Revealing gut microbiome alterations in prolonged water only fasting

Revealing gut microbiome alterations in prolonged water only fasting | 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 Revealing gut microbiome alterations in prolonged water only fasting Urszula Godlewska, Karol Pilis, Anna Anna Pilis, Krzysztof Stec, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8892199/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 The effectiveness of fasting and calorie restriction as a dietary intervention for addressing overnutrition is well established; however, the impact of prolonged water-only fasting, characterized by complete food deprivation, on the gut microbiome remains largely unexplored. This study examines the changes in the gut microbiome resulting from an 8-day fast. Our findings reveal that extended water-only fasting significantly impacts the relative abundance of Firmicutes and Bacteroidetes within the microbiome. Notably, the deprivation of food disrupts the populations of beneficial bacteria that play a critical role in the production of short-chain fatty acids (SCFAs), which may precipitate adverse health outcomes. Furthermore, these microbiome alterations have been associated with elevated cortisol levels. Importantly, the observed changes in the microbiome are reversible upon the reintroduction of a regular diet. This research underscores the potential role of prolonged water only fasting in reconfiguring the gut microbiome. Health sciences/Gastroenterology Biological sciences/Microbiology Fasting gut microbiome SCFA HPA ketosis BHB Figures Figure 1 Figure 2 Figure 3 Introduction Overnutrition is identified as a significant risk factor for a range of human diseases, including neurodegenerative conditions, cardiovascular diseases, metabolic disorders, and various forms of cancer. Caloric restriction and fasting are increasingly recognized as effective strategies for addressing obesity-related diseases in human. Prolonged fasting, characterized by the abstention from energy intake for ≥ 4 consecutive days, has been historically practiced for a variety of cultural, spiritual, and health-related purposes [ 1 ]. Recent research emphasizes fasting as a promising intervention for health promotion, highlighting its potential to enhance antioxidant status, mitigate cellular aging, boost autophagy, and, most importantly, facilitate weight loss and improve lipid metabolism [ 2 – 6 ]. On the contrary, fasting increases the risk of ketoacidosis, which can lead to liver damage [ 7 ]. Additionally, fasting raises uric acid levels, resulting in hyperuricemia, which negatively affects hemodynamics, hypertension, and renal function [ 2 , 8 , 9 ]. Furthermore, prolonged fasting stimulates protein utilization, as released amino acids are converted into substrates for gluconeogenesis [ 10 ]. Although, dietary practices and food scarcity can significantly alter the microbiome [ 11 – 13 ], the specific effects of prolonged fasting on the human microbiome remain unexplored. Humans can adapt to fasting, going weeks without food while remaining hydrated. The metabolic response includes three phases: post-absorption (0–24 hours), gluconeogenetic (24 hours to 10 days), and protein preservation (beyond 10 days) [ 10 ]. The gluconeogenesis phase is essential for providing energy and maintaining relatively constant blood glucose levels. This process reaches its peak efficiency during the first 3–4 days of fasting. It then gradually slows down as lipolysis and ketogenesis increase, producing ketone bodies such as β-hydroxybutyrate (BHB), which serve as an energy source and information molecules that respond to stress [ 10 , 14 – 16 ]. Indeed, fasting activates the hypothalamic-pituitary-adrenal (HPA) axis, leading to increased levels of cortisol and corticosterone in response to food deprivation stress (e.g., to stimulate gluconeogenesis) [ 4 , 5 ]. Although cortisol aids in coping with metabolic stress, it can negatively impact the central nervous system and is linked to mental illness [ 17 ]. Interestingly, during 8 days of water-only fasting, higher cortisol levels were found to correlate with lower perceived stress and anxiety [ 4 ]. The findings indicate that psychological responses to prolonged fasting may operate largely independently of the HPA axis. For instance, ketone bodies may enhance mood, concentration, and reduce symptoms of anxiety and depression [ 18 – 20 ]. Gut bacteria, interacting with the nervous system through the gut-brain axis, may also influence mood during prolonged fasting. Various pathways, including neurotransmitter metabolism and immune regulation, play a role, with short-chain fatty acids (SCFAs) being critical for cognitive processes [ 21 – 23 ]. However, the effects of prolonged fasting on gut microbiota and its implications for mental health remain unstudied. Here, we examine the effects of an extended 8-day water-only fast on the gut microbiome. Our findings showed that prolonged fasting negatively affects the microbiome by altering butyrate-producing bacteria, which reduces the pool of SCFAs. Additionally, changes in the microbiome have been linked to increased cortisol levels. However, the microbiome can return to its original state after refeeding. Methods Participant details Eleven healthy, middle-aged participants (10 men and 1 woman, aged 60.8 ± 10.96 years) volunteered to undergo an 8-day water-only fasting period. During this time, each individual was allowed to consume moderately mineralized water with the same composition. Medical supervision was provided before the study, throughout its duration, and for three days afterward. Medical examinations confirmed that there were no health contraindications for participation in the experiment. Participants were required to sign a declaration stating that they had not taken any medications or dietary supplements, nor had they engaged in smoking or alcohol consumption for at least one month prior to the study. It was also determined that all participants had prior experience with water fasting for varying durations, and none had undertaken such a fast in the six months leading up to the study. Before starting the study, all participants were thoroughly informed about the procedures involved and any potential risks. Written consent to participate was obtained from each individual. The experimental protocol received approval from the Research Ethics Committee of Jan Długosz University in Częstochowa, Poland (number KE-0/9/2024). All procedures were carried out in accordance with the Declaration of Helsinki. Sample Collection and analysis Participants arrived at the laboratory on the first day between 8:00 a.m. and 9:30 a.m. Their ages were recorded, along with various somatic measurements: body height (BH), body weight (BM), fat tissue content (BF), fat-free mass (FFM), total body water (TBW), and body mass index (BMI) using the Tanita TBF 300A body composition analyzer (Tanita, Amsterdam, the Netherlands). These baseline anthropometric data are presented in Table S1 . Afterward, participants were seated, and blood samples were drawn from the antecubital vein. The blood was allowed to clot at room temperature and then centrifuged at 1500 x g for 15 minutes. The resulting serum was divided into aliquots and stored at -80°C for later analysis. After 8 days of fasting, the research procedures were subsequently repeated in accordance with the outlined scheme. Stool samples were collected on the first day of fasting (referred to as "pre- fasting" samples) and again on the fourth day of fasting (referred to as "fasting" samples). A portion of 1 to 2 grams was collected immediately after defecation from various sites and placed into StoolSave™ DNA Protection kit tubes (A&A Biotechnology, Poland). This method ensured the protection and stabilization of nucleic acids in the collected material at room temperature during transport. In the lab, the samples were stored at -80°C until further analysis. SCFA concentration was measured using a Human Short-Chain Fatty Acids (ScFA) ELISA Kit (SunLong Biotech Hangzhou City, China). BHB concentration was determined enzymatically using the RANBUD diagnostic kit (Randox Laboratories Ltd., Crumlin, County Antrim, UK). Lactate concentration was determined using the EPOC® Blood Analysis System (Ottawa, Canada). The CLIA method was used to determine the levels of cortisol in serum using the MAGLUMI™ -1000 apparatus with Cortisol-CLIA kit (Shenzhen New Industries Biomedical Engineering Co. Ltd., Shenzhen, China). with appropriate kits. Other hormones, including serotonin, oxytocin, and α-endorphin, were measured using appropriate kits (ELK Biotechnology, Denver, USA). DNA extraction, PCR and illumina MiSeq sequencing Bacterial DNA was extracted from frozen human fecal samples using a MagnifiQ™ 1 Genomic DNA kit (A&A Biotechnology, Poland) and quantified fluorometrically using Quant-iT™ PicoGreen™ dsDNA Assay Kit (Invitrogen, USA). To conduct a metagenomic analysis of bacterial and archaeal populations, the hypervariable V3-V4 region of the 16S rRNA gene was amplified using the specific primers 341F (5′-CCTACGGGAGGCAGCAG-3′) and 785R (5′-GACTACHVGGGTATCTAATCC-3′). PCR was performed with Q5 Hot Start High-Fidelity 2X Master Mix (New England Biolabs, Germany), following the manufacturer's recommended reaction conditions. Sequencing was conducted by Genomed (Warsaw, Poland) on a MiSeq instrument using paired-end (PE) technology with 2x300 nt reads, employing the MiSeq v3 kit (Illumina) as per the manufacturer’s protocol. Automatic data analysis on the MiSeq sequencer using MiSeq Reporter v2.6 software (Illumina) involved two steps: demultiplexing samples and generating FASTQ files with raw reads. Data processing and analysis Bioinformatics analysis for species-level classification of reads was performed using the QIIME 2 software package, which employed the Silva 138 reference sequence database. The raw sequences underwent quality filtering and noise reduction, and taxonomy was assigned to the amplicon sequence variants (ASVs) using the DADA2 pipeline. Data analysis and statistics were conducted using Microsoft 365 Excel, GraphPad Prism 8.0.1, and R version 4.4.2. Graphics were created with R Studio, along with the tidyverse and ggplot2 package and GraphPad Prism 8.0.1. Quantification and statistical analysis Statistical analyses were conducted using GraphPad Prism 8.0.1, as detailed in the figure legends. The specific comparisons are indicated in the figure panels. Comparisons between two groups were performed using a t-test, while comparisons involving three or more groups were analyzed with one-way ANOVA. Correlation analysis was calculated by Spearman’s correlation method. All tests were two-tailed. In this