Impact of physical activity level on adult gut microbiome composition and metabolic function

Impact of physical activity level on adult gut microbiome composition and metabolic function | 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 Article Impact of physical activity level on adult gut microbiome composition and metabolic function Ana Viera, Adolfo Pozo, Ximena Lopez, Javier Morán, Carolina Sanchez-Doñas, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7595052/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Physical activity has been linked to improvements in metabolic health and gut microbiota composition. However, evidence connecting physical activity level (PAL) with the microbial metabolic potential in adults remains limited, particularly in South American populations. Methods We employed Gut Metabolic Modules (GMM) functional inference on 16S V4 rRNA data to elucidate how PAL shapes the gut microbiota’s metabolic potential in 233 Chilean adults. PAL was assessed via the self-reported IPAQ-SF questionnaire and categorized into low, medium, and high levels. Stratification by body mass index (BMI) and evaluation of physical activity volume (PAV) were also performed. Results No significant differences in overall microbial diversity were observed by PAL alone; however, when stratified by BMI, PAL was associated with shifts in the relative abundance of bacterial genera including Dorea , Holdemanella and Parabacteroides . Functionally, we identified 39 GMMs (37.8% of those evaluated) across the cohort, of which 18 modules differed by PAL, particularly protein and carbohydrate degradation pathways. PAV was positively associated with GMMs linked to energy metabolism, notably butyrate and propionate production. Conclusions PAL, especially when considered alongside BMI and activity volume, modulates the gut microbiome’s metabolic potential. As the largest Chilean cohort to apply 16S based functional profiling, this study provides foundational evidence from Latin America, highlighting physical activity as a modifiable factor for shaping microbiota functionality and host metabolic health. Health sciences/Gastroenterology Biological sciences/Microbiology Physical activity level Gut metabolic modules Gut microbiome Metabolic potential Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background The global increase in overweight and obesity has paralleled sociocultural development, prompting numerous efforts to improve dietary and physical activity habits especially in developed countries ( 1 – 5 ). Physical activity has been widely recognized for its role in preventing metabolic, musculoskeletal, neurodegenerative, inflammatory, and psychosocial diseases, and in promoting mood stability and overall quality of life ( 6 – 10 ). Concurrently, the human gut microbiota has emerged as a key regulator of host metabolism, immunity, and homeostasis ( 11 ). Disruptions in microbial balance, known as dysbiosis, are linked to gastrointestinal disorders and broader health outcomes ( 12 , 13 ). Specific microbial genera, such as Faecalibacterium , Eubacterium , Coprococcus , and Roseburia , are known for producing short-chain fatty acids (SCFAs), which serve as crucial energy sources and signaling molecules ( 14 ). Studies suggest that physical activity may influence gut microbiota composition and metabolic function. For instance, athletes display distinct microbial profiles, while sedentary individuals and those with metabolic disorders show reduced diversity ( 15 , 16 ). However, much of this evidence comes from North America and Europe. A Swedish study found higher Escherichia coli abundance in sedentary individuals and greater SCFA-producing bacteria in those with higher physical activity levels (PALs) ( 17 ), but research in Latin America remains scarce despite high levels of physical inactivity in the region ( 17 – 19 ). We evaluated how PAL (total minutes per week), physical activity frequency (PAF; days per week) and physical activity volume (PAV; MET-minutes per week), when considered alongside body mass index (BMI), relate to microbial diversity, taxonomy, and function. We observed no change in overall microbial diversity by PAL alone; however, when stratified by BMI, we identified 18 differential gut metabolic modules (GMMs), including carbohydrate degradation and SCFA production pathways, and shifts in the relative abundance of genera such as Dorea , Holdemanella , and Parabacteroides . This study provides novel insights into the relationship between PAL, microbial metabolic potential, and host phenotype. It represents the first report from Latin America to explore these associations. Our results show that differences in microbial composition and function are associated with PAL when BMI is considered. Specifically, we observed alterations in GMMs related to carbohydrate, protein, and lipid metabolism, and changes in the abundance of bacterial genera. These findings contribute foundational knowledge that may inform future strategies to improve metabolic health through microbiota modulation. 2. Methods Study Population Participants in this study were Chilean men and women aged 18 to 70 years, residing in Chile, who had not lived abroad for more than three consecutive years. These individuals provided informed consent for the analysis of their stool microbiome. Informed consent was obtained from all the participants. To establish a health baseline, we excluded individuals with a Body Mass Index (BMI) below 18.5 or above 30, classifying them as normal weight and overweight based on participants' BMI. Additionally, participants who had used certain medications in the past month (antibiotics, antifungals, corticosteroids, cytokines, immunosuppressants, commercially available probiotics, including daily use and mouthwashes) were excluded, as well as those with a history of specific diagnosed diseases (pulmonary, cardiovascular, gastrointestinal, hepatic or renal diseases, HIV, Hepatitis B and/or C, autoimmune diseases). Individuals with a history of gastrointestinal tract diseases such as ulcerative colitis, Crohn's disease, irritable bowel syndrome, colitis, gastritis, and infections caused by Clostridium difficile or Helicobacter pylori were also excluded, along with those who had a history of major gastrointestinal surgeries. Pregnant, nursing, or breastfeeding women were likewise excluded. Furthermore, participants who could not independently understand the physical activity questionnaire were excluded. These criteria were carefully established to minimize the presence of factors known to significantly influence the composition of the gut microbiota, ensuring a clearer understanding of the specific relationship between physical activity and the gut microbiota. Of the participants with stool microbiome data (n = 251), 233 completed the physical activity questionnaire and had BMI data; the remaining 18 (7.2%) were excluded due to missing information. Missing data were handled by listwise deletion, and no imputation was applied. Participants in this study were drawn from Chile’s three macro-zones: the north (7.29%, 17), the center (67.38%, 157), and the south (25.33%, 59), covering a large part of the national territory. The sample was not designed to be proportionally representative at the national level; representation is closer to population distribution in the center and south, whereas the north is underrepresented. Overall, 84.8% of participants reported university-level or higher education, which may limit generalizability to the national population. It is important to note that health information was self-reported by the participants and was not collected by a medical professional. Consequently, the reliability of the data depends on the accuracy of the participants' responses. This project has been approved by the Institutional Ethical Scientific Committee of Universidad Mayor No. 0274. All methods were performed in accordance with the relevant guidelines and regulations. Data Collection To assess the physical activity habits of the participants, we utilized the IPAQ-SF ("International Physical Activity Questionnaire - Short Form"; Appendix 1). This instrument was validated through a systematic review in 2011 ( 20 ) and subsequently translated into Spanish for populations in Spain ( 21 ) and Mexico ( 22 ). It has also been applied to the Chilean population in local studies ( 23 ). This tool is designed to gather specific information from the general population and determine their PAL, classifying them as low, medium, or high based on details about exercise intensity (light, moderate, and vigorous), physical activity frequency (PAF; sessions per week), and time spent per session (minutes) ( 24 ). These variables, considered independently in previous studies, provide additional value for moderate to vigorous intensities ( 25 ), frequencies exceeding three times per week ( 26 ), or total weekly time spent ( 25 , 27 ). We administered the instrument to the study population using online forms, collecting data on the intensity, frequency, and weekly duration of physical activity for each participant. METs (Metabolic Equivalent of Task) are units of metabolic index used to classify activity intensity. The calculation was performed by multiplying the number of minutes per session by the number of sessions per week, yielding the weekly metabolic index units (METs x minutes per session x sessions per week = weekly metabolic index units). Activity intensity was classified as follows: low intensity was assigned 3.3 METs, medium intensity 4 METs, and high intensity 8 METs. Participants classified as having a low level of physical activity either did not engage in physical activity or did not meet the criteria for medium or high levels. The medium level of physical activity applies to those who engage in physical activity at least 5 times per week at a low to medium intensity for at least 30 minutes per session, or at least 3 times per week at a high intensity for 25 minutes, or a combination of medium to high intensities resulting in an energy expenditure of 600 METs/min/week. The high level of physical activity is defined as engaging in high physical activity at least 3 days per week, achieving an energy expenditure of 1500 METs/min/week, or participating in 7 or more sessions per week of any combined intensity that reaches 3000 METs/min/week. Sample Collection Each participant was provided with a sampling kit that included tubes for gut microbiome collection (OMR-200; DNA Genotek) and a toilet accessory (OM-AC1; DNA Genotek). The gut microbiome sampling tube and the accessory paper are designed to facilitate self-collection of samples by participants in their own homes, maximizing convenience and minimizing potential disruptions to their daily routines. Once collected, the samples were returned to the research team and transported to the laboratory, with this process scheduled to not exceed two weeks. Upon arrival at the laboratory, samples were stored at room temperature and protected from light until DNA extraction was conducted within a month of sample collection. This home collection approach was essential for maintaining sample integrity and ensuring the reliability of our study, which focuses on the intricate relationship between physical activity and the composition of the gut microbiota. Sample Processing DNA extraction was carried out using the Quick-DNA Fecal/Soil Microbe kit from ZYMO RESEARCH (D6010), in accordance with the manufacturer's instructions. To verify the integrity of the extracted DNA, quality control was performed using 1% agarose gel electrophoresis. Sequencing DNA sequencing was conducted at Novogene (Beijing, China) using the Illumina NovaSeq PE250 platform. The V3-V4 region of the 16S rRNA gene was sequenced using the primers 341F: CCTAYGGGRBGCASCAG and 806R: GGACTACHVGGGTWTCTAAT. Data Analysis Data analysis was performed in R version 4.1.2 within RStudio, utilizing the DADA2 and Phyloseq packages ( 28 , 29 ). We implemented a preprocessing step on the reads to remove the first 30 bases and discard low-quality reads. Standard software parameters were used, except for maxEE and truncQ, which were set to 2. For the learning error model, 1x10 8 bases were utilized. The taxonomy of ASVs was assigned using the SILVA V138 database as a reference. Additionally, ASVs that could not be taxonomically identified were discarded. Each sample contained at least 10,000 reads. Subsequent analyses included the Centered Log Ratio (CLR) transformation of the ASV matrix. We used the iNext library to calculate Shannon entropy index. A Principal Component Analysis (PCA) was conducted on CLR-transformed data using Euclidean distances, a technique previously cited in the literature for analyzing compositional data. For compositional analyses, we calculated relative abundances at the phylum and genus levels. PISCRUSt2 (version 2.5.2) was executed with default parameters to infer genetic content using the KEGG database as a reference ( 30 – 32 ), and GMMs were determined using the omixerRpm and Tjazi packages. The GMM table was transformed to CLR using the vegan package. Statistical comparisons were performed as described in the following section, including correction for multiple testing using the Benjamini-Hochberg method. Statistical Analysis The Wilcoxon test was used for statistical comparisons, conducted in R. Significance levels were denoted as *p < 0.05, **p < 0.01, and N.S. for non-significant results. P-values were adjusted for multiple comparisons using the Benjamini-Hochberg method to control the false discovery rate (FDR), with a q-value threshold of < 0.15 considered significant. Asterisks in the figures indicate significance based on unadjusted p-values, and all reported associations also met the adjusted q-value criterion (q < 0.15). Analyses were conducted in a blinded manner. A participant was considered to have provided a sample if they completed both questionnaires. 3. Results Studied population, anthropometric variables, and physical activity. This study included 149 female and 84 male Chilean participants, aged 19 to 69 years (mean = 34 years), who met the inclusion criteria, did not meet any exclusion criteria, and were recruited from multiple zones across Chile (for more details see Methodology). The average BMI was 27.1 ± 5.4 kg/m², with 57% classified as normal weight and 43% as overweight (Table 1 ). Physical activity variables were assessed using the self-reported IPAQ-SF, which categorizes PALs into low (L-PAL), medium (M-PAL), or high (H-PAL), based on validated MET scores. ( 27 ). In our sample, 39% of participants were classified as L-PAL, 34.4% M-PAL, and 26.6% as H-PAL. In this analysis, we focused exclusively on PAL, derived from the IPAQ-SF, as a proxy for total physical activity. Although the questionnaire also captures sedentary behavior and strength training, these were excluded to prioritize the relationship between general activity patterns and the gut microbiome. Future studies may further explore these complementary dimensions. From participants’ responses, we derived the PAF as the total number of activity instances per week, summing vigorous, moderate, and walking activities. The overall mean PAF was 8.7 ± 3.9 times/week. When stratified by PAL, participants with H-PAL had a training frequency of 12.3 ± 3.6 times/week, those with M-PAL trained 8.3 ± 3.3 times/week, and those with L-PAL trained 6.5 ± 2.7 times/week. This measure reflects the total number of activity occurrences but not necessarily the number of distinct days of activity. We calculated the PAV as the total number of minutes of activity per week. The mean PAV in our sample was 504.3 ± 403.9 min/week, with the following distribution: H-PAL reported 913.7 ± 512.7 min/week of physical activity, those with M-PAL reported 468.8 ± 188.8 min/week, and those with L-PAL reported 256.7 ± 177.4 min/week. Table 1 Characteristics of the study population stratified by sex and physical activity variables (mean ± standard deviation, SD). Variable Male Female Total Number of participants 84 (36%) 149 (64%) 233 Age (years) 32 ± 9.9 35 ± 10.7 34 ± 10.5 Body Mass Index (BMI; Kg/m 2 ) 27.8 ± 2.8 25.3 ± 6.4 27.1 ± 5.4 Normal weight 51 (38.3%) 82 (61.7%) 133 (57%) Overweight 33 (33%) 67 (67%) 100 (43%) Physical Activity Level (PAL) Low 21 (23.1%) 69 (75.8%) 91 (39%) Medium 32 (40%) 48 (60%) 80 (34.4%) High 30 (48.4%) 32 (51.6%) 62 (26.6%) Physical Activity Frequency (PAF) (mean ± standard deviation, SD) (times/week) Low 6.3 ± 3.0 6.5 ± 2.6 6.5 ± 2.7 Medium 8.2 ± 3.9 8.4 ± 2.8 8.3 ± 3.3 High 12.3 ± 3.5 12.3 ± 3.7 12.3 ± 3.6 Physical Activity Volume (PAV) (mean ± standard deviation, SD) (min/week) Low 253.4 ± 183.6 257.8 ± 176.8 256.7 ± 177.4 Medium 488.9 ± 195.1 393.4 ± 199.0 468.8 ± 188.8 High 902.8 ± 358.5 923.8 ± 629.9 913.7 ± 512.7 Relationship Between Gut Microbiome Bacterial and Physical Activity, and Body Mass Index. This study examined the taxonomic composition of the gut microbiome in a cohort of 233 Chilean adults to evaluate its relationship with physical activity and other anthropometric