Role of gut microbiomes in different ocular pathologies: A systematic review

Abstract

BACKGROUND

The gut microbiome is integral to human health, with emerging research underscoring its potential impact on ocular health through the gut-eye axis. Various ocular disorders, such as dry eye syndrome, retinal vascular diseases, macular degeneration, and glaucoma, may be influenced by gut dysbiosis, which could significantly contribute to their development and progression.

AIM

To evaluate the influence of the gut microbiome on the pathogenesis and progression of various ocular diseases.

METHODS

An extensive search of the scientific literature was undertaken by adhering to Preferred Reporting Items for Systematic Reviews & Meta-Analyses standards, using PubMed (MEDLINE), Scopus, EMBASE, and the Cochrane Library as sources to locate studies addressing the relationship between the gut microbiome and human health. To capture all relevant publications, search terms were systematically applied across these major databases, without limiting the search by language or publication date. Inclusion criteria covered randomized controlled trials, non-randomized controlled trial, prospective studies, cross-sectional studies, and case-control studies. Out of the 3077 articles, 36 full texts were included in the review.

RESULTS

Ocular health appears to be shaped by the gut microbial community through mechanisms such as immune regulation, preservation of the blood–retinal barrier, and the generation of protective metabolites. Disturbances in this microbial balance can provoke measurable alterations in host immunity, providing a plausible immunopathogenic pathway that connects intestinal dysbiosis with eye disease. Both laboratory models and early human data suggest that targeted interventions, including prebiotics, probiotics, synbiotics, and faecal microbiota transfer, hold therapeutic potential.

CONCLUSION

The gut–eye relationship reflects a multifaceted interaction in which the intestinal microbiome contributes to ocular health through complex biological pathways. Integrating microbiome assessments into diagnostic methods can revolutionize disease management through early detection and targeted interventions. Further, randomised controlled clinical trials are necessary for ocular diseases to prove causal relationships.

Key Words: Gut microbiome; Ocular diseases; Dysbiosis; Gut-eye axis; Genomics; Prebiotics; Probiotics

Core Tip: The gut microbiome significantly influences ocular health through immune modulation, metabolic interactions, and maintenance of physiological barriers. Dysbiosis is linked to the onset and progression of major eye diseases such as uveitis, macular degeneration, dry eye, and glaucoma. Therapeutic interventions—including prebiotics, probiotics, synbiotics, and fecal microbiota transplantation—show promising results in preclinical and preliminary human studies, although further large-scale clinical trials are warranted to confirm their efficacy and safety in the context of ocular disease management.

INTRODUCTION

Human health is strongly influenced by the microbiome, a complex network of microbial species that interact symbiotically to maintain normal biological function. It is composed of microbial populations together with their collective genomes, and its composition changes according to the body site in which they reside. It represents both the microbial communities and their genetic repertoire, with diversity shaped by the specific environment within the host. Of all human microbial habitats, the gut harbors the largest community, with an estimated 3.8 × 10¹³ bacteria roughly matching the total human cell count of 3.0 × 10¹³[1,2]. The composition of gut flora is individualised and dynamic, influenced by habits, nutrition, body weight, and cultural environment[3].

The dominant bacterial phyla in the gut include Firmicutes, Bacteroidetes, Actinobacteria, Fusobacteria, Proteobacteria, and Verrucomicrobia. The phyla Firmicutes and Bacteroidetes are the most predominant among these, comprising 70%-90% of the gut microbiota[3]. Firmicutes include both beneficial organisms, such as Lactobacillus and fiber-fermenting Ruminococcus, and potential pathogens like Clostridium and Enterococcus. Bacteroidetes largely comprise Bacteroides and Prevotella, while Actinobacteria are represented by beneficial Bifidobacterium. Verrucomicrobia includes Akkermansia muciniphila, a mucin-degrading species of growing clinical interest[3-5]. Dysbiosis in Firmicutes and Bacteroidetes correlates with systemic inflammation and immune dysregulation in ocular pathology, suggesting potential therapeutic strategies through probiotics and dietary interventions[6].

Under physiological conditions, these microbial communities maintain homeostasis by aiding digestion, synthesizing vitamins, and producing short-chain fatty acids such as acetate, propionate, and butyrate, which preserve gut barrier integrity and modulate immune responses[7-10]. They also defend the host by competing with pathogenic organisms for nutrients and attachment sites, and by stimulating gut-associated lymphoid tissue[11]. This balance is highly sensitive to environmental influences. Diets rich in fat and sugar, sedentary behavior, or inappropriate antibiotic use can disturb microbial diversity, leading to dysbiosis. Such disruption has systemic consequences, including heightened inflammation and altered immune regulation[12-14].

Emerging evidence supports the concept of a gut-eye axis, wherein intestinal microbial imbalance may contribute to ocular disease[15]. Gut dysregulation can be assessed through several parameters, including overall species diversity and the ratio between the bacterial phyla Firmicutes and Bacteroidetes. These measures have been extensively examined in preclinical models investigating the gut-eye axis, yielding supportive findings[16,17]. Studies demonstrate that dysbiosis can activate dendritic cells and promote the secretion of pro-inflammatory cytokines such as interleukin-6 and tumor necrosis factor alpha, while suppressing regulatory T-cell function. These changes may compromise the blood–retinal barrier, increase vascular permeability, and foster immune-mediated damage in the eye. In addition, microbial metabolites such as trimethylamine-N-oxide (TMAO) can enter systemic circulation and exert deleterious effects on ocular tissues[18].

Therapeutic approaches focused on modulating the intestinal microbiome present promising alternatives to traditional treatments for ocular inflammatory conditions. These strategies leverage the gut-eye connection to potentially control inflammation and disease progression in the eye by restoring or balancing microbiome health[1]. Treatment options include the use of prebiotics, probiotics, and fecal transplant therapy. Recently, attention has been focused on microbial components enhancing human health by promoting the growth of beneficial microbes like Lactobacillus, Bacteroides fragilis[19-21]. A prebiotic-rich diet that boosts butyrate-producing bacteria may help ease dry eye symptoms in Sjögren’s syndrome, linking gut microbiome changes to better eye health[22]. Probiotics influence critical signalling pathways within gut epithelial cells, promote mucus production, and enhance the tight junctions between these cells, hence, strengthening the intestinal barrier’s integrity[23,24]. However, there are major gaps, risks and limitations of the therapeutic promise of treatment of ocular disease related to gut dysbiosis. Precise pathways linking gut dysbiosis to ocular disease remain unclear, as most findings are based on animal models or small human associations. Modulating the gut microbiome could trigger systemic immune response, and gut-targeted therapies may affect other tissues or organs. Microbiome composition varies widely, making personalised therapy challenging when long-term effects of gut-modifying treatments are uncertain. Many microbiome-based therapies are still experimental and under investigation.

