Investigating the uncertain causal link between gut microbiota and glaucoma: A genetic correlation and Mendelian randomisation study
Jiayong Li MD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Department of Ophthalmology, The First People's Hospital of Kashi Prefecture (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashi, China
Search for more papers by this authorXin Ma MD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Department of Ophthalmology, The First People's Hospital of Kashi Prefecture (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashi, China
Search for more papers by this authorKaichen Zhuo MD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Department of Ophthalmology, The First People's Hospital of Kashi Prefecture (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashi, China
Search for more papers by this authorYuxian He MD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Department of Ophthalmology, The First People's Hospital of Kashi Prefecture (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashi, China
Search for more papers by this authorMingkai Lin MD, PhD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Search for more papers by this authorWei Wang MD, PhD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Search for more papers by this authorShicheng Guo PhD
School of Life Sciences, Fudan University, Shanghai, China
Search for more papers by this authorChao Tang PhD
National Clinical Research Center for Child Health of the Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
Search for more papers by this authorCorresponding Author
Xu Zhang MD
Center for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Center for Reproductive Medicine, Chongqing Health Center for Women and Children, Chongqing Reproductive Genetics Institute, Chongqing, China
Correspondence
Xinbo Gao, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, No. 7 Jinsui Rd, Tianhe District, Guangzhou 510623, China.
Email: [email protected]
Xu Zhang, Center for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Center for Reproductive Medicine, Chongqing Health Center for Women and Children, Chongqing Reproductive Genetics Institute, No. 64 Jintang Street of Qixinggang, Yuzhong District, Chongqing 400013, China.
Email: [email protected]
Search for more papers by this authorCorresponding Author
Xinbo Gao MD, PhD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Department of Ophthalmology, The First People's Hospital of Kashi Prefecture (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashi, China
Correspondence
Xinbo Gao, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, No. 7 Jinsui Rd, Tianhe District, Guangzhou 510623, China.
Email: [email protected]
Xu Zhang, Center for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Center for Reproductive Medicine, Chongqing Health Center for Women and Children, Chongqing Reproductive Genetics Institute, No. 64 Jintang Street of Qixinggang, Yuzhong District, Chongqing 400013, China.
Email: [email protected]
Search for more papers by this authorJiayong Li MD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Department of Ophthalmology, The First People's Hospital of Kashi Prefecture (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashi, China
Search for more papers by this authorXin Ma MD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Department of Ophthalmology, The First People's Hospital of Kashi Prefecture (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashi, China
Search for more papers by this authorKaichen Zhuo MD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Department of Ophthalmology, The First People's Hospital of Kashi Prefecture (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashi, China
Search for more papers by this authorYuxian He MD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Department of Ophthalmology, The First People's Hospital of Kashi Prefecture (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashi, China
Search for more papers by this authorMingkai Lin MD, PhD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Search for more papers by this authorWei Wang MD, PhD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Search for more papers by this authorShicheng Guo PhD
School of Life Sciences, Fudan University, Shanghai, China
Search for more papers by this authorChao Tang PhD
National Clinical Research Center for Child Health of the Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
Search for more papers by this authorCorresponding Author
Xu Zhang MD
Center for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Center for Reproductive Medicine, Chongqing Health Center for Women and Children, Chongqing Reproductive Genetics Institute, Chongqing, China
Correspondence
Xinbo Gao, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, No. 7 Jinsui Rd, Tianhe District, Guangzhou 510623, China.
Email: [email protected]
Xu Zhang, Center for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Center for Reproductive Medicine, Chongqing Health Center for Women and Children, Chongqing Reproductive Genetics Institute, No. 64 Jintang Street of Qixinggang, Yuzhong District, Chongqing 400013, China.
Email: [email protected]
Search for more papers by this authorCorresponding Author
Xinbo Gao MD, PhD
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
Department of Ophthalmology, The First People's Hospital of Kashi Prefecture (The Affiliated Kashi Hospital of Sun Yat-Sen University), Kashi, China
Correspondence
Xinbo Gao, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, No. 7 Jinsui Rd, Tianhe District, Guangzhou 510623, China.