parameters. We employed 16S rRNA sequencing analysis to identify microbial variations associated with different PALs and BMI. At the phylum level, which provides a broad overview of microbial structure, the four most abundant phyla across all PAL categories were Firmicutes, Bacteroidota, Actinobacteriota, and Proteobacteria (Fig. 1 ). Firmicutes was the most predominant across all PAL groups (L-PAL: mean = 68.3%, SD = 12.8; M-PAL: mean = 66.4%, SD = 12.1; H-PAL: mean = 68.1%, SD = 11.7), followed by Bacteroidota (L-PAL: mean = 24.9%, SD = 11.5; M-PAL: mean = 26%, SD = 11.9; H-PAL: mean = 25.8%, SD = 11.7) and Proteobacteria (L-PAL: mean = 1.8%, SD = 3; M-PAL: mean = 2.3%, SD = 3.7; H-PAL: mean = 1.6%, SD = 1.7). However, no statistically significant differences were found in phylum level composition across PAL or BMI categories. We also assessed alpha diversity using the Shannon index, which showed no significant differences among PALs (L-PAL = 2.87 ± 0.33, M-PAL = 2.88 ± 0.30, H-PAL = 2.98 ± 0.33; Supplementary Fig. 1A). Similarly, beta diversity analysis did not reveal any clustering or significant associations among PAL or BMI groups (Supplementary Fig. 1B). No significant differences in gut microbiota composition were observed across PAL groups, and PAF, PAV, and sex also showed no significant associations. Only when PAL was stratified by BMI did specific bacterial genera exhibit differential abundance. Among individuals with normal weight, L-PAL was associated with increased abundance of Adlercreutzia , Lachnospira and Monoglobus compared to M-PAL (q = 0.064, q = 0.085, q = 0.104, respectively; Fig. 2 ). Conversely, Dorea , Parabacteroides , and Paraprevotella were less abundant in L-PAL relative to M-PAL (q = 0.051, q = 0.085, q = 0.085; Fig. 2 ). Collinsella abundance was higher in H-PAL than in L-PAL (q = 0.085; Fig. 2 ). Additionally, Dorea abundance was higher in H-PAL than in M-PAL (q = 0.050; Fig. 2 ). Comparing M-PAL and H-PAL groups, Holdemanella and Paraprevotella were more abundant in M-PAL (q = 0.091 and q = 0.085, respectively; Fig. 2 ). Notably, Paraprevotella was also increased in M-PAL compared to L-PAL (q = 0.085). In contrast, among overweight individuals no statistically significant differences in bacterial genera were observed across PAL categories. The analysis of the relative abundances of specific bacterial genera reveals significant differences associated with BMI and PAL. Specifically, the genus Parabacteroides exhibited an increase in its relative abundance in individuals with a normal BMI (q = 0.019; Fig. 3 ). Moreover, it was noted that the relative abundances of the genus Dorea increased in individuals overweight, suggesting a possible correlation between these genus and weight gain (q = 0.023, Fig. 3 ). In the case of Holdemanella , an increase in its relative abundance was observed in individuals reporting H-PAL, indicating an association between this genus and a more active lifestyle (q = 0.131; Fig. 3 ). On the other hand, Lachnospiraceae ND3007 group and Romboutsia exhibit distinct patterns in L-PAL. Specifically, a decrease in Lachnospiraceae ND3007 group was observed in overweight individuals compared to those with normal weight, while Romboutsia showed an increase in overweight individuals (q = 0.131 and q = 0.131, respectively; Fig. 3 ). Impact of PAL and PAV on Gut Metabolic Modules To revealed insights into microbiome functionality we performed a detailed examination of the microbiota’s metabolic potential. By sequencing the V3-V4 region of the 16S rRNA gene and applying PICRUSt2 functional prediction (see Methodology), we inferred the gut microbiome’s potential gene repertoire and organized these predicted genes into GMMs. This analysis revealed 39 GMMs, accounting for 37.8% of the total evaluated, involved in various metabolic pathways within the gut microbiome of the studied cohort. By interpreting the genetic content inferred from 16S rRNA sequencing, we identified specific GMMs. This analysis revealed 39 GMMs, accounting for 37.8% of the total evaluated, involved in various metabolic pathways within the gut microbiome of the studied cohort. The distribution of these GMMs exhibited notable uniformity, with 33 presents in over 90% of individuals, emphasizing a common metabolic basis in the participants’ gut microbiome. Among these, proteolysis pathways were particularly noteworthy because every protein metabolism module detected corresponded exclusively to degradation processes, highlighting the microbiome’s consistent capacity to break down essential amino acids such as arginine, threonine, cysteine, serine, glutamate, aspartate, valine, isoleucine, methionine, phenylalanine and proline. Additionally, we identified modules associated with carbohydrate metabolism, such as the degradation of various sugars and key pathways like glycolysis and lactate production. Other relevant modules included those related to glycerol degradation, conversion of acetyl-CoA to crotonyl-CoA and acetate, as well as butyrate and propionate production (Fig. 4 A). Approximately 48.7% of the GMMs were involved in saccharolytic metabolism, while 43.6% participated in other metabolic processes. When analyzing the abundance of these modules according to PAL, we observed that those present in more than 90% of individuals maintained high abundance across all PAL categories. In contrast, less prevalent modules showed decreased abundance, a pattern consistent across all PALs. These findings underscore the relevance of GMMs in regulating intestinal metabolism and suggest a correlation between module prevalence and abundance, highlighting a role of the gut microbiome in sustaining diverse metabolic functions. Our analysis revealed an association between the abundance of specific GMMs and PAL. We identified 18 modules with significantly different abundances across PAL groups, particularly between M-PAL and H-PAL, as well as between L-PAL and M-PAL. Notably, no significant differences were found between the extremes of activity (L-PAL and H-PAL) (Fig. 4 B). Modules showing reduced abundance in L-PAL compared to M-PAL included glycerol degradation I, propionate production II, and valine degradation (p < 0.05; Fig. 4 B), suggesting a metabolic shift in response to increased physical activity. Conversely, GMMs related to proteolysis, such as arginine degradation IV and V, aspartate degradation I, cysteine degradation II, methionine degradation I, and threonine degradation I and II, were less abundant in H-PAL compared to M-PAL (Fig. 4 B). This trend was not observed between L-PAL and H-PAL, suggesting a threshold beyond which further increases in activity suppress proteolysis pathways. Carbohydrate degradation modules, including galactose degradation, glycerol degradation I and II, glycolysis (pay-off-phase), and mannose degradation, melibiose degradation, sucrose degradation I, and trehalose degradation (Fig. 4 B), also showed reduced abundance in both M-PAL and H-PAL compared to L-PAL. This pattern mirrors findings from proteolysis pathways, reinforcing the notion of metabolic adaptation linked to PAL. These results illustrate the complex interplay between physical activity and the functional composition of the gut microbiome, highlighting how varying levels of exercise can differentially modulate microbial metabolism. Finally, we observed a clear influence of PAV on GMM abundance. As weekly time dedicated to physical activity increased, several modules showed significant changes. Specifically, higher PAV was associated with increased abundance of modules involved in ribose degradation; isoleucine degradation; butyrate production II; acetyl-CoA to crotonyl-CoA conversion; propionate production II; lactate production; lactose and galactose degradation; 4-aminobutyrate degradation; and valine degradation I (Fig. 4 C). Conversely, modules involved in glutamate degradation II, putrescine degradation, and anaerobic fatty acid beta-oxidation decreased in abundance with increasing PAV (Fig. 4 C). 4. Discussion Physical activity plays a crucial role in overall health ( 4 , 33 – 35 ). Numerous studies in pathological populations show that regular exercise reduces the impact of diverse conditions, including cancers, neurodegenerative diseases, psychiatric disorders, autoimmune conditions, musculoskeletal disorders, chronic pain, pathological aging, and metabolic disorders ( 33 , 36 – 38 ). In non-pathological populations and athletes, habitual physical activity lowers the risk of developing chronic disease across the life course ( 34 , 39 , 40 ). Moreover, it has been established that individuals who exercise frequently experience improved quality of life and self-esteem, personal potential development, feelings of self-satisfaction and self-fulfillment, healthy longevity, and the formation of secure interpersonal relationships ( 41 – 46 ). Several studies have also highlighted a bidirectional crosstalk between skeletal muscle and the gut, often referred to as the gut-muscle axis ( 47 ). During exercise, contracting muscle fibers release myokines, which exert systemic anti-inflammatory effects ( 48 ) that can influence gut barrier function, microbial composition, and metabolite production ( 23 , 25 , 49 ). Conversely, gut-derived metabolites such as SCFAs may modulate muscle metabolism and performance, underscoring the integrated nature of this axis in maintaining host health. A balanced and diverse gut microbiome is crucial for healthy food digestion, robust immune function, and the prevention of metabolic diseases ( 34 ). The relationship between PALs (low, medium, high) and the gut microbiome is an emerging field of study that remains underexplored. In this Chilean adult cohort, we classified PALs using the IPAQ-SF and examined associations with gut microbiota composition and functional potential. Our findings suggest that the metabolic potential of gut bacteria varies according to PAL, raising the possibility that physical activity could influence the functionality of the gut microbiota. Furthermore, both BMI and PAL are related to the composition of the gut microbiota, affecting the abundance of certain bacterial genera. The IPAQ-SF is a validated instrument for self-reporting physical activity at international ( 27 ) and national ( 23 ) levels. However, it is important to consider that individuals tend to overestimate both their PAL, and the time spent in sedentary behavior [40], which should be considered when interpreting our results. The instrument captures frequency and duration of walking, moderate, and vigorous activity, as well as sitting time ( 25 , 34 , 39 , 49 , 50 ). This frequency measure may overestimate the number of active days because the IPAQ records days for walking, moderate, and vigorous activity separately, so multiple sessions or intensities performed on the same day can be counted more than once ( 27 ). Although we observed no significant differences in alpha diversity by PAL alone, higher alpha diversity has been reported in elite athletes compared with sedentary controls without a consistent relationship to activity level. This suggests that shifts in community composition may be more sensitive to physical activity than diversity measures. Our results for M-PAL were inconclusive, reinforcing the need for objective measurement (e.g., accelerometry) and larger sample sizes. In a large Swedish cohort (n = 8,416) assessed by accelerometry, taxonomic differences at the species level were detected across physical activity categories, though patterns differed between vigorous and moderate activity ( 17 ). Such work illustrates the value of device-based measures for dissecting dose-response relationships. All functional insights in this study derive from 16S rRNA based taxonomic profiles mapped to GMMs rather than directly measured genes, transcripts, or metabolites; consequently, pathway presence reflects predicted potential and not confirmed activity. Future studies using whole-genome shotgun sequencing and complementary omics will be needed for more precise metabolic profiling ( 51 , 52 ). From a functional potential perspective, 37.8% of the identified GMMs were present in ≥ 90% of participants, suggesting a shared core of microbial metabolic functions in this cohort. The GMMs that displayed PAL-related differences were dominated by degradation pathways, with the exceptions of propionate production II and the payoff (energy-yielding) phase of glycolysis. This pattern underscores the role of the gut microbiome in macromolecule turnover. We observed a PAL-associated duality in protein metabolism, amino acid degradation pathways were more prominent in M-PAL than in H-PAL. Arginine, aspartate, and cysteine degradation tracked with this pattern; valine degradation increased in M-PAL vs L-PAL. With increasing PAV isoleucine degradation increased, whereas glutamate and putrescine degradation declined. These trends may reflect differing energetic demands: certain amino acids (e.g., isoleucine) can feed gluconeogenic/ketogenic pathways under activity-related energy stress ( 53 ). Glutamate plays an active role in the nervous system ( 54 ), putrescine has been implicated in muscle protein synthesis and growth ( 55 ). In a lifestyle intervention among older adults, greater increases in activity were accompanied by parallel shifts in amino acid degradation and energy-precursor pathways, supporting links between exercise and microbiome functional capacity ( 56 ). Carbohydrate-metabolism GMMs decreased in H-PAL relative to M-PAL, suggesting that higher activity levels may remodel microbial carbohydrate processing or reflect substrate competition under elevated energy turnover. Propionate production II was higher in M-PAL vs L-PAL, potentially reflecting increased microbial fermentation to meet host energy needs. Propionate can support energy balance and stimulate further fermentation, increasing SCFA yield available for absorption ( 57 , 58 ). Several studies have also reported positive associations between physical activity or cardiorespiratory fitness and fecal SCFA concentrations ( 59 , 60 ). SCFAs are readily absorbed, fuel colonocytes, enter peripheral circulation, and act as key mediators of skeletal muscle mitochondrial energy metabolism that contribute to whole-body glucose homeostasis. This provides further evidence implicating the gut-muscle axis in exercise responses ( 61 ). Physical activity has been linked to higher fecal butyrate and enrichment of butyrate-producing taxa ( 62 , 63 ). Indeed, fecal butyrate concentrations have been positively associated with cardiorespiratory fitness ( 59 ). Exercise-induced alterations in gut barrier physiology may favor butyrogenic bacteria ( 64 – 66 ). Butyrate can stimulate PGC-1α expression, mitochondrial biogenesis, β-oxidation, and glucose transport in muscle, enhancing oxidative capacity ( 65 ). Acetate and butyrate also promote muscle fat oxidation, improving metabolic flexibility and the ability to switch between lipid and carbohydrate fuels ( 67 ). Butyrate’s histone deacetylase inhibition may protect against muscle protein catabolism and age-related muscle loss ( 68 ). Medium- to long-term exercise interventions increase SCFA concentrations ( 62 ), which scale positively with PAL ( 60 ) and cardiorespiratory fitness ( 59 ). This finding reinforces exercise as a driver of SCFA-linked muscle-gut signaling. Metagenomic work combining diet and activity interventions has likewise shown coordinated shifts in metabolite biosynthesis and degradation pathways, indicating that lifestyle change can remodel both microbiome structure and function ( 56 ). In contrast to a previous study conducted in Chile in 2017, which found that the phylum Verrucomicrobia (now known as Verrucomicrobiota) was the third most abundant at 8.5% relative abundance ( 69 ), our analysis indicates that this phylum is the fifth most abundant, with 0.54% abundance (Fig. 1 ). In part, this discrepancy may be due to the distinct characteristics of the sample, as it included only 41 normal-weight individuals aged 18 to 39 from the city of Santiago. Paraprevotella produces succinate, which can engage pro-inflammatory signaling; links to hypertension and metabolic/inflammatory states have been reported ( 70 ). Its enrichment in M-PAL vs both L- and H-PAL highlights the complex, non-linear relationships between self-reported activity and microbial taxa. Misclassification of PAL, dietary heterogeneity, or unmeasured host factors could contribute. Collinsella has been associated with dysglycemia, atherosclerotic risk, and adverse lipid profiles ( 71 ). Its relative reduction in L-PAL vs H-PAL could point (cautiously) toward activity-related metabolic benefits. Dorea has been linked to glucose metabolism and higher abundance in prediabetes and obesity ( 72 , 73 ); its increase in normal-weight M- and H-PAL groups may reflect activity-BMI interactions that warrant targeted follow-up. Holdemanella produces both SCFAs ( 74 ) that can supply energy during prolonged light-to-moderate activity ( 75 ) and has been tied to improved carbohydrate metabolism and anti-inflammatory effects in experimental models ( 21 , 74 ). In our cohort, Holdemanella decreased in normal-weight H-PAL vs M-PAL but increased in overweight H-PAL, suggesting that host adiposity shapes microbial responses to activity. Lachnospira , a producer of butyrate and propionate, is inversely associated with body fat and adverse lipid profiles ( 76 , 77 ). Its decrease from L-PAL to M-PAL (but not to H-PAL) again underscores non-linear patterns in self-reported activity categories. These SCFAs are rapidly absorbed and routed into fatty acid metabolic pathways ( 78 ). High-performance athletes with large training volumes often show enrichment of SCFA-producing taxa ( 79 ). The PAL-associated differences we observed, particularly in amino acid degradation, SCFA production, and lipid/energy metabolism modules, map onto pathways implicated in glycemic regulation, inflammation, muscle energetics, and cardiometabolic risk. For example, propionate and butyrate related functions could support metabolic flexibility and muscle-gut signaling, whereas shifts in Collinsella or Paraprevotella may align with cardiometabolic or inflammatory states. Interpretation should be cautious because this cross-sectional, 16S inference-based study lacks direct metabolite, inflammatory, dietary, and clinical measures. Because data were collected at a single time point, temporal directionality and causality cannot be established. Still, the patterns highlight candidate microbial functions through which physical activity might confer metabolic benefits and suggest testable targets for longitudinal and intervention studies that integrate objective activity tracking, dietary assessment, metagenomics, and metabolomics. 