Microbiome biomarkers in precise diagnostics andnutrition, mental health and longevity of working andelderly populations

Підписуйтесь на наші соціальні мережі, щоб стежити за останніми новинами тут 💜:

Сайт: www.ediens.me

Facebook: www.facebook.com/profile.php?id=61561258499410  

LinkedIn: www.linkedin.com/ediens 

Instagram: www.instagram.com/ediens_official

TikTok: www.tiktok.com/@ediens_official 

BIO Web of Conferences 187, 07003 (2025) https://doi.org/10.1051/bioconf/202518707003

ISPH-ISNPR 2025

Nadiya Boyko1,2,3,*  , Tamara Meleshko1,2  , Taras Chendey4,5  , Ivan Lypey2  , Oleksandra Pallah1,2 , Svitlana  Burmei1,2,3 , Volodymyr Drobnych2  , Volodymyr Artyomenko6  , and Lesya Yusko1,2 1Uzhhorod National University, Department of Medical and Biological Disciplines, 2Uzhhorod National University, RDE Centre for Molecular Microbiology and Mucosal Immunology, 3Ediens LLC, Ukraine, 4Uzhhorod National University, Department of Hospital therapy, Faculty of Medicine 5Communal Non-profit Enterprise "Transcarpathian Regional Clinical Center of Cardiology and Cardiac Surgery" of the Transcarpathian Regional Council,

Abstract The role of the gut microbiome and environment in influencing mental health and longevity has

been recently determined and highlighted. To identify gut (oral) microbiome signatures and biochemical

markers associated with mental health and longevity in working-age and elderly individuals. To enable the

development of personalized diagnostics and prevention strategies based on mathematical models for

mental-age-relevant diseases. Fecal and blood samples were collected from participants across two cohort

groups, including working-age, elderly local and refugees. Gut microbiota composition was analyzed via

16S rRNA gene sequencing. Blood samples underwent standard biochemical testing, including lipid

profiles, liver and kidney function, glucose levels, and oxidative stress markers. Mental health status was

assessed through validated psychological questionnaires and cognitive screening tools. Correlations between

microbiome patterns, blood biomarkers, and mental health indicators were analyzed using multivariate

statistical methods. PCA were applied to build prognostic models. Participants with greater microbial

diversity and higher abundance of beneficial taxa such as Lactobacillus and Bifidobacterium showed more

favorable biochemical profiles and better mental health scores. Distinct microbiome patterns were observed

between working and elderly individuals, some of which were linked to markers of healthy aging. The

integration of sequencing and clinical data supports the potential of gut microbiota as a predictive tool for

assessing psychological resilience and longevity. Lacticaseibacillus rhamnosus S25 strain was selected as

the potential component of pharmabiotic DefendeXTM for PTSD prevention and treatment. Individual

nutrition plans are created by using the algorithm of Ediens (BioQuantum).

Keywords: microbiome, biomarkers, precision diagnostics, mental health, longevity, PTSD, prognostic

