Changes in Intestinal flora is associated with chronic diseases

Abstract

BACKGROUND

The intestinal flora (IF) has been linked to risks of non-communicable diseases, especially various cancers, stroke, and Alzheimer’s disease. However, many uncertainties of these associations during different stages of growth, development, and aging still exist. Therefore, further in-depth explorations are warranted.

AIM

To explore the associations of the human IF with disease risks during different stages of growth, development, and aging to achieve more accurate and convincing conclusions.

METHODS

Cohort, cross-sectional, case-control, and Mendelian randomization studies published in the PubMed and Web of Science databases until December 31, 2023 were systematically reviewed to clarify the associations of the IF at the genus level with the risks of various non-communicable diseases, which were grouped in accordance with the 10^th revision of the International Classification of Diseases.

RESULTS

In total, 57 studies were included to quantitatively examine the influence of the IF on the risks of 30 non-communicable diseases during different stages of growth, development, and aging. Population studies and Mendelian randomization studies confirmed positive associations of the abundances of Bifidobacterium and Ruminococcus with multiple sclerosis.

CONCLUSION

These findings contribute to a deeper understanding of the roles of the IF and provide novel evidence for effective strategies for the prevention and treatment of non-communicable diseases. In the future, it will be necessary to explore a greater variety of research techniques to uncover the specific mechanisms by which gut microbiota trigger diseases and conduct in-depth studies on the temporal relationship between microbiota alterations and diseases, so as to clarify the causal relationship more accurately.

Key Words: Intestinal flora; Non-communicable diseases; Occurrence risk; Systematic review; Meta-analysis

Core Tip: The present study revealed significant associations between intestinal flora and numerous chronic non-communicable diseases, including cancer, cardiovascular disorders, and neurological conditions, along with the growth, development, and aging processes of the human body. These findings provide valuable insights into the pathogenic mechanisms of specific bacterial genera on the host, facilitating enhanced disease prevention and control strategies within the population and offering potential treatment options for related disorders.

INTRODUCTION

The human intestinal flora (IF) is composed of a large and complex community of microorganisms and related metabolic products in the gastrointestinal tract, as well as various potential genetic factors. An estimated 10 trillion individual bacteria, archaea, fungi, viruses, and parasites reside in the intestinal mucosa, which has an average surface area of about 200-300 m²[1]. Bacteria are relatively the most suitable microorganisms for growth in the intestinal microenvironment, accounting for up to 90% of the IF, among which Firmicutes, Bacteroidetes, Actinobacteria, Clostridium, Proteobacteria, Verrucobacteria, and Cyanobacteria are typically the seven most abundant genera[2]. The bacterial components of the IF, often classified as “beneficial”, “harmful”, or “neutral”, influence digestion, absorption, immune maturation, vitamin synthesis, development, aging, tumor susceptibility, blood regulation, and maintenance of physiological dynamics[3-5]. Disruption to the homeostasis of the IF has been linked to the onset of various diseases[6,7].

Epidemiological studies have suggested that the IF is closely associated with the occurrence and progression of various cancers[8,9], cardiovascular diseases[10,11], cerebrovascular disorders[12,13], and mental and psychological conditions[14,15]. However, the conclusions of previous studies are inconsistent owing to disparities in design, sample size, and population stratification[16-19]. Therefore, the aim of this systematic review and meta-analysis was to clarify the role of the IF in the risk of common non-communicable diseases during different stages of growth, development, and aging to provide effective strategies to maintain health.

MATERIALS AND METHODS

Search strategy

Relevant articles were retrieved from the PubMed and Web of Science databases published until December 31, 2023 to investigate the associations of the IF and the development of various diseases at different stages of birth, physical development, and aging using the terms “microbiome” OR “gut microbiome” OR “microbial community” OR “gastrointestinal microbiome” OR “gut flora” OR “gastrointestinal microbiota” OR “microflora” OR “gastric microbiome” OR “intestinal microbiota” OR “intestinal flora” AND “odds ratio” OR “hazard risk” OR “hazard ratio”.

Inclusion criteria

This analysis was limited to cohort, cross-sectional, case-control, and Mendelian randomization studies designed to evaluate the correlation of the IF with the risk of chronic non-communicable diseases at different stages of birth, physical development, and aging, and provided the odds ratio (OR) or hazard ratio (HR) with the 95% confidence interval (CI), or supplied relevant data to calculate the 95%CI. For multiple reports of the same or overlapping participants, the study with the largest sample size was included.

Statistical analysis

Cochran’s Q test was employed to evaluate across-study heterogeneity and the I² value was calculated to assess the degree of variance due to heterogeneity. The DerSimonian and Laird random effects model was used if heterogeneity was evident (Q test P value < 0.05); otherwise, the fixed effects model was applied. The pooled effect size and 95%CI were calculated using Comprehensive Meta-Analysis software (version 2.0; https://meta-analysis.com/). A forest plot was generated using R software (version 3.4.1; https://cran-archive.r-project.org/bin/windows/base/old/3.4.1/).