context, "n" refers to the number of participants in the analysis. Results We conducted a study involving eleven healthy adult participants who fasted for 8 days without food but had unlimited access to water. Before and after the fasting period, we monitored several basic anthropometric parameters. The changes observed included weight alterations, which are detailed in Table S1 . Morning stool samples were collected on the first and fourth days of fasting to analyze any changes. Our prior experience indicates that lack of food intake leads to inhibited defecation. Considering this, we decided to collect stool on day four to capture the last stool passed during the fasting period. Overall, we obtained a total of 1086478 raw reads from the 11 samples taken before the fast, and 1209338 raw reads from the 11 samples collected during the fasting period. After filtering, a total of 662797 reads from the pre-fasting study and 760642 reads from during fasting study met the quality criteria for inclusion in the analysis, and were classified into the bacteria kingdom. Additionally, two samples from the fasting group contained readings for Archaea (28 and 70, respectively), but these were not analyzed further. Fasting Alters the Human Gut Microbiome To reveal the effect of an 8-day water-only fast on human gut microbiome, we examined bacteria in the feces from all participants both before and during fasting. Fasting resulted in changes in the microbial composition, leading to significant changes in relative abundance at the phylum level (Fig. 1 .A). Specifically, there was a significant decrease in Firmicutes (p = 0.0002) during fasting. In contrast, Bacteroidota exhibited an increase in relative abundance, although this change was not statistically significant (p = 0.2230) (Fig. 1 .B). This shift is also evident in the Firmicutes to Bacteroidota (F/B) ratio, which was calculated by dividing the relative abundance of Firmicutes by that of Bacteroidota. The F/B ratio decreased during fasting, but this decrease was not statistically significant either (p = 0.1455) (Fig. 1 .C). Fasting also resulted in an increased relative abundance of the phylum Actinobacteriota, Proteobacteria, Verrucomicrobiota and other phyla collectively labeled as "others." These "others" include Campylobacterota, Cyanobacteria, Desulfobacterota, Fusobacteriota, Patescibacteria, Spirochaetota, and Synergistota, each of which accounted for less than 1% individually. However, these changes were not statistically significant (Fig. 1 .A). Alpha diversity was assessed using the Shannon diversity index to evaluate taxa diversity before and during the fasting period (Fig. 1 .E). The mean of total Shannon index at phylum level before fasting was 0.912 (SD ± 0.116), with an equitability score of 0.496 (SD ± 0.07). During the fasting period, the Shannon index increased to 0.958 (SD ± 0.187), while the equitability score decreased slightly to 0.483 (SD ± 0.1) (Fig. S1 .A). A more detailed analysis of diversity within the phylum showed that Firmicutes had the highest sample diversity, with no significant difference between the two study: pre-fasting (3.121, SD ± 0.26) and during fasting (3.132, SD ± 0.26). Additionally, fasting resulted in a slight increase in diversity within the Bacteroidota group, although this change was not statistically significant (Sidak multiple comparison test p = 0.227) (Fig. 1 .E). Furthermore, principal component analysis (PCA) of the gut microbiome indicated a shift in composition during fasting. However, there was no clear clustering compared to the microbiome before fasting (Fig. 1 .D). Fasting Influences On SCFAs Producing Bacteria Given that inadequate fiber intake can lead to decreased SCFAa production, we examined the changes occurring at the genus level during fasting. Then, we conducted a more in-depth analysis at the genus level, focusing on the trends in relative abundance changes for bacteria that made up more than 1% of the total population. The observed alterations predominantly affected taxa known for their capability to produce SCFAs. These genera are detailed in Fig. 2 .A, which also provides annotations on the specific SCFAs produced by each. We present only the primary SCFAs—acetate, butyrate, and propionate—because they are more extensively documented in the literature [based on 24–32]. The most notable changes at the genus level were observed within the phylum Firmicutes, where fasting led to a significant reduction in abundance across all analyzed genera except for Oscillospiraceae UCG − 005, which increased during starvation (Fig. 2 .A). The greatest impact of fasting was seen in the family Lachnospiraceae (p < 0.0001) (Fig. S1 .B), where relative abundance was significantly reduced for genera such as Agathobacter, Blautia, Fusicatenibacter, Lachnospira , and Roseburia. In the case of butyrate-producing bacteria, both Faecalibacterium and Butyricicoccus showed a decrease in their abundance during fasting. Conversely, Akkermansia (belonging to phylum Verrucomicrobiota) tended to increase abundance during fasting, although these changes were not statistically significant, as they occurred in only a small number of individuals. Regarding the genus Bacteroidota, we found that fasting resulted in an increase in the abundance of Bacteroides, Odoribacter , and Alistipes , while Prevotella showed a tendency to decrease. A similar trend of decline was noted in the entire family Prevotellaceae (p = 0.0899), although this decrease did not reach statistical significance (Fig. S1 .B). Our analysis included two additional genera, Monoglobus and Christensenellaceae_R-7_group (Fig. 2 .A), which, according to existing literature, are associated with the levels of SCFAs [ 33 – 35 ]. Both genera belong to the class Clostridia, whose abundance significantly decreases during fasting (p = 0.0167) (Fig. 2 .D). Fasting led to a reduction in the abundance of Monoglobus (p = 0.0217), while the relative abundance of Christensenellaceae_R-7_group increased during the fasting period (p = 0.0223). In summary, our findings indicate that fasting causes notable changes in microbial composition, particularly affecting the abundance of genera within the Bacteroidota and Firmicutes phyla, with the most significant variations occurring in SCFA-producing genera of the Lachnospiraceae family. SCFAs change during fasting We subsequently investigated whether a decrease in the relative abundance of SCFA producers corresponds to a changes in the total amount of SCFAs. For this, we analyzed the total serum levels of SCFAs before and after fasting. In line with expectations, we noted a trend indicating a decrease in SCFAs following fasting; however, this trend was not statistically significant (p = 0.1981) (Fig. 2 .C). In contrast, a significant ketone body, BHB, which is chemically and functionally similar to butyrate [ 36 ], exhibits a substantial increase during fasting (p < 0.0001) (Fig. 2 .C). Furthermore, while lactate is not classified as a SCFAs, it is synthesized by various members of the microbiota, including bifidobacteria, and proteobacteria and can be converted into different SCFAs, such as butyrate and propionate [ 37 ]. To investigate potential variations in lactate levels during fasting, we conducted an analysis of serum lactate concentrations. Our findings indicate that fasting results in a significant elevation of lactate levels (p = 0.0272) (Fig. 2 C). This, along with the increase in BHB, may help to counteract the physiological effects of decreased SCFAs resulting from a lack of exogenous carbohydrates necessary for SCFA production. Fasting increases serotonin and cortisol levels In line with the hypothesis that alterations in gut microbiota during fasting could affect brain function, we subsequently analyzed levels of hormones linked to stress perception and mood enhancement. Our study observed a significant elevation in serum cortisol levels following an 8-day fasting period (p = 0.0021) (Fig. 2 C). In our investigation of the impact of fasting on happiness-related hormones, we observed a statistically significant increase in serum serotonin levels (p = 0.0066). However, it is noteworthy that levels of oxytocin and α-endorphin did not exhibit any significant changes during the same fasting interval (Fig. S1 .D). Next, we investigated whether the fluctuation of bacterial genera during fasting correlates with levels of stress and happiness-related molecules, specifically SCFA, lactate, BHB, cortisol, and serotonin (Fig. 2 .B). We found that elevated levels of the Christensenellaceae R-7 group were strongly correlated with SCFAs during fasting (r = 0.7939, p = 0.0088). In contrast, these same bacteria exhibited a negative correlation with lactate (r=-0.6182, p = 0.0478). Moreover, Monoglobus showed a positive correlation with lactate (r = 0.6273, p = 0.0440), as did Butyricicoccus (r = 0.7091, p = 0.0182). Additionally, the level of BHB during fast showed a negative correlation with Odoribacter (r=-0.7364, p = 0.0128), and Alistipes (r=-0.6364, p = 0.0402) which tends to increase in number during this period, and a positive correlation with Prevotella (r = 0.7062, p = 0.0186), whose abundance decreases. Although, we observed a positive correlation between cortisol levels and Faecalibacterium (r = 0.618, p = 0.0478), whose abundance diminishes during fasting. However, the exact mechanism by which these individual bacteria influence BHB or cortisol levels, or vice versa, has yet to be determined. Unexpectedly, we did not find a correlation between serotonin levels and the selected bacteria. However, further analysis revealed that serotonin tends to correlate with elevated levels of BHB during fasting (r = 0.6167, p = 0.0857) (Fig. S1 .C). Refeeding restores the microbiome back to state prior to fasting Considering that prolonged fasting can result in both beneficial and harmful changes to the microbiome, we collected data from seven volunteers to determine whether the changes caused by fasting are permanent. We compared the microbiome changes before fasting, during fasting, and 14 days after fasting for each of these individuals. The results are summarized in Fig. 3 . Our observations indicate that the microbiome generally shows signs of recovery 14 days after fasting ends, as evidenced by the comparison of relative abundance at the phylum level (Fig. 3 A). The F/B ratio tends to revert to its pre-fasting state (Fig. 3 B), and the Shannon index also returns to baseline levels (Fig. 3 C). In terms of changes at the genus level, we noticed a trend where the abundance of bacteria tended to return to pre-fasting levels (Fig. 3 .D). Specifically, all bacteria that had decreased in abundance due to fasting rebuilt their populations after feeding