Introduction: The longevity is influenced by genetic, environmental, and lifestyle factors. The specific changes that occur in the gut microbiome during the aging process, and their relationship to longevity and immune function, have not yet been fully understood. The ongoing research of other microbiome based on longevity cohort in Kazakhstan provides preliminary information on longevity-related aging, where cytokine expression is associated with specific microbial communities and microbial functions. Methods: Metagenomic shotgun sequencing study of 40 long-lived individuals aged 90 years and over was carried out, who were conditionally healthy and active, able to serve themselves, without a history of serious infection and cancer, who had not taken any antimicrobials, including probiotics. Blood serum was analyzed for clinical and laboratory characteristics. The cytokine and chemokine profile in serum and stool samples was assessed using multiplex analysis. Results: We found a significant increase in the expression of pro-inflammatory cytokines IL-1a, IL-6, 12p70, IP-10, IFNα2, IL-15, TNFa, as well as chemokines MIP-1a/CCL3 and MIP-1b/CCL4, chemokine motif ligands MCP-3/CCL7 and MDC/CCL22(1c). Nonagenerians and centenarians demonstrated a greater diversity of core microbiota genera and showed an elevated prevalence of the genera Bacteroides, Clostridium, Escherichia, and Alistipes. Conversely, there was a decrease in the abundance of the genera Ruminococcus, Fusicatenibacter, Dorea, as well as the species Fusicatenibacter saccharivorans. Furthermore, functional analysis revealed that the microbiome in long-lived group has a high capacity for lipid metabolism, amino acid degradation, and potential signs of chronic inflammatory status. Conclusion: Long-lived individuals exhibit an immune system imbalance and observed changes in the composition of the gut microbiota at the genus level between to the two age-groups. Age-related changes in the gut microbiome, metabolic functions of the microbial community, and chronic inflammation all contribute to immunosenescence. In turn, the inflammatory state and microbial composition of the gut is related to nutritional status.

Aging is a natural process where the body’s physiological functions gradually decline, making individuals more susceptible to diseases and increasing mortality rates as they get older [1, 2]. The role of the human gut microbiome in the aging process is becoming increasingly recognized. In its turn, various factors such as medications, diet, and a sedentary lifestyle contribute to changes in the gut microbiome composition as individuals age, leading to a decrease in beneficial microbes and a condition called dysbiosis. Dysbiosis in the gut microbiome has been linked to age-related conditions like chronic inflammation, metabolic disorders, neurodegenerative diseases, and impaired immune function [3]. Research indicates that age-related changes in the gut microbiome may contribute to inflammation, impaired immune function, metabolic disorders, and other health issues associated with aging [4]. One suggested mechanism by which the gut microbiome influences aging is through its impact on inflammation. Dysbiosis can lead to increased levels of pro-inflammatory molecules in the gut, which can enter the bloodstream and contribute to systemic inflammation [5]. The measurement of circulating cytokines and pro-inflammatory markers can provide insights into chronic inflammation, aiding in the assessment of specific factors contributing to it [6, 7]. The composition of the host microbiome throughout an individual’s life, from birth to death, significantly influences the development, operation, and regulation of the immune system [8, 9]. Recent findings suggest a link between an enrichment of Bacteroides fragilis in the gut and the expression of the anti-inflammatory factor IL-10, which is crucial for maintaining balance within the body and promoting longevity [10].

The gut microbiome also plays a role in nutrient metabolism and absorption. Changes in the gut microbiome with age can affect the breakdown and utilization of dietary nutrients, influencing energy balance, metabolism, and overall nutrient status, all of which are important factors in the aging process [11]. Additionally, the gut microbiome influences the integrity and function of the intestinal barrier. With age, the intestinal barrier may become more permeable, allowing bacterial components and toxins to enter the bloodstream, triggering immune responses and promoting inflammation, often referred to as “leaky gut.” Ongoing research aims to understand the complex interactions between the gut microbiome, aging, and age-related diseases.

The uniqueness of this study lies in the study of stool cytokines in persons over 90 years of age in combination with the gut metagenome and systemic cytokine patterns. Furthermore, the study focuses on the metagenome of long-lived (LL) individuals and of Kazakhstan, representing the Central Asian population. These aspects contribute to a more nuanced understanding of the role of stool cytokines, gut metagenome, and systemic cytokine patterns in the aging process, specifically in the context of aging individuals, and shed light on the unique characteristics of the gut microbiome in nonagenerians and centenarians from Central Asia.

Subject Recruitment and Clinical Information Collection

Subjects over 90 years old from the cities of Astana and Karaganda, Kazakhstan, were invited to the study. The study included 46 (aged 93–103 years) conditionally healthy and active nonagenerians and centenarians (LL individuals), who served themselves (Fig. 1). Subjects without a history of chronic serious infection, without any current infection, and without any type of malignant cancer were included according to the inclusion criteria of the study and individuals who had not taken any antimicrobials, including probiotics, in the past 3 months prior to enrollment in the study. The study included two control groups, the first control group (Cntrl-M) included 31 volunteers (aged 35–48 years), of which 23 subjects were included in the group from the preliminary study. The second control group (Cntrl-I) included 50 people (46–60 years old). All volunteers who expressed a desire to participate in the study completed a survey that included both demographic and physical data (Table 1) and medical history, food preferences. The clinical and laboratory parameters were analyzed according to Table 2. All subjects signed a written consent to participate in the study and the study was heard and approved by the Ethics Committee of the National Laboratory Astana, Nazarbayev University, protocol extract #04-2020 from August 27, 2020.

Fig. 1.

Scheme of study design.

Fig. 1.

Scheme of study design.

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Table 1.