RESULTS

Literature selection

Among the 82 articles that were initially screened (Supplementary material), 14, 37, 18, and 13 were conducted in China, the United States, Japan, and Ghana and other countries, respectively. Of these, 57 articles met the selection criteria, as shown in Figure 1, and were included for analysis. The final analysis included 8, 12, 21, and 16 cross-sectional, cohort, case-control, and Mendelian randomization studies, respectively, collectively involving more than one million participants to assess the correlation of the IF with the incidences of various cancers[16,20-28], cardiovascular diseases[17,29-34], cerebrovascular diseases[35-37], neurological diseases[18,38-40], mental and behavioral disorders[41-43], digestive diseases[18,44-54], respiratory diseases[55], urinary diseases[56], endocrine, nutritional, and metabolic diseases[29,57-64], skin and subcutaneous tissue diseases[55,65-70], skeletal and muscular system diseases[71], and reproductive and fertility disorders[72-74] (Figures 2, 3, and 4).

Figure 1  The flow chart of the study selection.

Figure 2 Population studies combined forest plot. OR: Odds ratio; CI: Confidence interval.

Figure 3 Mendelian randomization studies combined forest plot. OR: Odds ratio; CI: Confidence interval.

Figure 4  Study of the association between intestinal flora and chronic noncommunicable diseases, growth and development characteristics.

Associations of the IF with cancers

Colorectal cancer: Five cross-sectional studies[16,20-23] reported positive correlations of colorectal cancer (CRC) with Fusobacterium (pooled OR = 5.19, 95%CI: 2.63-10.23), Atopobium (pooled OR = 14.36, 95%CI: 2.78-74.30), Incertae sedis (pooled OR = 2.38, 95%CI: 1.12-5.06), Porphyromonas (pooled OR = 5.17, 95%CI: 1.75-15.25), Megasphaera (pooled OR = 4.02, 95%CI: 1.03-15.70), Candida (pooled OR = 5.44, 95%CI: 1.45-20.46), and Bacteroides (pooled OR = 1.80, 95%CI: 1.06-3.04); a negative association with Ruminococcus (pooled OR = 0.14, 95%CI: 0.02-0.85) and uncertain associations with Escherichia (pooled OR = 1.25, 95%CI: 0.85-1.85), Peptostreptococcus (pooled OR = 4.98, 95%CI: 0.87-28.53), and Coprococcus (pooled OR = 0.33, 95%CI: 0.06-2.36).

Gastric cancer: One case-control study[24] reported that that Prevotella (pooled OR = 0.90, 95%CI: 0.85-0.95) was negatively associated with gastric cancer, while Streptococcus (pooled OR = 1.28, 95%CI: 1.00-1.63) and Gemella (pooled OR = 1.10, 95%CI: 0.75-1.62) were not significantly associated.

Breast cancer: Two observational studies[25,26] found that Bacteroides (pooled OR = 1.05, 95%CI: 1.03-1.06), Parabacteroides (pooled OR = 1.42, 95%CI: 1.14-1.77), and Lachnoclostridium (pooled OR = 1.15, 95%CI: 1.03-1.28) were positively associated with breast cancer, while Enterococcus (pooled OR = 0.60, 95%CI: 0.57-0.63), Coprococcus (pooled OR = 0.45, 95%CI: 0.33-0.60), Romboutsia (pooled OR = 0.09, 95%CI: 0.03-0.24), Collinsella (pooled OR = 0.32, 95%CI: 0.12-0.87), Alloprevotella (pooled OR = 0.95, 95%CI: 0.91-0.99), Lachnospira (pooled OR = 0.82, 95%CI: 0.68-0.98), Roseburia (pooled OR = 0.85, 95%CI: 0.77-0.94), Pseudobutyrivibrio (pooled OR = 0.89, 95%CI: 0.85-0.94), Dialister (pooled OR = 0.66, 95%CI: 0.56-0.78), Anaerotruncus (pooled OR = 0.53, 95%CI: 0.33-0.84), and Haemophilus (pooled OR = 0.54, 95%CI: 0.37-0.78) were negatively associated. A two-sample Mendelian randomization study indicated that increased abundances of Erysipelatoclostridium (pooled OR = 1.25, 95%CI: 1.10-1.41) and Sellimonas (pooled OR = 1.09, 95%CI: 1.04-1.14) were causally associated with breast cancer risk, while Adlercreutzia (pooled OR = 0.88, 95%CI: 0.81-0.95) and Ruminococcus_2 (pooled OR = 0.77, 95%CI: 0.67-0.89) were negatively associated[27].

Prostate cancer: A two-sample Mendelian randomization study suggested that the relative abundance of Eubacterium (pooled OR = 1.16, 95%CI: 1.01-1.34) was positively associated with the risk of prostate cancer, while Akkermansia (pooled OR = 0.79, 95%CI: 0.67-0.99) and Bacteroides (pooled OR = 0.90, 95%CI: 0.83-0.99) were negatively associated, whereas Alistipes (pooled OR = 1.17, 95%CI: 0.91-1.52), Prevotella (pooled OR = 0.82, 95%CI: 0.63-1.06), Roseburia (pooled OR = 0.90, 95%CI: 0.74-1.09), Veillonella (pooled OR = 1.07, 95%CI: 0.96-1.19), and Ruminococcus (pooled OR = 0.96, 95%CI: 0.87-1.05) were unrelated[28].

