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691 results on '"Perera, Shamira"'

1. Introducing the Biomechanics-Function Relationship in Glaucoma: Improved Visual Field Loss Predictions from intraocular pressure-induced Neural Tissue Strains

Author

Chuangsuwanich, Thanadet, Nongpiur, Monisha E., Braeu, Fabian A., Tun, Tin A., Thiery, Alexandre, Perera, Shamira, Ho, Ching Lin, Buist, Martin, Barbastathis, George, Aung, Tin, and Girard, Michaël J. A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence
Abstract

Objective. (1) To assess whether neural tissue structure and biomechanics could predict functional loss in glaucoma; (2) To evaluate the importance of biomechanics in making such predictions. Design, Setting and Participants. We recruited 238 glaucoma subjects. For one eye of each subject, we imaged the optic nerve head (ONH) using spectral-domain OCT under the following conditions: (1) primary gaze and (2) primary gaze with acute IOP elevation. Main Outcomes: We utilized automatic segmentation of optic nerve head (ONH) tissues and digital volume correlation (DVC) analysis to compute intraocular pressure (IOP)-induced neural tissue strains. A robust geometric deep learning approach, known as Point-Net, was employed to predict the full Humphrey 24-2 pattern standard deviation (PSD) maps from ONH structural and biomechanical information. For each point in each PSD map, we predicted whether it exhibited no defect or a PSD value of less than 5%. Predictive performance was evaluated using 5-fold cross-validation and the F1-score. We compared the model's performance with and without the inclusion of IOP-induced strains to assess the impact of biomechanics on prediction accuracy. Results: Integrating biomechanical (IOP-induced neural tissue strains) and structural (tissue morphology and neural tissues thickness) information yielded a significantly better predictive model (F1-score: 0.76+-0.02) across validation subjects, as opposed to relying only on structural information, which resulted in a significantly lower F1-score of 0.71+-0.02 (p < 0.05). Conclusion: Our study has shown that the integration of biomechanical data can significantly improve the accuracy of visual field loss predictions. This highlights the importance of the biomechanics-function relationship in glaucoma, and suggests that biomechanics may serve as a crucial indicator for the development and progression of glaucoma., Comment: 19 pages, 2 figures

Published
2024

2. The 3D Structural Phenotype of the Glaucomatous Optic Nerve Head and its Relationship with The Severity of Visual Field Damage

Author

Braeu, Fabian A., Chuangsuwanich, Thanadet, Tun, Tin A., Perera, Shamira A., Husain, Rahat, Kadziauskiene, Aiste, Schmetterer, Leopold, Thiéry, Alexandre H., Barbastathis, George, Aung, Tin, and Girard, Michaël J. A.

Subjects
Computer Science - Machine Learning
Abstract

$\bf{Purpose}$: To describe the 3D structural changes in both connective and neural tissues of the optic nerve head (ONH) that occur concurrently at different stages of glaucoma using traditional and AI-driven approaches. $\bf{Methods}$: We included 213 normal, 204 mild glaucoma (mean deviation [MD] $\ge$ -6.00 dB), 118 moderate glaucoma (MD of -6.01 to -12.00 dB), and 118 advanced glaucoma patients (MD < -12.00 dB). All subjects had their ONHs imaged in 3D with Spectralis optical coherence tomography. To describe the 3D structural phenotype of glaucoma as a function of severity, we used two different approaches: (1) We extracted human-defined 3D structural parameters of the ONH including retinal nerve fiber layer (RNFL) thickness, lamina cribrosa (LC) shape and depth at different stages of glaucoma; (2) we also employed a geometric deep learning method (i.e. PointNet) to identify the most important 3D structural features that differentiate ONHs from different glaucoma severity groups without any human input. $\bf{Results}$: We observed that the majority of ONH structural changes occurred in the early glaucoma stage, followed by a plateau effect in the later stages. Using PointNet, we also found that 3D ONH structural changes were present in both neural and connective tissues. In both approaches, we observed that structural changes were more prominent in the superior and inferior quadrant of the ONH, particularly in the RNFL, the prelamina, and the LC. As the severity of glaucoma increased, these changes became more diffuse (i.e. widespread), particularly in the LC. $\bf{Conclusions}$: In this study, we were able to uncover complex 3D structural changes of the ONH in both neural and connective tissues as a function of glaucoma severity. We hope to provide new insights into the complex pathophysiology of glaucoma that might help clinicians in their daily clinical care.