Demographics characteristics of the longevity cohort

ParameterLL (n = 46)Cntrl-M (n = 31)Cntrl-I (n = 50)p value
Age, mean±SD, years 95.3±2.8 (93.0–96.0) 43.6±11.4 (35.2–48.0) 53.0±10.2 (46.8–60.0) ≤0.0001 
Gender, n (%) 
 Male 11 (24) 7 (23) 18 (35.3) ≤0.01 
 Female 35 (76) 24 (77) 32 (64.7) ns 
BMI, mean±SD, kg/m2 23.2±3.1 (21.0–25.4) 24.2±3.9 (22.1–24.9) 26.4±4.4 (23.6–29.7) ≤0.001 
Diastolic 87.6±11.3 (80.0–100.0) 80.7±18.8 (70.0–95.3) 77.9±6.2 (75.0–80.0) ≤0.01 
Systolic 138.0±18.2 (130.0–150.0) 122.9±28.0 (100.0–139.2) 120.7±14.4 (116.2–125.0) ≤0.01 
ParameterLL (n = 46)Cntrl-M (n = 31)Cntrl-I (n = 50)p value
Age, mean±SD, years 95.3±2.8 (93.0–96.0) 43.6±11.4 (35.2–48.0) 53.0±10.2 (46.8–60.0) ≤0.0001 
Gender, n (%) 
 Male 11 (24) 7 (23) 18 (35.3) ≤0.01 
 Female 35 (76) 24 (77) 32 (64.7) ns 
BMI, mean±SD, kg/m2 23.2±3.1 (21.0–25.4) 24.2±3.9 (22.1–24.9) 26.4±4.4 (23.6–29.7) ≤0.001 
Diastolic 87.6±11.3 (80.0–100.0) 80.7±18.8 (70.0–95.3) 77.9±6.2 (75.0–80.0) ≤0.01 
Systolic 138.0±18.2 (130.0–150.0) 122.9±28.0 (100.0–139.2) 120.7±14.4 (116.2–125.0) ≤0.01 
Table 2.

Clinical characteristics of the longevity cohort

ParameterLL (n = 46)Cntrl-M (n = 31)Cntrl-I (n = 50)p value
Glu, mmol/L 4.6±1.3 (4.0–4.8) 5.2±1.5 (4.6–5.2) 5.5±1.2 (5.0–6.1) ≤0.0001 
TC, mmol/L 4.7±1.2 (3.9–5.6) 4.9±0.9 (4.4–5.4) 5.9±5.6 (4.5–5.8) ns 
LDL, mmol/L 3.2±1.7 (2.3–3.5) 2.8±0.9 (2.2–3.2) 2.8±0.9 (2.2–3.2) ≤0.01 
HDL, mmol/L 1.3±0.4 (1.1–1.5) 1.5±0.9 (1.1–1.6) 1.3±0.3 (1.1–1.5) ns 
TG, mmol/L 1.1±0.4 (0.8–1.3) 1.2±1.1 (0.8–1.2) 1.3±0.6 (0.9–1.7) ns 
CRP, mg/L 5.2±7.5 (0.9–6.4) 2.7±3.3 (0.7–2.6) 1.9±2.3 (0.5–2.0) ≤0.01 
Hb, g/L 118.5±12.6 (111.0–128.0) 127.1±13.8 (122.5–134.0) 135.1±16.4 (124.5–147.2) ≤0.0001 
RBC, 1012/L 4.0±0.4 (3.7–4.3) 4.3±0.4 (4.0–4.6) 4.5±0.4 (4.2–4.9) ≤0.0001 
WBC, 109/L 5.9±1.8 (4.7–6.4) 5.8±1.2 (5.0–6.4) 6.0±1.8 (4.9–6.7) ns 
Lymph, 109/L 29.9±9.2 (23.1–36.0) 29.5±6.7 (26.0–33.3) 35.4±8.6 (32.2–38.6) ≤0.001 
PLT, % 230.2±53.4 (188.0–255.5) 284.7±80.9 (235.5–322.0) 234.0±60.2 (197.5–274.0) ≤0.01 
ParameterLL (n = 46)Cntrl-M (n = 31)Cntrl-I (n = 50)p value
Glu, mmol/L 4.6±1.3 (4.0–4.8) 5.2±1.5 (4.6–5.2) 5.5±1.2 (5.0–6.1) ≤0.0001 
TC, mmol/L 4.7±1.2 (3.9–5.6) 4.9±0.9 (4.4–5.4) 5.9±5.6 (4.5–5.8) ns 
LDL, mmol/L 3.2±1.7 (2.3–3.5) 2.8±0.9 (2.2–3.2) 2.8±0.9 (2.2–3.2) ≤0.01 
HDL, mmol/L 1.3±0.4 (1.1–1.5) 1.5±0.9 (1.1–1.6) 1.3±0.3 (1.1–1.5) ns 
TG, mmol/L 1.1±0.4 (0.8–1.3) 1.2±1.1 (0.8–1.2) 1.3±0.6 (0.9–1.7) ns 
CRP, mg/L 5.2±7.5 (0.9–6.4) 2.7±3.3 (0.7–2.6) 1.9±2.3 (0.5–2.0) ≤0.01 
Hb, g/L 118.5±12.6 (111.0–128.0) 127.1±13.8 (122.5–134.0) 135.1±16.4 (124.5–147.2) ≤0.0001 
RBC, 1012/L 4.0±0.4 (3.7–4.3) 4.3±0.4 (4.0–4.6) 4.5±0.4 (4.2–4.9) ≤0.0001 
WBC, 109/L 5.9±1.8 (4.7–6.4) 5.8±1.2 (5.0–6.4) 6.0±1.8 (4.9–6.7) ns 
Lymph, 109/L 29.9±9.2 (23.1–36.0) 29.5±6.7 (26.0–33.3) 35.4±8.6 (32.2–38.6) ≤0.001 
PLT, % 230.2±53.4 (188.0–255.5) 284.7±80.9 (235.5–322.0) 234.0±60.2 (197.5–274.0) ≤0.01 

Sample Collection

Participants were instructed to collect stool samples at home following standard protocols for stool collection. For metagenomic and immunoassay analysis, centenarians were provided with a specific collection tube containing DNA/RNA Shield Fecal Collection Tube (R1101, Zymo Research). These samples were then transported to the laboratory and stored at a temperature of −80°C until further analysis.