Email: [email protected]
Xu Zhang, Center for Reproductive Medicine, Women and Children's Hospital of Chongqing Medical University, Center for Reproductive Medicine, Chongqing Health Center for Women and Children, Chongqing Reproductive Genetics Institute, No. 64 Jintang Street of Qixinggang, Yuzhong District, Chongqing 400013, China.
Email: [email protected]
Search for more papers by this authorAbstract
Background
Glaucoma is the most common cause of irreversible blindness, and gut microbiota (GM) is associated with glaucoma. Whether this association represents a causal role remains unknown. This study aims to assess the potential association and causal link between GM and various forms of glaucoma, emphasising the need for cautious interpretation of the strength of these associations.
Methods
Employing a two-sample bidirectional Mendelian randomisation (MR) framework with false discovery rate correction and various sensitivity analyses, supplemented by genetic correlation analysis via linkage disequilibrium score regression (LDSC) and colocalisation for European summary-level data between MiBioGen consortium and FinnGen Study, we sought to explore the relationship between GM and glaucoma.
Results
While certain microbial taxa showed potential associations with glaucoma subtypes (e.g., Erysipelotrichaceae with primary angle closure glaucoma, Senegalimassilia with exfoliation glaucoma), the overall findings suggest a complex and not definitively causal relationship between GM and glaucoma. Notably, reverse MR analysis did not establish a significant causal effect of glaucoma on GM composition, and no consistent genetic correlations were observed between GM and glaucoma.
Conclusions
While our study provides some evidence of associations between specific GM taxa and glaucoma, the results underscore the complexity of these relationships and the need for further research to clarify the potential causal links. The findings highlight the importance of interpreting the gut-eye axis with caution and suggest that while GM may play a role in glaucoma, it is unlikely to be a predominant causal factor.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available in MRCIEU/opengwas at https://gwas.mrcieu.ac.uk, reference number from ebi-a-GCST90016908 to ebi-a-GCST90017118. These data were derived from the following resources available in the public domain: MiBioGen consortium, https://mibiogen.gcc.rug.nl; FinnGen Study, https://r9.finngen.fi.
Supporting Information
| Filename | Description |
|---|---|
| ceo14440-sup-0001-Figures.pdfPDF document, 9.7 MB | Figure S1. The forest plot showed primary results of the forward causal associations between GM and glaucoma. (A) GM: order NB1n id.3953, glaucoma: POAG; (B) GM: family Veillonellaceae id.2172, glaucoma: PACG; (C) GM: genus Escherichia Shigella id.3504, glaucoma: XFG; (D) GM: genus Oscillospira id.2064, glaucoma: XFG; (E) GM: genus Ruminococcaceae UCG003 id.11361, glaucoma: XFG; (F) GM: genus Ruminococcaceae NK4A214 group id.11358, glaucoma: NTG; (G) GM: genus Coprococcus3 id.11303, glaucoma: POAG; (H) GM: class Erysipelotrichia id.2147, glaucoma: PACG; (I) GM: order Erysipelotrichales id.2148, glaucoma: PACG; (J) GM: family Erysipelotrichaceae id.2149, glaucoma: PACG; (K) GM: genus Anaerotruncus id.2054, glaucoma: PACG; (L) GM: class Bacteroidia id.912, glaucoma: XFG; (M) GM: order Bacteroidales id.913, glaucoma: XFG; (N) GM: genus Senegalimassilia id.11160, glaucoma: XFG; (O) GM: genus Ruminococcus gauvreauii group