Published
2023

4. The Three-Dimensional Structural Configuration of the Central Retinal Vessel Trunk and Branches as a Glaucoma Biomarker

Author

Panda, Satish K., Cheong, Haris, Tun, Tin A., Chuangsuwanich, Thanadet, Kadziauskiene, Aiste, Senthil, Vijayalakshmi, Krishnadas, Ramaswami, Buist, Martin L., Perera, Shamira, Cheng, Ching-Yu, Aung, Tin, Thiery, Alexandre H., and Girard, Michael J. A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Purpose: To assess whether the three-dimensional (3D) structural configuration of the central retinal vessel trunk and its branches (CRVT&B) could be used as a diagnostic marker for glaucoma. Method: We trained a deep learning network to automatically segment the CRVT&B from the B-scans of the optical coherence tomography (OCT) volume of the optic nerve head (ONH). Subsequently, two different approaches were used for glaucoma diagnosis using the structural configuration of the CRVT&B as extracted from the OCT volumes. In the first approach, we aimed to provide a diagnosis using only 3D CNN and the 3D structure of the CRVT&B. For the second approach, we projected the 3D structure of the CRVT&B orthographically onto three planes to obtain 2D images, and then a 2D CNN was used for diagnosis. The segmentation accuracy was evaluated using the Dice coefficient, whereas the diagnostic accuracy was assessed using the area under the receiver operating characteristic curves (AUC). The diagnostic performance of the CRVT&B was also compared with that of retinal nerve fiber layer (RNFL) thickness. Results: Our segmentation network was able to efficiently segment retinal blood vessels from OCT scans. On a test set, we achieved a Dice coefficient of 0.81\pm0.07. The 3D and 2D diagnostic networks were able to differentiate glaucoma from non-glaucoma subjects with accuracies of 82.7% and 83.3%, respectively. The corresponding AUCs for CRVT&B were 0.89 and 0.90, higher than those obtained with RNFL thickness alone. Conclusions: Our work demonstrated that the diagnostic power of the CRVT&B is superior to that of a gold-standard glaucoma parameter, i.e., RNFL thickness. Our work also suggested that the major retinal blood vessels form a skeleton -- the configuration of which may be representative of major ONH structural changes as typically observed with the development and progression of glaucoma.

Published
2021

7. Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence

Author

Panda, Satish K., Cheong, Haris, Tun, Tin A., Devella, Sripad K., Krishnadas, Ramaswami, Buist, Martin L., Perera, Shamira, Cheng, Ching-Yu, Aung, Tin, Thiéry, Alexandre H., and Girard, Michaël J. A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The optic nerve head (ONH) typically experiences complex neural- and connective-tissue structural changes with the development and progression of glaucoma, and monitoring these changes could be critical for improved diagnosis and prognosis in the glaucoma clinic. The gold-standard technique to assess structural changes of the ONH clinically is optical coherence tomography (OCT). However, OCT is limited to the measurement of a few hand-engineered parameters, such as the thickness of the retinal nerve fiber layer (RNFL), and has not yet been qualified as a stand-alone device for glaucoma diagnosis and prognosis applications. We argue this is because the vast amount of information available in a 3D OCT scan of the ONH has not been fully exploited. In this study we propose a deep learning approach that can: \textbf{(1)} fully exploit information from an OCT scan of the ONH; \textbf{(2)} describe the structural phenotype of the glaucomatous ONH; and that can \textbf{(3)} be used as a robust glaucoma diagnosis tool. Specifically, the structural features identified by our algorithm were found to be related to clinical observations of glaucoma. The diagnostic accuracy from these structural features was $92.0 \pm 2.3 \%$ with a sensitivity of $90.0 \pm 2.4 \% $ (at $95 \%$ specificity). By changing their magnitudes in steps, we were able to reveal how the morphology of the ONH changes as one transitions from a `non-glaucoma' to a `glaucoma' condition. We believe our work may have strong clinical implication for our understanding of glaucoma pathogenesis, and could be improved in the future to also predict future loss of vision.

Published
2020

8. Towards Label-Free 3D Segmentation of Optical Coherence Tomography Images of the Optic Nerve Head Using Deep Learning

Author

Devalla, Sripad Krishna, Pham, Tan Hung, Panda, Satish Kumar, Zhang, Liang, Subramanian, Giridhar, Swaminathan, Anirudh, Yun, Chin Zhi, Rajan, Mohan, Mohan, Sujatha, Krishnadas, Ramaswami, Senthil, Vijayalakshmi, de Leon, John Mark S., Tun, Tin A., Cheng, Ching-Yu, Schmetterer, Leopold, Perera, Shamira, Aung, Tin, Thiery, Alexandre H., and Girard, Michael J. A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Since the introduction of optical coherence tomography (OCT), it has been possible to study the complex 3D morphological changes of the optic nerve head (ONH) tissues that occur along with the progression of glaucoma. Although several deep learning (DL) techniques have been recently proposed for the automated extraction (segmentation) and quantification of these morphological changes, the device specific nature and the difficulty in preparing manual segmentations (training data) limit their clinical adoption. With several new manufacturers and next-generation OCT devices entering the market, the complexity in deploying DL algorithms clinically is only increasing. To address this, we propose a DL based 3D segmentation framework that is easily translatable across OCT devices in a label-free manner (i.e. without the need to manually re-segment data for each device). Specifically, we developed 2 sets of DL networks. The first (referred to as the enhancer) was able to enhance OCT image quality from 3 OCT devices, and harmonized image-characteristics across these devices. The second performed 3D segmentation of 6 important ONH tissue layers. We found that the use of the enhancer was critical for our segmentation network to achieve device independency. In other words, our 3D segmentation network trained on any of 3 devices successfully segmented ONH tissue layers from the other two devices with high performance (Dice coefficients > 0.92). With such an approach, we could automatically segment images from new OCT devices without ever needing manual segmentation data from such devices.

