Purpose: To identify plasma proteins that are causally related to primary open-angle glaucoma (POAG) for potential therapeutic targeting. Methods: Summary statistics of plasma protein quantitative trait loci (pQTL) were derived from two extensive genome-wide analysis study (GWAS) datasets and one systematic review, with over 100 thousand participants covering thousands of plasma proteins. POAG data were sourced from the largest FinnGen study, comprising 8,530 DR cases and 391,275 European controls. A two-sample MR analysis, supplemented by bidirectional MR, Bayesian co-localization analysis, and phenotype scanning, was conducted to examine the causal relationships between plasma proteins and POAG. The analysis was validated by identifying associations between plasma proteins and POAG-related traits, including intraocular pressure (IOP), retinal nerve fibre layer (RNFL), and ganglion cell and inner plexiform layer (GCIPL). By searching druggable gene lists, the ChEMBL database, and the ClinicalTrials.gov database, the druggability and clinical development activity of the identified proteins were systematically evaluated. Results: Eighteen proteins were identified with significant associations with POAG risk after multiple comparison adjustments. The ORs per standard deviation increase in protein levels ranged from 0.39 (95% CI: 0.24–0.62; P = 7.70×10-5) for phospholipase C gamma 1 (PLCG1) to 1.29 (95% CI: 1.16–1.44; P = 6.72×10-6) for nidogen-1 (NID1). Bidirectional MR indicated that reverse causality did not interfere with the results of the main MR analyses. Five proteins exhibited strong co-localization evidence (PH4 ≥ 0.8): protein sel-1 homolog 1 (SEL1L), tyrosine-protein kinase receptor UFO (AXL), nidogen-1 (NID1) and FAD-linked sulfhydryl oxidase ALR (GFER) were negatively associated with POAG risk, while roundabout homolog 1 (ROBO1) showed a positive association. The phenotype scanning did not reveal any confounding factors between pQTLs and POAG. Further, validation analyses identified nine proteins causally related to POAG traits, with five proteins including interleukin-18 receptor 1 (IL18R1), interleukin-1 receptor type 1 (IL1R1), phospholipase C gamma 1 (PLCG1), ribonuclease pancreatic (RNASE1), serine protease inhibitor Kazal-type 6 (SPINK6) revealing consistent directional associations. In addition, 18 causal proteins were highlighted for their druggability, of which 5 proteins are either already approved drugs or in clinical trials and 13 proteins are novel drug targets. Conclusions: This study identifies 18 plasma proteins as potential therapeutic targets for POAG, particularly emphasizing the role of genomic and proteomic integration in drug discovery. Future experimental and clinical studies should be conducted to validate the efficacy of these proteins and to conduct more comprehensive proteomic explorations, thus taking a significant leap toward innovative POAG treatments.

Purpose: To identify plasma proteins that are causally related to primary open-angle glaucoma (POAG) for potential therapeutic targeting. Methods: Summary statistics of plasma protein quantitative trait loci (pQTL) were derived from two extensive genome-wide analysis study (GWAS) datasets and one systematic review, with over 100 thousand participants covering thousands of plasma proteins. POAG data were sourced from the largest FinnGen study, comprising 8,530 DR cases and 391,275 European controls. A two-sample MR analysis, supplemented by bidirectional MR, Bayesian co-localization analysis, and phenotype scanning, was conducted to examine the causal relationships between plasma proteins and POAG. The analysis was validated by identifying associations between plasma proteins and POAG-related traits, including intraocular pressure (IOP), retinal nerve fibre layer (RNFL), and ganglion cell and inner plexiform layer (GCIPL). By searching druggable gene lists, the ChEMBL database, and the ClinicalTrials.gov database, the druggability and clinical development activity of the identified proteins were systematically evaluated. Results: Eighteen proteins were identified with significant associations with POAG risk after multiple comparison adjustments. The ORs per standard deviation increase in protein levels ranged from 0.39 (95% CI: 0.24–0.62; P = 7.70×10-5) for phospholipase C gamma 1 (PLCG1) to 1.29 (95% CI: 1.16–1.44; P = 6.72×10-6) for nidogen-1 (NID1). Bidirectional MR indicated that reverse causality did not interfere with the results of the main MR analyses. Five proteins exhibited strong co-localization evidence (PH4 ≥ 0.8): protein sel-1 homolog 1 (SEL1L), tyrosine-protein kinase receptor UFO (AXL), nidogen-1 (NID1) and FAD-linked sulfhydryl oxidase ALR (GFER) were negatively associated with POAG risk, while roundabout homolog 1 (ROBO1) showed a positive association. The phenotype scanning did not reveal any confounding factors between pQTLs and POAG. Further, validation analyses identified nine proteins causally related to POAG traits, with five proteins including interleukin-18 receptor 1 (IL18R1), interleukin-1 receptor type 1 (IL1R1), phospholipase C gamma 1 (PLCG1), ribonuclease pancreatic (RNASE1), serine protease inhibitor Kazal-type 6 (SPINK6) revealing consistent directional associations. In addition, 18 causal proteins were highlighted for their druggability, of which 5 proteins are either already approved drugs or in clinical trials and 13 proteins are novel drug targets. Conclusions: This study identifies 18 plasma proteins as potential therapeutic targets for POAG, particularly emphasizing the role of genomic and proteomic integration in drug discovery. Future experimental and clinical studies should be conducted to validate the efficacy of these proteins and to conduct more comprehensive proteomic explorations, thus taking a significant leap toward innovative POAG treatments.