resumed, with this recovery being particularly noticeable among butyrate producers ( Butyricicoccus, Faecalibacterium, Fusicatenibacter, Roseburia, Lachnospira ). Blautia exhibited the slowest rate of return to its initial pre-fasting abundance, showing a significant difference between pre-fasting and post-fasting levels (p = 0.0148). Nevertheless, the results indicated a pronounced trend toward an increase in the abundance of Blautia subsequent to the fasting period. Similarly, after 14 days of resuming a nutrition, the numbers of Clostridia returned to their pre-fasting levels (pre-fasting vs. post-fasting, p = 0.9995) (Fig. 3 .E). In contrast, the bacteria ( Akkermansia , Alistipes, Bacteroides, Christensenellaceae_R-7_group, Odoribacte r, UCG-005) that increase during fasting decrease back to pre-fasting levels after food is consumed (Fig. 3 .D). In summary, these findings indicate that fasting leads to temporary changes in the human microbiome, which gradually normalize after returning to a regular diet. Discussion Our research indicates that prolonged water-only fasting strongly influences the levels of Firmicutes and Bacteroidetes, which are among the most prevalent bacterial phyla in the gastrointestinal tract. The ratio of these two groups, known as the F/B ratio, is a widely used marker for assessing imbalance in gut microbiota, associated with several pathological conditions, including obesity, type 2 diabetes, and cardiovascular diseases [ 38 , 39 ]. The main effect of these imbalances is primarily linked to SCFAs, which are predominantly produced by Firmicutes through the fermentation of dietary polysaccharides [ 40 ]. Undoubtedly, during water-only fasting, these bacteria must adapt by using alternative substrates for metabolism. While Firmicutes and Bacteroidota feed mainly on dietary carbohydrates, Bacteroidota can quickly adapt to the lack of carbohydrates by utilizing other available nutrients [ 41 , 42 ]. Here, fasting has been associated with a notable reduction in the population SCFA producers within the Firmicutes phylum, particularly affecting butyrate producers such as Butyricicoccus, Faecalibacterium, Fusicatenibacter, Lachnospira , and Roseburia . Interestingly, our analysis did not reveal a significant reduction in SCFAs during fasting, despite a noted decrease in the levels of specific SCFA-producing microbial populations. We hypothesize that this phenomenon can be explained by cross-feeding, in which metabolites produced by one microbial species are utilized as substrates by another [ 42 , 43 ]. Such intermicrobial interactions are critical in balancing the overall equilibrium of intestinal SCFAs, as the production of butyrate may be facilitated by an increased availability of acetate, propionate, or lactate derived from microbial or host metabolism [ 43 ]. For instance, Akkermansia is known for efficiently utilizing circulating lactate for metabolism [ 44 ]. In our study, we noted an increase in Akkermansia during fasting, possibly due to higher lactate availability. However, no correlation with lactate levels was observed, likely because Akkermansia was present only in a few participants, and fasting did not trigger a de novo increase in those who lacked it initially. Alternatively, in the absence of available carbohydrate sources, undigested proteins and amino acids can be used to produce SCFA. Indeed, gut bacteria can metabolize amino acids like threonine, which is key for synthesizing acetate, butyrate, and propionate [ 45 ]. Furthermore, the phyla Firmicutes, Bacteroidetes, and Verrucomicrobia exhibit distinct patterns of nutrient utilization. Firmicutes primarily use dietary proteins and circulating urea as their main nitrogen sources, whereas Bacteroidota tend to metabolize proteins secreted by the host. Similarly, Akkermansia, a prominent member of the Verrucomicrobiota, shows a preference for degrading host proteins, especially mucins [ 44 ]. The metabolic flexibility of Bacteroidota and Akkermansia, in contrast to Firmicutes, may explain their better adaptation to fasting conditions. According to our previous observations, an 8-day water-only fast leads to changes in intestinal transit due to the lack of food, which results in the inhibition of defecation before the fast concludes. We hypothesize that inconsistent availability of food affects the speed of intestinal transit, which may have influenced the alterations in the microbiome observed during the fasting period. Overall, variations in intestinal transit times have an impact on the diversity and composition of the gut microbiome [ 46 , 47 ]. In addition, cortisol levels rise during fasting, which can influence the gut microbiota by changing gut transit time [ 48 , 49 ]. Prolonged colonic transit times are associated with a metabolic shift in the colon from carbohydrate fermentation to protein breakdown, causing a slight increase in pH [ 47 ]. An increase in pH promotes the growth of Bacteroides and enhances propionate production, while in low pH, butyrate-forming bacteria are more prevalent [ 13 , 50 , 51 ]. Moreover, higher fecal pH and low transit rates are associated with a high abundance of Akkermansia [ 51 ]. However, the transition in bacterial metabolism from saccharolytic processes to protein degradation can lead to increased urinary levels of potentially harmful protein-derived metabolites, which may negatively impact overall health [ 47 ]. Nevertheless these changes are likely temporary; when returning to a normal diet, the gut microbiome seems to revert to its pre-fasting state. To gain further insight into the potential role of changes in the gut microbiome resulting from fasting, we examined levels of hormones associated with stress perception and mood improvement. In our study, we found a positive correlation between cortisol levels and 11 out of the 15 genera we compared. The strongest correlation was observed with Faecalibacterium. This finding suggests that cortisol affects bacteria during fasting; however, the specific mechanisms underlying these interactions require further examination. In contrast, serotonin, which increases during fasting, does not seem to be correlated with changes in the microbiome that are induced by fasting, particularly SCFAs are known to elevate its levels [ 52 ]. However, our findings indicated a correlation between serotonin levels during fasting and BHB. Given that BHB and butyrate share a similar chemical structure and both bind to the same receptors—GPR109A and GPR41 [ 53 , 54 ]—this suggests a potential mechanism involving these receptors. Nonetheless, further research is needed to explore this possibility. In conclusion, our study highlights the significant effects of prolonged water-only fasting on the gut microbiome, particularly regarding the relative abundances of Firmicutes and Bacteroidetes. Prolonged fasting disrupts the populations of beneficial bacteria crucial for the production of SCFAs, which may lead to adverse health outcomes. Notably, the observed microbiome changes are reversible upon reintroduction of a regular diet, suggesting that temporary fasting can be a safe dietary intervention when followed by a refeeding period. This underscores the importance of considering the gut microbiome's role in health when implementing fasting as a strategy for addressing overnutrition, while emphasizing the need for careful management to support the recovery of fiber-digesting bacteria. Limitations of the study The study presented herein possesses several noteworthy limitations. The foremost limitation is the relatively small sample size, comprising only 11 healthy volunteers. This limitation raises concerns, as critics contend that such a small sample may not provide sufficient statistical power. Unfortunately, recruiting participants for an eight-day, water-only fasting regimen is challenging, resulting in a limited dataset. Another limitation is the predominance of men among the study participants, which arises from the availability of volunteers willing to participate. Additionally, stool samples were collected during the fasting period rather than on the last day. This decision was made because fasting can inhibit intestinal transit for several days after the fast begins. Collecting samples on the final day would require enemas, which could dilute the stool and potentially lead to inaccurate results. We initially dismissed the option of surgically collecting intestinal contents from healthy volunteers due to ethical concerns. Lastly, the use of the ELISA method to measure total SCFAs in serum samples is another limitation. This choice was made based on the availability of biological material, the buffers used to preserve the stool, and various technical issues faced during the study. Declarations Disclosure of potential conflicts of interest The authors report there are no competing interests to declare . Acknowledgments The authors would like to thank all study participants who contributed their time to this project. Funding This work was supported by the National Science Centre, Poland (grant number 2020/37/B/NZ7/01794). Author contributions Urszula Godlewska: Writing – original draft, Writing – review and editing, Data curation, Investigation, Methodology, Software, Visualization, Formal analysis, Conceptualization. Karol Pilis: Investigation, Methodology, Data curation, Formal analysis. Anna Pilis: Investigation, Methodology, Data curation, Formal analysis. Krzysztof Stec: Investigation, Methodology, Data curation, Formal analysis, Resources. Wiesław Pilis : Writing – review and editing, Investigation, Resources, Funding acquisition, Conceptualization. Elżbieta Nowara: Writing – review and editing, Formal analysis. Jędrzej Antosiewicz: Validation, Review and editing Funding acquisition, Supervision, Project administration, Conceptualization. Data availability The 16S rRNA gene sequencing data can be found in the NCBI's Sequence Read Archive (SRA) under accession number PRJNA1298881. Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request. References Koppold, D. A. et al. International consensus on fasting terminology. Cell. Metab. 36 (8), 1779–1794e4. 10.1016/j.cmet.2024.06.013 (2024). Pilis, K. et al. Metabolic and hormonal effects of an 8 days water only fasting combined with exercise in middle aged men. Sci. Rep. 15 (1), 22805. 10.1038/s41598-025-05164-0 (2025). Pilis, K. et al. 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Interactions and competition within the microbial community of the human colon: links between diet and health. Environ. Microbiol. 9 (5), 1101–1111. 10.1111/j.1462-2920.2007.01281.x (2007). Magne, F. et al. The Firmicutes/Bacteroidetes Ratio: A Relevant Marker of Gut Dysbiosis in Obese Patients? Nutrients 12 (5), 1474. 