Associations of the IF with cardiovascular diseases

Arterial disease: Two case-control studies[30,31] found that the Enterobacteriaceae (pooled OR = 1.87, 95%CI: 1.19-2.92) may increase the risk of arterial disease, while Lactobacillus (pooled OR = 0.68, 95%CI: 0.49-0.95) may reduce this risk, and both Lachnospira (pooled OR = 0.54, 95%CI: 0.27-1.10) and Ruminococcus (pooled OR = 1.19, 95%CI: 0.63-2.25) were not related to disease risk. Two Mendelian randomization studies[29,32] reported that Oxalobacter (pooled OR = 1.06, 95%CI: 1.03-1.10) was positively correlated with arterial disease, whereas Bifidobacterium (pooled OR = 0.96 95%CI: 0.94-0.98) was negatively correlated and Lachnospira (pooled OR = 1.10, 95%CI: 1.00-1.20) was not correlated.

Hypertension: One case-control study[33] found that the relative abundances of Bifidobacterium (pooled OR = 0.90, 95%CI: 0.81-1.00), Bacteroides (pooled OR = 1.01, 95%CI: 0.99-1.03), Blautia (pooled OR = 0.97, 95%CI: 0.92-1.03), and Prevotella (pooled OR = 1.00, 95%CI: 0.97-1.03) were not associated with the risk of hypertensive disorders of pregnancy.

Heart failure: Two Mendelian randomization studies[17,34] reported that heart failure was associated with the abundances of Bacteroides (pooled OR = 1.06, 95%CI: 1.02-1.10) and Lachnospiraceae_noname (pooled OR = 1.09, 95%CI: 1.03-1.14), while Bilophila (pooled OR = 0.94, 95%CI: 0.89-1.00) may be a protective factor. However, Dorea (pooled OR = 1.11, 95%CI: 1.00-1.23), Alistipes (pooled OR = 1.06, 95%CI: 1.00-1.13), Eubacterium (pooled OR = 1.04, 95%CI: 1.00-1.09), Candida (pooled OR = 0.99, 95%CI: 0.97-1.02), Shigella (pooled OR = 0.99, 95%CI: 0.98-1.01), and Campylobacter (pooled OR = 1.01, 95%CI: 0.91-1.13) were not associated with heart failure.

Associations of the IF with cerebrovascular diseases

Stroke: Two Mendelian randomization studies[35,36] showed that Alistipes (pooled OR = 1.52, 95%CI: 1.04-2.24), Dorea (pooled OR = 1.53, 95%CI: 1.25-1.86), Eisenbergiella (pooled OR = 1.24, 95%CI: 1.05-1.48), Eubacterium_rectale_group (pooled OR = 1.46, 95%CI: 1.01-2.09), Fusicatenibacter (pooled OR = 1.42, 95%CI: 1.02-1.97), Gordonibacter (pooled OR = 1.36, 95%CI: 1.05-1.76), Lachnospiraceae_UCG_008 (pooled OR = 1.28, 95%CI: 1.01-1.62), Parabacteroides (pooled OR = 1.78, 95%CI: 1.15-2.75), Parasutterella (pooled OR = 1.27, 95%CI: 1.06-1.52), and Prevotella_7 (pooled OR = 1.23, 95%CI: 1.08-1.41) were positively correlated with stroke risk. In contrast, Butyrivibrio (pooled OR = 0.80, 95%CI: 0.65-0.99), Candidatus_Soleaferrea (pooled OR = 0.74, 95%CI: 0.59-0.92), Coprobacter (pooled OR = 0.71, 95%CI: 0.57-0.89), Eubacterium_hallii_group (pooled OR = 0.79, 95%CI: 0.65-0.96), FamilyXIIIAD3011 (pooled OR = 0.73, 95%CI: 0.55-0.97), Intestinimonas (pooled OR = 0.83, 95%CI: 0.73-0.94), Lachnospiraceae_NK4A136_group (pooled OR = 0.78, 95%CI: 0.67-0.91), Methanobrevibacter (pooled OR = 0.70, 95%CI: 0.54-0.91), Rikenellaceae_RC9_gut_group (pooled OR = 0.81, 95%CI: 0.67-0.99), and Tyzzerella_3 (pooled OR = 0.81, 95%CI: 0.68-0.97) were negatively associated, whereas Eubacterium_xylanophilum_group (pooled OR = 1.26, 95%CI: 0.91-1.74). Finally, Lachnospiraceae_ND_3007 (pooled OR = 0.96, 95%CI: 0.61-1.51), and Ruminococcaceae_UCG_009 (pooled OR = 0.94, 95%CI: 0.80-1.10) were not associated with an increased risk of stroke.