models

1. Introduction Recent research convincingly demonstrates the critical role of the gut microbiome in regulating mental health, metabolic resilience, and longevity [1]. In the context of global population aging and the exacerbation of mental disorders among the working-age population, there is an urgent need for accurate, personalized diagnostic tools using microbiome biomarkers. These biomarkers promise new ways to identify early risks and preventive interventions according to the physiological characteristics of people of different age categories, taking into account the individual approach [2]. According to the United Nations and other demographic agencies, the proportion of the population aged 60 and over is steadily increasing and is likely to exceed 20% of the world's population by 2050. This puts pressure on health care systems, social security and the economy [3]. In turn, the increasing prevalence of anxiety and depressive disorders among the working-age population is associated with urbanization, social isolation, stress, and poor nutrition [4]. The gut microbiome interacts with the nervous system through pathways, the immune system, metabolites, and neurotransmitters – describing the  “microbiome-gut-brain axis” or “gut–brain axis” mechanism [5]. For example, the microbiota is a source of SCFA (an aspect that regulates intestinal barrier function and neurochemical balance), and also produces serotonin, gamma-aminobutyric acid (GABA), dopamine, and other neurotransmitters that significantly affect mood, cognition, and stress reactivity [6]. It is commonly accepted that gut microbiota composition in old and young [human] generations is significantly different. The microbiome of older adults often exhibits increased diversity, increased xenobiotic metabolism and antioxidant activity, and characteristic species associated with longevity. For example, Akkermansia, Roseburia, Lactobacillus, Methanobrevibacter, which secrete SCFA or participate in oxidoreductase activity [7]. Bacterial changes with age, delivery mode, and body shape. The infants’, born through vagina, are initially colonized by vaginal microorganisms – Lactobacillus and Prevotella. The infants’, born through cesarean section, are dominated by Streptococcus, Corynebacterium, and Propionibacterium, similar to the skin of humans. In children up to 3 years old, the gut microbiota is stable, with 60–70% similarity to the adult gut microbiota. The gut microbiota of adults with  © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/). BIO Web of Conferences 187, 07003 (2025) https://doi.org/10.1051/bioconf/202518707003 ISPH-ISNPR 2025  Gynecology  Email of correspondent author: *  normal shape is mainly composed of five phyla: Bacteroidetes, Firmicutes, Actinobacteria, Proteobacteria, and Cerrucomicrobia. The adults who are so slim are prone to show the F/B ratio increasing, while the F/B ratio of the obese decreasing. The gut microbiota of the elder changed: the diversity decreasing, with facultative anaerobic bacteria increasing [8]. Anxiety is the major “illness”, or rather, trigger and main symptom (characteristic) of the behaviour of the working population in various countries. This is the result of living with less comfortable economic conditions and a high-stress, pressured society, following data from the population project [https://thepopulationproject.org/]. There are countries like Ukraine which has traditionally higher level of cardiovascular disease deaths, and this tendency has dramatically increased in recent years, affected by war. Our cohort study reported of the difference between microbiome characteristics in patients with symptomatic and asymptomatic atherosclerosis [9]. In addition to this, pollution is recognised as one of the factors related to longevity and health living not only because of the food quality or proper nutrition but also air contaminants concentration. The relationship  between air pollution and life expectancy is well- established and profoundly concerning for scholars,  stakeholders and government [10]. Prolonged exposure to high levels of air pollution, particularly fine particulate matter and toxic gases like nitrogen dioxide and carbon emission, has been linked to various serious health issues, including respiratory diseases, cardiovascular problems, and even cancers [11]. Over time, these health effects can significantly reduce an individual's life expectancy. At the same time, previous reviews have shown that changes in microbiota composition are associated with anxiety, depression, and other psychiatric conditions, and that certain probiotics (e.g., Bifidobacterium, Lactobacillus) may exacerbate symptoms through effects on immunity and neurotransmitter regulation [12]. Trimethylamine (TMA) is produced in the gut through the activity of several key microbial enzymes, primarily classified into three types. One of the most studied enzymes is cut C/D, which is found in bacterial genera such as Desulforibrio, Desulfuricans, and within the classes Gammaproteobacteria and Deltaproteobacteria (phylum Proteobacteria), as well as in Proteus mirabilis and some representatives of Firmicutes, including Clostridia and Bacillus. Research by Wang and colleagues has demonstrated that 3,3- dimethyl-1- butanol (DMB), a structural analog of choline, can suppress the activity of cut C/D choline TMA lyase, thereby reducing serum TMAO levels and offering potential for therapeutic intervention in cardiometabolic diseases [13]. However, Orman et al. reported that DMB is ineffective in inhibiting cut C activity. Instead, betaine aldehyde has shown