Differential Gut Microbial Signatures Induced by Exercise Intensity and Mode in Aging SAMP6 Mice

Differential Gut Microbial Signatures Induced by Exercise Intensity and Mode in Aging SAMP6 Mice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Differential Gut Microbial Signatures Induced by Exercise Intensity and Mode in Aging SAMP6 Mice Xiang Tang, Pengfei Li, Hongzhi Gao, Xinlong Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8899298/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: The aging population faces increased risks of metabolic and cognitive decline, with emerging evidence linking gut microbiota to exercise-mediated health benefits. Senescence-accelerated mouse prone 6 (SAMP6) exhibits early aging phenotypes, while senescence-resistant 1 (SAMR1) serves as a healthy control. Methods: Thirty-six male SAMP6 and twelve SAMR1 mice (5- and 7-month-old) were assigned to eight groups (n = 6/group). Seven-month-old SAMP6 mice underwent 8 weeks of treadmill training: low-intensity continuous (12 m/min), moderate-intensity continuous (15 m/min), high-intensity interval training (HIIT; 12/20 m/min), or progressively intensified protocol (12→15 m/min). Fecal samples were collected post-intervention for 16S rRNA sequencing (V3–V4 region). Alpha diversity, beta diversity, and taxonomic composition were analyzed. Gut microbiome health index (GMHI) and microbial dysbiosis index (MDI) were calculated to assess health-associated microbial configuration. Results: Sedentary SAMR1 mice exhibited higher alpha diversity than SAMP6 controls, indicating a link between microbial richness and healthy aging. HIIT significantly restructured gut microbiota composition in SAMP6 mice, characterized by enrichment of Eggerthella lenta and Lactobacillus johnsonii , increased Proteobacteria abundance, and reduced overall alpha diversity. Moderate-intensity continuous training (MICT) enriched Faecalibaculum rodentium with milder compositional shifts. Progressively intensified training resulted in an intermediate microbial phenotype. GMHI was positive in SAMR1 and all exercised SAMP6 groups, with HIIT showing the highest score; sedentary SAMP6 groups exhibited negative GMHI. MDI values were consistent with these health-associated shifts. Conclusions: HIIT is associated with distinct compositional shifts and improved gut microbiome health indices in aging-prone mice, despite reduced alpha diversity. These findings highlight exercise intensity as a critical determinant of gut microbial ecology and support HIIT as a candidate precision exercise strategy for older adults. Future studies employing fecal microbiota transplantation are necessary to test causality. Exercise intensity Gut microbiota Aging SAMP6 mouse HIIT Akkermansia muciniphila 16S rRNA sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The aging population is a significant global challenge, with an increasing prevalence of age-related diseases such as metabolic syndrome and cognitive decline. Physical exercise has emerged as a promising intervention to mitigate these effects, enhancing both physical health and cognitive function in older adults. However, the specific mechanisms through which exercise influences these outcomes—particularly in different genetic backgrounds and age groups—remain inadequately explored. This study focuses on the senescence-accelerated mouse prone 6 (SAMP6) and senescence-accelerated mouse resistant 1 (SAMR1) models, which are widely recognized for their utility in aging research. Previous studies have demonstrated that exercise can improve insulin sensitivity and increase muscle mass, which are crucial for maintaining metabolic health in older individuals [ 1 ]. Moreover, exercise has been associated with increased levels of brain-derived neurotrophic factor (BDNF), which plays a vital role in promoting neurogenesis and enhancing cognitive abilities [ 2 ]. Despite these findings, the effects of exercise on different age groups and genetic backgrounds, particularly in mouse models, have not been systematically analyzed. Emerging evidence suggests that the gut microbiota is a key mediator of the systemic benefits of exercise [ 3 , 4 ]. Exercise modulates gut microbial composition, diversity, and functional capacity, which in turn may influence host metabolism, inflammation, and brain function via the gut–brain axis [ 5 , 6 ]. However, most studies have focused on moderate-intensity continuous training (MICT) in young or obese cohorts [ 7 ], and the comparative effects of high-intensity interval training (HIIT) on gut microbiota in aging models remain underexplored [ 8 ]. In this context, our research aims to investigate the effects of varying intensities of exercise on the gut microbial composition of SAMP6 and SAMR1 mice at different ages. By employing 16S rRNA sequencing, we seek to characterize exercise-induced shifts in the gut microbiota and assess whether these shifts are associated with a health-aligned microbial configuration using validated indices (GMHI, MDI). This approach allows for a nuanced understanding of how exercise intensity and genetic predisposition interact to influence the gut ecosystem in aging. 2. Methods 2.1. Experimental Animals and Grouping This study utilized 36 SAMP6 and 12 SAMR1 mice (all 5-month-old males), randomly divided into eight groups (n = 6). The grouping design included duplicate sets of controls to account for experimental variability: G1 and G3: SAMP6 sedentary controls (sampled at 5 and 7 months, respectively) G2 and G4: SAMR1 sedentary controls (sampled at 5 and 7 months, respectively) G5–G8: SAMP6 exercise groups (5-month-old at intervention start) All mice (36 SAMP6 and 12 SAMR1, all 5-month-old males) used in this study were procured from the Animal Experimentation Center of Peking University (Beijing, China). Upon arrival, the animals were acclimatized to the laboratory environment for one week prior to any experimental procedures. They were housed under specific pathogen-free (SPF) conditions with controlled temperature (22 ± 2°C), humidity (50 ± 10%), and a 12-hour light/dark cycle, with standard rodent chow and autoclaved water provided ad libitum. Following this initial acclimatization, all animals were further acclimatized to the treadmill environment for one week before the start of the exercise intervention. All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of (Approval No. TJU-2021-029) and were conducted in accordance with the relevant guidelines and regulations to ensure animal welfare. All experiments were consistently performed during the light phase (e.g., between 09:00 and 12:00) to minimize circadian variations. The strain maintenance and breeding protocols adhere to institutional standards for genetically defined rodent models. All euthanasia procedures were performed strictly in accordance with the Guide for the Care and Use of Laboratory Animals (National Research Council, 8th Edition), the American Veterinary Medical Association (AVMA) Guidelines on Euthanasia (2020 Edition), and protocols approved by the Institutional Animal Care and Use Committee (IACUC). Mice were euthanized via intraperitoneal injection of sodium pentobarbital (150 mg/kg body weight) using a 1 mL syringe fitted with a 27-gauge needle. Loss of consciousness occurred within 30–60 seconds, followed by respiratory arrest. Death was confirmed by the permanent absence of respiration, heartbeat, and corneal reflex. No secondary physical methods (e.g., cervical dislocation) were applied, as the administered dose ensured rapid, irreversible, and humane termination without distress. All procedures were conducted by trained personnel certified in laboratory animal euthanasia techniques. Sodium pentobarbital was the sole anesthetic agent used in this study for euthanasia purposes. No other anesthetics were administered to the animals. 2.2. Exercise Intervention Protocols Sedentary control mice (G1: SAMP6, G2: SAMR1) were sampled at 5 months of age. An additional set of sedentary controls (G3: SAMP6, G4: SAMR1) were housed under the same conditions and placed on a stationary treadmill to match the handling of the exercise group; these mice were sampled at 7 months of age. All exercise groups underwent a treadmill training protocol for 8 weeks, 5 sessions per week: G5 (Low-intensity continuous): 12 m/min, 30 min/session G6 (Moderate-intensity continuous, MICT): 15 m/min, 30 min/session G7 (High-intensity interval training, HIIT): 5 cycles of 3 min at 12 m/min + 3 min at 20 m/min (total 30 min/session) G8 (Progressively intensified): 12 m/min for 2 weeks, increased by 1 m/min every 2 weeks until reaching 15 m/min (total 8 weeks) 2.3. Fecal Sample Collection and 16S rRNA Sequencing Fecal samples were collected immediately after the 8-week intervention, frozen in liquid nitrogen, and stored at -80°C until DNA extraction. 2.3.1. DNA Extraction and PCR Amplification Genomic DNA was extracted using the FastDNA® Spin Kit for Soil (MP Biomedicals) following the manufacturer’s instructions. A negative control (non-template control) was included during DNA extraction and PCR amplification to monitor potential contamination. DNA integrity was verified by 1% agarose gel electrophoresis. The V3–V4 hypervariable region of the 16S rRNA gene was amplified using primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) with sample-specific barcodes. PCR was performed using TransStart FastPfu DNA Polymerase on an ABI GeneAmp® 9700 thermocycler. Although the amplicon length is approximately 460 bp, paired-end sequencing (PE150) generates two 150 bp reads; overlapping regions were merged to reconstruct full-length amplicons. 2.3.2. Library Construction and Sequencing PCR products were pooled in equimolar ratios and purified using the AxyPrep DNA Gel Extraction Kit (Axygen). Sequencing libraries were prepared using the TruSeq™ DNA Sample Prep Kit and sequenced on the Illumina NextSeq 550 platform (PE150 mode). 2.4. Bioinformatics Analysis Raw paired-end reads were merged using FLASH (v1.2.11) with a minimum overlap of 10 bp and mismatch rate ≤ 0.2. Quality filtering was performed using QIIME (v1.9.1) to remove reads with average quality score 8 bp. Chimeric sequences were identified and removed using UCHIME (v4.2) against the Silva 138 database. 2.4.1. Operational taxonomic units (OTUs) Clustering OTUs were clustered at 97% similarity using UPARSE (v7.0.1090). Taxonomic assignment was performed with the RDP Classifier (v2.11) against the Silva 138 database (confidence threshold ≥ 0.7). We acknowledge that amplicon sequence variant (ASV)-based methods (e.g., DADA2) offer higher resolution; however, due to the nature of our historical dataset, OTUs clustering was applied. Future studies should adopt ASV approaches to improve taxonomic granularity. 2.4.2. Diversity Analysis Alpha diversity indices (Shannon, Simpson, Chao1, coverage) were calculated using QIIME. Normality was assessed with the Shapiro–Wilk test. Differences in alpha diversity were evaluated using one-way ANOVA with Tukey’s HSD post hoc test or the Kruskal–Wallis test, as appropriate. Beta diversity was assessed using Bray–Curtis dissimilarity and visualized via principal component analysis (PCA). Permutational multivariate analysis of variance (PERMANOVA; Adonis) and analysis of similarities (ANOSIM) were used to test group differences in community structure. 2.4.3. Differential Abundance Linear discriminant analysis effect size (LEfSe) was performed to identify taxa differentially enriched between groups (LDA score > 3.6, p < 0.05). 2.4.4. Gut Microbiome Health Index (GMHI) and Microbial Dysbiosis Index(MDI) GMHI and MDI were calculated based on previously validated algorithms [ 9 , 10 ] using species-level relative abundance profiles. GMHI > 0 indicates a health-associated configuration; MDI > 0 indicates deviation from a healthy reference. 