To assess the overall immune status of participants in both groups, qualified medical personnel collected fasting venous blood using plastic vacuum tubes containing a clotting activator gel. The volume of blood collected was approximately 10 mL.

Сytokines Analysis

Multiplex analyses were performed to analyze the cytokine/chemokine profile of participants using Luminex BioRad Bio-Plex 200 technology and Milliplex HCYTMAG-60K-PX41 kits (Millipore). For immunoassay of intestinal immunity of LL individuals, stools collected in R1101 tubes, Zymo Research, were used. To do this, the test tube with feces was vortexed until a homogeneous consistency was obtained, and 2 mL of the suspension was taken into clean test tubes. After centrifugation at 140,000 rpm for 15 min, 200 μL of the supernatant was taken for further analysis.

Blood serum was used to analyze the general immune status of LL group. Blood serum was obtained by centrifugation for 10 min at 4,000 rpm. The resulting serum aliquots were stored at −80°C for further analysis.

According to the assay protocol, 25 μL of blood serum (stool supernatant) was mixed with antibody-bound magnetic beads in a 96-well plate and incubated overnight at 4°C with constant shaking. The incubation steps at cold and room temperature were carried out on an orbital shaker at 500–600 rpm. After incubation, the samples were washed twice with wash buffer. After 1 h incubation at room temperature with biotinylated detection antibody, streptavidin-PE was added for 30 min with shaking. The plates were washed as above and sheet fluids were added to the wells for reading on the Bio-Plex 200 instrument.

Metagenomic Analysis

For metagenomic analysis, 40 stool samples of nonagenerians and centenarians were used (4 samples were not taken due to the low depth of sequencing) and 31 samples of the control group. Shotgun sequencing of stool DNA was used to analyze the entire metagenome to assess taxonomic and functional changes. DNA was obtained using a commercial Quick-DNA Fecal/Soil Microbe Miniprep Kit (Zymo Research, D4300). DNA concentrations were measured using a Nanodrop 2000/2000c (ThermoFisher). The sequencing was performed on the Illumina NovaSeq6000 at Novogene laboratory (Beijing, China) using standard Illumina protocols.

Bioinformatics Analysis

The NovoSeq platforms generate paired-end 150 bp reads, resulting in each sample producing a minimum of 5–10 Gb raw data, with over 80% of bases having a quality score of ≥Q30. MetaPhlAn v4.0.3 was utilized with default parameters to profile the taxonomic composition. The functional gene pathway was profiled using the default settings of HUMAnN v3.6.1. To perform functional potential profiling of microbial communities, HUMAnN v3.6.1 was annotated with UniRef90 on all detectable species per sample as determined by MetaPhlAn. The functional annotations were based on the MetaCyc database.

Statistical Analysis

All statistical calculations were performed in Python 3 using NumPy 1.21.5, SciPy 1.7.0, and statsmodels 0.13.5 libraries. Visualization was performed with the Matplotlib 3.7.1, Seaborn 0.11.2, and Sankey flow 0.3.8 libraries. Two-group analysis was performed using the Mann-Whitney U test, and correlation analysis was performed using the Spearman r coefficient. All sets of p values were adjusted using the Benjamin-Hochberg procedure (FDR), p ≤ 0.5. Microbial alpha diversity was assessed using Shannon and Simpson indices, and beta diversity was assessed using Bray-Curtis dissimilarity. Ordination of microbial composition was performed using principal coordinate analysis and analysis of separation was performed using ANOSIM and PERMANOVA tests with 999 permutations. Diversity calculation, ordination, ANOSIM and PERMANOVA tests were performed using the scikit-bio 0.5.6 package. STAMP 2.1.3 software was used to identify significant differences in abundance ratios in the microbial and functional data (Welsh t test, p < 0.05, FDR, effect size ≥0.2). Linear discriminant analysis with effect size was used to identify the most differentially distributed taxa, requiring an LDA effect size of at least 1.5 and p ≤ 0.05 for each significant call. Only bacterial taxa and functional data present in at least 25% of the samples in each group were included in the analysis.

General Immune Status of LL Individuals

Age-related changes in the immune system, known as immunosenescence, lead to a shift in cytokine production toward a pro-inflammatory phenotype and characterized by an imbalance in the immune system, which can lead to the production of both pro- and anti-inflammatory cytokines. We have confirmed most of the previous observations regarding cytokines in aging. A picture emerges of an increase in pro-inflammatory cytokines that, in longevity, are at least partially offset by elevated levels of anti-inflammatory molecules [12].

In confirmation, in the study group older than 90 years compared to the control group, we found a significant increase in the expression of pro-inflammatory cytokines interleukin-1a (IL-1a) (p = 0.00001), interleukin-6 (IL-6), interleukin-12p70 (IL-12), gamma-induced protein 10 (IP-10; p = 0.00001), interferon alpha-2 (IFNα2), interleukin-15 (IL-15; p = 0.00672), tumor necrosis factor alpha (TNFα; p = 0.04906), as well as chemokines macrophage inflammatory protein-1 alpha (MIP-1a/CCL3; p = 0.00044) and macrophage inflammatory protein-1 beta (MIP-1b/CCL4), monocyte-specific chemokine 3 (MCP-3/CCL7; p = 0.00333) and macrophage-derived chemokine (MDC/CCL22) (Fig. 2). Along with this, we observe increased values of anti-inflammatory cytokine interleukin-1 receptor antagonist (IL-1RA; p = 0.00044) (Fig. 2b). IL-1a and IL-1RA are members of the interleukin-1 (IL-1) family of cytokines. IL-1RA counterbalances the pro-inflammatory effects of IL-1a by blocking its activity and reducing inflammation and tissue damage. At the same time, we do not observe a significant correlation between them, which may indicate some imbalance associated with age-related changes. Of the anti-inflammatory cytokines, elevated values were also determined for interleukin-4 (IL-4; p = 0.00001), FLT-3L, and interleukin-15 (IL-15) (Fig. 2b). The analysis of growth factors may also provide insight into the mechanisms underlying healthy aging; in our study, epidermal growth factor (EGF), granulocyte-macrophage colony-stimulating factor (GM-CSF), and vascular endothelial growth factor-A (VEGF-A) were elevated in the serum of LL individuals (Fig. 2d).