id.11342, glaucoma: NTG; GM, gut microbiota; POAG, primary open-angle glaucoma; PACG, primary angle closure glaucoma; XFG, exfoliation glaucoma; NTG, normal tension glaucoma. Figure S2. Scatter plots for the forward causal association between GM and glaucoma. (A) GM: order NB1n id.3953, glaucoma: POAG; (B) GM: genus Coprococcus3 id.11303, glaucoma: POAG; (C) GM: class Erysipelotrichia id.2147, glaucoma: PACG; (D) GM: order Erysipelotrichales id.2148, glaucoma: PACG; (E) GM: family Erysipelotrichaceae id.2149, glaucoma: PACG; (F) GM: family Veillonellaceae id.2172, glaucoma: PACG; (G), GM: genus Anaerotruncus id.2054, glaucoma: PACG; (H) GM: class Bacteroidia id.912, glaucoma: XFG; (I) GM: order Bacteroidales id.913, glaucoma: XFG; (J) GM: genus Escherichia Shigella id.3504, glaucoma: XFG; (K) GM: genus Oscillospira id.2064, glaucoma: XFG; (L) GM: genus Ruminococcaceae UCG003 id.11361, glaucoma: XFG; (M) GM: genus Senegalimassilia id.11160, glaucoma: XFG; (N) GM: genus Ruminococcaceae NK4A214 group id.11358, glaucoma: NTG; (O) GM: genus Ruminococcus gauvreauii group id.11342, glaucoma: NTG; GM, gut microbiota; POAG, primary open-angle glaucoma; PACG, primary angle closure glaucoma; XFG, exfoliation glaucoma; NTG, normal tension glaucoma. From top to bottom panels, MR egger and inverse variance weighted, respectively. Figure S3. Results of Radial MR for the forward causal association between GM and glaucoma. (A) GM: order NB1n id.3953, glaucoma: POAG; (B) GM: genus Coprococcus3 id.11303, glaucoma: POAG; (C) GM: class Erysipelotrichia id.2147, glaucoma: PACG; (D) GM: order Erysipelotrichales id.2148, glaucoma: PACG; (E) GM: family Erysipelotrichaceae id.2149, glaucoma: PACG; (F) GM: family Veillonellaceae id.2172, glaucoma: PACG; (G) GM: genus Anaerotruncus id.2054, glaucoma: PACG; (H) GM: class Bacteroidia id.912, glaucoma: XFG; (I) GM: order Bacteroidales id.913, glaucoma: XFG; (J) GM: genus Escherichia Shigella id.3504, glaucoma: XFG; (K) GM: genus Oscillospira id.2064, glaucoma: XFG; (L) GM: genus Ruminococcaceae UCG003 id.11361, glaucoma: XFG; (M) GM: genus Ruminococcaceae NK4A214 group id.11358, glaucoma: NTG; (N) GM: genus Ruminococcus gauvreauii group id.11342, glaucoma: NTG; MR, Mendelian randomisation; GM, gut microbiota; POAG, primary open-angle glaucoma; PACG, primary angle closure glaucoma; XFG, exfoliation glaucoma; NTG, normal tension glaucoma. Figure S4. The reverse causal effect of the gut microbiota on glaucoma at five levels based on MR analysis. From outside to inside, the p values of MR Egger, weighted median, and IVW of POAG, XFG, and NTG, respectively. The outside number for ID of gut microbiota; Red for p < 0.05; Blue for p > 0.05; MR, Mendelian randomisation; IVW, inverse variance weighted; POAG, primary open-angle glaucoma; XFG, exfoliation glaucoma; NTG, normal tension glaucoma. Figure S5. The forest plot showed primary results of the reverse causal associations between GM and glaucoma. (A) GM: family Acidaminococcaceae id.2166, glaucoma: POAG; (B) GM: phylum Euryarchaeota id.55, glaucoma: XFG; (C) GM: class Methanobacteria id.119, glaucoma: XFG; (D) GM: order Bifidobacteriales id.432, glaucoma: XFG; (E) GM: order Methanobacteriales id.120, glaucoma: XFG; (F) GM: family Bifidobacteriaceae id.433, glaucoma: XFG; (G) GM: family Methanobacteriaceae id.121, glaucoma: XFG; (H) GM: genus Marvinbryantia id.2005, glaucoma: XFG; (I) GM: genus Methanobrevibacter id.123, glaucoma: XFG; (J) GM: phylum Euryarchaeota id.55, glaucoma: NTG; (K) GM: class Methanobacteria id.119, glaucoma: NTG; (L) GM: order Methanobacteriales id.120, glaucoma: NTG; (M) GM: family Methanobacteriaceae id.121, glaucoma: NTG; (N) GM: genus Marvinbryantia id.2005, glaucoma: NTG; (O) GM: genus Ruminococcus gnavus group id.14376, glaucoma: NTG; GM, gut microbiota; POAG, primary open-angle glaucoma; PACG, primary angle closure glaucoma; XFG, exfoliation glaucoma; NTG, normal tension glaucoma. Figure S6. Scatter plots for the reverse causal association between GM and glaucoma. (A) GM: family Acidaminococcaceae id.2166, glaucoma: POAG; (B) GM: phylum Euryarchaeota id.55, glaucoma: XFG; (C) GM: class Methanobacteria id.119, glaucoma: XFG; (D) GM: order Bifidobacteriales id.432, glaucoma: XFG; (E) GM: order Methanobacteriales id.120, glaucoma: XFG; (F) GM: family Bifidobacteriaceae id.433, glaucoma: XFG; (G) GM: family Methanobacteriaceae id.121, glaucoma: XFG; (H) GM: genus Marvinbryantia id.2005, glaucoma: XFG; (I) GM: genus Methanobrevibacter id.123, glaucoma: XFG; (J) GM: phylum Euryarchaeota id.55, glaucoma: NTG; (K) GM: class Methanobacteria id.119, glaucoma: NTG; (L) GM: order Methanobacteriales id.120, glaucoma: NTG; (M) GM: family Methanobacteriaceae id.121, glaucoma: NTG; (N) GM: genus Marvinbryantia id.2005, glaucoma: NTG; (O) GM: genus Ruminococcus gnavus group id.14376, glaucoma: NTG; GM, gut microbiota; POAG, primary open-angle glaucoma; PACG, primary angle closure glaucoma; XFG, exfoliation glaucoma; NTG, normal tension glaucoma. From top to bottom panels, MR egger and inverse variance weighted, respectively. Figure S7. Leave-one-out plots for the forward causal association between gut microbiota and glaucoma (A) GM: order NB1n id.3953, glaucoma: POAG; (B) GM: family Veillonellaceae id.2172, glaucoma: PACG; (C) GM: genus Escherichia Shigella id.3504, glaucoma: XFG; (D) GM: genus Oscillospira id.2064, glaucoma: XFG; (E) GM: genus Ruminococcaceae UCG003 id.11361, glaucoma: XFG; (F) GM: genus Ruminococcaceae NK4A214 group id.11358, glaucoma: NTG; (G) GM: genus Coprococcus3 id.11303, glaucoma: POAG; (H) GM: class Erysipelotrichia id.2147, glaucoma: PACG; (I) GM: order Erysipelotrichales id.2148, glaucoma: PACG; (J) GM: family Erysipelotrichaceae id.2149, glaucoma: PACG; (K) GM: genus Anaerotruncus id.2054, glaucoma: PACG; (L) GM: class Bacteroidia id.912, glaucoma: XFG; (M) GM: order Bacteroidales id.913, glaucoma: XFG; (N) GM: genus Senegalimassilia id.11160, glaucoma: XFG; (O) GM: genus Ruminococcus gauvreauii group id.11342, glaucoma: NTG; GM, gut microbiota; POAG, primary open-angle glaucoma; PACG, primary angle closure glaucoma; XFG, exfoliation glaucoma; NTG, normal tension glaucoma. Figure S8. Leave-one-out plots for the reverse causal association between gut microbiota and glaucoma. (A) GM: family Acidaminococcaceae id.2166, glaucoma: POAG; (B) GM: phylum Euryarchaeota id.55, glaucoma: XFG; (C) GM: class Methanobacteria id.119, glaucoma: XFG; (D) GM: order Bifidobacteriales id.432, glaucoma: XFG; (E) GM: order Methanobacteriales id.120, glaucoma: XFG; (F) GM: family Bifidobacteriaceae id.433, glaucoma: XFG; (G) GM: family Methanobacteriaceae id.121, glaucoma: XFG; (H) GM: genus Marvinbryantia id.2005, glaucoma: XFG; (I) GM: genus Methanobrevibacter id.123, glaucoma: XFG; (J) GM: phylum Euryarchaeota id.55, glaucoma: NTG; (K) GM: class Methanobacteria id.119, glaucoma: NTG; (L) GM: order Methanobacteriales id.120, glaucoma: NTG; (M) GM: family Methanobacteriaceae id.121, glaucoma: NTG; (N) GM: genus Marvinbryantia