The microbiome and metabolome of patients with acute or severe and enduring anorexia nervosa influence gamma-aminobutyric acid (GABA) metabolism and microbial fermentation

The microbiome and metabolome of patients with acute or severe and enduring anorexia nervosa influence gamma-aminobutyric acid (GABA) metabolism and microbial fermentation | 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 The microbiome and metabolome of patients with acute or severe and enduring anorexia nervosa influence gamma-aminobutyric acid (GABA) metabolism and microbial fermentation Petra Prochazkova, Janet Jezkova, Radka Roubalova, Katerina Zadakova, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6610537/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Nov, 2025 Read the published version in npj Biofilms and Microbiomes → Version 1 posted 11 You are reading this latest preprint version Abstract Anorexia nervosa (AN), an eating disorder, is associated with marked changes in the microbiome and metabolic profile of those affected. In this study, we compared the diversity and composition of the gut microbiota and the differences in various parameters between 29 female patients with acute AN, 33 severe and enduring AN (SEAN), and 30 healthy controls. Both patient groups differed in the assessment of eating behaviors, depression symptoms, stressful events in adulthood, and the use of antidepressants. The SEAN group showed elevated markers of gut damage and the greatest inter-individual variation in the gut microbiome. Certain bacterial taxa, such as Faecalibacterium , Fusicatenbacter , Lachnospiraceae, and CAG-56, were less abundant in the patients' microbiomes, while Erysipelatoclostridium and UBA1819 were more abundant, but with no difference between patient groups. Functional prediction of the microbiome revealed differences in metabolic pathways, particularly in amino acid metabolism and oxidative stress responses, which were more pronounced in patients with SEAN. Certain bacteria such as Christensenellaceae, Ruminococcaceae, and Escherichia-Shigella negatively affect GABA metabolism, as evidenced by the lower serum and fecal concentrations in both patient groups compared to healthy women. Members of the Christensenellaceae affect microbial fermentation, resulting in significant differences in acetic, propionic, and butyric acid levels in stool and serum samples from AN patients. These findings highlight the complex interplay between the gut microbiota and metabolic changes in AN patients and provide insights into potential microbial biomarkers and therapeutic targets for this disease. Biological sciences/Microbiology Health sciences/Health care microbiome SCFA neurotransmitter dysbiosis anorexia nervosa gut-brain-microbiota axis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Anorexia nervosa (AN) is a severe eating disorder characterized by pathological eating behavior with extreme restriction of energy intake and compulsive excessive exercising that leads to dangerously low body weight and malnutrition. In patients diagnosed with AN, their reluctance to engage with therapeutic interventions, in conjunction with their gastrointestinal (GI), cardiovascular, immunological, and metabolic symptoms, as well as their frequent psychiatric comorbidities, poses a significant challenge to the effectiveness of early interventions. It is therefore evident that further research into the efficacy of complementary therapies is required. In addition, psychotherapy is likely needed to achieve a full, long-term recovery. The long duration of this disease enables the delineation of its phases into acute anorexia and severe and enduring anorexia nervosa (SEAN). Definitions of SEAN vary in some respects. The major features of SEAN include the presence of a persistent state of food restriction, underweight, impaired weight/shape perception, and completion of at least two medical procedures (e.g., feeding tubes or intravenous interventions) along with a diagnostic evaluation. One controversial point is the duration of the disease. While some researchers use a value of more than 3 years, others use more than 7 years. While the pathophysiology of AN is not yet fully understood, there is a growing body of evidence that recognizes the gut microbiome as an important factor affecting the course and outcome of this disorder. Research into the human microbiome with regard to the physiological regulation of health and disease has increased dramatically in recent years. Many diseases, from autoimmune disorders to neurodegenerative diseases and even cancer, have been linked to an imbalance in the microbiota, known as dysbiosis 1 . Emerging data point to gut-brain communication and bidirectional interactions between the gut-brain axis and the microbiota. The commensal microbiota inhabiting the GI tract actively influences human health, both through microbial metabolism and direct interactions with the host. Most studies on the microbiota in patients with AN show significant changes in the composition and diversity of their gut microbiota. Although the results of individual studies are quite variable in terms of bacterial taxonomic composition, most studies show decreased alpha diversity in these patients, which is attributed to reduced caloric intake and very low food variability 2 – 7 . Decreased alpha diversity is usually associated with metabolic endotoxemia, a form of chronic, low-grade systemic inflammation resulting from impaired gut barrier function and increased intestinal permeability (leaky gut syndrome). As a result, bacterial components and metabolic products from the gut cross the blood-brain barrier and enter the bloodstream and possibly the brain. Indigestible substances such as dietary fiber are metabolized by intestinal microbes and used to produce short-chain fatty acids (SCFAs). SCFAs supply the intestinal epithelial cells with energy and help regulate their function, thereby improving the function of the intestinal barrier and the host’s metabolism. At the intestinal level, SCFAs interact with intestinal epithelial and immune cells. In the systemic circulation and peripheral tissues, they moderately influence systemic inflammation by regulating cytokine secretion. SCFAs can also cross the hematoencephalic barrier and enter the central nervous system. Furthermore, SCFAs stimulate enteroendocrine cells in the intestines to release specific gut hormones, which, in turn, exert indirect effects on brain function. SCFAs can also directly activate the vagus nerve. The interaction of SCFAs with these gut-brain pathways can directly or indirectly influence processes related to brain function, learning, memory, and mood 8 . People with AN often have lower numbers of gut bacteria that produce butyrate and therefore have lower amounts of butyrate in their gut. This is explained by the reduced consumption of fiber as part of their restrictive and pathological diet. Butyrate, one of the three most abundant and best-studied SCFAs, has been shown to improve the metabolic profile of the host 9 . Butyrate in the gut is negatively correlated with anxiety and depression, which may explain some of the psychiatric symptoms frequently associated with AN. The gut microbiota influences the production of neurotransmitters in the host but can also produce several neurotransmitters independently. The hormone serotonin (5-HT), for example, is synthesized, in both the brain and the gut, from tryptophan, an essential amino acid derived from protein-rich foods like chicken, eggs, and dairy. Under physiological conditions, 5-HT has many functions since 5-HT receptors are found throughout the body. In particular, 5-HT regulates smooth muscle in the GI tract and supports intestinal peristalsis. In the brain, 5-HT is involved in mood regulation and is colloquially referred to as the "good mood hormone". Lower concentrations of the neurotransmitters 5-HT, GABA, and dopamine (DA) have been found in the stools of AN patients compared to healthy controls. These differences may contribute to anxiety or depression 6 , 10 . As with many other diseases, there is an intensive search for possible therapeutic mechanisms that could restore the gut microbiota to a "healthy" state and help to achieve "healthy" eating behaviors. Given the role that the gut microbiota plays in regulating appetite, mood, and metabolism, restoring the gut microbiota in people with AN is a stepping stone to a better, fuller recovery. The gut dysbiosis present in AN patients on admission to hospital, for example, only partially normalizes during hospital stays and treatments, even though their body mass index (BMI) increases and psychometric parameters improve 6 . This raises the question of whether a long-term disrupted microbiome is sufficiently effective in chronic AN and also during nutritional therapy. The purpose of this study was to investigate the composition of the gut microbiome and metabolome in patients with anorexia nervosa in the early and late stages of the disease and to identify the predictors of AN persistence. We found the gut microbiota and milieu of microbial metabolites differ between patients with early-stage AN, patients with severe and enduring anorexia nervosa (SEAN), and healthy individuals. Furthermore, we showed that increased intestinal permeability, leading to low-grade systemic inflammation, is less supported in patients with early-stage acute AN than in patients with SEAN. This research fills a gap in the literature by further distinguishing between the acute and chronic course of the disease. Clarifying the mechanisms and the role of microbiota in the transition of anorexia from the early phase to the chronic phase of AN could help to discover new preventive and therapeutic approaches to AN. Results Clinical, anthropometric, and biochemical description of the study cohort The study included 62 anorexic patients and 30 healthy women (Table 1). The age of patients with acute AN was less compared to the other participants due to the nature of the acute illness, which typically has an onset between 12 and 25 years 11 . After categorizing patients into acute AN and SEAN, there was also a distinction between the two groups relative to disease duration (Table 1). Both groups of AN patients differ from healthy controls in physical parameters related to malnutrition, such as body weight, BMI, body fat percentage, waist and hip line circumference, but also in the presence of hyperactivity, which is a common feature of AN (Table 1). AN participants also differed from the healthy controls in their greater use of antidepressants, antipsychotics, and anxiolytics (Table 2). Patients with long-standing SEAN primarily differed from those with acute AN relative to the presence of stressful events in adulthood (Table 1), which was reflected by greater use of antidepressants (Table 2). AN patients had many different comorbidities, but their prevalence did not differ significantly between the two patient groups (Table 3) . The results of the biochemical serum tests showed that both groups of AN patients had lower levels of cholinesterase and free triiodothyronine compared to healthy controls. The two groups of patients also differed in IgA and IgM levels, which could be a consequence of intestinal damage (Table 4). However, the Spearman correlation of these anthropometric and biochemical parameters showed that the parameters related to body size were closely related to the amount of cholinesterase, thyroid hormones fT3 and fT4, and IgG levels, as shown in Fig. 1. Patients with acute AN had a higher EDE-Q and HAMD total score To compare the psychiatric differences between the two groups of patients with anorexia nervosa (acute AN and SEAN), they were assessed with the Eating Disorder Examination questionnaire (EDE-Q), Hamilton Anxiety Rating Scale (HAMA), and Hamilton Psychiatric Rating Scale for depression (HAMD). Patients with SEAN had lower total EDE-Q scores and dietary restraint subscores (extreme attempts to limit food intake) compared to patients with acute AN (Fig. 2A-B). The patient groups did not show significant differences in HAMA, which determines anxiety. However, Principal Component Analysis (PCA) analysis suggested that patients with acute AN were more likely to exhibit features of psychological anxiety, whereas patients with SEAN were more likely to exhibit features of somatic anxiety (Fig. 2C-D). Patients with acute AN also had higher HAMD total depression severity scores, reflecting their lower use of antidepressants compared to patients with SEAN (Fig. 2E). I-FABP levels are higher in patients with SEAN and correspond with BMI To assess the potential damage to the intestinal mucosa, we tracked serum levels of intestinal fatty acid binding protein (I-FABP), which is expressed in the epithelial cells of the small intestinal mucosa and released into the bloodstream when the intestinal mucosa is damaged. We detected slightly elevated levels of I-FABP in the serum of SEAN patients, but this was largely due to high I-FABP levels in several patients who had clear signs of intestinal damage (Fig. 3A). Spearman correlation was used to investigate whether I-FABP levels might berelated to BMI or disease duration in all AN patients. Figure 3 shows that I-FABP levels correlate negatively with BMI and positively with disease duration. Patients with anorexia have different levels of markers of bacterial translocation To determine whether anorexia nervosa is associated with an inflammatory condition, we measured serum levels of the inflammatory biomarkers amyloid A (SAA) and calprotectin in the serum of all participants. Surprisingly, we discovered reduced SAA levels in both patient groups and reduced calprotectin levels in SEAN patients compared to healthy controls (Fig. 4A-B). To investigate whether AN is associated with increased microbial entry into the bloodstream, we measured the levels of the potential translocation biomarkers lipopolysaccharide-binding protein (LBP) and secretory CD14. We found that sCD14 levels were decreased in both patient groups compared to healthy controls, but LBP levels were slightly higher (Fig. 4C-D). This discrepancy could be partly explained by the Spearman correlation analysis (Fig. 4E), which showed that the decreased levels of SAA, calprotectin and sCD14 were positively correlated with BMI — a variable that was significantly lower in AN patients compared to healthy controls. In contrast, LBP values showed no dependence on BMI. However, despite these correlations, statistical modeling revealed that the reduction in sCD14 (p = 0.97), SAA (p = 0.74) and calprotectin (p = 0.31) was not driven by BMI. These results suggest that the lower levels of these inflammatory markers are related to the disease status itself rather than simply being a consequence of reduced BMI. Patients with both acute AN and SEAN have different microbiome characteristics Patients suffering from acute AN and SEAN had a lower alpha diversity than healthy controls based on the Shannox index (Anova: p = 0.018, F = 4.423, Tukey post hoc test: p = 0.034 for HC vs. acute AN and p = 0.036 for HC vs. SEAN, Fig. 5A). Based on ASV richness, we observed similar trend, although it was marginally non-significant (Anova: p = 0.051, F = 3.09, Fig. 5B). We observed differences in gut microbiota composition between healthy controls and the two patient groups, as well as between acute AN and SEAN, based on the Bray-Curtis and Jaccard dissimilarity metrics (Fig. 6A-B, Table 5). The differences in gut microbiota composition between the studied groups remained significant even after adjustment for BMI and body fat percentage (PERMANOVA: Bray-Cutis: p = 0.003, F = 1.619, Jaccard: 0.003, F = 1.331). In addition, Betadisper analysis showed increased interindividual variation in gut microbiota in SEAN participants compared to healthy controls and acute AN participants based on both dissimilarity metrics (Fig. 7A-B, Table 5). Acute AN showed increased interindividual variation in gut microbiota compared to healthy controls, but only when using the Bray-Curtis dissimilarity (Table 5). The relative representation of the dominant bacterial classes and genera (Fig. S2A-B) was comparable in the studied groups. Using ANCOM-BC2, we detected 17 bacterial taxa that were differentially distributed in the analyzed groups. Six of them also passed sensitivity filtering: Erysipelatoclostridium , CAG-56, Fusicatenibacter , Faecalibacterium , UBA1819, and Lachnospiraceae (Fig. 8). A post-hoc analysis (Fig. S5) showed that (1) UBA1819 was less abundant and Lachnospiraceae was more abundant in HCs compared to both patient groups, (2) Erysipelatoclostridium was more abundant in SEAN compared to acute AN, and (3) CAG-56 was more abundant in HCs compared to acute AN. We found significant associations (p < 0.05) between alpha diversity (Shannon index) and BMI, body fat percentage, and the use of antidepressants and antipsychotics (Table 6). No associations were found between alpha diversity (Shannon index) and AN type (restrictive/purgative), severity of AN (based on the DSM), hyperactivity, stress, psychiatric disorders, physical disorders, other medications taken, hypothyroidism, malnutrition, EDEQ, HAMA, and HAMD questionnaire results, or biochemical data. We found significant (p < 0.05) associations between beta diversity and body fat, AN type (restrictive/purgative), psychiatric disorders, OCD, antidepressant and hormonal contraceptive use, stress, and fT3 (Table 6). We found no significant associations between beta diversity and severity of AN (based on the DSM), hyperactivity, hypothyroidism, malnutrition, EDEQ, HAMA, and HAMD questionnaire results, medications taken, or biochemical data other than fT3. Predictive functional profiling of the gut microbiota We used the PICTRUSt2 pipeline to estimate the variation in predicted metabolites based on 16S rRNA profiling. We found that the predicted metagenome varied between the analyzed groups (PERMANOVA: Bray-Curtis: p = 0.046, F = 2.684). ANCOM-BC2 analyses revealed significant alterations in abundance of predicted metabolites between the groups (Fig. S3, Table S3). The most pronounced differences were observed in the comparison between HC and SEAN, where the majority of metabolites showed a positive correlation. The most affected category was amino acid metabolism and detoxification as well as the response to oxidative stress. Notable differences were also observed in glycogen, sugar, and energy metabolism. Fecal levels of neurotransmitters, tryptophan, and SCFAs differed between AN patients and healthy controls Using liquid chromatography, the concentrations of various neurotransmitters or their precursors and SCFAs were measured in the feces of healthy controls and AN patients. We found only slightly higher concentrations of GABA in healthy women than in AN patients. The concentrations of serotonin and its precursor tryptophan showed no significant differences between the studied groups (Fig. 9). Kynurenine (a tryptophan metabolite) was only detected in a quarter of the samples, therefore, it was not included in the analyses. The other measured molecules, i.e., dopamine and hydroxytryptophan, were below the quantification limit. In contrast, gas chromatography, which was used to measure fecal SCFA concentrations, found significant differences between healthy controls and AN patients. Healthy controls had significantly higher concentrations of acetic acid, propionic acid, butyric acid, valeric acid, and hexanoic acid than either group of patients. There were no differences in isobutyric acid and isovaleric acid among groups. The amount of heptanoic acid was higher in healthy controls than in either group of AN patients, but this was only true for SCFAs in which a difference in the amount of heptanoic acid was found between patients with acute AN and SEAN; although the difference were relatively small (Fig. 9). The other SCFAs measured, i.e., formic acid and 2-methylvaleric acid, were only detected in a limited number of samples and were therefore not included in further analyses. The concentrations of valeric acid, butyric acid, acetic acid, and propionic acid were strongly correlated (Fig. S4), as were those of isovaleric acid and isobutyric acid (rho < 0.8). GABA showed moderate correlations with valeric acid, butyric acid, acetic acid, and propionic acid. Tryptophan exhibited a significant correlation exclusively with propionic acid. Serotonin showed no significant correlation with other SCFAs or other neurotransmitters. Serum levels of neurotransmitters, tryptophan, and SCFAs differ between AN patients and healthy controls We also measured the amount of neurotransmitters, tryptophan, and SCFAs in the serum of healthy controls and AN patients. In general, the amounts of neurotransmitters and SCFAs detected per milliliter of serum were lower than the per-gram levels in stool. The concentrations of GABA, serotonin, and kynurenine were lower in the sera of both groups of AN patients vs. HCs. The tryptophan concentrations showed no differences between AN groups and were similar to stool concentrations. The other molecules measured, i.e., dopamine and hydroxytryptophan, were below the quantification limit (Fig. 10). Regarding the amount of SCFAs in the serum, differences were found between HCs vs. AN patients relative to hexanoic acid, valeric acid, and isovaleric acid (Fig. 10). In contrast to stool samples, propionic acid and isobutyric acid were only detected in a small number of serum samples, therefore, these were not included in further analyses. GABA, serotonin, and tryptophan correlated with several bacterial taxa After statistically controlling for differences between the study groups, we found a significant association between the abundance of three bacterial taxa and GABA, two with serotonin, and one with tryptophan (Fig. S6). However, Christensenellaceae, Ruminococcaceae, and Escherichia-Shigella , despite having a negative correlation with GABA, but passed the ANCOM-BC2 sensitivity test (Fig. 11). A total of 11 associations were found between SCFA concentrations and the abundance of bacterial