stronger inhibitory potential against this enzyme [14]. Besides cut C/D, other related enzymes such as Cnt A/B and Yea X/Y have been identified and are noted for their high sequence homology. In atherosclerotic plaques, the  phylum Proteobacteria is often dominant, although Firmicutes are also present. In the gut microbiome of individuals with atherosclerosis, a higher Firmicutes/Bacteroidetes ratio is commonly observed, along with an enrichment of the Escherichia genus. Taken together, these findings underscore the importance of the gut microbiome as a key modulator of physical and mental health throughout the human lifespan. The interconnectedness of microbial activity with neurochemical signaling, immune responses, metabolic function, and environmental stressors like pollution highlights the multifactorial nature of disease risk and resilience. Furthermore, distinct microbial signatures observed in aging populations, individuals with cardiometabolic conditions, and those experiencing psychological distress point to the microbiota as a promising source of diagnostic and prognostic information. Emerging evidence also emphasizes the potential of microbiome-targeted interventions – ranging from dietary modifications and psychobiotics to enzyme inhibitors such as DMB or betaine aldehyde –  to correct dysbiosis and reduce the burden of non- communicable diseases. However, the variability of  microbial communities between individuals of different ages, geographic regions, and health statuses presents a challenge for the development of universal strategies. Thus, there is a growing consensus on the need for precision approaches that consider host physiology, lifestyle, and environmental context. In this regard, the  identification and validation of specific microbiome- derived biomarkers become critically relevant.  Biomarkers capable of reflecting early shifts in health status, particularly those linked to atherosclerosis, anxiety, and aging, could offer new avenues for stratified prevention and treatment. Therefore, the present study aimsto explore the predictive value of gut microbial markers and their associations with metabolic and neurobehavioral parameters, thereby contributing to the advancement of personalized health strategiesin at-risk populations. The microbiome changes to be potentially valued for precise and earlier diagnostics and numerous cohort studies for collecting big data are required. We aimed to identify gut (oral) microbiome signatures and biochemical markers associated with mental health and longevity in young, working age and elderly individuals during a cohort study in Ukraine connected to atherosclerosis. Detecting the difference in gut / oral microbiome compositions connected (associated) with anxiety, lipid profile, and wassels’ elasticity will enable the development of personalized diagnostics and prevention  strategies based on mathematical models for mental- age-relevant diseases.The mathematical model is  established by principal component analysis used for biomarkers prioritisation.  2  BIO Web of Conferences 187, 07003 (2025) https://doi.org/10.1051/bioconf/202518707003 ISPH-ISNPR 2025  2. Methods 2.1 Study design For a comprehensive analysis of the course of atherosclerosis at different stages, patients were selected to participate in a cohort study and personal patient profiles were created. The examination was conducted among patients diagnosed with cerebral and coronary atherosclerosis, followed by a differentiated approach to personalized therapy. Additional selection criteria included the presence of lipid metabolism disorders, markers of chronic inflammation, and altered structural and functional characteristics of blood vessels as determined by instrumental examination. Patients were informed about the purpose and methodology of the study and provided written informed consent to participate. In all patients, seven groups of parameters were assessed: 1. endothelial function and elasticity of elastic arteries 2. biochemical status 3. local immunity status 4. gut microbiome status 5. psychosomatic state 6. characteristics of food panel perception 7. physical examination findings and level of physical activity Participants were excluded from the study if they had acute infections or systemic inflammatory diseases at the time of recruitment. Individuals who had undergone antibiotic therapy within the previous three months, which could significantly influence gut microbiota composition, were also excluded. Patients with a history of diagnosed gastrointestinal disorders (such as inflammatory bowel disease or irritable bowel syndrome) or malignancies were not considered for inclusion. Additionally, individuals receiving immunosuppressive agents or corticosteroid medications were excluded to avoid potential confounding effects on immune status and biochemical parameters. 2.2 Clinical assessments Patients underwent duplex scanning ofthe neck and head vessels, ultrasound Doppler examination of the major arteries, and measurement of the vascular wall stiffness index. Morphometric assessment of atherosclerotic changes included determining the intima-media thickness, degree of stenosis, presence of calcification, unstable plaques, and the level of blood flow turbulence. Mental health status was assessed through validated psychological questionnaires and cognitive screening tools. For a more detailed and in-depth analysis of each participant’s eating behavior and psychological state, we processed the data from an online questionnaire completed by the patient. The developed questionnaire is unique and contains more than 320 questions characterizing the patient’s lifestyle. An important part  of the questionnaire involves the patient’s self- assessment of their psychological state, including  perceived levels of stress, tension, fear, anxiety, panic,  alertness, satisfaction, feelings of slowed-down perception, and ability to relax, among others. Most questions focus on dietary habits – specifically, which foods the individual consumes, in what quantities and proportions, and in what form, among other aspects. 