Moyamoya disease: One case-control study[37] reported that Ruminococcus was highly associated with an increased incidence of moyamoya disease (OR = 16.76, 95%CI: 2.73-102.84).

Associations of the IF with neurological diseases

Multiple sclerosis: A matched case-control study[75] found that Bilophila (pooled OR = 3.00, 95%CI: 2.90-3.20), Bifidobacterium (pooled OR = 4.20, 95%CI: 3.90-4.50), Desulfovibrio (pooled OR = 5.10, 95%CI: 4.70-5.70), Prevotella_copri (pooled OR = 5.00, 95%CI: 4.40-5.60), Oscillospira (pooled OR = 2.30, 95%CI: 2.20-2.50), and Ruminococcus (pooled OR = 4.30, 95%CI: 3.60-5.10) were all positively associated with the risk of multiple sclerosis. By contrast, Lachnospira (pooled OR = 0.17, 95%CI: 0.16-0.19), Paraprevotella (pooled OR = 0.15, 95%CI: 0.14-0.17), Peptostreptococcus (pooled OR = 0.01, 95%CI: 0.01-0.02), Butyricimonas (pooled OR = 0.19, 95%CI: 0.16-0.22), Parabacteroides (pooled OR = 0.08, 95%CI: 0.07-0.10), and Mitsuokella (pooled OR = 0.10, 95%CI: 0.08-0.13) were all negatively associated. One Mendelian randomization study revealed that higher relative abundances of Bifidobacterium and Ruminococcus were associated with an increased risk of multiple sclerosis by 1.38 fold (pooled OR = 1.38, 95%CI: 1.13-1.70) and 2.89 fold (pooled OR = 2.89, 95%CI: 1.67-5.00), respectively[18].

Alzheimer’s disease: One cross-sectional study[39] reported that Escherichia was positively associated with the risk of Alzheimer’s disease (pooled OR = 5.17, 95%CI: 4.08-6.54).

Parkinson’s disease: One two-sample bidirectional Mendelian randomization study[40] found that Candidatus_Soleaferrea (pooled OR = 1.32, 95%CI: 1.04-1.68), Clostridium_sensustricto_1 (pooled OR = 1.38, 95%CI: 1.07-1.78), Eubacterium_hallii_group (pooled OR = 1.29, 95%CI: 1.03-1.62), Lachnospiraceae_UCG_010 (pooled OR = 1.35, 95%CI: 1.01-1.81), Ruminococcaceae_UCG_002 (pooled OR = 1.34, 95%CI: 1.03-1.75), and Senegalimassilia (pooled OR = 1.58, 95%CI: 1.17-2.13) were all positively correlated with Parkinson’s disease, while Bifidobacterium (pooled OR = 0.77, 95%CI: 0.60-0.99) was negatively correlated, whereas Erysipelato_clostridium (pooled OR = 1.11, 95%CI: 0.84-1.47) and Roseburia (pooled OR = 1.08, 95%CI: 0.78-1.50) were not associated with a markedly increased risk.

Associations of the IF with mental and behavioral disorders

Autism spectrum disorder: One case-control study[41] found that Candida albicans was associate with an increased incidence of autism spectrum disorder by about 3.45 fold (pooled OR = 3.45, 95%CI: 1.04-11.47).

Bipolar disorder: One case-control study[42] reported that the relative abundance of Flavonifractor was greater in patients with bipolar disorder as compared to a healthy population (pooled OR = 2.90, 95%CI: 1.60-5.20).

Depression: One case-control study[43] demonstrated a possible association of Bifidobacterium (pooled OR = 3.23, 95%CI: 1.38-7.54) and Lactobacillus (pooled OR = 2.57, 95%CI: 1.14-5.78) with the risk of major depression.

Associations of the IF with digestive diseases

Inflammatory bowel disease: One two-sample Mendelian randomization study[18] showed that Bifidobacterium (pooled OR = 1.18, 95%CI: 1.04-1.35) and Ruminococcus (pooled OR = 2.14, 95%CI: 1.43-3.22) were positively correlated with inflammatory bowel disease.

Necrotizing enterocolitis: One case-control study[44] demonstrated that Clostridium could significantly increase the risk of necrotizing enterocolitis (pooled OR = 45.40, 95%CI: 26.20-78.60).

Irritable bowel syndrome: A large cohort study[45] reported that Blautia was positively correlated with irritable bowel syndrome (IBS) (pooled OR = 1.43, 95%CI: 1.21-1.69). One Mendelian randomization study[46] showed that Eubacterium_hallii_group (pooled OR = 1.08, 95%CI: 1.02-1.15), Flavonifractor (pooled OR = 1.10, 95%CI: 1.03-1.18), and Prevotella_9 (pooled OR = 1.06, 95%CI: 1.02-1.11) were positively correlated with IBS, while Coprococcus_1 (pooled OR = 1.07, 95%CI: 1.00-1.14), Eisenbergiella (pooled OR = 0.95, 95%CI: 0.91-1.00), and Ruminiclostridium_6 (pooled OR = 1.06, 95%CI: 1.00-1.12) were not significantly associated.