10.3390/nu12051474 (2020). Stojanov, S., Berlec, A. & Štrukelj, B. The Influence of Probiotics on the Firmicutes/Bacteroidetes Ratio in the Treatment of Obesity and Inflammatory Bowel disease. Microorganisms 8 (11), 1715. 10.3390/microorganisms8111715 (2020). Houtman, T. A., Eckermann, H. A., Smidt, H. & de Weerth, C. Gut microbiota and BMI throughout childhood: the role of firmicutes, bacteroidetes, and short-chain fatty acid producers. Sci. Rep. 12 (1), 3140. 10.1038/s41598-022-07176-6 (2022). Shin, J. H. et al. Bacteroides and related species: The keystone taxa of the human gut microbiota. Anaerobe 85 , 102819. 10.1016/j.anaerobe.2024.102819 (2024). Rios-Covian, D., Salazar, N., Gueimonde, M. & de Los Reyes-Gavilan, C. G. Shaping the Metabolism of Intestinal Bacteroides Population through Diet to Improve Human Health. Front. Microbiol. 8 , 376. 10.3389/fmicb.2017.00376 (2017). Culp, E. J. & Goodman, A. L. Cross-feeding in the gut microbiome: Ecology and mechanisms. Cell. Host Microbe . 31 (4), 485–499. 10.1016/j.chom.2023.03.016 (2023). Zeng, X. et al. Gut bacterial nutrient preferences quantified in vivo. Cell 185 (18), 3441–3456e19. 10.1016/j.cell.2022.07.020 (2022). Neis, E. P., Dejong, C. H. & Rensen, S. S. The role of microbial amino acid metabolism in host metabolism. Nutrients 7 (4), 2930–2946. 10.3390/nu7042930 (2015). Ducarmon, Q. R. et al. Remodelling of the intestinal ecosystem during caloric restriction and fasting. Trends Microbiol. 31 (8), 832–844. 10.1016/j.tim.2023.02.009 (2023). Roager, H. M. et al. Colonic transit time is related to bacterial metabolism and mucosal turnover in the gut. Nat. Microbiol. 1 (9), 16093. 10.1038/nmicrobiol.2016.93 (2016). Rusch, J. A., Layden, B. T. & Dugas, L. R. Signalling cognition: the gut microbiota and hypothalamic-pituitaryadrenal axis. Front. Endocrinol. (Lausanne) . 14 , 1130689. 10.3389/fendo.2023.1130689 (2023). Mayer, E. A. The neurobiology of stress and gastrointestinal disease. Gut 47 , 861–869. 10.1136/gut.47.6.861 (2000). Rios-Covian, D., Salazar, N., Gueimonde, M. & de Los Reyes-Gavilan, C. G. Shaping the Metabolism of Intestinal Bacteroides Population through Diet to Improve Human Health. Front. Microbiol. 8 , 376. 10.3389/fmicb.2017.00376 (2017). Adamberg, S. & Adamberg, K. Prevotella enterotype associates with diets supporting acidic faecal pH and production of propionic acid by microbiota. Heliyon 10 (10), e31134. 10.1016/j.heliyon.2024.e31134 (2024). Reigstad, C. S. et al. Gut microbes promote colonic serotonin production through an effect of short-chain fatty acids on enterochromaffin cells. FASEB J. 29 (4), 1395–1403. 10.1096/fj.14-259598 (2015). Newman, J. C. & Verdin, E. β-Hydroxybutyrate: A Signaling Metabolite. Annu. Rev. Nutr. 37 , 51–76. 10.1146/annurev-nutr-071816-064916 (2017). Hodgkinson, K. et al. Butyrate's role in human health and the current progress towards its clinical application to treat gastrointestinal disease. Clin. Nutr. 42 (2), 61–75. 10.1016/j.clnu.2022.10.024 (2023). Additional Declarations No competing interests reported. Supplementary Files FigureS1.pdf Figure S1. (A) The Shannon diversity index box plot at phylum level. Grey box plot represent individuals before fasting, red box plot indicate individuals during fasting. Error bars represent SD. Statistical significance was determined by two-sided paired t-tests, n=11. (B) Relative abundance (%) of Lachnospiraceae and Prevotellaceae . Grey circles represent individuals before fasting, and red circles indicate individuals during fasting, with dotted lines used to highlight individual changes. Error bars represent SD. Statistical significance was determined by two-sided paired t-tests, ***p ≤ 0.001, n=11. (C) Heatmap of Spearman correlations between BHB with SCFAs, lactate, cortisol and serotonin before and after 8 days of fasting, n=11 (D) Serum levels of α-endorphin and oxytocin. Grey circles represent individuals before fasting, and red circles indicate individuals after fasting, with dotted lines used to highlight individual changes. Error bars represent SD. Statistical significance was determined by two-sided paired t-tests, n=11. Appendix.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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8892199","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":601883947,"identity":"d395fe01-f691-4757-8c42-0337174eb38a","order_by":0,"name":"Urszula Godlewska","email":"","orcid":"","institution":"Jan Długosz University","correspondingAuthor":false,"prefix":"","firstName":"Urszula","middleName":"","lastName":"Godlewska","suffix":""},{"id":601883948,"identity":"3bf16738-2bce-4c72-84ad-3130bf0482b0","order_by":1,"name":"Karol Pilis","email":"","orcid":"","institution":"Jan Długosz University","correspondingAuthor":false,"prefix":"","firstName":"Karol","middleName":"","lastName":"Pilis","suffix":""},{"id":601883949,"identity":"e845e53a-8e8d-42b3-9af8-938db6a2bec5","order_by":2,"name":"Anna Anna Pilis","email":"","orcid":"","institution":"Jan Długosz University","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"Anna","lastName":"Pilis","suffix":""},{"id":601883950,"identity":"754e5ae7-4be4-4f46-9092-a11d7d9c5a1f","order_by":3,"name":"Krzysztof Stec","email":"","orcid":"","institution":"Jan Długosz University","correspondingAuthor":false,"prefix":"","firstName":"Krzysztof","middleName":"","lastName":"Stec","suffix":""},{"id":601883951,"identity":"a3e0662e-c1ab-4d60-9d24-40d8d2acfa17","order_by":4,"name":"Wiesław Pilis","email":"","orcid":"","institution":"Jan Długosz University","correspondingAuthor":false,"prefix":"","firstName":"Wiesław","middleName":"","lastName":"Pilis","suffix":""},{"id":601883952,"identity":"cdd249c7-245e-438b-b8a9-2845cc52335a","order_by":5,"name":"Elżbieta Nowara","email":"","orcid":"","institution":"Jan Długosz University","correspondingAuthor":false,"prefix":"","firstName":"Elżbieta","middleName":"","lastName":"Nowara","suffix":""},{"id":601883953,"identity":"c107f6c2-7baf-4ce0-95a5-fc2787ad49c6","order_by":6,"name":"Jędrzej Antosiewicz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACxgYg8QHMTGBg4AFSfERoYWycgayFjRiLmnlI0sLcfvb4Y5tfh6P52RMYH7xtY8gjqIWxJy+xObfvcO7MngfMhnPbGIoJa2nIMWzO7Tmcu+FGAps0bxtDYhtBLf1vDJstgVr230hg/02clhlAWxh+AG2RSGBjJlLLG8OZvQ3puTPOPGyWnHNOgrBfDPtzDD78+GOd29+efPDDmzKbPH6CWhpAVoEdA04IEgmEdDDIg8k/CAHCWkbBKBgFo2DEAQBftULDARAwpQAAAABJRU5ErkJggg==","orcid":"","institution":"Medical University of Gdańsk","correspondingAuthor":true,"prefix":"","firstName":"Jędrzej","middleName":"","lastName":"Antosiewicz","suffix":""}],"badges":[],"createdAt":"2026-02-16 10:25:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8892199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8892199/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104489631,"identity":"4fdb3205-3b1a-4bb8-9d4f-8f3ba9567aea","added_by":"auto","created_at":"2026-03-12 11:19:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":329750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobiota diversity and composition at phylum level.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)\u003cstrong\u003e \u003c/strong\u003eThe taxon bar plot of the relative abundance (%) of predominant bacteria at phylum level before and during fasting for each participant. The letter \"F\" next to the participant's number represents the data collected during fasting. Phyla with a mean relative abundance of less than 1% are combined and plotted as others, n=11.\u003c/p\u003e\n\u003cp\u003e(B) Relative abundance (%) of Firmicutes and Bacteroidota. Grey circles represent individuals before fasting, and red circles indicate individuals during fasting, with dotted lines used to highlight individual changes. Error bars represent SD. Statistical significance was determined by Sidak's multiple comparisons test ***p ≤ 0.001, n=11.\u003c/p\u003e\n\u003cp\u003e(C) F/B ratio. Grey circles represent individuals before fasting, and red circles indicate individuals during fasting, with dotted lines used to highlight individual changes. Error bars represent SD. Statistical significance was determined by two-sided paired t-tests, n=11.\u003c/p\u003e\n\u003cp\u003e(D) Principal component analysis (PCA) representation for the fecal microbiota before (grey circles) and during fasting (red circles).\u003c/p\u003e\n\u003cp\u003e(E) The Shannon diversity index box plot across the phyla: Actinobacteriota, Bacteroidota, Firmicutes, and Proteobacteria. Grey box plot represent individuals before fasting, and red box plot indicate individuals during fasting. Error bars represent SD. Statistical significance was determined by Tukey's multiple comparisons test, n=11.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8892199/v1/0fad6db861f7ee6462af049f.png"},{"id":104489632,"identity":"400f7822-1677-4197-8932-2426c242988f","added_by":"auto","created_at":"2026-03-12 11:19:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":860893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of Fasting on SCFAa and Hormones\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e(A) Relative abundance (%) of dominant bacteria at the genus level during fasting, categorized by their ability to synthesize selected SCFAs. Grey circles represent individuals before fasting, and red circles indicate individuals during fasting, with dotted lines used to highlight individual changes. Error bars represent SD. Statistical significance was determined by two-sided paired t-tests, ***p ≤ 0.001, **p ≤ 0.01, *p ≤ 0.05, n=11.\u003c/p\u003e\n\u003cp\u003e(B) Heatmap of Spearman correlations between SCFAs, lactate, BHB, cortisol and serotonin with certain bacteria that differ during fasting, *p ≤ 0.05, n=11.\u003c/p\u003e\n\u003cp\u003e(C) Serum levels of total SCFAs, lactate, BHB, cortisol and serotonin (5-HT). Grey circles represent individuals before fasting, and red circles indicate individuals after fasting, with dotted lines used to highlight individual changes. Error bars represent SD. Statistical significance was determined by two-sided paired t-tests, ***p ≤ 0.001, **p ≤ 0.01, *p ≤ 0.05, n=11.\u003c/p\u003e\n\u003cp\u003e(D) Relative abundance (%) of Clostridia. Grey circles represent individuals before fasting, and red circles indicate individuals during fasting, with dotted lines used to highlight individual changes. Error bars represent SD. Statistical significance was determined by two-sided paired t-tests, *p ≤ 0.05, n=11\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8892199/v1/72f267f5dd14154ce3648a11.png"},{"id":104489635,"identity":"07172a9c-f267-4794-a035-87df17b9a1f6","added_by":"auto","created_at":"2026-03-12 11:19:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":764686,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobiome changes after 2 weeks of fasting\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e(A)\u003cstrong\u003e \u003c/strong\u003eThe taxon bar plot of the relative abundance (%) of predominant bacteria at the phylum level before, during, and 14 days after finishing fasting. Phyla with a mean relative abundance of less than 1% are combined and plotted as others, n=7.