2.3 Biochemical analysis Fecal and blood samples were collected from participants across two cohort groups, including working-age, elderly local and refugees. Gut microbiota  composition was analyzed via 16S rRNA Next- generation sequencing (NGS). Blood samples  underwent standard biochemical testing, including lipid profiles, liver and kidney function, glucose levels, and oxidative stress markers. The determination of  biochemical parameters was carried out at the “ASTRA- DIA” medical laboratory and in the clinical laboratory  of the Center for Reconstructive and Restorative Medicine (University Clinic) of Odesa National Medical University. Correlations between microbiome patterns, blood biomarkers, and mental health indicators were analyzed using multivariate statistical methods. PCA were applied to build prognostic models. 2.4 Gut microbiome profiling Bacterial DNA extraction was performed using the ZymoBIOMICS DNA Mini Kit (Zymo Research, USA) according to the manufacturer’s instructions. The concentration of the extracted DNA in the samples was measured using a DeNovix DS-11 FX+ spectrophotometer/fluorometer (DeNovix Inc., USA). qRT-PCR was carried out on an AriaMx instrument (Agilent Technologies, USA) using specific primers. In the process of identifying and prioritizing new markers of atherosclerosis, as well as developing disease diagnostics and risk assessment models, both parametric and nonparametric methods for comparing data groups were used, along with correlation analysis and multivariate statistical techniques such as Principal Component Analysis (PCA) and cluster analysis. The analyses were performed using modern tools implemented in Data Science and Machine Learning libraries for the Python programming language. Calculations were carried out in the well-known Anaconda–Python environment, designed for scientific research. 3. Statistics The study results were evaluated using descriptive statistics (frequency, percentage), one-way analysis of variance (ANOVA) (Minitab Inc., version 11.24, UK), and the chi- square test. The relationship between two variables – symptomatic and asymptomatic patient groups—was assessed using the one-way ANOVA test (Minitab Inc., version 11.24, UK). Relationships among more than two variables (symptomatic patients, asymptomatic patients, and the control group) were evaluated using Tukey’s test (Minitab Inc., version 11.24, UK). Data were considered statistically significant at p < 0.05.  3  BIO Web of Conferences 187, 07003 (2025) https://doi.org/10.1051/bioconf/202518707003 ISPH-ISNPR 2025  4. Results 4.1 Gut Microbiome Diversity and Enterotype Shifts The microbiome consistently demonstrates considerable interindividual variability, even in the absence of disease. This complicates the determination of the functional and etiological roles of the many distinct microbial communities present in healthy individuals. However, the most challenging task is to detect the initial shifts (disturbances) in microorganisms  that play an etiological role in the onset of non- communicable diseases (NCDs), with the aim of  correcting them for predictive prevention and treatment. A healthy and stable microbiome is characterized by high diversity, whereas a lack of gut microbiome diversity is observed in conditions such as obesity and type 1 and type 2 diabetes mellitus. We have previously demonstrated the key differences in various indicators and ratios of the gut microbiome in different diseases— overweight, type 2 diabetes, and cardiovascular diseases (CVDs) – which makes it possible to use them for early diagnosis as well as for targeted microbial and nutritional therapy. As part of the study, a comparative analysis of the gut microbiome composition was conducted in two groups: individuals with chronic anxiety (n = 10) and conditionally healthy controls (n = 10), based on 16S rRNA sequencing. Particular attention was given to the quantitative composition of key functional and conditionally pathogenic bacterial taxa (Akkermansia muciniphila, Faecalibacterium prausnitzii,  Lactobacillus spp., Bacteroidetes, Escherichia- Shigella), as well as to the alpha diversity index  (Shannon index). The abundance of Escherichia- Shigella representatives was elevated in both groups,  with a mean value of 18.52%. However, it is noteworthy that extreme values (52.1% and 58.9%) were recorded exclusively among the “healthy” individuals, suggesting a possible latent dysfunction or hidden pathology in part of the control sample. Elevated Escherichia- Shigella levels are associated with endotoxemia and enhanced systemic inflammation via LPS-induced activation of the TLR4 cascade. In the anxiety group, a marked decrease in Faecalibacterium prausnitzii content was observed (1.53% vs. the reference range of 1.9–5.0%), along with complete or partial absence of Akkermansia muciniphila  in most patients. These bacteria are responsible for anti- inflammatory regulation (via butyrate) and maintenance  of the intestinal mucosal barrier, respectively. A reduction in their levels is associated with impaired barrier function and immune homeostasis, which