The experimental studies on autoimmune uveitis, dry eye, and diabetic retinopathy (DR) have shown efficacy in modulating immune responses in mouse models[25-27]. A study on a synbiotic formulation demonstrated improvement in uveitis and dry eye disease patients[28,29]. Current clinical evidence for the use of probiotics in ocular diseases remains limited, emphasizing the urgent need for large-scale randomized controlled trials to confirm their preventive and therapeutic efficacy[30].

Fecal microbiota transplantation (FMT) re-establishes eubiosis, reduces levels of inflammation, increases short-chain fatty acids (SCFA), and improves integrity of the gut barrier[31-34]. Emerging research has explored FMT as a modulator of the gut–eye axis, particularly focusing on its potential impact in autoimmune uveitis and Sjögren’s syndrome[35,36]. Although FMT is generally well tolerated, notable risks require careful consideration[37]. These include immediate complications from microbial translocation, potential long-term disruptions to the gut microbiome ecology, and increased sepsis risk in immunocompromised patients. Stringent donor screening and meticulous patient selection are crucial, especially for vulnerable individuals such as those with retinal disorders, to ensure both safety and treatment efficacy.

This systematic review provides a critical evaluation of how alterations in the gut microbiome contribute to the development of ocular disorders, highlighting new therapeutic opportunities for ophthalmology. It explores the composition and functional dynamics of the intestinal microbiota, along with dysbiosis-related changes, with particular emphasis on inflammatory eye diseases such as age-related macular degeneration, DR, glaucoma, retinal artery occlusion, uveitis, and dry eye. Potential interventions directed at restoring microbial balance are assessed as emerging approaches to reduce disease burden.

MATERIALS AND METHODS

Search strategy

This study was conducted in accordance with Preferred Reporting Items for Systematic Reviews & Meta-Analyses (PRISMA) guidelines and has been registered on the international prospective register of systematic reviews “PROSPERO” (registration number: CRD420251067162). A comprehensive literature search was carried out across multiple databases, including PubMed (MEDLINE), Scopus, EMBASE, and Cochrane Library. The primary keywords used in this extensive literature search included "gut microbiome", "intestinal flora", "dysbiosis", "ocular disease", "eye", "uveitis", "macular degeneration", "dry eye", "glaucoma", "diabetic retinopathy", "probiotics", "prebiotics", and "FMT". The search was confined to publications from 1997 to 2025 with no restriction on language.

Study selection and quality assessment

A systematic search was performed using PubMed, Scopus, EMBASE, and the Cochrane Library to identify studies investigating the role of the gut microbiome in ocular health. The initial screening of published articles was conducted by examining titles and abstracts, followed by a full-text review completed independently by two reviewers. The inclusion criteria focused on human studies related to gut microbiome-associated ocular diseases, encompassing randomized controlled trials, prospective studies, cross-sectional studies, and case-control studies. Studies excluded from the analysis were reviews, case reports, opinion articles lacking full-text access, animal studies, and those investigating gut microbiome associations with systemic diseases unrelated to ocular conditions. Discrepancies in screening and quality assessment were resolved through consensus discussions between the reviewers.

Data extraction

Data extraction covered multiple variables, including study details (author, publication year, design, sample size), participant information (age, gender, diagnosis, disease severity, treatment goals, length of follow-up), methods used to assess the gut microbiome [such as ribosomal RNA (rRNA) sequencing, metagenomics, metabolomics], any interventions applied, and outcomes related to both ocular and gut health.

Data synthesis

The primary objective was to systematically compile evidence regarding the link between gut microbiome dysbiosis and the occurrence, progression, severity, outcomes, and prognosis of ocular diseases. The secondary objective involved assessing the proposed mechanisms through which the gut microbiome affects ocular health. For ocular diseases with fully documented outcomes, a detailed disease-specific analysis was performed and included in the narrative review.

Risk of bias assessment

The risk of bias of randomized controlled trials (RCTs) was assessed by the Revised Cochrane Risk-of-Bias Tool for Randomized Trials (ROB2) tool, non-randomized trials were assessed by the risk of bias in non-randomized studies-of interventions (ROBINS-I) tool and for observational studies, quality assessment was done with the National Institutes of health (NIH) tool.

RESULTS

Study selection followed predefined eligibility standards and was summarized using a PRISMA flow diagram in Figure 1. The database search across PubMed (MEDLINE), EMBASE, Scopus, and the Cochrane Library identified 3077 records. After duplicates were removed, 3007 records were screened. Of these, 2952 were excluded after title and abstract review because they did not meet inclusion criteria or were not directly relevant to the study aims, often due to being experimental in design (Supplementary Tables 1-5). The remaining 55 studies underwent full-text evaluation, and 19 were excluded at this stage for not fulfilling eligibility requirements. Ultimately, 36 studies satisfied all criteria and were included in the final analysis of this review. This study reviewed various ocular pathologies involving either microbial communities or interference of the gut-inflammatory axis and summarised in Table 1. The bias assessment was displayed in Table 2. The bias assessment risk of two included RCTs (ROB2) and one non-randomised trial (ROBINS-I) was of low risk of bias. Quality assessment of observational studies was done with the NIH tool. Most of the included studies were of fair quality, with scores ranging from 6 to 8 among 14 questions (Supplementary Table 6).

Figure 1 Preferred Reporting Items for Systematic Reviews & Meta-Analyses flow chart.

Table 1 Summaries of studies related to ocular diseases based on gut dysbiosis examination.