Fig. 2.

Serum cytokine levels in controls (Cntrl-I) and long-lived (LL) individuals. Only those analytes are shown whose levels differed significantly between groups. Data are expressed in pg/mL. *p < 0.05, **p < 0.01, ***p < 0.0001, ****p < 0.00001. a Serum levels of pro-inflammatory cytokines. b Serum levels of anti-inflammatory cytokines. c Serum levels of chemokines. d Serum levels of growth factors.

Fig. 2.

Serum cytokine levels in controls (Cntrl-I) and long-lived (LL) individuals. Only those analytes are shown whose levels differed significantly between groups. Data are expressed in pg/mL. *p < 0.05, **p < 0.01, ***p < 0.0001, ****p < 0.00001. a Serum levels of pro-inflammatory cytokines. b Serum levels of anti-inflammatory cytokines. c Serum levels of chemokines. d Serum levels of growth factors.

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Correlation analysis of cytokine patterns with clinical parameters (Fig. 3a) revealed a positive relationship between systolic and diastolic blood pressure levels and MIP-1b. MIP-1b, also known as CCL4, is one of the two major MIP factor proteins produced by macrophages after being stimulated by bacterial endotoxins. Serum MIP-1b level is a predictor of neurovascular events in hypertensive patients [13]. In confirmation, we observe a correlation of MIP-1b with growth factors, as vascular endothelial growth factor A (VEGF-A), and with IP-10 (interferon gamma-inducible protein), which indicates the presence of neurovascular pathologies among the participants in the study group.

Fig. 3.

Correlation analysis of serum (general immunity), stool (intestinal immunity) cytokine levels and clinical laboratory parameters (clinical data) in the long-lived (LL) group. a Between general and clinical data. b Within parameters of general immunity. c Between general and intestinal immunity. Positive correlations are shown in red, negative correlations in blue. Spearman r, FDR, p ≤ 0.5. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

Fig. 3.

Correlation analysis of serum (general immunity), stool (intestinal immunity) cytokine levels and clinical laboratory parameters (clinical data) in the long-lived (LL) group. a Between general and clinical data. b Within parameters of general immunity. c Between general and intestinal immunity. Positive correlations are shown in red, negative correlations in blue. Spearman r, FDR, p ≤ 0.5. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

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We also observe a positive correlation of VEGF-A (vascular endothelial) with EGF expression. In our case, this correlation is associated with age-related renal dysfunction, described for this group in a previous publication. We consider this to be a compensatory mechanism, where VEGF-A and EGF are important growth factors involved in the regulation of proximal tubular cell proliferation (Fig. 3b).

We also found a correlation between serum and intestinal cytokines (Fig. 3c). By its nature, the intestinal cytokine profile relates predominantly to the Th1 pathway, i.e., producing interferon-gamma (IFN-γ) under physiological conditions. Normally, the intestines of an adult are in a state of physiological inflammation; in an aging organism, this physiological inflammation is more pronounced. Expression of intestinal cytokines MDC and interleukin-15 (IL-15) positively correlates with the level of IL-1a in the blood, indicating a probable chronic mucosal inflammation. Also in favor of chronic inflammation is the negative correlation of intestinal interleukin-6 (IL-6) with blood GM-CSF. GM-CSF may contribute to intestinal homeostasis by maintaining the integrity of the intestinal epithelium, and IL-6 may play a role in stimulating epithelial proliferation. Experiments in mouse models have shown that inhibition of IL-6 leads to disruption of the integrity of the intestinal wall and increased permeability [14].

Characterization of Gut Microbiota Compositional Profiles between Two Age-Groups

The alpha diversity using Shannon and Simpson indices was significantly higher in LL (online suppl. Table S1; for all online suppl. material, see https://doi.org/10.1159/000536082; Fig. 4a). Relative abundance analysis allowed species-level identification of significant dominant species in the longevity cohort, such as Odoribacter splanchnicus, Parabacteroides distasonis, Bacteroides uniformis, when Fusicanibacter saccharivorans abundance was reduced (Fig. 4b).

Fig. 4.

Gut microbial composition and diversity in Cntrl-M and LL age-groups. a Mann-Whitney U test, FDR, p ≤ 0.5. b Stack plots showing the mean relative abundance of the phylum Firmicutes, Bacteroidetes, and Proteobacteria. c PCoA ordination based on Bray-Curtis distance. Genera significantly correlated with the ordination are shown as arrows, where length of the arrow indicates the goodness of fit – squared correlation coefficient. Spearman r with permutation test (999 repeats), p ≤ 0.5. Test for the degree of separation – ANOSIM. d, e Linear discriminant analysis (LDA) with effect size (LEfSe). d LEfSe cladogram depicting the phylogenetic distribution of differentially abundant microbiota. The central point marks the root of the tree and extends to lower taxonomic levels from phylum to species. e LEfSe (LDA effect size) scores for differentially abundant taxa at each level. LEfSe, p ≤ 0.05, LDA ≥1.5. f Relative abundance (left) and difference in mean proportions with 95% CI (right) of significantly differentially abundant genera. STAMP software, 2-group analysis, FDR, p ≤ 0.5. g Significant changes in bacterial species abundances. Mann-Whitney U test, FDR, p ≤ 0.5. Only bacterial taxa present in at least 25% of all samples were included in the analysis. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

Fig. 4.