may play a role in the pathogenesis of anxiety disorders according to the microbiota–gut–brain axis concept. At the same time, Lactobacillus spp. exhibited atypically  high levelsin some patients with anxiety (up to 22.19%), which is likely a compensatory microbiota response to chronic stress. In the control group, the average Lactobacillus level was lower and more evenly distributed. The Bacteroidetes phylum level was critically reduced in most individuals from both groups (<5% vs. the reference range of 27–36%), indicating Firmicutes dominance and profound disruption of enterotype architecture. In many cases, the Firmicutes/Bacteroidetes ratio exceeded the normal range by several orders of magnitude, which is associated with impaired short-chain fatty acid (SCFA)  metabolism and a potential metabolic shift toward pro- inflammatory states.  Thus, it can be assumed that individuals with anxiety disorders exhibit signs of profound dysbiosis, a decrease in key anti-inflammatory bacteria (Faecalibacterium, Akkermansia), as well as a shift in enterotype. At the same time, high levels of conditionally pathogenic Escherichia-Shigella in some “healthy” participants may indicate the need for more in-depth diagnostics, even in the absence of clinical symptoms. 4.2 Psychoemotional States and Microbiome Composition Continuing the search for potential biological markers of human psychoemotional states—ideally capable of determining such a state for each individual patient—we investigated the correlations between psychoemotional parameters and (a) gut microbiome indicators and (b) blood biochemistry parameters. This study used data from the two aforementioned groups of patients, namely individuals with chronic anxiety (n = 10) and conditionally healthy controls (n = 10). Since psychoemotional parameters are categorical variables, while gut microbiome and blood biochemistry indicators are numerical characteristics, “psyche– microbiome” and “psyche–blood” correlations were determined using Spearman’s rank correlation coefficients (r). All statistically significant r coefficients for these correlations are presented in the heatmap in Fig. 1 (to ensure an acceptable level of compactness, the diagram does not include parameters for which no statistically significant r coefficient was found). It should be noted that the group of 20 individuals considered here is relatively small; therefore, only the largest absolute r coefficients reached statistical significance (p < 0.05). Smaller coefficients did not attain statistical significance, and their calculated values are thus uncertain; in Fig. 1, they are represented by black cells marked “--”. It should also be noted that for the “psyche– microbiome” relationship, most of the correlation coefficients presented in Fig. 1 have negative values. Since all psychoemotional state parameters are  categorical and scaled such that a deterioration in condition corresponds to an increase in the numerical value of the parameter, an improvement in the psychoemotional state would, in the case of a negative correlation, correspond to an increase in a given microbiome parameter – for example, an increase in the  abundance of a specific bacterial species. As shown in Fig. 1, tension negatively correlates with lactate production (r = –0.57) and the abundance of Bifidobacterium spp. (r = –0.58). These findings are consistent with previous studies indicating dysbiosis as a potential factor in increased anxiety. A decrease in  4  BIO Web of Conferences 187, 07003 (2025) https://doi.org/10.1051/bioconf/202518707003 ISPH-ISNPR 2025  lactate levels with elevated tension (r = –0.57) may point to impaired function of certain metabolically active bacteria involved in carbohydrate fermentation with the production of short-chain fatty acids (SCFAs), including lactate. Lactate, along with other SCFAs, contributes to maintaining the integrity of the intestinal barrier and possesses neuromodulatory properties, influencing the central nervous system through gut–brain axis  Figure –1. Statistically significant correlation coefficients (r) between psychoemotional state parameters and gut microbiome and blood biochemistry indicators for the selected group of 20 individuals. Positive and negative correlations are shown in shades of red and blue, respectively. Correlations that are not statistically significant are indicated in black. mechanisms. Insufficient levels of this metabolite may be associated with dysbiosis and disruption of neurochemical balance, which can promote the development of anxiety disorders. Bifidobacteria are key representatives of the normal gut microbiota and perform a number of important functions: they produce SCFAs, reduce inflammation, improve the intestinal mucosal barrier function, and exert modulatory effects on the immune and nervous systems. Their reduction may indicate dysbiosis, which in turn can trigger or exacerbate anxiety disorder symptoms through disruption of the microbiome–gut–brain axis. In addition, panic attacks and stress also show negative correlations with lactate production (r = –0.48 and r =  –0.46, respectively). Restlessness showed a negative correlation with Bacteroides spp. (r = –0.46), suggesting a possible role of these bacteria in modulating behavioral responses to stress. Fear positively correlated with the abundance of Dorea spp. (r = 0.49), while trembling attacks positively correlated with Eubacterium spp. (r = 0.47). Dorea spp. belong to the family Lachnospiraceae, which are generally part of the commensal microbiota; however, their excessive abundance is associated with dysbiotic changes.