2.5. Data Availability The 16S rRNA sequencing data have been deposited in the NCBI Sequence Read Archive under accession number PRJNA1379550. All other data are available from the corresponding author upon reasonable request. 2.6. Bioinformatics Tools Qiime 1.9.1: OTU clustering, diversity analysis, https://qiime.org Mega 7: Phylogenetic tree construction (Neighbor-Joining method), https://www.megasoftware.net/ SILVA 138: Bacterial 16S rRNA database (> 200,000 sequences), https://www.arb-silva.de/ UNITE 8: Fungal ITS database (> 150,000 sequences), https://unite.ut.ee/ RDP 11.5: Archaeal classification database (> 10,000 sequences), http://rdp.cme.msu.edu/ 3. Result 3.1. Sequencing Data Quality A total of 48 fecal samples (6 per group) were sequenced. After quality filtering, an average of 51,437 high-quality reads per sample were obtained, with an average read length of 419 bp and a validity rate > 88% (Table 1 ). Rarefaction curves indicated that sequencing depth was sufficient across all groups (coverage > 96%), although G7 showed slightly lower OTU saturation at equivalent read depth (Fig. 1 ). Table 1 Summary of 16S rRNA sequencing data quality after filtering Group Raw Reads Valid Reads Validity Rate (%) Avg Length (bp) G1 120,345 108,210 90.0 382 G3 118,760 106,540 89.7 380 G4 135,780 121,650 90.3 385 G5 110,230 97,850 88.8 379 G6 114,670 101,230 88.3 378 G7 112,450 99,320 88.4 378 G8 115,890 102,560 88.5 379 Table 1 . Values are means per group (n = 6). Raw reads: paired-end reads before filtering; Valid reads: high-quality merged reads after quality control; Validity rate: (Valid reads / Raw reads) × 100%; Avg length: mean length of merged reads (bp). All groups achieved validity rates > 88% and average read lengths > 370 bp, confirming sufficient sequencing quality for downstream analysis. Figure 1 . Each curve represents a single sample; colors indicate experimental groups. All curves approached plateau at > 30,000 reads, confirming sufficient sequencing depth. G7 (HIIT) exhibited slightly lower OTU saturation at equivalent read depth compared to G4 (SAMR1 sedentary) and G1 (SAMP6 sedentary), consistent with its reduced alpha diversity. 3.2. Gut Microbial Composition at the Phylum Level To characterize the overall structure of the gut microbiota, we examined the relative abundance of dominant phyla across groups (Fig. 2 ). In sedentary SAMP6 mice (G1), Bacillota (formerly Firmicutes) was the most abundant phylum (38.7%), followed by Bacteroidota (27.4%) and Actinobacteria (12.3%). In contrast, sedentary SAMR1 mice (G4) exhibited a higher relative abundance of Bacteroidota (31.5%) and a lower proportion of Bacillota (22.7%). HIIT (G7) increased the relative abundance of Proteobacteria (18.4%) while reducing Bacillota (25.6%). MICT (G6) enriched Faecalibaculum rodentium (Table 5 ) and showed a modest increase in Bacteroidota. The progressively intensified protocol (G8) resulted in a phylum-level profile intermediate between sedentary and HIIT groups. Figure 2 . Stacked bar plot showing taxonomic composition at the phylum level in representative groups: 5-month-old sedentary SAMP6 (G1), 7-month-old sedentary SAMR1 (G4), and 7-month-old SAMP6 subjected to 8-week HIIT (G7) (n = 6 per group). Only phyla with relative abundance > 1% are shown; others are grouped as “Others”. 3.3 Alpha Diversity Alpha diversity indices were calculated to assess within-sample microbial richness and evenness (Table 2 , Fig. 3 ). Sedentary SAMR1 mice (G4) displayed the highest Shannon (4.56 ± 0.12) and Chao1 (412 ± 18) indices, consistent with their healthy aging phenotype. SAMP6 sedentary controls (G1, G3) showed intermediate diversity. Among exercise groups, HIIT (G7) was associated with significantly lower Shannon (3.58 ± 0.09) and Chao1 (298 ± 15) indices compared to both sedentary SAMP6 (G3) and SAMR1 (G4) controls (p < 0.01, ANOVA). MICT (G6) did not significantly alter alpha diversity relative to G3, whereas low-intensity (G5) and progressively intensified (G8) protocols resulted in modest, non-significant reductions. Coverage exceeded 96% in all groups, confirming adequate sequencing depth. Table 2 Alpha diversity indices of gut microbiota across experimental groups (mean ± SEM, n = 6 per group). Group Shannon Simpson Chao1 Coverage (%) G1 4.23 ± 0.09 0.89 ± 0.02 387 ± 14 98.2 ± 0.3 G3 4.18 ± 0.10 0.88 ± 0.02 381 ± 16 98.0 ± 0.4 G4 4.56 ± 0.12 0.91 ± 0.01 412 ± 18 98.7 ± 0.2 G5 4.05 ± 0.11 0.86 ± 0.03 363 ± 19 97.8 ± 0.5 G6 3.92 ± 0.11 0.85 ± 0.03 356 ± 21 97.5 ± 0.5 G7 3.58 ± 0.09 0.78 ± 0.04 298 ± 15 96.5 ± 0.6 G8 4.01 ± 0.13 0.87 ± 0.02 370 ± 20 97.9 ± 0.4 Table 2 . Values are mean ± SEM (n = 6 per group). Shannon and Simpson indices reflect community diversity (higher values indicate greater diversity); Chao1 estimates species richness; Coverage represents the proportion of total OTUs detected. Statistical significance was assessed by one-way ANOVA with Tukey’s HSD post hoc test. G4 (SAMR1 sedentary) exhibited the highest diversity, while G7 (HIIT) showed significantly lower richness and diversity compared to both sedentary SAMP6 (G3) and SAMR1 (G4) controls (p 96% in all groups confirms adequate sequencing depth. Figure 3 . Box plots showing (A) Shannon diversity index and (B) Chao1 richness estimator across all eight groups (n = 6 per group). Center line: median; box limits: IQR; whiskers: 1.5× IQR. Statistical significance: one-way ANOVA with Tukey’s HSD. *p < 0.05, **p < 0.01, ***p < 0.001. G4 showed significantly higher diversity/richness than all SAMP6 groups. Among exercise groups, G7 (HIIT) exhibited the lowest alpha diversity (p < 0.01 vs. G3 and G4). 3.4. Beta Diversity Principal component analysis (PCA) based on Bray–Curtis dissimilarity revealed distinct clustering of gut microbial communities (Fig. 4 ). PC1 explained 11.99% of the total variance, and PC2 explained 6.43%. Sedentary SAMR1 mice (G4) separated clearly from all SAMP6 groups along PC1 (negative axis), indicating strong strain-specific compositional differences. Within SAMP6 mice, exercise groups (G5–G8) were partially separated from sedentary controls (G3) along PC2. HIIT (G7) samples clustered separately from both sedentary and other exercise groups, suggesting a distinct compositional shift. MICT (G6) and progressively intensified (G8) groups overlapped with each other and with G5, indicating less pronounced remodeling. PERMANOVA confirmed significant group effects (Adonis R² = 0.25, p = 0.001; ANOSIM R = 0.43, p = 0.002). Figure 4 . Principal component analysis (PCA) of gut microbial community structure. PCA ordination plot based on Bray–Curtis dissimilarity at the OTU level. Each point represents a fecal sample (n = 6 per group). Colors indicate experimental groups. PC1: 11.99%, PC2: 6.43% of total variance. G4 clusters separately from all SAMP6 groups along PC1; G7 forms a distinct cluster along PC2. PERMANOVA: R² = 0.25, p = 0.001. 3.5. Shared and Unique OTUs A flower plot was constructed to visualize shared and unique OTUs across all eight groups (Fig. 5 ). A total of 350 OTUs were shared among all groups, representing the core gut microbiota conserved across aging and exercise interventions. Group-specific OTUs highlighted key differences: G4 (SAMR1 sedentary) harbored the highest number of unique OTUs (69), including OTU272 classified as Akkermansia muciniphila (2.1% relative abundance). G7 (HIIT) uniquely enriched 16 OTUs, including OTU417 ( Eggerthella lenta , 1.8%) and showed depletion of several Lachnospiraceae OTUs. G6 and G8 exhibited fewer unique OTUs (16 and 24, respectively) and shared most of their microbiota with sedentary SAMP6 controls. Figure 5 . Flower plot showing distribution of shared and group-specific OTUs across eight groups (n = 6 per group). Central number (350): OTUs detected in all groups (core microbiota). Peripheral numbers: OTUs uniquely identified in each group. G4 has the highest number of unique OTUs (69), including Akkermansia muciniphila; G7 uniquely enriches 16 OTUs, including Eggerthella lenta. 3.6 GMHI and MDI To assess the health relevance of exercise-induced compositional shifts, we calculated GMHI and MDI for each group (Table 3 , Table 4 ; Fig. 6 , Fig. 7 ). Sedentary SAMR1 mice (G4) and all exercised SAMP6 groups (G6, G7, G8) exhibited positive GMHI scores, with HIIT (G7) showing the highest value (+ 1.90 ± 0.10). In contrast, sedentary SAMP6 groups (G1, G3) displayed negative GMHI scores, indicating a dysbiosis-associated configuration. MDI values were consistent with this pattern: sedentary SAMP6 mice had negative MDI (reference), while HIIT (G7) showed a slight shift toward positive MDI, suggesting mild deviation from the sedentary SAMP6 baseline. G4 and other exercise groups displayed intermediate MDI values. Table 3 Gut Microbiome Health Index (GMHI) across experimental groups Group GMHI (mean ± SEM) G1 -0.59 ± 0.12 G3 -0.54 ± 0.11 G4 + 1.83 ± 0.15 G5 -0.22 ± 0.09 G6 + 1.50 ± 0.13 G7 + 1.90 ± 0.10 G8 + 0.95 ± 0.14 Table 3 . Values are mean ± SEM (n = 6 per group). GMHI is a validated index based on the prevalence of health- and disease-associated microbial species; positive values indicate a microbiota profile resembling healthy individuals, while negative values indicate dysbiosis-associated profile. Sedentary SAMR1 mice (G4) and all exercised SAMP6 groups (G6, G7, G8) exhibited positive GMHI scores, with HIIT (G7) showing the highest value. In contrast, sedentary SAMP6 groups (G1, G3) displayed negative GMHI scores. Table 4 Microbial Dysbiosis Index (MDI) across experimental groups Group MDI (mean ± SEM) G1 -0.46 ± 0.03 G3 -0.41 ± 0.04 G4 -0.10 ± 0.06 G5 -0.17 ± 0.05 G6 -0.05 ± 0.04 G7 + 0.04 ± 0.03 G8 -0.12 ± 0.05 Table 4 . Values are mean ± SEM (n = 6 per group). MDI quantifies the degree of deviation from a healthy baseline microbiota; higher values indicate greater dysbiosis. Sedentary SAMP6 mice (G1, G3) exhibited negative MDI values (non-dysbiotic reference), while HIIT (G7) showed a slight shift toward positive MDI. G4 (SAMR1) and other exercise groups displayed intermediate values. Together with GMHI, these data indicate that exercise-induced compositional shifts are associated with a more health-aligned microbial profile, despite reduced alpha diversity in G7. Figure 6 . Box plots showing GMHI scores in fecal samples from all eight groups (n = 6 per group). Center line: median; box limits: interquartile range (IQR); whiskers: 1.5 × IQR; points: individual mice. GMHI > 0 indicates a health-associated microbiota profile; GMHI < 0 indicates dysbiosis-associated profile. Sedentary SAMR1 mice (G4) and all exercised SAMP6 groups (G6, G7, G8) exhibited positive GMHI scores, with HIIT (G7) showing the highest value. Sedentary SAMP6 groups (G1, G3) displayed negative GMHI scores. **p < 0.01, ***p < 0.001 (one-way ANOVA with Tukey’s HSD). Figure 7 . Box plots showing MDI scores in fecal samples from all eight groups (n = 6 per group). Center line: median; box limits: IQR; whiskers: 1.5 × IQR; points: individual mice. MDI quantifies deviation from a healthy baseline; higher values indicate greater dysbiosis. Horizontal dashed line represents MDI = 0 (healthy reference). Sedentary SAMP6 mice (G1, G3) exhibited negative MDI values, while HIIT (G7) showed a slight shift toward positive MDI. G4 (SAMR1) and other exercise groups displayed intermediate values. *p < 0.05 vs. G3 (ANOVA). 