Published
2020

9. DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images

Author

Cheong, Haris, Devalla, Sripad Krishna, Pham, Tan Hung, Liang, Zhang, Tun, Tin Aung, Wang, Xiaofei, Perera, Shamira, Schmetterer, Leopold, Tin, Aung, Boote, Craig, Thiery, Alexandre H., and Girard, Michael J. A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Purpose: To remove retinal shadows from optical coherence tomography (OCT) images of the optic nerve head(ONH). Methods:2328 OCT images acquired through the center of the ONH using a Spectralis OCT machine for both eyes of 13 subjects were used to train a generative adversarial network (GAN) using a custom loss function. Image quality was assessed qualitatively (for artifacts) and quantitatively using the intralayer contrast: a measure of shadow visibility ranging from 0 (shadow-free) to 1 (strong shadow) and compared to compensated images. This was computed in the Retinal Nerve Fiber Layer (RNFL), the Inner Plexiform Layer (IPL), the Photoreceptor layer (PR) and the Retinal Pigment Epithelium (RPE) layers. Results: Output images had improved intralayer contrast in all ONH tissue layers. On average the intralayer contrast decreased by 33.7$\pm$6.81%, 28.8$\pm$10.4%, 35.9$\pm$13.0%, and43.0$\pm$19.5%for the RNFL, IPL, PR, and RPE layers respectively, indicating successful shadow removal across all depths. This compared to 70.3$\pm$22.7%, 33.9$\pm$11.5%, 47.0$\pm$11.2%, 26.7$\pm$19.0%for compensation. Output images were also free from artifacts commonly observed with compensation. Conclusions: DeshadowGAN significantly corrected blood vessel shadows in OCT images of the ONH. Our algorithm may be considered as a pre-processing step to improve the performance of a wide range of algorithms including those currently being used for OCT image segmentation, denoising, and classification. Translational Relevance: DeshadowGAN could be integrated to existing OCT devices to improve the diagnosis and prognosis of ocular pathologies.

Published
2019

11. A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Author

Devalla, Sripad Krishna, Subramanian, Giridhar, Pham, Tan Hung, Wang, Xiaofei, Perera, Shamira, Tun, Tin A., Aung, Tin, Schmetterer, Leopold, Thiery, Alexandre H., and Girard, Michael J. A.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance of the de-noising algorithm was assessed qualitatively, and quantitatively on 1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio (CNR), and mean structural similarity index metrics (MSSIM). Results: The proposed algorithm successfully denoised unseen single-frame OCT B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR increased from $4.02 \pm 0.68$ dB (single-frame) to $8.14 \pm 1.03$ dB (denoised). For all the ONH tissues, the mean CNR increased from $3.50 \pm 0.56$ (single-frame) to $7.63 \pm 1.81$ (denoised). The MSSIM increased from $0.13 \pm 0.02$ (single frame) to $0.65 \pm 0.03$ (denoised) when compared with the corresponding multi-frame B-scans. Conclusions: Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort.

Published
2018

12. DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images

Author

Devalla, Sripad Krishna, Renukanand, Prajwal K., Sreedhar, Bharathwaj K., Perera, Shamira, Mari, Jean-Martial, Chin, Khai Sing, Tun, Tin A., Strouthidis, Nicholas G., Aung, Tin, Thiery, Alexandre H., and Girard, Michael J. A.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning
Abstract

Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm was designed and trained to digitally stain (i.e. highlight) 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall dice coefficient (mean of all tissues) was $0.91 \pm 0.05$ when assessed against manual segmentations performed by an expert observer. We offer here a robust segmentation framework that could be extended for the automated parametric study of the ONH tissues.

Published
2018

18. Deep Learning System Outperforms Clinicians in Identifying Optic Disc Abnormalities

Author

Vasseneix, Caroline, Nusinovici, Simon, Xu, Xinxing, Hwang, Jeong-Min, Hamann, Steffen, Chen, John J., Loo, Jing Liang, Milea, Leonard, Tan, Kenneth B.K., Ting, Daniel S.W., Liu, Yong, Newman, Nancy J., Biousse, Valerie, Wong, Tien Ying, Milea, Dan, Najjar, Raymond P., Gohier, Philippe, Miller, Neil, Vanikieti, Kavin, La Morgia, Chiara, Rougier, Marie-Bénédicte, Ambika, Selvakumar, Fonseca, Pedro, Lagrèze, Wolf Alexander, Sanda, Nicolae, Chiquet, Christophe, Yang, Hui, Chan, Carmen K. M., Cheung, Carol Y., Ha Chau, Tran Thi, Jurkute, Neringa, Yu-Wai-Man, Patrick, Kho, Richard, Jonas, Jost B, Vignal-Clermont, Catherine, Kim, Dong Hyun, Yang, Hee Kyung, Aung, Tin, Singhal, Shweta, Tow, Sharon, Nongpiur, Monisha Esther, Perera, Shamira, Narayanaswamy, Arun, Thirugnanam, Umapathi N., Fraser, Clare L., Mejico, Luis J., and Fard, Masoud Aghsaei