No.	Ref.	Type of study	Participants	Age (mean ± SD)	Ocular disease category	Methodology	Intervention, if any	Primary ocular outcome	GI outcome
1	Jayasudha et al[38], 2019	Case-control	14 uveitis, 24 HC	43.64 ± 14.37, 45.92 ± 16.91	Uveitis	Fecal fungal rRNA sequencing	None	None	Significant ↓ in gut fungal richness and diversity in uveitis patients compared to HC
2	Kalyana Chakravarthy et al[39], 2018	Case-control	13 uveitis, 13 HC	44.54 ± 12.64, 43.08 ± 12.99	Uveitis	Fecal 16S rRNA gene sequencing	None	None	Reduced diversity of several anti-inflammatory organisms in uveitis microbiomes; also ↓ probiotic and antibacterial organisms in uveitis
3	Huang et al[40], 2018	Case-control	38 AAU, 40 HC	33.87 ± 8.77, 36.01 ± 6.87	Uveitis	Fecal 16S rRNA gene sequencing	None	None	Significant difference in beta diversity of gut microbiota composition between AAU and controls; and also significant difference in fecal metabolite phenotype in uveitis patients from HC
4	Morandi et al[41], 2024	Case-control study	20 HLA-B27 uveitis, 27 control	44.5 ± 16.3, 42.1 ± 14.9	Uveitis	Fecal DNA sequencing	None	Bacteroides caccae may therefore play a protective role in the development of AU in HLA-B27-positive individual	AU development is associated with compositional and functional alterations of the GM
5	Wang et al[42], 2023	Case-control	37 Behcet’s uveitis, control-40		Behcet uveitis	Fecal 16S rRNA gene sequencing	None	Restoration of healthy gut microbiota composition correlated with reduced ocular inflammation and slower progression of retinal disease	Targeting gut microbiota showed potential for modulating systemic immunity and ocular pathology
			31 VKH, control-40		VKH		None
6	Ye et al[43], 2020	Case-control	71 active VKH, 11 inactive VKH, 67 HC	40.5 ± 15.1, 41.1 ± 13.5	VKH	Fecal DNA sequencing	None	None	Depleted butyrate-producing bacteria, lactate-producing bacteria and methanogens as well as enriched Gram-negative bacteria were identified in the active VKH patients
7	Tecer et al[44], 2020	Case-control	7 BS, 12 (FMF), 9 CD and 16 HC	35.57 ± 6.60, 32.17 ± 8.64, 35.00 ± 5.27, 39.38 ± 7.69	Behcet’s syndrome	Fecal 16S rRNA gene sequencing	None	None	Significant differences in alpha diversity between four groups. Prevotella copri was dominant in BS group
8	Kim et al[45], 2021	Case-control	9 BD, 7 with RAU, 9 BD-matched HC, and 7 RAU-matched HC	Median 33, 47, 53, 44	Behcet’s syndrome	Fecal 16S rRNA gene sequencing	None	BD patients with uveitis had different abundances of various taxa, compared to those without uveitis. Alterations in the fecal microbiome in patients with BD according to disease activity, and an association of the abundance of fecal bacterial species with BD disease activity and uveitis symptoms	A tendency toward clustering in the beta diversity & ↓ in alpha diversity of the fecal microbiome was observed between the active BD patients and HC. Active BD patients had a significantly higher abundance of fecal Bacteroides uniformis than their matched HC and patients with inactive disease state (P = 0.038)
9	Yasar Bilge et al[46], 2020	Prospective cohort	27 BD, 10 HC	40.8 ± 9.3, 38.9 ± 4.9	Behcet’s syndrome	Fecal 16S rRNA gene sequencing	None	None	No differences between the BD group and the control group in terms of alpha and beta microbial diversity and abundance indices (P > 0.05), significant differences in the relative abundance of some bacterial taxa between patient with BD and HC
10	Ye et al[47], 2018	Case-control	32 active BD and 74 HC	47.1 ± 5.3, 45.9 ± 7.2	Behcet’s syndrome	Fecal metagenomic DNA sequencing	None	None	Enriched in a SRB along with a lower level of butyrate-producing bacteria and methanogens
11	Zysset-Burri et al[48], 2019	Case-control	29 non-arteritic (RAO) and 30 HC	69.4 ± 1.9, 69.0 ± 1.7	RAO	Fecal DNA sequencing	None	None	Gut derived, TMAO was significantly higher in patients with RAO compared to controls (P = 0.023)
12	Zhang Y et al[49], 2023	Case-control	30 AMD, 17 control	Not applicable	AMD	Fecal 16S rRNA gene sequencing	None	None	Different bacterial compositions noted in the AMD compared to controls
13	Zysset-Burri et al[50], 2020	Case-control	12 nAMD, 11 control	75.4 ± 8.3, 75.3 ± 7.1	AMD	Fecal DNA sequencing	None	None	AMD patients show distinct gut microbiome composition and functional gene enrichment; genetic complement variants modulate these associations; microbiome–complement axis may contribute to AMD pathogenesis
14	Xue et al[51], 2023	Case-control	30 AMD, 30 control	66.05 ± 9.26, 78.4 ± 7.4	AMD	Fecal metagenomic DNA sequencing	None	Depleted Bacteroidaceae in patients with AMD was negatively associated with hemorrhage size	Gut microbiome may affect AMD severity by increasing intestinal permeability, thereby facilitating the translocation of microbes
15	Huang et al[52], 2021	Cross sectional study	25 DM without DR, 25 DM with DR, 25 control	62.5 ± 5.2, 60.3 ± 9.1, 57.8 ± 7.5	DR	Fecal 16S rRNA gene sequencing	None	None	Reduced alpha & beta diversity in both DM and DR groups compared to control
16	Moubayed et al[53], 2019	Case-control	9 diabetic patients without retinopathy, 8 diabetic patients with retinopathy, 18 HC	Not applicable	DR	Fecal DNA sequencing	None	None	Higher ratio of Bacteroides in diabetic groups than controls but no difference between those with and without retinopathy
17	Ye et al[54], 2021	Case-control	45 PDR, 90 diabetic without DR as control	59.9 ± 11.3, 60.9 ± 9.9	DR	Fecal 16S rRNA gene sequencing	None	None	Significantly lower bacterial diversity with significant depletion of 22 families and enrichment of 2 families in the PDR group as compared with the NDR group
18	Das et al[55], 2021	Case-control	25 with T2DM without DR, 28 with T2DM and DR, 30 HC	57.3, 55.2, 52.2	DR	Fecal 16S rRNA gene sequencing	None	None	Dysbiosis more pronounced in DR compared to DM, control & ↓ in anti-inflammatory, probiotic and pathogenic bacteria compared to HC
19	Jayasudha et al[56], 2020	Cohort study	21 DM, 24 DR, 30 HC	57.5, 54.5, 52.2	DR	Fecal DNA sequencing	None	None	More mycobiome dysbiosis in people with T2DM and DR than compared to HC
20	Khan R et al[57], 2021	Case-control	37 with sight Threatening DR, 21 control	57.45 ± 8.08, 57.50 ± 7.60	DR	Fecal DNA sequencing	None	None	No difference in gut microbial abundance between the 2 populations
21	Omar et al[58], 2024	Prospective study	23 were NM, 8 PM, and 21 SM	31.96 ± 7.5, 32.35 ± 4.8, 30.62 ± 8.1	Myopia	Fecal 16S rRNA gene sequencing	None	None	No significant differences in alpha and beta diversity between the three groups (NM, PM, and SM), Prevotella copri was predominant in stable myopia
22	Sun et al[59], 2024	Case-control study	35 myopia, 45 HC		Myopia	Fecal 16S rRNA gene sequencing	None	None	No significant difference in α diversity while β diversity reached a significant level
23	Skondra et al[60], 2021	Cross sectional study	6 infants with type 1 ROP and 4 preterm infants without any ROP	24.1 weeks, 25.6 weeks	ROP	Fecal 16S rRNA gene sequencing	None	Absence of Enterobacteriaceae overabundance, in addition to enrichment of amino acid biosynthesis pathways, may protect against severe ROP in high-risk preterm infants	Significant enrichment of Enterobacteriaceae & ↓ amino acid biosynthesis pathways in type 1 ROP
24	Chang et al[61], 2024	Case-control study	13 with type 1 ROP, 44 with type 2, and 53 without ROP		ROP	Fecal 16S rRNA gene sequencing	None	Reduced gut microbial diversity may be associated with ROP development in high-risk preterm infants	Type 1 ROP showed no significant difference in microbial diversity up to 8 postnatal weeks (P = 0.057), while type 2 and no ROP groups displayed increased diversity (P = 0.0015 and P = 0.049, respectively)
25	Berkowitz et al[62], 2022	Case-control	25 IIH, 20 HC	35.12, 48.5	IIH	Fecal DNA sequencing	Acetazolamide examined in IIH patients	None	Lower diversity of bacterial species in IIH patients compared with HC, ↑ in Lactobacillus brevis, (beneficial bacterium) in acetazolamide treated patients
26	Gong et al[63], 2020	Case-control	30 POAG, 30 control	54.77 ± 9.32, 53.80 ± 7.87	POAG	Fecal 16S rRNA gene sequencing	None	Mean visual acuity was negatively correlated with Blautia, mean VF-MD was negatively correlated with Faecalibacterium, and average RNFL thickness was positively correlated with Streptococcus	Bacterial profile in the gut microbiome had significant differences between the POAG and control
27	Kalyana Chakravarthy et al[64], 2018	Case-control	32 fungal keratitis, 31 HC	47.1, 42.2	Fungal keratitis	Fecal 16S rRNA gene sequencing	None	None	No significant difference in fungal dysbiosis, but bacterial richness and diversity was significantly decreased in FK patients, strong association of disease phenotype with ↓ in beneficial bacteria and increase in pro-inflammatory and pathogenic bacteria in FK patients
28	Jayasudha et al[65], 2018	Case-control	19 BK, 21 HC	48.8	Bacterial keratitis	Fecal DNA sequencing	None	None	↑ In number of antiinflammatory organisms in HC compared to BK
29	Mendez et al[66], 2020	Case-control	13 with Sjögren + dry eye, 8 with Sjögren without dry eye, 21 HC	58.8 ± 10, 58.4 ± 726.0	Sjögren syndrome	Fecal 16S rRNA gene sequencing	None	None	Shannon’s diversity index showed no differences between groups Faith’s phylogenetic diversity showed increased diversity in cases vs controls, which reached significance when comparing SDE and controls (P = 0.02)
30	Moon et al[67], 2020	Case-control	10 SS, 14 with environmental DES, 12 HC	58.5 ± 3.05, 46.29 ± 2.6, 47.5 ± 4.05	Sjögren syndrome	Fecal 16S rRNA gene sequencing	None	Bacteroidetes, Actinobacteria and Bifidobacterium were significantly related with dry eye signs (P < 0.05), multivariate linear regression analysis revealed tear secretion was strongly affected by Prevotella (P = 0.025)	Gut microbiome showed significant differences in patients with Sjögren than compared to controls & DES; no significant difference in alpha-diversity across all 3 groups
31	Watane et al[68], 2021	Non-randomized clinical trial	10 dry eye due to Sjögren syndrome	60.4 ± 4.2	Sjögren syndrome	Fecal DNA sequencing	FMT	Improvement of subjective dry eye symptoms in 5 individuals after FMT at 3 month follow up	No side effect after FMT
32	Filippelli et al[69], 2021	RCT	26 children with chalazion	8.3	Chalazion	None	Probiotic supplementation	Decreased time to resolution of chalazion in probiotic group (P < 0.0001)	No adverse effect
33	Filippelli et al[70], 2022	RCT	20 adults with chalazion	48.25	Chalazion	None	Probiotic supplementation	Decreased time to resolution of small size. Chalazion in probiotic group (P < 0.039). Failure to resolve medium or large chalazion	No adverse effect
34	Yang et al[71], 2024	Case-control study	145 GD, 156 GO, 100 HC	36, 44.50, 38	Grave’s disease	Fecal 16S rRNA gene sequencing	None	Levels of IAA were negatively correlated with clinical activity score and serum TRAb in GO patients	Trp metabolites IAA maybe a novel biomarker for GO progression. and IPA, ILA and IAA may play a protective role in GO
35	Zhang et al[72], 2024	Case-control study	30 TAO, 29 HC	43.40 ± 10.38, 41.79 ± 7.61	Thyroid ophthalmopathy	Fecal 16S rRNA gene sequencing	None	Veillonella demonstrated a positive correlation with exophthalmos. Conversely, Alloprevotella showed a negative correlation with exophthalmos. Dialister and Clostridium sensu stricto 1 exhibited positive correlations with disease severity, while Streptococcus showed a negative correlation with disease severity	Reduced gut richness and diversity observed in patients with TAO
36	Zhang et al[73], 2023	Case-control study	62 GO, 18 HC		Graves orbitopathy	Fecal 16S rRNA gene sequencing	None	Klebsiella pneumoniae was positively correlated with disease severity	No remarkable difference in gut microbiota diversity between groups; however, the gut microbial community and dominant microbiota significantly differed among groups