Nonalcoholic fatty liver disease: Five studies[47-51] found a positive correlation between Helicobacter pylori (H. pylori) and nonalcoholic fatty liver disease (pooled OR = 1.18, 95%CI: 1.09-1.28).

Gallstone: Twenty-four articles reported a positive association of Escherichia with gallstone formation (pooled OR = 2.48, 95%CI: 1.92-3.20)[52]. Helicobacter was not correlated with gallstones (pooled OR = 0.93, 95%CI: 0.48-1.82)[52]. One Mendelian randomization study[53] indicated that Butyrivibrio (pooled OR = 1.00, 95%CI: 1.00-1.00), Lachnospiraceae_UCG_001 (pooled OR = 1.00, 95%CI: 1.00-1.01), Ruminococcaceae_NK4A214_group (pooled OR = 1.01, 95%CI: 1.00-1.01), Ruminococcaceae_UCG_011 (pooled OR = 1.00, 95%CI: 1.00-1.01), Actinomyces (pooled OR = 1.00, 95%CI: 0.99-1.00), Phascolarctobacterium (pooled OR = 1.00, 95%CI: 0.99-1.00), Rikenellaceae_RC9_gut_group (pooled OR = 1.00, 95%CI: 1.00-1.00), and Ruminococcaceae_UCG_013 (pooled OR = 1.00, 95%CI: 0.99-1.00) were correlated with gallstone formation.

Associations of the IF with respiratory and urinary diseases

Asthma: One cohort study[55] found a negative correlation of Escherichia with the risk of asthma (pooled OR = 0.41, 95%CI: 0.22-0.80).

Overactive bladder disorder: One three-year longitudinal study[56] demonstrated that Streptococcus was an independent risk factor for overactive bladder disorder (pooled OR = 1.05, 95%CI: 1.01-1.11).

Associations of the IF with endocrine, nutritional, and metabolic diseases

Obesity: Two observational studies determine whether changes in the gut microbiota are associated with obesity[57,58]. Lactobacillus was positively correlated with obesity (pooled OR = 1.79, 95%CI: 1.03-3.10), Akkermansia (pooled OR = 0.78, 95%CI: 0.63-0.95) and Methanobrevibacter were negatively correlated with obesity (pooled OR = 0.76, 95%CI: 0.59-0.97), while Bifidobacterium was not statistically correlated with obesity (pooled OR = 0.63, 95%CI: 0.39-1.01). A Mendelian randomization analysis found that Bifidobacterium (pooled OR = 0.99, 95%CI: 0.98-1.00) and Faecalibacterium (pooled OR = 0.99, 95%CI: 0.98-1.00) were not correlated with obesity[29].

Type 2 diabetes mellitus: Five observational studies[59-61,63,64] indicated that Flavonifractor (pooled OR = 1.22, 95%CI: 1.08-1.38), Tyzzerella (pooled HR = 1.17, 95%CI: 1.01-1.36), and Ruminococcus (pooled HR = 1.17, 95%CI: 1.01-1.36) were positively correlated with the risk of type 2 diabetes mellitus (T2DM), while Intestinibacter (pooled OR = 0.60, 95%CI: 0.48-0.76), Romboutsia (pooled OR = 0.55, 95%CI: 0.44-0.70), Coprococcus (pooled OR = 0.91, 95%CI: 0.85-0.97), Odoribacter (pooled OR = 0.94, 95%CI: 0.90-0.99), Blautia (pooled OR = 0.45, 95%CI: 0.24-0.86), and Roseburia (pooled OR = 0.54, 95%CI: 0.30-0.96) were negatively associated. However, Clostridium (pooled OR = 0.79, 95%CI: 0.34-1.83), Alistipes (pooled OR = 0.94, 95%CI: 0.88-1.00), Anaerostipes (pooled OR = 0.97, 95%CI: 0.89-1.07), Bifidobacterium (pooled OR = 1.20, 95%CI: 0.58-2.47), Oscillibacter (pooled OR = 0.95, 95%CI: 0.90-1.00), Pseudoflavonifractor (pooled OR = 0.99, 95%CI: 0.91-1.08), and Streptococcus (pooled OR = 1.47, 95%CI: 0.93-2.32) were not statistically associated with the risk of T2DM. Two Mendelian randomization studies[29,64] found that Streptococcaceae (pooled OR = 1.17, 95%CI: 1.04-1.31) and Acidaminococcaceae (pooled OR = 1.17, 95%CI: 1.04-1.31) were both positively associated with T2DM, but not Anaerostipes (pooled OR = 0.96, 95%CI: 0.93-1.00)[29,64].