5. Conclusion Our findings show that self-reported PAL and PAV relate differentially to gut microbial composition and predicted metabolic functions in a Chilean human gut microbiome cohort to integrate 16S based functional profiling (GMMs) with multiple physical activity metrics and BMI. Because physical activity is a modifiable behavior worldwide, the PAL and PAV associated shifts we observed in amino acid degradation and SCFAs and energy related pathways highlight microbiome functions that may be leveraged to improve metabolic health across diverse populations. Future longitudinal and intervention studies that pair accelerometry with whole genome sequencing, fecal and plasma metabolomics, dietary assessment, and structured exercise dosing are needed to validate these associations and identify responsive microbial targets. Such evidence could inform precision activity prescriptions, potentially stratified by BMI and microbiome profile, to modulate gut metabolic capacity and support cardiometabolic and functional health. Abbreviations BMI: Body Mass Index PAL: Physical Activity Level L-PAL: Low Physical Activity Level M-PAL: Medium Physical Activity Level H-PAL: High Physical Activity Level SCFAs: Short-Chain Fatty Acids PAV: physical activity volume PAF: physical activity frequency GMM: Gut Metabolic Modules IPAQ-SF: International Physical Activity Questionnaire - Short Form MET: Metabolic Equivalent (kcal/kg/hour) Declarations Ethics approval and consent to participate: This study was approved by the Institutional Ethical Scientific Committee of Universidad Mayor (Protocol No. 0274). Informed consent was obtained from all the participants. The research team worked exclusively with de-identified data and had no access to personal identifiers. Consent for publication: All authors have read and approved the final version of the manuscript and consent to its publication. Availability of data and materials: The data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB91588 (https://www.ebi.ac.uk/ena/browser/view/PRJEB91588). Competing interests: The authors declare no competing interests. Funding: This work was supported by Proyecto P29 “Microbioma Chileno”, grant 16PTECAI-66648 -P16, IFAN, CORFO. Geroscience Center for Brain Health and Metabolism FONDAP-15150012 and Financiamiento Basal para Centros Científicos y Tecnológicos de Excelencia Centro Ciencia y Vida, FB210008 (FAC). Authors’ contributions: PCR, XL, AV, AP, JM, and SMM were responsible for conceptualization, methodology, and experimental/analytical design; PCR prepared the visualizations, and together with AV, conducted the formal analysis, investigation, and data curation; CSD, PMA and GMM provided resources; PCR and AV drafted the original manuscript; all authors reviewed and edited the paper; and XL, AP, and FAC secured funding. All authors read and approved the final version. Acknowledgments: Not applicable. References Panter, J., Tanggaard Andersen, P., Aro, A. R. & Samara, A. Obesity Prevention: A Systematic Review of Setting-Based Interventions from Nordic Countries and the Netherlands. J. Obes. 2018 , 1–34 (2018). Ahrens, W. et al. Dietary behaviour and physical activity policies in Europe: learnings from the Policy Evaluation Network (PEN). Eur. J. Public. Health . 32 (Supplement_4), iv114–iv125 (2022). Blundell, J. E. et al. Variations in the Prevalence of Obesity Among European Countries, and a Consideration of Possible Causes. Obes. Facts . 10 (1), 25–37 (2017). WHO. WHO European Regional Obesity Report. Licence: CC BY-NC-SA 3.0 IGO. 2022; (2022). Munir, M. et al. Global human obesity and political globalization; asymmetric relationship through world human development levels. Nutr. Health . 30 (3), 489–497 (2024). Hong, S. et al. Association between cardiorespiratory fitness and the prevalence of metabolic syndrome among Korean adults: a cross sectional study. BMC Public. Health . 14 (1), 481 (2014). Chomiuk, T., Niezgoda, N., Mamcarz, A. & Śliż, D. Physical activity in metabolic syndrome. Front. Physiol. 15 , 1365761 (2024). De La Corte-Rodriguez, H., Roman-Belmonte, J. M., Resino-Luis, C., Madrid-Gonzalez, J. & Rodriguez-Merchan, E. C. The Role of Physical Exercise in Chronic Musculoskeletal Pain: Best Medicine—A. Narrative Rev. Healthc. 12 (2), 242 (2024). Stubbs, B., Chen, L. J., Chang, C. Y., Sun, W. J. & Ku, P. W. Accelerometer-assessed light physical activity is protective of future cognitive ability: A longitudinal study among community dwelling older adults. Exp. Gerontol. 91 , 104–109 (2017). Burini, R. C., Anderson, E., Durstine, J. L. & Carson, J. A. Inflammation, physical activity, and chronic disease: An evolutionary perspective. Sports Med. Health Sci. 2 (1), 1–6 (2020). Valdes, A. M., Walter, J., Segal, E. & Spector, T. D. Role of the gut microbiota in nutrition and health. BMJ ;k2179. (2018). Tamboli, C. P. Dysbiosis in inflammatory bowel disease. Gut 53 (1), 1–4 (2004). Qiu, P. et al. The Gut Microbiota in Inflammatory Bowel Disease. Front. Cell. Infect. Microbiol. 12 , 733992 (2022). Morrison, D. J. & Preston, T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. Gut Microbes . 7 (3), 189–200 (2016). Przewłócka, K., Folwarski, M., Kaźmierczak-Siedlecka, K., Skonieczna-Żydecka, K. & Kaczor, J. J. Gut-Muscle Axis Exists and May Affect Skeletal Muscle Adaptation to Training. Nutrients 12 (5), 1451 (2020). Hamamah, S., Hajnal, A. & Covasa, M. Influence of Bariatric Surgery on Gut Microbiota Composition and Its Implication on Brain and Peripheral Targets. Nutrients 16 (7), 1071 (2024). Baldanzi, G. et al. Accelerometer-based physical activity is associated with the gut microbiota in 8416 individuals in SCAPIS. eBioMedicine 100 , 104989 (2024). Qian, H. et al. Impact of exercise training on gut microbiome imbalance in obese individuals: a study based on Mendelian randomization analysis. Front. Physiol. 14 , 1264931 (2024). Holzhausen, E. A. et al. Assessing the relationship between physical activity and the gut microbiome in a large, population-based sample of Wisconsin adults. Fatouros IG, editor. PLOS ONE. ;17(10):e0276684. (2022). Lee, P. H., Macfarlane, D. J., Lam, T. & Stewart, S. M. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. Int. J. Behav. Nutr. Phys. Act. 8 (1), 115 (2011). Roman-Viñas, B. et al. International Physical Activity Questionnaire: Reliability and validity in a Spanish population. Eur. J. Sport Sci. 10 (5), 297–304 (2010). Medina, C., Barquera, S. & Janssen, I. Validity and reliability of the International Physical Activity Questionnaire among adults in Mexico. Rev. Panam. Salud Publica Pan Am. J. Public. Health . 34 (1), 21–28 (2013). Serón, P., Muñoz, S. & Lanas, F. Nivel de actividad física medida a través del cuestionario internacional de actividad física en población Chilena. Rev Médica Chile [Internet]. 2010 Oct [cited 2024 Mar 20];138(10). Available from: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0034-98872010001100004&lng=en&nrm=iso&tlng=en Hagströmer, M., Oja, P. & Sjöström, M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public. Health Nutr. 9 (6), 755–762 (2006). Bull, F. C. et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br. J. Sports Med. 54 (24), 1451–1462 (2020). Garber, C. E. et al. Quantity and Quality of Exercise for Developing and Maintaining Cardiorespiratory, Musculoskeletal, and Neuromotor Fitness in Apparently Healthy Adults: Guidance for Prescribing Exercise. Med. Sci. Sports Exerc. 43 (7), 1334–1359 (2011). Craig, C. L. et al. International Physical Activity Questionnaire: 12-Country Reliability and Validity. Med. Sci. Sports Exerc. 35 (8), 1381–1395 (2003). Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods . 13 (7), 581–583 (2016). McMurdie, P. J. & Holmes, S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. Watson M, editor. PLoS ONE. ;8(4):e61217. (2013). Vieira-Silva, S. et al. Species–function relationships shape ecological properties of the human gut microbiome. Nat. Microbiol. 1 (8), 16088 (2016). Bastiaanssen, T. F. S., Quinn, T. P. & Loughman, A. Bugs as features (part 1): concepts and foundations for the compositional data analysis of the microbiome–gut–brain axis. Nat. Ment Health . 1 (12), 930–938 (2023). Bastiaanssen, T. F. S., Quinn, T. P. & Loughman, A. Bugs as features (part 2): a perspective on enriching microbiome–gut–brain axis analyses. Nat. Ment Health . 1 (12), 939–949 (2023). WHO. The inequality epidemic: low-income teens face higher risks of obesity, inactivity and poor diet. ; (2024). Ruegsegger, G. N. & Booth, F. W. Health Benefits of Exercise. Cold Spring Harb Perspect. Med. 8 (7), a029694 (2018). Pray, L. The Human Microbiome, Diet, and Health [Internet]. 1ra ed. Vol. 1. Washington DC: The National Academies Press.; 196 p. (2013). Available from: http://www.nap.edu Valles-Colomer, M. et al. The neuroactive potential of the human gut microbiota in quality of life and depression. Nat. Microbiol. 4 (4), 623–632 (2019). Anderson, E. & Durstine, J. L. Physical activity, exercise, and chronic diseases: A brief review. Sports Med. Health Sci. 1 (1), 3–10 (2019). Kissow, A. M. Participation in physical activity and the everyday life of people with physical disabilities: a review of the literature. Scand. J. Disabil. Res. 17 (2), 144–166 (2015). Warburton, D. E. R. Health benefits of physical activity: the evidence. Can. Med. Assoc. J. 174 (6), 801–809 (2006). Saqib, Z. A. et al. Physical Activity is a Medicine for Non-Communicable Diseases: A Survey Study Regarding the Perception of Physical Activity Impact on Health Wellbeing. Risk Manag Healthc. Policy . 13 , 2949–2962 (2020). Da Silva, C. C. M. et al. The Association between Perceived Stress, Quality of Life, and Level of Physical Activity in Public School Teachers. Int. J. Environ. Res. Public. Health . 21 (1), 88 (2024). Hovland, J. F., Langeland, E., Ness, O. & Skogvang, B. O. Experiences with physical activity, health and well-being among young adults with serious mental illness. Int. J. Qual. Stud. Health Well-Being . 18 (1), 2221911 (2023). White, R. L., Ryan, D., Young, C., Elston, R. & Rossi, T. How does the context of physical activity influence perceived mood and wellbeing after exercise? Ment Health Phys. Act. 24 , 100504 (2023). Harrison, E. A. et al. Perceptions, Opinions, Beliefs, and Attitudes About Physical Activity and Exercise in Urban-Community-Residing Older Adults. J. Prim. Care Community Health . 11 , 215013272092413 (2020). Svantesson, U., Jones, J., Wolbert, K. & Alricsson, M. Impact of Physical Activity on the Self-Perceived Quality of Life in Non-Frail Older Adults. J. Clin. Med. Res. 7 (8), 585–593 (2015). Marquez, D. X. et al. A systematic review of physical activity and quality of life and well-being. Transl Behav. Med. 10 (5), 1098–1109 (2020). Chen, J., Guo, Y., Gui, Y. & Xu, D. Physical exercise, gut, gut microbiota, and atherosclerotic cardiovascular diseases. Lipids Health Dis. 17 (1), 17 (2018). Pedersen, B. K. The diseasome of physical inactivity – and the role of myokines in muscle–fat cross talk. J. Physiol. 587 (23), 5559–5568 (2009). Stamatakis, E. et al. Does Strength-Promoting Exercise Confer Unique Health Benefits? A Pooled Analysis of Data on 11 Population Cohorts With All-Cause, Cancer, and Cardiovascular Mortality Endpoints. Am. J. Epidemiol. 187 (5), 1102–1112 (2018). Duclos, M. Les bénéfices de la pratique d’une activité physique pour la santé. Rev. Infirm . 70 (275), 16–19 (2021). Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31 (9), 814–821 (2013). Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J. & Segata, N. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35 (9), 833–844 (2017). Dimou, A., Tsimihodimos, V. & Bairaktari, E. The Critical Role of the Branched Chain Amino Acids (BCAAs) Catabolism-Regulating Enzymes, Branched-Chain Aminotransferase (BCAT) and Branched-Chain α-Keto Acid Dehydrogenase (BCKD), in Human Pathophysiology. Int. J. Mol. Sci. 23 (7), 4022 (2022). Zhou, Y. & Danbolt, N. C. Glutamate as a neurotransmitter in the healthy brain. J. Neural Transm . 121 (8), 799–817 (2014). Hu, C. et al. Putrescine promotes MMP9-induced angiogenesis in skeletal muscle through hydrogen peroxide/METTL3 pathway. Free Radic Biol. Med. 212 , 433–447 (2024). Ruiz-Limón, P. et al. Physical activity shifts gut microbiota structure in aged subjectswith overweight/obesity and metabolic syndrome. Biol. Sport . 41 (3), 47–60 (2024). Baur, T., Wentzel, A. & Dürre, P. Production of propionate using metabolically engineered strains of Clostridium saccharoperbutylacetonicum. Appl. Microbiol. Biotechnol. 106 (22), 7547–7562 (2022). Sales, K. M. & Reimer, R. A. Unlocking a novel determinant of athletic performance: The role of the gut microbiota, short-chain fatty acids, and biotics in exercise. J. Sport Health Sci. 12 (1), 36–44 (2023). Estaki, M. et al. Cardiorespiratory fitness as a predictor of intestinal microbial diversity and distinct metagenomic functions. Microbiome 4 (1), 42 (2016). Barton, W. et al. The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level. Gut 2017;gutjnl-2016-313627. Ortiz-Alvarez, L., Xu, H. & Martinez-Tellez, B. Influence of Exercise on the Human Gut Microbiota of Healthy Adults: A Systematic Review. Clin. Transl Gastroenterol. 11 (2), e00126 (2020). Allen, J. M. et al. Exercise Alters Gut Microbiota Composition and Function in Lean and Obese Humans. Med. Sci. Sports Exerc. 50 (4), 747–757 (2018). Lambert, J. E. et al. Exercise training modifies gut microbiota in normal and diabetic mice. Appl. Physiol. Nutr. Metab. 40 (7), 749–752 (2015). Clark, A. & Mach, N. The Crosstalk between the Gut Microbiota and Mitochondria during Exercise. Front. Physiol. 8 , 319 (2017). Gao, W., Howden, B. P. & Stinear, T. P. Evolution of virulence in Enterococcus faecium, a hospital-adapted opportunistic pathogen. Curr. Opin. Microbiol. 41 , 76–82 (2018). Vargas-Ortiz, K., Pérez-Vázquez, V. & Macías-Cervantes, M. H. Exercise and Sirtuins: A Way to Mitochondrial Health in Skeletal Muscle. Int. J. Mol. Sci. 20 (11), 2717 (2019). Canfora, E. E., Jocken, J. W. & Blaak, E. E. Short-chain fatty acids in control of body weight and insulin sensitivity. Nat. Rev. Endocrinol. 11 (10), 577–591 (2015). Walsh, M. E. et al. The histone deacetylase inhibitor butyrate improves metabolism and reduces muscle atrophy during aging. Aging Cell. 14 (6), 957–970 (2015). Fujio-Vejar, S. et al. The Gut Microbiota of Healthy Chilean Subjects Reveals a High Abundance of the Phylum Verrucomicrobia. Front. Microbiol. 8 , 1221 (2017). Larsen, J. M. The immune response to Prevotella bacteria in chronic inflammatory disease. Immunology 151 (4), 363–374 (2017). Frost, F. et al. A structured weight loss program increases gut microbiota phylogenetic diversity and reduces levels of Collinsella in obese type 2 diabetics: A pilot study. Taheri S, editor. PLOS ONE. ;14(7):e0219489. (2019). Palmnäs-Bédard, M. S. et al. The human gut microbiota and glucose metabolism: a scoping review of key bacteria and the potential role of SCFAs. Am. J. Clin. Nutr. 116 (4), 862–874 (2022). Carrizales-Sánchez, A. K. et al. Characterization of gut microbiota associated with metabolic syndrome and type-2 diabetes mellitus in Mexican pediatric subjects. BMC Pediatr. 23 (1), 210 (2023). Romaní-Pérez, M. et al. Holdemanella biformis improves glucose tolerance and regulates GLP‐1 signaling in obese mice. FASEB J [Internet]. Jul [cited 2024 Jul 24];35(7). Available from: https://onlinelibrary.wiley.com/doi/ (2021). 