taxa (Fig. S7), five of which passed the ANCOM-BC2 sensitivity test (Fig. 11). Of these five bacterial taxa, the Christensenellaceae family correlated negatively with all dominant SCFAs. Additionally, the bacterial genera UBA1819, Turicibacter , Terrisporobacter , and Ruminococcaceae were negatively correlated with propionic acid (Fig. 12). Discussion This study provides a comprehensive examination of the clinical, anthropometric, biochemical, psychiatric, and microbiome characteristics of patients with anorexia nervosa, comparing both the acute and the severe and enduring (SEAN) forms of the disorder with healthy controls. Previous studies have examined the composition of the microbiome associated with AN, however, those studies did not explicitly distinguish between the two forms of the disease, which differ markedly in duration. A comparison of microbiota involvement in both phases of AN could help identify factors responsible for the persistence of the disease. Our results found significant differences between physical health, psychiatric profiles, and microbiome composition of AN patients and HCs. These underscore the complexity of AN and show that both physical and psychological factors can evolve throughout the disease. Physical and Biochemical Differences Between AN Patients and Healthy Controls Consistent with the recognized physical manifestations in both acute AN and SEAN patients, we observed significant anthropometric differences compared to healthy controls. Differences included lower body weight, BMI, and body fat percentage, and smaller waist and hip circumference. These findings reflect the characteristic feature of AN, i.e., severe malnutrition. Notably, both groups of AN patients had elevated levels of hyperactivity, a symptom often associated with the neurobiological underpinnings of the disorder. In addition, the use of antidepressants, antipsychotics, and anxiolytics was notably higher in AN patients than HCs, reflecting the high comorbidity of psychiatric disorders in the AN population, as previously reported 12 . The higher use of antidepressants in SEAN compared to acute AN reflects the duration of the disease. SEAN patients reported a higher prevalence of stress in adulthood than HCs or patients with acute AN. High levels of stress hormones can lead to changes in eating behavior; additionally, people with eating disorders have a higher risk of chronic stress exposure, which can lead to stress-induced anorexia. Part of the link between stress and eating disorders is related to how individuals cope with stress (Tables 1 – 3 ) 13 . The biochemical profile of AN patients also showed alterations in several key markers. Both groups of AN patients had lower levels of cholinesterase and free triiodothyronine (fT3) compared to HCs, indicating impaired thyroid function, which is commonly observed in AN 6 , 7 , 14 . AN patients have also been found to have altered levels of immunoglobulins (IgA and IgM), possibly indicating gut damage or dysfunction, which is consistent with previous studies examining the gut-brain axis in AN 15 , 16 . Spearman correlation analyses between anthropometric and biochemical parameters also suggest that the physiological and biochemical disturbances in AN are closely related (Table 4 , Fig. 1 ). Psychiatric Differences and Symptom Severity The psychiatric differences between acute AN and SEAN were substantial. As expected, patients with acute AN had higher scores on the Eating Disorder Examination Questionnaire (EDE-Q), particularly in “restraint concern,” and on the Hamilton Anxiety Rating Scale (HAMD), indicating more severe eating disorder symptoms and higher levels of depressive symptoms. These findings may reflect the more pronounced and acute psychological distress that often accompanies the early stages of AN. Interestingly, SEAN patients had lower EDE-Q scores, which may be due to the chronic nature of their illness, in which extreme restrictive behaviors are less pronounced because they have been replaced by more entrenched disordered eating patterns. The greater use of antidepressants in the SEAN group may also be an indication of the long-term psychological burden associated with the illness and the corresponding need for pharmacological interventions (Fig. 2 ). Although anxiety scores measured by the Hamilton Anxiety Rating Scale (HAMA) were similar, principal component analysis (PCA) indicated that acute AN patients exhibited a tendency towards psychological anxiety symptoms, whereas SEAN patients were more inclined to display somatic anxiety symptoms. This distinction may be important for tailoring clinical treatments, since patients with the chronic form of the disorder may need more targeted interventions to address somatic symptoms and physical health concerns, along with the more typical psychological features of AN. Gut health and inflammation in AN We detected elevated serum levels of intestinal fatty acid binding protein (I-FABP) in SEAN patients, suggesting that the chronic form may lead to intestinal mucosal damage (Fig. 3 ). The correlation of I-FABP levels with disease duration and lower BMIs also suggests that prolonged malnutrition may contribute to structural damage of the intestinal mucosa. This aligns with other studies reporting GI dysfunction in AN, which may hinder proper nutritional management and worsen the physical consequences of the disorder 17 . Regarding inflammatory markers, we found unexpectedly low levels of amyloid A and calprotectin in the serum of both acute AN and SEAN patients, suggesting a suppression of inflammatory responses in AN, possibly due to severe malnutrition. In contrast, lipopolysaccharide-binding protein (LBP), a marker of bacterial translocation, was slightly elevated in both patient groups, suggesting that intestinal permeability may be impaired even in the absence of overt inflammatory markers (Fig. 4 ). Studies on cytokine levels in AN have found that it is strongly associated with a dysregulated immune system, which may be mainly influenced by oxidative stress, chronic psychological stress, and an altered microbiome 18 , 19 . These findings emphasize the complex relationship between malnutrition, immune function, and intestinal permeability in AN. The composition of the microbiota and its clinical implications Here we show that the gut microbiota of both acute AN and SEAN patients shows remarkable differences in diversity and composition compared to HCs. Both patient groups had lower alpha diversity as measured by the Shannon index, reflecting a less diverse microbiome (Fig. 5 ). This finding is consistent with some previous studies that have associated lower diversity of the microbiome with AN 3 , 20 – 23 , although other studies failed to describe different levels of alpha diversity in patients with AN vs. healthy individuals 2 , 6 , 24 – 26 . In addition, analysis of beta diversity showed marked differences in the composition of the gut microbiome between the two patient groups and healthy controls, with the SEAN group showing greater interindividual variation in the composition of the microbiome (Figs. 6 , 7 ). The increase in interindividual variation in the composition of the microbiota as the disease progresses indicates a gradual deterioration in the ability to regulate the symbiotic microbiota. This possibility is supported by biochemical data indicating a progressive decrease in the inflammatory immune response to gut bacteria (evidenced by decreasing calprotectin levels), increasing mucosal damage (indicated by increased I-FABP levels) and increased bacterial translocation (evidenced by increased levels of lipopolysaccharide-binding protein) in the SEAN group. These results suggest that the microbiome undergoes significant changes during the course of AN, possibly contributing to the chronicity and complexity of the disease. Similar interindividual variability was demonstrated in a previous study 6 . Interestingly, certain bacterial taxa such as Erysipelatoclostridium , Faecalibacterium , UBA1819, Fusicatenibacter , Lachnospiraceae, and Firmicutes bacterium CAG-56 were differently represented in patient groups vs. HCs. These changes in the composition of the microbiota may reflect the underlying physiological and metabolic adaptations associated with AN. For example, Lachnospiraceae and Faecalibacterium , which were more abundant in the HCs, are known for their role in butyrate production, a short-chain fatty acid (SCFA) associated with gut health and anti-inflammatory properties 27 . The lower prevalence of butyrate-producing bacteria is a common feature of AN 2 , 6 , 24 . Lower abundance of Faecalibacterium sp. is often described in other diseases, and their presence is likely related to a healthier state of the organism and has been considered for possible therapeutic use. Microbiomes of healthy individuals also contained a higher abundance of Fusicatenibacter , which forms formate, lactate, acetate, and succinate as the main products of glucose fermentation. Fusicatenibacter is associated with unhealthy eating behaviors and obesity, and its low levels in patients with AN correspond to their low calorie consumption 28 . The Firmicutes bacterium CAG-56, belonging to the Lachnospiraceae family, was only decreased in acute AN patients compared to HCs; additionally, it likely contributes to the production of SCFAs. Conversely, our findings of elevated levels of UBA1819 from the Ruminococcaceae family in both AN groups are supported by a study showing that low-energy diets lead to an increase in UBA181940 and by another study demonstrating a negative correlation between UBA1819 and subcutaneous fat as well as body weight 29 . The higher abundance of Erysipelatoclostridium in SEAN patients may indicate microbial shifts associated with prolonged malnutrition or altered metabolic pathways (Fig. 8 ). Consistent with previous findings, we found that gut microbiota composition was associated with BMI, body fat percentage, antidepressant use, antipsychotic use, hormonal contraception, AN type (restrictive/purgative), psychiatric disorders, obsessive-compulsive disorder, stress, and fT3 levels (Tables 6 ). Understandably, the composition and quantity of food have a strong direct influence on the composition of the microbiota. The use of antidepressants and antipsychotics may alter both the diversity and composition of the gut microbiota, primarily by altering the environment for microbial growth and also through their antimicrobial activity 30 , 31 . The diversity and composition of the microbiome are also influenced by hormones, be it gender, stress, or other hormones, which was confirmed in our study 32 . The difference between the diversity of the microbiome in patients with restrictive or purgative anorexia can be explained by the influence of vomiting or laxatives on the microbiome in purgative patients. Furthermore, we can assume that other psychiatric illnesses or obsessive-compulsive disorders also influence the gut microbiome in some way, either through the possible effects of medication or other factors. Metabolic Alterations The functional predictions of microbiome activity in this study, as assessed by PICTRUSt2, revealed significant differences in predicted metabolism of amino acids, oxidative stress response, and energy metabolism between patient groups and HCs, mainly between SEAN patients and HCs (Fig. S3). The SEAN group showed significant predicted disturbances in metabolism, particularly in amino acid and energy metabolism, suggesting long-term adaptation to malnutrition. Changes in predicted metabolites regulating oxidative stress and detoxification indicate chronic stress and cellular damage repair mechanisms in these patients. Differences in the abundance of metabolites regulating glucose metabolism suggest altered glucose homeostasis, presumably related to fasting in SEAN patients. These findings reflect the complex metabolic adaptations that occur in AN, potentially involving altered energy utilization and nutrient processing, which may be exacerbated in chronic conditions such as SEAN. These results confirm the results of our previous study in which we observed similar metabolic changes in patients with AN, such as the development of oxidative stress, vitamin deficits, loss of muscle mass, and a decrease in ketone bodies 7 . Furthermore, we found subtle differences in neurotransmitter levels but significant differences in SCFA concentrations between HCs and AN patients, which may reflect disturbances in gut-brain interactions. Concentrations of GABA, serotonin, and tryptophan in stool samples showed no significant differences between HCs and AN patients, although GABA concentrations were slightly elevated in HCs compared to AN patients (Fig. 9 ). This is consistent with some previous studies indicating minimal changes in gut-derived neurotransmitters in AN. It appears that the pathology of AN does not substantially disrupt the synthesis of serotonin and other neurotransmitters in the gut, or if it does, the changes are subtle 6 . The detection of kynurenine in only a small proportion of samples and the absence of quantifiable dopamine and hydroxytryptophan in stools suggest that these molecules may not be as reliably detected in the gut of AN patients, limiting our ability to draw conclusions about their involvement in the disorder. In contrast, serum analysis in AN patients revealed consistently lower levels of neurotransmitters GABA, serotonin, and kynurenine compared to HCs, which may indicate systemic changes in neurotransmitter signaling in patients with AN (Fig. 10 ). These decreased serum neurotransmitter levels may reflect gut-brain axis dysregulation or altered central nervous system function in AN, although the relatively low levels of these molecules in serum limit our ability to infer direct links to disease pathology. Compared to neurotransmitter levels, SCFAs showed significant differences between HCs and AN patients, indicating more pronounced metabolic alterations of the microbiome. Healthy women had higher concentrations of acetic acid, propionic acid, butyric acid, valeric acid, and hexanoic acid in their stool samples than AN patients, suggesting that alterations in the gut microbiota lead to lower SCFA production in AN (Fig. 9 ). These findings are consistent with previous studies showing that SCFAs play a role in maintaining gut health and modulating the gut-brain axis, and their low levels corroborate findings of higher levels of the intestinal mucosal damage marker I-FABP in their blood (Fig. 3 ). Lower levels of SCFAs in AN could indicate dysbiosis or a disturbed microbiota composition, both of which have previously been associated with AN 2 , 6 , 24 , 33 . Interestingly, levels of heptanoic acid were higher in HCs than in both groups of AN patients, with a small but significant difference between acute AN and SEAN. This suggests a potential marker of disease progression or a response to longer-term treatment, although the physiological significance of heptanoic acid in AN requires further investigation. In contrast, isobutyric acid and isovaleric acid did not differ significantly between groups, suggesting that these specific SCFAs may not be as sensitive to the metabolic changes associated with AN. The analysis of SCFA levels in serum revealed significantly lower values than in the stool samples, but differences between HCs and AN patients were still evident, especially for hexanoic acid, valeric acid, and isovaleric acid (Fig. 10 ). These changes in serum SCFA levels could reflect systemic alterations in metabolism, but given the lower levels detected in serum, their direct role in the pathophysiology of AN remains unclear. The limited detection of propionic acid and isobutyric acid in serum samples further complicates the interpretation of SCFA dynamics outside the GI tract. However, the results of this study are consistent with those of previous studies 33 , 34 . The correlation between metabolites and gut microbiota offers intriguing insights into the gut-brain axis in AN. We found significant negative correlations between several bacterial taxa, e.g., Christensenellaceae, Ruminococcaceae and Escherichia-Shigella and GABA neurotransmitter levels, suggesting that certain bacterial species influence GABA metabolism and may contribute to neurochemical disturbances in AN (Fig. 11 ). Several microbial species such as Bifidobacterium , Lactobacillus , and Bacteroides contain the gene encoding glutamic acid decarboxylase, which is capable of converting glutamate to GABA 35 . On the other hand, some bacteria, such as E. coli , have been shown to degrade GABA to succinic semialdehyde via GABA aminotransferase 36 . In our previous study, we found a higher prevalence of Christensenellaceae in patients with AN as well as lower fecal GABA levels. Although we did not observe altered levels of Christensenellaceae in our current study, we found a negative association between this group of bacteria and GABA levels, which is consistent with our previous study 6 . The bacterial species Evtepia gabavorous , belonging to the Ruminococcaceae family, has also been described as using GABA for growth 35 , which supports our results. Among the SCFAs, a broader spectrum of bacterial taxa showed correlations with the concentrations of acetic acid, propionic acid, butyric acid, and other SCFAs. Christensenellaceae in particular was negatively correlated with all dominant SCFAs, while other genera such as UBA1819, Turicibacter, Terrisporobacter , and Ruminococcaceae were negatively correlated with propionic acid (Fig. 12 ). The negative correlation of Christensenellaceae with SCFA levels is consistent with the finding that Christensenellaceae tend to be found in individuals with low BMIs; reduced SCFA levels are also observed in AN patients 37 . These results emphasize the complexity of the gut microbiota in shaping SCFA levels and suggest that microbiota-related metabolic shifts may contribute to the altered metabolic profile observed in AN. The negative correlations between certain bacterial taxa and SCFAs may reflect microbial influences on fermentation processes or an impaired microbiota in AN that fails to produce optimal levels of beneficial metabolites. This study presents several notable strengths, particularly the robust bioinformatics analysis of microbiome data. While the findings should be considered within the context of certain limitations, they still provide valuable insights. Although the small sample size may affect the generalizability of the results, it highlights important trends worth exploring further. The study also faced challenges in detecting some neurotransmitters, such as dopamine, likely due to molecular degradation during sample handling and storage. Nevertheless, this opens up opportunities for improved methodologies in future research. Finally, the observed metabolic differences between groups are predictive rather than definitive, since 16S rDNA pathway sequencing does not account for alternative possibilities. Conclusions This study provides valuable insights into the complex interplay between physical health, psychiatric symptoms, and microbiome alterations in anorexia nervosa. Our findings suggest that the gut microbiota plays a significant role in the pathophysiology of AN and that changes in the diversity and composition of the microbiome may contribute to both the psychological and physical manifestations. The gut microbiome influences both our eating habits and our mental health, including anxiety and depression. The role of the gut microbiota in the development and maintenance of eating disorders has only recently begun to be explored. Perhaps because of the incomplete understanding of the etiology of these disorders, treatment remains insufficient, and patients often relapse. The gut microbiota and its influence on mental health may be the missing element in understanding the etiology of eating disorders. In particular, the gut and metabolic disturbances in patients with SEAN highlight the need for therapeutic strategies that address both the psychological and physiological aspects of the disorder. Future studies should investigate the potential of microbiome-based interventions, such as probiotics or dietary changes, as complementary treatments for anorexia nervosa. The relationship between the composition of the microbiome and anorexia nervosa is complicated. Gut microbiome changes observed in anorexia nervosa are likely the result of both significant caloric restriction and psychological stress. However, our and other studies show that AN-induced gut microbiome dysbiosis may contribute to the persistence of disease symptoms. Methods Participants The study was conducted per the Declaration of Helsinki and was approved by the Ethics Committee of the General University Hospital in Prague. Written informed consent was obtained from all participants. Women aged 18–40 years were eligible to participate. Exclusion criteria included: pregnancy, breastfeeding, infectious diseases, severe active diseases or chronic diseases of the cardiovascular system, the hematopoietic system, the liver or urinary tract, and the use of antibiotics or antimycotics within three months of participation. Mental capacity that precluded informed consent was also exclusionary. Healthy subjects had no history of eating disorders or other psychiatric illnesses, and there was no evidence of a genetic predisposition toward eating disorders in their family history. A total of 29 patients with early-stage disease (less than 3 years since the first clinically significant AN symptoms; inpatients, outpatients or day-care patients) and 33 patients with severe and enduring AN (more than 7 years since the first clinically significant AN symptoms; inpatients, outpatients or day-care patients), and 30 healthy, age-matched women (HC group) were included (Table 1 ). The healthy women were recruited from college students, various employers, and office workers. Healthy controls were screened for hidden eating disorders using the SCOFF questionnaire. Patients with AN were examined at the Center for Eating