Published
2023
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20. Novel hyperparallel optical coherence tomography for angle closure assessment: comparison with swept-source OCT and gonioscopy

Author

Porporato, Natalia, Xu, Benjamin Y, Tan, Bingyao, Chang, Yu Q, Tun, Tin A, Perera, Shamira, Husain, Rahat, Ho, Ching Lin, Sultana, Rehena, Aung, Tin, and Schmetterer, Leopold

Abstract

AimTo evaluate reproducibility and agreement of angle closure assessment by a novel hyperparallel optical coherence tomography (OCT) system (HP-OCT, Cylite Optics, Melbourne, Australia), in comparison with swept-source OCT (SS-OCT, CASIA SS-1000, Tomey Corporation, Nagoya, Japan) and gonioscopy.MethodsCross-sectional study. Phakic subjects >40 years, with no relevant ophthalmic history were consecutively recruited from the glaucoma clinic. Subjects underwent same-day evaluation with HP-OCT, SS-OCT and gonioscopy. The primary outcome was the presence of angle closure, defined as iridotrabecular contact in HP-OCT and SS-OCT images at 0°−180° meridional and as non-visibility of the posterior trabecular meshwork (TM) by gonioscopy. Visibility of TM was also assessed (secondary outcome). Intra and interdevice agreement analysis (Gwet AC1) and logistic regression analysis were performed for primary and secondary outcomes, respectively.Results154 sectors from horizontal scans of 77 subjects were analysed. The reproducibility of angle closure assessment by HP-OCT was excellent (AC1 of 0.95 for temporal angle and 1.00 for nasal). Agreement for angle closure detection was very good between HP-OCT and SS-OCT (AC1 of 0.88 for temporal and 0.81 for nasal angle) and good between HP-OCT and gonioscopy (AC1 of 0.71 for temporal and 0.78 for nasal angle). TM was identifiable in 64.4% (94/146) of unprocessed HP-OCT images (both open and closed angles), however not visible in any of the SS-OCT unprocessed images.ConclusionsHP-OCT showed excellent reproducibility for angle closure assessment and good agreement with SS-OCT and gonioscopy. HP-OCT technology also provides a unique capability to visualise regions around TM and Schlemm’s canal, opening new avenues for clinical research of distal outflow pathways.

Published
2025
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21. Genetic association study of exfoliation syndrome identifies a protective rare variant at LOXL1 and five new susceptibility loci.

Author

Aung, Tin, Ozaki, Mineo, Lee, Mei, Schlötzer-Schrehardt, Ursula, Thorleifsson, Gudmar, Mizoguchi, Takanori, Igo, Robert, Haripriya, Aravind, Williams, Susan, Astakhov, Yury, Orr, Andrew, Burdon, Kathryn, Nakano, Satoko, Mori, Kazuhiko, Abu-Amero, Khaled, Hauser, Michael, Li, Zheng, Prakadeeswari, Gopalakrishnan, Bailey, Jessica, Cherecheanu, Alina, Kang, Jae, Nelson, Sarah, Hayashi, Ken, Manabe, Shin-Ichi, Kazama, Shigeyasu, Zarnowski, Tomasz, Inoue, Kenji, Irkec, Murat, Coca-Prados, Miguel, Sugiyama, Kazuhisa, Järvelä, Irma, Schlottmann, Patricio, Lerner, S, Lamari, Hasnaa, Nilgün, Yildirim, Bikbov, Mukharram, Park, Ki, Cha, Soon, Yamashiro, Kenji, Zenteno, Juan, Jonas, Jost, Kumar, Rajesh, Perera, Shamira, Chan, Anita, Kobakhidze, Nino, George, Ronnie, Vijaya, Lingam, Do, Tan, Edward, Deepak, de Juan Marcos, Lourdes, Pakravan, Mohammad, Moghimi, Sasan, Ideta, Ryuichi, Bach-Holm, Daniella, Kappelgaard, Per, Wirostko, Barbara, Thomas, Samuel, Gaston, Daniel, Bedard, Karen, Greer, Wenda, Yang, Zhenglin, Chen, Xueyi, Huang, Lulin, Sang, Jinghong, Jia, Hongyan, Jia, Liyun, Qiao, Chunyan, Zhang, Hui, Liu, Xuyang, Zhao, Bowen, Wang, Ya-Xing, Xu, Liang, Leruez, Stéphanie, Reynier, Pascal, Chichua, George, Tabagari, Sergo, Uebe, Steffen, Zenkel, Matthias, Berner, Daniel, Mossböck, Georg, Weisschuh, Nicole, Hoja, Ursula, Welge-Luessen, Ulrich-Christoph, Mardin, Christian, Founti, Panayiota, Chatzikyriakidou, Anthi, Pappas, Theofanis, Anastasopoulos, Eleftherios, Lambropoulos, Alexandros, Ghosh, Arkasubhra, Shetty, Rohit, Porporato, Natalia, Saravanan, Vijayan, Venkatesh, Rengaraj, Shivkumar, Chandrashekaran, Kalpana, Narendran, Sarangapani, Sripriya, Kanavi, Mozhgan, Beni, Afsaneh, and Yazdani, Shahin