Background: Diabetic retinopathy (DR) urgently needs novel and effective therapeutic targets. Integrated analyses of plasma proteomic and genetic markers can clarify the causal relevance of proteins and discover novel targets for diseases, but no systematic screening for DR has been performed.

Methods: Summary statistics of plasma protein quantitative trait loci (pQTL) were derived from two extensive genome-wide analysis study (GWAS) datasets and one systematic review, with over 100 thousand participants covering thousands of plasma proteins. DR data were sourced from the largest FinnGen study, comprising 10,413 DR cases and 308,633 European controls. Genetic instrumental variables were identified using multiple filters. In the two-sample MR analysis, Wald ratio and inverse variance-weighted (IVW) MR were utilized to investigate the

causality of plasma proteins with DR. Bidirectional MR, Bayesian Co-localization, and phenotype scanning were employed to test for potential reverse causality and confounding factors in the main MR analyses. By systemically searching druggable gene lists, the ChEMBL database, DrugBank, and Gene Ontology database, the druggability and relevant functional pathways of the identified proteins were systematically evaluated.

Results: Genetically predicted levels of 24 proteins were significantly associated with DR risk at a false discovery rate <0.05 including 11 with positive associations and 13 with negative associations. For each standard deviation increase in plasm protein levels, the odds ratios (ORs) for DR varied from 0.51 (95% CI: 0.36-0.73; P=2.22×10-5) for tubulin polymerization-promoting protein family member 3 (TPPP3) to 2.02 (95% CI: 1.44-2.83; P=5.01×10-5) for olfactomedin like 3 (OLFML3). Bidirectional MR indicated there was no reverse causality that interfered with the results of the main MR analyses. Four proteins exhibited strong co-localization evidence (PH4 ≥0.8): cytoplasmic tRNA synthetase (WARS), acrosin binding protein(ACRBP), and intercellular adhesion molecule 1 (ICAM1) were negatively associated with DR risk, while neurogenic locus notch homolog protein 2 (NOTCH2) showed a positive association. No confounding factors were detected between pQTLs and DR according to the phenotypic scan. Drugability assessments highlighted 6 proteins already in drug development endeavor and 18 novel drug targets, with metalloproteinase inhibitor 3 (TIMP) currently in phase I clinical trials for DR. GO analysis identified 18 of 24 plasma proteins enriching 22 pathways related to cell differentiation and proliferation regulation.

Conclusions:Twenty-four promising drug targets for DR were identified, including four plasma proteins with particular co-localization evidence. These findings offer new insights into DR's etiology and therapeutic targeting, exemplifying the value of genomic and proteomic data in drug target discovery.

Background: Diabetic retinopathy (DR) urgently needs novel and effective therapeutic targets. Integrated analyses of plasma proteomic and genetic markers can clarify the causal relevance of proteins and discover novel targets for diseases, but no systematic screening for DR has been performed.