Gut microbial composition and diversity in Cntrl-M and LL age-groups. a Mann-Whitney U test, FDR, p ≤ 0.5. b Stack plots showing the mean relative abundance of the phylum Firmicutes, Bacteroidetes, and Proteobacteria. c PCoA ordination based on Bray-Curtis distance. Genera significantly correlated with the ordination are shown as arrows, where length of the arrow indicates the goodness of fit – squared correlation coefficient. Spearman r with permutation test (999 repeats), p ≤ 0.5. Test for the degree of separation – ANOSIM. d, e Linear discriminant analysis (LDA) with effect size (LEfSe). d LEfSe cladogram depicting the phylogenetic distribution of differentially abundant microbiota. The central point marks the root of the tree and extends to lower taxonomic levels from phylum to species. e LEfSe (LDA effect size) scores for differentially abundant taxa at each level. LEfSe, p ≤ 0.05, LDA ≥1.5. f Relative abundance (left) and difference in mean proportions with 95% CI (right) of significantly differentially abundant genera. STAMP software, 2-group analysis, FDR, p ≤ 0.5. g Significant changes in bacterial species abundances. Mann-Whitney U test, FDR, p ≤ 0.5. Only bacterial taxa present in at least 25% of all samples were included in the analysis. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

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At the phylum level, we observed a moderately significant enrichment of Proteobacteria and Verrucomicrobia in the LL group compared to the control. The Firmicutes/Bacteroidetes/Proteobacteria ratio was identified as an indicator of the structure of the intestinal microbiota in the groups. The relative abundance of Firmicutes was higher in the Cntrl-M group, while a higher Bacteroidetes/Proteobacteria ratio was found in the longevity cohort (Fig. 4c). Representatives of the genus Bacteroides are potential colonizers of the intestinal microbiome, are often useful opportunistic flora, and metabolize polysaccharides and oligosaccharides, providing nutrition and vitamins to the host and other inhabitants of the intestine [15]. The persistent abundance of the phylum Proteobacteria is mainly caused by an imbalance in the intestinal microbial community, where the natural human intestinal flora usually contains only a minor part of this phylum [16].

Changes in the microbiota with relative abundance of genera with significant differences between groups were displayed according to Figure 4d, e. The aging signature included longevity-specific genera and is enriched with the opportunistic genera Bacteroides, Clostridium, Escherichia, and Alistipes. The genera Faecalibacterium, Ruminococcus, Roseburia are markedly enriched in Cntrl-M.

Further, to determine the similarity in the structure of the bacterial composition (at the genus level) of the gut microbiome, principal coordinate analysis was used, in which the two groups show a different composition of the microbiota, where gut microbial structures constantly shifted with aging (Fig. 4f). The similarity analysis test (ANOSIM) using the Bray-Curtis distance showed that there were significant differences in the composition of the intestinal microbiota at the genus level between the control group and LL group (R = 0.37, p < 0.0001). The genera that significantly correlate with ordination are shown as arrow (permutation test, p ≤ 0.05), with the length of the arrow indicating the goodness-of-fit statistic, squared correlation coefficient.

According to linear discriminant analysis with effect size, it was found that the microbiome of LL individuals is enriched in the Proteobacteria type, Gammaproteobacteria class, and the Enterobacterales and Firmicutes orders are also abundant. In addition, the accumulation of the families Bacteroidaceae, Clostridiaceae, Enterobacteriaceae, and Rikenellaceae was revealed, while Cntrl-M shows an abundance of the Firmicutes phylum, classes Clostridia and Bacilli and enrichment of the families Lachnospiraceae and Ruminococcaceae. At the genus level, the LL group was dominated by the genera Bacteroides, Clostridium, Escherichia, and Alistipes, while the Cntrl-M group was dominated by Faecalibacterium. An abundance of the species Escherichia coli was revealed (online suppl. Table S2; Fig. 4g).

Functional Annotations of Gut Microbiota in the Two Age-Groups

Analysis of microbial function showed that 37 metabolic pathways differed significantly between LL and Cntrl-M (online suppl. Table S3). The relative abundance of differentially abundant metabolic pathways in the two age-groups, regrouped into six major metabolic functional classes, is shown in Figure 5a.

Fig. 5.

Differential analysis of functional data abundance in Cntrl-M and LL groups and functional data association with clinical parameters (clinical data) in LL group. a Differentially abundant metabolic pathways. Mean relative abundance of functional features (left), difference in mean proportions (right). MetaCyc functional annotation. STAMP software, 2-group analysis, FDR, p ≤ 0.05. b Correlation analysis. Association of functional and clinical data in the LL group. Spearman r, FDR, p ≤ 0.5. *p ≤ 0.5, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

Fig. 5.

Differential analysis of functional data abundance in Cntrl-M and LL groups and functional data association with clinical parameters (clinical data) in LL group. a Differentially abundant metabolic pathways. Mean relative abundance of functional features (left), difference in mean proportions (right). MetaCyc functional annotation. STAMP software, 2-group analysis, FDR, p ≤ 0.05. b Correlation analysis. Association of functional and clinical data in the LL group. Spearman r, FDR, p ≤ 0.5. *p ≤ 0.5, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

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Differentially abundant metabolic pathways involved in carbohydrate metabolism had a lower abundance in the intestinal microbiota of LL individuals. However, in the intestinal microbiota of nonagenerians and centenarians, we found a high activity of the hexitol fermentation process to lactate, formate, ethanol, and acetate, which can promote the transfer of carbohydrates into the bacterial cytoplasm. Notably, the metabolism of polysaccharides such as glycans in the gut microbiome has, meanwhile, the peptidoglycan maturation pathway, which controls the assembly of outer membrane proteins, has been proliferated. Moreover, LL individuals have amino acids metabolic pathways and amino acids showed lower abundance in most biosynthetic pathways. Differentially metabolic pathways associated with vitamin metabolism in LL groups demonstrated a significant enrichment of gene pathways for biotin biosynthesis. The metabolic pathways of nucleotide metabolism of the gut microbiome were significantly reduced with aging. However, the activity of gene pathways for the degradation of adenosine nucleotides and guanosine nucleotides in the microbiome was increased.