IIH: Idiopathic intracranial hypertension; DM: Diabetes mellitus; PM: Progressive myopes; SM: Stable myopes; NM: Nonmyopes; HC: Healthy controls; rRNA: Ribosomal ribonucleic acid; AAU: Acute anterior uveitis; AU: Anterior uveitis; HLA: Human leukocyte antigen; GM: Gut microbiota; VKH: Vogt Koyanagi Harada; FMF: Familial mediterranean fever; BS: Behcet’s syndrome; BD: Behcet disease; RAU: Recurrent aphthous ulcer; SRB: Sulfate-reducing bacteria; RAO: Retinal artery occlusion; TMAO: Trimethylamine-N-oxide; AMD: Age related macular degeneration; nAMD: Neovascular Age related macular degeneration; DR: Diabetic retinopathy; PDR: Proliferative diabetic retinopathy; NDR: Non-proliferative diabetic retinopathy; ROP: Retinopathy of prematurity; POAG: Primary open angle glaucoma; RNFL: Retinal nerve fiber layer; FK: Fungal keratitis; BK: Bacterial keratitis; SDE: Sjogren associated dry eye; SS: Sjogren syndrome; DES: Dry eye syndrome; FMT: Fecal microbiota transplant; RCT: Randomized controlled trial; GO: Graves’ orbitopathy; GD: Graves’ disease; IAA: Indole-3- acetic acid; TRAb: Thyrotropin receptor antibody; ILA: Lndole-3-lactate; IPA: Indole propionic acid; TAO: Thyroid associated orbitopathy.