Methods: Summary statistics of plasma protein quantitative trait loci (pQTL) were derived from two extensive genome-wide analysis study (GWAS) datasets and one systematic review, with over 100 thousand participants covering thousands of plasma proteins. DR data were sourced from the largest FinnGen study, comprising 10,413 DR cases and 308,633 European controls. Genetic instrumental variables were identified using multiple filters. In the two-sample MR analysis, Wald ratio and inverse variance-weighted (IVW) MR were utilized to investigate the

causality of plasma proteins with DR. Bidirectional MR, Bayesian Co-localization, and phenotype scanning were employed to test for potential reverse causality and confounding factors in the main MR analyses. By systemically searching druggable gene lists, the ChEMBL database, DrugBank, and Gene Ontology database, the druggability and relevant functional pathways of the identified proteins were systematically evaluated.

Results: Genetically predicted levels of 24 proteins were significantly associated with DR risk at a false discovery rate <0.05 including 11 with positive associations and 13 with negative associations. For each standard deviation increase in plasm protein levels, the odds ratios (ORs) for DR varied from 0.51 (95% CI: 0.36-0.73; P=2.22×10-5) for tubulin polymerization-promoting protein family member 3 (TPPP3) to 2.02 (95% CI: 1.44-2.83; P=5.01×10-5) for olfactomedin like 3 (OLFML3). Bidirectional MR indicated there was no reverse causality that interfered with the results of the main MR analyses. Four proteins exhibited strong co-localization evidence (PH4 ≥0.8): cytoplasmic tRNA synthetase (WARS), acrosin binding protein(ACRBP), and intercellular adhesion molecule 1 (ICAM1) were negatively associated with DR risk, while neurogenic locus notch homolog protein 2 (NOTCH2) showed a positive association. No confounding factors were detected between pQTLs and DR according to the phenotypic scan. Drugability assessments highlighted 6 proteins already in drug development endeavor and 18 novel drug targets, with metalloproteinase inhibitor 3 (TIMP) currently in phase I clinical trials for DR. GO analysis identified 18 of 24 plasma proteins enriching 22 pathways related to cell differentiation and proliferation regulation.

Conclusions:Twenty-four promising drug targets for DR were identified, including four plasma proteins with particular co-localization evidence. These findings offer new insights into DR's etiology and therapeutic targeting, exemplifying the value of genomic and proteomic data in drug target discovery.

目的：探讨无糖尿病性视网膜病变(diabetic retinopathy，DR)的糖尿病人群中，糖尿病与近视对黄斑区节细胞-内丛状层(ganglion cell layer and inner plexiform layer，GCIPL)厚度纵向变化的影响。方法：纳入广州糖尿病眼病研究中1165名基线无视网膜病变的糖尿病和正常对照者，纵向随访2年。根据是否存在近视[等效球镜(spherical equivalent，SE)≤-3屈光度(diopter，D)]和糖尿病分为健康组(n=508)、糖尿病组(n=525)及糖尿病合并近视组(n=132)。扫频光学相干断层成像(swept source-optical coherence tomography，SS-OCT)技术测量并比较三组间GCIPL厚度的变化，以确定糖尿病和近视的影响，三组间差异使用协方差分析，采用线性混合模型分析评估GCIPL厚度与相关因素的关系。结果：对照组的SE为(1.07±1.06) D，糖尿病组为(1.02±1.00) D，糖尿病合并近视组为(-5.36±2.30) D，组间差异有统计学意义(P<0.001)。对照组基线GCIPL厚度为(71.1±0.3) μm，糖尿病组为(74.4±0.2)μm，糖尿病合并近视组为(71.7±0.5) μm。在2年随访过程中，对照组GCIPL厚度下降-0.10(95%CI：-2.03~0.05) μm/年，糖尿病组GCIPL厚度下降的速度为对照组的12倍[-1.21(95%CI：-24.04~0.05) μm/年，P<0.001]，糖尿病合并近视组GCIPL厚度下降的速度为对照组的22倍[-2.17(95%CI：-21.63~0.10)μm/年，P<0.001]。结论：近视是无DR的糖尿病患者中GCIPL加速变薄的危险因素，糖尿病和近视在GCIPL损伤中可能存在协同作用。