\u003c/p\u003e\n\u003cp\u003e(B) F/B ratio. Grey circles represent individuals before fasting, red circles indicate individuals during fasting and black circles showing data from 14 days after the fasting period has ended, with dotted lines used to highlight individual changes. Error bars represent SD. Statistical significance was determined by two-sided paired t-tests, n=7.\u003c/p\u003e\n\u003cp\u003e(C) The Shannon diversity index box plot at phylum level. Grey box plot represent individuals before fasting, red box plot indicate individuals during fasting and black box showing data from 14 days after the fasting period has ended. Error bars represent SD. Statistical significance was determined by Tukey's multiple comparisons test, n=7.\u003c/p\u003e\n\u003cp\u003e(D) Relative abundance (%) of dominant bacteria at the genus level after finishing fasting, categorized by their ability to synthesize selected SCFAs. Grey circles represent individuals before fasting, red circles indicate individuals during fasting, and black circles show data from 14 days after the fasting period ended, with dotted lines used to highlight individual changes. Error bars represent SD. Statistical significance was determined by Dunnett's multiple comparisons test, **p ≤ 0.01, *p ≤ 0.05, n=7.\u003c/p\u003e\n\u003cp\u003e(E) Relative abundance (%) of Clostridia. Grey circles represent individuals before fasting, red circles indicate individuals during fasting, black circles show data from 14 days after the fasting period ended, with dotted lines used to highlight individual changes. Error bars represent SD. Statistical significance was determined by Dunnett's multiple comparisons test to compare the results with the post-fasting data, n=7.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8892199/v1/503bc2843d29ed45ffb2ba09.png"},{"id":108005793,"identity":"de60d3d0-9045-4532-a830-250dfec8d5cf","added_by":"auto","created_at":"2026-04-28 12:48:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2952700,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8892199/v1/d1ba543f-3d05-42f0-ac7a-4e9f45c77870.pdf"},{"id":104489633,"identity":"93a3181e-9561-458d-9958-69ea74edd633","added_by":"auto","created_at":"2026-03-12 11:19:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1402771,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The Shannon diversity index box plot at phylum level. Grey box plot represent individuals before fasting, red box plot indicate individuals during fasting. Error bars represent SD. Statistical significance was determined by two-sided paired t-tests, n=11.\u003c/p\u003e\n\u003cp\u003e(B) Relative abundance (%) of \u003cem\u003eLachnospiraceae\u003c/em\u003eand \u003cem\u003ePrevotellaceae\u003c/em\u003e. Grey circles represent individuals before fasting, and red circles indicate individuals during fasting, with dotted lines used to highlight individual changes. Error bars represent SD. Statistical significance was determined by two-sided paired t-tests, ***p ≤ 0.001, n=11.\u003c/p\u003e\n\u003cp\u003e(C) Heatmap of Spearman correlations between BHB with SCFAs, lactate, cortisol and serotonin before and after 8 days of fasting, n=11\u003c/p\u003e\n\u003cp\u003e(D) Serum levels of α-endorphin and oxytocin. Grey circles represent individuals before fasting, and red circles indicate individuals after fasting, with dotted lines used to highlight individual changes. Error bars represent SD. Statistical significance was determined by two-sided paired t-tests, n=11.\u003c/p\u003e","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8892199/v1/d08a449a0c5137e711426eec.pdf"},{"id":104489634,"identity":"189efb27-e9e4-41c2-97b6-e8f68c0f0bf5","added_by":"auto","created_at":"2026-03-12 11:19:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15310,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8892199/v1/50bd9ca0b7eca3af5a8a6475.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revealing gut microbiome alterations in prolonged water only fasting","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvernutrition is identified as a significant risk factor for a range of human diseases, including neurodegenerative conditions, cardiovascular diseases, metabolic disorders, and various forms of cancer. Caloric restriction and fasting are increasingly recognized as effective strategies for addressing obesity-related diseases in human. Prolonged fasting, characterized by the abstention from energy intake for \u0026ge;\u0026thinsp;4 consecutive days, has been historically practiced for a variety of cultural, spiritual, and health-related purposes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recent research emphasizes fasting as a promising intervention for health promotion, highlighting its potential to enhance antioxidant status, mitigate cellular aging, boost autophagy, and, most importantly, facilitate weight loss and improve lipid metabolism [\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. On the contrary, fasting increases the risk of ketoacidosis, which can lead to liver damage [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, fasting raises uric acid levels, resulting in hyperuricemia, which negatively affects hemodynamics, hypertension, and renal function [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, prolonged fasting stimulates protein utilization, as released amino acids are converted into substrates for gluconeogenesis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although, dietary practices and food scarcity can significantly alter the microbiome [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the specific effects of prolonged fasting on the human microbiome remain unexplored.\u003c/p\u003e \u003cp\u003eHumans can adapt to fasting, going weeks without food while remaining hydrated. The metabolic response includes three phases: post-absorption (0\u0026ndash;24 hours), gluconeogenetic (24 hours to 10 days), and protein preservation (beyond 10 days) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The gluconeogenesis phase is essential for providing energy and maintaining relatively constant blood glucose levels. This process reaches its peak efficiency during the first 3\u0026ndash;4 days of fasting. It then gradually slows down as lipolysis and ketogenesis increase, producing ketone bodies such as β-hydroxybutyrate (BHB), which serve as an energy source and information molecules that respond to stress [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Indeed, fasting activates the hypothalamic-pituitary-adrenal (HPA) axis, leading to increased levels of cortisol and corticosterone in response to food deprivation stress (e.g., to stimulate gluconeogenesis) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although cortisol aids in coping with metabolic stress, it can negatively impact the central nervous system and is linked to mental illness [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Interestingly, during 8 days of water-only fasting, higher cortisol levels were found to correlate with lower perceived stress and anxiety [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The findings indicate that psychological responses to prolonged fasting may operate largely independently of the HPA axis. For instance, ketone bodies may enhance mood, concentration, and reduce symptoms of anxiety and depression [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Gut bacteria, interacting with the nervous system through the gut-brain axis, may also influence mood during prolonged fasting. Various pathways, including neurotransmitter metabolism and immune regulation, play a role, with short-chain fatty acids (SCFAs) being critical for cognitive processes [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the effects of prolonged fasting on gut microbiota and its implications for mental health remain unstudied.\u003c/p\u003e \u003cp\u003eHere, we examine the effects of an extended 8-day water-only fast on the gut microbiome. Our findings showed that prolonged fasting negatively affects the microbiome by altering butyrate-producing bacteria, which reduces the pool of SCFAs. Additionally, changes in the microbiome have been linked to increased cortisol levels. However, the microbiome can return to its original state after refeeding.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipant details\u003c/h2\u003e \u003cp\u003eEleven healthy, middle-aged participants (10 men and 1 woman, aged 60.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.96 years) volunteered to undergo an 8-day water-only fasting period. During this time, each individual was allowed to consume moderately mineralized water with the same composition. Medical supervision was provided before the study, throughout its duration, and for three days afterward. Medical examinations confirmed that there were no health contraindications for participation in the experiment. Participants were required to sign a declaration stating that they had not taken any medications or dietary supplements, nor had they engaged in smoking or alcohol consumption for at least one month prior to the study. It was also determined that all participants had prior experience with water fasting for varying durations, and none had undertaken such a fast in the six months leading up to the study. Before starting the study, all participants were thoroughly informed about the procedures involved and any potential risks. Written consent to participate was obtained from each individual. The experimental protocol received approval from the Research Ethics Committee of Jan Długosz University in Częstochowa, Poland (number KE-0/9/2024). All procedures were carried out in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample Collection and analysis\u003c/h3\u003e\n\u003cp\u003eParticipants arrived at the laboratory on the first day between 8:00 a.m. and 9:30 a.m. Their ages were recorded, along with various somatic measurements: body height (BH), body weight (BM), fat tissue content (BF), fat-free mass (FFM), total body water (TBW), and body mass index (BMI) using the Tanita TBF 300A body composition analyzer (Tanita, Amsterdam, the Netherlands). These baseline anthropometric data are presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Afterward, participants were seated, and blood samples were drawn from the antecubital vein. The blood was allowed to clot at room temperature and then centrifuged at 1500 x g for 15 minutes. The resulting serum was divided into aliquots and stored at -80\u0026deg;C for later analysis. After 8 days of fasting, the research procedures were subsequently repeated in accordance with the outlined scheme. Stool samples were collected on the first day of fasting (referred to as \"pre- fasting\" samples) and again on the fourth day of fasting (referred to as \"fasting\" samples). A portion of 1 to 2 grams was collected immediately after defecation from various sites and placed into StoolSave\u0026trade; DNA Protection kit tubes (A\u0026amp;A Biotechnology, Poland). This method ensured the protection and stabilization of nucleic acids in the collected material at room temperature during transport. In the lab, the samples were stored at -80\u0026deg;C until further analysis.