Table 2 Quality of evidence of studies examined.

Ref.	Type of study	Quality of evidence (NIH tool for observational studies) (ROBINS-I for non-randomized trial) (ROB2 for RCT)
Jayasudha et al[38], 2019	Case-control	8
Kalyana Chakravarthy et al[39], 2018	Case-control	6
Huang et al[40], 2018	Case-control	8
Morandi et al[41], 2024	Case-control	8
Wang et al[42], 2023	Case-control	8
Ye et al[43], 2020	Case-control	8
Tecer et al[44], 2020	Case-control	8
Kim et al[45], 2021	Case-control	8
Yasar Bilge et al[46], 2020	Cohort	9
Ye et al[47], 2018	Case-control	8
Zysset-Burri et al[48], 2019	Case-control	8
Zhang et al[49], 2023	Case-control	8
Zysset-Burri et al[50], 2020	Case-control	8
Xue et al[51], 2023	Case-control	6
Huang et al[52], 2021	Cross-sectional	8
Moubayed et al[53], 2019	Case-control	8
Ye et al[54], 2021	Case-control	8
Das et al[55], 2021	Case-control	8
Jayasudha et al[56], 2020	Cohort study	9
Khan et al[57], 2021	Case-control	8
Omar et al[58], 2024	Cohort	8
Sun et al[59], 2024	Case-control	8
Skondra et al[60], 2021	Cross-sectional	8
Chang et al[61], 2024	Case-control	7
Berkowitz et al[62], 2022	Case-control	8
Gong et al[63], 2020	Case-control	8
Kalyana Chakravarthy et al[64], 2018	Case-control	8
Jayasudha et al[65], 2018	Case-control	8
Mendez et al[66], 2020	Case-control	8
Moon et al[67], 2020	Case-control	8
Watane et al[68], 2021	Non-randomized clinical trial	Low risk of bias
Filippelli et al[69], 2021	Randomized control Trial	Low risk of bias
Filippelli et al[70], 2022	Randomized control trial	Low risk of bias
Yang et al[71], 2024	Case-control	8
Zhang et al[72], 2024	Case-control	8
Zhang et al[73], 2023	Case-control	8

RCT: Randomized controlled trial; NIH: National institutes of health; ROB2: Revised Cochrane Risk-of-Bias Tool for Randomized Trials; ROBINS-I: Risk of Bias in Non-Randomized Studies-of Interventions.

Recent studies have linked dysbiosis to various ocular diseases such as bacterial and fungal keratitis, dry eye syndrome, uveitis, age-related macular degeneration, DR, and glaucoma, suggesting a potential association between gut microbiome imbalance and these eye conditions. The integrated analysis was outlined in Table 3 and Figure 2. Out of 36 included studies, 28 were case-control studies, 2 were cross-sectional studies, 3 were cohort studies, 1 was a non-randomized clinical trial, and 2 were RCTs. The studies included a total of 2489 participants. More female participants were noted in 15 studies out of 36 included studies. Among microbiome assessment methods of 36 studies, 23 had fecal 16S rRNA gene sequencing, while 11 had fecal metagenomic DNA sequencing.

Figure 2 Integrated gut microbiota associated with ocular disease. A: Uveitis; B: Behcet’s syndrome; C: Vogt-Koyanagi-Harada disease; D: Retinal artery occlusion; E: Age-related macular degeneration; F: Diabetic retinopathy; G: Myopia; H: Retinopathy of prematurity; I: Idiopathic intracranial hypertension; J: Glaucoma; K: Fungal keratitis; L: Bacterial keratitis; M: Sjögren’s syndrome; N: Grave’s disease. Grey color depicting disease; light blue depicting decrease; dark blue depicting increase.

Table 3 Microorganisms highlighted in dysbiosis of ocular diseases.

Ocular disease category	Implicated micro-organisms increase	Implicated micro-organisms decrease
Uveitis	Malassezia, Candida, Candida, Aspergillus gracilis	Faecalibacterium, Lachnospira, Ruminococcus, Bacteroides
Behcet’s syndrome	Veillonellaceae, Succinivibrionaceae	Bacteroidaceae
Vogt-Koyanagi-Harada syndrome	Ramularia, Alternaria and Rhizophagus, Alistipes	Methanoculleus, Candidatus Methanomethylophilus and Azospirillum Dorea
Retinal artery occlusion	Actinobacter, Bifidobacterium, Bacteroides, Faecalibacterium
Age related macular degeneration	Ruminococcus, Oscillibacter, Anaerotruncus, Eubacterium
Diabetic retinopathy	Bacteriodes, Bifidobacterium, Burkholderiaceae	Firmicutes, Actinobacteria, Faecalibacterium, Clostridium, Escherichia Shigella, Coriobacteriaceae, Veillonellaceae, Streptococcaceae
Myopia	Bifidobacterium, Bacteroides, Megamonas, Faecalibacterium, Coprococcus, Dorea, Roseburia, and Blautia
Retinopathy of prematurity	Enterobacteriaceae
Idiopathic intracranial hypertension		Lactobacillus, Atopobium, Megamonas, Ruminococcus, Streptococcus
Glaucoma	Prevotellaceae, Enterobacteriaceae, Escherichia coli	Megamonas, Bacterioides
Fungal keratitis		Bifidobacterium, Lactospira, Faecalibacterium, Lachnospira, Ruminococcus, Mitsuokella, Megasphera and Lachnospiraceae
Bacterial keratitis		Dialister, Megasphaera, Faecalibacterium, Lachnospira, Ruminococcus, Mitsuokella, Firmicutes, Veillonellaceae, Ruminococcaceae and Lachnospiraceae
Sjögren syndrome	Bacteriodetes, Alistipes, Streptococcus, Prevotella, Odoribacter, Actinomycetaceae, Eggerthellaceae, Lactobacillaceae, Akkermanciaceae, Coriobacteriaceae, and Eubacteriaceae	Faecalibacterium, Prevotella, and Ruminococcus, Actinobacteria, Bifidobacterium, Dorea, Agathobacter
Grave’s disease	Firmicutes, Bacteroidetes, Proteobacteria, Bacteroides, Ruminococcus gnavus