3.7 Differential Taxa Enrichment LEfSe analysis was performed to identify taxa differentially enriched between key comparisons (G4 vs. G3, G7 vs. G3, G6 vs. G3; Table 5 ). In sedentary SAMR1 mice (G4), Akkermansia muciniphila (LDA = 4.21, p = 0.005) and Bifidobacterium longum (LDA = 3.65, p < 0.001) were significantly enriched. In HIIT-treated SAMP6 mice (G7), Eggerthella lenta (LDA = 3.76, p = 0.003) and Lactobacillus johnsonii (LDA = 3.45, p = 0.02) were overrepresented. Moderate-intensity training (G6) enriched Faecalibaculum rodentium (LDA = 3.38, p = 0.01) and several unclassified Ruminococcaceae OTUs. No taxa reached the LDA threshold in G5 or G8 comparisons. Table 5 Differentially enriched bacterial taxa identified by LEfSe (LDA > 3.6, p < 0.05). Species Group LDA score p-value Akkermansia muciniphila G4 4.21 0.005 Bifidobacterium longum G4 3.65 < 0.001 Eggerthella lenta G7 3.76 0.003 Lactobacillus johnsonii G7 3.45 0.02 Faecalibaculum rodentium G6 3.38 0.01 Table 5 . Only taxa meeting the LDA threshold in key pairwise comparisons (G4 vs. G3, G7 vs. G3, G6 vs. G3) are shown. G4 (healthy aging control) enriched in Akkermansia muciniphila and Bifidobacterium longum; G7 (HIIT) enriched in Eggerthella lenta and Lactobacillus johnsonii; G6 (MICT) enriched in Faecalibaculum rodentium. No taxa reached the LDA threshold in G5 or G8 comparisons. 3.8. Inter-group Distribution of Dominant Genera To visualize the relationship between experimental groups and dominant bacterial genera, a Circos plot was generated (Fig. 8 ). Bacteroides and Lactobacillu showed distinct distribution patterns between sedentary and exercised groups. HIIT (G7) exhibited a higher proportion of Eggerthella and Lactobacillus , consistent with LEfSe results. The width of ribbons represents the relative contribution of each genus to each group; the length of outer bars indicates total relative abundance across all groups. HIIT (G7) shows higher proportions of Eggerthella and Lactobacillus , consistent with Table 5 . Figure 8 . Circos plot visualizing the relationship between experimental groups and dominant bacterial genera. The width of the ribbons represents the relative contribution of each genus to each group, and the length of the outer bars indicates the total relative abundance of the genus across all groups. Bacteroides and Lactobacillus showed distinct distribution patterns between sedentary and exercised groups. Akkermansia was predominantly observed in the sedentary SAMR1 group (G4), consistent with LEfSe results ( Table 5 ). HIIT (G7) exhibited a higher proportion of Eggerthella and Lactobacillus. 4. Discussion Aging is a critical public health issue, characterized by a decline in physiological functions and an increased susceptibility to age-related diseases. Physical exercise is widely recognized as an effective intervention to mitigate these effects, yet the optimal intensity and underlying mechanisms—particularly those involving the gut microbiota—remain incompletely understood [ 3 , 4 ]. In this study, we comprehensively compared the effects of four treadmill training protocols on the gut microbial composition of aging SAMP6 mice, with SAMR1 mice serving as healthy controls. Our results demonstrate that exercise intensity is a major determinant of gut microbiota remodeling. HIIT (G7) induced the most pronounced compositional shift, characterized by enrichment of Eggerthella lenta and Lactobacillus johnsonii, increased Proteobacteria abundance, and a marked reduction in overall alpha diversity. MICT (G6) produced more subtle changes, enriching Faecalibaculum rodentium, while low-intensity (G5) and progressively intensified (G8) protocols had intermediate effects. These findings align with previous reports that HIIT elicits distinct physiological responses compared to continuous endurance training [ 11 , 12 ]. The enrichment of Akkermansia muciniphila was observed exclusively in the healthy aging control group (G4), not in any exercised SAMP6 group. This suggests that this mucin-degrading bacterium may represent an intrinsic feature of the SAMR1 strain’s healthy gut ecosystem rather than an exercise-induced effect in the SAMP6 background. Its established roles in improving metabolic health and gut barrier function [ 13 , 14 ] are consistent with the superior metabolic phenotype of SAMR1 mice [ 5 ], but our data indicate that exercise, at least in this aging-prone model, does not recapitulate this specific microbial signature. The concurrent reduction in alpha diversity and positive GMHI in the HIIT group presents an intriguing dissociation. While some have proposed that loss of diversity can be offset by proliferation of functionally critical taxa (“functional specialization”) [ 15 ], our current dataset cannot distinguish this interpretation from alternative explanations such as exercise-induced physiological stress leading to ecological simplification. Crucially, GMHI—a validated composite index based on prevalence of health- and disease-associated species—was positive in all exercised SAMP6 groups and highest in HIIT, directly demonstrating that the compositional shifts are associated with a health-aligned microbial configuration. This finding substantially strengthens the interpretation that HIIT-induced remodeling, despite lower alpha diversity, is not merely dysbiotic but may reflect adaptive specialization. Importantly, our study highlights the distinction between compositional associations and causal mediation. Although we observed robust correlations between exercise intensity, specific taxa, and positive GMHI, we did not collect paired host phenotype data in the same animals used for microbiota analysis. Therefore, any inference that the microbial shifts mediate exercise benefits remains speculative. We explicitly frame our conclusions in terms of association rather than causation, and we emphasize the need for fecal microbiota transplantation (FMT) experiments to test causality [ 16 ]. Our multi-intensity design also revealed that the progressively intensified protocol (G8), despite reaching the same terminal speed as MICT, did not replicate the microbial effects of either MICT or HIIT (G8 exhibited alpha diversity and GMHI scores comparable to G5/G6, Tables 2 – 3 ). This suggests that the pattern of intensity—specifically the intermittent bursts of high speed—may be more critical than cumulative workload in driving gut microbiota remodeling. This observation is consistent with studies showing that interval training activates AMPK and PGC-1α pathways more robustly than constant-load exercise [ 17 ]. Several limitations should be acknowledged. First, the sample size (n = 6 per group) is modest, and while adequate for detecting large compositional differences, it may limit statistical power for subtle taxa shifts. Second, our use of OTU clustering (97% similarity) rather than ASV resolution may have reduced taxonomic precision; future studies should adopt denoising methods such as DADA2. Third, we did not perform functional metagenomics or metabolomics, preventing us from assessing whether the observed compositional changes translate into altered microbial metabolic capacity. Fourth, as noted above, the lack of paired host physiological and cognitive data in the sequenced cohorts precludes formal mediation analysis. Despite these limitations, this study provides a comprehensive, intensity-resolved description of exercise-induced gut microbiota shifts in a clinically relevant aging model, validated by established health indices (GMHI, MDI). It underscores that exercise intensity is not merely a quantitative parameter but a qualitative determinant of microbial ecology. Our findings support the potential of HIIT as a candidate precision exercise strategy for older adults, while also highlighting that “more” (intensity) is not always “better” in terms of diversity. 5. Conclusions This study demonstrates that different exercise intensities are associated with distinct compositional changes in the gut microbiota of aging SAMP6 mice. High-intensity interval training is associated with enrichment of Eggerthella lenta and Lactobacillus johnsonii, positive GMHI, and reduced alpha diversity. In contrast, enrichment of Akkermansia muciniphila was unique to the healthy aging control group (G4) and was not induced by any exercise protocol. Moderate-intensity continuous training enriches Faecalibaculum rodentium with milder compositional shifts, while progressive intensity escalation fails to recapitulate the HIIT-associated microbial profile. These findings suggest that exercise prescription for gut microbiota modulation should consider both the absolute workload and the pattern of intensity application. Crucially, our conclusions are framed as descriptive associations, not causal mechanisms. To advance the field, future research must integrate paired host phenotyping, functional metagenomics, and interventional FMT studies. Only through such approaches can we determine whether exercise-induced gut microbiota remodeling is a true mediator of healthy aging or merely a correlate. Declarations Ethics approval and consent to participate All animal procedures were approved by the Institutional Animal Care and Use Committee of Tianjin University (Approval No. TJU-2021-029) and conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Consent for publication Not applicable. Availability of data and materials The 16S rRNA gene sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) under accession number [PRJNA1379550]. Other datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by [Key Research and Development Plan for Active Health and Population Aging Response, 2022YFC3601904]. The funder had no role in study design, data collection, analysis, or manuscript preparation. Authors' contributions Xiang Tang, Pengfei Li, and Hongzhi Gao contributed equally to this work and share first authorship. Xiang Tang: Conceptualization, Methodology, Animal experiments, Data curation, Formal analysis, Writing – original draft. Pengfei Li: Methodology, Bioinformatics analysis, Visualization, Validation, Writing – original draft. Hongzhi Gao: Investigation, Sample collection, Microbiome data processing, Statistical analysis, Writing – review & editing. Xinlong Ma: Supervision, Project administration, Funding acquisition, Conceptualization, Writing – review & editing, Final approval of the manuscript. All authors read and approved the final manuscript. References Plaza-Florido A, Pérez-Prieto I, Lucia A. The aging lipidome: exercise is medicine. Trends Mol Med. 2024;30(11):1001–3. https://doi.org/10.1016/j.molmed.2024.06.006 Saucedo Marquez CM, Vanaudenaerde B, Troosters T, Wenderoth N. High-intensity interval training evokes larger serum BDNF levels compared with intense continuous exercise. J Appl Physiol. 2015;119(12):1363–73. https://doi.org/10.1152/japplphysiol.00126.2015 Mailing LJ, Allen JM, Buford TW, Fields CJ, Woods JA. Exercise and the gut microbiome: a review of the evidence, potential mechanisms, and implications for human health. Exerc Sport Sci Rev. 2019;47(2):75-85. https://doi.org/10.1249/JES.0000000000000183 Allen JM, Mailing LJ, Niemiro GM, et al. Exercise alters gut microbiota composition and function in lean and obese humans. Med Sci Sports Exerc. 2018;50(4):747-757. https://doi.org/10.1249/MSS.0000000000001495 Niimi K, Takahashi E, Itakura C. Adiposity-related biochemical phenotype in senescence-accelerated mouse prone 6 (SAMP6). Comp Med. 2009;59(5):431-436. https://pubmed.ncbi.nlm.nih.gov/19887026/ Castillo CA, Albasanz JL, León D, Ferrer-Montiel A, Martín M. Age-related expression of adenosine receptors in brain from the senescence-accelerated mouse. Exp Gerontol. 2009;44(6-7):453-461. https://doi.org/10.1016/j.exger.2009.04.006 Niimi K, Takahashi E, Itakura C. Age-related difference in nociceptive behavior between SAMP6 and SAMR1 strains. Neurosci Lett. 2008;444(1):60-63. https://doi.org/10.1016/j.neulet.2008.08.003 Gibala MJ, Little JP, Macdonald MJ, Hawley JA. Physiological adaptations to low-volume, high-intensity interval training in health and disease. J Physiol. 2012;590(5):1077-1084. https://doi.org/10.1113/jphysiol.2011.224725 Gupta VK, Kim M, Bakshi U, Cunningham KY, Davis JM 3rd, Lazaridis KN, et al. A predictive index for health status using species-level gut microbiome profiling. Nat Commun. 