inflammation, and increased gas production, which may affect both somatic comfort and overall anxiety levels. Their positive correlation with fear may indicate involvement in mechanisms related to generalized anxiety, potentially through effects on neurotransmitter synthesis or activation of low-grade immune responses. Eubacterium spp., on the other hand, also participate in the synthesis of short-chain fatty acids, but certain species within this genus are associated with impaired bilirubin metabolism, the formation of toxic metabolites, and altered immune responses. Their association with trembling attacks may indicate involvement in the modulation of the autonomic nervous system or an indirect influence on the “stress– microbiota–brain” axis. Overall, the identified associations support the rationale for further investigation of microbial biomarkers in the context of psychoemotional disorders and for developing microbiome-based risk profiles to enable individualized diagnostic and therapeutic approaches.  5  BIO Web of Conferences 187, 07003 (2025) https://doi.org/10.1051/bioconf/202518707003 ISPH-ISNPR 2025  4.3 Inflammatory Signatures Associated with Anxiety The observed positive correlations between psychoemotional manifestations and immunoinflammatory markers indicate a close link between neuropsychological state and systemic immune response. In particular, the association between a sense of slowed-down perception and elevated homocysteine (r = 0.55), as well as IL-6 (r = 0.50), may suggest a role for chronic low-grade inflammation and disturbances in sulfur-containing amino acid metabolism in the development of cognitive and psychomotor retardation. Homocysteine is known to be a neurotoxic compound,  while elevated IL-6 is a classical marker of pro- inflammatory status, accompanying both depressive  conditions and neurodegenerative processes. Similarly, the positive correlation between restlessness and homocysteine (r = 0.56) may indicate a different spectrum of this metabolite’s influence, possibly through the reduction of dopamine levels or  dysregulation of nervous system excitability. This relationship reinforces the hypothesis of homocysteine’s multifaceted involvement in the pathogenesis of anxiety and behavioral disorders. The positive correlation between tension and elevated levels of secretory immunoglobulin A (SIgA) (r = 0.48) may indicate activation of local mucosal immunity in response to chronic stress. SIgA is a key component of mucosal defense, and its increase may represent a compensatory reaction to psychoemotional stress, accompanied by activation of the hypothalamic–pituitary–adrenal axis. Overall, the obtained data enhance the understanding of the role of immunoinflammatory markers in the pathogenesis of anxiety symptoms and highlight their potential use as objective biomarkers for risk stratification and for monitoring the effectiveness of interventions. Figure 2 presentsthe Pearson correlation matrix between blood parameters and gut microbiome indicators.