\u003c/p\u003e \u003cp\u003eSCFA concentration was measured using a Human Short-Chain Fatty Acids (ScFA) ELISA Kit (SunLong Biotech Hangzhou City, China). BHB concentration was determined enzymatically using the RANBUD diagnostic kit (Randox Laboratories Ltd., Crumlin, County Antrim, UK). Lactate concentration was determined using the EPOC\u0026reg; Blood Analysis System (Ottawa, Canada). The CLIA method was used to determine the levels of cortisol in serum using the MAGLUMI\u0026trade; -1000 apparatus with Cortisol-CLIA kit (Shenzhen New Industries Biomedical Engineering Co. Ltd., Shenzhen, China). with appropriate kits. Other hormones, including serotonin, oxytocin, and α-endorphin, were measured using appropriate kits (ELK Biotechnology, Denver, USA).\u003c/p\u003e\n\u003ch3\u003eDNA extraction, PCR and illumina MiSeq sequencing\u003c/h3\u003e\n\u003cp\u003eBacterial DNA was extracted from frozen human fecal samples using a MagnifiQ\u0026trade; 1 Genomic DNA kit (A\u0026amp;A Biotechnology, Poland) and quantified fluorometrically using Quant-iT\u0026trade; PicoGreen\u0026trade; dsDNA Assay Kit (Invitrogen, USA). To conduct a metagenomic analysis of bacterial and archaeal populations, the hypervariable V3-V4 region of the 16S rRNA gene was amplified using the specific primers 341F (5\u0026prime;-CCTACGGGAGGCAGCAG-3\u0026prime;) and 785R (5\u0026prime;-GACTACHVGGGTATCTAATCC-3\u0026prime;). PCR was performed with Q5 Hot Start High-Fidelity 2X Master Mix (New England Biolabs, Germany), following the manufacturer's recommended reaction conditions. Sequencing was conducted by Genomed (Warsaw, Poland) on a MiSeq instrument using paired-end (PE) technology with 2x300 nt reads, employing the MiSeq v3 kit (Illumina) as per the manufacturer\u0026rsquo;s protocol. Automatic data analysis on the MiSeq sequencer using MiSeq Reporter v2.6 software (Illumina) involved two steps: demultiplexing samples and generating FASTQ files with raw reads.\u003c/p\u003e\n\u003ch3\u003eData processing and analysis\u003c/h3\u003e\n\u003cp\u003eBioinformatics analysis for species-level classification of reads was performed using the QIIME 2 software package, which employed the Silva 138 reference sequence database. The raw sequences underwent quality filtering and noise reduction, and taxonomy was assigned to the amplicon sequence variants (ASVs) using the DADA2 pipeline. Data analysis and statistics were conducted using Microsoft 365 Excel, GraphPad Prism 8.0.1, and R version 4.4.2. Graphics were created with R Studio, along with the tidyverse and ggplot2 package and GraphPad Prism 8.0.1.\u003c/p\u003e\n\u003ch3\u003eQuantification and statistical analysis\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were conducted using GraphPad Prism 8.0.1, as detailed in the figure legends. The specific comparisons are indicated in the figure panels. Comparisons between two groups were performed using a t-test, while comparisons involving three or more groups were analyzed with one-way ANOVA. Correlation analysis was calculated by Spearman\u0026rsquo;s correlation method. All tests were two-tailed. In this context, \"n\" refers to the number of participants in the analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe conducted a study involving eleven healthy adult participants who fasted for 8 days without food but had unlimited access to water. Before and after the fasting period, we monitored several basic anthropometric parameters. The changes observed included weight alterations, which are detailed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Morning stool samples were collected on the first and fourth days of fasting to analyze any changes. Our prior experience indicates that lack of food intake leads to inhibited defecation. Considering this, we decided to collect stool on day four to capture the last stool passed during the fasting period. Overall, we obtained a total of 1086478 raw reads from the 11 samples taken before the fast, and 1209338 raw reads from the 11 samples collected during the fasting period. After filtering, a total of 662797 reads from the pre-fasting study and 760642 reads from during fasting study met the quality criteria for inclusion in the analysis, and were classified into the bacteria kingdom. Additionally, two samples from the fasting group contained readings for Archaea (28 and 70, respectively), but these were not analyzed further.\u003c/p\u003e\n\u003ch3\u003eFasting Alters the Human Gut Microbiome\u003c/h3\u003e\n\u003cp\u003eTo reveal the effect of an 8-day water-only fast on human gut microbiome, we examined bacteria in the feces from all participants both before and during fasting. Fasting resulted in changes in the microbial composition, leading to significant changes in relative abundance at the phylum level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.A). Specifically, there was a significant decrease in Firmicutes (p\u0026thinsp;=\u0026thinsp;0.0002) during fasting. In contrast, Bacteroidota exhibited an increase in relative abundance, although this change was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.2230) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.B). This shift is also evident in the Firmicutes to Bacteroidota (F/B) ratio, which was calculated by dividing the relative abundance of Firmicutes by that of Bacteroidota. The F/B ratio decreased during fasting, but this decrease was not statistically significant either (p\u0026thinsp;=\u0026thinsp;0.1455) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.C). Fasting also resulted in an increased relative abundance of the phylum Actinobacteriota, Proteobacteria, Verrucomicrobiota and other phyla collectively labeled as \"others.\" These \"others\" include Campylobacterota, Cyanobacteria, Desulfobacterota, Fusobacteriota, Patescibacteria, Spirochaetota, and Synergistota, each of which accounted for less than 1% individually. However, these changes were not statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.A).\u003c/p\u003e \u003cp\u003eAlpha diversity was assessed using the Shannon diversity index to evaluate taxa diversity before and during the fasting period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.E). The mean of total Shannon index at phylum level before fasting was 0.912 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;0.116), with an equitability score of 0.496 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07). During the fasting period, the Shannon index increased to 0.958 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;0.187), while the equitability score decreased slightly to 0.483 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.A). A more detailed analysis of diversity within the phylum showed that Firmicutes had the highest sample diversity, with no significant difference between the two study: pre-fasting (3.121, SD\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26) and during fasting (3.132, SD\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26). Additionally, fasting resulted in a slight increase in diversity within the Bacteroidota group, although this change was not statistically significant (Sidak multiple comparison test p\u0026thinsp;=\u0026thinsp;0.227) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.E). Furthermore, principal component analysis (PCA) of the gut microbiome indicated a shift in composition during fasting. However, there was no clear clustering compared to the microbiome before fasting (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.D).\u003c/p\u003e\n\u003ch3\u003eFasting Influences On SCFAs Producing Bacteria\u003c/h3\u003e\n\u003cp\u003eGiven that inadequate fiber intake can lead to decreased SCFAa production, we examined the changes occurring at the genus level during fasting. Then, we conducted a more in-depth analysis at the genus level, focusing on the trends in relative abundance changes for bacteria that made up more than 1% of the total population. The observed alterations predominantly affected taxa known for their capability to produce SCFAs. These genera are detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.A, which also provides annotations on the specific SCFAs produced by each. We present only the primary SCFAs\u0026mdash;acetate, butyrate, and propionate\u0026mdash;because they are more extensively documented in the literature [based on 24\u0026ndash;32]. The most notable changes at the genus level were observed within the phylum Firmicutes, where fasting led to a significant reduction in abundance across all analyzed genera except for \u003cem\u003eOscillospiraceae\u003c/em\u003e UCG\u0026thinsp;\u0026minus;\u0026thinsp;005, which increased during starvation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.A). The greatest impact of fasting was seen in the family \u003cem\u003eLachnospiraceae\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.B), where relative abundance was significantly reduced for genera such as \u003cem\u003eAgathobacter, Blautia, Fusicatenibacter, Lachnospira\u003c/em\u003e, and \u003cem\u003eRoseburia.\u003c/em\u003e In the case of butyrate-producing bacteria, both \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eButyricicoccus\u003c/em\u003e showed a decrease in their abundance during fasting. Conversely, \u003cem\u003eAkkermansia\u003c/em\u003e (belonging to phylum Verrucomicrobiota) tended to increase abundance during fasting, although these changes were not statistically significant, as they occurred in only a small number of individuals. Regarding the genus Bacteroidota, we found that fasting resulted in an increase in the abundance of \u003cem\u003eBacteroides, Odoribacter\u003c/em\u003e, and \u003cem\u003eAlistipes\u003c/em\u003e, while \u003cem\u003ePrevotella\u003c/em\u003e showed a tendency to decrease. A similar trend of decline was noted in the entire family \u003cem\u003ePrevotellaceae\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0899), although this decrease did not reach statistical significance (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.B).