2020;11(1):4635. https://doi.org/10.1038/s41467-020-18476-8 Geerlings SY, Kostopoulos I, de Vos WM, Belzer C. The human gut microbiota and its interactive capacity to metabolize dietary glycans. Physiol Rev. 2021;101(3):1107-1179. https://doi.org/10.1152/physrev.00018.2020 Brisebois MF, Biggerstaff KD, Nichols DL. Cardiorespiratory responses to acute bouts of high-intensity functional training and traditional exercise in physically active adults. J Sports Med Phys Fitness. 2022;62(2):199-206. https://doi.org/10.23736/S0022-4707.21.12115-2 Little JP, Safdar A, Wilkin GP, Tarnopolsky MA, Gibala MJ. A practical model of low-volume high-intensity interval training induces mitochondrial biogenesis in human skeletal muscle: potential mechanisms. J Physiol. 2010;588(Pt 6):1011-1022. https://doi.org/10.1113/jphysiol.2009.181743 Cani PD, de Vos WM. Next-generation beneficial microbes: the case of Akkermansia muciniphila. Front Microbiol. 2017;8:1765. https://doi.org/10.3389/fmicb.2017.01765 Dao MC, Everard A, Aron-Wisnewsky J, et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut. 2016;65(3):426-436. https://doi.org/10.1136/gutjnl-2014-308778 Valdes AM, Walter J, Segal E, Spector TD. Role of the gut microbiota in nutrition and health. BMJ. 2018;361:k2179. https://doi.org/10.1136/bmj.k2179 Cryan JF, O'Riordan KJ, Cowan CSM, et al. The microbiota-gut-brain axis. Physiol Rev. 2019;99(4):1877-2013. https://doi.org/10.1152/physrev.00018.2018 Scheiman J, Luber JM, Chavkin TA, et al. Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism. Nat Med. 2019;25(7):1104-1109. https://doi.org/10.1038/s41591-019-0485-4 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers invited by journal 26 Feb, 2026 Editor assigned by journal 21 Feb, 2026 Editor invited by journal 20 Feb, 2026 Submission checks completed at journal 19 Feb, 2026 First submitted to journal 19 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8899298","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597645532,"identity":"19622507-2291-477c-a733-3caf612ba0e1","order_by":0,"name":"Xiang Tang","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Tang","suffix":""},{"id":597645533,"identity":"5cc51c65-42ae-4980-8cb9-ee1dfd252c97","order_by":1,"name":"Pengfei Li","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Li","suffix":""},{"id":597645534,"identity":"e31f5856-574a-4577-955f-5506557b13b2","order_by":2,"name":"Hongzhi Gao","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongzhi","middleName":"","lastName":"Gao","suffix":""},{"id":597645535,"identity":"fd02926b-42da-42e3-ae81-bd42b595ced6","order_by":3,"name":"Xinlong Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYJCCAwwFQJKZIeHwnwoJOX7itBgASfaGhw94zlgYSzYQZQ9IC8/Bxwa8bRWJGwhp0W0/Y3i4wMAmTz4iOU1Ccp4E4wYG5oePbuDRYnYmLeHwDIO0YsMbaWkShtskmM0Z2IyNc/BpOZB84DCPweHEjTNy0iQSt0mwWTbwsEnj1XL+YQNUS/43iYNzJHgMDhDScgNqy3yeA8mGjQ0SEkRoeZYA1JKWuIG9IfExwzEJA8lmQn45n2P8mafCJnF+MzAqGWrq6vvZmx8+xqcFDgwOwFjMxCgHAfkGYlWOglEwCkbBiAMA9oZQ8oogMVMAAAAASUVORK5CYII=","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xinlong","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2026-02-17 08:56:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8899298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8899298/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103849185,"identity":"13852ead-837f-4428-b98e-af8be0cc28fd","added_by":"auto","created_at":"2026-03-03 16:13:07","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":258951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRarefaction curves of observed OTUs (Sobs) across sequencing depth\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1. Each curve represents a single sample; colors indicate experimental groups. All curves approached plateau at \u0026gt;30,000 reads, confirming sufficient sequencing depth. G7 (HIIT) exhibited slightly lower OTU saturation at equivalent read depth compared to G4 (SAMR1 sedentary) and G1 (SAMP6 sedentary), consistent with its reduced alpha diversity.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8899298/v1/2cecba9032e995b0c6152ec0.jpeg"},{"id":104401041,"identity":"4a1130aa-8800-4224-b742-6ec6b0eaeff8","added_by":"auto","created_at":"2026-03-11 12:11:44","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":276731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative abundance of dominant bacterial phyla in fecal microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2.\u003cstrong\u003e \u003c/strong\u003eStacked bar plot showing taxonomic composition at the phylum level in representative groups: 5-month-old sedentary SAMP6 (G1), 7-month-old sedentary SAMR1 (G4), and 7-month-old SAMP6 subjected to 8-week HIIT (G7) (n=6 per group). Only phyla with relative abundance \u0026gt;1% are shown; others are grouped as “Others”.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8899298/v1/170d07cf1a0da4180f76b149.jpeg"},{"id":103849190,"identity":"7a3874f6-4740-4256-8e99-cc6ad0c04963","added_by":"auto","created_at":"2026-03-03 16:13:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlpha diversity comparisons across experimental groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3.\u003c/strong\u003e Box plots showing (A) Shannon diversity index and (B) Chao1 richness estimator across all eight groups (n=6 per group). Center line: median; box limits: IQR; whiskers: 1.5× IQR. Statistical significance: one-way ANOVA with Tukey’s HSD. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001. G4 showed significantly higher diversity/richness than all SAMP6 groups. Among exercise groups, G7 (HIIT) exhibited the lowest alpha diversity (p \u0026lt; 0.01 vs. G3 and G4).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8899298/v1/2e28547710ab00cbf50213dd.png"},{"id":104400971,"identity":"25322f2c-3f39-4072-9a72-830df164e013","added_by":"auto","created_at":"2026-03-11 12:11:36","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":154423,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePrincipal component analysis (PCA) of gut microbial community structure\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4\u003cstrong\u003e.\u003c/strong\u003e \u003cstrong\u003ePrincipal component analysis (PCA) of gut microbial community structure.\u003c/strong\u003e\u003cbr\u003e\nPCA ordination plot based on Bray–Curtis dissimilarity at the OTU level. Each point represents a fecal sample (n=6 per group). Colors indicate experimental groups. PC1: 11.99%, PC2: 6.43% of total variance. G4 clusters separately from all SAMP6 groups along PC1; G7 forms a distinct cluster along PC2. PERMANOVA: R² = 0.25, p = 0.001.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8899298/v1/be51afd4ad920c8eab2d57ba.jpeg"},{"id":103849186,"identity":"1d921ef6-6278-4da8-954f-a18df80f2c34","added_by":"auto","created_at":"2026-03-03 16:13:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":216395,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eShared and unique OTUs among all experimental groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 5. Flower plot showing distribution of shared and group-specific OTUs across eight groups (n=6 per group). Central number (350): OTUs detected in all groups (core microbiota). Peripheral numbers: OTUs uniquely identified in each group. G4 has the highest number of unique OTUs (69), including Akkermansia muciniphila; G7 uniquely enriches 16 OTUs, including Eggerthella lenta.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8899298/v1/6a843c136152460d8582e470.png"},{"id":104400664,"identity":"166f31c2-958f-466c-8c68-b37a6f57fdcf","added_by":"auto","created_at":"2026-03-11 12:10:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":66298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGut Microbiome Health Index (GMHI) across experimental groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 6. Box plots showing GMHI scores in fecal samples from all eight groups (n = 6 per group). Center line: median; box limits: interquartile range (IQR); whiskers: 1.5 × IQR; points: individual mice. GMHI \u0026gt; 0 indicates a health-associated microbiota profile; GMHI \u0026lt; 0 indicates dysbiosis-associated profile. Sedentary SAMR1 mice (G4) and all exercised SAMP6 groups (G6, G7, G8) exhibited positive GMHI scores, with HIIT (G7) showing the highest value. Sedentary SAMP6 groups (G1, G3) displayed negative GMHI scores. **p \u0026lt; 0.01, ***p \u0026lt; 0.001 (one-way ANOVA with Tukey’s HSD).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8899298/v1/7257a785657edcb8fde49448.png"},{"id":104401074,"identity":"0b4679c2-247b-413d-a617-03b5436aa58f","added_by":"auto","created_at":"2026-03-11 12:11:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":75855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial Dysbiosis Index (MDI) across experimental groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 7. Box plots showing MDI scores in fecal samples from all eight groups (n = 6 per group). Center line: median; box limits: IQR; whiskers: 1.5 × IQR; points: individual mice. MDI quantifies deviation from a healthy baseline; higher values indicate greater dysbiosis. Horizontal dashed line represents MDI = 0 (healthy reference). Sedentary SAMP6 mice (G1, G3) exhibited negative MDI values, while HIIT (G7) showed a slight shift toward positive MDI. G4 (SAMR1) and other exercise groups displayed intermediate values. *p \u0026lt; 0.05 vs. G3 (ANOVA).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8899298/v1/4c025d72b5d33a11ab51550c.png"},{"id":104401206,"identity":"e64f125d-35ef-40f4-bdb9-cfae3b633031","added_by":"auto","created_at":"2026-03-11 12:12:06","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":342410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircos plot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 8. Circos plot visualizing the relationship between experimental groups and dominant bacterial genera. The width of the ribbons represents the relative contribution of each genus to each group, and the length of the outer bars indicates the total relative abundance of the genus across all groups. Bacteroides and Lactobacillus showed distinct distribution patterns between sedentary and exercised groups. Akkermansia was predominantly observed in the sedentary SAMR1 group (G4), consistent with LEfSe results (Table 5). HIIT (G7) exhibited a higher proportion of Eggerthella and Lactobacillus.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8899298/v1/da865cf064c102e5ff2b4570.jpeg"},{"id":104412793,"identity":"1b964c01-9819-4019-851a-bfbe564b1c6b","added_by":"auto","created_at":"2026-03-11 13:01:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2553294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8899298/v1/9ae74b2f-0d4b-4635-9933-867f2cc99dfa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differential Gut Microbial Signatures Induced by Exercise Intensity and Mode in Aging SAMP6 Mice","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe aging population is a significant global challenge, with an increasing prevalence of age-related diseases such as metabolic syndrome and cognitive decline. Physical exercise has emerged as a promising intervention to mitigate these effects, enhancing both physical health and cognitive function in older adults. However, the specific mechanisms through which exercise influences these outcomes\u0026mdash;particularly in different genetic backgrounds and age groups\u0026mdash;remain inadequately explored. This study focuses on the senescence-accelerated mouse prone 6 (SAMP6) and senescence-accelerated mouse resistant 1 (SAMR1) models, which are widely recognized for their utility in aging research.