Uveitis

Out of 4 studies on uveitis, one study reported dysbiosis of pathogenic Aspergillus and Candida in uveitic eyes compared to controls[38], while another found no significant differences in composition of microbiota between cases and controls. Affected patients exhibit a decreased diversity of anti-inflammatory microbial species[39]. A number of butyrate-producing bacteria, including Lachnospira, Dialister, Dorea, Blautia, Faecalibacterium, Akkermansia muciniphila, Megasphaera, and Roseburia, were found to be diminished in abundance[40]. Bacteroides caccae play a protective role in the development of anterior uveitis in HLA-B27-positive individual[41].

Vogt–Koyanagi–Harada disease

Out of 2 studies on Vogt–Koyanagi–Harada disease (VKH), one study showed reduced ocular inflammation and slower progression of retinal disease after restoration of healthy gut microbiota[42], while the other showed a mix of Bacteroides enterotype enriched with Bacteroides species, Paraprevotella species, Prevotella species, and Parabacteroides species, accompanied by a decrease in lactate-producing bacteria[43].

Behcet's disease

Among five available studies on Behçet’s disease, two demonstrated notable changes in alpha and beta microbial diversity[44,45], while one study did not detect any variation between cases and controls[46]. One study showed a decrease in Lachnospiraceae, Dorea, Blautia, Coprococcus, Erysipelotrichaceae and an increase in Bilophila and Stenotrophomonas in Behçet's-related uveitis patients[42]. An enrichment of sulfate-reducing bacteria has been observed, along with a decline in butyrate producers and methanogenic species[47].

Retinal artery occlusion

There are reports of significantly higher atherogenic metabolite TMAO in retinal artery occlusion (RAO) patients compared to controls, with a direct association between TMAO and an increase in Akkermansia[48].

Age-related macular degeneration

Reports indicate that the gut microbiome shows distinct alterations, with markedly reduced Firmicutes and relatively increased Proteobacteria and Bacteroidota[49,50]. One study showed a negative association of hemorrhage size in age-related macular degeneration (AMD) patients with depleted Bacteroidaceae[51].

DR

Six studies have been published on the association of dysbiosis with DR, out of which 4 showed significant bacterial dysbiosis[52-55], while one study showed more mycobiome dysbiosis in DR compared to healthy controls[56]. However, one study has shown no difference in gut microbial flora in sight threatening DR[57].

Myopia

Research in myopic patients revealed enrichment of gut bacteria linked to dopamine signaling and gamma-aminobutyric acid production, implicating these taxa in the development and progression of myopia. Prevotella copri was predominant in stable myopia[58]. The other study revealed a close relation of adolescent myopia to the gut microbiota[59].

Retinopathy of prematurity

A study on retinopathy of prematurity (ROP) showed depletion of Enterobacteriaceae as a protecting factor against ROP in preterm infants at high-risk[60] while another study showed reduced gut microbial diversity associated with ROP development in high-risk preterm infants[61].

Idiopathic intracranial hypertension

The study also reported an increase in lactobacillus in acetazolamide-treated patients with idiopathic intracranial hypertension, suggesting gut dysbiosis serving as an immunometabolic and neurovascular modulator[62].

Glaucoma

A study on primary open-angle glaucoma (POAG) demonstrated an alteration in the bacterial profile of the gut microbiome. Findings indicated that higher abundance of Blautia correlated with poorer visual acuity, and Faecalibacterium levels were inversely related to mean visual field mean deviation. In contrast, retinal nerve fiber layer thickness showed a positive relationship with Streptococcus abundance[63].

Keratitis

In a study on fungal keratitis, no significant differences in fungal community composition were observed; however, bacterial richness and diversity were markedly reduced[64], while another study on bacterial keratitis found abundance in Prevotella copri (pro-inflammatory action)[65].

Sjögren syndrome

Out of 3 studies, two showed significant differences in gut microbiome diversity, with one study giving a significant relation of Bacteroidetes, Actinobacteria, and Bifidobacterium with dry eye signs and Prevotella with tear secretion[66,67]. An interventional study evaluating FMT reported improvement in subjective dry eye symptoms in half of the patients at a 3-month follow-up[68].

Chalazion

Probiotic therapy has been assessed in two studies of chalazion. The pediatric trial reported a reduced time to lesion resolution[69], whereas the adult trial showed benefit only for smaller chalazia, with no improvement seen in larger ones[70].

Graves’ disease

One study reported significantly reduced serum concentrations of indolepropionic acid, indole-3-lactate, and indoleacetic acid (IAA) in patients with both Graves’ disease and Graves’ orbitopathy, with IAA identified as a potential biomarker for Graves’ orbitopathy progression[71]. Another investigation found strong associations between Veillonella and Megamonas and the clinical features of thyroid-associated ophthalmopathy, while Klebsiella pneumoniae showed a positive correlation with disease severity[72,73].

DISCUSSION

This systematic review offers a comprehensive synthesis of current evidence linking gut microbiota with ocular diseases. Expanding upon the foundational work of Bu et al[74], our analysis not only reinforces the concept of a gut–eye axis but also critically appraises methodological quality, biological plausibility, and the translational relevance of this emerging field. While prior reviews have catalogued preliminary associations between gut dysbiosis and ocular pathology, they often lacked mechanistic findings and a predictive strategy for future research. This review addresses these deficiencies by incorporating structured quality assessment tools and qualitative analysis of included studies.

Traditional culture-based methods yield a far less diverse microbial profile compared with modern molecular approaches[75]. Genomic techniques, particularly 16S rRNA polymerase chain reaction, allow broader identification of bacterial taxa from a given niche than culture techniques[76]. In this review, 23 of the 36 included studies employed 16S rRNA gene sequencing. Research on the intestinal microbiome faces several methodological challenges: Appropriate sample collection, preservation of specimen stability, and reliable downstream processing are critical for accurate DNA sequencing[77]. In clinical settings, factors such as diet, medication use, gut motility, and stool characteristics must also be considered when interpreting microbiota data[78,79]. Cross-sectional studies may be confounded by such environmental influences, and observed associations cannot establish causality. Therefore, randomized controlled clinical trials, including those investigating interventions like FMT, are essential in the context of ocular disease. Several investigations have proposed links between dysbiosis and ocular pathology, but current data do not establish a causal role. The nature of this association remains unresolved, as no particular microbial species has been reproducibly associated, underscoring the challenges posed by the intricate structure of the microbiome.