id.2005, glaucoma: NTG; (O) GM: genus Ruminococcus gnavus group id.14376, glaucoma: NTG; GM, gut microbiota; POAG, primary open-angle glaucoma; PACG, primary angle closure glaucoma; XFG, exfoliation glaucoma; NTG, normal tension glaucoma. Figure S9. Funnel plots for the forward causal association between gut microbiota and glaucoma. (A) GM: order NB1n id.3953, glaucoma: POAG; (B) GM: genus Coprococcus3 id.11303, glaucoma: POAG; (C) GM: class Erysipelotrichia id.2147, glaucoma: PACG; (D) GM: order Erysipelotrichales id.2148, glaucoma: PACG; (E) GM: family Erysipelotrichaceae id.2149, glaucoma: PACG; (F) GM: family Veillonellaceae id.2172, glaucoma: PACG; (G) GM: genus Anaerotruncus id.2054, glaucoma: PACG; (H) GM: class Bacteroidia id.912, glaucoma: XFG; (I) GM: order Bacteroidales id.913, glaucoma: XFG; (J) GM: genus Escherichia Shigella id.3504, glaucoma: XFG; (K) GM: genus Oscillospira id.2064, glaucoma: XFG; (L) GM: genus Ruminococcaceae UCG003 id.11361, glaucoma: XFG; (M) GM: genus Senegalimassilia id.11160, glaucoma: XFG; (N) GM: genus Ruminococcaceae NK4A214 group id.11358, glaucoma: NTG; (O) GM: genus Ruminococcus gauvreauii group id.11342, glaucoma: NTG; GM, gut microbiota; POAG, primary open-angle glaucoma; PACG, primary angle closure glaucoma; XFG, exfoliation glaucoma; NTG, normal tension glaucoma. Figure S10. Funnel plots for the reverse causal association between gut microbiota and glaucoma. (A) GM: family Acidaminococcaceae id.2166, glaucoma: POAG; (B) GM: phylum Euryarchaeota id.55, glaucoma: XFG; (C) GM: class Methanobacteria id.119, glaucoma: XFG; (D) GM: order Bifidobacteriales id.432, glaucoma: XFG; (E) GM: order Methanobacteriales id.120, glaucoma: XFG; (F) GM: family Bifidobacteriaceae id.433, glaucoma: XFG; (G) GM: family Methanobacteriaceae id.121, glaucoma: XFG; (H) GM: genus Marvinbryantia id.2005, glaucoma: XFG; (I) GM: genus Methanobrevibacter id.123, glaucoma: XFG; (J) GM: phylum Euryarchaeota id.55, glaucoma: NTG; (K) GM: class Methanobacteria id.119, glaucoma: NTG; (L) GM: order Methanobacteriales id.120, glaucoma: NTG; (M) GM: family Methanobacteriaceae id.121, glaucoma: NTG; (N) GM: genus Marvinbryantia id.2005, glaucoma: NTG; (O) GM: genus Ruminococcus gnavus group id.14376, glaucoma: NTG; GM, gut microbiota; POAG, primary open-angle glaucoma; PACG, primary angle closure glaucoma; XFG, exfoliation glaucoma; NTG, normal tension glaucoma. Figure S11. Colocalisation analysis between gut microbiota and glaucoma. (A) GM: genus Coprococcus3 id.11303, glaucoma: POAG; (B) GM: class Erysipelotrichia id.2147, glaucoma: PACG; (C) GM: order Erysipelotrichales id.2148, glaucoma: PACG; (D) GM: family Erysipelotrichaceae id.2149, glaucoma: PACG; (E) GM: genus Anaerotruncus id.2054, glaucoma: PACG; (F) GM: class Bacteroidia id.912, glaucoma: XFG; (G) GM: order Bacteroidales id.913, glaucoma: XFG; (H) GM: genus Senegalimassilia id.11160, glaucoma: XFG; (I) GM: genus Ruminococcus gauvreauii group id.11342, glaucoma: NTG; GM, gut microbiota; POAG, primary open-angle glaucoma; PACG, primary angle closure glaucoma; XFG, exfoliation glaucoma; NTG, normal tension glaucoma. From left to right panels, gassocplot, locus comparison plot, respectively. |
| ceo14440-sup-0002-Tables.xlsxExcel 2007 spreadsheet , 1.2 MB | Tables S1–S17. |
| ceo14440-sup-0003-supinfo.docxWord 2007 document , 41.7 KB | Data S1: STROBE-MR. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
REFERENCES