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that exercise can improve insulin sensitivity and increase muscle mass, which are crucial for maintaining metabolic health in older individuals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Moreover, exercise has been associated with increased levels of brain-derived neurotrophic factor (BDNF), which plays a vital role in promoting neurogenesis and enhancing cognitive abilities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite these findings, the effects of exercise on different age groups and genetic backgrounds, particularly in mouse models, have not been systematically analyzed.\u003c/p\u003e \u003cp\u003eEmerging evidence suggests that the gut microbiota is a key mediator of the systemic benefits of exercise [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Exercise modulates gut microbial composition, diversity, and functional capacity, which in turn may influence host metabolism, inflammation, and brain function via the gut\u0026ndash;brain axis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, most studies have focused on moderate-intensity continuous training (MICT) in young or obese cohorts [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and the comparative effects of high-intensity interval training (HIIT) on gut microbiota in aging models remain underexplored [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, our research aims to investigate the effects of varying intensities of exercise on the gut microbial composition of SAMP6 and SAMR1 mice at different ages. By employing 16S rRNA sequencing, we seek to characterize exercise-induced shifts in the gut microbiota and assess whether these shifts are associated with a health-aligned microbial configuration using validated indices (GMHI, MDI). This approach allows for a nuanced understanding of how exercise intensity and genetic predisposition interact to influence the gut ecosystem in aging.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Experimental Animals and Grouping\u003c/h2\u003e \u003cp\u003eThis study utilized 36 SAMP6 and 12 SAMR1 mice (all 5-month-old males), randomly divided into eight groups (n\u0026thinsp;=\u0026thinsp;6). The grouping design included duplicate sets of controls to account for experimental variability:\u003c/p\u003e \u003cp\u003eG1 and G3: SAMP6 sedentary controls (sampled at 5 and 7 months, respectively)\u003c/p\u003e \u003cp\u003eG2 and G4: SAMR1 sedentary controls (sampled at 5 and 7 months, respectively)\u003c/p\u003e \u003cp\u003eG5\u0026ndash;G8: SAMP6 exercise groups (5-month-old at intervention start)\u003c/p\u003e \u003cp\u003eAll mice (36 SAMP6 and 12 SAMR1, all 5-month-old males) used in this study were procured from the Animal Experimentation Center of Peking University (Beijing, China). Upon arrival, the animals were acclimatized to the laboratory environment for one week prior to any experimental procedures. They were housed under specific pathogen-free (SPF) conditions with controlled temperature (22\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C), humidity (50\u0026thinsp;\u0026plusmn;\u0026thinsp;10%), and a 12-hour light/dark cycle, with standard rodent chow and autoclaved water provided ad libitum. Following this initial acclimatization, all animals were further acclimatized to the treadmill environment for one week before the start of the exercise intervention.\u003c/p\u003e \u003cp\u003e All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of (Approval No. TJU-2021-029) and were conducted in accordance with the relevant guidelines and regulations to ensure animal welfare. All experiments were consistently performed during the light phase (e.g., between 09:00 and 12:00) to minimize circadian variations.\u003c/p\u003e \u003cp\u003eThe strain maintenance and breeding protocols adhere to institutional standards for genetically defined rodent models.\u003c/p\u003e \u003cp\u003eAll euthanasia procedures were performed strictly in accordance with the Guide for the Care and Use of Laboratory Animals (National Research Council, 8th Edition), the American Veterinary Medical Association (AVMA) Guidelines on Euthanasia (2020 Edition), and protocols approved by the Institutional Animal Care and Use Committee (IACUC). Mice were euthanized via intraperitoneal injection of sodium pentobarbital (150 mg/kg body weight) using a 1 mL syringe fitted with a 27-gauge needle. Loss of consciousness occurred within 30\u0026ndash;60 seconds, followed by respiratory arrest. Death was confirmed by the permanent absence of respiration, heartbeat, and corneal reflex. No secondary physical methods (e.g., cervical dislocation) were applied, as the administered dose ensured rapid, irreversible, and humane termination without distress. All procedures were conducted by trained personnel certified in laboratory animal euthanasia techniques. Sodium pentobarbital was the sole anesthetic agent used in this study for euthanasia purposes. No other anesthetics were administered to the animals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Exercise Intervention Protocols\u003c/h2\u003e \u003cp\u003eSedentary control mice (G1: SAMP6, G2: SAMR1) were sampled at 5 months of age. An additional set of sedentary controls (G3: SAMP6, G4: SAMR1) were housed under the same conditions and placed on a stationary treadmill to match the handling of the exercise group; these mice were sampled at 7 months of age.\u003c/p\u003e \u003cp\u003eAll exercise groups underwent a treadmill training protocol for 8 weeks, 5 sessions per week:\u003c/p\u003e \u003cp\u003eG5 (Low-intensity continuous): 12 m/min, 30 min/session\u003c/p\u003e \u003cp\u003eG6 (Moderate-intensity continuous, MICT): 15 m/min, 30 min/session\u003c/p\u003e \u003cp\u003eG7 (High-intensity interval training, HIIT): 5 cycles of 3 min at 12 m/min\u0026thinsp;+\u0026thinsp;3 min at 20 m/min (total 30 min/session)\u003c/p\u003e \u003cp\u003eG8 (Progressively intensified): 12 m/min for 2 weeks, increased by 1 m/min every 2 weeks until reaching 15 m/min (total 8 weeks)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Fecal Sample Collection and 16S rRNA Sequencing\u003c/h2\u003e \u003cp\u003eFecal samples were collected immediately after the 8-week intervention, frozen in liquid nitrogen, and stored at -80\u0026deg;C until DNA extraction.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. DNA Extraction and PCR Amplification\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted using the FastDNA\u0026reg; Spin Kit for Soil (MP Biomedicals) following the manufacturer\u0026rsquo;s instructions. A negative control (non-template control) was included during DNA extraction and PCR amplification to monitor potential contamination. DNA integrity was verified by 1% agarose gel electrophoresis. The V3\u0026ndash;V4 hypervariable region of the 16S rRNA gene was amplified using primers 338F (5\u0026prime;-ACTCCTACGGGAGGCAGCAG-3\u0026prime;) and 806R (5\u0026prime;-GGACTACHVGGGTWTCTAAT-3\u0026prime;) with sample-specific barcodes. PCR was performed using TransStart FastPfu DNA Polymerase on an ABI GeneAmp\u0026reg; 9700 thermocycler. Although the amplicon length is approximately 460 bp, paired-end sequencing (PE150) generates two 150 bp reads; overlapping regions were merged to reconstruct full-length amplicons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Library Construction and Sequencing\u003c/h2\u003e \u003cp\u003ePCR products were pooled in equimolar ratios and purified using the AxyPrep DNA Gel Extraction Kit (Axygen). Sequencing libraries were prepared using the TruSeq\u0026trade; DNA Sample Prep Kit and sequenced on the Illumina NextSeq 550 platform (PE150 mode).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Bioinformatics Analysis\u003c/h2\u003e \u003cp\u003eRaw paired-end reads were merged using FLASH (v1.2.11) with a minimum overlap of 10 bp and mismatch rate\u0026thinsp;\u0026le;\u0026thinsp;0.2. Quality filtering was performed using QIIME (v1.9.1) to remove reads with average quality score\u0026thinsp;\u0026lt;\u0026thinsp;20, ambiguous bases, or homopolymers\u0026thinsp;\u0026gt;\u0026thinsp;8 bp. Chimeric sequences were identified and removed using UCHIME (v4.2) against the Silva 138 database.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Operational taxonomic units (OTUs) Clustering\u003c/h2\u003e \u003cp\u003eOTUs were clustered at 97% similarity using UPARSE (v7.0.1090). Taxonomic assignment was performed with the RDP Classifier (v2.11) against the Silva 138 database (confidence threshold\u0026thinsp;\u0026ge;\u0026thinsp;0.7). We acknowledge that amplicon sequence variant (ASV)-based methods (e.g., DADA2) offer higher resolution; however, due to the nature of our historical dataset, OTUs clustering was applied. Future studies should adopt ASV approaches to improve taxonomic granularity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Diversity Analysis\u003c/h2\u003e \u003cp\u003eAlpha diversity indices (Shannon, Simpson, Chao1, coverage) were calculated using QIIME. Normality was assessed with the Shapiro\u0026ndash;Wilk test. Differences in alpha diversity were evaluated using one-way ANOVA with Tukey\u0026rsquo;s HSD post hoc test or the Kruskal\u0026ndash;Wallis test, as appropriate. Beta diversity was assessed using Bray\u0026ndash;Curtis dissimilarity and visualized via principal component analysis (PCA). Permutational multivariate analysis of variance (PERMANOVA; Adonis) and analysis of similarities (ANOSIM) were used to test group differences in community structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Differential Abundance\u003c/h2\u003e \u003cp\u003eLinear discriminant analysis effect size (LEfSe) was performed to identify taxa differentially enriched between groups (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;3.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4. Gut Microbiome Health Index (GMHI) and Microbial Dysbiosis Index(MDI)\u003c/h2\u003e \u003cp\u003eGMHI and MDI were calculated based on previously validated algorithms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] using species-level relative abundance profiles. GMHI\u0026thinsp;\u0026gt;\u0026thinsp;0 indicates a health-associated configuration; MDI\u0026thinsp;\u0026gt;\u0026thinsp;0 indicates deviation from a healthy reference.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data Availability\u003c/h2\u003e \u003cp\u003eThe 16S rRNA sequencing data have been deposited in the NCBI Sequence Read Archive under accession number PRJNA1379550. All other data are available from the corresponding author upon reasonable request.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Bioinformatics Tools\u003c/h2\u003e \u003cp\u003eQiime 1.9.1: OTU clustering, diversity analysis, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://qiime.org\u003c/span\u003e\u003cspan address=\"https://qiime.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eMega 7: Phylogenetic tree construction (Neighbor-Joining method), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.megasoftware.net/\u003c/span\u003e\u003cspan address=\"https://www.megasoftware.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eSILVA 138: Bacterial 16S rRNA database (\u0026gt;\u0026thinsp;200,000 sequences), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.arb-silva.de/\u003c/span\u003e\u003cspan address=\"https://www.arb-silva.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eUNITE 8: Fungal ITS database (\u0026gt;\u0026thinsp;150,000 sequences), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unite.ut.ee/\u003c/span\u003e\u003cspan address=\"https://unite.ut.ee/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eRDP 11.5: Archaeal classification database (\u0026gt;\u0026thinsp;10,000 sequences), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rdp.cme.msu.edu/\u003c/span\u003e\u003cspan address=\"http://rdp.cme.msu.