10.1096/fj.202100126R Bongiovanni, T., Yin, M. O. L. & Heaney, L. M. The Athlete and Gut Microbiome: Short-chain Fatty Acids as Potential Ergogenic Aids for Exercise and Training. Int. J. Sports Med. 42 (13), 1143–1158 (2021). Chen, S. J. et al. Association of Fecal and Plasma Levels of Short-Chain Fatty Acids With Gut Microbiota and Clinical Severity in Patients With Parkinson Disease. Neurology [Internet]. 2022 Feb 22 [cited 2024 Jul 24];98(8). Available from: https://www.neurology.org/doi/ 10.1212/WNL.0000000000013225 Companys, J. et al. Gut Microbiota Profile and Its Association with Clinical Variables and Dietary Intake in Overweight/Obese and Lean Subjects: A Cross-Sectional Study. Nutrients 13 (6), 2032 (2021). Frampton, J., Murphy, K. G., Frost, G. & Chambers, E. S. Short-chain fatty acids as potential regulators of skeletal muscle metabolism and function. Nat. Metab. 2 (9), 840–848 (2020). Hampton-Marcell, J. T. et al. Comparative Analysis of Gut Microbiota Following Changes in Training Volume Among Swimmers. Int. J. Sports Med. 41 (05), 292–299 (2020). Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYMATERIAL.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7595052","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":525633980,"identity":"54d7fd51-c535-48ae-b5b3-52b05355eb6c","order_by":0,"name":"Ana Viera","email":"","orcid":"","institution":"Universidad Mayor","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Viera","suffix":""},{"id":525633984,"identity":"716a5596-199a-4a37-a4a0-bb16b82802dc","order_by":1,"name":"Adolfo Pozo","email":"","orcid":"","institution":"IMAS incubadora de innovación más alimentos saludables SPA","correspondingAuthor":false,"prefix":"","firstName":"Adolfo","middleName":"","lastName":"Pozo","suffix":""},{"id":525633986,"identity":"1fa14390-e4fd-47fa-8e53-4a3af171d08c","order_by":2,"name":"Ximena Lopez","email":"","orcid":"","institution":"IMAS incubadora de innovación más alimentos saludables SPA","correspondingAuthor":false,"prefix":"","firstName":"Ximena","middleName":"","lastName":"Lopez","suffix":""},{"id":525633988,"identity":"81b3614d-0a99-4059-8791-b1459e9edee7","order_by":3,"name":"Javier Morán","email":"","orcid":"","institution":"University Institute of Food Innovation. Catholic University of Murcia (UCAM)","correspondingAuthor":false,"prefix":"","firstName":"Javier","middleName":"","lastName":"Morán","suffix":""},{"id":525633990,"identity":"ca540dce-0205-4673-a588-b8eb34c343d0","order_by":4,"name":"Carolina Sanchez-Doñas","email":"","orcid":"","institution":"Universidad Mayor","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Sanchez-Doñas","suffix":""},{"id":525633993,"identity":"772c3217-87ff-48fa-acee-a3701679801e","order_by":5,"name":"Pamela Mieres-Amigo","email":"","orcid":"","institution":"Universidad Mayor","correspondingAuthor":false,"prefix":"","firstName":"Pamela","middleName":"","lastName":"Mieres-Amigo","suffix":""},{"id":525633994,"identity":"2521060f-ee45-4640-8fa6-e363ac63c523","order_by":6,"name":"Giannina Massoni-Mora","email":"","orcid":"","institution":"Universidad Mayor","correspondingAuthor":false,"prefix":"","firstName":"Giannina","middleName":"","lastName":"Massoni-Mora","suffix":""},{"id":525633995,"identity":"910eab43-a913-4f6f-ac42-5cac6c272ac3","order_by":7,"name":"Felipe A. Court","email":"","orcid":"","institution":"Universidad Mayor","correspondingAuthor":false,"prefix":"","firstName":"Felipe","middleName":"A.","lastName":"Court","suffix":""},{"id":525633996,"identity":"4c4c298b-654b-47a7-ab90-934cd9853c4b","order_by":8,"name":"Sandra Mahecha-Matsudo","email":"","orcid":"","institution":"Unidad de Medicina del Deporte y Actividad Física, Clínica MEDS","correspondingAuthor":false,"prefix":"","firstName":"Sandra","middleName":"","lastName":"Mahecha-Matsudo","suffix":""},{"id":525633997,"identity":"606087eb-3362-42b0-96fe-71b639c97177","order_by":9,"name":"Paulina Calderón-Romero","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYHACxgMJYJq5QQJE8cMl2HHrgWphhGiRbICJM+PRwoCsxeAAAS3m7GcMDjzcYcfA336w8cbHPTZ5xjeyEz/zVNxj4G/GrsWyJ8fgQOKZZAaJM4nNljOepRWb3cjdLM1zpphB4jB2LQYHQFramBkMGBLbpHkOHE7cdiN3gzRvWwKDAQ6HGZx/A9JSz2DA/7BN+g9Qy+YZuZt/49VyA2zLYQYDCaAtDEAtGyRyt+G1Bej8AqCW4zwSNx42W/YcSEuccebtNss5ZxJ4cPnFnD9548OfbdVy/P3JB2/8OGCT2N+eu/nGm4oEOf72BuwOg9I8KKJMPOgiWLSgAsYfuNSPglEwCkbBSAQAxC9ik5Xt43cAAAAASUVORK5CYII=","orcid":"","institution":"Universidad Mayor","correspondingAuthor":true,"prefix":"","firstName":"Paulina","middleName":"","lastName":"Calderón-Romero","suffix":""}],"badges":[],"createdAt":"2025-09-11 22:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7595052/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7595052/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92970339,"identity":"f327aedd-bdf9-4599-b437-0c5c3ac56fb2","added_by":"auto","created_at":"2025-10-07 16:39:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1618105,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscritpPALPMCV2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/6cc99f847ebf21d3b669cc66.docx"},{"id":92970194,"identity":"3980ebfa-37c7-4caf-a080-dd056f4ab8fe","added_by":"auto","created_at":"2025-10-07 16:39:34","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10667,"visible":true,"origin":"","legend":"","description":"","filename":"d9ebfb906877470eb7f12f1e094cb76c.json","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/f70da03e7a7b670ffab5e0f3.json"},{"id":92970411,"identity":"322a98a8-f859-4d02-b3f1-6028ac81c165","added_by":"auto","created_at":"2025-10-07 16:39:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72268,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIAL.docx","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/0b01a1d26cf9780493f1ad37.docx"},{"id":92970262,"identity":"d3241f81-ecb1-45e5-884a-36484ab5b73f","added_by":"auto","created_at":"2025-10-07 16:39:35","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":160602,"visible":true,"origin":"","legend":"","description":"","filename":"d9ebfb906877470eb7f12f1e094cb76c1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/d2f100e6369361e897de6757.xml"},{"id":92970373,"identity":"2172cceb-fa04-4fd4-9e68-62d39635b03b","added_by":"auto","created_at":"2025-10-07 16:39:38","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41490,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/6bf4309f9565ecc474f99b53.png"},{"id":92970264,"identity":"8cd2c597-4a56-40af-8ed7-585dac4443e3","added_by":"auto","created_at":"2025-10-07 16:39:35","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":403998,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/ca87f4beeb4824abe0915171.jpeg"},{"id":92970372,"identity":"b1910d16-df9f-49a6-9bf0-f4b944dbf0ee","added_by":"auto","created_at":"2025-10-07 16:39:38","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134614,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/d5418ee327e88890ffb96d2b.png"},{"id":92970338,"identity":"e1a2f94e-9dbf-47f7-9025-33edcb182e09","added_by":"auto","created_at":"2025-10-07 16:39:37","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1101811,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/ca3d31767c0c953017631958.jpeg"},{"id":92970319,"identity":"28650903-ea47-4395-8efe-aba5f6f28c76","added_by":"auto","created_at":"2025-10-07 16:39:36","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13737,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/595f8f8081a719f7a2cbd74b.png"},{"id":92970409,"identity":"22deb05d-c36d-46f5-9684-6dc5688d7e73","added_by":"auto","created_at":"2025-10-07 16:39:41","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92230,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/e772ffccce9a3f37a1e34230.png"},{"id":92970259,"identity":"e73f9880-815a-4f78-8cb6-8f150fb7628b","added_by":"auto","created_at":"2025-10-07 16:39:35","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33095,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/6163de9a330f488198fdefc8.png"},{"id":92970278,"identity":"837c9b7c-6890-4f25-be65-a18c7545e444","added_by":"auto","created_at":"2025-10-07 16:39:36","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146506,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/1741be1cca79bdbb3f3eeaf0.png"},{"id":92970425,"identity":"1a1de462-318a-48f3-9ec3-3a2460aeab26","added_by":"auto","created_at":"2025-10-07 16:39:45","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":158064,"visible":true,"origin":"","legend":"","description":"","filename":"d9ebfb906877470eb7f12f1e094cb76c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/cc459b3adf046e5779c13d33.xml"},{"id":92970334,"identity":"83f55d56-9611-48b0-af55-3061b8663b03","added_by":"auto","created_at":"2025-10-07 16:39:36","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":175860,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/cfb7a57da6743e9dd3809fa3.html"},{"id":92970280,"identity":"92d022ce-83fa-467c-a947-69735ff0d34f","added_by":"auto","created_at":"2025-10-07 16:39:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109113,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTaxonomic characterization of the gut microbiome associated with different PALs. \u003c/strong\u003eThe relative abundance of microorganisms present in the gut microbiome of 233 participants from the cohort was analyzed using V4 region 16S rRNA sequencing via the Illumina system. Participants were categorized based on their PAL. This analysis provides a detailed view of the microbial composition at the phylum level and its association with physical activity habits, offering insights into how different levels of exercise may influence the diversity and structure of the gut microbiome.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/89791a55b51b8ae72bb10709.png"},{"id":92970380,"identity":"1f551b21-9c6c-4db3-91d9-bd066bfaa9e5","added_by":"auto","created_at":"2025-10-07 16:39:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153581,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhysical activity level modulates gut bacterial genera in normal‑BMI adults. \u003c/strong\u003eThis figure illustrates the bacterial genera whose relative abundances significantly differ among physical activity groups: low activity (blue), medium activity (green), and high activity (red). Statistical significance values are indicated: * p \u0026lt; 0.05, ** for p \u0026lt; 0.01; non-significant differences are marked as N.S.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/fdf89c4ccc0b40936106e506.png"},{"id":92970309,"identity":"11e3d4b2-cebd-4845-8057-a68ddde18329","added_by":"auto","created_at":"2025-10-07 16:39:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":222146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhysical activity level and BMI influence on gut microbiota genera abundance. \u003c/strong\u003eThis figure presents bacterial genera with significant differences in their relative abundances, intersecting the PALs, low activity (blue), medium activity (green), and high activity (red), with BMI categories: normal and overweight. The significance values are: * p \u0026lt; 0.05, ** p \u0026lt; 0.01; and non-significant differences are indicated as N.S.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/caab487c6f202dd2c2c6c301.png"},{"id":92970374,"identity":"5ff1bbb7-b595-4b1d-9121-1f4f90b61c3b","added_by":"auto","created_at":"2025-10-07 16:39:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":199583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of the metabolic potential of the gut microbiome.\u003c/strong\u003e(A) Illustration of the prevalence of GMMs among participants, with those exhibiting a prevalence above 90% displayed in dark gray and those with a prevalence below 90% in light gray. The dashed blue lines indicate the prevalence ranges between 5% and 90%. (B) The GMMs with significant differences in their relative abundances are presented, intersecting the PALs: low activity (blue), medium activity (green), and high activity (red). (C) GMMs with significant differences in relative abundance associated with PAV. The significance values are indicated as follows: * p \u0026lt; 0.05, ** p \u0026lt; 0.01; and non-significant differences are marked as N.S. In panel B, the PAV in minutes per week is displayed alongside some modules that demonstrate changes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/f3787c9f65d2d82c36ce5289.png"},{"id":94469876,"identity":"4462477a-ed33-498b-8a30-0027bdd845c9","added_by":"auto","created_at":"2025-10-27 15:30:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1343403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/aba56b6c-c2c3-471c-bd16-4cd7c6e23ac9.pdf"},{"id":92970258,"identity":"2f2ae3a5-a521-445f-a24f-b579e2bf1ec8","added_by":"auto","created_at":"2025-10-07 16:39:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":72268,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIAL.docx","url":"https://assets-eu.researchsquare.com/files/rs-7595052/v1/be9e76d66e45ae35cda6b45e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of physical activity level on adult gut microbiome composition and metabolic function","fulltext":[{"header":"1. Background","content":"\u003cp\u003eThe global increase in overweight and obesity has paralleled sociocultural development, prompting numerous efforts to improve dietary and physical activity habits especially in developed countries (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePhysical activity has been widely recognized for its role in preventing metabolic, musculoskeletal, neurodegenerative, inflammatory, and psychosocial diseases, and in promoting mood stability and overall quality of life (\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConcurrently, the human gut microbiota has emerged as a key regulator of host metabolism, immunity, and homeostasis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Disruptions in microbial balance, known as dysbiosis, are linked to gastrointestinal disorders and broader health outcomes (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Specific microbial genera, such as \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEubacterium\u003c/em\u003e, \u003cem\u003eCoprococcus\u003c/em\u003e, and \u003cem\u003eRoseburia\u003c/em\u003e, are known for producing short-chain fatty acids (SCFAs), which serve as crucial energy sources and signaling molecules (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudies suggest that physical activity may influence gut microbiota composition and metabolic function. For instance, athletes display distinct microbial profiles, while sedentary individuals and those with metabolic disorders show reduced diversity (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, much of this evidence comes from North America and Europe. A Swedish study found higher \u003cem\u003eEscherichia coli\u003c/em\u003e abundance in sedentary individuals and greater SCFA-producing bacteria in those with higher physical activity levels (PALs) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), but research in Latin America remains scarce despite high levels of physical inactivity in the region (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe evaluated how PAL (total minutes per week), physical activity frequency (PAF; days per week) and physical activity volume (PAV; MET-minutes per week), when considered alongside body mass index (BMI), relate to microbial diversity, taxonomy, and function. We observed no change in overall microbial diversity by PAL alone; however, when stratified by BMI, we identified 18 differential gut metabolic modules (GMMs), including carbohydrate degradation and SCFA production pathways, and shifts in the relative abundance of genera such as \u003cem\u003eDorea\u003c/em\u003e, \u003cem\u003eHoldemanella\u003c/em\u003e, and \u003cem\u003eParabacteroides\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThis study provides novel insights into the relationship between PAL, microbial metabolic potential, and host phenotype. It represents the first report from Latin America to explore these associations. Our results show that differences in microbial composition and function are associated with PAL when BMI is considered. Specifically, we observed alterations in GMMs related to carbohydrate, protein, and lipid metabolism, and changes in the abundance of bacterial genera. These findings contribute foundational knowledge that may inform future strategies to improve metabolic health through microbiota modulation.