In addition to other genes, we also found age-related differences in metabolic pathways associated with lipid metabolism. The microbiome of group older than 90 years demonstrates a large contribution of genes involved in oxidation, elongation-saturation of fatty acids, and ethanolamine utilization. In the gene pathways for the biosynthesis of butyrate, stearate, palmitolate, oleate, and (5Z)-dodesanate, an increase in these metabolic processes in the intestinal microbial community of longevity was revealed. Correlation analysis between MetaCyc-based functional data and clinical laboratory parameters revealed a significant negative correlation of LPH with biotin biosynthesis, ethanolamine utilization, fatty acid and beta-oxidation, and guanosine nucleotides degradation (Fig. 5b).

Relationship of the Immune System across the Microbiome and Metabolic Pathways in Aging

Individual differences in the cytokine profile, intestinal microbial profile, and their pathways of function were revealed (Fig. 6-10, online suppl. Table S4). We analyzed the co-occurrence of bacterial genera in the gut microbiome of the Cntrl-M and CN groups (Fig. 6a, b). Impressively, in Cntrl-M, bacterial genera of the Firmicutes phylum positively correlated with other significant phyla in the gut; Faecalibacterium was significantly associated with Clostridium and the genera Escherichia, Gordonibacter. However, Ruminococcus was negatively correlated with Prevotella (r = 0.55; p = 0.032103) and Fusicatenibacter with Blautia (r = 0.59; p = 0.005714). The LL group microbiome showed negative associations with certain bacterial genera. Specifically, there was a negative correlation between Escherichia and Dorea (r = −0.57; p = 0.001993), as well as between Dorea and Akkermansia (r = −0.6; p = 0.001279). Similarly, Prevotella showed a negative association with both Erysipelatoclostridium (r = −0.63; p = 0.000231) and Ruthenibacterium (r = −0.52; p = 0.009196). Clostridium exhibited negativity with Fusicatenibacter (r = 0.65; p = 0.000155).

Fig. 6.

Network plots describing co-occurrence of bacterial genera in the gut microbiota of Cntrl-M and LL groups. a In the Cntrl-M group. b In the LL group; positive correlations are shown in red, negative correlations in blue. The size of the nodes is proportional to the abundance of the taxa. Spearman r, FDR, p ≤ 0.5, r ≥ 0.5. Only bacterial taxa present in at least 25% of the samples in each group were included in the analysis. Only significant correlations are shown.

Fig. 6.

Network plots describing co-occurrence of bacterial genera in the gut microbiota of Cntrl-M and LL groups. a In the Cntrl-M group. b In the LL group; positive correlations are shown in red, negative correlations in blue. The size of the nodes is proportional to the abundance of the taxa. Spearman r, FDR, p ≤ 0.5, r ≥ 0.5. Only bacterial taxa present in at least 25% of the samples in each group were included in the analysis. Only significant correlations are shown.

Close modal
Fig. 7.

Correlation analysis of serum (general immunity), stool (intestinal immunity) cytokine levels, functional data (metabolic pathway), and taxonomic features abundance in Cntrl-M and long-lived (LL) groups. a Between functional and taxonomic data in Cntrl-M (left) and LL (right) groups. b Between functional data and general immunity in LL group. c Between functional data and intestinal immunity in LL group. Positive correlations are shown in red, negative correlations in blue. Spearman r, FDR, p ≤ 0.5. p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

Fig. 7.

Correlation analysis of serum (general immunity), stool (intestinal immunity) cytokine levels, functional data (metabolic pathway), and taxonomic features abundance in Cntrl-M and long-lived (LL) groups. a Between functional and taxonomic data in Cntrl-M (left) and LL (right) groups. b Between functional data and general immunity in LL group. c Between functional data and intestinal immunity in LL group. Positive correlations are shown in red, negative correlations in blue. Spearman r, FDR, p ≤ 0.5. p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

Close modal
Fig. 8.

Correlation analysis between bacterial genera and serum (general immunity), stool (intestinal immunity) cytokine levels. a, b Between genera and general immunity. c, d (in blue) Correlation heatmaps (left), corresponding to the heatmaps correlation network plots (right). Spearman r, FDR, p ≤ 0.5. p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

Fig. 8.

Correlation analysis between bacterial genera and serum (general immunity), stool (intestinal immunity) cytokine levels. a, b Between genera and general immunity. c, d (in blue) Correlation heatmaps (left), corresponding to the heatmaps correlation network plots (right). Spearman r, FDR, p ≤ 0.5. p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

Close modal
Fig. 9.

Sankey flow diagrams illustrating the co-association of taxonomic and functional data abundance, serum (general) and stool (intestinal) cytokine levels in the long-lived (LL) group. Positive correlations are shown in red, negative correlations in blue. Only significant correlations are shown. Spearman r, FDR, left r > 0.4, right r > 0, p ≤ 0.5. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

Fig. 9.

Sankey flow diagrams illustrating the co-association of taxonomic and functional data abundance, serum (general) and stool (intestinal) cytokine levels in the long-lived (LL) group. Positive correlations are shown in red, negative correlations in blue. Only significant correlations are shown. Spearman r, FDR, left r > 0.4, right r > 0, p ≤ 0.5. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

Close modal
Fig. 10.

Network plot summarizing all significant correlations identified between functional, taxonomic data, cytokine levels, and clinical data in the LL group. Positive correlations are shown in red; negative correlations are shown in blue. The size of the nodes is proportional to the relative abundance of the feature. Only significant correlations are shown. Spearman r, FDR, p ≤ 0.5, r ≥ 0.3.

Fig. 10.