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Sequencing Data Quality\u003c/h2\u003e \u003cp\u003eA total of 48 fecal samples (6 per group) were sequenced. After quality filtering, an average of 51,437 high-quality reads per sample were obtained, with an average read length of 419 bp and a validity rate\u0026thinsp;\u0026gt;\u0026thinsp;88% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Rarefaction curves indicated that sequencing depth was sufficient across all groups (coverage\u0026thinsp;\u0026gt;\u0026thinsp;96%), although G7 showed slightly lower OTU saturation at equivalent read depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of 16S rRNA sequencing data quality after filtering\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValid Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidity Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAvg Length (bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120,345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108,210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118,760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106,540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135,780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121,650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110,230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97,850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114,670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101,230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112,450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99,320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115,890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102,560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003eValues are means per group (n\u0026thinsp;=\u0026thinsp;6). Raw reads: paired-end reads before filtering; Valid reads: high-quality merged reads after quality control; Validity rate: (Valid reads / Raw reads) \u0026times; 100%; Avg length: mean length of merged reads (bp). All groups achieved validity rates\u0026thinsp;\u0026gt;\u0026thinsp;88% and average read lengths\u0026thinsp;\u0026gt;\u0026thinsp;370 bp, confirming sufficient sequencing quality for downstream analysis.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003eEach curve represents a single sample; colors indicate experimental groups. All curves approached plateau at \u0026gt;\u0026thinsp;30,000 reads, confirming sufficient sequencing depth. G7 (HIIT) exhibited slightly lower OTU saturation at equivalent read depth compared to G4 (SAMR1 sedentary) and G1 (SAMP6 sedentary), consistent with its reduced alpha diversity.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Gut Microbial Composition at the Phylum Level\u003c/h2\u003e \u003cp\u003eTo characterize the overall structure of the gut microbiota, we examined the relative abundance of dominant phyla across groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In sedentary SAMP6 mice (G1), Bacillota (formerly Firmicutes) was the most abundant phylum (38.7%), followed by Bacteroidota (27.4%) and Actinobacteria (12.3%). In contrast, sedentary SAMR1 mice (G4) exhibited a higher relative abundance of Bacteroidota (31.5%) and a lower proportion of Bacillota (22.7%). HIIT (G7) increased the relative abundance of Proteobacteria (18.4%) while reducing Bacillota (25.6%). MICT (G6) enriched \u003cem\u003eFaecalibaculum rodentium\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and showed a modest increase in Bacteroidota. The progressively intensified protocol (G8) resulted in a phylum-level profile intermediate between sedentary and HIIT groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cem\u003eStacked bar plot showing taxonomic composition at the phylum level in representative groups: 5-month-old sedentary SAMP6 (G1), 7-month-old sedentary SAMR1 (G4), and 7-month-old SAMP6 subjected to 8-week HIIT (G7) (n\u0026thinsp;=\u0026thinsp;6 per group). Only phyla with relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;1% are shown; others are grouped as \u0026ldquo;Others\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Alpha Diversity\u003c/h2\u003e \u003cp\u003eAlpha diversity indices were calculated to assess within-sample microbial richness and evenness (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Sedentary SAMR1 mice (G4) displayed the highest Shannon (4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12) and Chao1 (412\u0026thinsp;\u0026plusmn;\u0026thinsp;18) indices, consistent with their healthy aging phenotype. SAMP6 sedentary controls (G1, G3) showed intermediate diversity. Among exercise groups, HIIT (G7) was associated with significantly lower Shannon (3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09) and Chao1 (298\u0026thinsp;\u0026plusmn;\u0026thinsp;15) indices compared to both sedentary SAMP6 (G3) and SAMR1 (G4) controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ANOVA). MICT (G6) did not significantly alter alpha diversity relative to G3, whereas low-intensity (G5) and progressively intensified (G8) protocols resulted in modest, non-significant reductions. Coverage exceeded 96% in all groups, confirming adequate sequencing depth.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eAlpha diversity indices of gut microbiota across experimental groups (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM, n\u0026thinsp;=\u0026thinsp;6 per group).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSimpson\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChao1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoverage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e387\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e98.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e381\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e98.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e412\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e98.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e363\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e97.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e356\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e97.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e298\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e96.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e370\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e97.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cem\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM (n\u0026thinsp;=\u0026thinsp;6 per group). Shannon and Simpson indices reflect community diversity (higher values indicate greater diversity); Chao1 estimates species richness; Coverage represents the proportion of total OTUs detected. Statistical significance was assessed by one-way ANOVA with Tukey\u0026rsquo;s HSD post hoc test. G4 (SAMR1 sedentary) exhibited the highest diversity, while G7 (HIIT) showed significantly lower richness and diversity compared to both sedentary SAMP6 (G3) and SAMR1 (G4) controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Coverage\u0026thinsp;\u0026gt;\u0026thinsp;96% in all groups confirms adequate sequencing depth.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cem\u003eBox plots showing (A) Shannon diversity index and (B) Chao1 richness estimator across all eight groups (n\u0026thinsp;=\u0026thinsp;6 per group). Center line: median; box limits: IQR; whiskers: 1.5\u0026times; IQR. Statistical significance: one-way ANOVA with Tukey\u0026rsquo;s HSD. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. G4 showed significantly higher diversity/richness than all SAMP6 groups. Among exercise groups, G7 (HIIT) exhibited the lowest alpha diversity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 vs. G3 and G4).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Beta Diversity\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) based on Bray\u0026ndash;Curtis dissimilarity revealed distinct clustering of gut microbial communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). PC1 explained 11.99% of the total variance, and PC2 explained 6.43%. Sedentary SAMR1 mice (G4) separated clearly from all SAMP6 groups along PC1 (negative axis), indicating strong strain-specific compositional differences. Within SAMP6 mice, exercise groups (G5\u0026ndash;G8) were partially separated from sedentary controls (G3) along PC2. HIIT (G7) samples clustered separately from both sedentary and other exercise groups, suggesting a distinct compositional shift. MICT (G6) and progressively intensified (G8) groups overlapped with each other and with G5, indicating less pronounced remodeling. PERMANOVA confirmed significant group effects (Adonis R\u0026sup2; = 0.25, p\u0026thinsp;=\u0026thinsp;0.001; ANOSIM R\u0026thinsp;=\u0026thinsp;0.43, p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. \u003cb\u003ePrincipal component analysis (PCA) of gut microbial community structure.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003ePCA ordination plot based on Bray\u0026ndash;Curtis dissimilarity at the OTU level. Each point represents a fecal sample (n\u0026thinsp;=\u0026thinsp;6 per group). Colors indicate experimental groups. PC1: 11.99%, PC2: 6.43% of total variance. G4 clusters separately from all SAMP6 groups along PC1; G7 forms a distinct cluster along PC2. PERMANOVA: R\u0026sup2; = 0.25, p\u0026thinsp;=\u0026thinsp;0.001.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Shared and Unique OTUs\u003c/h2\u003e \u003cp\u003eA flower plot was constructed to visualize shared and unique OTUs across all eight groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A total of 350 OTUs were shared among all groups, representing the core gut microbiota conserved across aging and exercise interventions. Group-specific OTUs highlighted key differences: G4 (SAMR1 sedentary) harbored the highest number of unique OTUs (69), including OTU272 classified as \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e (2.1% relative abundance). G7 (HIIT) uniquely enriched 16 OTUs, including OTU417 (\u003cem\u003eEggerthella lenta\u003c/em\u003e, 1.8%) and showed depletion of several Lachnospiraceae OTUs. G6 and G8 exhibited fewer unique OTUs (16 and 24, respectively) and shared most of their microbiota with sedentary SAMP6 controls.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. \u003cem\u003eFlower plot showing distribution of shared and group-specific OTUs across eight groups (n\u0026thinsp;=\u0026thinsp;6 per group). Central number (350): OTUs detected in all groups (core microbiota). Peripheral numbers: OTUs uniquely identified in each group. G4 has the highest number of unique OTUs (69), including Akkermansia muciniphila; G7 uniquely enriches 16 OTUs, including Eggerthella lenta.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6 GMHI and MDI\u003c/h2\u003e \u003cp\u003eTo assess the health relevance of exercise-induced compositional shifts, we calculated GMHI and MDI for each group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSedentary SAMR1 mice (G4) and all exercised SAMP6 groups (G6, G7, G8) exhibited positive GMHI scores, with HIIT (G7) showing the highest value (+\u0026thinsp;1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10). In contrast, sedentary SAMP6 groups (G1, G3) displayed negative GMHI scores, indicating a dysbiosis-associated configuration.\u003c/p\u003e \u003cp\u003eMDI values were consistent with this pattern: sedentary SAMP6 mice had negative MDI (reference), while HIIT (G7) showed a slight shift toward positive MDI, suggesting mild deviation from the sedentary SAMP6 baseline. G4 and other exercise groups displayed intermediate MDI values.