Associations of the IF with skin and subcutaneous tissue diseases

Atopic dermatitis and eczema: Five studies[55,65-68] concurred that Clostridium was positively correlated with atopic dermatitis (pooled OR = 1.73, 95%CI: 1.28-2.34), while Bifidobacterium (pooled OR = 0.88, 95%CI: 0.59-1.31), Escherichia (pooled OR = 0.84, 95%CI: 0.51-1.39), Bacteroides (pooled OR = 1.16, 95%CI: 0.77-1.75), and Lactobacillus (pooled OR = 0.90, 95%CI: 0.52-1.55) were not. Meanwhile, Escherichia (pooled OR = 1.87, 95%CI: 1.15-3.04) and Clostridium (pooled OR = 1.40, 95%CI: 1.02-1.91) were positively correlated with eczema, while Lachnospira was negatively correlated (pooled OR = 0.88, 95%CI: 0.79-0.96), whereas Bifidobacterium (pooled OR = 1.02, 95%CI: 0.77-1.35), Bacteroides (pooled OR = 1.02, 95%CI: 0.71-1.47), and Lactobacillus (pooled OR = 1.23, 95%CI: 0.91-1.65) were not statistically correlated.

Psoriasis and psoriatic arthritis: Two Mendelian randomization studies[69,70] demonstrated that Eusicatena_fissicatena_group was correlated with an increased risk of psoriasis (pooled OR = 1.22, 95%CI: 1.10-1.35), but not Prevotella_9 (pooled OR = 0.87, 95%CI: 0.76-1.00). In addition, Blautia (pooled OR = 1.41, 95%CI: 1.14-1.75), Methanobrevibacter (pooled OR = 1.29, 95%CI: 1.11-1.50), and Eubacterium_fissicatena_group (pooled OR = 1.24, 95%CI: 1.10-1.40) were positively correlated psoriatic arthritis, while Butyricicoccus (pooled OR = 0.67, 95%CI: 0.51-0.87) and Ruminococcaceae_UCG_002 (pooled OR = 0.80, 95%CI: 0.69-0.93) were negatively correlated. Meanwhile, Christensenella (pooled OR = 0.85, 95%CI: 0.54-1.34), Defluviitalea (pooled OR = 1.31, 95%CI: 0.98-1.77), Family_XIII_AD3011_group (pooled OR = 1.32, 95%CI: 0.94-1.84), Fusicatenibacter (pooled OR = 1.25, 95%CI: 0.98-1.61), and Oscillospira (pooled OR = 0.80, 95%CI: 0.55-1.16) were not associated with psoriatic arthritis.

Associations of the IF with musculoskeletal system diseases

One cross-sectional study[71] reported that Bacteroides was associated with an increased risk of fracture by about 10.69 fold (pooled OR = 10.69, 95%CI: 1.88-60.93), while Blautia (pooled OR = 2.13, 95%CI: 0.46-9.84), Oscillospira (pooled OR = 1.57, 95%CI: 0.34-10.72), Ruminococcus (pooled OR = 1.40, 95%CI: 0.33-5.93), Bifidobacterium (pooled OR = 1.03, 95%CI: 0.24-4.41), and Sutterella (pooled OR = 2.48, 95%CI: 0.54-11.40) were not significantly associated with fracture.

Associations of the IF with reproduction and fertility

Development: One cohort study[72] showed that the Bacteroide-dominated enterotype was correlated with later menarche and voice break (pooled OR = 0.53, 95%CI: 0.28-0.98).

Reproduction: One cohort study[73] found that a greater abundance of Actinomyces was positively associated with spontaneous preterm birth (pooled OR = 3.27, 95%CI: 1.35-7.95).

Aging: One cohort study[74] demonstrated that Bacteroides was not associated with the risk of death of community-dwelling participants at any age (pooled HR = 1.28, 95%CI: 1.00-1.65).

DISCUSSION

Summary

The current meta-analysis included more than 57 articles to assess the impact of the IF on the risks of 30 non-communicable disease during different stages of growth, development, and aging. Population and Mendelian randomization studies simultaneously confirmed positive associations of Bifidobacterium and Ruminococcus with multiple sclerosis. These findings provide more effective strategies for disease control and promote healthy growth and development.

Mechanisms

Fusobacterium nucleatum (F. nucleatum) has been implicated in the pathogenesis of CRC by stimulating growth of CRC cells via increased expression of the transcription factor nuclear factor-kappaB and various oncogenes, such as c-Myc and cyclin D1, by binding fadA and E-cadherin[76]. The proliferation of CRC cells can also be increased by up-regulating expression of microRNA-21[76]. F. nucleatum can also down-regulate T cells and natural killer cells to inhibit cellular anti-tumor immunity[76]. In addition, F. nucleatum can induce the production of pro-inflammatory cytokines and form a pro-inflammatory microenvironment, which can influence the efficacy of anti-tumor chemotherapy drugs and treatment outcomes[76]. Escherichia coli (E. coli) can promote oncogenesis by inducing the mucosal antibody response of immunoglobulin A and/or the systemic antibody response of immunoglobulin G[23]. The carcinogenic potential of H. pylori is realized primarily through the virulence factors cytotoxin-associated gene A and vacuolating cytotoxin A, which inhibit expression of immune and pro-inflammatory cytokines[24]. In addition, lymphocytic gastritis caused by Propionibacterium acnes may enhance the development of gastric cancer by increased production of pro-inflammatory cytokines, such as interleukin (IL)-15[24]. On the contrary, Lactobacillus lactis can cause cell cycle arrest at the G0/G1 phase, thereby playing an anti-tumor role by inducing apoptosis[24]. For breast cancer, the IF plays an important role in regulating systemic estrogen[77]. Bacteria with estrogen-decoupling enzyme activity, characterized by the presence of the β-glucuronidase or β-galactosidase genes, have enzyme activity that promotes the reabsorption of unbound estrogen into the circulation, thereby increasing the estrogen load, which promotes the occurrence and development of breast cancer[77]. Members of the genus Ruminococcus produce butyrate, which can promote fat deposition and subsequently accelerate the growth of prostate tumors by inducing inflammation[78]. In addition, alterations to lipid metabolism, especially excessive accumulation of cholesterol and fatty acids, promote the malignant transformation of prostate cancer through the formation of cholesterol esters[78,79]. Akkermansia_muciniphila and Amuc_1100 may reduce the risk of prostate cancer by improving inflammation and regulating glucose and lipid metabolism[28].