- 1Tham Y-C, Li X, Wong TY, Quigley HA, Aung T, Cheng C-Y. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014; 121(11): 2081-2090. doi:10.1016/j.ophtha.2014.05.013
- 2Jayaram H, Kolko M, Friedman DS, Gazzard G. Glaucoma: now and beyond. Lancet (London, England). 2023; 402(10414): 1788-1801. doi:10.1016/S0140-6736(23)01289-8
- 3Aboobakar IF, Johnson WM, Stamer WD, Hauser MA, Allingham RR. Major review: exfoliation syndrome; advances in disease genetics, molecular biology, and epidemiology. Exp Eye Res. 2017; 154: 88-103. doi:10.1016/j.exer.2016.11.011
- 4Stein JD, Khawaja AP, Weizer JS. Glaucoma in adults-screening, diagnosis, and management: a review. JAMA. 2021; 325(2): 164-174. doi:10.1001/jama.2020.21899
- 5Hakim A, Guido B, Narsineni L, Chen D-W, Foldvari M. Gene therapy strategies for glaucoma from IOP reduction to retinal neuroprotection: progress towards non-viral systems. Adv Drug Deliv Rev. 2023; 196:114781. doi:10.1016/j.addr.2023.114781
- 6Tribble JR, Hui F, Quintero H, et al. Neuroprotection in glaucoma: mechanisms beyond intraocular pressure lowering. Mol Aspects Med. 2023; 92:101193. doi:10.1016/j.mam.2023.101193
- 7Ferreiro AL, Choi J, Ryou J, et al. Gut microbiome composition may be an indicator of preclinical Alzheimer's disease. Sci Transl Med. 2023; 15(700):eabo2984. doi:10.1126/scitranslmed.abo2984
- 8de Wit DF, Hanssen NMJ, Wortelboer K, Herrema H, Rampanelli E, Nieuwdorp M. Evidence for the contribution of the gut microbiome to obesity and its reversal. Sci Transl Med. 2023; 15(723):eadg2773. doi:10.1126/scitranslmed.adg2773
- 9Danne C, Skerniskyte J, Marteyn B, Sokol H. Neutrophils: from IBD to the gut microbiota. Nat Rev Gastroenterol Hepatol. 2023; 21: 184-197. doi:10.1038/s41575-023-00871-3
- 10Huang L, Hong Y, Fu X, et al. The role of the microbiota in glaucoma. Mol Aspects Med. 2023; 94:101221. doi:10.1016/j.mam.2023.101221
- 11Xue W, Li JJ, Zou Y, Zou B, Wei L. Microbiota and ocular diseases. Front Cell Infect Microbiol. 2021; 11:759333. doi:10.3389/fcimb.2021.759333
- 12Pezzino S, Sofia M, Greco LP, et al. Microbiome dysbiosis: a pathological mechanism at the intersection of obesity and glaucoma. Int J Mol Sci. 2023; 24(2): 1166. doi:10.3390/ijms24021166
- 13Sanderson E, Glymour MM, Holmes MV, et al. Mendelian randomization. Nat Rev Methods Primers. 2022; 2:6. doi:10.1038/s43586-021-00092-5
- 14Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ. 2021; 375:n2233. doi:10.1136/bmj.n2233
- 15Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR statement. JAMA. 2021; 326(16): 1614-1621. doi:10.1001/jama.2021.18236
- 16Bulik-Sullivan BK, Loh P-R, Finucane HK, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015; 47(3): 291-295. doi:10.1038/ng.3211
- 17Grotzinger AD, Rhemtulla M, de Vlaming R, et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behav. 2019; 3(5): 513-525. doi:10.1038/s41562-019-0566-x
- 18Giambartolomei C, Vukcevic D, Schadt EE, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014; 10(5):e1004383. doi:10.1371/journal.pgen.1004383
- 19Wallace C. Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLoS Genet. 2020; 16(4):e1008720. doi:10.1371/journal.pgen.1008720
- 20Wallace C. A more accurate method for colocalisation analysis allowing for multiple causal variants. PLoS Genet. 2021; 17(9):e1009440. doi:10.1371/journal.pgen.1009440
- 21Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA. 2017; 318(19): 1925-1926. doi:10.1001/jama.2017.17219
- 22Kurilshikov A, Medina-Gomez C, Bacigalupe R, et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet. 2021; 53(2): 156-165. doi:10.1038/s41588-020-00763-1