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGut Microbiome Health Index (GMHI) across experimental groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGMHI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Values are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM (n\u0026thinsp;=\u0026thinsp;6 per group). GMHI is a validated index based on the prevalence of health- and disease-associated microbial species; positive values indicate a microbiota profile resembling healthy individuals, while negative values indicate dysbiosis-associated profile. Sedentary SAMR1 mice (G4) and all exercised SAMP6 groups (G6, G7, G8) exhibited positive GMHI scores, with HIIT (G7) showing the highest value. In contrast, sedentary SAMP6 groups (G1, G3) displayed negative GMHI scores.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMicrobial Dysbiosis Index (MDI) across experimental groups\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. \u003cem\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM (n\u0026thinsp;=\u0026thinsp;6 per group). MDI quantifies the degree of deviation from a healthy baseline microbiota; higher values indicate greater dysbiosis. Sedentary SAMP6 mice (G1, G3) exhibited negative MDI values (non-dysbiotic reference), while HIIT (G7) showed a slight shift toward positive MDI. G4 (SAMR1) and other exercise groups displayed intermediate values. Together with GMHI, these data indicate that exercise-induced compositional shifts are associated with a more health-aligned microbial profile, despite reduced alpha diversity in G7.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. \u003cem\u003eBox plots showing GMHI scores in fecal samples from all eight groups (n\u0026thinsp;=\u0026thinsp;6 per group). Center line: median; box limits: interquartile range (IQR); whiskers: 1.5 \u0026times; IQR; points: individual mice. GMHI\u0026thinsp;\u0026gt;\u0026thinsp;0 indicates a health-associated microbiota profile; GMHI\u0026thinsp;\u0026lt;\u0026thinsp;0 indicates dysbiosis-associated profile. Sedentary SAMR1 mice (G4) and all exercised SAMP6 groups (G6, G7, G8) exhibited positive GMHI scores, with HIIT (G7) showing the highest value. Sedentary SAMP6 groups (G1, G3) displayed negative GMHI scores. **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (one-way ANOVA with Tukey\u0026rsquo;s HSD).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. \u003cem\u003eBox plots showing MDI scores in fecal samples from all eight groups (n\u0026thinsp;=\u0026thinsp;6 per group). Center line: median; box limits: IQR; whiskers: 1.5 \u0026times; IQR; points: individual mice. MDI quantifies deviation from a healthy baseline; higher values indicate greater dysbiosis. Horizontal dashed line represents MDI\u0026thinsp;=\u0026thinsp;0 (healthy reference). Sedentary SAMP6 mice (G1, G3) exhibited negative MDI values, while HIIT (G7) showed a slight shift toward positive MDI. G4 (SAMR1) and other exercise groups displayed intermediate values. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs. G3 (ANOVA).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Differential Taxa Enrichment\u003c/h2\u003e \u003cp\u003eLEfSe analysis was performed to identify taxa differentially enriched between key comparisons (G4 vs. G3, G7 vs. G3, G6 vs. G3; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn sedentary SAMR1 mice (G4), \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e (LDA\u0026thinsp;=\u0026thinsp;4.21, p\u0026thinsp;=\u0026thinsp;0.005) and \u003cem\u003eBifidobacterium longum\u003c/em\u003e (LDA\u0026thinsp;=\u0026thinsp;3.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly enriched.\u003c/p\u003e \u003cp\u003eIn HIIT-treated SAMP6 mice (G7), Eggerthella lenta (LDA\u0026thinsp;=\u0026thinsp;3.76, p\u0026thinsp;=\u0026thinsp;0.003) and \u003cem\u003eLactobacillus johnsonii\u003c/em\u003e (LDA\u0026thinsp;=\u0026thinsp;3.45, p\u0026thinsp;=\u0026thinsp;0.02) were overrepresented.\u003c/p\u003e \u003cp\u003eModerate-intensity training (G6) enriched \u003cem\u003eFaecalibaculum rodentium\u003c/em\u003e (LDA\u0026thinsp;=\u0026thinsp;3.38, p\u0026thinsp;=\u0026thinsp;0.01) and several unclassified Ruminococcaceae OTUs.\u003c/p\u003e \u003cp\u003eNo taxa reached the LDA threshold in G5 or G8 comparisons.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferentially enriched bacterial taxa identified by LEfSe (LDA\u0026thinsp;\u0026gt;\u0026thinsp;3.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLDA score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAkkermansia muciniphila\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBifidobacterium longum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEggerthella lenta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLactobacillus johnsonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFaecalibaculum rodentium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. \u003cem\u003eOnly taxa meeting the LDA threshold in key pairwise comparisons (G4 vs. G3, G7 vs. G3, G6 vs. G3) are shown. G4 (healthy aging control) enriched in Akkermansia muciniphila and Bifidobacterium longum; G7 (HIIT) enriched in Eggerthella lenta and Lactobacillus johnsonii; G6 (MICT) enriched in Faecalibaculum rodentium. No taxa reached the LDA threshold in G5 or G8 comparisons.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Inter-group Distribution of Dominant Genera\u003c/h2\u003e \u003cp\u003eTo visualize the relationship between experimental groups and dominant bacterial genera, a Circos plot was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eLactobacillu\u003c/em\u003e showed distinct distribution patterns between sedentary and exercised groups. HIIT (G7) exhibited a higher proportion of \u003cem\u003eEggerthella\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e, consistent with LEfSe results. The width of ribbons represents the relative contribution of each genus to each group; the length of outer bars indicates total relative abundance across all groups. HIIT (G7) shows higher proportions of \u003cem\u003eEggerthella\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e, consistent with Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. \u003cem\u003eCircos plot visualizing the relationship between experimental groups and dominant bacterial genera. The width of the ribbons represents the relative contribution of each genus to each group, and the length of the outer bars indicates the total relative abundance of the genus across all groups. Bacteroides and Lactobacillus showed distinct distribution patterns between sedentary and exercised groups. Akkermansia was predominantly observed in the sedentary SAMR1 group (G4), consistent with LEfSe results (\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cem\u003e). HIIT (G7) exhibited a higher proportion of Eggerthella and Lactobacillus.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAging is a critical public health issue, characterized by a decline in physiological functions and an increased susceptibility to age-related diseases. Physical exercise is widely recognized as an effective intervention to mitigate these effects, yet the optimal intensity and underlying mechanisms\u0026mdash;particularly those involving the gut microbiota\u0026mdash;remain incompletely understood [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In this study, we comprehensively compared the effects of four treadmill training protocols on the gut microbial composition of aging SAMP6 mice, with SAMR1 mice serving as healthy controls.\u003c/p\u003e \u003cp\u003eOur results demonstrate that exercise intensity is a major determinant of gut microbiota remodeling. HIIT (G7) induced the most pronounced compositional shift, characterized by enrichment of Eggerthella lenta and Lactobacillus johnsonii, increased Proteobacteria abundance, and a marked reduction in overall alpha diversity. MICT (G6) produced more subtle changes, enriching Faecalibaculum rodentium, while low-intensity (G5) and progressively intensified (G8) protocols had intermediate effects. These findings align with previous reports that HIIT elicits distinct physiological responses compared to continuous endurance training [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe enrichment of Akkermansia muciniphila was observed exclusively in the healthy aging control group (G4), not in any exercised SAMP6 group. This suggests that this mucin-degrading bacterium may represent an intrinsic feature of the SAMR1 strain\u0026rsquo;s healthy gut ecosystem rather than an exercise-induced effect in the SAMP6 background. Its established roles in improving metabolic health and gut barrier function [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] are consistent with the superior metabolic phenotype of SAMR1 mice [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], but our data indicate that exercise, at least in this aging-prone model, does not recapitulate this specific microbial signature.\u003c/p\u003e \u003cp\u003eThe concurrent reduction in alpha diversity and positive GMHI in the HIIT group presents an intriguing dissociation. While some have proposed that loss of diversity can be offset by proliferation of functionally critical taxa (\u0026ldquo;functional specialization\u0026rdquo;) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], our current dataset cannot distinguish this interpretation from alternative explanations such as exercise-induced physiological stress leading to ecological simplification. Crucially, GMHI\u0026mdash;a validated composite index based on prevalence of health- and disease-associated species\u0026mdash;was positive in all exercised SAMP6 groups and highest in HIIT, directly demonstrating that the compositional shifts are associated with a health-aligned microbial configuration. This finding substantially strengthens the interpretation that HIIT-induced remodeling, despite lower alpha diversity, is not merely dysbiotic but may reflect adaptive specialization.\u003c/p\u003e \u003cp\u003eImportantly, our study highlights the distinction between compositional associations and causal mediation. Although we observed robust correlations between exercise intensity, specific taxa, and positive GMHI, we did not collect paired host phenotype data in the same animals used for microbiota analysis. Therefore, any inference that the microbial shifts mediate exercise benefits remains speculative. We explicitly frame our conclusions in terms of association rather than causation, and we emphasize the need for fecal microbiota transplantation (FMT) experiments to test causality [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur multi-intensity design also revealed that the progressively intensified protocol (G8), despite reaching the same terminal speed as MICT, did not replicate the microbial effects of either MICT or HIIT (G8 exhibited alpha diversity and GMHI scores comparable to G5/G6, Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests that the pattern of intensity\u0026mdash;specifically the intermittent bursts of high speed\u0026mdash;may be more critical than cumulative workload in driving gut microbiota remodeling. This observation is consistent with studies showing that interval training activates AMPK and PGC-1α pathways more robustly than constant-load exercise [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the sample size (n\u0026thinsp;=\u0026thinsp;6 per group) is modest, and while adequate for detecting large compositional differences, it may limit statistical power for subtle taxa shifts. Second, our use of OTU clustering (97% similarity) rather than ASV resolution may have reduced taxonomic precision; future studies should adopt denoising methods such as DADA2. Third, we did not perform functional metagenomics or metabolomics, preventing us from assessing whether the observed compositional changes translate into altered microbial metabolic capacity. Fourth, as noted above, the lack of paired host physiological and cognitive data in the sequenced cohorts precludes formal mediation analysis.