Disorders located at the Psychiatric Department of the 1st Faculty of Medicine of Charles University and General University Hospital, Prague, CZ. All patients were examined by physicians specializing in eating disorders to determine if they met the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for AN1. This screening was also used to determine the severity of AN (0–5: none, mild, moderate, severe, and extreme). The Exercise and Eating Disorders questionnaire, version 3 (EED19) was used to assess hyperactivity (0–2: none, present, considerable). Participants were also screened for the presence of allergies, stressful events in their past (none, present before age 3 years, present in adolescence, present in adulthood), and use of antidepressants and other medications (Table 1 , 2). In patients with anorexia, i.e., anorexia identified as restrictive, or purgative (Table 1 ), the presence of comorbid disorders was also investigated (Table 3 ). Anthropometric measurements using bioimpedance (TANITA, Japan) (i.e., body fat percentage) as well as height, weight, and hip and waist circumference) were measured (Table 4 ) by experienced nursing staff. Blood samples came from the cubital vein, and all samples were drawn early in the morning, i.e., a fasted state, by psychiatric department staff. Half of the samples were sent to the hospital's central laboratory for biochemical analysis. The rest were processed at the Institute of Microbiology, and the prepared serum aliquots were frozen at − 80°C until analyzed. On the same day, stool samples were collected from all participants and immediately frozen at − 80°C. Before the stool collection, participants were asked to refrain from drinking alcohol, coffee, or black tea, not to eat any products containing cocoa, chocolate, nuts, or bananas, and not to take any probiotics. Biochemical analysis of blood samples Blood samples from controls and patients were used to determine serum levels of triacylglycerols (TAG), cholinesterase, albumin, immunoglobulins IgA, IgG, IgM, thyroid-stimulating hormone (TSH), free thyroxine (fT4), and free triiodothyronine (fT3; Table 4 ). Serum was also used for enzyme-linked immunosorbent assays (ELISA) to determine the levels of intestinal fatty acid binding protein (I-FABP), lipopolysaccharide-binding protein (LBP), calprotectin, amyloid A, and soluble CD14 (sCD14). Serum levels of intestinal fatty acid binding protein (I-FABP), a marker of intestinal damage, were analyzed in duplicate using ELISA for selective detection of human I-FABP (HyCult Biotechnology, Netherlands). The test was performed according to the manufacturer's recommendations, which included diluting the serum samples 1:5 with a supplied calibrator diluent. Commercial ELISA (Human Amyloid A DuoSet ELISA Kit; R&D Systems, USA) kits were used to determine serum amyloid A (SAA) levels as an acute phase reactant. Analyses were performed in duplicate, with serum diluted 10-fold. Commercial ELISAs were also used to determine levels of LBP and sCD14 as biomarkers for microbial transmission through the intestinal wall (Human LBP and Human sCD14 DuoSet ELISA kits, respectively; R&D Systems, USA). Analyses were performed in duplicate, with serum diluted 100-fold. The analyses were performed according to the manufacturer’s instructions. Serum calprotectin levels (S1008/S100A9) were analyzed using commercial ELISA kits (Human S100A8/S100A9 DuoSet ELISA Kit, R&D Systems, USA). The analysis was performed in duplicate with serum diluted 300-fold according to the manufacturer’s instructions. For all analyses, the resulting absorbances at 450 and 650 nm were measured using a Multiskan Ascent Plate Reader Spectrometer, MTX Lab Systems (USA). Eating Disorder Evaluation Questionnaire (EDE-Q) AN patients completed the EDE-Q 6.0 38 , which consists of 28 items derived from the Eating Disorder Examination (EDE) 39 . Twenty-two of the items were broken down into four subscales that assessed (1) restraint, (2) concern about body shape, (3) concern about body weight, and (4) concern about their eating in the past 28 days. The items were rated on a 7-point scale (0–6). The sum of the individual subscales was averaged to determine the general subscale score. Overall scores were calculated by summing and averaging the subscale scores. Higher scores indicate a greater ED psychopathology. An additional 6 questions assessed the frequency (number of times or days) of certain behaviors that had occurred in the previous 28 days, e.g., objective binge eating, self-induced vomiting, use of laxatives, or excessive exercise. Results from these items are not included in the subscale values. Data are presented as boxplots with minimum and maximum whiskers of the EDE-Q subscale and the total values. Hamilton Anxiety Rating Scale (HAMA) AN patients completed the HAMA questionnaire to measure the severity of anxiety symptoms. The scale consists of 14 items that measure both psychological and somatic anxiety. Each question is graded on a scale from 0 (absent) to 4 (severe), with a total score that can range from 0–56. Total scores less than 17 represent mild anxiety, 18–24 represent mild to moderate, 25–30 represent moderate to severe, and greater than 30 represents severe anxiety 40 . Hamilton Psychiatric Rating Scale for Depression (HAMD) AN patients were also screened for signs of depression using HAMD questionnaires. We used a structured interview conducted by a trained psychiatrist using an interview guide. A total of 21 items were analyzed, but only the first 17 were scored (HAMD17) 41 , 42 . Eight items are rated on a five-point scale (0–4), with numbers representing absent, doubtful, mild, moderate, and severe. Nine items are rated on a three-point scale (0–2), with the numbers representing absent, slight or clear, marked or severe. The sum of the first seventeen items gives the total score, with scores below 7 indicating an absence of depression, scores between 7–17 indicating mild depression, 18–24 indicating moderate depression, and scores over 24 indicating severe depression. Statistical analysis The comparison of age between the groups was analyzed using the Kruskal-Wallis test with Dunn´s multiple comparison test. Comparisons of 2 groups of AN patients were analyzed using the Mann-Whitney test. All categorical data were analyzed using Fisher's exact test (i.e., severity of AN, AN type, presence of stressful events, hyperactivity, allergy, medication use, and comorbidities). Comparisons of anthropometric, biochemical, and questionnaire variables were analyzed using a two-way ANOVA with Tukey’s multiple comparison test after data normalization with the Box-Cox transformation (λ = 0.1). Levels of serum biomarkers linked to inflammation or microbial transition were assessed using a one-way ANOVA with Box-Cox (λ = 0.1) transformed values and the Tukey’s multiple comparison test. Spearman’s rank test was used to evaluate all correlations. Statistical analyses were performed in GraphPad Prism 8 or R Studio (version 2023.6.0.421). Gut microbiota analysis Genomic DNA was isolated from collected stool samples using ZymoBIOMICS DNA Miniprep Kits (Zymo Research) and used for high-throughput sequencing (HTS) of the bacterial V3-V4 region of the 16S rRNA gene. Amplicon libraries were prepared by two separate PCR reactions 43 . The first PCR reaction was performed using the specific primer pair S-D-Bact-341-a-A-21 (5′-CCTACGGNGGCWGCAG-3′) and S-D-Bact-0785-a-A-21 (5′-GACTACHVGGGTATCTAATCC-3′), with the “tails” in the second PCR reaction serving as priming sites for the outer primers 44 . The outer primers contained sample-specific barcodes and sequencing adaptors. Kapa HiFi HotStart ReadyMix (Roche) was used in both PCR reactions as follows: first PCR reaction: 95°C for 3 min, (98°C 20 s, 55°C 30 s, 72°C 30 s) 28 cycles and 72°C for 5 min; second PCR reaction: 95°C for 3 min, (98°C 20 s, 55°C 30 s, 72°C 30 s) 12 cycles and 72°C for 5 min. The sequenced samples contained duplicates with different inline barcodes. The library was quantified by capillary electrophoresis using a DNA Screening Kit 2400 (QIAxcel Advanced, QIAGEN), pooled in equimolar amounts, and purified using SPRIselect magnetic beads (Beckman Coulter). Amplicons were sequenced using MGIEasy Universal Library Conversion Kits (App-A) on an MGI DNB-SEQ-G400 (2 x 300-bp pair-end reads, 600 cycles, MGI, USA) at Ceitec, Brno, Czech Republic. The ZymoBIOMICS Microbial Community Standard and Standard II (log distribution) and the ZymoBIOMICS Microbial Community DNA Standard and Standard II (log distribution; Zymo Research) were used to assess the quality of DNA processing, sequencing, and amplicon library preparation workflows. The relative bacterial composition of these standards was comparable to the original composition. The microbial composition of the original standards and the sequencing data obtained are shown in Table S1 and Fig. S1 . In addition, we used DNAse-free water instead of stool to check for possible contamination during the isolation process (Kitom) and a sample with DNAse-free water as the negative control sample. None of the negative control samples produced any sequences, except for one of the three DNAse-free water samples, which yielded 28 sequences classified under Bacteroides. Bioinformatic pipeline The sequencing data were generated in Fastq file format; they were demultiplexed and trimmed using Skewer software 44 . The trimmed sequences were filtered based on quality scores (expected number of errors per read < 2). High-quality sequences were denoised using DADA2 45 and an abundance matrix was created. Next, we identified and eliminated chimeric sequences with DADA2 (removeBimeraDenovo function) and performed taxonomy identification with the RDP classifier with a confidence threshold of 80 using the SILVA v.138.14 reference database. Using Procrustes analysis (p = 0.001, R = 0.987), we determined the consistency of amplicon sequence variants (ASVs) representing technical duplicates and retained only those ASVs that were present in both samples. Prior to statistical analysis, the bacterial dataset consisted of 1893 ASVs represented by 1,355,626 high-quality reads with an average sequencing depth of 15,582 (range 1,689–80,375) sequences per sample. Prediction of bacterial metagenome function was performed with the PICRUSt2 pipeline using default settings 46 . The weighted NSTI scores were comparable between the analyzed groups (Anova p = 0.556, F = 0.591). The different frequencies of predicted metabolites among the analyzed groups were identified using ANCOM-BC2. Statistical analyses of microbiome data All statistical analyses were performed in R Studio (version 2023.6.0.421). Due to the different sequencing coverage of the samples, we assigned a rarefaction threshold to the ASVs corresponding to the minimum sequencing depth. For alpha diversity analysis, we included the Shannon index and ASV richness measures as response variables, normalized the data using Box-Cox transformations, and compared alpha diversity between study groups using ANOVA. To assess the effects of different parameters (e.g., BMI, body fat percentage, and AN type) on alpha diversity, we first selected relevant variables using lasso regression, and those with a non-zero regression coefficient were used as predictors in standard linear models. Beta diversity was visualized using principal coordinate analysis (PCoA). Systematic differences based on Bray-Curtis or Jaccard dissimilarities between the analyzed groups were tested using pairwise PERMANOVA (i.e., adonis2, R package vegan) 47 . Interindividual variation between groups was tested using PERMDISP (i.e., betadisper, R package vegan) 47 . To test the effects of the different parameters on beta diversity, we performed a distance-based redundancy analysis (db-RDA) with Bray-Curtis or Jaccard dissimilarities as dependent variables. In these db-RDA models, we used a forward stepwise model selection in which the parameters with the largest effect on the model were added sequentially to the null model containing only the dependent variable. Differences in the abundance of bacterial taxa between study groups were tested using ANCOM-BC2 48 . We performed both a global analysis to test whether there were differences between study groups and a post-hoc analysis to test for pairwise differences between study groups. Analyses were conducted at the microbial genus level. Subsequent ANCOM-BC2 analyses also aimed to identify microbial taxa whose abundance is associated with the concentration of individual SCFAs or neurotransmitters. To avoid multicollinearity, these analyses were performed separately for each SCFA or neurotransmitter. In addition, the concentrations of all neurotransmitters and SCFAs exhibited highly skewed distributions and were therefore square root transformed prior to analysis to avoid the effect of high leverage data points. The analyses were statistically adjusted for differences between the study groups. Measurement of neurotransmitter levels in stool and serum by LC-MS/MS analysis Approximately 100 mg of stool (if less stool was present, the volumes of the following substances were proportionally reduced) was mixed with 1 mL of Milli-Q water (Smart2Pure™ Water Purification System, Thermo Scientific™) and homogenized using a vortex mixer. The samples were centrifuged at 30,000 x g at 4°C for 10 minutes. The supernatant was transferred, and 1 mL of acetonitrile (LC-MS grade, CHROMASOLV™ Honeywell) was added. The samples were then placed in a freezer (− 20°C) for 30 minutes. The cooled samples were centrifuged as second time at 30,000 x g at 4°C for 10 minutes. Subsequently, 1 mL of the supernatant was used for LC-MS/MS analysis. Fifty µL of serum was mixed with 200 µL of extraction reagent (acetonitrile:methanol (3:5)), both LC-MS grade. CHROMASOLV™ Honeywell was added and allowed to stand for 30 minutes at − 20°C, followed by centrifugation at 7,700 x g at 4°C for 10 minutes. Subsequently, the supernatant was used for LC-MS/MS analysis. LC-MS/MS analysis Samples were analyzed using an Agilent Infinity 1260 liquid chromatograph coupled with an Agilent 6470 LC/TQ mass spectrometer for targeted analysis. The analytes were separated on a Kinetex Polar C18 (2.6 µm, 3 mm × 100 mm) column with a SecurityGuard Polar C18 (2.6 µm, 3 mm × 2 mm) precolumn (Phenomenex), both heated to 40°C. The gradient elution consisted of phase A (0.1% formic acid LC-MS grade, Honeywell; in Milli-Q water) and phase B (0.1% formic acid in acetonitrile, LC-MS grade, CHROMASOLV™ Honeywell); the elution program was as follows: (time [min], % phase B): 0/0; 1/0; 5/20; 6/100; 8/100; 8.5/0; 9/0. The mobile phase flow rate was 0.6 mL/min, and the injected sample volume was 2.0 µL. To eliminate the matrix effect, each sample was injected and then quantified by automatic standard additions. Ion transitions and other mass spectrometric parameters were optimized using MassHunter Workstation Optimizer and Source Optimizer software (both version 10.0, SR1, Agilent). The gas temperature was 210°C and the gas flow was 12 L/min. All analytical standards were purchased from Sigma-Aldrich®. Additional details of analytical measurements can be found in Table S2 . Analysis of short-chain fatty acids in stool and serum by GC-MS analysis Approximately 200 mg of stool (if less stool was present, the volumes of the following substances were proportionally reduced) was mixed with 1 mL of Milli-Q water (Smart2Pure™ Water Purification System, Thermo Scientific™) and homogenized using a vortex mixer. The samples were then centrifuged at 30,000 x g at 4°C for 10 minutes. After centrifugation, 500 µL of the supernatant was mixed with 50 µL of concentrated 30% HCl (Lachner, Czech Republic). The solution was briefly mixed using a vortex mixer. Subsequently, 600 µL of stabilized diethyl ether (VWR Chemicals BDH®, USA) was added. Samples were extracted for 3 minutes at 4°C using a vortex mixer, followed by centrifugation at 30,000 x g at 4°C for 10 minutes. The supernatant diethyl ether fraction was transferred to a 2 mL iron vial, which was weighed beforehand. The extraction process was repeated once more. The fractions of both extractions were combined, weighed, and used for GC-MS analysis. Five hundred µL of serum was mixed with 50 µL of concentrated 30% HCl (Lachner, Czech Republic). If less serum was available, the volume of serum was brought up to 500 µL with Milli-Q water (Smart2Pure™ Water Purification System, Thermo Scientific™). The solution was briefly mixed on a vortex mixer and then 600 µL of stabilized diethyl ether (VWR Chemicals BDH®, USA) was added. The samples were then extracted on a vortex mixer at 4°C for 3 minutes and then centrifuged at 6,000 x g at 4°C for 4 minutes. The supernatant diethyl ether fraction was transferred to a 2 mL iron vial, which was weighed beforehand. The extraction process was repeated two more times. The fractions of the extractions were combined, weighed, and used for GC-MS analysis. GC-MS analysis The analysis was performed using a Varian 450-GC gas chromatograph in conjunction with a Varian 240-MS mass spectrometer (both Varian, Inc., USA) equipped with a DB-WAXETR column (0.25 µm film thickness, 30 m × 0.25 mm i.d.). Helium was used as the carrier gas at a constant flow rate of 1.4 mL/min. The injection volume was set to 1 µL, and the injection was performed in split/splitless mode with a split ratio of 1:50, which was set one minute after injection. The oven temperature program was set as follows: The initial temperature was held at 50°C for one minute, then increased to 140°C at a rate of 20°C/min, followed by an increase to 150°C at 10°C/min, where it was held for one minute. The temperature was then increased to 180°C at 15°C/min and finally to 230°C at 20°C/min and held for one minute. The total duration of the program was 13.5 minutes. The temperature of the injector was set to 230°C. The temperature of the electron ionization (EI) ion source and the temperature of the transfer line were set to 250°C and 280°C, respectively. A solvent delay of 3 minutes was selected. Mass spectra were collected in the total ion current (TIC) mode with a mass range of 30–300 m/z during the analysis, which ran from the 3 to 13.5 minute mark. Detection of analytes was performed in the selected ion monitoring (SIM) mode with m/z values derived from the Q1 mass data reported by Zhu et al 49 . Samples were diluted 10-fold for measurements. Quantification of analytes was performed using external calibration curves. The detection limits for the analytes ranged from 0.1 to 25 µg/g, depending on the specific analyte. When measuring from serum, the detection limits for the analytes ranged from 0.01 to 25 µg/g, depending on the specific analyte. Concentrations were converted to mL of serum. Declarations Declaration of interest statement All authors declare no financial or non-financial competing interests. Author Contribution P.P. led the conceptualization of the study, with large contributions from R.R. J.J. and J.K. were responsible for data curation. Formal analysis was conducted primarily by PP, with supporting contributions from K.C., G.K., and K.Z. PP and HP secured funding for the project. The investigation was carried out by J.S., A.N., and T.C., who performed metabolomic measurements. Samples from patients were collected by P.H., A.L., and H.P. Supervision and validation were undertaken by H-T.H. Visualization was led by P.P, with support from J.J. The original draft of the manuscript was written by P.P, with supporting input from R.R. and H.P . All authors contributed to the review and editing of the manuscript, with P.P. taking the lead, and R.R. and J.J providing supporting contributions. Acknowledgements The study was supported by the Ministry of Health of the Czech Republic under grant nr. NU22-04-00010 and NU23-04-00381 and by the Ministry of Education, Youth, and Sports of the Czech Republic under grant Talking microbes - understanding microbial interactions within One Health framework (CZ.02.01.01/00/22_008/0004597). Data availability statement Sequencing data are archived in the European Nucleotide Archive under project PRJEB77672. Accession numbers with metadata for each sample and R scripts are available at github repository ( https://github.com/JanetJezkova/Gut-microbiota-in-patients-with-Anorexia-Nervosa---acute-vs.-chronic-patients ). The data from this study are available in the Open Research Repository Zenodo: doi: 10.5281/zenodo.15102456 . References Liang, D., Leung, R. K., Guan, W. & Au, W. W. Involvement of gut microbiome in human health and disease: brief overview, knowledge gaps and research opportunities. 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Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures. Nat Methods 21 , 83-91, doi:10.1038/s41592-023-02092-7 (2024). Zhu, J. H. et al. Optimization and validation of direct gas chromatography-mass spectrometry method for simultaneous quantification of ten short-chain fatty acids in rat feces. J Chromatogr A 1669 , 462958, doi:10.1016/j.chroma.2022.462958 (2022). Tables Table 1. Clinical parameters of controls and AN patients. Variable Controls (HC) (n=30) Acute AN (n=29) SEAN (n=33) p-value s HC vs. Acute AN HC vs. SEAN Acute AN vs. SEAN Age (years) 26 (23.8; 30) 20 (18; 26) 27 (21.5; 31) **0.0030 NS **0.0014 Disease duration (months) - 24 (15; 35) 102 (84; 150) - - ****<0.0001 Severity of AN (DSM) - 3 (2; 4) 4 (2.5; 4) - - NS AN type (R/P) - 18/11 21/12 - - NS Childhood stress (%) 3.3 10.3 6.1 NS NS NS Adolescent stress (%) 30 27.6 51.5 NS NS NS Adulthood stress (%) 20 13.8 51.5 NS *0.0175 **0.004 Hyperactivity 0 (0; 0) 1 (0; 2) 0 (0; 2) ****<0.0001 ***0.007 NS Allergy (%) 33.4 48.3 45.5 NS NS NS The comparison of age between groups was evaluated by the Kruskal-Wallis test with Dunn´s multiple comparison test. Comparison between the 2 groups of AN patients was evaluated using the Mann-Whitney test. Categorical data were evaluated using Fisher's exact test . The results are shown as median with IQR (age, disease duration, severity of AN, hyperactivity), proportional numbers (AN type), or percentage (stress, allergy). NS – non-significant, SEAN - severe and enduring AN. Stars indicate p-values for each comparison. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Table24. Medications used by the study cohort. Medication Controls (HC) (n=30) Acute AN (n=29) SEAN (n=33) p-value s HC vs. Acute AN HC vs. SEAN Acute AN vs. SEAN Antidepressants (%) 0 65.5 90.9 ****<0.0001 ****<0.0001 *0.0263 Antipsychotics (%) 0 31.0 48.5 ***0.0008 ****<0.0001 NS Anxiolytics (%) 0 24.1 15.2 **0.0046 *0.054 NS Contraceptives (%) 16.7 10.3 24.3 NS NS NS Thyroid hormones (%) 6.7 17.2 30.0 NS NS NS The results are shown as percentages. To test differences between groups, Fisher´s exact test was used. Stars indicate p-values for each comparison. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Table 3. Proportional representation of comorbid disorders or features in acute AN and SEAN. Comorbid disorder/feature Acute AN SEAN Acute AN vs. SEAN p-value Alcohol-, Sedative-, Hypnotic-, or Anxiolytic-related disorders 0/29 (0%) 4/33 (12.12%) 0.12 Manic episode 1/29 (3.45%) 0/33 (0%) 0.47 Major depressive disorder 8/29 (27.59%) 9/33 (27.27%) 1 Dysthymic disorder 3/29 (10.34%) 3/33 (9.09%) 1 Agoraphobia 0/29 (0%) 2/33 (6.06%) 0.49 Social anxiety disorder 3/29 (10.34%) 4/33 (12.12%) 1 Panic disorder 5/29 (17.24%) 2/33 (6.06%) 0.24 Generalized anxiety disorder 5/29 (17.24%) 4/33 (12.12%) 0.72 Mixed anxiety and depressive disorders 6/29 (20.69%) 9/33 (27.27%) 0.77 Obsessive-compulsive disorder 1/29 (3.45%) 3/33 (9.09%) 0.62 Mixed obsessional thoughts and acts 0/29 (0%) 5/33 (15.15%) 0.12 Post-traumatic stress disorder 2/29 (6.9%) 0/33 (0%) 0.21 Persistent somatoform pain disorder 1/30 (3.45%) 0/33 (0%) 0.47 Emotionally unstable personality disorders 2/29 (6.9%) 1/33 (3.03%) 0.60 Mixed personality disorders 0/29 (0%) 1/33 (3.03%) 1 Suicidality 9/29 (31.03%) 6/33 (18.18%) 0.37 Differences between groups were assessed using Fisher's exact test. SEAN - severe and enduring AN. All comparisons were non-significant. Table 4. Anthropometric and biochemical parameters for controls and AN patients. Variable Control (H C) (n=30) Acute AN (n=29) SEAN (n=33) p-value s H C vs. Acute AN H C vs. SEAN Acute AN vs. SEAN Height (cm) 169.6 ± 6.89 167.00 ± 5.27 164.60 ± 6.73 NS NS NS Weight (kg) 64.34 ± 7.29 43.48 ± 4.32 38.32 ± 6.97 ****<0.0001 ****<0.0001 NS BMI (kg/m 2 ) 22.35 ± 2.00 15.60 ± 1.33 14.14 ± 2.31 ****<0.0001 ****<0.0001 NS Body Fat (%) 27.73 ± 5.03 6.23 ± 4.28 5.52 ± 4.54 ****<0.0001 ****<0.0001 NS Waistline (cm) 70.70 ± 6.20 61.10 ± 4.29 59.15 ± 8.67 *0.0395 **<0.0041 NS Hipline (cm) 95.70 ± 5.31 80.10 ± 4.07 76.42 ± 5.55 **<0.0059 ***<0.0002 NS TAG (mmol/l) 0.90 ± 0.33 0.88 ± 0.34 1.09 ± 0.97 NS NS NS Cholinesterase (ukat/l) 118.50 ± 24.47 96.10 ± 25.90 110.10 ± 48.14 ****<0.0003 ***0.0315 NS Albumin (g/l) 47.17 ± 2.96 46.51 ± 3.24 45.63 ± 3.45 NS NS NS IgG (g/l) 12.01 ± 2.14 10.61 ± 1.89 10.19 ± 1.70 NS NS NS IgA (g/l) 2.06 ± 0.79 1.76 ± 0.64 2.21 ± 0.53 NS NS **0.0062 IgM (g/l) 1.58 ± 0.74 1.57 ± 0.84 1.28 ± 0.63 NS *0.0354 *0.0474 TSH (mIU/L) 2.68 ± 1.28 2.41 ± 1.25 2.74 ± 1.56 NS NS NS fT4 (pmol/l) 14.65 ± 1.35 12.84 ± 1.73 12.84 ± 2.40 NS NS NS fT3 (pmol/l) 5.54 ± 0.50 3.88 ± 1.04 4.04 ± 0.91 ****<0.0001 ****<0.0001 NS Multiple comparisons between groups were evaluated using 2-way ANOVA with Tukey corrections on transformed data (λ = 0.1). TAG – triacylglyceride; TSH – thyroid-stimulating hormone; fT4 – free thyroxine; fT3 – free triiodothyronine; NS – non-significant. The results are shown as mean ± SD. Stars indicate p-values for each comparison. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Table 5. Comparisons of interindividual variation and composition of gut microbiomes. Comparisons of (A) microbial composition and (B) interindividual variation between studied groups based on PERMANOVA and Betadisper tests, respectively. Tests were conducted using relative abundance-based (Bray-Curtis) and prevalence-based (Jaccard) dissimilarities. Values of (pseudo-) F statistics (F), associated degrees of freedom (df), resulting probability values (p), and proportions of explained variance (R2) are shown. Significant values are in bold type. Table 6. Alpha and beta diversity associations. Alpha diversity (Shannon index) F - value p - value BMI 5.149 0.0258 Body fat % 5.127 0.0261 Antidepressants 5.700 0.0192 Antipsychotics 5.813 0.0181 Beta diversity Bray-Curtis Jaccard F - value p- value F - value p - value Body fat % 2.092 0.001 1.643 0.002 AN type (res/purg) 1.405 0.031 NS NS Antidepressants 1.743 0.005 1.446 0.005 Hormonal contraception 1.553 0.020 1.314 0.030 Stress 1.596 0.010 1.327 0.015 Other psychiatric disorders 1.820 0.005 1.467 0.005 OCD 1.586 0.015 1.301 0.010 fT3 2.388 0.005 1.791 0.005 AN type – restrictive (res) or purgative (purg); BMI – body mass index; fT3 - free triiodothyronine; OCD – obsessive-compulsive disorder. Additional Declarations No competing interests reported. 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parameters for all patients and healthy controls. Yellow indicates a positive association and blue indicates a negative association. Asterisks denote the p-values for each comparison: **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001. Significance less than p \u0026lt; 0.01 is not shown. BMI – body mass index; TAG – triacylglycerides; TSH – thyroid-stimulating hormone; fT4 – free thyroxine; fT3 – free triiodothyronine.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/da291d18fe29916e181a34c0.jpg"},{"id":84203747,"identity":"c120d454-8395-4bd0-b7a2-4b82fd1f1c28","added_by":"auto","created_at":"2025-06-09 08:45:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":245528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComparison of scores and principal component analyses (PCA) of the subscales and total scores of the Eating Disorder Examination Questionnaire (EDE-Q), the Hamilton Anxiety Rating Scale (HAMA), and the Hamilton Depression Rating Scale (HAMD) in patients with acute AN or SEAN.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A) Subscales and total scores from the EDE-Q; (B) PCA analysis of the subscales and total scores from the EDE-Q; (C) subscales and total scores from the HAMD; (D) PCA of the subscales and total scores from the HAMD; (E) total scores from the HAMD17. Data were transformed using the Box-Cox transformation (lambda 0.1) and analyzed using two-way ANOVA with Tukey´s multiple comparison test. Asterisks indicate *p \u0026lt; 0.05, ****p \u0026lt; 0.0001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/f2223ec494bd9d13a65667e0.jpg"},{"id":84202030,"identity":"01bc5219-ddf4-4c31-b336-3181dc1c1e8e","added_by":"auto","created_at":"2025-06-09 08:37:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":115162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLevels of intestinal fatty acid-binding protein (I-FABP) in the serum of healthy controls and patients with acute AN or SEAN and Spearman correlation\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A) Serum levels of I-FABP; (B) Spearman correlation of I-FABP values and BMI with 95% confidence intervals for patients with AN and healthy controls; (C) Spearman correlation of I-FABP values and disease duration with 95% confidence intervals for patients with AN. Data were transformed using the Box-Cox transformation (lambda 0.1) and analyzed using one-way ANOVA with Tukey´s multiple comparison test. HC – healthy controls; SEAN – severe and enduring AN. Asterisks indicate *p \u0026lt; 0.05, **p \u0026lt; 0.01. Boxplots show medians with interquartile ranges.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/a1a3b75259a967025f1e139a.jpg"},{"id":84202034,"identity":"b75c297b-c962-476c-99de-fdc392763438","added_by":"auto","created_at":"2025-06-09 08:37:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":216019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSerum levels of microbial translocation and correlations.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A) Amyloid A level; (B) LBP level; (C) sCD14 level; (D) calprotectin level; (E) Spearman correlation coefficient between serum parameters and BMI. Green indicates a positive correlation and purple indicates a negative correlation. The data were transformed using the Box-Cox transformation (lambda 0.1) and analyzed using a one-way ANOVA with Tukey´s multiple comparison test. The asterisks indicate the p-values for each comparison. *p \u0026lt; 0.05; **p \u0026lt; 0.01. BMI – Body Mass Index. nHC = 30, nAcute AN = 29, nSEAN = 33. Boxplots show medians with interquartile ranges. SAA – amyloid A; LBP – lipopolysaccharide-binding protein; sCD14 – secretory CD14.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/a36524829ee45c5b434c7fbb.jpg"},{"id":84202032,"identity":"0e8754ba-60cc-421a-aa16-0100eb80f1f0","added_by":"auto","created_at":"2025-06-09 08:37:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":89722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eVariation in alpha diversity among studied groups.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe boxplots show the variation in: (A) Shannon diversity and (B) ASV richness between the groups. The boxplots show the median values and the interquartile range.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/2a11a69f5e3fb84ccb75d9b5.png"},{"id":84202042,"identity":"027120c9-9c83-4544-b246-82b0d7162e51","added_by":"auto","created_at":"2025-06-09 08:37:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":149003,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eVariation in the composition of the microbiota.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePrincipal coordinate analysis (PCoA) for (A) Bray-Curtis and (B) Jaccard dissimilarities showed differences in microbiota composition between sample groups. The PCoA plots for Bray-Curtis showed that the x-axis explains 7.8% and the y-axis 7.5% of the variation in the data. The PCoA plots for Jaccard show that the x-axis explains 5.9% and the y-axis explains 3.8% of the variation in the data. Blue dots represent PCoA values of healthy controls; red dots represent PCoA values of patients with acute AN; green dots represent PCoA values of SEAN patients. Shaded ellipses represent inter-individual variation between groups and indicate 90% confidence intervals for all points. HC – healthy controls; SEAN – severe and enduring AN.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/faf10e0b4ad84065b3819a61.png"},{"id":84203748,"identity":"d9e101ec-6c64-4e08-80b9-5c24e4629054","added_by":"auto","created_at":"2025-06-09 08:45:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":110944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMultivariate analysis of microbial dispersion.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A) based on Bray-Curtis and (B) Jaccard dissimilarities. Individual points in boxplots show distances to the centroid in multivariate space for each group. HC – healthy controls; SEAN – severe and enduring AN.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/5daae474ccfbd614738a22c5.png"},{"id":84202040,"identity":"4ea976f5-7c32-471c-819f-19e99a750a33","added_by":"auto","created_at":"2025-06-09 08:37:47","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":330642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDifferential frequency analysis at the genus level.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBoxplots show the bacterial genera whose abundance varied between study groups based on ANCOM-BC2. Genera that passed the sensitivity filter are shown in bold type.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/24cbb4128335f9234747752d.png"},{"id":84202043,"identity":"71801815-460d-45c2-8787-b8dc793f8621","added_by":"auto","created_at":"2025-06-09 08:37:47","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":233022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConcentrations of fecal neurotransmitters, serotonin precursors, and short-chain fatty acids (SCFAs) in the three studied groups.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A) GABA; (B) serotonin; (C) tryptophan; (D) acetic acid; (E) propionic acid; (F) butyric acid; (G) isobutyric acid; (H) valeric acid; (I) isovaleric acid; (J) hexanoic acid; (K) heptanoic acid. Data were transformed using the Box-Cox transformation (λ = 0.1) and analyzed using a one-way ANOVA with Tukey´s multiple comparison test. Asterisks indicate p-values for each comparison. *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001; ****p \u0026lt; 0.0001. Boxplots show medians with interquartile ranges. GABA – gamma-aminobutyric acid; HC – healthy controls; SEAN – severe and enduring AN.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/51dbb2422c532ddd3e34b728.jpeg"},{"id":84202036,"identity":"1947ba00-023e-492a-8c99-30cf1caf5f36","added_by":"auto","created_at":"2025-06-09 08:37:47","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":225392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConcentrations of serum neurotransmitters, serotonin precursors, and short-chain fatty acids (SCFA) in the three studied groups.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A) GABA; (B) serotonin; (C) tryptophan; (D) acetic acid; (E) propionic acid; (F) butyric acid; (G) isobutyric acid; (H) valeric acid; (I) isovaleric acid; (J) hexanoic acid; (K) heptanoic acid. Data were transformed using the Box-Cox transformation (λ = 0.1) and analyzed using a one-way ANOVA with Tukey´s multiple comparison test. Asterisks indicate p-values for each comparison. *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001; ****p \u0026lt; 0.0001. Boxplots show medians with interquartile ranges. GABA – gamma-aminobutyric acid; HC – healthy controls; SEAN – severe and enduring AN.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/d8790bebfc94f69d61a51080.jpeg"},{"id":84203749,"identity":"e9b2b4b7-cb8c-42be-81ed-5b2b47d97c6b","added_by":"auto","created_at":"2025-06-09 08:45:47","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":148013,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAssociations between GABA concentrations and the bacterial taxa.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSignificant associations between GABA concentrations and the abundance of bacterial taxa that passed the ANCOM-BC2 sensitivity test.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/55605e232acb2a2428d40dc9.png"},{"id":84202039,"identity":"6de19211-fb3a-49e5-845e-501693179031","added_by":"auto","created_at":"2025-06-09 08:37:47","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":279603,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAssociations between SCFA concentrations and bacterial taxa.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSignificant associations between SCFA concentrations [(A) propionic acid, (B) acetic acid, and (C) butyric acid] and the abundance of bacterial taxa that passed the ANCOM-BC2 sensitivity test.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/9497eb30647ad46a5f80916e.png"},{"id":97179861,"identity":"c044b474-faed-43ae-a3af-b0b6c208c570","added_by":"auto","created_at":"2025-12-01 16:17:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4160528,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/a13d8e57-4eb5-4bbd-a8ba-53c4eb9147f4.pdf"},{"id":84202028,"identity":"d2b9eb62-d8a0-49ac-b55f-1de2ed38d6c5","added_by":"auto","created_at":"2025-06-09 08:37:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2282443,"visible":true,"origin":"","legend":"","description":"","filename":"ProchazkovaSupplementdataBaM.docx","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/63d9512539203e96424b55bf.docx"},{"id":84203746,"identity":"c466253a-3613-4348-9a47-5fffd195a007","added_by":"auto","created_at":"2025-06-09 08:45:47","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1490878,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract1Prochazkova.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6610537/v1/ba5a672b078cf83cd05ab162.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"The microbiome and metabolome of patients with acute or severe and enduring anorexia nervosa influence gamma-aminobutyric acid (GABA) metabolism and microbial fermentation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnorexia nervosa (AN) is a severe eating disorder characterized by pathological eating behavior with extreme restriction of energy intake and compulsive excessive exercising that leads to dangerously low body weight and malnutrition. In patients diagnosed with AN, their reluctance to engage with therapeutic interventions, in conjunction with their gastrointestinal (GI), cardiovascular, immunological, and metabolic symptoms, as well as their frequent psychiatric comorbidities, poses a significant challenge to the effectiveness of early interventions. It is therefore evident that further research into the efficacy of complementary therapies is required. In addition, psychotherapy is likely needed to achieve a full, long-term recovery. The long duration of this disease enables the delineation of its phases into acute anorexia and severe and enduring anorexia nervosa (SEAN). Definitions of SEAN vary in some respects. The major features of SEAN include the presence of a persistent state of food restriction, underweight, impaired weight/shape perception, and completion of at least two medical procedures (e.g., feeding tubes or intravenous interventions) along with a diagnostic evaluation. One controversial point is the duration of the disease. While some researchers use a value of more than 3 years, others use more than 7 years.\u003c/p\u003e \u003cp\u003eWhile the pathophysiology of AN is not yet fully understood, there is a growing body of evidence that recognizes the gut microbiome as an important factor affecting the course and outcome of this disorder. Research into the human microbiome with regard to the physiological regulation of health and disease has increased dramatically in recent years. Many diseases, from autoimmune disorders to neurodegenerative diseases and even cancer, have been linked to an imbalance in the microbiota, known as dysbiosis\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Emerging data point to gut-brain communication and bidirectional interactions between the gut-brain axis and the microbiota. The commensal microbiota inhabiting the GI tract actively influences human health, both through microbial metabolism and direct interactions with the host.\u003c/p\u003e \u003cp\u003eMost studies on the microbiota in patients with AN show significant changes in the composition and diversity of their gut microbiota. Although the results of individual studies are quite variable in terms of bacterial taxonomic composition, most studies show decreased alpha diversity in these patients, which is attributed to reduced caloric intake and very low food variability\u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Decreased alpha diversity is usually associated with metabolic endotoxemia, a form of chronic, low-grade systemic inflammation resulting from impaired gut barrier function and increased intestinal permeability (leaky gut syndrome). As a result, bacterial components and metabolic products from the gut cross the blood-brain barrier and enter the bloodstream and possibly the brain.\u003c/p\u003e \u003cp\u003eIndigestible substances such as dietary fiber are metabolized by intestinal microbes and used to produce short-chain fatty acids (SCFAs). SCFAs supply the intestinal epithelial cells with energy and help regulate their function, thereby improving the function of the intestinal barrier and the host\u0026rsquo;s metabolism. At the intestinal level, SCFAs interact with intestinal epithelial and immune cells. In the systemic circulation and peripheral tissues, they moderately influence systemic inflammation by regulating cytokine secretion. SCFAs can also cross the hematoencephalic barrier and enter the central nervous system. Furthermore, SCFAs stimulate enteroendocrine cells in the intestines to release specific gut hormones, which, in turn, exert indirect effects on brain function. SCFAs can also directly activate the vagus nerve. The interaction of SCFAs with these gut-brain pathways can directly or indirectly influence processes related to brain function, learning, memory, and mood\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePeople with AN often have lower numbers of gut bacteria that produce butyrate and therefore have lower amounts of butyrate in their gut. This is explained by the reduced consumption of fiber as part of their restrictive and pathological diet. Butyrate, one of the three most abundant and best-studied SCFAs, has been shown to improve the metabolic profile of the host\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Butyrate in the gut is negatively correlated with anxiety and depression, which may explain some of the psychiatric symptoms frequently associated with AN. The gut microbiota influences the production of neurotransmitters in the host but can also produce several neurotransmitters independently. The hormone serotonin (5-HT), for example, is synthesized, in both the brain and the gut, from tryptophan, an essential amino acid derived from protein-rich foods like chicken, eggs, and dairy. Under physiological conditions, 5-HT has many functions since 5-HT receptors are found throughout the body. In particular, 5-HT regulates smooth muscle in the GI tract and supports intestinal peristalsis. In the brain, 5-HT is involved in mood regulation and is colloquially referred to as the \"good mood hormone\". Lower concentrations of the neurotransmitters 5-HT, GABA, and dopamine (DA) have been found in the stools of AN patients compared to healthy controls. These differences may contribute to anxiety or depression\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs with many other diseases, there is an intensive search for possible therapeutic mechanisms that could restore the gut microbiota to a \"healthy\" state and help to achieve \"healthy\" eating behaviors. Given the role that the gut microbiota plays in regulating appetite, mood, and metabolism, restoring the gut microbiota in people with AN is a stepping stone to a better, fuller recovery. The gut dysbiosis present in AN patients on admission to hospital, for example, only partially normalizes during hospital stays and treatments, even though their body mass index (BMI) increases and psychometric parameters improve\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This raises the question of whether a long-term disrupted microbiome is sufficiently effective in chronic AN and also during nutritional therapy.