\u003c/p\u003e \u003cp\u003eOur analysis included two additional genera, \u003cem\u003eMonoglobus\u003c/em\u003e and \u003cem\u003eChristensenellaceae_R-7_group\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.A), which, according to existing literature, are associated with the levels of SCFAs [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Both genera belong to the class Clostridia, whose abundance significantly decreases during fasting (p\u0026thinsp;=\u0026thinsp;0.0167) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.D). Fasting led to a reduction in the abundance of \u003cem\u003eMonoglobus\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0217), while the relative abundance of \u003cem\u003eChristensenellaceae_R-7_group\u003c/em\u003e increased during the fasting period (p\u0026thinsp;=\u0026thinsp;0.0223). In summary, our findings indicate that fasting causes notable changes in microbial composition, particularly affecting the abundance of genera within the Bacteroidota and Firmicutes phyla, with the most significant variations occurring in SCFA-producing genera of the \u003cem\u003eLachnospiraceae\u003c/em\u003e family.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSCFAs change during fasting\u003c/h2\u003e \u003cp\u003eWe subsequently investigated whether a decrease in the relative abundance of SCFA producers corresponds to a changes in the total amount of SCFAs. For this, we analyzed the total serum levels of SCFAs before and after fasting. In line with expectations, we noted a trend indicating a decrease in SCFAs following fasting; however, this trend was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.1981) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.C). In contrast, a significant ketone body, BHB, which is chemically and functionally similar to butyrate [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], exhibits a substantial increase during fasting (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.C). Furthermore, while lactate is not classified as a SCFAs, it is synthesized by various members of the microbiota, including bifidobacteria, and proteobacteria and can be converted into different SCFAs, such as butyrate and propionate [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. To investigate potential variations in lactate levels during fasting, we conducted an analysis of serum lactate concentrations. Our findings indicate that fasting results in a significant elevation of lactate levels (p\u0026thinsp;=\u0026thinsp;0.0272) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This, along with the increase in BHB, may help to counteract the physiological effects of decreased SCFAs resulting from a lack of exogenous carbohydrates necessary for SCFA production.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFasting increases serotonin and cortisol levels\u003c/h2\u003e \u003cp\u003eIn line with the hypothesis that alterations in gut microbiota during fasting could affect brain function, we subsequently analyzed levels of hormones linked to stress perception and mood enhancement. Our study observed a significant elevation in serum cortisol levels following an 8-day fasting period (p\u0026thinsp;=\u0026thinsp;0.0021) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In our investigation of the impact of fasting on happiness-related hormones, we observed a statistically significant increase in serum serotonin levels (p\u0026thinsp;=\u0026thinsp;0.0066). However, it is noteworthy that levels of oxytocin and α-endorphin did not exhibit any significant changes during the same fasting interval (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.D).\u003c/p\u003e \u003cp\u003eNext, we investigated whether the fluctuation of bacterial genera during fasting correlates with levels of stress and happiness-related molecules, specifically SCFA, lactate, BHB, cortisol, and serotonin (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.B). We found that elevated levels of the \u003cem\u003eChristensenellaceae\u003c/em\u003e R-7 group were strongly correlated with SCFAs during fasting (r\u0026thinsp;=\u0026thinsp;0.7939, p\u0026thinsp;=\u0026thinsp;0.0088). In contrast, these same bacteria exhibited a negative correlation with lactate (r=-0.6182, p\u0026thinsp;=\u0026thinsp;0.0478). Moreover, \u003cem\u003eMonoglobus\u003c/em\u003e showed a positive correlation with lactate (r\u0026thinsp;=\u0026thinsp;0.6273, p\u0026thinsp;=\u0026thinsp;0.0440), as did \u003cem\u003eButyricicoccus\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.7091, p\u0026thinsp;=\u0026thinsp;0.0182). Additionally, the level of BHB during fast showed a negative correlation with \u003cem\u003eOdoribacter\u003c/em\u003e (r=-0.7364, p\u0026thinsp;=\u0026thinsp;0.0128), and \u003cem\u003eAlistipes\u003c/em\u003e (r=-0.6364, p\u0026thinsp;=\u0026thinsp;0.0402) which tends to increase in number during this period, and a positive correlation with \u003cem\u003ePrevotella\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.7062, p\u0026thinsp;=\u0026thinsp;0.0186), whose abundance decreases. Although, we observed a positive correlation between cortisol levels and \u003cem\u003eFaecalibacterium\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.618, p\u0026thinsp;=\u0026thinsp;0.0478), whose abundance diminishes during fasting. However, the exact mechanism by which these individual bacteria influence BHB or cortisol levels, or vice versa, has yet to be determined. Unexpectedly, we did not find a correlation between serotonin levels and the selected bacteria. However, further analysis revealed that serotonin tends to correlate with elevated levels of BHB during fasting (r\u0026thinsp;=\u0026thinsp;0.6167, p\u0026thinsp;=\u0026thinsp;0.0857) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.C).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRefeeding restores the microbiome back to state prior to fasting\u003c/h2\u003e \u003cp\u003eConsidering that prolonged fasting can result in both beneficial and harmful changes to the microbiome, we collected data from seven volunteers to determine whether the changes caused by fasting are permanent. We compared the microbiome changes before fasting, during fasting, and 14 days after fasting for each of these individuals. The results are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Our observations indicate that the microbiome generally shows signs of recovery 14 days after fasting ends, as evidenced by the comparison of relative abundance at the phylum level (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The F/B ratio tends to revert to its pre-fasting state (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), and the Shannon index also returns to baseline levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In terms of changes at the genus level, we noticed a trend where the abundance of bacteria tended to return to pre-fasting levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.D). Specifically, all bacteria that had decreased in abundance due to fasting rebuilt their populations after feeding resumed, with this recovery being particularly noticeable among butyrate producers (\u003cem\u003eButyricicoccus, Faecalibacterium, Fusicatenibacter, Roseburia, Lachnospira\u003c/em\u003e). \u003cem\u003eBlautia\u003c/em\u003e exhibited the slowest rate of return to its initial pre-fasting abundance, showing a significant difference between pre-fasting and post-fasting levels (p\u0026thinsp;=\u0026thinsp;0.0148). Nevertheless, the results indicated a pronounced trend toward an increase in the abundance of \u003cem\u003eBlautia\u003c/em\u003e subsequent to the fasting period. Similarly, after 14 days of resuming a nutrition, the numbers of Clostridia returned to their pre-fasting levels (pre-fasting vs. post-fasting, p\u0026thinsp;=\u0026thinsp;0.9995) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.E). In contrast, the bacteria (\u003cem\u003eAkkermansia\u003c/em\u003e, \u003cem\u003eAlistipes, Bacteroides, Christensenellaceae_R-7_group, Odoribacte\u003c/em\u003er, UCG-005) that increase during fasting decrease back to pre-fasting levels after food is consumed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.D). In summary, these findings indicate that fasting leads to temporary changes in the human microbiome, which gradually normalize after returning to a regular diet.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur research indicates that prolonged water-only fasting strongly influences the levels of Firmicutes and Bacteroidetes, which are among the most prevalent bacterial phyla in the gastrointestinal tract. The ratio of these two groups, known as the F/B ratio, is a widely used marker for assessing imbalance in gut microbiota, associated with several pathological conditions, including obesity, type 2 diabetes, and cardiovascular diseases [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The main effect of these imbalances is primarily linked to SCFAs, which are predominantly produced by Firmicutes through the fermentation of dietary polysaccharides [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Undoubtedly, during water-only fasting, these bacteria must adapt by using alternative substrates for metabolism. While Firmicutes and Bacteroidota feed mainly on dietary carbohydrates, Bacteroidota can quickly adapt to the lack of carbohydrates by utilizing other available nutrients [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, fasting has been associated with a notable reduction in the population SCFA producers within the Firmicutes phylum, particularly affecting butyrate producers such as \u003cem\u003eButyricicoccus, Faecalibacterium, Fusicatenibacter, Lachnospira\u003c/em\u003e, and \u003cem\u003eRoseburia\u003c/em\u003e. Interestingly, our analysis did not reveal a significant reduction in SCFAs during fasting, despite a noted decrease in the levels of specific SCFA-producing microbial populations. We hypothesize that this phenomenon can be explained by cross-feeding, in which metabolites produced by one microbial species are utilized as substrates by another [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Such intermicrobial interactions are critical in balancing the overall equilibrium of intestinal SCFAs, as the production of butyrate may be facilitated by an increased availability of acetate, propionate, or lactate derived from microbial or host metabolism [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. For instance, \u003cem\u003eAkkermansia\u003c/em\u003e is known for efficiently utilizing circulating lactate for metabolism [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In our study, we noted an increase in \u003cem\u003eAkkermansia\u003c/em\u003e during fasting, possibly due to higher lactate availability. However, no correlation with lactate levels was observed, likely because \u003cem\u003eAkkermansia\u003c/em\u003e was present only in a few participants, and fasting did not trigger a de novo increase in those who lacked it initially. Alternatively, in the absence of available carbohydrate sources, undigested proteins and amino acids can be used to produce SCFA. Indeed, gut bacteria can metabolize amino acids like threonine, which is key for synthesizing acetate, butyrate, and propionate [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Furthermore, the phyla Firmicutes, Bacteroidetes, and Verrucomicrobia exhibit distinct patterns of nutrient utilization. Firmicutes primarily use dietary proteins and circulating urea as their main nitrogen sources, whereas Bacteroidota tend to metabolize proteins secreted by the host. Similarly, Akkermansia, a prominent member of the Verrucomicrobiota, shows a preference for degrading host proteins, especially mucins [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The metabolic flexibility of Bacteroidota and Akkermansia, in contrast to Firmicutes, may explain their better adaptation to fasting conditions.