Figure 2 – Statistically significant correlation coefficients (r) between gut microbiome indicators and blood biochemistry parameters for the selected group of 20 individuals. Positive and negative correlations are shown in shades of red and blue, respectively. Correlationsthat are notstatisticallysignificant are indicated in black. 4.4 Integration of Microbiome, Biochemical, and Psychological Profiles The presented correlations demonstrate a multilevel interaction between the gut microbiome, metabolic parameters, and the body’s immunological status.  Homocysteine showed positive correlations with the Firmicutes/Bacteroidetes ratio, the Prevotella spp./Bacteroides ratio, the abundance of Fusobacteria, as well as the counts of B. dentium, Enterobacter spp., Klebsiella spp., and Lactobacillus spp., and a negative correlation with Oscillibacter spp. This pattern may indicate impaired intestinal barrier function and the presence of endotoxemia as a mechanism contributing to elevated homocysteine levels. The negative correlation between homocysteine and Oscillibacter spp. (a representative of potentially anti-inflammatory microbiota) further supports this hypothesis.  6  BIO Web of Conferences 187, 07003 (2025) https://doi.org/10.1051/bioconf/202518707003 ISPH-ISNPR 2025  A similar trend was observed in the context of kidney function. The glomerular filtration rate negatively correlated with the Firmicutes/Bacteroidetes ratio, the Prevotella spp./Bacteroides ratio, and the abundance of Klebsiella spp. and Lactobacillus spp., which likely reflects the systemic influence of the gut microbiota on nephrological parameters via immune and metabolic pathways. At the same time, high SIgA levels showed negative correlations with the abundance of Bacteroidetes (r = –0.80), Prevotella copri (r = –0.80), Oscillibacter spp. (r = –0.76), Oxalobacter formigenes (r = –0.91), among others, which may indicate activation of mucosal immunity in response to excessive presence of these bacterial taxa or their metabolites. Lipid disorders, such as elevated VLDL and triglycerides, showed a  negative correlation with the abundance of SCFA- producing microbiota—Ruminococcus spp. (r = – 0.47)  – and a positive correlation with Enterococcus spp. (r = 0.51) as well as with the Firmicutes/Bacteroidetes ratio (r = 0.45), further underscoring the influence of microbiome composition on the metabolic profile. It should be noted that distinct microbiome patterns were observed between working-age and elderly individuals, some of which were associated with markers of healthy aging. The integration of sequencing and clinical data supports the potential of the gut microbiota as a predictive tool for assessing psychological resilience and longevity. The obtained data reinforce the concept of the “gut–vascular” and “gut–metabolic” axes and indicate the relevance of further research to validate microbiome biomarkers of systemic disorders. 5. Conclusion Alterations in the gut microbiota composition may play a significant role in modulating psychoemotional states via multiple interconnected mechanisms along the microbiota–gut–brain axis. The observed reduction in Faecalibacterium prausnitzii and Akkermansia muciniphila among individuals with chronic anxiety suggests a loss of key anti-inflammatory functions. Both taxa are known producers of SCFAs, such as butyrate, which help maintain intestinal barrier integrity, suppress systemic inflammation, and modulate neurotransmitter synthesis. Reduced SCFA availability may impair the production of GABA and serotonin precursors, leading to increased vulnerability to anxiety and mood dysregulation.  Conversely, elevated levels of Escherichia- Shigella and Enterococcus spp., which are associated  with lipopolysaccharide (LPS) release, may contribute to low-grade systemic inflammation through TLR4 activation. This pro-inflammatory state can influence hypothalamic–pituitary–adrenal (HPA) axis hyperactivation, promoting stress responses and exacerbating anxiety symptoms. Interestingly, higher abundances of Lactobacillus spp. observed in some anxiety patients may represent a compensatory adaptation. Certain Lactobacillus species produce lactate and bioactive neuropeptides that can modulate vagal nerve signaling, potentially mitigating stress effects. However, this response may not be sufficient to  counterbalance the detrimental effects of dysbiosis. These findings support a bidirectional relationship between gut microbial imbalances and psychoemotional disturbances, mediated through immune activation, neurotransmitter pathways, and metabolic signaling. Understanding these mechanisms may enable the development of microbiome-targeted therapeutic strategies for managing psychoemotional disorders. Microbiome-based diagnostics test systems may provide a valuable framework for personalized approaches to mental health and healthy aging across populations. Individualized nutrition plans are developed using the EdiensUniCodeTM algorithm. The Lacticaseibacillus rhamnosus S25 strain was identified as a potential key component of the pharmabiotic DefendeXTM, aimed at the prevention and treatment of PTSD.