Arterial and cerebrovascular diseases are mostly due to the breakdown of Proteobacteria in dietary metabolism to produce trimethylamine-N-oxide, which promotes oxidative stress, inflammatory responses, and the formation of atherosclerotic plaques[80]. An imbalance to the IF will cause intestinal barrier dysfunction, increase the proportions of harmful bacteria, as well as concentrations of hydrogen sulfide and lipopolysaccharides, while decreasing the abundances of beneficial bacteria, contents of short-chain fatty acids (SCFAs), and formation of intestinal tight junction proteins, thereby increasing intestinal permeability, which will lead to inflammatory responses, resulting in endothelial dysfunction and subsequent hypertension[81]. The intestinal hypothesis suggests that damage to the intestinal barrier may increase intestinal permeability and promote microbial translocation, resulting in the entry of microorganisms and related metabolites into the blood circulation, triggering low-grade chronic inflammation, ultimately inducing heart failure[81].

The gut-brain axis is thought to be a bidirectional biochemical signaling pathway between the gut and brain[15]. Disturbances to the IF can alter brain function by changing the levels of neuroactive compounds, increasing the levels of pro-inflammatory cytokines, and altering microbial metabolism[82]. Changes to the IF of multiple sclerosis patients may be associated with changes to the gene expression profiles of circulating T cells and monocytes involved in dendritic cell maturation, interferon signaling, and the nuclear factor-kappaB signaling pathway[83]. Alzheimer’s disease is characterized by decreased abundances of protective microbiota (e.g., Bacteroides, Spirillum, and Ruminococcus) and an increased proportion of Prevotella, which promotes inflammation[84]. Patients with depressive episodes generally have decreased proportions of bacteria that produce SCFAs and increased proportions of pro-inflammatory bacteria that are involved in lipid metabolism[85].

Many studies have shown that disruption to the IF is the main environmental driver of inflammatory bowel disease. A decrease in the number of butyrate-producing bacteria results in decreased activity of peroxisome-enhanced activator receptor γ and the agonist level/activity of the nuclear transcription factor aromatic receptor (AhR), increased glycolysis, decreased oxygen consumption, and increased production of carbon monoxide, leading to increased abundances of pathogenic facultative anaerobic bacteria (e.g., Enterobacteriaceae and Proteobacteria)[1]. Inflammatory bowel disease is characterized by reduced function of regulatory T cells, increased inflammation, and dysregulation of the intestinal barrier and detoxification function[1]. Several animal models have shown that Clostridium_butyricum can reproduce NEC-like lesions, which have significant cytotoxicity and cell adhesion, resulting in increased oxidative stress and immunosuppression[44]. An elevated Firmicutes/Bacteroidetes ratio in subgroups of patients with IBS are associated with changes in epithelial permeability and low-grade inflammation, which are considered possible mechanisms of IBS[44]. In addition, changes to the bidirectional interaction of the brain-gut axis play an important role in the pathophysiology of IBS[86]. H. pylori infection is associated with chronic inflammation, insulin resistance, diabetes, dyslipidemia, and metabolic syndrome, which are also risk factors for nonalcoholic fatty liver disease[47]. In addition, H. pylori infection can alter lipid metabolism, resulting in increased triacylglycerol accumulation in the liver[48], as well as local infiltration of lymphocytes and granulocytes by releasing inflammatory factors, such as IL-6, IL-1, and tumor necrosis factor-α, thereby inducing inflammation and exacerbating liver damage[47].

For asthma, lipopolysaccharides of Gram-negative bacteria, such as E. coli, activate signaling of toll-like receptor (TLR)-4 and cytokine production mediated by the myeloid differentiation primary response 88 protein[55]. Persistent exposure to lipopolysaccharides leads to tolerance and can cause downregulation of the TLR-4 signaling pathway[55]. Thus, colonization of the neonatal intestine by E. coli may lead to downregulation of TLR-4 signaling and remission of inflammatory responses, thereby reducing the risk of respiration-related inflammation[55]. An overactive bladder is closely associated with IBS and explained by crosstalk between the bladder and gastrointestinal tract via parasympathetic and sympathetic nerves[56]. Since IBS is thought to be closely related to nerve substances produced by the IF, dysregulation of the IF may also affect bladder function through convergent sensory pathways among the peripheral intestine, bladder, and spine[56].