Objective: To investigate the association between myopia and ganglion cell layer and inner plexiform layer (GCIPL) in diabetic population without diabetic retinopathy (DR). Methods: In this Guangzhou Diabetic Eye study, a total of 1 165 patients aged 30–80 years were recruited followed up longitudinally for 2 years. According to the presence or absence of myopia [spherical equivalence (SE)≤-3 diopter (D)] and diabetics, the patients were divided into a healthy group (n=508), a diabetes mellitus group (n=525), and a diabetes mellitus + myopia group (n=132). GCIPL was measured via swept-source optical coherence tomography. Univariable and multivariable mixed models were used to show the association of GCIPL change and baseline parameters. Results: SE was (1.07±1.06) D in the healthy group, (1.02±1.00) D in the diabetes mellitus group and (-5.36±2.30) D in the diabetes mellitus + myopia group (P<0.001). The baseline GCIPL thickness were (71.1±0.3), (74.4±0.2), and (71.7±0.5) μm, respectively. The slope of GCIPL thickness was -0.10 (95% CI: -2.03 to 0.05) μm/year in the healthy group, which was 12 folds faster than those in the diabetes mellitus group [-1.21(95% CI: -24.04 to 0.05 μm/year, P<0.001] and 22 folds higher among those in diabetes mellitus + myopia group [-2.17 (95% CI: -21.63 to 0.10) μm/year, P=0.009]. Conclusion: Both myopia and diabetes status accelerate macular ganglion cell layer and inner plexiform layer thinning in diabetic patients without diabetic retinopathy.

目的：探讨无糖尿病性视网膜病变(diabetic retinopathy，DR)的糖尿病人群中，糖尿病与近视对黄斑区节细胞-内丛状层(ganglion cell layer and inner plexiform layer，GCIPL)厚度纵向变化的影响。方法：纳入广州糖尿病眼病研究中1 165名基线无视网膜病变的糖尿病和正常对照者，纵向随访2年。根据是否存在近视[等效球镜(spherical equivalent，SE)≤-3 屈光度(diopter，D)]和糖尿病分为健康组(n =508)、糖尿病组(n =525)及糖尿病合并近视组(n =132)。扫频光学相干断层成像(swept source-optical coherence tomography，SS-OCT)技术测量并比较三组间GCIPL厚度的变化，以确定糖尿病和近视的影响，三组间差异使用协方差分析，采用线性混合模型分析评估GCIPL厚度与相关因素的关系。结果：对照组的SE为(1.07±1.06) D，糖尿病组为(1.02±1.00) D，糖尿病合并近视组为(-5.36±2.30D)，组间差异有统计学意义(P<0.001)。对照组基线GCIPL厚度为(71.1±0.3) μm，糖尿病组为(74.4±0.2) μm，糖尿病合并近视组为(71.7±0.5) μm。在2年随访过程中，对照组GCIPL厚度下降-0.10(95%CI：0.05~-2.03) μm/年，糖尿病组GCIPL厚度下降的速度为对照组的12倍[-1.21(95%CI：0.05~?24.04) μm/年，P<0.001]，糖尿病合并近视组GCIPL厚度下降的速度为对照组的22倍[-2.17(95%CI：0.10~-21.63) μm/年，P<0.001]。结论：近视是无DR的糖尿病患者中GCIPL加速变薄的危险因素，糖尿病和近视GCIPL损伤中可能存在协同作用。

Objective: To investigate the association between myopia and ganglion cell layer and inner plexiform layer(GCIPL) in diabetic population without diabetic retinopathy (DR). Methods: In this Guangzhou Diabetic Eyestudy, a total of 1165 patients aged 30–80 years were recruited followed up longitudinally for 2 years. According tothe presence or absence of myopia [spherical equivalence (SE)≤-3 diopter (D)] and diabetics, the patients weredivided into a healthy group (n=508), a diabetes mellitus group (n=525), and a diabetes mellitus + myopia group(n=132). GCIPL was measured via swept-source optical coherence tomography. Univariable and multivariablemixed models were used to show the association of GCIPL change and baseline parameters. Results: SE was(1.07±1.06) D in the healthy group, (1.02±1.00) D in the diabetes mellitus group and (-5.36±2.30) D in thediabetes mellitus + myopia group (P<0.001). The baseline GCIPL thickness were (71.1±0.3), (74.4±0.2), and(71.7±0.5) μm, respectively. The slope of GCIPL thickness was ?0.10 (95% CI: 0.05 to -2.03) μm/year in the healthy group, which was 12 folds faster than those in the diabetes mellitus group [-1.21(95% CI: 0.05 to-24.04) μm/year, P<0.001] and 22 folds higher among those in diabetes mellitus + myopia group [-2.17 (95%CI: 0.10 to ?21.63) μm/year, P=0.009]. Conclusion: Both myopia and diabetes status accelerate macular ganglioncell layer and inner plexiform layer thinning in diabetic patients without diabetic retinopathy.