References

1. R.G. Xiong, J. Li, J. Cheng, D.D. Zhou, S.X. Wu, S.Y. Huang, A. Saimaiti, Z.J. Yang, R.Y. Gan, H.B. Li, The role of gut microbiota in anxiety, depression, and other mental disorders as well as the protective effects of dietary components. Nutrients. 15(14), 3258 (2023). https://doi.org/10.3390/nu15143258 2. D.A. Schupack, R.A.T. Mars, D.H. Voelker, J.P. Abeykoon, P.C. Kashyap, The promise of the gut microbiome as part of individualized treatment strategies. Nat. Rev. Gastroenterol. Hepatol. 19(1), 7- 25 (2022). https://doi.org/10.1038/s41575-021- 00499-1 3. D. Ochnik, B. Buława, P. Nagel, M. Gachowski, M. Budziński, Urbanization, loneliness and mental health model - A cross-sectional network analysis with a representative sample. Sci Rep 14, 24974 (2024). https://doi.org/10.1038/s41598-024- 76813- z 4. S.-M. Petrut, A.M. Bragaru, A.E. Munteanu, A.-D. Moldovan, C.-A. Moldovan, E. Rusu, Gut over mind: Exploring the powerful gut–brain axis. Nutrients. 17(5), 842 (2025). https://doi.org/10.3390/nu17050842 5. V. Bamicha, P. Pergantis, A. Drigas, The effect of gut microbiome, neurotransmitters, and digital insights in autism. Appl. Microbiol. 4(4), 1677-1701 (2024). https://doi.org/10.3390/applmicrobiol4040114 6. L. Wu, X. Xie, Y. Li, et al., Gut microbiota as an antioxidant system in centenarians associated with high antioxidant activities of gut-resident Lactobacillus. npj Biofilms Microbiomes. 8, 102 (2022). https://doi.org/10.1038/s41522-022-00366-0 7. V.D. Badal, E.D. Vaccariello, E.R. Murray, K.E. Yu, R. Knight, D.V. Jeste, T.T. Nguyen, The gut microbiome, aging, and longevity: A systematic review. Nutrients. 12(12), 3759 (2020). https://doi.org/10.3390/nu12123759 8. . Yusko, T. Chendey, V. Lohoida, A. Konic-Ristic, N. Boyko, Gut microbiome in acute coronary syndrome. In: Proc. Shevchenko Sci. Soc. Med. Sci. 72(2) (2023). https://doi.org/10.25040/ntsh2023.02.16 9. F.H. Karlsson, F. Fåk, I. Nookaew, V. Tremaroli, B.  7  BIO Web of Conferences 187, 07003 (2025) https://doi.org/10.1051/bioconf/202518707003 ISPH-ISNPR 2025  Fagerberg, D. Petranovic, et al, Symptomatic atherosclerosis is associated with an altered gut metagenome. Nat Commun. 3, 1245. https://doi.org/10.1038/ncomms2266 10. S.E. Oziegbe, H. Yao, K.S. Agyemang, Air pollution and life expectancy: New evidence from the MINT economies. Heliyon. 9(12), e22396 (2023). https://doi.org/10.1016/ j.heliyon.2023.e22396 11. K. Nikel, M. Stojko, J. Smolarczyk, M. Piegza, The impact of gut microbiota on the development of anxiety symptoms – A narrative review. Nutrients. 17(6), 933 (2025). https://doi.org/10.3390/nu17060933 12. X. Shen, L. Li, Z. Sun, G. Zang, L. Zhang, C. Shao, Z. Wang, Gut microbiota and atherosclerosis –  Focusing on the plaque stability. Front. Cardiovasc. Med. 8, 668532 (2021). https://doi.org/10.3389/fcvm.2021.668532 13. Z. Wang, A.B. Roberts, J.A. Buffa, B.S. Levison, W. Zhu, E. Org, et al., Non-lethal inhibition of gut microbial trimethylamine production for the treatment of atherosclerosis. Cell. 163, 1585-1595 (2015). https://doi.org/10.1016/j.cell.2015.11.055 14. M. Orman, S. Bodea, M.A. Funk, A.M. Campo, M. M. Bollenbach, C.L.Drennan, E.P. Balskus, Structure-Guided Identification of a Small Molecule That Inhibits Anaerobic Choline Metabolism by Human Gut Bacteria. J Am Chem Soc. 2019 Jan 9;141(1):33-37. https://doi.org/10.1021/jacs.8b04883