Uveitis

Huang et al[40] reported no significant difference in gut microbial composition between uveitis cases and controls, suggesting bacteria may not play a central role in disease onset. This was further supported by Jayasudha et al[38], who identified increased fungal pathogens such as Aspergillus and Candida in affected patients, while Kalyana Chakravarthy et al[39] observed reduced diversity of anti-inflammatory bacterial species. Multiple butyrate-producing taxa, including Lachnospira, Dialister, Dorea, Blautia, Faecalibacterium, Akkermansia muciniphila, Magasphaera, and Roseburia, were found to be decreased. The decline in these protective microbes may exacerbate inflammatory activity. Napolitano et al[79] documented improved visual acuity and reduced clinical signs of uveitis after two months of probiotic therapy with Bifidobacterium lacti, Bifidobacterium bifidum, and Bifidobacterium breve in a patient with longstanding anterior uveitis.

VKH

In VKH, active disease is associated with depletion of butyrate/Lactate producers and methanogens, alongside enrichment of Gram-negative taxa. Parallel studies also demonstrate distinct mycobiome changes, indicating that bacteria and fungi jointly influence disease expression[43].

Behcet’s disease

Tecer et al[44] documented a significant decline in gut microbial diversity among patients with Behçet’s disease compared with controls. Reduced butyrate production was also observed, which impaired regulatory T-cell activity and may contribute to disease pathogenesis[80]. Consistently, the genera Roseburia and Subdoligranulum were diminished in the fecal microbiota of affected individuals[81]. Across different clinical phenotypes, additional microbial variations were noted, with Lachnospiraceae particularly associated with Behçet’s patients presenting with uveitis, reinforcing the connection between gut imbalance and ocular pathology[46].

RAO

Beyond traditional risk factors such as hypertension, diabetes, and atherosclerosis, recent evidence suggests a role for gut dysbiosis in the development of RAO. Translocation of microbial products from the gut may aggravate systemic inflammation and endothelial dysfunction. Zysset-Burri et al[48] reported elevated TMAO levels in RAO patients, which correlated positively with Akkermansia abundance, indicating a possible mechanistic link between gut microbiota and RAO. The detection of microbial DNA within atherosclerotic plaques, together with raised concentrations of the pro-atherogenic metabolite TMAO, further supports the concept of a “gut–retina–vascular axis” in this disease[82,83].

AMD

Dysbiosis of the gut microbiota has been increasingly linked to AMD, particularly via mechanisms involving impaired intestinal barrier function[49,84]. Risk has been correlated with enrichment of Peptococcaceae, Bilophila, Faecalibacterium, and Roseburia, while protective associations were seen with Candidatus Solaeferrea, Desulfovibrio, and the Eubacterium ventriosum group[85]. While Zhang et al[49] found an increase in wet AMD cases, Baldi et al[84] found reductions in Bacteroidota, Bacteroidales, and Prevotellaceae abundance. Patients with AMD exhibit a significant decline in saturated fatty acid production, and wet AMD cases show decreased abundance of SCFA-producing microbes such as Phascolarctobacterium and Parabacteroides, underscoring the anti-inflammatory potential of SCFAs and their therapeutic implications in ocular health[85-87]. Several studies have demonstrated that patients with AMD display a unique gut microbial signature with diminished diversity and shifts in critical taxa. This dysbiosis compromises SCFA metabolism and lowers production of protective metabolites, while simultaneously enhancing systemic exposure to endotoxins and pathogen-associated molecular patterns, a pattern also described in other retinal disorders[88]. These molecules can activate retinal microglia and complement pathways, both of which are implicated in drusen formation and choroidal neovascularisation[89].

DR

Gut microbial dysbiosis in DR drives immune dysregulation, which in turn accelerates vascular injury within the retina[90]. Alterations in microbial composition include a decline in Actinobacteria and Bacteroidetes, together with an overrepresentation of Escherichia, Faecalibacterium, and Prevotella[55,91]. Contradictory findings show that certain DR groups have elevated Bacteroidetes[52]. Qin and colleagues demonstrated that DR patients had diminished levels of Butyricicoccus and Ruminococcus torques, coupled with lower circulating SCFAs, consistent with compromised gut barrier function and heightened inflammatory signalling[92]. Microbial metabolites such as TMAO were found to be elevated in DR and associated with disease severity[93]. Similarly, succinate, a metabolite produced by microbial fermentation, was decreased in the ocular fluids of DR patients, suggesting metabolic contributions from the gut microbiota. Serban et al[94] analysed eight prospective cohort studies from 2020-2022 involving over 200 T2DM patients with DR and corresponding controls, focusing on gut microbiome diversity and composition using mainly 16S rRNA sequencing. Findings consistently showed a distinctive gut microbiota profile in DR characterised by decreased abundance of anti-inflammatory and short-chain fatty acid-producing bacteria. It also highlighted the potential therapeutic approaches to modulate gut dysbiosis in DR, including probiotics (Lactobacillus, Bifidobacterium), dietary fibre, and engineered probiotics targeting pathways to reduce retinal inflammation and damage.

Myopia

Omar et al[58] found that myopic individuals, including those with simple myopia (SM) and pathological myopia (PM), exhibited significantly higher abundances of Bacteroides, Bifidobacterium, Clostridium, and Ruminococcus. Furthermore, Prevotella copri was notably more abundant in SM compared to PM. These findings suggest distinct gut microbiome profiles in myopia, warranting further investigation into their potential role in myopia pathogenesis and progression. All of these bacterial genera are involved in modulating dopaminergic signalling pathways[95].

ROP

Emerging evidence suggests a potential role of the gut–retina axis, where intestinal dysbiosis may contribute to abnormal retinal angiogenesis by impairing vascular repair and disrupting vascular endothelial growth factor (VEGF) signalling. Increased gut permeability and endotoxemia may worsen retinal hypoxia and neovascularisation. While these associations support the gut–retina axis in ROP pathogenesis, causality remains unproven, and local oxygen fluctuations remain the primary driver of VEGF expression[96]. Small cohort studies support these associations, though results are limited by confounding factors such as antibiotic use, sepsis, and necrotizing enterocolitis[92,97]. The study by Skondra et al[60] concluded that a decline in Enterobacteriaceae and upregulation of amino acid biosynthesis pathways has been identified as protective factor against the development of severe ROP in high-risk preterm infants.