Subjects
Aged, 80 and over, Alleles, Amino Acid Oxidoreductases, Amino Acid Substitution, Asian People, Calcium Channels, Cell Adhesion, Exfoliation Syndrome, Extracellular Matrix, Eye, Female, Gene Expression Profiling, Genetic Predisposition to Disease, Genome-Wide Association Study, Haplotypes, Humans, Male, Molecular Chaperones, Mutation, Missense, Point Mutation, RNA, Messenger, Spheroids, Cellular
Abstract

Exfoliation syndrome (XFS) is the most common known risk factor for secondary glaucoma and a major cause of blindness worldwide. Variants in two genes, LOXL1 and CACNA1A, have previously been associated with XFS. To further elucidate the genetic basis of XFS, we collected a global sample of XFS cases to refine the association at LOXL1, which previously showed inconsistent results across populations, and to identify new variants associated with XFS. We identified a rare protective allele at LOXL1 (p.Phe407, odds ratio (OR) = 25, P = 2.9 × 10-14) through deep resequencing of XFS cases and controls from nine countries. A genome-wide association study (GWAS) of XFS cases and controls from 24 countries followed by replication in 18 countries identified seven genome-wide significant loci (P < 5 × 10-8). We identified association signals at 13q12 (POMP), 11q23.3 (TMEM136), 6p21 (AGPAT1), 3p24 (RBMS3) and 5q23 (near SEMA6A). These findings provide biological insights into the pathology of XFS and highlight a potential role for naturally occurring rare LOXL1 variants in disease biology.

Published
2017

24. Efficiency, Precision, Validity, and Reliability of GlauCAT-Asian Computerized Adaptive Tests in Measuring Glaucoma-Related Quality of Life

Author

Fenwick, Eva K., primary, Man, Ryan E. K., additional, Lim, Belicia, additional, Baskaran, Mani, additional, Nongpiur, Monisha, additional, Sng, Chelvin C. A., additional, Iyer, Jayant Venkatramani, additional, Husain, Rahat, additional, Perera, Shamira, additional, Wong, Tina, additional, Low, Jin Rong, additional, Huang, Olivia Shimin, additional, Lun, Katherine, additional, Loe, Bao Sheng, additional, Aung, Tin, additional, and Lamoureux, Ecosse L., additional

Published
2024
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25. Genome-wide association study identifies five new susceptibility loci for primary angle closure glaucoma

Author

Khor, Chiea Chuen, Do, Tan, Jia, Hongyan, Nakano, Masakazu, George, Ronnie, Abu-Amero, Khaled, Duvesh, Roopam, Chen, Li Jia, Li, Zheng, Nongpiur, Monisha E, Perera, Shamira A, Qiao, Chunyan, Wong, Hon-Tym, Sakai, Hiroshi, Barbosa de Melo, Mônica, Lee, Mei-Chin, Chan, Anita S, Azhany, Yaakub, Dao, Thi Lam Huong, Ikeda, Yoko, Perez-Grossmann, Rodolfo A, Zarnowski, Tomasz, Day, Alexander C, Jonas, Jost B, Tam, Pancy OS, Tran, Tuan Anh, Ayub, Humaira, Akhtar, Farah, Micheal, Shazia, Chew, Paul TK, Aljasim, Leyla A, Dada, Tanuj, Luu, Tam Thi, Awadalla, Mona S, Kitnarong, Naris, Wanichwecharungruang, Boonsong, Aung, Yee Yee, Mohamed-Noor, Jelinar, Vijayan, Saravanan, Sarangapani, Sripriya, Husain, Rahat, Jap, Aliza, Baskaran, Mani, Goh, David, Su, Daniel H, Wang, Huaizhou, Yong, Vernon K, Yip, Leonard W, Trinh, Tuyet Bach, Makornwattana, Manchima, Nguyen, Thanh Thu, Leuenberger, Edgar U, Park, Ki-Ho, Wiyogo, Widya Artini, Kumar, Rajesh S, Tello, Celso, Kurimoto, Yasuo, Thapa, Suman S, Pathanapitoon, Kessara, Salmon, John F, Sohn, Yong Ho, Fea, Antonio, Ozaki, Mineo, Lai, Jimmy SM, Tantisevi, Visanee, Khaing, Chaw Chaw, Mizoguchi, Takanori, Nakano, Satoko, Kim, Chan-Yun, Tang, Guangxian, Fan, Sujie, Wu, Renyi, Meng, Hailin, Nguyen, Thi Thuy Giang, Tran, Tien Dat, Ueno, Morio, Martinez, Jose Maria, Ramli, Norlina, Aung, Yin Mon, Reyes, Rigo Daniel, Vernon, Stephen A, Fang, Seng Kheong, Xie, Zhicheng, Chen, Xiao Yin, Foo, Jia Nee, Sim, Kar Seng, Wong, Tina T, Quek, Desmond T, Venkatesh, Rengaraj, Kavitha, Srinivasan, Krishnadas, Subbiah R, Soumittra, Nagaswamy, Shantha, Balekudaru, Lim, Boon-Ang, Ogle, Jeanne, de Vasconcellos, José PC, Costa, Vital P, Abe, Ricardo Y, de Souza, Bruno B, and Sng, Chelvin C