The mechanisms of the IF leading to metabolic and inflammatory diseases, such as obesity and T2DM, may include decreased abundances of SCFA-producing bacteria, decreased activity of peroxisome proliferator-activated receptor gamma, increased glycolysis, increased proportions of pathogenic facultative anaerobic bacteria and anaerobic bacteria, decreased T cell function, and increased inflammatory responses[80]. Decreased levels/activities of AhR decreases intestinal barrier function and increases the abundance of harmful bacteria[80]. Endotoxins produced by Gram-negative bacteria can activate inflammation via specific pattern recognition receptors, such as TLR2 and TLR4[80]. Decreased activity of the myeloid differentiation primary response 88 protein results in decreased production of antimicrobial peptides and T cell activities, which can result in metabolic disorders[80]. Changes to the endocannabinoid system signaling pathway increases production of arachidonic acid glycolamide, fluctuations in intestinal permeability, and increased lipogenesis, which can destroy the intestinal barrier[80]. In addition, the disturbance to the IF acts on the enteric nervous system, altering the brain-gut axis and promoting glucose absorption, leading to hyperglycemia[80].

In recent years, the concept of the “gut-skin axis” has been gradually expanded. Disturbance to the IF can affect the gut-skin axis, resulting in decreased AhR, impaired differentiation and repair of the epidermis, and vulnerability to skin infections and barrier damage[80]. In psoriatic arthritis, harmful bacteria can reach the joints through the circulation or intestinal lymphatic system[70]. Dysregulation of the IF and gut inflammation may lead to reduced production of SCFAs, upregulation of the IL-23/Th17 axis and tumor necrosis factor-α, leading to inflammation, thereby increasing the risk of psoriasis[87,88]. Low blood vitamin K concentrations have been linked to higher incidences of femoral, cervical, and vertebral compression fractures in older women[71]. Notably, the abundance of Bacteroides was significantly correlated to vitamin K levels, which may explain the influence of Bacteroides on the occurrence of fractures[71].

The composition of the IF changes with age. Prevotella and Staphylococcus can cause inflammation, while Bacteroides produce SCFAs with anti-inflammatory properties[72]. Inflammation can alter the function of neurons via gonadotropin-releasing hormone, ultimately altering the course of pubertal development[72]. For preterm mothers, low IF diversity may be one of the root causes of inflammation, as an increased abundance of Actinomycetes has been linked to intense inflammation and energy loss[73]. Spontaneous preterm birth has been attributed to the higher tissue inflammatory status of obese women[89]. The microbiome of healthy older adults is characterized by the depletion of core genera found in most humans and an increase in rare taxa capable of synthesizing bioactive microbial metabolites[74]. There is an increasing burden of intestinal heterogenic metabolites in aging hosts, such as mildly toxic phenylalanine/tyrosine microbial fermentation products[90].

Limitations

This study has illuminated the connections between gut microbiota and disease risks, but it is not without limitations. Firstly, common confounding factors such as diet, lifestyle, and medication use, which can influence the relationship between gut microbiota and disease risk, were not fully accounted for in previous studies due to inadequate data acquisition. Secondly, inherent limitations such as small sample sizes, flawed study designs, and suboptimal microbiological analysis techniques have led to significant heterogeneity among studies. Consequently, the findings should be interpreted with caution. Thirdly, the inherent limitations of observational studies affect the establishment of causal temporal relationships. Future research should include a multitude of prospective cohort studies to further elucidate the link between gut microbiota and chronic disease risk. Additionally, while most current studies have examined the correlation between IF and disease risk, there is a notable absence of discussion regarding the underlying biological mechanisms. Future investigations should incorporate more animal and in vitro experiments to explore the biological mechanisms by which IF may treat diseases.

CONCLUSION

The present study revealed significant associations of the IF and numerous chronic non-communicable diseases, including various cancers, cardiovascular disorders, and neurological conditions during different stages of growth, development, and aging. These findings provide valuable insights into the pathogenic mechanisms of specific bacterial genera on the host, facilitating enhanced disease prevention and control strategies for related disorders. Nevertheless, in the future, more standardized analytical techniques need to be explored to discover the specific mechanisms by which gut microbiota induce diseases. Additionally, longitudinal studies and interventional studies should be increased. Moreover, by combining multi-omics data (such as genomics and metabolomics) with machine learning methods, we can gain a deeper understanding of the temporal relationship between changes in gut microbiota and disease onset, understand the dynamic changes of the microbiota over time or under therapeutic interventions, and thus clarify the causal relationship more definitively.

ACKNOWLEDGEMENTS

We are deeply indebted to the funding agencies that provided financial support for this study. Their generous support enabled us to conduct extensive experiments, access necessary resources, and complete the data collection and analysis.