\u003c/p\u003e \u003cp\u003eThe purpose of this study was to investigate the composition of the gut microbiome and metabolome in patients with anorexia nervosa in the early and late stages of the disease and to identify the predictors of AN persistence. We found the gut microbiota and milieu of microbial metabolites differ between patients with early-stage AN, patients with severe and enduring anorexia nervosa (SEAN), and healthy individuals. Furthermore, we showed that increased intestinal permeability, leading to low-grade systemic inflammation, is less supported in patients with early-stage acute AN than in patients with SEAN. This research fills a gap in the literature by further distinguishing between the acute and chronic course of the disease. Clarifying the mechanisms and the role of microbiota in the transition of anorexia from the early phase to the chronic phase of AN could help to discover new preventive and therapeutic approaches to AN.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eClinical, anthropometric, and biochemical description of the study cohort\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study included\u0026nbsp;62 anorexic patients and 30 healthy\u0026nbsp;women (Table 1). The age of patients with acute AN was less compared to the other participants due to the nature of the acute illness, which typically has an onset between 12 and 25 years\u003csup\u003e11\u003c/sup\u003e. \u0026nbsp;After categorizing patients into acute AN and SEAN, there was also a distinction between the two groups relative to disease duration (Table 1). Both groups of AN patients differ from healthy controls in physical parameters related to malnutrition, such as body weight, BMI, body fat percentage, waist and hip line circumference, but also in the presence of hyperactivity, which is a common feature of AN (Table 1). AN participants also differed from the healthy controls in their greater use of antidepressants, antipsychotics, and anxiolytics (Table 2). Patients with long-standing SEAN primarily differed from those with acute AN relative to the presence of stressful events in adulthood (Table 1), which was reflected by greater use of antidepressants (Table 2). AN patients had many different comorbidities, but their prevalence did not differ significantly between the two patient groups (Table 3)\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the biochemical serum tests showed that both groups of AN patients had lower levels of cholinesterase and free triiodothyronine compared to healthy controls. The two groups of patients also differed in IgA and IgM levels, which could be a consequence of intestinal damage (Table 4). However, the Spearman correlation of these anthropometric and biochemical parameters showed that the parameters related to body size were closely related to the amount of cholinesterase, thyroid hormones fT3 and fT4, and IgG levels, as shown in Fig. 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatients with acute AN had a higher EDE-Q and HAMD total score\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo compare the psychiatric differences between the two groups of patients with anorexia nervosa (acute AN and SEAN), they were assessed with the Eating Disorder Examination questionnaire (EDE-Q), Hamilton Anxiety Rating Scale (HAMA), and Hamilton Psychiatric Rating Scale for depression (HAMD). Patients with SEAN had lower total EDE-Q scores and dietary restraint subscores (extreme attempts to limit food intake) compared to patients with acute AN (Fig. 2A-B). The patient groups did not show significant differences in HAMA, which determines anxiety. However, Principal Component Analysis (PCA) analysis suggested that patients with acute AN were more likely to exhibit features of psychological anxiety, whereas patients with SEAN were more likely to exhibit features of somatic anxiety (Fig. 2C-D). Patients with acute AN also had higher HAMD total depression severity scores, reflecting their lower use of antidepressants compared to patients with SEAN (Fig. 2E).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eI-FABP levels are higher in patients with SEAN and correspond with BMI\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the potential damage to the intestinal mucosa, we tracked serum levels of intestinal fatty acid binding protein (I-FABP), which is expressed in the epithelial cells of the small intestinal mucosa and released into the bloodstream when the intestinal mucosa is damaged. We detected slightly elevated levels of I-FABP in the serum of SEAN patients, but this was largely due to high I-FABP levels in several patients who had clear signs of intestinal damage (Fig. 3A). Spearman correlation was used to investigate whether I-FABP levels might berelated to BMI or disease duration in all AN patients. Figure 3 shows that I-FABP levels correlate negatively with BMI and positively with disease duration.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatients with anorexia have different levels of markers of bacterial translocation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo determine whether anorexia nervosa is associated with an inflammatory condition, we measured serum levels of the inflammatory biomarkers amyloid A (SAA) and calprotectin in the serum of all participants. Surprisingly, we discovered reduced SAA levels in both patient groups and reduced calprotectin levels in SEAN patients compared to healthy controls (Fig. 4A-B).\u003c/p\u003e\n\u003cp\u003eTo investigate whether AN is associated with increased microbial entry into the bloodstream, we measured the levels of the potential translocation biomarkers lipopolysaccharide-binding protein (LBP) and secretory CD14. We found that sCD14 levels were decreased in both patient groups compared to healthy controls, but LBP levels were slightly higher (Fig. 4C-D). This discrepancy could be partly explained by the Spearman correlation analysis (Fig. 4E), which showed that the decreased levels of SAA, calprotectin and sCD14 were positively correlated with BMI — a variable that was significantly lower in AN patients compared to healthy controls. In contrast, LBP values showed no dependence on BMI. However, despite these correlations, statistical modeling revealed that the reduction in sCD14 (p = 0.97), SAA (p = 0.74) and calprotectin (p = 0.31) was not driven by BMI. These results suggest that the lower levels of these inflammatory markers are related to the disease status itself rather than simply being a consequence of reduced BMI.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatients with both acute AN and SEAN have different microbiome characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatients suffering from acute AN and SEAN had a lower alpha diversity than healthy controls based on the Shannox index (Anova: p = 0.018, F = 4.423, Tukey post hoc test: p = 0.034 for HC vs. acute AN and p = 0.036 for HC vs. SEAN, Fig. 5A). Based on ASV richness, we observed similar trend, although it was marginally non-significant (Anova: p = 0.051, F = 3.09, Fig. 5B). We observed differences in gut microbiota composition between healthy controls and the two patient groups, as well as between acute AN and SEAN, based on the Bray-Curtis and Jaccard dissimilarity metrics (Fig. 6A-B, Table 5). The differences in gut microbiota composition between the studied groups remained significant even after adjustment for BMI and body fat percentage (PERMANOVA: Bray-Cutis: p = 0.003, F = 1.619, Jaccard: 0.003, F = 1.331).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, Betadisper analysis showed increased interindividual variation in gut microbiota in SEAN participants compared to healthy controls and acute AN participants based on both dissimilarity metrics (Fig. 7A-B, Table 5). Acute AN showed increased interindividual variation in gut microbiota compared to healthy controls, but only when using the Bray-Curtis dissimilarity (Table 5). The relative representation of the dominant bacterial classes and genera (Fig. S2A-B) was comparable in the studied groups. Using ANCOM-BC2, we detected 17 bacterial taxa that were differentially distributed in the analyzed groups. Six of them also passed sensitivity filtering: \u003cem\u003eErysipelatoclostridium\u003c/em\u003e, CAG-56, \u003cem\u003eFusicatenibacter\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, UBA1819, and Lachnospiraceae (Fig. 8). A post-hoc analysis (Fig. S5) showed that (1) UBA1819 was less abundant and Lachnospiraceae was more abundant in HCs compared to both patient groups, (2) \u003cem\u003eErysipelatoclostridium\u0026nbsp;\u003c/em\u003ewas more abundant in SEAN compared to acute AN, and (3) CAG-56 was more abundant in HCs compared to acute AN.\u003c/p\u003e\n\u003cp\u003eWe found significant associations (p \u0026lt; 0.05) between alpha diversity (Shannon index) and BMI, body fat percentage, and the use of antidepressants and antipsychotics (Table 6). No associations were found between alpha diversity (Shannon index) and AN type (restrictive/purgative), severity of AN (based on the DSM), hyperactivity, stress, psychiatric disorders, physical disorders, other medications taken, hypothyroidism, malnutrition, EDEQ, HAMA, and HAMD questionnaire results, or biochemical data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe found significant (p \u0026lt; 0.05) associations between beta diversity and body fat, AN type (restrictive/purgative), psychiatric disorders, OCD, antidepressant and hormonal contraceptive use, stress, and fT3 (Table 6). We found no significant associations between beta diversity and severity of AN (based on the DSM), hyperactivity, hypothyroidism, malnutrition, EDEQ, HAMA, and HAMD questionnaire results, medications taken, or biochemical data other than fT3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePredictive functional profiling of the gut microbiota\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the PICTRUSt2 pipeline to estimate the variation in predicted metabolites based on 16S rRNA profiling. We found that the predicted metagenome varied between the analyzed groups (PERMANOVA: Bray-Curtis: p = 0.046, F = 2.684). ANCOM-BC2 analyses revealed significant alterations in abundance of predicted metabolites between the groups (Fig. S3, Table S3). The most pronounced differences were observed in the comparison between HC and SEAN, where the majority of metabolites showed a positive correlation. The most affected category was amino acid metabolism and detoxification as well as the response to oxidative stress. Notable differences were also observed in glycogen, sugar, and energy metabolism.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFecal levels of neurotransmitters, tryptophan, and SCFAs differed between AN patients and healthy controls\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing liquid chromatography, the concentrations of various neurotransmitters or their precursors and SCFAs were measured in the feces of healthy controls and AN patients. We found only slightly higher concentrations of GABA in healthy women than in AN patients. The concentrations of serotonin and its precursor tryptophan showed no significant differences between the studied groups (Fig. 9). Kynurenine (a tryptophan metabolite) was only detected in a quarter of the samples, therefore, it was not included in the analyses. The other measured molecules, i.e., dopamine and hydroxytryptophan, were below the quantification limit.\u003c/p\u003e\n\u003cp\u003eIn contrast, gas chromatography, which was used to measure fecal SCFA concentrations, found significant differences between healthy controls and AN patients. Healthy controls had significantly higher concentrations of acetic acid, propionic acid, butyric acid, valeric acid, and hexanoic acid than either group of patients. There were no differences in isobutyric acid and isovaleric acid among groups. The amount of heptanoic acid was higher in healthy controls than in either group of AN patients, but this was only true for SCFAs in which a difference in the amount of heptanoic acid was found between patients with acute AN and SEAN; although the difference were relatively small (Fig. 9). The other SCFAs measured, i.e., formic acid and 2-methylvaleric acid, were only detected in a limited number of samples and were therefore not included in further analyses.\u003c/p\u003e\n\u003cp\u003eThe concentrations of valeric acid, butyric acid, acetic acid, and propionic acid were strongly correlated (Fig. S4), as were those of isovaleric acid and isobutyric acid (rho \u0026lt; 0.8). GABA showed moderate correlations with valeric acid, butyric acid, acetic acid, and propionic acid. Tryptophan exhibited a significant correlation exclusively with propionic acid. Serotonin showed no significant correlation with other SCFAs or other neurotransmitters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSerum levels of neurotransmitters, tryptophan, and SCFAs\u0026nbsp;\u003c/em\u003e\u003cem\u003ediffer between AN patients and healthy controls\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe also measured the amount of neurotransmitters, tryptophan, and SCFAs in the serum of healthy controls and AN patients. In general, the amounts of neurotransmitters and SCFAs detected per milliliter of serum were lower than the per-gram levels in stool. The concentrations of GABA, serotonin, and kynurenine were lower in the sera of both groups of AN patients vs. HCs. The tryptophan concentrations showed no differences between AN groups and were similar to stool concentrations. The other molecules measured, i.e., dopamine and hydroxytryptophan, were below the quantification limit (Fig. 10).\u003c/p\u003e\n\u003cp\u003eRegarding the amount of SCFAs in the serum, differences were found between HCs vs. AN patients relative to hexanoic acid, valeric acid, and isovaleric acid (Fig. 10). In contrast to stool samples, propionic acid and isobutyric acid were only detected in a small number of serum samples, therefore, these were not included in further analyses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGABA, serotonin, and tryptophan\u003c/em\u003e\u003cem\u003e\u0026nbsp;correlated with several bacterial taxa\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAfter statistically controlling for differences between the study groups, we found a significant association between the abundance of three bacterial taxa and GABA, two with serotonin, and one with tryptophan (Fig. S6). However, Christensenellaceae, Ruminococcaceae, and \u003cem\u003eEscherichia-Shigella\u003c/em\u003e, despite having a negative correlation with GABA, but passed the ANCOM-BC2 sensitivity test (Fig. 11). A total of 11 associations were found between SCFA concentrations and the abundance of bacterial taxa (Fig. S7), five of which passed the ANCOM-BC2 sensitivity test (Fig. 11). Of these five bacterial taxa, the Christensenellaceae family correlated negatively with all dominant SCFAs. Additionally, the bacterial genera UBA1819, \u003cem\u003eTuricibacter\u003c/em\u003e, \u003cem\u003eTerrisporobacter\u003c/em\u003e, and Ruminococcaceae were negatively correlated with propionic acid (Fig. 12).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a comprehensive examination of the clinical, anthropometric, biochemical, psychiatric, and microbiome characteristics of patients with anorexia nervosa, comparing both the acute and the severe and enduring (SEAN) forms of the disorder with healthy controls. Previous studies have examined the composition of the microbiome associated with AN, however, those studies did not explicitly distinguish between the two forms of the disease, which differ markedly in duration. A comparison of microbiota involvement in both phases of AN could help identify factors responsible for the persistence of the disease. Our results found significant differences between physical health, psychiatric profiles, and microbiome composition of AN patients and HCs. These underscore the complexity of AN and show that both physical and psychological factors can evolve throughout the disease.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePhysical and Biochemical Differences Between AN Patients and Healthy Controls\u003c/h2\u003e \u003cp\u003eConsistent with the recognized physical manifestations in both acute AN and SEAN patients, we observed significant anthropometric differences compared to healthy controls. Differences included lower body weight, BMI, and body fat percentage, and smaller waist and hip circumference. These findings reflect the characteristic feature of AN, i.e., severe malnutrition. Notably, both groups of AN patients had elevated levels of hyperactivity, a symptom often associated with the neurobiological underpinnings of the disorder. In addition, the use of antidepressants, antipsychotics, and anxiolytics was notably higher in AN patients than HCs, reflecting the high comorbidity of psychiatric disorders in the AN population, as previously reported\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The higher use of antidepressants in SEAN compared to acute AN reflects the duration of the disease. SEAN patients reported a higher prevalence of stress in adulthood than HCs or patients with acute AN. High levels of stress hormones can lead to changes in eating behavior; additionally, people with eating disorders have a higher risk of chronic stress exposure, which can lead to stress-induced anorexia. Part of the link between stress and eating disorders is related to how individuals cope with stress (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe biochemical profile of AN patients also showed alterations in several key markers. Both groups of AN patients had lower levels of cholinesterase and free triiodothyronine (fT3) compared to HCs, indicating impaired thyroid function, which is commonly observed in AN\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. AN patients have also been found to have altered levels of immunoglobulins (IgA and IgM), possibly indicating gut damage or dysfunction, which is consistent with previous studies examining the gut-brain axis in AN\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Spearman correlation analyses between anthropometric and biochemical parameters also suggest that the physiological and biochemical disturbances in AN are closely related (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePsychiatric Differences and Symptom Severity\u003c/h2\u003e \u003cp\u003eThe psychiatric differences between acute AN and SEAN were substantial. As expected, patients with acute AN had higher scores on the Eating Disorder Examination Questionnaire (EDE-Q), particularly in \u0026ldquo;restraint concern,\u0026rdquo; and on the Hamilton Anxiety Rating Scale (HAMD), indicating more severe eating disorder symptoms and higher levels of depressive symptoms. These findings may reflect the more pronounced and acute psychological distress that often accompanies the early stages of AN. Interestingly, SEAN patients had lower EDE-Q scores, which may be due to the chronic nature of their illness, in which extreme restrictive behaviors are less pronounced because they have been replaced by more entrenched disordered eating patterns. The greater use of antidepressants in the SEAN group may also be an indication of the long-term psychological burden associated with the illness and the corresponding need for pharmacological interventions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although anxiety scores measured by the Hamilton Anxiety Rating Scale (HAMA) were similar, principal component analysis (PCA) indicated that acute AN patients exhibited a tendency towards psychological anxiety symptoms, whereas SEAN patients were more inclined to display somatic anxiety symptoms. This distinction may be important for tailoring clinical treatments, since patients with the chronic form of the disorder may need more targeted interventions to address somatic symptoms and physical health concerns, along with the more typical psychological features of AN.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGut health and inflammation in AN\u003c/h2\u003e \u003cp\u003eWe detected elevated serum levels of intestinal fatty acid binding protein (I-FABP) in SEAN patients, suggesting that the chronic form may lead to intestinal mucosal damage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The correlation of I-FABP levels with disease duration and lower BMIs also suggests that prolonged malnutrition may contribute to structural damage of the intestinal mucosa. This aligns with other studies reporting GI dysfunction in AN, which may hinder proper nutritional management and worsen the physical consequences of the disorder\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRegarding inflammatory markers, we found unexpectedly low levels of amyloid A and calprotectin in the serum of both acute AN and SEAN patients, suggesting a suppression of inflammatory responses in AN, possibly due to severe malnutrition. In contrast, lipopolysaccharide-binding protein (LBP), a marker of bacterial translocation, was slightly elevated in both patient groups, suggesting that intestinal permeability may be impaired even in the absence of overt inflammatory markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Studies on cytokine levels in AN have found that it is strongly associated with a dysregulated immune system, which may be mainly influenced by oxidative stress, chronic psychological stress, and an altered microbiome\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These findings emphasize the complex relationship between malnutrition, immune function, and intestinal permeability in AN.