Idiopathic intracranial hypertension

The gut–brain–eye axis interconnects intestinal microbiota with neuro-ophthalmic health through immune modulation, molecular mimicry, and vascular/metabolic influence (affecting intracranial pressure and perfusion). Decreased levels of Lactobacillus, Atopobium, Megamonas, Ruminococcus, Streptococcus have been implicated in idiopathic intracranial hypertension. Berkowitz et al[62] noticed the increase in lactobacillus was noted in acetazolamide treated patients. These findings reinforce that gut dysbiosis serves as an immunometabolic and neurovascular modulator rather than a primary cause.

Glaucoma

Increasing evidence implicates the gut microbiome in modulating both susceptibility and progression of glaucoma through systemic immune and metabolic pathways. A pilot study conducted by Chen et al[98] explored the correlation of POAG and gut microbiota. A bidirectional Mendelian randomisation analysis conducted by Wu et al[99] concluded that the bacterial family Oxalobacteraceae and the genus Eggerthella negatively impact glaucoma, while the genera Bilophila, Lachnospiraceae, and Ruminiclostridium exert a positive effect. Additionally, increased levels of microbial metabolites like trimethylamine have been detected in the aqueous humor of patients with POAG[100]. McPherson et al[101] reported increased probability of glaucoma in patients with irritable bowel syndrome (OR = 5.84).

Keratitis

Kalyana Chakravarthy et al[64] and Jayasudha et al[65] found variation in gut bacterial and fungal microbiota in patients with keratitis compared to control patients. An increase in Aspergillus and Malassezia species in patients with bacterial keratitis suggests these fungi may contribute to heightened host vulnerability to bacterial corneal infections. In fungal keratitis, gut dysbiosis has been shown to disrupt Th17/Treg balance, promoting excessive inflammation that aggravates stromal damage. Jayasudha et al[65] observed that a significant decline in bacterial genera such as Dialister, Megasphaera, Lachnospira, Ruminococcus, Veillonellaceae, Ruminococcaceae, and Lachnospiraceae and increase in pro-inflammatory bacteria like Prevotella copri, Bilophila, Enterococcus, Dysgonomonas.

Sjögren syndrome

Studies have identified distinct changes in the gut microbiota of individuals with dry eye disease, especially those with Sjögren’s syndrome. These changes include a decline in beneficial bacteria such as Faecalibacterium and Bacteroides, alongside an increase in potentially harmful bacteria like Streptococcus and Escherichia/Shigella[102]. Regarding dry eye secondary to Sjögren’s syndrome, a decline in Firmicutes/Bacteroidetes ratio and Bifidobacterium has been observed, suggesting that the disease’s severity is related to the groups of bacteria involved[67]. Interventions aimed at modulating the gut microbiome, including dietary changes, probiotic supplementation, and prebiotics, have demonstrated potential in enhancing tear film stability and alleviating ocular surface inflammation[103]. An RCT showed that a synbiotic mixture significantly improved tear secretion and ocular surface health in patients with dry eye disease[104].

Chalazion

Gut microbiome may indirectly modulate the risk and recurrence of chalazion through systemic inflammatory and metabolic pathways. Zhang et al[105] performed a two-sample Mendelian randomization study that demonstrated a causal link between the gut microbiome and chalazion. Their findings identified Catenibacterium, belonging to the phylum Firmicutes, as associated with a higher risk of chalazion, whereas Veillonella was found to have a protective effect[106]. Filippelli et al[69,70] reported a substantial reduction in the resolution time of the chalazion post probiotic supplementation.

Grave’s disease

Microbiota-derived tryptophan metabolites may protect against Graves’ orbitopathy by modulating inflammation and cell proliferation in orbital fibroblasts through inhibition of the Akt signaling pathway[107]. This suggests that targeting these metabolites could offer therapeutic potential for Graves’ orbitopathy through dietary interventions or oral probiotic supplementation. The decreased abundance of Ruminococcus gnavus in patients with thyroid-associated orbitopathy may contribute to disease development by promoting the activation of effector T cells[72].

While this review offers a critical advance over previous syntheses, several limitations must be acknowledged. The heterogeneity of included studies precluded quantitative meta-analysis. This review lacks causal association and also mechanistic link. Research article or RCT is mandated to establish a causal relationship, which is currently not available in the present literature. Many aspects, like specific pathogenic mechanisms, are still not fully clarified. Given the emerging nature of this field, many included studies were exploratory and lacked replication cohorts. Nonetheless, these limitations reflect broader challenges in microbiome research and reinforce the need for methodological standardization.

Future research direction

Given the established connection between gut microbiota and immune-related diseases, future research should focus on clarifying the role of gut commensal bacteria in shaping and sustaining the host's immune homeostasis. Uveitis patients exhibited changes in gut fungal diversity, underscoring the importance of considering the entire gut microbiome to optimize the effectiveness of FMT. Since probiotic effects are highly strain-specific, their therapeutic benefits cannot be broadly applied to entire genera or species. This highlights the necessity for precise selection and detailed characterization of probiotic strains for clinical use. Given the cooperative and dynamic interactions among gut bacteria, future therapeutic approaches may be more successful by employing multi-strain probiotic formulations that can synergistically and sustainably reshape the microbial ecosystem. Future research should aim to identify specific prebiotic compounds, optimise their dosage and administration, and clinically validate their benefits.

CONCLUSION

Accumulating evidence accentuates the role of the gut microbiome in the pathogenesis of ophthalmic diseases, positioning it as a promising target for both preventive and therapeutic strategies. Disruptions in microbial balance can alter immune regulation, promote inflammation, and impair the blood–retinal barrier, thereby contributing to conditions such as uveitis, AMD, DR, dry eye disease, and glaucoma. Numerous studies demonstrate that microbial imbalance affects systemic immunity and metabolism, with changes observed in T- and B-cell activity, cytokine profiles, and host metabolic responses. Although the underlying mechanisms remain incompletely defined, interventions that modify the microbiome, such as probiotics, prebiotics, synbiotics, and FMT, offer emerging therapeutic potential. At present, most supporting data derive from animal models; thus, well-designed human clinical trials are urgently needed to establish their efficacy and safety in ophthalmic care. A deeper understanding of gut–eye interactions may also lead to novel biomarkers for disease risk stratification and treatment responsiveness, paving the way toward personalized therapeutic approaches in ophthalmology.