tab

   OSA starch modulates gut microbiota–metabolite–host axis to alleviate
   ALD. (A) Interaction network depicting relationships among gut
   microbiota, microbial metabolism, fecal metabolites, serum metabolites,
   and serum indicators. (B) SEM illustrating the pathways from OSA starch
   intervention through gut microbiota, microbial functions, fecal and
   serum metabolites, and serum markers to alcoholic liver disease
   outcome. * p < 0.05, ** p < 0.01.

   A structural equation model (SEM) was constructed using AMOS software
   version 28 (Armonk, NY, USA) to quantify the direct and indirect
   pathways involved ([129]Figure 7B). The final model exhibited good fit
   to the data, with fit indices of RMSEA = 0.065 and CFI = 0.975,
   indicating an acceptable model fit. OSA starch positively regulated gut
   microbiota (path coefficient = 0.45, p < 0.01) and microbial functions
   (0.48, p < 0.01), which sequentially affected fecal metabolites (0.53,
   p < 0.01) and serum metabolites (0.41, p < 0.05). Both fecal and serum
   metabolites significantly influenced serum markers (0.51, p < 0.01 and
   0.44, p < 0.05, respectively). OSA starch exhibited a strong negative
   correlation with ALD progression (−0.39, p < 0.05). These findings
   suggest that the gut microbiota–metabolite axis serves as a critical
   mediator of OSA starch’s therapeutic benefits in ALD.

   Network and SEM analyses strongly support the hypothesis that OSA
   starch alleviates ALD by modulating the gut microbiota–metabolite–host
   axis. This central role of the gut microbiota aligns with the
   protective mechanisms attributed to other prebiotics and dietary fibers
   in ALD research, such as polysaccharides [[130]49]. The unique
   structure and physical and chemical properties of OSA starch may affect
   the way this regulatory mechanism is realized, thereby influencing the
   succession of microorganisms and the availability of ecological niches.
   This indicates that although the core protective axis (gut–liver
   communication) is the common goal, different prebiotic fibers
   participate in it through subtle mechanisms.

   While these findings show the beneficial effects of OSA starch on the
   gut–liver axis in ALD mice, several limitations exist. First, murine
   models differ from humans in alcohol metabolism and disease
   progression, limiting translation to human ALD. Second, the 28-day
   intervention may not reflect long-term OSA starch effects or chronic
   ALD progression, warranting longer studies. Third, replacing native
   starch entirely with OSA starch does not mimic typical human diets,
   suggesting that future studies should examine OSA starch in more
   complex dietary contexts.

4. Conclusions

   In conclusion, this study suggests that dietary OSA starch may
   ameliorate alcoholic liver disease by orchestrating a gut
   microbiota–metabolite–host axis. OSA starch selectively enriched
   beneficial gut microbes such as Faecalibaculum rodentium and
   Bifidobacterium adolescentis, which appeared associated with enhanced
   microbial metabolic functions, including pyruvate, butanoate, and
   propanoate metabolism. Consistent with this, favorable changes in fecal
   and serum metabolites (e.g., elevated pyruvate and reduced lactic
   acid), along with improvements in serum lipid profiles and hepatic
   injury markers, were observed. Structural equation modeling indicates
   that OSA starch exerts its protective effects through a multi-level
   regulatory cascade spanning the gut microbiota, microbial functions,
   metabolic intermediates, and serum indicators. These findings highlight
   the potential of OSA starch as a dietary intervention for alcoholic
   fatty liver disease by modulating the gut–liver metabolic axis.

Abbreviations

   The following abbreviations are used in this manuscript:
   ALD Alcoholic liver disease
   OSA Octenyl succinic anhydride
   [131]Open in a new tab

Supplementary Materials

   The following supporting information can be downloaded at:
   [132]https://www.mdpi.com/article/10.3390/nu17172779/s1, Figure S1: Key
   Gut Microbial Metabolic Pathways Modulated by OSA Starch in ALD mice.
   [133]nutrients-17-02779-s001.zip^ (441.7KB, zip)

Author Contributions

   C.L. and X.P. designed and performed the experiments, analyzed the
   data, interpreted the results, and wrote the article. T.L., R.M. and
   Y.T. designed the experiments, interpreted the results. All authors
   have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

   All animal procedures received ethical approval from the Animal Ethics
   Committee of Jiangnan University (JN. No. 20240930c0641123 (17 July
   2025)), following the ARRIVE guidelines for animal research.

Informed Consent Statement

   Not applicable.

Data Availability Statement

   The Dataset available on request from the authors.

Conflicts of Interest

   The authors declare no conflicts of interest.

Funding Statement

   This work is financially supported by the China Postdoctoral Science
   Foundation (2024M761182), Postdoctoral Fellowship Program of CPSF (No.
   GZB20240279), and Jiangsu Funding Program for Excellent Postdoctoral
   Talent (No. 2024ZB209).

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References