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Chang KJ, Wu HY, Chiang PH, Hsu YT, Weng PY, Yu TH, Li CY, Chen YH, Dai HJ, Tsai HY, Chang YJ, Wu YR, Yang YP, Li CT, Hsu CC, Chen SJ, Chen YC, Cheng CY, Hsieh AR, Chiou SH. Decoding and reconstructing disease relations between dry eye and depression: a multimodal investigation comprising meta-analysis, genetic pathways and Mendelian randomization. J Adv Res 2024:S2090-1232(24)00115-2. [PMID: 38548265 DOI: 10.1016/j.jare.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 03/07/2024] [Accepted: 03/23/2024] [Indexed: 04/05/2024] Open
Abstract
INTRODUCTION The clinical presentations of dry eye disease (DED) and depression (DEP) often comanifest. However, the robustness and the mechanisms underlying this association were undetermined. OBJECTIVES To this end, we set up a three-segment study that employed multimodality results (meta-analysis, genome-wide association study [GWAS] and Mendelian randomization [MR]) to elucidate the association, common pathways and causality between DED and DEP. METHODS A meta-analysis comprising 26 case-control studies was first conducted to confirm the DED-DEP association. Next, we performed a linkage disequilibrium (LD)-adjusted GWAS and targeted phenotype association study (PheWAS) in East Asian TW Biobank (TWB) and European UK Biobank (UKB) populations. Single-nucleotide polymorphisms (SNPs) were further screened for molecular interactions and common pathways at the functional gene level. To further elucidate the activated pathways in DED and DEP, a systemic transcriptome review was conducted on RNA sequencing samples from the Gene Expression Omnibus. Finally, 48 MR experiments were implemented to examine the bidirectional causation between DED and DEP. RESULTS Our meta-analysis showed that DED patients are associated with an increased DEP prevalence (OR = 1.83), while DEP patients have a concurrent higher risk of DED (OR = 2.34). Notably, cross-disease GWAS analysis revealed that similar genetic architecture (rG = 0.19) and pleiotropic functional genes contributed to phenotypes in both diseases. Through protein-protein interaction and ontology convergence, we summarized the pleiotropic functional genes under the ontology of immune activation, which was further validated by a transcriptome systemic review. Importantly, the inverse variance-weighted (IVW)-MR experiments in both TWB and UKB populations (p value <0.001) supported the bidirectional exposure-outcome causation for DED-to-DEP and DEP-to-DED. Despite stringent LD-corrected instrumental variable re-selection, the bidirectional causation between DED and DEP remained. CONCLUSION With the multi-modal evidence combined, we consolidated the association and causation between DED and DEP.
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Affiliation(s)
- Kao-Jung Chang
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Department of Ophthalmology, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Department of Medical Education, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan
| | - Hsin-Yu Wu
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Pin-Hsuan Chiang
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Big Data Center, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Department of Statistics, Tamkang University, 251301 No.151, Yingzhuan Rd., Tamsui District, New Taipei, Taiwan
| | - Yu-Tien Hsu
- Department of Social & Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, 02115 No.677 Huntington Avenue, MA, USA
| | - Pei-Yu Weng
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan
| | - Ting-Han Yu
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Cheng-Yi Li
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Yu-Hsiang Chen
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - He-Jhen Dai
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Han-Ying Tsai
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Big Data Center, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Department of Statistics, Tamkang University, 251301 No.151, Yingzhuan Rd., Tamsui District, New Taipei, Taiwan
| | - Yu-Jung Chang
- Department of Statistics, Tamkang University, 251301 No.151, Yingzhuan Rd., Tamsui District, New Taipei, Taiwan
| | - You-Ren Wu
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Institute of Pharmacology, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Yi-Ping Yang
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Institute of Pharmacology, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Cheng-Ta Li
- Department of Psychiatry, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Institute of Cognitive Neuroscience, National Central University, 320317 No. 300, Zhongda Rd., Zhongli District, Jhongli, Taiwan
| | - Chih-Chien Hsu
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Department of Ophthalmology, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Shih-Jen Chen
- Big Data Center, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan
| | - Yu-Chun Chen
- School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Big Data Center, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Department of Family Medicine, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751 No.11 Third Hospital Ave, Singapore; Department of Ophthalmology, Yong Loo Lin school of Medicine, National University of Singapore, 119228 No.21 Lower Kent Ridge Road, Singapore
| | - Ai-Ru Hsieh
- Department of Statistics, Tamkang University, 251301 No.151, Yingzhuan Rd., Tamsui District, New Taipei, Taiwan.
| | - Shih-Hwa Chiou
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Department of Ophthalmology, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Institute of Pharmacology, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan.
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Li L, Zhu H, Zhang Z, Zhao L, Xu L, Jonas RA, Garway-Heath DF, Jonas JB, Wang YX. Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation. JMIR Med Inform 2021; 9:e22664. [PMID: 34003137 PMCID: PMC8170554 DOI: 10.2196/22664] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 09/17/2020] [Accepted: 04/13/2021] [Indexed: 01/27/2023] Open
Abstract
Background Due to the axial elongation–associated changes in the optic nerve and retina in high myopia, traditional methods like optic disc evaluation and visual field are not able to correctly differentiate glaucomatous lesions. It has been clinically challenging to detect glaucoma in highly myopic eyes. Objective This study aimed to develop a neural network to adjust for the dependence of the peripapillary retinal nerve fiber layer (RNFL) thickness (RNFLT) profile on age, gender, and ocular biometric parameters and to evaluate the network’s performance for glaucoma diagnosis, especially in high myopia. Methods RNFLT with 768 points on the circumferential 3.4-mm scan was measured using spectral-domain optical coherence tomography. A fully connected network and a radial basis function network were trained for vertical (scaling) and horizontal (shift) transformation of the RNFLT profile with adjustment for age, axial length (AL), disc-fovea angle, and distance in a test group of 2223 nonglaucomatous eyes. The performance of RNFLT compensation was evaluated in an independent group of 254 glaucoma patients and 254 nonglaucomatous participants. Results By applying the RNFL compensation algorithm, the area under the receiver operating characteristic curve for detecting glaucoma increased from 0.70 to 0.84, from 0.75 to 0.89, from 0.77 to 0.89, and from 0.78 to 0.87 for eyes in the highest 10% percentile subgroup of the AL distribution (mean 26.0, SD 0.9 mm), highest 20% percentile subgroup of the AL distribution (mean 25.3, SD 1.0 mm), highest 30% percentile subgroup of the AL distribution (mean 24.9, SD 1.0 mm), and any AL (mean 23.5, SD 1.2 mm), respectively, in comparison with unadjusted RNFLT. The difference between uncompensated and compensated RNFLT values increased with longer axial length, with enlargement of 19.8%, 18.9%, 16.2%, and 11.3% in the highest 10% percentile subgroup, highest 20% percentile subgroup, highest 30% percentile subgroup, and all eyes, respectively. Conclusions In a population-based study sample, an algorithm-based adjustment for age, gender, and ocular biometric parameters improved the diagnostic precision of the RNFLT profile for glaucoma detection particularly in myopic and highly myopic eyes.
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Affiliation(s)
- Lei Li
- State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing, China.,Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China
| | - Haogang Zhu
- State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing, China.,NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, London, United Kingdom
| | - Zhenyu Zhang
- State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Liang Zhao
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China
| | - Liang Xu
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China
| | - Rahul A Jonas
- Department of Ophthalmology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - David F Garway-Heath
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, London, United Kingdom
| | - Jost B Jonas
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China.,Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital University of Medical Science, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China
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