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eThe composition of the microbiota and its clinical implications\u003c/h2\u003e \u003cp\u003eHere we show that the gut microbiota of both acute AN and SEAN patients shows remarkable differences in diversity and composition compared to HCs. Both patient groups had lower alpha diversity as measured by the Shannon index, reflecting a less diverse microbiome (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This finding is consistent with some previous studies that have associated lower diversity of the microbiome with AN\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, although other studies failed to describe different levels of alpha diversity in patients with AN vs. healthy individuals\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In addition, analysis of beta diversity showed marked differences in the composition of the gut microbiome between the two patient groups and healthy controls, with the SEAN group showing greater interindividual variation in the composition of the microbiome (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The increase in interindividual variation in the composition of the microbiota as the disease progresses indicates a gradual deterioration in the ability to regulate the symbiotic microbiota. This possibility is supported by biochemical data indicating a progressive decrease in the inflammatory immune response to gut bacteria (evidenced by decreasing calprotectin levels), increasing mucosal damage (indicated by increased I-FABP levels) and increased bacterial translocation (evidenced by increased levels of lipopolysaccharide-binding protein) in the SEAN group. These results suggest that the microbiome undergoes significant changes during the course of AN, possibly contributing to the chronicity and complexity of the disease. Similar interindividual variability was demonstrated in a previous study\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, certain bacterial taxa such as \u003cem\u003eErysipelatoclostridium\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, UBA1819, \u003cem\u003eFusicatenibacter\u003c/em\u003e, Lachnospiraceae, and Firmicutes bacterium CAG-56 were differently represented in patient groups vs. HCs. These changes in the composition of the microbiota may reflect the underlying physiological and metabolic adaptations associated with AN. For example, Lachnospiraceae and \u003cem\u003eFaecalibacterium\u003c/em\u003e, which were more abundant in the HCs, are known for their role in butyrate production, a short-chain fatty acid (SCFA) associated with gut health and anti-inflammatory properties\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The lower prevalence of butyrate-producing bacteria is a common feature of AN\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Lower abundance of \u003cem\u003eFaecalibacterium\u003c/em\u003e sp. is often described in other diseases, and their presence is likely related to a healthier state of the organism and has been considered for possible therapeutic use. Microbiomes of healthy individuals also contained a higher abundance of \u003cem\u003eFusicatenibacter\u003c/em\u003e, which forms formate, lactate, acetate, and succinate as the main products of glucose fermentation. \u003cem\u003eFusicatenibacter\u003c/em\u003e is associated with unhealthy eating behaviors and obesity, and its low levels in patients with AN correspond to their low calorie consumption\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Firmicutes bacterium CAG-56, belonging to the Lachnospiraceae family, was only decreased in acute AN patients compared to HCs; additionally, it likely contributes to the production of SCFAs. Conversely, our findings of elevated levels of UBA1819 from the Ruminococcaceae family in both AN groups are supported by a study showing that low-energy diets lead to an increase in UBA181940 and by another study demonstrating a negative correlation between UBA1819 and subcutaneous fat as well as body weight\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The higher abundance of \u003cem\u003eErysipelatoclostridium\u003c/em\u003e in SEAN patients may indicate microbial shifts associated with prolonged malnutrition or altered metabolic pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsistent with previous findings, we found that gut microbiota composition was associated with BMI, body fat percentage, antidepressant use, antipsychotic use, hormonal contraception, AN type (restrictive/purgative), psychiatric disorders, obsessive-compulsive disorder, stress, and fT3 levels (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Understandably, the composition and quantity of food have a strong direct influence on the composition of the microbiota. The use of antidepressants and antipsychotics may alter both the diversity and composition of the gut microbiota, primarily by altering the environment for microbial growth and also through their antimicrobial activity \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The diversity and composition of the microbiome are also influenced by hormones, be it gender, stress, or other hormones, which was confirmed in our study\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The difference between the diversity of the microbiome in patients with restrictive or purgative anorexia can be explained by the influence of vomiting or laxatives on the microbiome in purgative patients. Furthermore, we can assume that other psychiatric illnesses or obsessive-compulsive disorders also influence the gut microbiome in some way, either through the possible effects of medication or other factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic Alterations\u003c/h2\u003e \u003cp\u003eThe functional predictions of microbiome activity in this study, as assessed by PICTRUSt2, revealed significant differences in predicted metabolism of amino acids, oxidative stress response, and energy metabolism between patient groups and HCs, mainly between SEAN patients and HCs (Fig. S3). The SEAN group showed significant predicted disturbances in metabolism, particularly in amino acid and energy metabolism, suggesting long-term adaptation to malnutrition. Changes in predicted metabolites regulating oxidative stress and detoxification indicate chronic stress and cellular damage repair mechanisms in these patients. Differences in the abundance of metabolites regulating glucose metabolism suggest altered glucose homeostasis, presumably related to fasting in SEAN patients. These findings reflect the complex metabolic adaptations that occur in AN, potentially involving altered energy utilization and nutrient processing, which may be exacerbated in chronic conditions such as SEAN. These results confirm the results of our previous study in which we observed similar metabolic changes in patients with AN, such as the development of oxidative stress, vitamin deficits, loss of muscle mass, and a decrease in ketone bodies\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, we found subtle differences in neurotransmitter levels but significant differences in SCFA concentrations between HCs and AN patients, which may reflect disturbances in gut-brain interactions. Concentrations of GABA, serotonin, and tryptophan in stool samples showed no significant differences between HCs and AN patients, although GABA concentrations were slightly elevated in HCs compared to AN patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This is consistent with some previous studies indicating minimal changes in gut-derived neurotransmitters in AN. It appears that the pathology of AN does not substantially disrupt the synthesis of serotonin and other neurotransmitters in the gut, or if it does, the changes are subtle\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The detection of kynurenine in only a small proportion of samples and the absence of quantifiable dopamine and hydroxytryptophan in stools suggest that these molecules may not be as reliably detected in the gut of AN patients, limiting our ability to draw conclusions about their involvement in the disorder. In contrast, serum analysis in AN patients revealed consistently lower levels of neurotransmitters GABA, serotonin, and kynurenine compared to HCs, which may indicate systemic changes in neurotransmitter signaling in patients with AN (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). These decreased serum neurotransmitter levels may reflect gut-brain axis dysregulation or altered central nervous system function in AN, although the relatively low levels of these molecules in serum limit our ability to infer direct links to disease pathology.\u003c/p\u003e \u003cp\u003eCompared to neurotransmitter levels, SCFAs showed significant differences between HCs and AN patients, indicating more pronounced metabolic alterations of the microbiome. Healthy women had higher concentrations of acetic acid, propionic acid, butyric acid, valeric acid, and hexanoic acid in their stool samples than AN patients, suggesting that alterations in the gut microbiota lead to lower SCFA production in AN (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). These findings are consistent with previous studies showing that SCFAs play a role in maintaining gut health and modulating the gut-brain axis, and their low levels corroborate findings of higher levels of the intestinal mucosal damage marker I-FABP in their blood (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Lower levels of SCFAs in AN could indicate dysbiosis or a disturbed microbiota composition, both of which have previously been associated with AN\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Interestingly, levels of heptanoic acid were higher in HCs than in both groups of AN patients, with a small but significant difference between acute AN and SEAN. This suggests a potential marker of disease progression or a response to longer-term treatment, although the physiological significance of heptanoic acid in AN requires further investigation. In contrast, isobutyric acid and isovaleric acid did not differ significantly between groups, suggesting that these specific SCFAs may not be as sensitive to the metabolic changes associated with AN.\u003c/p\u003e \u003cp\u003eThe analysis of SCFA levels in serum revealed significantly lower values than in the stool samples, but differences between HCs and AN patients were still evident, especially for hexanoic acid, valeric acid, and isovaleric acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). These changes in serum SCFA levels could reflect systemic alterations in metabolism, but given the lower levels detected in serum, their direct role in the pathophysiology of AN remains unclear. The limited detection of propionic acid and isobutyric acid in serum samples further complicates the interpretation of SCFA dynamics outside the GI tract. However, the results of this study are consistent with those of previous studies\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe correlation between metabolites and gut microbiota offers intriguing insights into the gut-brain axis in AN. We found significant negative correlations between several bacterial taxa, e.g., Christensenellaceae, Ruminococcaceae and \u003cem\u003eEscherichia-Shigella\u003c/em\u003e and GABA neurotransmitter levels, suggesting that certain bacterial species influence GABA metabolism and may contribute to neurochemical disturbances in AN (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Several microbial species such as \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e contain the gene encoding glutamic acid decarboxylase, which is capable of converting glutamate to GABA\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. On the other hand, some bacteria, such as \u003cem\u003eE. coli\u003c/em\u003e, have been shown to degrade GABA to succinic semialdehyde via GABA aminotransferase\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In our previous study, we found a higher prevalence of Christensenellaceae in patients with AN as well as lower fecal GABA levels. Although we did not observe altered levels of Christensenellaceae in our current study, we found a negative association between this group of bacteria and GABA levels, which is consistent with our previous study\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The bacterial species \u003cem\u003eEvtepia gabavorous\u003c/em\u003e, belonging to the Ruminococcaceae family, has also been described as using GABA for growth\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, which supports our results.\u003c/p\u003e \u003cp\u003eAmong the SCFAs, a broader spectrum of bacterial taxa showed correlations with the concentrations of acetic acid, propionic acid, butyric acid, and other SCFAs. Christensenellaceae in particular was negatively correlated with all dominant SCFAs, while other genera such as UBA1819, \u003cem\u003eTuricibacter, Terrisporobacter\u003c/em\u003e, and Ruminococcaceae were negatively correlated with propionic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). The negative correlation of Christensenellaceae with SCFA levels is consistent with the finding that Christensenellaceae tend to be found in individuals with low BMIs; reduced SCFA levels are also observed in AN patients\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. These results emphasize the complexity of the gut microbiota in shaping SCFA levels and suggest that microbiota-related metabolic shifts may contribute to the altered metabolic profile observed in AN. The negative correlations between certain bacterial taxa and SCFAs may reflect microbial influences on fermentation processes or an impaired microbiota in AN that fails to produce optimal levels of beneficial metabolites.\u003c/p\u003e \u003cp\u003eThis study presents several notable strengths, particularly the robust bioinformatics analysis of microbiome data. While the findings should be considered within the context of certain limitations, they still provide valuable insights. Although the small sample size may affect the generalizability of the results, it highlights important trends worth exploring further. The study also faced challenges in detecting some neurotransmitters, such as dopamine, likely due to molecular degradation during sample handling and storage. Nevertheless, this opens up opportunities for improved methodologies in future research. Finally, the observed metabolic differences between groups are predictive rather than definitive, since 16S rDNA pathway sequencing does not account for alternative possibilities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides valuable insights into the complex interplay between physical health, psychiatric symptoms, and microbiome alterations in anorexia nervosa. Our findings suggest that the gut microbiota plays a significant role in the pathophysiology of AN and that changes in the diversity and composition of the microbiome may contribute to both the psychological and physical manifestations.\u003c/p\u003e \u003cp\u003eThe gut microbiome influences both our eating habits and our mental health, including anxiety and depression. The role of the gut microbiota in the development and maintenance of eating disorders has only recently begun to be explored. Perhaps because of the incomplete understanding of the etiology of these disorders, treatment remains insufficient, and patients often relapse. The gut microbiota and its influence on mental health may be the missing element in understanding the etiology of eating disorders.\u003c/p\u003e \u003cp\u003eIn particular, the gut and metabolic disturbances in patients with SEAN highlight the need for therapeutic strategies that address both the psychological and physiological aspects of the disorder. Future studies should investigate the potential of microbiome-based interventions, such as probiotics or dietary changes, as complementary treatments for anorexia nervosa.\u003c/p\u003e \u003cp\u003eThe relationship between the composition of the microbiome and anorexia nervosa is complicated. Gut microbiome changes observed in anorexia nervosa are likely the result of both significant caloric restriction and psychological stress. However, our and other studies show that AN-induced gut microbiome dysbiosis may contribute to the persistence of disease symptoms.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003e The study was conducted per the Declaration of Helsinki and was approved by the Ethics Committee of the General University Hospital in Prague. Written informed consent was obtained from all participants.\u003c/p\u003e\u003cp\u003eWomen aged 18–40 years were eligible to participate. Exclusion criteria included: pregnancy, breastfeeding, infectious diseases, severe active diseases or chronic diseases of the cardiovascular system, the hematopoietic system, the liver or urinary tract, and the use of antibiotics or antimycotics within three months of participation. Mental capacity that precluded informed consent was also exclusionary. Healthy subjects had no history of eating disorders or other psychiatric illnesses, and there was no evidence of a genetic predisposition toward eating disorders in their family history.\u003c/p\u003e\u003cp\u003eA total of 29 patients with early-stage disease (less than 3 years since the first clinically significant AN symptoms; inpatients, outpatients or day-care patients) and 33 patients with severe and enduring AN (more than 7 years since the first clinically significant AN symptoms; inpatients, outpatients or day-care patients), and 30 healthy, age-matched women (HC group) were included (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The healthy women were recruited from college students, various employers, and office workers. Healthy controls were screened for hidden eating disorders using the SCOFF questionnaire. Patients with AN were examined at the Center for Eating Disorders located at the Psychiatric Department of the 1st Faculty of Medicine of Charles University and General University Hospital, Prague, CZ. All patients were examined by physicians specializing in eating disorders to determine if they met the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for AN1. This screening was also used to determine the severity of AN (0–5: none, mild, moderate, severe, and extreme). The Exercise and Eating Disorders questionnaire, version 3 (EED19) was used to assess hyperactivity (0–2: none, present, considerable). Participants were also screened for the presence of allergies, stressful events in their past (none, present before age 3 years, present in adolescence, present in adulthood), and use of antidepressants and other medications (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 2). In patients with anorexia, i.e., anorexia identified as restrictive, or purgative (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the presence of comorbid disorders was also investigated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnthropometric measurements using bioimpedance (TANITA, Japan) (i.e., body fat percentage) as well as height, weight, and hip and waist circumference) were measured (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) by experienced nursing staff. Blood samples came from the cubital vein, and all samples were drawn early in the morning, i.e., a fasted state, by psychiatric department staff. Half of the samples were sent to the hospital's central laboratory for biochemical analysis. The rest were processed at the Institute of Microbiology, and the prepared serum aliquots were frozen at − 80°C until analyzed. On the same day, stool samples were collected from all participants and immediately frozen at − 80°C. Before the stool collection, participants were asked to refrain from drinking alcohol, coffee, or black tea, not to eat any products containing cocoa, chocolate, nuts, or bananas, and not to take any probiotics.\u003c/p\u003e\u003ch2\u003eBiochemical analysis of blood samples\u003c/h2\u003e\u003cp\u003eBlood samples from controls and patients were used to determine serum levels of triacylglycerols (TAG), cholinesterase, albumin, immunoglobulins IgA, IgG, IgM, thyroid-stimulating hormone (TSH), free thyroxine (fT4), and free triiodothyronine (fT3; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Serum was also used for enzyme-linked immunosorbent assays (ELISA) to determine the levels of intestinal fatty acid binding protein (I-FABP), lipopolysaccharide-binding protein (LBP), calprotectin, amyloid A, and soluble CD14 (sCD14).\u003c/p\u003e\u003cp\u003eSerum levels of intestinal fatty acid binding protein (I-FABP), a marker of intestinal damage, were analyzed in duplicate using ELISA for selective detection of human I-FABP (HyCult Biotechnology, Netherlands). The test was performed according to the manufacturer's recommendations, which included diluting the serum samples 1:5 with a supplied calibrator diluent.\u003c/p\u003e\u003cp\u003eCommercial ELISA (Human Amyloid A DuoSet ELISA Kit; R\u0026amp;D Systems, USA) kits were used to determine serum amyloid A (SAA) levels as an acute phase reactant. Analyses were performed in duplicate, with serum diluted 10-fold.\u003c/p\u003e\u003cp\u003eCommercial ELISAs were also used to determine levels of LBP and sCD14 as biomarkers for microbial transmission through the intestinal wall (Human LBP and Human sCD14 DuoSet ELISA kits, respectively; R\u0026amp;D Systems, USA). Analyses were performed in duplicate, with serum diluted 100-fold. The analyses were performed according to the manufacturer’s instructions.\u003c/p\u003e\u003cp\u003eSerum calprotectin levels (S1008/S100A9) were analyzed using commercial ELISA kits (Human S100A8/S100A9 DuoSet ELISA Kit, R\u0026amp;D Systems, USA). The analysis was performed in duplicate with serum diluted 300-fold according to the manufacturer’s instructions. For all analyses, the resulting absorbances at 450 and 650 nm were measured using a Multiskan Ascent Plate Reader Spectrometer, MTX Lab Systems (USA).