- 23Wang J, Kurilshikov A, Radjabzadeh D, et al. Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative. Microbiome. 2018; 6(1): 101. doi:10.1186/s40168-018-0479-3
- 24van der Velde KJ, Imhann F, Charbon B, et al. MOLGENIS research: advanced bioinformatics data software for non-bioinformaticians. Bioinformatics. 2019; 35(6): 1076-1078. doi:10.1093/bioinformatics/bty742
- 25Elsworth B, Lyon M, Alexander T, et al. The MRC IEU OpenGWAS data infrastructure. bioRxiv. 2020:2020.08.10.244293. doi:10.1101/2020.08.10.244293
- 26Hemani G, Zheng J, Elsworth B, et al. The MR-base platform supports systematic causal inference across the human phenome. Elife. 2018; 7:e3440. doi:10.7554/eLife.34408
- 27Kurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023; 613(7944): 508-518. doi:10.1038/s41586-022-05473-8
- 28Sanna S, van Zuydam NR, Mahajan A, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat Genet. 2019; 51(4): 600-605. doi:10.1038/s41588-019-0350-x
- 29Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013; 37(7): 658-665. doi:10.1002/gepi.21758
- 30Li P, Wang H, Guo L, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Med. 2022; 20(1): 443. doi:10.1186/s12916-022-02657-x
- 31Kamat MA, Blackshaw JA, Young R, et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics. 2019; 35(22): 4851-4853. doi:10.1093/bioinformatics/btz469
- 32Staley JR, Blackshaw J, Kamat MA, et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics. 2016; 32(20): 3207-3209.
- 33Canela-Xandri O, Rawlik K, Tenesa A. An atlas of genetic associations in UK Biobank. Nat Genet. 2018; 50(11): 1593-1599. doi:10.1038/s41588-018-0248-z
- 34Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015; 44(2): 512-525. doi:10.1093/ije/dyv080
- 35Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016; 40(4): 304-314. doi:10.1002/gepi.21965
- 36Brion M-JA, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013; 42(5): 1497-1501. doi:10.1093/ije/dyt179
- 37Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol. 2016; 45(6): 1961-1974. doi:10.1093/ije/dyw220
- 38Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018; 50(5): 693-698. doi:10.1038/s41588-018-0099-7
- 39Hemani G, Tilling K, Davey SG. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017; 13(11):e1007081. doi:10.1371/journal.pgen.1007081
- 40Corbin LJ, Richmond RC, Wade KH, et al. BMI as a modifiable risk factor for type 2 diabetes: refining and understanding causal estimates using Mendelian randomization. Diabetes. 2016; 65(10): 3002-3007. doi:10.2337/db16-0418
- 41Yu W, Mei Y, Lu Z, et al. The causal relationship between genetically determined telomere length and meningiomas risk. Front Neurol. 2023; 14:1178404. doi:10.3389/fneur.2023.1178404
- 42Bowden J, Spiller W, Del Greco MF, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol. 2018; 47(4): 1264-1278. doi:10.1093/ije/dyy101
- 43Storm CS, Kia DA, Almramhi MM, et al. Finding genetically-supported drug targets for Parkinson's disease using Mendelian randomization of the druggable genome. Nat Commun. 2021; 12(1): 7342. doi:10.1038/s41467-021-26280-1
- 44Napolitano P, Filippelli M, Davinelli S, Bartollino S, dell'Omo R, Costagliola C. Influence of gut microbiota on eye diseases: an overview. Ann Med. 2021; 53(1): 750-761. doi:10.1080/07853890.2021.1925150
- 45Campagnoli LIM, Varesi A, Barbieri A, Marchesi N, Pascale A. Targeting the gut-eye axis: an emerging strategy to face ocular diseases. Int J Mol Sci. 2023; 24(17):13338. doi:10.3390/ijms241713338