Network plot summarizing all significant correlations identified between functional, taxonomic data, cytokine levels, and clinical data in the LL group. Positive correlations are shown in red; negative correlations are shown in blue. The size of the nodes is proportional to the relative abundance of the feature. Only significant correlations are shown. Spearman r, FDR, p ≤ 0.5, r ≥ 0.3.

Close modal

The correlation analysis was conducted between the key genera of the intestinal microbiome, which showed significant differences between the groups, and the MetaCyc pathways of functioning (Fig. 7a). Specifically, the abundance of the Escherichia genus in LL individuals displayed a significant positive correlation with the P461-PWY and SALVADEHYPOX-PWY pathways in both groups. Additionally, in LL groups, Escherichia positively correlated with the PWY-6608 pathway. However, it was negatively correlated with S-adenosyl-L-methionine salvage and PWY-7357. In the control group, the biotin biosynthesis and PWY-6519 metabolic pathways were positively correlated with the genera Alistipes, Bacteroides, and Parabacteroides whereas, in the LL group, these pathways were positively associated with Escherichia but negatively correlated with the genera Eubacterium, Dorea, and Roseburia. Interestingly, in the longevity cohort, the genera Clostridium, Roseburia, Oscillibacter, and Eubacterium were negatively correlated with multiple metabolic pathways. In turn, a positive association was found between the genus Dorea and PWY-7357. In addition, the abundance of Fusicatenibacter was positively correlated with the PWY-7357, PWY-6151, and glycogen biosynthesis pathways.

It is well known that the immune system has the ability to recognize and respond to small molecules produced by gut microbes. Interestingly, these microbial functional pathways are often regulated by multiple microorganisms. We conducted an analysis to examine the relationship between cytokine production, which represents the general and intestinal immune responses, and the MetaCyc metabolic pathways. This analysis is illustrated in Figure 7b, c. Serum pro-inflammatory cytokines TNF-alpha, IL-1alpha, and GM-CSF were positively correlated with adenosine and guanosine nucleotide breakdown, and IL-1α was correlated with L-valine biosynthesis. GM-CSF was negatively associated with S-adenosyl-L-methionine salvage I. In addition, we investigated the relationship between bacterial metabolic pathways and immune cytokines in circulating feces. We found a positive correlation of these cytokines with MDC expression and the metabolic pathway of glycogen synthesis (GLYCOGENSYNTH-PWY). Furthermore, a positive relationship was found between IL-15 production and the S-adenosyl-L-methionine biosynthesis pathway, as well as myo-, chiro-, and scyllo-inositol degradation. Simultaneously, a negative correlation was found between the level of MCP-3 and the biosynthesis of 8-amino-7-oxononanoate and vitamin B5.

Analysis of the serum cytokine profile showed (Fig. 9, 10) that the abundance of Escherichia positively correlates with most of the studied cytokines indicated in Figure 10a, revealing a significant correlation with the level of FLT-3L, EGF, IL-4, and TNFα. In addition, we found that Odoribacter enrichment was positively associated with IL-15, IL-1α, IFNα2 levels and negatively correlated with EGF. Parabacteroides showed a positive association with IFNα2 levels and a negative association with IL-15 and MIP-1β levels. A negative correlation was found between Alistipes and IL-4. Between Bacteroides and IL-15, MIP-1β showed a negative correlation but was positively associated with IL-1RA.

Nine important gut microbiome genera showed correlations with significant local immune status cytokines, as depicted in Figure 9and 10. As illustrated in Figure 9, an increase in Odoribacter, Eubacterium, Oscillibacter, Bacteroides, and Alistipes genera was positively associated with all significant stool immune markers. Conversely, both Clostridium and Ruthenibacterium were negatively correlated with the cytokines IL-15, VEGF-A, MDC, IL-6, and IL-4. Additionally, Clostridium showed a negative correlation with TNFα and IP-10, while Ruthenibacterium exhibited a negative association with MCP-3. These findings highlight the important role of these bacterial genera in modulating the immune response during aging.

The balance between the flexibility and stability of the gut microbiota and the balance between pro- and anti-inflammatory activity may be an indication of successful aging. With aging, there is a chronic inflammatory condition associated with continuous exposure to antigens, which affects the commensal bacteria that are beneficial to the body and causes changes in the intestinal microbial community [9, 17, 18]. The changes in the intestinal microbiome are also linked to changes in the immune status and play a role in healthy aging [19].

The study suggests that the abundance of certain genera, such as Odoribacter, Eubacterium, Oscillibacter, Bacteroides, and Alistipes, is positively associated with significant stool cytokines patterns. In contrast, genera like Clostridium and Ruthenibacterium exhibit negative associations with several cytokines, such as IL-15, VEGF-A, MDC, IL-6, IL-4, TNFα, IP-10, and MCP-3. These findings imply that the presence or abundance of specific gut microbiome genera can impact the immune response during the aging process. Some genera seem to have a beneficial effect on the immune system as their increase is correlated with positive immune markers. It is important to acknowledge that the interactions between bacterial genera and immune markers can vary among individuals due to the complexity and dynamism of the human gut microbiome. Nevertheless, these findings contribute to our understanding of how the gut microbiome influences immune responses in aging.

The study revealed significant changes in the taxonomic composition of the stool microbiome in centenarians. Compared to younger individuals, LL individuals had a higher ratio of Bacteroidetes to Proteobacteria and showed a depletion of certain genera such as Ruminococcaceae, Fusicatenibacter, Dorea, and the species Fusicatenibacter saccharivorans. Conversely, there was an enrichment in the abundance of genera like Bacteroides, Clostridium, and Escherichia [20‒23]. Wu et al. [24] achieved similar findings in their study, observing shifts in microbiome diversity among LL individuals. These shifts were marked by fluctuations in both advantageous and detrimental bacterial strains.

Bacteroides abundance in the microbiome has been linked to dietary habits, particularly excessive consumption of proteins and fats [15]. Interestingly, Bacteroides showed a negative correlation with serum IL-15 production and a positive correlation with IL-6 expression.