Subjects
Neurodegenerative, Human Genome, Eye Disease and Disorders of Vision, Aging, Genetics, Cell Line, Chromosome Mapping, Female, Gene Expression, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Glaucoma, Angle-Closure, Humans, Male, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Primary angle closure glaucoma (PACG) is a major cause of blindness worldwide. We conducted a genome-wide association study (GWAS) followed by replication in a combined total of 10,503 PACG cases and 29,567 controls drawn from 24 countries across Asia, Australia, Europe, North America, and South America. We observed significant evidence of disease association at five new genetic loci upon meta-analysis of all patient collections. These loci are at EPDR1 rs3816415 (odds ratio (OR) = 1.24, P = 5.94 × 10(-15)), CHAT rs1258267 (OR = 1.22, P = 2.85 × 10(-16)), GLIS3 rs736893 (OR = 1.18, P = 1.43 × 10(-14)), FERMT2 rs7494379 (OR = 1.14, P = 3.43 × 10(-11)), and DPM2-FAM102A rs3739821 (OR = 1.15, P = 8.32 × 10(-12)). We also confirmed significant association at three previously described loci (P < 5 × 10(-8) for each sentinel SNP at PLEKHA7, COL11A1, and PCMTD1-ST18), providing new insights into the biology of PACG.

Published
2016

26. Genome-wide association analysis identifies TXNRD2, ATXN2 and FOXC1 as susceptibility loci for primary open-angle glaucoma

Author

Bailey, Jessica N Cooke, Loomis, Stephanie J, Kang, Jae H, Allingham, R Rand, Gharahkhani, Puya, Khor, Chiea Chuen, Burdon, Kathryn P, Aschard, Hugues, Chasman, Daniel I, Igo, Robert P, Hysi, Pirro G, Glastonbury, Craig A, Ashley-Koch, Allison, Brilliant, Murray, Brown, Andrew A, Budenz, Donald L, Buil, Alfonso, Cheng, Ching-Yu, Choi, Hyon, Christen, William G, Curhan, Gary, De Vivo, Immaculata, Fingert, John H, Foster, Paul J, Fuchs, Charles, Gaasterland, Douglas, Gaasterland, Terry, Hewitt, Alex W, Hu, Frank, Hunter, David J, Khawaja, Anthony P, Lee, Richard K, Li, Zheng, Lichter, Paul R, Mackey, David A, McGuffin, Peter, Mitchell, Paul, Moroi, Sayoko E, Perera, Shamira A, Pepper, Keating W, Qi, Qibin, Realini, Tony, Richards, Julia E, Ridker, Paul M, Rimm, Eric, Ritch, Robert, Ritchie, Marylyn, Schuman, Joel S, Scott, William K, Singh, Kuldev, Sit, Arthur J, Song, Yeunjoo E, Tamimi, Rulla M, Topouzis, Fotis, Viswanathan, Ananth C, Verma, Shefali Setia, Vollrath, Douglas, Wang, Jie Jin, Weisschuh, Nicole, Wissinger, Bernd, Wollstein, Gadi, Wong, Tien Y, Yaspan, Brian L, Zack, Donald J, Zhang, Kang, Study, EPIC-Norfolk Eye, Weinreb, Robert N, Pericak-Vance, Margaret A, Small, Kerrin, Hammond, Christopher J, Aung, Tin, Liu, Yutao, Vithana, Eranga N, MacGregor, Stuart, Craig, Jamie E, Kraft, Peter, Howell, Gareth, Hauser, Michael A, Pasquale, Louis R, Haines, Jonathan L, and Wiggs, Janey L

Subjects
Biological Sciences, Genetics, Eye Disease and Disorders of Vision, Neurodegenerative, Neurosciences, Human Genome, Aging, Eye, Ataxin-2, Forkhead Transcription Factors, Genetic Predisposition to Disease, Genome-Wide Association Study, Glaucoma, Open-Angle, Humans, Polymorphism, Single Nucleotide, Thioredoxin Reductase 2, ANZRAG Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Primary open-angle glaucoma (POAG) is a leading cause of blindness worldwide. To identify new susceptibility loci, we performed meta-analysis on genome-wide association study (GWAS) results from eight independent studies from the United States (3,853 cases and 33,480 controls) and investigated the most significantly associated SNPs in two Australian studies (1,252 cases and 2,592 controls), three European studies (875 cases and 4,107 controls) and a Singaporean Chinese study (1,037 cases and 2,543 controls). A meta-analysis of the top SNPs identified three new associated loci: rs35934224[T] in TXNRD2 (odds ratio (OR) = 0.78, P = 4.05 × 10(-11)) encoding a mitochondrial protein required for redox homeostasis; rs7137828[T] in ATXN2 (OR = 1.17, P = 8.73 × 10(-10)); and rs2745572[A] upstream of FOXC1 (OR = 1.17, P = 1.76 × 10(-10)). Using RT-PCR and immunohistochemistry, we show TXNRD2 and ATXN2 expression in retinal ganglion cells and the optic nerve head. These results identify new pathways underlying POAG susceptibility and suggest new targets for preventative therapies.