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStudy Population\u003c/span\u003e\u003c/p\u003e\u003cp\u003eParticipants in this study were Chilean men and women aged 18 to 70 years, residing in Chile, who had not lived abroad for more than three consecutive years. These individuals provided informed consent for the analysis of their stool microbiome. Informed consent was obtained from all the participants. To establish a health baseline, we excluded individuals with a Body Mass Index (BMI) below 18.5 or above 30, classifying them as normal weight and overweight based on participants' BMI. Additionally, participants who had used certain medications in the past month (antibiotics, antifungals, corticosteroids, cytokines, immunosuppressants, commercially available probiotics, including daily use and mouthwashes) were excluded, as well as those with a history of specific diagnosed diseases (pulmonary, cardiovascular, gastrointestinal, hepatic or renal diseases, HIV, Hepatitis B and/or C, autoimmune diseases). Individuals with a history of gastrointestinal tract diseases such as ulcerative colitis, Crohn's disease, irritable bowel syndrome, colitis, gastritis, and infections caused by \u003cem\u003eClostridium difficile\u003c/em\u003e or \u003cem\u003eHelicobacter pylori\u003c/em\u003e were also excluded, along with those who had a history of major gastrointestinal surgeries. Pregnant, nursing, or breastfeeding women were likewise excluded. Furthermore, participants who could not independently understand the physical activity questionnaire were excluded. These criteria were carefully established to minimize the presence of factors known to significantly influence the composition of the gut microbiota, ensuring a clearer understanding of the specific relationship between physical activity and the gut microbiota.\u003c/p\u003e\u003cp\u003eOf the participants with stool microbiome data (n\u0026thinsp;=\u0026thinsp;251), 233 completed the physical activity questionnaire and had BMI data; the remaining 18 (7.2%) were excluded due to missing information. Missing data were handled by listwise deletion, and no imputation was applied.\u003c/p\u003e\u003cp\u003eParticipants in this study were drawn from Chile\u0026rsquo;s three macro-zones: the north (7.29%, 17), the center (67.38%, 157), and the south (25.33%, 59), covering a large part of the national territory. The sample was not designed to be proportionally representative at the national level; representation is closer to population distribution in the center and south, whereas the north is underrepresented. Overall, 84.8% of participants reported university-level or higher education, which may limit generalizability to the national population.\u003c/p\u003e\u003cp\u003eIt is important to note that health information was self-reported by the participants and was not collected by a medical professional. Consequently, the reliability of the data depends on the accuracy of the participants' responses. This project has been approved by the Institutional Ethical Scientific Committee of Universidad Mayor No. 0274.\u003c/p\u003e\u003cp\u003e All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eData Collection\u003c/span\u003e\u003c/p\u003e\u003cp\u003eTo assess the physical activity habits of the participants, we utilized the IPAQ-SF (\"International Physical Activity Questionnaire - Short Form\"; Appendix 1). This instrument was validated through a systematic review in 2011 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) and subsequently translated into Spanish for populations in Spain (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and Mexico (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). It has also been applied to the Chilean population in local studies (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis tool is designed to gather specific information from the general population and determine their PAL, classifying them as low, medium, or high based on details about exercise intensity (light, moderate, and vigorous), physical activity frequency (PAF; sessions per week), and time spent per session (minutes) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). These variables, considered independently in previous studies, provide additional value for moderate to vigorous intensities (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), frequencies exceeding three times per week (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), or total weekly time spent (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). We administered the instrument to the study population using online forms, collecting data on the intensity, frequency, and weekly duration of physical activity for each participant. METs (Metabolic Equivalent of Task) are units of metabolic index used to classify activity intensity. The calculation was performed by multiplying the number of minutes per session by the number of sessions per week, yielding the weekly metabolic index units (METs x minutes per session x sessions per week\u0026thinsp;=\u0026thinsp;weekly metabolic index units). Activity intensity was classified as follows: low intensity was assigned 3.3 METs, medium intensity 4 METs, and high intensity 8 METs.\u003c/p\u003e\u003cp\u003eParticipants classified as having a low level of physical activity either did not engage in physical activity or did not meet the criteria for medium or high levels. The medium level of physical activity applies to those who engage in physical activity at least 5 times per week at a low to medium intensity for at least 30 minutes per session, or at least 3 times per week at a high intensity for 25 minutes, or a combination of medium to high intensities resulting in an energy expenditure of 600 METs/min/week. The high level of physical activity is defined as engaging in high physical activity at least 3 days per week, achieving an energy expenditure of 1500 METs/min/week, or participating in 7 or more sessions per week of any combined intensity that reaches 3000 METs/min/week.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSample Collection\u003c/span\u003e\u003c/p\u003e\u003cp\u003eEach participant was provided with a sampling kit that included tubes for gut microbiome collection (OMR-200; DNA Genotek) and a toilet accessory (OM-AC1; DNA Genotek). The gut microbiome sampling tube and the accessory paper are designed to facilitate self-collection of samples by participants in their own homes, maximizing convenience and minimizing potential disruptions to their daily routines. Once collected, the samples were returned to the research team and transported to the laboratory, with this process scheduled to not exceed two weeks. Upon arrival at the laboratory, samples were stored at room temperature and protected from light until DNA extraction was conducted within a month of sample collection. This home collection approach was essential for maintaining sample integrity and ensuring the reliability of our study, which focuses on the intricate relationship between physical activity and the composition of the gut microbiota.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSample Processing\u003c/span\u003e\u003c/p\u003e\u003cp\u003eDNA extraction was carried out using the Quick-DNA Fecal/Soil Microbe kit from ZYMO RESEARCH (D6010), in accordance with the manufacturer's instructions. To verify the integrity of the extracted DNA, quality control was performed using 1% agarose gel electrophoresis.\u003c/p\u003e\u003cp\u003eSequencing\u003c/p\u003e\u003cp\u003eDNA sequencing was conducted at Novogene (Beijing, China) using the Illumina NovaSeq PE250 platform. The V3-V4 region of the 16S rRNA gene was sequenced using the primers 341F: CCTAYGGGRBGCASCAG and 806R: GGACTACHVGGGTWTCTAAT.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eData Analysis\u003c/span\u003e\u003c/p\u003e\u003cp\u003eData analysis was performed in R version 4.1.2 within RStudio, utilizing the DADA2 and Phyloseq packages (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). We implemented a preprocessing step on the reads to remove the first 30 bases and discard low-quality reads. Standard software parameters were used, except for maxEE and truncQ, which were set to 2. For the learning error model, 1x10\u003csup\u003e8\u003c/sup\u003e bases were utilized. The taxonomy of ASVs was assigned using the SILVA V138 database as a reference. Additionally, ASVs that could not be taxonomically identified were discarded. Each sample contained at least 10,000 reads. Subsequent analyses included the Centered Log Ratio (CLR) transformation of the ASV matrix. We used the iNext library to calculate Shannon entropy index. A Principal Component Analysis (PCA) was conducted on CLR-transformed data using Euclidean distances, a technique previously cited in the literature for analyzing compositional data. For compositional analyses, we calculated relative abundances at the phylum and genus levels. PISCRUSt2 (version 2.5.2) was executed with default parameters to infer genetic content using the KEGG database as a reference (\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), and GMMs were determined using the omixerRpm and Tjazi packages. The GMM table was transformed to CLR using the vegan package. Statistical comparisons were performed as described in the following section, including correction for multiple testing using the Benjamini-Hochberg method.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStatistical Analysis\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThe Wilcoxon test was used for statistical comparisons, conducted in R. Significance levels were denoted as *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and N.S. for non-significant results. P-values were adjusted for multiple comparisons using the Benjamini-Hochberg method to control the false discovery rate (FDR), with a q-value threshold of \u0026lt;\u0026thinsp;0.15 considered significant. Asterisks in the figures indicate significance based on unadjusted p-values, and all reported associations also met the adjusted q-value criterion (q\u0026thinsp;\u0026lt;\u0026thinsp;0.15). Analyses were conducted in a blinded manner. A participant was considered to have provided a sample if they completed both questionnaires.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStudied population, anthropometric variables, and physical activity.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThis study included 149 female and 84 male Chilean participants, aged 19 to 69 years (mean\u0026thinsp;=\u0026thinsp;34 years), who met the inclusion criteria, did not meet any exclusion criteria, and were recruited from multiple zones across Chile (for more details see Methodology). The average BMI was 27.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4 kg/m\u0026sup2;, with 57% classified as normal weight and 43% as overweight (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePhysical activity variables were assessed using the self-reported IPAQ-SF, which categorizes PALs into low (L-PAL), medium (M-PAL), or high (H-PAL), based on validated MET scores. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In our sample, 39% of participants were classified as L-PAL, 34.4% M-PAL, and 26.6% as H-PAL.\u003c/p\u003e\u003cp\u003eIn this analysis, we focused exclusively on PAL, derived from the IPAQ-SF, as a proxy for total physical activity. Although the questionnaire also captures sedentary behavior and strength training, these were excluded to prioritize the relationship between general activity patterns and the gut microbiome. Future studies may further explore these complementary dimensions.\u003c/p\u003e\u003cp\u003eFrom participants\u0026rsquo; responses, we derived the PAF as the total number of activity instances per week, summing vigorous, moderate, and walking activities. The overall mean PAF was 8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 times/week. When stratified by PAL, participants with H-PAL had a training frequency of 12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 times/week, those with M-PAL trained 8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 times/week, and those with L-PAL trained 6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 times/week. This measure reflects the total number of activity occurrences but not necessarily the number of distinct days of activity.\u003c/p\u003e\u003cp\u003eWe calculated the PAV as the total number of minutes of activity per week. The mean PAV in our sample was 504.3\u0026thinsp;\u0026plusmn;\u0026thinsp;403.9 min/week, with the following distribution: H-PAL reported 913.7\u0026thinsp;\u0026plusmn;\u0026thinsp;512.7 min/week of physical activity, those with M-PAL reported 468.8\u0026thinsp;\u0026plusmn;\u0026thinsp;188.8 min/week, and those with L-PAL reported 256.7\u0026thinsp;\u0026plusmn;\u0026thinsp;177.4 min/week.\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\u003eCharacteristics of the study population stratified by sex and physical activity variables (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, SD).\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNumber of participants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e149 (64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBody Mass Index (BMI; Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNormal weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (38.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (61.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e133 (57%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67 (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100 (43%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePhysical Activity Level (PAL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69 (75.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91 (39%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80 (34.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (48.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (51.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62 (26.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePhysical Activity Frequency (PAF)\u003c/p\u003e\u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, SD) (times/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePhysical Activity Volume (PAV) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, SD) (min/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e253.4\u0026thinsp;\u0026plusmn;\u0026thinsp;183.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e257.8\u0026thinsp;\u0026plusmn;\u0026thinsp;176.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e256.7\u0026thinsp;\u0026plusmn;\u0026thinsp;177.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e488.9\u0026thinsp;\u0026plusmn;\u0026thinsp;195.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e393.4\u0026thinsp;\u0026plusmn;\u0026thinsp;199.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e468.8\u0026thinsp;\u0026plusmn;\u0026thinsp;188.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e902.8\u0026thinsp;\u0026plusmn;\u0026thinsp;358.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e923.8\u0026thinsp;\u0026plusmn;\u0026thinsp;629.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e913.7\u0026thinsp;\u0026plusmn;\u0026thinsp;512.7\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\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eRelationship Between Gut Microbiome Bacterial and Physical Activity, and Body Mass Index.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThis study examined the taxonomic composition of the gut microbiome in a cohort of 233 Chilean adults to evaluate its relationship with physical activity and other anthropometric parameters. We employed 16S rRNA sequencing analysis to identify microbial variations associated with different PALs and BMI. At the phylum level, which provides a broad overview of microbial structure, the four most abundant phyla across all PAL categories were Firmicutes, Bacteroidota, Actinobacteriota, and Proteobacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Firmicutes was the most predominant across all PAL groups (L-PAL: mean\u0026thinsp;=\u0026thinsp;68.3%, SD\u0026thinsp;=\u0026thinsp;12.8; M-PAL: mean\u0026thinsp;=\u0026thinsp;66.4%, SD\u0026thinsp;=\u0026thinsp;12.1; H-PAL: mean\u0026thinsp;=\u0026thinsp;68.1%, SD\u0026thinsp;=\u0026thinsp;11.7), followed by Bacteroidota (L-PAL: mean\u0026thinsp;=\u0026thinsp;24.9%, SD\u0026thinsp;=\u0026thinsp;11.5; M-PAL: mean\u0026thinsp;=\u0026thinsp;26%, SD\u0026thinsp;=\u0026thinsp;11.9; H-PAL: mean\u0026thinsp;=\u0026thinsp;25.8%, SD\u0026thinsp;=\u0026thinsp;11.7) and Proteobacteria (L-PAL: mean\u0026thinsp;=\u0026thinsp;1.8%, SD\u0026thinsp;=\u0026thinsp;3; M-PAL: mean\u0026thinsp;=\u0026thinsp;2.3%, SD\u0026thinsp;=\u0026thinsp;3.7; H-PAL: mean\u0026thinsp;=\u0026thinsp;1.6%, SD\u0026thinsp;=\u0026thinsp;1.7). However, no statistically significant differences were found in phylum level composition across PAL or BMI categories.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe also assessed alpha diversity using the Shannon index, which showed no significant differences among PALs (L-PAL\u0026thinsp;=\u0026thinsp;2.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33, M-PAL\u0026thinsp;=\u0026thinsp;2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30, H-PAL\u0026thinsp;=\u0026thinsp;2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33; Supplementary Fig.\u0026nbsp;1A). Similarly, beta diversity analysis did not reveal any clustering or significant associations among PAL or BMI groups (Supplementary Fig.\u0026nbsp;1B).