\u003c/p\u003e\u003ch2\u003eEating Disorder Evaluation Questionnaire (EDE-Q)\u003c/h2\u003e\u003cp\u003eAN patients completed the EDE-Q 6.0\u003csup\u003e38\u003c/sup\u003e, which consists of 28 items derived from the Eating Disorder Examination (EDE)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Twenty-two of the items were broken down into four subscales that assessed (1) restraint, (2) concern about body shape, (3) concern about body weight, and (4) concern about their eating in the past 28 days. The items were rated on a 7-point scale (0–6). The sum of the individual subscales was averaged to determine the general subscale score. Overall scores were calculated by summing and averaging the subscale scores. Higher scores indicate a greater ED psychopathology. An additional 6 questions assessed the frequency (number of times or days) of certain behaviors that had occurred in the previous 28 days, e.g., objective binge eating, self-induced vomiting, use of laxatives, or excessive exercise. Results from these items are not included in the subscale values. Data are presented as boxplots with minimum and maximum whiskers of the EDE-Q subscale and the total values.\u003c/p\u003e\u003ch2\u003eHamilton Anxiety Rating Scale (HAMA)\u003c/h2\u003e\u003cp\u003eAN patients completed the HAMA questionnaire to measure the severity of anxiety symptoms. The scale consists of 14 items that measure both psychological and somatic anxiety. Each question is graded on a scale from 0 (absent) to 4 (severe), with a total score that can range from 0–56. Total scores less than 17 represent mild anxiety, 18–24 represent mild to moderate, 25–30 represent moderate to severe, and greater than 30 represents severe anxiety\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eHamilton Psychiatric Rating Scale for Depression (HAMD)\u003c/h2\u003e\u003cp\u003eAN patients were also screened for signs of depression using HAMD questionnaires. We used a structured interview conducted by a trained psychiatrist using an interview guide. A total of 21 items were analyzed, but only the first 17 were scored (HAMD17)\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Eight items are rated on a five-point scale (0–4), with numbers representing absent, doubtful, mild, moderate, and severe. Nine items are rated on a three-point scale (0–2), with the numbers representing absent, slight or clear, marked or severe. The sum of the first seventeen items gives the total score, with scores below 7 indicating an absence of depression, scores between 7–17 indicating mild depression, 18–24 indicating moderate depression, and scores over 24 indicating severe depression.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe comparison of age between the groups was analyzed using the Kruskal-Wallis test with Dunn´s multiple comparison test. Comparisons of 2 groups of AN patients were analyzed using the Mann-Whitney test. All categorical data were analyzed using Fisher's exact test (i.e., severity of AN, AN type, presence of stressful events, hyperactivity, allergy, medication use, and comorbidities). Comparisons of anthropometric, biochemical, and questionnaire variables were analyzed using a two-way ANOVA with Tukey’s multiple comparison test after data normalization with the Box-Cox transformation (λ = 0.1). Levels of serum biomarkers linked to inflammation or microbial transition were assessed using a one-way ANOVA with Box-Cox (λ = 0.1) transformed values and the Tukey’s multiple comparison test. Spearman’s rank test was used to evaluate all correlations. Statistical analyses were performed in GraphPad Prism 8 or R Studio (version 2023.6.0.421).\u003c/p\u003e\u003ch2\u003eGut microbiota analysis\u003c/h2\u003e\u003cp\u003eGenomic DNA was isolated from collected stool samples using ZymoBIOMICS DNA Miniprep Kits (Zymo Research) and used for high-throughput sequencing (HTS) of the bacterial V3-V4 region of the 16S rRNA gene. Amplicon libraries were prepared by two separate PCR reactions\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The first PCR reaction was performed using the specific primer pair S-D-Bact-341-a-A-21 (5′-CCTACGGNGGCWGCAG-3′) and S-D-Bact-0785-a-A-21 (5′-GACTACHVGGGTATCTAATCC-3′), with the “tails” in the second PCR reaction serving as priming sites for the outer primers \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The outer primers contained sample-specific barcodes and sequencing adaptors. Kapa HiFi HotStart ReadyMix (Roche) was used in both PCR reactions as follows: first PCR reaction: 95°C for 3 min, (98°C 20 s, 55°C 30 s, 72°C 30 s) 28 cycles and 72°C for 5 min; second PCR reaction: 95°C for 3 min, (98°C 20 s, 55°C 30 s, 72°C 30 s) 12 cycles and 72°C for 5 min. The sequenced samples contained duplicates with different inline barcodes. The library was quantified by capillary electrophoresis using a DNA Screening Kit 2400 (QIAxcel Advanced, QIAGEN), pooled in equimolar amounts, and purified using SPRIselect magnetic beads (Beckman Coulter).\u003c/p\u003e\u003cp\u003eAmplicons were sequenced using MGIEasy Universal Library Conversion Kits (App-A) on an MGI DNB-SEQ-G400 (2 x 300-bp pair-end reads, 600 cycles, MGI, USA) at Ceitec, Brno, Czech Republic. The ZymoBIOMICS Microbial Community Standard and Standard II (log distribution) and the ZymoBIOMICS Microbial Community DNA Standard and Standard II (log distribution; Zymo Research) were used to assess the quality of DNA processing, sequencing, and amplicon library preparation workflows. The relative bacterial composition of these standards was comparable to the original composition. The microbial composition of the original standards and the sequencing data obtained are shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. In addition, we used DNAse-free water instead of stool to check for possible contamination during the isolation process (Kitom) and a sample with DNAse-free water as the negative control sample. None of the negative control samples produced any sequences, except for one of the three DNAse-free water samples, which yielded 28 sequences classified under Bacteroides.\u003c/p\u003e\u003ch2\u003eBioinformatic pipeline\u003c/h2\u003e\u003cp\u003eThe sequencing data were generated in Fastq file format; they were demultiplexed and trimmed using Skewer software\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The trimmed sequences were filtered based on quality scores (expected number of errors per read \u0026lt; 2). High-quality sequences were denoised using DADA2\u003csup\u003e45\u003c/sup\u003e and an abundance matrix was created. Next, we identified and eliminated chimeric sequences with DADA2 (removeBimeraDenovo function) and performed taxonomy identification with the RDP classifier with a confidence threshold of 80 using the SILVA v.138.14 reference database. Using Procrustes analysis (p = 0.001, R = 0.987), we determined the consistency of amplicon sequence variants (ASVs) representing technical duplicates and retained only those ASVs that were present in both samples. Prior to statistical analysis, the bacterial dataset consisted of 1893 ASVs represented by 1,355,626 high-quality reads with an average sequencing depth of 15,582 (range 1,689–80,375) sequences per sample. Prediction of bacterial metagenome function was performed with the PICRUSt2 pipeline using default settings\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The weighted NSTI scores were comparable between the analyzed groups (Anova p = 0.556, F = 0.591). The different frequencies of predicted metabolites among the analyzed groups were identified using ANCOM-BC2.\u003c/p\u003e\u003ch2\u003eStatistical analyses of microbiome data\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed in R Studio (version 2023.6.0.421). Due to the different sequencing coverage of the samples, we assigned a rarefaction threshold to the ASVs corresponding to the minimum sequencing depth. For alpha diversity analysis, we included the Shannon index and ASV richness measures as response variables, normalized the data using Box-Cox transformations, and compared alpha diversity between study groups using ANOVA. To assess the effects of different parameters (e.g., BMI, body fat percentage, and AN type) on alpha diversity, we first selected relevant variables using lasso regression, and those with a non-zero regression coefficient were used as predictors in standard linear models. Beta diversity was visualized using principal coordinate analysis (PCoA). Systematic differences based on Bray-Curtis or Jaccard dissimilarities between the analyzed groups were tested using pairwise PERMANOVA (i.e., adonis2, R package vegan)\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Interindividual variation between groups was tested using PERMDISP (i.e., betadisper, R package vegan)\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. To test the effects of the different parameters on beta diversity, we performed a distance-based redundancy analysis (db-RDA) with Bray-Curtis or Jaccard dissimilarities as dependent variables. In these db-RDA models, we used a forward stepwise model selection in which the parameters with the largest effect on the model were added sequentially to the null model containing only the dependent variable.\u003c/p\u003e\u003cp\u003eDifferences in the abundance of bacterial taxa between study groups were tested using ANCOM-BC2\u003csup\u003e48\u003c/sup\u003e. We performed both a global analysis to test whether there were differences between study groups and a post-hoc analysis to test for pairwise differences between study groups. Analyses were conducted at the microbial genus level.\u003c/p\u003e\u003cp\u003eSubsequent ANCOM-BC2 analyses also aimed to identify microbial taxa whose abundance is associated with the concentration of individual SCFAs or neurotransmitters. To avoid multicollinearity, these analyses were performed separately for each SCFA or neurotransmitter. In addition, the concentrations of all neurotransmitters and SCFAs exhibited highly skewed distributions and were therefore square root transformed prior to analysis to avoid the effect of high leverage data points. The analyses were statistically adjusted for differences between the study groups.\u003c/p\u003e\u003ch2\u003eMeasurement of neurotransmitter levels in stool and serum by LC-MS/MS analysis\u003c/h2\u003e\u003cp\u003eApproximately 100 mg of stool (if less stool was present, the volumes of the following substances were proportionally reduced) was mixed with 1 mL of Milli-Q water (Smart2Pure™ Water Purification System, Thermo Scientific™) and homogenized using a vortex mixer. The samples were centrifuged at 30,000 x g at 4°C for 10 minutes. The supernatant was transferred, and 1 mL of acetonitrile (LC-MS grade, CHROMASOLV™ Honeywell) was added. The samples were then placed in a freezer (− 20°C) for 30 minutes. The cooled samples were centrifuged as second time at 30,000 x g at 4°C for 10 minutes. Subsequently, 1 mL of the supernatant was used for LC-MS/MS analysis. Fifty µL of serum was mixed with 200 µL of extraction reagent (acetonitrile:methanol (3:5)), both LC-MS grade. CHROMASOLV™ Honeywell was added and allowed to stand for 30 minutes at − 20°C, followed by centrifugation at 7,700 x g at 4°C for 10 minutes. Subsequently, the supernatant was used for LC-MS/MS analysis.\u003c/p\u003e\u003ch3\u003eLC-MS/MS analysis\u003c/h3\u003e\u003cp\u003eSamples were analyzed using an Agilent Infinity 1260 liquid chromatograph coupled with an Agilent 6470 LC/TQ mass spectrometer for targeted analysis. The analytes were separated on a Kinetex Polar C18 (2.6 µm, 3 mm × 100 mm) column with a SecurityGuard Polar C18 (2.6 µm, 3 mm × 2 mm) precolumn (Phenomenex), both heated to 40°C. The gradient elution consisted of phase A (0.1% formic acid LC-MS grade, Honeywell; in Milli-Q water) and phase B (0.1% formic acid in acetonitrile, LC-MS grade, CHROMASOLV™ Honeywell); the elution program was as follows: (time [min], % phase B): 0/0; 1/0; 5/20; 6/100; 8/100; 8.5/0; 9/0. The mobile phase flow rate was 0.6 mL/min, and the injected sample volume was 2.0 µL. To eliminate the matrix effect, each sample was injected and then quantified by automatic standard additions. Ion transitions and other mass spectrometric parameters were optimized using MassHunter Workstation Optimizer and Source Optimizer software (both version 10.0, SR1, Agilent). The gas temperature was 210°C and the gas flow was 12 L/min. All analytical standards were purchased from Sigma-Aldrich®. Additional details of analytical measurements can be found in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eAnalysis of short-chain fatty acids in stool and serum by GC-MS analysis\u003c/h2\u003e\u003cp\u003eApproximately 200 mg of stool (if less stool was present, the volumes of the following substances were proportionally reduced) was mixed with 1 mL of Milli-Q water (Smart2Pure™ Water Purification System, Thermo Scientific™) and homogenized using a vortex mixer. The samples were then centrifuged at 30,000 x g at 4°C for 10 minutes. After centrifugation, 500 µL of the supernatant was mixed with 50 µL of concentrated 30% HCl (Lachner, Czech Republic). The solution was briefly mixed using a vortex mixer. Subsequently, 600 µL of stabilized diethyl ether (VWR Chemicals BDH®, USA) was added. Samples were extracted for 3 minutes at 4°C using a vortex mixer, followed by centrifugation at 30,000 x g at 4°C for 10 minutes. The supernatant diethyl ether fraction was transferred to a 2 mL iron vial, which was weighed beforehand. The extraction process was repeated once more. The fractions of both extractions were combined, weighed, and used for GC-MS analysis. Five hundred µL of serum was mixed with 50 µL of concentrated 30% HCl (Lachner, Czech Republic). If less serum was available, the volume of serum was brought up to 500 µL with Milli-Q water (Smart2Pure™ Water Purification System, Thermo Scientific™). The solution was briefly mixed on a vortex mixer and then 600 µL of stabilized diethyl ether (VWR Chemicals BDH®, USA) was added. The samples were then extracted on a vortex mixer at 4°C for 3 minutes and then centrifuged at 6,000 x g at 4°C for 4 minutes. The supernatant diethyl ether fraction was transferred to a 2 mL iron vial, which was weighed beforehand. The extraction process was repeated two more times. The fractions of the extractions were combined, weighed, and used for GC-MS analysis.\u003c/p\u003e\u003ch2\u003eGC-MS analysis\u003c/h2\u003e\u003cp\u003eThe analysis was performed using a Varian 450-GC gas chromatograph in conjunction with a Varian 240-MS mass spectrometer (both Varian, Inc., USA) equipped with a DB-WAXETR column (0.25 µm film thickness, 30 m × 0.25 mm i.d.). Helium was used as the carrier gas at a constant flow rate of 1.4 mL/min. The injection volume was set to 1 µL, and the injection was performed in split/splitless mode with a split ratio of 1:50, which was set one minute after injection. The oven temperature program was set as follows: The initial temperature was held at 50°C for one minute, then increased to 140°C at a rate of 20°C/min, followed by an increase to 150°C at 10°C/min, where it was held for one minute. The temperature was then increased to 180°C at 15°C/min and finally to 230°C at 20°C/min and held for one minute. The total duration of the program was 13.5 minutes. The temperature of the injector was set to 230°C. The temperature of the electron ionization (EI) ion source and the temperature of the transfer line were set to 250°C and 280°C, respectively. A solvent delay of 3 minutes was selected. Mass spectra were collected in the total ion current (TIC) mode with a mass range of 30–300 m/z during the analysis, which ran from the 3 to 13.5 minute mark. Detection of analytes was performed in the selected ion monitoring (SIM) mode with m/z values derived from the Q1 mass data reported by Zhu et al\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Samples were diluted 10-fold for measurements. Quantification of analytes was performed using external calibration curves. The detection limits for the analytes ranged from 0.1 to 25 µg/g, depending on the specific analyte.\u003c/p\u003e\u003cp\u003eWhen measuring from serum, the detection limits for the analytes ranged from 0.01 to 25 µg/g, depending on the specific analyte. Concentrations were converted to mL of serum.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eDeclaration of interest statement\u003c/p\u003e\n\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u0026nbsp;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eP.P. led the conceptualization of the study, with large contributions from R.R. J.J. and J.K. were responsible for data curation. Formal analysis was conducted primarily by PP, with supporting contributions from K.C., G.K., and K.Z. PP and HP secured funding for the project. The investigation was carried out by J.S., A.N., and T.C., who performed metabolomic measurements. Samples from patients were collected by P.H., A.L., and H.P. Supervision and validation were undertaken by H-T.H. Visualization was led by P.P, with support from J.J. The original draft of the manuscript was written by P.P, with supporting input from R.R. and H.P . All authors contributed to the review and editing of the manuscript, with P.P. taking the lead, and R.R. and J.J providing supporting contributions.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe study was supported by the Ministry of Health of the Czech Republic under grant nr. NU22-04-00010 and NU23-04-00381 and by the Ministry of Education, Youth, and Sports of the Czech Republic under grant Talking microbes - understanding microbial interactions within One Health framework (CZ.02.01.01/00/22_008/0004597).\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eSequencing data are archived in the European Nucleotide Archive under project PRJEB77672. Accession numbers with metadata for each sample and R scripts are available at github repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/JanetJezkova/Gut-microbiota-in-patients-with-Anorexia-Nervosa---acute-vs.-chronic-patients\u003c/span\u003e\u003cspan address=\"https://github.com/JanetJezkova/Gut-microbiota-in-patients-with-Anorexia-Nervosa---acute-vs.-chronic-patients\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data from this study are available in the Open Research Repository Zenodo: doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.15102456\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.15102456\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiang, D., Leung, R. K., Guan, W. \u0026amp; Au, W. W. 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M.\u003cem\u003e et al.\u003c/em\u003e PICRUSt2 for prediction of metagenome functions. \u003cem\u003eNat Biotechnol\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 685-688, doi:10.1038/s41587-020-0548-6 (2020).\u003c/li\u003e\n\u003cli\u003eOksanen, J.\u003cem\u003e et al.\u003c/em\u003e \u003cem\u003eVegan: Community Ecology Package. R package version 2.6-7.\u003c/em\u003e, 2024).\u003c/li\u003e\n\u003cli\u003eLin, H. \u0026amp; Peddada, S. D. Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures. \u003cem\u003eNat Methods\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 83-91, doi:10.1038/s41592-023-02092-7 (2024).\u003c/li\u003e\n\u003cli\u003eZhu, J. H.\u003cem\u003e et al.\u003c/em\u003e Optimization and validation of direct gas chromatography-mass spectrometry method for simultaneous quantification of ten short-chain fatty acids in rat feces. \u003cem\u003eJ Chromatogr A\u003c/em\u003e \u003cstrong\u003e1669\u003c/strong\u003e, 462958, doi:10.1016/j.chroma.2022.462958 (2022).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1. Clinical parameters of controls and AN patients.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(HC) (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute AN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEAN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC vs.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAcute AN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC vs.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;SEAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute AN vs.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSEAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003cp\u003e(23.8; 30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e(18; 26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003cp\u003e(21.5; 31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e**0.0030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e**0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eDisease duration (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e(15; 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003cp\u003e(84; 150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eSeverity of AN (DSM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(2; 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(2.5; 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eAN type (R/P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e18/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e21/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eChildhood stress (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eAdolescent stress (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eAdulthood stress (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e*0.0175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e**0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eHyperactivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0; 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(0; 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0; 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e***0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eAllergy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e45.