Published
2016

27. A common variant mapping to CACNA1A is associated with susceptibility to exfoliation syndrome

Author

Aung, Tin, Ozaki, Mineo, Mizoguchi, Takanori, Allingham, R Rand, Li, Zheng, Haripriya, Aravind, Nakano, Satoko, Uebe, Steffen, Harder, Jeffrey M, Chan, Anita SY, Lee, Mei Chin, Burdon, Kathryn P, Astakhov, Yury S, Abu-Amero, Khaled K, Zenteno, Juan C, Nilgün, Yildirim, Zarnowski, Tomasz, Pakravan, Mohammad, Safieh, Leen Abu, Jia, Liyun, Wang, Ya Xing, Williams, Susan, Paoli, Daniela, Schlottmann, Patricio G, Huang, Lulin, Sim, Kar Seng, Foo, Jia Nee, Nakano, Masakazu, Ikeda, Yoko, Kumar, Rajesh S, Ueno, Morio, Manabe, Shin-ichi, Hayashi, Ken, Kazama, Shigeyasu, Ideta, Ryuichi, Mori, Yosai, Miyata, Kazunori, Sugiyama, Kazuhisa, Higashide, Tomomi, Chihara, Etsuo, Inoue, Kenji, Ishiko, Satoshi, Yoshida, Akitoshi, Yanagi, Masahide, Kiuchi, Yoshiaki, Aihara, Makoto, Ohashi, Tsutomu, Sakurai, Toshiya, Sugimoto, Takako, Chuman, Hideki, Matsuda, Fumihiko, Yamashiro, Kenji, Gotoh, Norimoto, Miyake, Masahiro, Astakhov, Sergei Y, Osman, Essam A, Al-Obeidan, Saleh A, Owaidhah, Ohoud, Al-Jasim, Leyla, Shahwan, Sami Al, Fogarty, Rhys A, Leo, Paul, Yetkin, Yaz, Oğuz, Çilingir, Kanavi, Mozhgan Rezaei, Beni, Afsaneh Naderi, Yazdani, Shahin, Akopov, Evgeny L, Toh, Kai-Yee, Howell, Gareth R, Orr, Andrew C, Goh, Yufen, Meah, Wee Yang, Peh, Su Qin, Kosior-Jarecka, Ewa, Lukasik, Urszula, Krumbiegel, Mandy, Vithana, Eranga N, Wong, Tien Yin, Liu, Yutao, Koch, Allison E Ashley, Challa, Pratap, Rautenbach, Robyn M, Mackey, David A, Hewitt, Alex W, Mitchell, Paul, Wang, Jie Jin, Ziskind, Ari, Carmichael, Trevor, Ramakrishnan, Rangappa, Narendran, Kalpana, Venkatesh, Rangaraj, Vijayan, Saravanan, Zhao, Peiquan, Chen, Xueyi, Guadarrama-Vallejo, Dalia, Cheng, Ching Yu, Perera, Shamira A, Husain, Rahat, and Ho, Su-Ling

Subjects
Biological Sciences, Genetics, Rare Diseases, Human Genome, Prevention, 2.1 Biological and endogenous factors, Aetiology, Animals, Asian People, Calcium Channels, Case-Control Studies, Chromosome Mapping, Exfoliation Syndrome, Genetic Predisposition to Disease, Genome-Wide Association Study, Glaucoma, Open-Angle, HEK293 Cells, HeLa Cells, Humans, Japan, MCF-7 Cells, Mice, Mice, Inbred C57BL, Polymorphism, Single Nucleotide, Tumor Cells, Cultured, Blue Mountains Eye Study GWAS Team, Wellcome Trust Case Control Consortium 2, Hela Cells, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Exfoliation syndrome (XFS) is the most common recognizable cause of open-angle glaucoma worldwide. To better understand the etiology of XFS, we conducted a genome-wide association study (GWAS) of 1,484 cases and 1,188 controls from Japan and followed up the most significant findings in a further 6,901 cases and 20,727 controls from 17 countries across 6 continents. We discovered a genome-wide significant association between a new locus (CACNA1A rs4926244) and increased susceptibility to XFS (odds ratio (OR) = 1.16, P = 3.36 × 10(-11)). Although we also confirmed overwhelming association at the LOXL1 locus, the key SNP marker (LOXL1 rs4886776) demonstrated allelic reversal depending on the ancestry group (Japanese: OR(A allele) = 9.87, P = 2.13 × 10(-217); non-Japanese: OR(A allele) = 0.49, P = 2.35 × 10(-31)). Our findings represent the first genetic locus outside of LOXL1 surpassing genome-wide significance for XFS and provide insight into the biology and pathogenesis of the disease.