- 46Radjabzadeh D, Bosch JA, Uitterlinden AG, et al. Gut microbiome-wide association study of depressive symptoms. Nat Commun. 2022; 13(1): 7128. doi:10.1038/s41467-022-34502-3
- 47Low L, Suleiman K, Shamdas M, et al. Gut dysbiosis in ocular mucous membrane pemphigoid. Front Cell Infect Microbiol. 2022; 12:780354. doi:10.3389/fcimb.2022.780354
- 48Vergroesen JE, Jarrar ZA, Weiss S, et al. Glaucoma patients have a lower abundance of butyrate-producing taxa in the gut. Invest Ophthalmol Vis Sci. 2024; 65(2):7. doi:10.1167/iovs.65.2.7
- 49Wang B, Liu J, Lei R, et al. Cold exposure, gut microbiota, and hypertension: a mechanistic study. Sci Total Environ. 2022; 833:155199. doi:10.1016/j.scitotenv.2022.155199
- 50López-García E, Benítez-Cabello A, Arenas-de Larriva AP, et al. Application of compositional data analysis to study the relationship between bacterial diversity in human faeces and sex, age, and weight. Biomedicines. 2023; 11(8): 2134. doi:10.3390/biomedicines11082134
- 51McCann SE, Hullar MAJ, Tritchler DL, et al. Enterolignan production in a flaxseed intervention study in postmenopausal US women of African ancestry and European ancestry. Nutrients. 2021; 13(3): 919. doi:10.3390/nu13030919
- 52Gu XM, Lu CY, Pan J, Ye JZ, Zhu QH. Alteration of intestinal microbiota is associated with diabetic retinopathy and its severity: samples collected from southeast coast Chinese. World J Diabetes. 2023; 14(6): 862-882. doi:10.4239/wjd.v14.i6.862
- 53Qian Y, Yang X, Xu S, et al. Alteration of the fecal microbiota in Chinese patients with Parkinson's disease. Brain Behav Immun. 2018; 70: 194-202. doi:10.1016/j.bbi.2018.02.016
- 54Santos-Marcos JA, Haro C, Vega-Rojas A, et al. Sex differences in the gut microbiota as potential determinants of gender predisposition to disease. Mol Nutr Food Res. 2019; 63(7):e1800870. doi:10.1002/mnfr.201800870
- 55Donabedian P, Dawson E, Li Q, Chen J. Gut microbes and eye disease. Ophthalmic Res. 2022; 65(3): 245-253. doi:10.1159/000519457
- 56Chen S, Wang N, Xiong S, Xia X. The correlation between primary open-angle glaucoma (POAG) and gut microbiota: a pilot study towards predictive, preventive, and personalized medicine. EPMA J. 2023; 14(3): 539-552. doi:10.1007/s13167-023-00336-2
- 57Zhou J, Ouyang J, Gao Z, Qin H, Jun W, Shi T. MagMD: database summarizing the metabolic action of gut microbiota to drugs. Comput Struct Biotechnol J. 2022; 20: 6427-6430. doi:10.1016/j.csbj.2022.11.021
- 58Gong H, Zhang S, Li Q, et al. Gut microbiota compositional profile and serum metabolic phenotype in patients with primary open-angle glaucoma. Exp Eye Res. 2020; 191:107921. doi:10.1016/j.exer.2020.107921
- 59Gong H, Zeng R, Li Q, et al. The profile of gut microbiota and central carbon-related metabolites in primary angle-closure glaucoma patients. Int Ophthalmol. 2022; 42(6): 1927-1938. doi:10.1007/s10792-021-02190-5
- 60Wu Y, Shi R, Chen H, et al. Effect of the gut microbiome in glaucoma risk from the causal perspective. BMJ Open Ophthalmol. 2024; 9(1):e001547. doi:10.1136/bmjophth-2023-001547
- 61Li C, Lu P. Association of gut microbiota with age-related macular degeneration and glaucoma: a bidirectional Mendelian randomization study. Nutrients. 2023; 15(21):4646. doi:10.3390/nu15214646
- 62Zhou X, Xu J, Zhang X, Zhao Y, Duan X. Causal relationships between Gut microbiota and primary open-angle Glaucoma: a Mendelian randomization and mediation analysis of Glaucoma endophenotypes. Exp Eye Res. 2024; 240:109788. doi:10.1016/j.exer.2024.109788