The study also found a high abundance of the genus Clostridium, which negatively correlated with the synthesis of essential amino acids, short-chain fatty acids, and pyrimidine nucleotides. The longevity microbiome exhibited alterations in amino acid metabolism, including a decrease in the biosynthesis of several branched-chain amino acids, arginine, and ornithine. This coincided with an increase in proteolytic functions as the microbiome degraded valine and isoleucine. Clostridium was observed to utilize amino acids for anaerobic growth in the absence of glucose [25].

In aging LL individuals, the microbiome loses its ability to produce essential amino acids, which may contribute to a decrease in overall body nutrition and potentially lead to sarcopenia [26]. Clostridium has both pro- and anti-inflammatory effects on the body, capable of exacerbating inflammation while also reducing inflammation and allergic diseases through positive interactions with intestinal epithelial cells and the immune system [27‒29].

On the other hand, Alistipes showed a negative correlation with the IL-4 cytokine and is known to have a protective effect in inflammatory bowel diseases, neurovascular disorders, and oncopathologies. However, it may have a negative role in chronic fatigue and depression [30]. In the longevity cohort, Alistipes exhibited a positive correlation with almost all studied intestinal cytokines and was significantly associated with growth factors and IFN-α. Nonetheless, it had a negative association with MDC and IL-15. It is important to highlight that the increased expression of IL-15 observed in the aging LL group can stimulate the proliferation of memory T-cells, which characterizes immunoaging [31].

Our research cohort exhibited a high abundance of Escherichia [20], which aligns with previous findings in centenarians from China [29] and Japan [32]. As observed in our study, Escherichia promotes the synthesis of biotin, a vital nutrient involved in various metabolic processes. Reduced biotin levels have been associated with several chronic diseases [33, 34]. Escherichia also facilitates the degradation of purine nucleotides [35], resulting in the release of ammonia and carbon dioxide. These byproducts can be utilized by Escherichia and other members of the gut microbiome for biotin synthesis [36]. Furthermore, the degradation of Escherichia purines has been linked to the expression of TNFα, an association more commonly observed in healthy individuals [37].

Notably, our study explored the cytokine profile of the gut in aging individuals, suggesting a pro-inflammatory phenotype. The positive correlation between intestinal cytokines MDC and IL-15 with blood IL-1a levels indicates chronic mucosal inflammation. Additionally, there was a negative correlation between intestinal IL-6 and blood GM-CSF, which may contribute to intestinal homeostasis by maintaining the integrity of the intestinal epithelium. The gut microbiome can influence the production and release of cytokines within the gut. Certain bacteria, such as Odoribacter, Eubacterium, Oscillibacter, Bacteroides and Alistipes, have been shown to modulate cytokine production through direct interactions. Furthermore, evidence suggests that specific dietary interventions, such as prebiotics and probiotics, can increase the production of anti-inflammatory cytokines while reducing the production of pro-inflammatory cytokines [38, 39]. Overall, our findings shed light on the relationship between the gut microbiome, cytokines, and inflammation in aging individuals, providing insights into potential interventions to promote intestinal health and immune balance.

The study has several limitations that must be considered. First, the sample size was relatively small, with only 46 long-lived individuals included in the study. Second, the study was conducted exclusively in central Kazakhstan, specifically in the cities of Astana and Karaganda. Third, the evaluation of the local immune status was based solely on the measurement of intestinal cytokines, which were only studied in LL individuals. In the fourth place, it is important to note that correlations do not inherently indicate causation. Lastly, comparative analyses were conducted using different control groups, which could potentially impact the accuracy and reliability of the results.

The study findings indicate that longevity displays an immune system imbalance characterized by increased pro-inflammatory cytokines and chemokines, along with higher levels of specific anti-inflammatory cytokines, which are associated with immunosenescence. Furthermore, notable differences were observed in the gut microbiota composition at the genus level between the two age-groups. Maintaining a balanced gut microbiota, with a harmonious interplay of flexibility and stability, as well as a proper equilibrium between pro- and anti-inflammatory activity, is crucial for successful aging. These findings provide valuable insights into the mechanisms of healthy aging and underscore the pivotal role of the gut microbiome.

The experiments and procedures were conducted in accordance with the Declaration of Helsinki of 1975 and were approved by the Ethics Committee of the National Laboratory Astana, Nazarbayev University (Astana, Kazakhstan) (No. 04-2020 from 08/27/2020), and written informed consent has been obtained from each subject.

Results presented in this paper have not been published previously in whole or part. The authors declare they have no conflicts of interest regarding the publication of this article.

This research was supported by the grants from the Ministry of Science and Higher Education of the Republic of Kazakhstan for financial support (AP09058099). This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. BR21882152). We are grateful to BIORENDER (biorender.com) for assisting in creating the graphic.

Laura Chulenbayeva conceived of the presented idea and developed the theory, supervised the project, and contributed to the analysis of the results and to the writing of the manuscript; Yulia Ganzhula and Zhanar Borykbay recruited participants; Zharkyn Jarmukhanov carried out the bioinformatic analysis; Madiyar Nurgaziyev, Nurislam Muhanbetzhanov, and Sanzhar Zhetkenev performed the extraction of DNA and preparation of DNA library; Ayaulym Nurgozhina performed the multiplex immunoassay; Shyngyz Sergazy contributed to the interpretation of the results; Viktor Tkachev and Saltanat Urazova aided in interpreting the results; Elizaveta Vinogradova performed the statistical analysis; and Almagul Kushugulova and Samat Kozhakhmetov conceived of the presented idea, developed the theory, and worked on the manuscript. All authors discussed the results and commented on the manuscript.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. The raw data of shotgun sequencing for this study can be found in the PRJNA973824 in NCBI. Supplementary data to this article can be found online at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA973824. Further inquiries can be directed to the corresponding author.

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