\u003c/p\u003e\u003cp\u003eNo significant differences in gut microbiota composition were observed across PAL groups, and PAF, PAV, and sex also showed no significant associations. Only when PAL was stratified by BMI did specific bacterial genera exhibit differential abundance.\u003c/p\u003e\u003cp\u003eAmong individuals with normal weight, L-PAL was associated with increased abundance of \u003cem\u003eAdlercreutzia\u003c/em\u003e, \u003cem\u003eLachnospira\u003c/em\u003e and \u003cem\u003eMonoglobus\u003c/em\u003e compared to M-PAL (q\u0026thinsp;=\u0026thinsp;0.064, q\u0026thinsp;=\u0026thinsp;0.085, q\u0026thinsp;=\u0026thinsp;0.104, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConversely, \u003cem\u003eDorea\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e, and \u003cem\u003eParaprevotella\u003c/em\u003e were less abundant in L-PAL relative to M-PAL (q\u0026thinsp;=\u0026thinsp;0.051, q\u0026thinsp;=\u0026thinsp;0.085, q\u0026thinsp;=\u0026thinsp;0.085; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003eCollinsella\u003c/em\u003e abundance was higher in H-PAL than in L-PAL (q\u0026thinsp;=\u0026thinsp;0.085; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, \u003cem\u003eDorea\u003c/em\u003e abundance was higher in H-PAL than in M-PAL (q\u0026thinsp;=\u0026thinsp;0.050; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eComparing M-PAL and H-PAL groups, \u003cem\u003eHoldemanella\u003c/em\u003e and \u003cem\u003eParaprevotella\u003c/em\u003e were more abundant in M-PAL (q\u0026thinsp;=\u0026thinsp;0.091 and q\u0026thinsp;=\u0026thinsp;0.085, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, \u003cem\u003eParaprevotella\u003c/em\u003e was also increased in M-PAL compared to L-PAL (q\u0026thinsp;=\u0026thinsp;0.085). In contrast, among overweight individuals no statistically significant differences in bacterial genera were observed across PAL categories.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe analysis of the relative abundances of specific bacterial genera reveals significant differences associated with BMI and PAL. Specifically, the genus \u003cem\u003eParabacteroides\u003c/em\u003e exhibited an increase in its relative abundance in individuals with a normal BMI (q\u0026thinsp;=\u0026thinsp;0.019; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, it was noted that the relative abundances of the genus \u003cem\u003eDorea\u003c/em\u003e increased in individuals overweight, suggesting a possible correlation between these genus and weight gain (q\u0026thinsp;=\u0026thinsp;0.023, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the case of \u003cem\u003eHoldemanella\u003c/em\u003e, an increase in its relative abundance was observed in individuals reporting H-PAL, indicating an association between this genus and a more active lifestyle (q\u0026thinsp;=\u0026thinsp;0.131; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, \u003cem\u003eLachnospiraceae ND3007 group\u003c/em\u003e and \u003cem\u003eRomboutsia\u003c/em\u003e exhibit distinct patterns in L-PAL. Specifically, a decrease in \u003cem\u003eLachnospiraceae ND3007 group\u003c/em\u003e was observed in overweight individuals compared to those with normal weight, while \u003cem\u003eRomboutsia\u003c/em\u003e showed an increase in overweight individuals (q\u0026thinsp;=\u0026thinsp;0.131 and q\u0026thinsp;=\u0026thinsp;0.131, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eImpact of PAL and PAV on Gut Metabolic Modules\u003c/span\u003e\u003c/p\u003e\u003cp\u003eTo revealed insights into microbiome functionality we performed a detailed examination of the microbiota\u0026rsquo;s metabolic potential. By sequencing the V3-V4 region of the 16S rRNA gene and applying PICRUSt2 functional prediction (see Methodology), we inferred the gut microbiome\u0026rsquo;s potential gene repertoire and organized these predicted genes into GMMs. This analysis revealed 39 GMMs, accounting for 37.8% of the total evaluated, involved in various metabolic pathways within the gut microbiome of the studied cohort. By interpreting the genetic content inferred from 16S rRNA sequencing, we identified specific GMMs. This analysis revealed 39 GMMs, accounting for 37.8% of the total evaluated, involved in various metabolic pathways within the gut microbiome of the studied cohort.\u003c/p\u003e\u003cp\u003e The distribution of these GMMs exhibited notable uniformity, with 33 presents in over 90% of individuals, emphasizing a common metabolic basis in the participants\u0026rsquo; gut microbiome. Among these, proteolysis pathways were particularly noteworthy because every protein metabolism module detected corresponded exclusively to degradation processes, highlighting the microbiome\u0026rsquo;s consistent capacity to break down essential amino acids such as arginine, threonine, cysteine, serine, glutamate, aspartate, valine, isoleucine, methionine, phenylalanine and proline. Additionally, we identified modules associated with carbohydrate metabolism, such as the degradation of various sugars and key pathways like glycolysis and lactate production. Other relevant modules included those related to glycerol degradation, conversion of acetyl-CoA to crotonyl-CoA and acetate, as well as butyrate and propionate production (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eApproximately 48.7% of the GMMs were involved in saccharolytic metabolism, while 43.6% participated in other metabolic processes. When analyzing the abundance of these modules according to PAL, we observed that those present in more than 90% of individuals maintained high abundance across all PAL categories. In contrast, less prevalent modules showed decreased abundance, a pattern consistent across all PALs.\u003c/p\u003e\u003cp\u003eThese findings underscore the relevance of GMMs in regulating intestinal metabolism and suggest a correlation between module prevalence and abundance, highlighting a role of the gut microbiome in sustaining diverse metabolic functions.\u003c/p\u003e\u003cp\u003eOur analysis revealed an association between the abundance of specific GMMs and PAL. We identified 18 modules with significantly different abundances across PAL groups, particularly between M-PAL and H-PAL, as well as between L-PAL and M-PAL. Notably, no significant differences were found between the extremes of activity (L-PAL and H-PAL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eModules showing reduced abundance in L-PAL compared to M-PAL included glycerol degradation I, propionate production II, and valine degradation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), suggesting a metabolic shift in response to increased physical activity. Conversely, GMMs related to proteolysis, such as arginine degradation IV and V, aspartate degradation I, cysteine degradation II, methionine degradation I, and threonine degradation I and II, were less abundant in H-PAL compared to M-PAL (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). This trend was not observed between L-PAL and H-PAL, suggesting a threshold beyond which further increases in activity suppress proteolysis pathways.\u003c/p\u003e\u003cp\u003eCarbohydrate degradation modules, including galactose degradation, glycerol degradation I and II, glycolysis (pay-off-phase), and mannose degradation, melibiose degradation, sucrose degradation I, and trehalose degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), also showed reduced abundance in both M-PAL and H-PAL compared to L-PAL. This pattern mirrors findings from proteolysis pathways, reinforcing the notion of metabolic adaptation linked to PAL.\u003c/p\u003e\u003cp\u003eThese results illustrate the complex interplay between physical activity and the functional composition of the gut microbiome, highlighting how varying levels of exercise can differentially modulate microbial metabolism.\u003c/p\u003e\u003cp\u003eFinally, we observed a clear influence of PAV on GMM abundance. As weekly time dedicated to physical activity increased, several modules showed significant changes. Specifically, higher PAV was associated with increased abundance of modules involved in ribose degradation; isoleucine degradation; butyrate production II; acetyl-CoA to crotonyl-CoA conversion; propionate production II; lactate production; lactose and galactose degradation; 4-aminobutyrate degradation; and valine degradation I (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Conversely, modules involved in glutamate degradation II, putrescine degradation, and anaerobic fatty acid beta-oxidation decreased in abundance with increasing PAV (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePhysical activity plays a crucial role in overall health (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Numerous studies in pathological populations show that regular exercise reduces the impact of diverse conditions, including cancers, neurodegenerative diseases, psychiatric disorders, autoimmune conditions, musculoskeletal disorders, chronic pain, pathological aging, and metabolic disorders (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). In non-pathological populations and athletes, habitual physical activity lowers the risk of developing chronic disease across the life course (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Moreover, it has been established that individuals who exercise frequently experience improved quality of life and self-esteem, personal potential development, feelings of self-satisfaction and self-fulfillment, healthy longevity, and the formation of secure interpersonal relationships (\u003cspan additionalcitationids=\"CR42 CR43 CR44 CR45\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral studies have also highlighted a bidirectional crosstalk between skeletal muscle and the gut, often referred to as the gut-muscle axis (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). During exercise, contracting muscle fibers release myokines, which exert systemic anti-inflammatory effects (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) that can influence gut barrier function, microbial composition, and metabolite production (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Conversely, gut-derived metabolites such as SCFAs may modulate muscle metabolism and performance, underscoring the integrated nature of this axis in maintaining host health. A balanced and diverse gut microbiome is crucial for healthy food digestion, robust immune function, and the prevention of metabolic diseases (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe relationship between PALs (low, medium, high) and the gut microbiome is an emerging field of study that remains underexplored. In this Chilean adult cohort, we classified PALs using the IPAQ-SF and examined associations with gut microbiota composition and functional potential. Our findings suggest that the metabolic potential of gut bacteria varies according to PAL, raising the possibility that physical activity could influence the functionality of the gut microbiota. Furthermore, both BMI and PAL are related to the composition of the gut microbiota, affecting the abundance of certain bacterial genera.\u003c/p\u003e\u003cp\u003eThe IPAQ-SF is a validated instrument for self-reporting physical activity at international (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and national (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) levels. However, it is important to consider that individuals tend to overestimate both their PAL, and the time spent in sedentary behavior [40], which should be considered when interpreting our results. The instrument captures frequency and duration of walking, moderate, and vigorous activity, as well as sitting time (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). This frequency measure may overestimate the number of active days because the IPAQ records days for walking, moderate, and vigorous activity separately, so multiple sessions or intensities performed on the same day can be counted more than once (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough we observed no significant differences in alpha diversity by PAL alone, higher alpha diversity has been reported in elite athletes compared with sedentary controls without a consistent relationship to activity level. This suggests that shifts in community composition may be more sensitive to physical activity than diversity measures.\u003c/p\u003e\u003cp\u003eOur results for M-PAL were inconclusive, reinforcing the need for objective measurement (e.g., accelerometry) and larger sample sizes. In a large Swedish cohort (n\u0026thinsp;=\u0026thinsp;8,416) assessed by accelerometry, taxonomic differences at the species level were detected across physical activity categories, though patterns differed between vigorous and moderate activity (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Such work illustrates the value of device-based measures for dissecting dose-response relationships.\u003c/p\u003e\u003cp\u003eAll functional insights in this study derive from 16S rRNA based taxonomic profiles mapped to GMMs rather than directly measured genes, transcripts, or metabolites; consequently, pathway presence reflects predicted potential and not confirmed activity. Future studies using whole-genome shotgun sequencing and complementary omics will be needed for more precise metabolic profiling (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e From a functional potential perspective, 37.8% of the identified GMMs were present in \u0026ge;\u0026thinsp;90% of participants, suggesting a shared core of microbial metabolic functions in this cohort. The GMMs that displayed PAL-related differences were dominated by degradation pathways, with the exceptions of propionate production II and the payoff (energy-yielding) phase of glycolysis. This pattern underscores the role of the gut microbiome in macromolecule turnover.\u003c/p\u003e\u003cp\u003eWe observed a PAL-associated duality in protein metabolism, amino acid degradation pathways were more prominent in M-PAL than in H-PAL. Arginine, aspartate, and cysteine degradation tracked with this pattern; valine degradation increased in M-PAL vs L-PAL. With increasing PAV isoleucine degradation increased, whereas glutamate and putrescine degradation declined.\u003c/p\u003e\u003cp\u003eThese trends may reflect differing energetic demands: certain amino acids (e.g., isoleucine) can feed gluconeogenic/ketogenic pathways under activity-related energy stress (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Glutamate plays an active role in the nervous system (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), putrescine has been implicated in muscle protein synthesis and growth (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). In a lifestyle intervention among older adults, greater increases in activity were accompanied by parallel shifts in amino acid degradation and energy-precursor pathways, supporting links between exercise and microbiome functional capacity (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCarbohydrate-metabolism GMMs decreased in H-PAL relative to M-PAL, suggesting that higher activity levels may remodel microbial carbohydrate processing or reflect substrate competition under elevated energy turnover. Propionate production II was higher in M-PAL vs L-PAL, potentially reflecting increased microbial fermentation to meet host energy needs. Propionate can support energy balance and stimulate further fermentation, increasing SCFA yield available for absorption (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Several studies have also reported positive associations between physical activity or cardiorespiratory fitness and fecal SCFA concentrations (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). SCFAs are readily absorbed, fuel colonocytes, enter peripheral circulation, and act as key mediators of skeletal muscle mitochondrial energy metabolism that contribute to whole-body glucose homeostasis. This provides further evidence implicating the gut-muscle axis in exercise responses (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePhysical activity has been linked to higher fecal butyrate and enrichment of butyrate-producing taxa (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Indeed, fecal butyrate concentrations have been positively associated with cardiorespiratory fitness (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Exercise-induced alterations in gut barrier physiology may favor butyrogenic bacteria (\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Butyrate can stimulate PGC-1α expression, mitochondrial biogenesis, β-oxidation, and glucose transport in muscle, enhancing oxidative capacity (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Acetate and butyrate also promote muscle fat oxidation, improving metabolic flexibility and the ability to switch between lipid and carbohydrate fuels (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). Butyrate\u0026rsquo;s histone deacetylase inhibition may protect against muscle protein catabolism and age-related muscle loss (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Medium- to long-term exercise interventions increase SCFA concentrations (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), which scale positively with PAL (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e) and cardiorespiratory fitness (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). This finding reinforces exercise as a driver of SCFA-linked muscle-gut signaling. Metagenomic work combining diet and activity interventions has likewise shown coordinated shifts in metabolite biosynthesis and degradation pathways, indicating that lifestyle change can remodel both microbiome structure and function (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast to a previous study conducted in Chile in 2017, which found that the phylum Verrucomicrobia (now known as Verrucomicrobiota) was the third most abundant at 8.5% relative abundance (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e), our analysis indicates that this phylum is the fifth most abundant, with 0.54% abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In part, this discrepancy may be due to the distinct characteristics of the sample, as it included only 41 normal-weight individuals aged 18 to 39 from the city of Santiago.