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eThe comparison of age between groups was evaluated by the Kruskal-Wallis test with Dunn\u0026acute;s multiple comparison test. Comparison between the 2 groups of AN patients was evaluated using the Mann-Whitney test. Categorical data were evaluated using\u0026nbsp;\u003c/em\u003e\u003cem\u003eFisher\u0026apos;s exact test\u003c/em\u003e. \u003cem\u003eThe results are shown as median with IQR (age, disease duration, severity of AN, hyperactivity), proportional numbers (AN type), or percentage (stress, allergy).\u0026nbsp;\u003c/em\u003e\u003cem\u003eNS \u0026ndash; non-significant,\u003c/em\u003e\u003cem\u003e\u0026nbsp;SEAN - severe and enduring AN.\u003c/em\u003e\u003cem\u003e\u0026nbsp;Stars indicate p-values for each comparison. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable24. Medications used by the study cohort.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(HC)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute AN (n=29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEAN (n=33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC vs.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAcute AN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC vs.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSEAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute AN vs.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSEAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eAntidepressants (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e65.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e90.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e*0.0263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eAntipsychotics (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e31.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e48.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e***0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eAnxiolytics (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e**0.0046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e*0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eContraceptives\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eThyroid hormones (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eThe results are shown as percentages. To test differences between groups, Fisher\u0026acute;s exact test was used.\u003c/em\u003e\u003cem\u003e\u0026nbsp;Stars indicate p-values for each comparison. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Proportional representation of comorbid disorders or features in acute AN and SEAN.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbid disorder/feature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute AN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute AN vs. SEAN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eAlcohol-, Sedative-, Hypnotic-, or Anxiolytic-related disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0/29 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4/33 (12.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eManic episode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1/29 (3.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0/33 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eMajor depressive disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e8/29 (27.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e9/33 (27.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eDysthymic disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3/29 (10.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3/33 (9.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eAgoraphobia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0/29 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2/33 (6.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eSocial anxiety disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3/29 (10.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4/33 (12.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003ePanic disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5/29 (17.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2/33 (6.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eGeneralized anxiety disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5/29 (17.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4/33 (12.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eMixed anxiety and depressive disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e6/29 (20.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e9/33 (27.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eObsessive-compulsive disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1/29 (3.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3/33 (9.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eMixed obsessional thoughts and acts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0/29 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e5/33 (15.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003ePost-traumatic stress disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2/29 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0/33 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003ePersistent somatoform pain disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1/30 (3.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0/33 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eEmotionally unstable personality disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2/29 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1/33 (3.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eMixed personality disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0/29 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1/33 (3.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 265px;\"\u003e\n \u003cp\u003eSuicidality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9/29 (31.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6/33 (18.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDifferences between groups were assessed using Fisher\u0026apos;s exact test. SEAN - severe and enduring AN. All comparisons were non-significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 4. Anthropometric and biochemical parameters for controls and AN patients.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(H\u003c/strong\u003e\u003cstrong\u003eC)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute AN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(n=29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEAN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003cstrong\u003eC vs.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAcute AN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003cstrong\u003eC vs.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSEAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute AN vs. SEAN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e169.6 \u0026plusmn; 6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e167.00 \u0026plusmn; 5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e164.60\u0026nbsp;\u0026plusmn; 6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e64.34 \u0026plusmn; 7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e43.48 \u0026plusmn; 4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e38.32\u0026nbsp;\u0026plusmn; 6.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e22.35 \u0026plusmn; 2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15.60 \u0026plusmn; 1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14.14 \u0026plusmn; 2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eBody Fat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e27.73 \u0026plusmn; 5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e6.23 \u0026plusmn; 4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5.52 \u0026plusmn; 4.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eWaistline (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e70.70 \u0026plusmn; 6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e61.10 \u0026plusmn; 4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e59.15 \u0026plusmn; 8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e*0.0395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e**\u0026lt;0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eHipline (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e95.70 \u0026plusmn; 5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e80.10 \u0026plusmn; 4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e76.42 \u0026plusmn; 5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e**\u0026lt;0.0059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e***\u0026lt;0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eTAG (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.90 \u0026plusmn; 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.88 \u0026plusmn; 0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.09 \u0026plusmn; 0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eCholinesterase\u003c/p\u003e\n \u003cp\u003e(ukat/l)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e118.50 \u0026plusmn; 24.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e96.10 \u0026plusmn; 25.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e110.10 \u0026plusmn; 48.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e***0.0315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eAlbumin (g/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e47.17\u0026nbsp;\u0026plusmn; 2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e46.51\u0026nbsp;\u0026plusmn; 3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e45.63\u0026nbsp;\u0026plusmn; 3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eIgG (g/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e12.01\u0026nbsp;\u0026plusmn; 2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10.61\u0026nbsp;\u0026plusmn; 1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10.19 \u0026plusmn; 1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eIgA (g/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.06\u0026nbsp;\u0026plusmn; 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.76\u0026nbsp;\u0026plusmn; 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.21\u0026nbsp;\u0026plusmn; 0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e**0.0062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eIgM (g/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.58\u0026nbsp;\u0026plusmn; 0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.57\u0026nbsp;\u0026plusmn; 0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.28 \u0026plusmn; 0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e*0.0354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e*0.0474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eTSH (mIU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.68\u0026nbsp;\u0026plusmn; 1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2.41\u0026nbsp;\u0026plusmn; 1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.74\u0026nbsp;\u0026plusmn; 1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003efT4 (pmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14.65\u0026nbsp;\u0026plusmn; 1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12.84 \u0026plusmn; 1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e12.84\u0026nbsp;\u0026plusmn; 2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003efT3\u0026nbsp;(pmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5.54\u0026nbsp;\u0026plusmn; 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e3.88\u0026nbsp;\u0026plusmn; 1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.04\u0026nbsp;\u0026plusmn; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e****\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMultiple comparisons between groups were evaluated using 2-way ANOVA with Tukey corrections on transformed data (\u0026lambda; = 0.1). TAG \u0026ndash; triacylglyceride; TSH \u0026ndash; thyroid-stimulating hormone; fT4 \u0026ndash; free thyroxine;\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003efT3 \u0026ndash; free triiodothyronine;\u0026nbsp;\u003c/em\u003e\u003cem\u003eNS \u0026ndash; non-significant.\u003c/em\u003e\u003cem\u003e\u0026nbsp;The results are shown as mean \u0026plusmn; SD.\u003c/em\u003e\u003cem\u003e\u0026nbsp;Stars indicate p-values for each comparison. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 5. Comparisons\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;of interindividual variation and composition of gut microbiomes.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" height=\"282\" width=\"670\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparisons of (A) microbial composition and (B) interindividual variation between studied groups based on PERMANOVA and Betadisper tests, respectively. Tests were conducted using relative abundance-based (Bray-Curtis) and prevalence-based (Jaccard) dissimilarities. Values of (pseudo-) F statistics (F), associated degrees of freedom (df), resulting probability values (p), and proportions of explained variance (R2) are shown. Significant values are in bold type.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 6.\u003c/em\u003e \u003cem\u003eAlpha and beta diversity associations.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 372px;\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003eAlpha diversity (Shannon index)\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 166px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003eF - value\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 166px;\"\u003ep - value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003eBMI\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e5.149\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 166px;\"\u003e0.0258\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003eBody fat %\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e5.127\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 166px;\"\u003e0.0261\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003eAntidepressants\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e5.700\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 166px;\"\u003e0.0192\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003eAntipsychotics\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e5.813\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 166px;\"\u003e0.0181\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 166px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\u003cem\u003eBeta diversity\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 166px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003eBray-Curtis\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 166px;\"\u003eJaccard\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003eF - value\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003ep- value\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003eF - value\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003ep - value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eBody fat %\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e2.092\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.001\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.643\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAN type (res/purg)\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.405\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.031\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eNS\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eNS\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAntidepressants\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.743\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.446\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eHormonal contraception\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.553\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.020\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.314\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.030\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eStress\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.596\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.010\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.327\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.015\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eOther psychiatric disorders\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.820\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.467\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eOCD\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.586\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.015\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.301\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.010\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003efT3\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e2.388\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e1.791\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eAN type \u0026ndash; restrictive (res) or purgative (purg); BMI \u0026ndash; body mass index; fT3 - free triiodothyronine; OCD \u0026ndash; obsessive-compulsive disorder.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-biofilms-and-microbiomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjbiofilms","sideBox":"Learn more about [npj Biofilms and Microbiomes](http://www.nature.com/npjbiofilms/)","snPcode":"41522","submissionUrl":"https://submission.springernature.com/new-submission/41522/3","title":"npj Biofilms and Microbiomes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"microbiome, SCFA, neurotransmitter, dysbiosis, anorexia nervosa, gut-brain-microbiota axis","lastPublishedDoi":"10.21203/rs.3.rs-6610537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6610537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnorexia nervosa (AN), an eating disorder, is associated with marked changes in the microbiome and metabolic profile of those affected. In this study, we compared the diversity and composition of the gut microbiota and the differences in various parameters between 29 female patients with acute AN, 33 severe and enduring AN (SEAN), and 30 healthy controls. Both patient groups differed in the assessment of eating behaviors, depression symptoms, stressful events in adulthood, and the use of antidepressants. The SEAN group showed elevated markers of gut damage and the greatest inter-individual variation in the gut microbiome.\u003c/p\u003e \u003cp\u003eCertain bacterial taxa, such as \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eFusicatenbacter\u003c/em\u003e, Lachnospiraceae, and CAG-56, were less abundant in the patients' microbiomes, while Erysipelatoclostridium and UBA1819 were more abundant, but with no difference between patient groups. Functional prediction of the microbiome revealed differences in metabolic pathways, particularly in amino acid metabolism and oxidative stress responses, which were more pronounced in patients with SEAN. Certain bacteria such as Christensenellaceae, Ruminococcaceae, and \u003cem\u003eEscherichia-Shigella\u003c/em\u003e negatively affect GABA metabolism, as evidenced by the lower serum and fecal concentrations in both patient groups compared to healthy women. Members of the Christensenellaceae affect microbial fermentation, resulting in significant differences in acetic, propionic, and butyric acid levels in stool and serum samples from AN patients. These findings highlight the complex interplay between the gut microbiota and metabolic changes in AN patients and provide insights into potential microbial biomarkers and therapeutic targets for this disease.\u003c/p\u003e","manuscriptTitle":"The microbiome and metabolome of patients with acute or severe and enduring anorexia nervosa influence gamma-aminobutyric acid (GABA) metabolism and microbial fermentation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 08:37:42","doi":"10.21203/rs.3.rs-6610537/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-15T04:15:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-12T15:22:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-11T17:00:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-07T11:35:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12591008241251285686621850805646882088","date":"2025-06-17T12:54:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74712026267743837977325724877926720761","date":"2025-06-16T16:00:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174397314003764394512710262335657155111","date":"2025-06-16T08:24:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-03T08:18:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-24T13:20:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-12T06:45:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Biofilms and Microbiomes","date":"2025-05-07T09:26:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-biofilms-and-microbiomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjbiofilms","sideBox":"Learn more about [npj Biofilms and Microbiomes](http://www.nature.com/npjbiofilms/)","snPcode":"41522","submissionUrl":"https://submission.springernature.com/new-submission/41522/3","title":"npj Biofilms and Microbiomes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5da8deb0-30a7-4676-b7f0-5f22b6108eeb","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49504452,"name":"Biological sciences/Microbiology"},{"id":49504453,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-12-01T16:15:35+00:00","versionOfRecord":{"articleIdentity":"rs-6610537","link":"https://doi.org/10.1038/s41522-025-00847-y","journal":{"identity":"npj-biofilms-and-microbiomes","isVorOnly":false,"title":"npj Biofilms and Microbiomes"},"publishedOn":"2025-11-26 15:57:16","publishedOnDateReadable":"November 26th, 2025"},"versionCreatedAt":"2025-06-09 08:37:42","video":"","vorDoi":"10.1038/s41522-025-00847-y","vorDoiUrl":"https://doi.org/10.1038/s41522-025-00847-y","workflowStages":[]},"version":"v1","identity":"rs-6610537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6610537","identity":"rs-6610537","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}