\u003c/p\u003e \u003cp\u003eAccording to our previous observations, an 8-day water-only fast leads to changes in intestinal transit due to the lack of food, which results in the inhibition of defecation before the fast concludes. We hypothesize that inconsistent availability of food affects the speed of intestinal transit, which may have influenced the alterations in the microbiome observed during the fasting period. Overall, variations in intestinal transit times have an impact on the diversity and composition of the gut microbiome [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In addition, cortisol levels rise during fasting, which can influence the gut microbiota by changing gut transit time [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Prolonged colonic transit times are associated with a metabolic shift in the colon from carbohydrate fermentation to protein breakdown, causing a slight increase in pH [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. An increase in pH promotes the growth of \u003cem\u003eBacteroides\u003c/em\u003e and enhances propionate production, while in low pH, butyrate-forming bacteria are more prevalent [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Moreover, higher fecal pH and low transit rates are associated with a high abundance of \u003cem\u003eAkkermansia\u003c/em\u003e [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. However, the transition in bacterial metabolism from saccharolytic processes to protein degradation can lead to increased urinary levels of potentially harmful protein-derived metabolites, which may negatively impact overall health [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Nevertheless these changes are likely temporary; when returning to a normal diet, the gut microbiome seems to revert to its pre-fasting state.\u003c/p\u003e \u003cp\u003eTo gain further insight into the potential role of changes in the gut microbiome resulting from fasting, we examined levels of hormones associated with stress perception and mood improvement. In our study, we found a positive correlation between cortisol levels and 11 out of the 15 genera we compared. The strongest correlation was observed with \u003cem\u003eFaecalibacterium.\u003c/em\u003e This finding suggests that cortisol affects bacteria during fasting; however, the specific mechanisms underlying these interactions require further examination. In contrast, serotonin, which increases during fasting, does not seem to be correlated with changes in the microbiome that are induced by fasting, particularly SCFAs are known to elevate its levels [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. However, our findings indicated a correlation between serotonin levels during fasting and BHB. Given that BHB and butyrate share a similar chemical structure and both bind to the same receptors\u0026mdash;GPR109A and GPR41 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u0026mdash;this suggests a potential mechanism involving these receptors. Nonetheless, further research is needed to explore this possibility.\u003c/p\u003e \u003cp\u003eIn conclusion, our study highlights the significant effects of prolonged water-only fasting on the gut microbiome, particularly regarding the relative abundances of Firmicutes and Bacteroidetes. Prolonged fasting disrupts the populations of beneficial bacteria crucial for the production of SCFAs, which may lead to adverse health outcomes. Notably, the observed microbiome changes are reversible upon reintroduction of a regular diet, suggesting that temporary fasting can be a safe dietary intervention when followed by a refeeding period. This underscores the importance of considering the gut microbiome's role in health when implementing fasting as a strategy for addressing overnutrition, while emphasizing the need for careful management to support the recovery of fiber-digesting bacteria.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of the study\u003c/h2\u003e \u003cp\u003eThe study presented herein possesses several noteworthy limitations. The foremost limitation is the relatively small sample size, comprising only 11 healthy volunteers. This limitation raises concerns, as critics contend that such a small sample may not provide sufficient statistical power. Unfortunately, recruiting participants for an eight-day, water-only fasting regimen is challenging, resulting in a limited dataset. Another limitation is the predominance of men among the study participants, which arises from the availability of volunteers willing to participate. Additionally, stool samples were collected during the fasting period rather than on the last day. This decision was made because fasting can inhibit intestinal transit for several days after the fast begins. Collecting samples on the final day would require enemas, which could dilute the stool and potentially lead to inaccurate results. We initially dismissed the option of surgically collecting intestinal contents from healthy volunteers due to ethical concerns. Lastly, the use of the ELISA method to measure total SCFAs in serum samples is another limitation. This choice was made based on the availability of biological material, the buffers used to preserve the stool, and various technical issues faced during the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure of potential conflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors report there are no competing interests to declare\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all study participants who contributed their time to this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This work was supported by the National Science Centre, Poland (grant number 2020/37/B/NZ7/01794).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrszula Godlewska:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; original draft, Writing \u0026ndash; review and editing, Data curation, Investigation, Methodology, Software, Visualization, Formal analysis, Conceptualization.\u0026nbsp;\u003cstrong\u003eKarol Pilis:\u0026nbsp;\u003c/strong\u003eInvestigation, Methodology, Data curation, \u0026nbsp;Formal analysis.\u003cstrong\u003e\u0026nbsp;Anna Pilis:\u0026nbsp;\u003c/strong\u003eInvestigation, Methodology, Data curation, \u0026nbsp;Formal analysis.\u0026nbsp;\u003cstrong\u003eKrzysztof Stec:\u0026nbsp;\u003c/strong\u003eInvestigation, Methodology, Data curation, \u0026nbsp;Formal analysis, Resources. \u003cstrong\u003eWiesław Pilis\u003c/strong\u003e: Writing \u0026ndash; review and editing, Investigation, Resources, Funding acquisition, Conceptualization.\u0026nbsp;\u003cstrong\u003eElżbieta Nowara:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review and editing, Formal analysis.\u0026nbsp;\u003cstrong\u003eJędrzej Antosiewicz:\u0026nbsp;\u003c/strong\u003eValidation, Review and editing Funding acquisition, Supervision, Project administration, Conceptualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 16S rRNA gene sequencing data can be found in the NCBI\u0026apos;s Sequence Read Archive (SRA) under accession number PRJNA1298881. 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Nutr.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (2), 61\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clnu.2022.10.024\u003c/span\u003e\u003cspan address=\"10.1016/j.clnu.2022.10.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\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":"Fasting; gut microbiome, SCFA, HPA, ketosis, BHB","lastPublishedDoi":"10.21203/rs.3.rs-8892199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8892199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe effectiveness of fasting and calorie restriction as a dietary intervention for addressing overnutrition is well established; however, the impact of prolonged water-only fasting, characterized by complete food deprivation, on the gut microbiome remains largely unexplored. This study examines the changes in the gut microbiome resulting from an 8-day fast. Our findings reveal that extended water-only fasting significantly impacts the relative abundance of Firmicutes and Bacteroidetes within the microbiome. Notably, the deprivation of food disrupts the populations of beneficial bacteria that play a critical role in the production of short-chain fatty acids (SCFAs), which may precipitate adverse health outcomes. Furthermore, these microbiome alterations have been associated with elevated cortisol levels. Importantly, the observed changes in the microbiome are reversible upon the reintroduction of a regular diet. This research underscores the potential role of prolonged water only fasting in reconfiguring the gut microbiome.\u003c/p\u003e","manuscriptTitle":"Revealing gut microbiome alterations in prolonged water only fasting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 11:18:59","doi":"10.21203/rs.3.rs-8892199/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":"71548c0b-3144-432d-9c67-7571baef1f10","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64054065,"name":"Health sciences/Gastroenterology"},{"id":64054066,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-04-10T05:56:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 11:18:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8892199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8892199","identity":"rs-8892199","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}