\u003c/p\u003e\u003cp\u003e\u003cem\u003eParaprevotella\u003c/em\u003e produces succinate, which can engage pro-inflammatory signaling; links to hypertension and metabolic/inflammatory states have been reported (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Its enrichment in M-PAL vs both L- and H-PAL highlights the complex, non-linear relationships between self-reported activity and microbial taxa. Misclassification of PAL, dietary heterogeneity, or unmeasured host factors could contribute.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCollinsella\u003c/em\u003e has been associated with dysglycemia, atherosclerotic risk, and adverse lipid profiles (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Its relative reduction in L-PAL vs H-PAL could point (cautiously) toward activity-related metabolic benefits.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDorea\u003c/em\u003e has been linked to glucose metabolism and higher abundance in prediabetes and obesity (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e); its increase in normal-weight M- and H-PAL groups may reflect activity-BMI interactions that warrant targeted follow-up.\u003c/p\u003e\u003cp\u003e\u003cem\u003eHoldemanella\u003c/em\u003e produces both SCFAs (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e) that can supply energy during prolonged light-to-moderate activity (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e) and has been tied to improved carbohydrate metabolism and anti-inflammatory effects in experimental models (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). In our cohort, \u003cem\u003eHoldemanella\u003c/em\u003e decreased in normal-weight H-PAL vs M-PAL but increased in overweight H-PAL, suggesting that host adiposity shapes microbial responses to activity.\u003c/p\u003e\u003cp\u003e\u003cem\u003eLachnospira\u003c/em\u003e, a producer of butyrate and propionate, is inversely associated with body fat and adverse lipid profiles (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). Its decrease from L-PAL to M-PAL (but not to H-PAL) again underscores non-linear patterns in self-reported activity categories. These SCFAs are rapidly absorbed and routed into fatty acid metabolic pathways (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). High-performance athletes with large training volumes often show enrichment of SCFA-producing taxa (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe PAL-associated differences we observed, particularly in amino acid degradation, SCFA production, and lipid/energy metabolism modules, map onto pathways implicated in glycemic regulation, inflammation, muscle energetics, and cardiometabolic risk. For example, propionate and butyrate related functions could support metabolic flexibility and muscle-gut signaling, whereas shifts in \u003cem\u003eCollinsella\u003c/em\u003e or \u003cem\u003eParaprevotella\u003c/em\u003e may align with cardiometabolic or inflammatory states. Interpretation should be cautious because this cross-sectional, 16S inference-based study lacks direct metabolite, inflammatory, dietary, and clinical measures. Because data were collected at a single time point, temporal directionality and causality cannot be established. Still, the patterns highlight candidate microbial functions through which physical activity might confer metabolic benefits and suggest testable targets for longitudinal and intervention studies that integrate objective activity tracking, dietary assessment, metagenomics, and metabolomics.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur findings show that self-reported PAL and PAV relate differentially to gut microbial composition and predicted metabolic functions in a Chilean human gut microbiome cohort to integrate 16S based functional profiling (GMMs) with multiple physical activity metrics and BMI. Because physical activity is a modifiable behavior worldwide, the PAL and PAV associated shifts we observed in amino acid degradation and SCFAs and energy related pathways highlight microbiome functions that may be leveraged to improve metabolic health across diverse populations. Future longitudinal and intervention studies that pair accelerometry with whole genome sequencing, fecal and plasma metabolomics, dietary assessment, and structured exercise dosing are needed to validate these associations and identify responsive microbial targets. Such evidence could inform precision activity prescriptions, potentially stratified by BMI and microbiome profile, to modulate gut metabolic capacity and support cardiometabolic and functional health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003ePAL: Physical Activity Level\u003c/p\u003e\n\u003cp\u003eL-PAL: Low Physical Activity Level\u003c/p\u003e\n\u003cp\u003eM-PAL: Medium Physical Activity Level\u003c/p\u003e\n\u003cp\u003eH-PAL: High Physical Activity Level\u003c/p\u003e\n\u003cp\u003eSCFAs: Short-Chain Fatty Acids\u003c/p\u003e\n\u003cp\u003ePAV:\u0026nbsp;physical activity volume\u003c/p\u003e\n\u003cp\u003ePAF:\u0026nbsp;physical activity frequency\u003c/p\u003e\n\u003cp\u003eGMM:\u0026nbsp;Gut Metabolic Modules\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIPAQ-SF:\u0026nbsp;International Physical Activity Questionnaire - Short Form\u003c/p\u003e\n\u003cp\u003eMET: Metabolic Equivalent (kcal/kg/hour)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: This study was approved by the Institutional Ethical Scientific Committee of Universidad Mayor (Protocol No. 0274). Informed consent was obtained from all the participants. The research team worked exclusively with de-identified data and had no access to personal identifiers.\u003c/p\u003e\n\u003cp\u003eConsent for publication: All authors have read and approved the final version of the manuscript and consent to its publication.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: The data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB91588 (https://www.ebi.ac.uk/ena/browser/view/PRJEB91588).\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding: This work was supported by Proyecto P29 \u0026ldquo;Microbioma Chileno\u0026rdquo;, grant 16PTECAI-66648 -P16, IFAN, CORFO.\u003c/p\u003e\n\u003cp\u003eGeroscience Center for Brain Health and Metabolism FONDAP-15150012 and Financiamiento Basal para Centros Cient\u0026iacute;ficos y Tecnol\u0026oacute;gicos de Excelencia Centro Ciencia y Vida, FB210008 (FAC).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions: PCR, XL, AV, AP, JM, and SMM were responsible for conceptualization, methodology, and experimental/analytical design; PCR prepared the visualizations, and together with AV, conducted the formal analysis, investigation, and data curation; CSD, PMA and GMM provided resources; PCR and AV drafted the original manuscript; all authors reviewed and edited the paper; and XL, AP, and FAC secured funding. All authors read and approved the final version.\u003c/p\u003e\n\u003cp\u003eAcknowledgments: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePanter, J., Tanggaard Andersen, P., Aro, A. R. \u0026amp; Samara, A. Obesity Prevention: A Systematic Review of Setting-Based Interventions from Nordic Countries and the Netherlands. \u003cem\u003eJ. Obes.\u003c/em\u003e \u003cb\u003e2018\u003c/b\u003e, 1\u0026ndash;34 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhrens, W. et al. Dietary behaviour and physical activity policies in Europe: learnings from the Policy Evaluation Network (PEN). \u003cem\u003eEur. J. Public. Health\u003c/em\u003e. \u003cb\u003e32\u003c/b\u003e (Supplement_4), iv114\u0026ndash;iv125 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlundell, J. E. et al. Variations in the Prevalence of Obesity Among European Countries, and a Consideration of Possible Causes. \u003cem\u003eObes. Facts\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e (1), 25\u0026ndash;37 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO. WHO European Regional Obesity Report. Licence: CC BY-NC-SA 3.0 IGO. 2022; (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMunir, M. et al. Global human obesity and political globalization; asymmetric relationship through world human development levels. \u003cem\u003eNutr. Health\u003c/em\u003e. \u003cb\u003e30\u003c/b\u003e (3), 489\u0026ndash;497 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHong, S. et al. Association between cardiorespiratory fitness and the prevalence of metabolic syndrome among Korean adults: a cross sectional study. \u003cem\u003eBMC Public. Health\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e (1), 481 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChomiuk, T., Niezgoda, N., Mamcarz, A. \u0026amp; Śliż, D. Physical activity in metabolic syndrome. \u003cem\u003eFront. Physiol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1365761 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe La Corte-Rodriguez, H., Roman-Belmonte, J. M., Resino-Luis, C., Madrid-Gonzalez, J. \u0026amp; Rodriguez-Merchan, E. C. The Role of Physical Exercise in Chronic Musculoskeletal Pain: Best Medicine\u0026mdash;A. \u003cem\u003eNarrative Rev. Healthc.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (2), 242 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStubbs, B., Chen, L. J., Chang, C. Y., Sun, W. J. \u0026amp; Ku, P. W. Accelerometer-assessed light physical activity is protective of future cognitive ability: A longitudinal study among community dwelling older adults. \u003cem\u003eExp. Gerontol.\u003c/em\u003e \u003cb\u003e91\u003c/b\u003e, 104\u0026ndash;109 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurini, R. C., Anderson, E., Durstine, J. L. \u0026amp; Carson, J. A. Inflammation, physical activity, and chronic disease: An evolutionary perspective. \u003cem\u003eSports Med. Health Sci.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e (1), 1\u0026ndash;6 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValdes, A. M., Walter, J., Segal, E. \u0026amp; Spector, T. D. Role of the gut microbiota in nutrition and health. \u003cem\u003eBMJ\u003c/em\u003e ;k2179. (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTamboli, C. P. Dysbiosis in inflammatory bowel disease. \u003cem\u003eGut\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e (1), 1\u0026ndash;4 (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQiu, P. et al. The Gut Microbiota in Inflammatory Bowel Disease. \u003cem\u003eFront. Cell. Infect. Microbiol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 733992 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorrison, D. J. \u0026amp; Preston, T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. \u003cem\u003eGut Microbes\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e (3), 189\u0026ndash;200 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrzewł\u0026oacute;cka, K., Folwarski, M., Kaźmierczak-Siedlecka, K., Skonieczna-Żydecka, K. \u0026amp; Kaczor, J. J. Gut-Muscle Axis Exists and May Affect Skeletal Muscle Adaptation to Training. \u003cem\u003eNutrients\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (5), 1451 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamamah, S., Hajnal, A. \u0026amp; Covasa, M. Influence of Bariatric Surgery on Gut Microbiota Composition and Its Implication on Brain and Peripheral Targets. \u003cem\u003eNutrients\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (7), 1071 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaldanzi, G. et al. Accelerometer-based physical activity is associated with the gut microbiota in 8416 individuals in SCAPIS. \u003cem\u003eeBioMedicine\u003c/em\u003e \u003cb\u003e100\u003c/b\u003e, 104989 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQian, H. et al. Impact of exercise training on gut microbiome imbalance in obese individuals: a study based on Mendelian randomization analysis. \u003cem\u003eFront. Physiol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1264931 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolzhausen, E. A. et al. Assessing the relationship between physical activity and the gut microbiome in a large, population-based sample of Wisconsin adults. Fatouros IG, editor. PLOS ONE. ;17(10):e0276684. (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee, P. H., Macfarlane, D. J., Lam, T. \u0026amp; Stewart, S. M. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. \u003cem\u003eInt. J. Behav. Nutr. Phys. Act.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (1), 115 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoman-Vi\u0026ntilde;as, B. et al. International Physical Activity Questionnaire: Reliability and validity in a Spanish population. \u003cem\u003eEur. J. Sport Sci.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (5), 297\u0026ndash;304 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMedina, C., Barquera, S. \u0026amp; Janssen, I. Validity and reliability of the International Physical Activity Questionnaire among adults in Mexico. \u003cem\u003eRev. Panam. Salud Publica Pan Am. J. Public. Health\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e (1), 21\u0026ndash;28 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSer\u0026oacute;n, P., Mu\u0026ntilde;oz, S. \u0026amp; Lanas, F. Nivel de actividad f\u0026iacute;sica medida a trav\u0026eacute;s del cuestionario internacional de actividad f\u0026iacute;sica en poblaci\u0026oacute;n Chilena. Rev M\u0026eacute;dica Chile [Internet]. 2010 Oct [cited 2024 Mar 20];138(10). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.scielo.cl/scielo.php?script=sci_arttext\u0026amp;pid=S0034-98872010001100004\u0026amp;lng=en\u0026amp;nrm=iso\u0026amp;tlng=en\u003c/span\u003e\u003cspan address=\"http://www.scielo.cl/scielo.php?script=sci_arttext\u0026amp;pid=S0034-98872010001100004\u0026amp;lng=en\u0026amp;nrm=iso\u0026amp;tlng=en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHagstr\u0026ouml;mer, M., Oja, P. \u0026amp; Sj\u0026ouml;str\u0026ouml;m, M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. \u003cem\u003ePublic. Health Nutr.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (6), 755\u0026ndash;762 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBull, F. C. et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. \u003cem\u003eBr. J. Sports Med.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e (24), 1451\u0026ndash;1462 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarber, C. E. et al. Quantity and Quality of Exercise for Developing and Maintaining Cardiorespiratory, Musculoskeletal, and Neuromotor Fitness in Apparently Healthy Adults: Guidance for Prescribing Exercise. \u003cem\u003eMed. Sci. Sports Exerc.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e (7), 1334\u0026ndash;1359 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCraig, C. L. et al. International Physical Activity Questionnaire: 12-Country Reliability and Validity. \u003cem\u003eMed. Sci. Sports Exerc.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e (8), 1381\u0026ndash;1395 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCallahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. \u003cem\u003eNat. Methods\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e (7), 581\u0026ndash;583 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcMurdie, P. J. \u0026amp; Holmes, S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. Watson M, editor. PLoS ONE. ;8(4):e61217. (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVieira-Silva, S. et al. Species\u0026ndash;function relationships shape ecological properties of the human gut microbiome. \u003cem\u003eNat. Microbiol.