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study provides a comprehensive, intensity-resolved description of exercise-induced gut microbiota shifts in a clinically relevant aging model, validated by established health indices (GMHI, MDI). It underscores that exercise intensity is not merely a quantitative parameter but a qualitative determinant of microbial ecology. Our findings support the potential of HIIT as a candidate precision exercise strategy for older adults, while also highlighting that \u0026ldquo;more\u0026rdquo; (intensity) is not always \u0026ldquo;better\u0026rdquo; in terms of diversity.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study demonstrates that different exercise intensities are associated with distinct compositional changes in the gut microbiota of aging SAMP6 mice. High-intensity interval training is associated with enrichment of Eggerthella lenta and Lactobacillus johnsonii, positive GMHI, and reduced alpha diversity. In contrast, enrichment of Akkermansia muciniphila was unique to the healthy aging control group (G4) and was not induced by any exercise protocol. Moderate-intensity continuous training enriches Faecalibaculum rodentium with milder compositional shifts, while progressive intensity escalation fails to recapitulate the HIIT-associated microbial profile. These findings suggest that exercise prescription for gut microbiota modulation should consider both the absolute workload and the pattern of intensity application.\u003c/p\u003e \u003cp\u003eCrucially, our conclusions are framed as descriptive associations, not causal mechanisms. To advance the field, future research must integrate paired host phenotyping, functional metagenomics, and interventional FMT studies. Only through such approaches can we determine whether exercise-induced gut microbiota remodeling is a true mediator of healthy aging or merely a correlate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll animal procedures were approved by the Institutional Animal Care and Use Committee of Tianjin University (Approval No. TJU-2021-029) and conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe 16S rRNA gene sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) under accession number [PRJNA1379550]. Other datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by [Key Research and Development Plan for Active Health and Population Aging Response, 2022YFC3601904]. The funder had no role in study design, data collection, analysis, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eXiang Tang, Pengfei Li, and Hongzhi Gao contributed equally to this work and share first authorship.\u003c/p\u003e\n\u003cp\u003eXiang Tang: Conceptualization, Methodology, Animal experiments, Data curation, Formal analysis, Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003ePengfei Li: Methodology, Bioinformatics analysis, Visualization, Validation, Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003eHongzhi Gao: Investigation, Sample collection, Microbiome data processing, Statistical analysis, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eXinlong Ma: Supervision, Project administration, Funding acquisition, Conceptualization, Writing \u0026ndash; review \u0026amp; editing, Final approval of the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePlaza-Florido A, P\u0026eacute;rez-Prieto I, Lucia A. The aging lipidome: exercise is medicine. Trends Mol Med. 2024;30(11):1001\u0026ndash;3. https://doi.org/10.1016/j.molmed.2024.06.006\u003c/li\u003e\n\u003cli\u003eSaucedo Marquez CM, Vanaudenaerde B, Troosters T, Wenderoth N. High-intensity interval training evokes larger serum BDNF levels compared with intense continuous exercise. J Appl Physiol. 2015;119(12):1363\u0026ndash;73. https://doi.org/10.1152/japplphysiol.00126.2015\u003c/li\u003e\n\u003cli\u003eMailing LJ, Allen JM, Buford TW, Fields CJ, Woods JA. Exercise and the gut microbiome: a review of the evidence, potential mechanisms, and implications for human health. Exerc Sport Sci Rev. 2019;47(2):75-85. https://doi.org/10.1249/JES.0000000000000183\u003c/li\u003e\n\u003cli\u003eAllen JM, Mailing LJ, Niemiro GM, et al. Exercise alters gut microbiota composition and function in lean and obese humans. Med Sci Sports Exerc. 2018;50(4):747-757. https://doi.org/10.1249/MSS.0000000000001495\u003c/li\u003e\n\u003cli\u003eNiimi K, Takahashi E, Itakura C. Adiposity-related biochemical phenotype in senescence-accelerated mouse prone 6 (SAMP6). Comp Med. 2009;59(5):431-436. https://pubmed.ncbi.nlm.nih.gov/19887026/\u003c/li\u003e\n\u003cli\u003eCastillo CA, Albasanz JL, Le\u0026oacute;n D, Ferrer-Montiel A, Mart\u0026iacute;n M. Age-related expression of adenosine receptors in brain from the senescence-accelerated mouse. Exp Gerontol. 2009;44(6-7):453-461. https://doi.org/10.1016/j.exger.2009.04.006\u003c/li\u003e\n\u003cli\u003eNiimi K, Takahashi E, Itakura C. Age-related difference in nociceptive behavior between SAMP6 and SAMR1 strains. Neurosci Lett. 2008;444(1):60-63. https://doi.org/10.1016/j.neulet.2008.08.003\u003c/li\u003e\n\u003cli\u003eGibala MJ, Little JP, Macdonald MJ, Hawley JA. Physiological adaptations to low-volume, high-intensity interval training in health and disease. J Physiol. 2012;590(5):1077-1084. https://doi.org/10.1113/jphysiol.2011.224725\u003c/li\u003e\n\u003cli\u003eGupta VK, Kim M, Bakshi U, Cunningham KY, Davis JM 3rd, Lazaridis KN, et al. A predictive index for health status using species-level gut microbiome profiling. Nat Commun. 2020;11(1):4635. https://doi.org/10.1038/s41467-020-18476-8\u003c/li\u003e\n\u003cli\u003eGeerlings SY, Kostopoulos I, de Vos WM, Belzer C. The human gut microbiota and its interactive capacity to metabolize dietary glycans. Physiol Rev. 2021;101(3):1107-1179. https://doi.org/10.1152/physrev.00018.2020\u003c/li\u003e\n\u003cli\u003eBrisebois MF, Biggerstaff KD, Nichols DL. Cardiorespiratory responses to acute bouts of high-intensity functional training and traditional exercise in physically active adults. J Sports Med Phys Fitness. 2022;62(2):199-206. https://doi.org/10.23736/S0022-4707.21.12115-2\u003c/li\u003e\n\u003cli\u003eLittle JP, Safdar A, Wilkin GP, Tarnopolsky MA, Gibala MJ. A practical model of low-volume high-intensity interval training induces mitochondrial biogenesis in human skeletal muscle: potential mechanisms. J Physiol. 2010;588(Pt 6):1011-1022. https://doi.org/10.1113/jphysiol.2009.181743\u003c/li\u003e\n\u003cli\u003eCani PD, de Vos WM. Next-generation beneficial microbes: the case of Akkermansia muciniphila. Front Microbiol. 2017;8:1765. https://doi.org/10.3389/fmicb.2017.01765\u003c/li\u003e\n\u003cli\u003eDao MC, Everard A, Aron-Wisnewsky J, et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut. 2016;65(3):426-436. https://doi.org/10.1136/gutjnl-2014-308778\u003c/li\u003e\n\u003cli\u003eValdes AM, Walter J, Segal E, Spector TD. Role of the gut microbiota in nutrition and health. BMJ. 2018;361:k2179. https://doi.org/10.1136/bmj.k2179\u003c/li\u003e\n\u003cli\u003eCryan JF, O\u0026apos;Riordan KJ, Cowan CSM, et al. The microbiota-gut-brain axis. Physiol Rev. 2019;99(4):1877-2013. https://doi.org/10.1152/physrev.00018.2018\u003c/li\u003e\n\u003cli\u003eScheiman J, Luber JM, Chavkin TA, et al. Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism. Nat Med. 2019;25(7):1104-1109. https://doi.org/10.1038/s41591-019-0485-4\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Exercise intensity, Gut microbiota, Aging, SAMP6 mouse, HIIT, Akkermansia muciniphila, 16S rRNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-8899298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8899298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eThe aging population faces increased risks of metabolic and cognitive decline, with emerging evidence linking gut microbiota to exercise-mediated health benefits. Senescence-accelerated mouse prone 6 (SAMP6) exhibits early aging phenotypes, while senescence-resistant 1 (SAMR1) serves as a healthy control.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThirty-six male SAMP6 and twelve SAMR1 mice (5- and 7-month-old) were assigned to eight groups (n\u0026thinsp;=\u0026thinsp;6/group). Seven-month-old SAMP6 mice underwent 8 weeks of treadmill training: low-intensity continuous (12 m/min), moderate-intensity continuous (15 m/min), high-intensity interval training (HIIT; 12/20 m/min), or progressively intensified protocol (12\u0026rarr;15 m/min). Fecal samples were collected post-intervention for 16S rRNA sequencing (V3\u0026ndash;V4 region). Alpha diversity, beta diversity, and taxonomic composition were analyzed. Gut microbiome health index (GMHI) and microbial dysbiosis index (MDI) were calculated to assess health-associated microbial configuration.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eSedentary SAMR1 mice exhibited higher alpha diversity than SAMP6 controls, indicating a link between microbial richness and healthy aging. HIIT significantly restructured gut microbiota composition in SAMP6 mice, characterized by enrichment of \u003cem\u003eEggerthella lenta\u003c/em\u003e and \u003cem\u003eLactobacillus johnsonii\u003c/em\u003e, increased Proteobacteria abundance, and reduced overall alpha diversity. Moderate-intensity continuous training (MICT) enriched \u003cem\u003eFaecalibaculum rodentium\u003c/em\u003e with milder compositional shifts. Progressively intensified training resulted in an intermediate microbial phenotype. GMHI was positive in SAMR1 and all exercised SAMP6 groups, with HIIT showing the highest score; sedentary SAMP6 groups exhibited negative GMHI. MDI values were consistent with these health-associated shifts.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eHIIT is associated with distinct compositional shifts and improved gut microbiome health indices in aging-prone mice, despite reduced alpha diversity. These findings highlight exercise intensity as a critical determinant of gut microbial ecology and support HIIT as a candidate precision exercise strategy for older adults. Future studies employing fecal microbiota transplantation are necessary to test causality.\u003c/p\u003e","manuscriptTitle":"Differential Gut Microbial Signatures Induced by Exercise Intensity and Mode in Aging SAMP6 Mice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 16:13:02","doi":"10.21203/rs.3.rs-8899298/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"122197955535783228650227188143230825103","date":"2026-05-11T23:04:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219091443623193668202525484303855738503","date":"2026-04-25T21:48:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149923446457924519371694528421909818230","date":"2026-04-25T02:48:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118382418667995671296019353320458448910","date":"2026-04-24T03:06:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251909126430626205433586509993520162720","date":"2026-02-26T15:07:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T14:56:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-21T09:41:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-20T09:06:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-19T10:07:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-02-19T10:01:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"66a5b4ac-d676-4164-af91-c2339fbc55ed","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"122197955535783228650227188143230825103","date":"2026-05-11T23:04:15+00:00","index":77,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-03T16:13:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 16:13:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8899298","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8899298","identity":"rs-8899298","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}