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e (8), 16088 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBastiaanssen, T. F. S., Quinn, T. P. \u0026amp; Loughman, A. Bugs as features (part 1): concepts and foundations for the compositional data analysis of the microbiome\u0026ndash;gut\u0026ndash;brain axis. \u003cem\u003eNat. Ment Health\u003c/em\u003e. \u003cb\u003e1\u003c/b\u003e (12), 930\u0026ndash;938 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBastiaanssen, T. F. S., Quinn, T. P. \u0026amp; Loughman, A. Bugs as features (part 2): a perspective on enriching microbiome\u0026ndash;gut\u0026ndash;brain axis analyses. \u003cem\u003eNat. Ment Health\u003c/em\u003e. \u003cb\u003e1\u003c/b\u003e (12), 939\u0026ndash;949 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO. The inequality epidemic: low-income teens face higher risks of obesity, inactivity and poor diet. ; (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuegsegger, G. N. \u0026amp; Booth, F. W. Health Benefits of Exercise. \u003cem\u003eCold Spring Harb Perspect. Med.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (7), a029694 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePray, L. The Human Microbiome, Diet, and Health [Internet]. 1ra ed. Vol. 1. Washington DC: The National Academies Press.; 196 p. (2013). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nap.edu\u003c/span\u003e\u003cspan address=\"http://www.nap.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValles-Colomer, M. et al. The neuroactive potential of the human gut microbiota in quality of life and depression. \u003cem\u003eNat. Microbiol.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e (4), 623\u0026ndash;632 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnderson, E. \u0026amp; Durstine, J. L. Physical activity, exercise, and chronic diseases: A brief review. \u003cem\u003eSports Med. Health Sci.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e (1), 3\u0026ndash;10 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKissow, A. M. Participation in physical activity and the everyday life of people with physical disabilities: a review of the literature. \u003cem\u003eScand. J. Disabil. Res.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (2), 144\u0026ndash;166 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWarburton, D. E. R. Health benefits of physical activity: the evidence. \u003cem\u003eCan. Med. Assoc. J.\u003c/em\u003e \u003cb\u003e174\u003c/b\u003e (6), 801\u0026ndash;809 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaqib, Z. A. et al. Physical Activity is a Medicine for Non-Communicable Diseases: A Survey Study Regarding the Perception of Physical Activity Impact on Health Wellbeing. \u003cem\u003eRisk Manag Healthc. Policy\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 2949\u0026ndash;2962 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDa Silva, C. C. M. et al. The Association between Perceived Stress, Quality of Life, and Level of Physical Activity in Public School Teachers. \u003cem\u003eInt. J. Environ. Res. Public. Health\u003c/em\u003e. \u003cb\u003e21\u003c/b\u003e (1), 88 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHovland, J. F., Langeland, E., Ness, O. \u0026amp; Skogvang, B. O. Experiences with physical activity, health and well-being among young adults with serious mental illness. \u003cem\u003eInt. J. Qual. Stud. Health Well-Being\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e (1), 2221911 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhite, R. L., Ryan, D., Young, C., Elston, R. \u0026amp; Rossi, T. How does the context of physical activity influence perceived mood and wellbeing after exercise? \u003cem\u003eMent Health Phys. Act.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 100504 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarrison, E. A. et al. Perceptions, Opinions, Beliefs, and Attitudes About Physical Activity and Exercise in Urban-Community-Residing Older Adults. \u003cem\u003eJ. Prim. Care Community Health\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 215013272092413 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSvantesson, U., Jones, J., Wolbert, K. \u0026amp; Alricsson, M. Impact of Physical Activity on the Self-Perceived Quality of Life in Non-Frail Older Adults. \u003cem\u003eJ. Clin. Med. Res.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (8), 585\u0026ndash;593 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarquez, D. X. et al. A systematic review of physical activity and quality of life and well-being. \u003cem\u003eTransl Behav. Med.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (5), 1098\u0026ndash;1109 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, J., Guo, Y., Gui, Y. \u0026amp; Xu, D. Physical exercise, gut, gut microbiota, and atherosclerotic cardiovascular diseases. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (1), 17 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePedersen, B. K. The diseasome of physical inactivity \u0026ndash; and the role of myokines in muscle\u0026ndash;fat cross talk. \u003cem\u003eJ. Physiol.\u003c/em\u003e \u003cb\u003e587\u003c/b\u003e (23), 5559\u0026ndash;5568 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStamatakis, E. et al. Does Strength-Promoting Exercise Confer Unique Health Benefits? A Pooled Analysis of Data on 11 Population Cohorts With All-Cause, Cancer, and Cardiovascular Mortality Endpoints. \u003cem\u003eAm. J. Epidemiol.\u003c/em\u003e \u003cb\u003e187\u003c/b\u003e (5), 1102\u0026ndash;1112 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuclos, M. Les b\u0026eacute;n\u0026eacute;fices de la pratique d\u0026rsquo;une activit\u0026eacute; physique pour la sant\u0026eacute;. \u003cem\u003eRev. Infirm\u003c/em\u003e. \u003cb\u003e70\u003c/b\u003e (275), 16\u0026ndash;19 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLangille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. \u003cem\u003eNat. Biotechnol.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (9), 814\u0026ndash;821 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuince, C., Walker, A. W., Simpson, J. T., Loman, N. J. \u0026amp; Segata, N. Shotgun metagenomics, from sampling to analysis. \u003cem\u003eNat. Biotechnol.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e (9), 833\u0026ndash;844 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDimou, A., Tsimihodimos, V. \u0026amp; Bairaktari, E. The Critical Role of the Branched Chain Amino Acids (BCAAs) Catabolism-Regulating Enzymes, Branched-Chain Aminotransferase (BCAT) and Branched-Chain α-Keto Acid Dehydrogenase (BCKD), in Human Pathophysiology. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (7), 4022 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, Y. \u0026amp; Danbolt, N. C. Glutamate as a neurotransmitter in the healthy brain. \u003cem\u003eJ. Neural Transm\u003c/em\u003e. \u003cb\u003e121\u003c/b\u003e (8), 799\u0026ndash;817 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu, C. et al. Putrescine promotes MMP9-induced angiogenesis in skeletal muscle through hydrogen peroxide/METTL3 pathway. \u003cem\u003eFree Radic Biol. Med.\u003c/em\u003e \u003cb\u003e212\u003c/b\u003e, 433\u0026ndash;447 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuiz-Lim\u0026oacute;n, P. et al. Physical activity shifts gut microbiota structure in aged subjectswith overweight/obesity and metabolic syndrome. \u003cem\u003eBiol. Sport\u003c/em\u003e. \u003cb\u003e41\u003c/b\u003e (3), 47\u0026ndash;60 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaur, T., Wentzel, A. \u0026amp; D\u0026uuml;rre, P. Production of propionate using metabolically engineered strains of Clostridium saccharoperbutylacetonicum. \u003cem\u003eAppl. Microbiol. Biotechnol.\u003c/em\u003e \u003cb\u003e106\u003c/b\u003e (22), 7547\u0026ndash;7562 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSales, K. M. \u0026amp; Reimer, R. A. Unlocking a novel determinant of athletic performance: The role of the gut microbiota, short-chain fatty acids, and biotics in exercise. \u003cem\u003eJ. Sport Health Sci.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (1), 36\u0026ndash;44 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEstaki, M. et al. Cardiorespiratory fitness as a predictor of intestinal microbial diversity and distinct metagenomic functions. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e (1), 42 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarton, W. et al. The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level. \u003cem\u003eGut\u003c/em\u003e 2017;gutjnl-2016-313627.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrtiz-Alvarez, L., Xu, H. \u0026amp; Martinez-Tellez, B. Influence of Exercise on the Human Gut Microbiota of Healthy Adults: A Systematic Review. \u003cem\u003eClin. Transl Gastroenterol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (2), e00126 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllen, J. M. et al. Exercise Alters Gut Microbiota Composition and Function in Lean and Obese Humans. \u003cem\u003eMed. Sci. Sports Exerc.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e (4), 747\u0026ndash;757 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLambert, J. E. et al. Exercise training modifies gut microbiota in normal and diabetic mice. \u003cem\u003eAppl. Physiol. Nutr. Metab.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e (7), 749\u0026ndash;752 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClark, A. \u0026amp; Mach, N. The Crosstalk between the Gut Microbiota and Mitochondria during Exercise. \u003cem\u003eFront. Physiol.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 319 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao, W., Howden, B. P. \u0026amp; Stinear, T. P. Evolution of virulence in Enterococcus faecium, a hospital-adapted opportunistic pathogen. \u003cem\u003eCurr. Opin. Microbiol.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 76\u0026ndash;82 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVargas-Ortiz, K., P\u0026eacute;rez-V\u0026aacute;zquez, V. \u0026amp; Mac\u0026iacute;as-Cervantes, M. H. Exercise and Sirtuins: A Way to Mitochondrial Health in Skeletal Muscle. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (11), 2717 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCanfora, E. E., Jocken, J. W. \u0026amp; Blaak, E. E. Short-chain fatty acids in control of body weight and insulin sensitivity. \u003cem\u003eNat. Rev. Endocrinol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (10), 577\u0026ndash;591 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalsh, M. E. et al. The histone deacetylase inhibitor butyrate improves metabolism and reduces muscle atrophy during aging. \u003cem\u003eAging Cell.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (6), 957\u0026ndash;970 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFujio-Vejar, S. et al. The Gut Microbiota of Healthy Chilean Subjects Reveals a High Abundance of the Phylum Verrucomicrobia. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 1221 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLarsen, J. M. The immune response to \u003cem\u003ePrevotella\u003c/em\u003e bacteria in chronic inflammatory disease. \u003cem\u003eImmunology\u003c/em\u003e \u003cb\u003e151\u003c/b\u003e (4), 363\u0026ndash;374 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrost, F. et al. A structured weight loss program increases gut microbiota phylogenetic diversity and reduces levels of Collinsella in obese type 2 diabetics: A pilot study. Taheri S, editor. PLOS ONE. ;14(7):e0219489. (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalmn\u0026auml;s-B\u0026eacute;dard, M. S. et al. The human gut microbiota and glucose metabolism: a scoping review of key bacteria and the potential role of SCFAs. \u003cem\u003eAm. J. Clin. Nutr.\u003c/em\u003e \u003cb\u003e116\u003c/b\u003e (4), 862\u0026ndash;874 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarrizales-S\u0026aacute;nchez, A. K. et al. Characterization of gut microbiota associated with metabolic syndrome and type-2 diabetes mellitus in Mexican pediatric subjects. \u003cem\u003eBMC Pediatr.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (1), 210 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoman\u0026iacute;-P\u0026eacute;rez, M. et al. \u003cem\u003eHoldemanella biformis\u003c/em\u003e improves glucose tolerance and regulates GLP‐1 signaling in obese mice. FASEB J [Internet]. Jul [cited 2024 Jul 24];35(7). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://onlinelibrary.wiley.com/doi/\u003c/span\u003e\u003cspan address=\"https://onlinelibrary.wiley.com/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1096/fj.202100126R\u003c/span\u003e\u003cspan address=\"10.1096/fj.202100126R\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBongiovanni, T., Yin, M. O. L. \u0026amp; Heaney, L. M. The Athlete and Gut Microbiome: Short-chain Fatty Acids as Potential Ergogenic Aids for Exercise and Training. \u003cem\u003eInt. J. Sports Med.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (13), 1143\u0026ndash;1158 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, S. J. et al. Association of Fecal and Plasma Levels of Short-Chain Fatty Acids With Gut Microbiota and Clinical Severity in Patients With Parkinson Disease. Neurology [Internet]. 2022 Feb 22 [cited 2024 Jul 24];98(8). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.neurology.org/doi/\u003c/span\u003e\u003cspan address=\"https://www.neurology.org/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/WNL.0000000000013225\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000013225\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCompanys, J. et al. Gut Microbiota Profile and Its Association with Clinical Variables and Dietary Intake in Overweight/Obese and Lean Subjects: A Cross-Sectional Study. \u003cem\u003eNutrients\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (6), 2032 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrampton, J., Murphy, K. G., Frost, G. \u0026amp; Chambers, E. S. Short-chain fatty acids as potential regulators of skeletal muscle metabolism and function. \u003cem\u003eNat. Metab.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e (9), 840\u0026ndash;848 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHampton-Marcell, J. T. et al. Comparative Analysis of Gut Microbiota Following Changes in Training Volume Among Swimmers. \u003cem\u003eInt. J. Sports Med.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e (05), 292\u0026ndash;299 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Physical activity level, Gut metabolic modules, Gut microbiome, Metabolic potential","lastPublishedDoi":"10.21203/rs.3.rs-7595052/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7595052/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePhysical activity has been linked to improvements in metabolic health and gut microbiota composition. However, evidence connecting physical activity level (PAL) with the microbial metabolic potential in adults remains limited, particularly in South American populations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe employed Gut Metabolic Modules (GMM) functional inference on 16S V4 rRNA data to elucidate how PAL shapes the gut microbiota\u0026rsquo;s metabolic potential in 233 Chilean adults. PAL was assessed via the self-reported IPAQ-SF questionnaire and categorized into low, medium, and high levels. Stratification by body mass index (BMI) and evaluation of physical activity volume (PAV) were also performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eNo significant differences in overall microbial diversity were observed by PAL alone; however, when stratified by BMI, PAL was associated with shifts in the relative abundance of bacterial genera including \u003cem\u003eDorea\u003c/em\u003e, \u003cem\u003eHoldemanella\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e. Functionally, we identified 39 GMMs (37.8% of those evaluated) across the cohort, of which 18 modules differed by PAL, particularly protein and carbohydrate degradation pathways. PAV was positively associated with GMMs linked to energy metabolism, notably butyrate and propionate production.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ePAL, especially when considered alongside BMI and activity volume, modulates the gut microbiome\u0026rsquo;s metabolic potential. As the largest Chilean cohort to apply 16S based functional profiling, this study provides foundational evidence from Latin America, highlighting physical activity as a modifiable factor for shaping microbiota functionality and host metabolic health.\u003c/p\u003e","manuscriptTitle":"Impact of physical activity level on adult gut microbiome composition and metabolic function","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 16:36:00","doi":"10.21203/rs.3.rs-7595052/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f524ca5-a9bc-4027-901a-08271b5c9acf","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55863068,"name":"Health sciences/Gastroenterology"},{"id":55863069,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-10-27T13:57:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-07 16:36:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7595052","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7595052","identity":"rs-7595052","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}