Published
2015

36. Handheld chromatic pupillometry can reliably detect functional glaucomatous damage in eyes with high myopia.

Author

Finkelstein, Maxwell Toan, Nongpiur, Monisha Esther, Husain, Rahat, Perera, Shamira, Baskaran, Mani, Wong, Tina T., Tin Aung, Milea, Dan, and Najjar, Raymond P.

Abstract

Background/aims To assess pupillary light responses (PLRs) in eyes with high myopia (HM) and evaluate the ability of handheld chromatic pupillometry (HCP) to identify glaucomatous functional loss in eyes with HM. Methods This prospective, cross-sectional study included 28 emmetropes (EM), 24 high myopes without glaucoma (HM) and 17 high myopes with confirmed glaucoma (HMG), recruited at the Singapore National Eye Center. Monocular PLRs were evaluated using a custom-built handheld pupillometer that recorded changes in horizontal pupil radius in response to 9 s of exponentially increasing blue (469.1 nm) and red (640.1 nm) lights. Fifteen pupillometric features were compared between groups. A logistic regression model (LRM) was used to distinguish HMG eyes from non-glaucomatous eyes (EM and HM). Results All pupillometric features were similar between EM and HM groups. Phasic constriction to blue (p<0.001) and red (p=0.006) lights, and maximum constriction to blue light (p<0.001) were reduced in HMG compared with EM and HM. Pupillometric features of melanopsin function (postillumination pupillary response, PIPR area under the curve (AUC) 0-12 s (p<0.001) and PIPR 6 s (p=0.01) to blue light) were reduced in HMG. Using only three pupillometric features, the LRM could classify glaucomatous from non-glaucomatous eyes with an AUC of 0.89 (95% CI 0.77 to 1.00), sensitivity 94.1% (95% CI 82.4% to 100.0%) and specificity 78.8% (95% CI 67.3% to 90.4%). Conclusion PLRs to ramping-up light stimuli are unaltered in highly myopic eyes without other diagnosed ocular conditions. Conversely, HCP can distinguish glaucomatous functional loss in eyes with HM and can be a useful tool to detect/confirm the presence of glaucoma in patients with HM. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Evaluation of Primary Angle-Closure Glaucoma Susceptibility Loci in Patients with Early Stages of Angle-Closure Disease

Author

Nongpiur, Monisha E., Cheng, Ching-Yu, Duvesh, Roopam, Vijayan, Saravanan, Baskaran, Mani, Khor, Chiea-Chuen, Allen, John, Kavitha, Srinivasan, Venkatesh, Rengaraj, Goh, David, Husain, Rahat, Boey, Pui Yi, Quek, Desmond, Ho, Ching Lin, Wong, Tina T., Perera, Shamira, Wong, Tien Yin, Krishnadas, Subbiah R., Sundaresan, Periasamy, Aung, Tin, and Vithana, Eranga N.

Published
2018
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39. Three-Dimensional Structural Phenotype of the Optic Nerve Head as a Function of Glaucoma Severity

Author

Braeu, Fabian A., primary, Chuangsuwanich, Thanadet, additional, Tun, Tin A., additional, Perera, Shamira A., additional, Husain, Rahat, additional, Kadziauskienė, Aiste, additional, Schmetterer, Leopold, additional, Thiéry, Alexandre H., additional, Barbastathis, George, additional, Aung, Tin, additional, and Girard, Michaël J. A., additional

Published
2023
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42. Bimatoprost Sustained-Release Implants for Glaucoma Therapy: 6-Month Results From a Phase I/II Clinical Trial

Author

Aung, Tin, Beck, Allen D., Christie, William C., Coote, Michael, Crane, Charles J., Craven, E. Randy, Crichton, Andrew, Day, Douglas G., Durcan, F. Jane, Flynn, William J., Gagné, Sébastien, Goldberg, Damien F., Jinapriya, Delan, Johnson, C. Starck, Kurtz, Shimon, Lewis, Richard A., Mansberger, Steven L., Perera, Shamira A., Rotberg, Michael H., Saltzmann, Robert M., Schenker, Howard I., Tepedino, Michael E., Yap-Veloso, Maria Imelda R., Uy, Harvey S., Walters, Thomas R., Walters, Thomas, Bejanian, Marina, Lee, Susan S., Goodkin, Margot L., Zhang, Jane, Whitcup, Scott M., and Robinson, Michael R.

Published
2017
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43. Adduction induces large optic nerve head deformations in subjects with normal-tension glaucoma.

Author

Chuangsuwanich, Thanadet, Tun, Tin A., Braeu, Fabian A., Xiaofei Wang, Zhi Yun Chin, Panda, Satish K., Buist, Martin, Milea, Dan, Strouthidis, Nicholas, Perera, Shamira, Nongpiur, Monisha Esther, Tin Aung, and Girard, Michael J. A.

Abstract

Purpose To assess intraocular pressure (IOP)-induced and gaze-induced optic nerve head (ONH) strains in subjects with high-tension glaucoma (HTG) and normal-tension glaucoma (NTG). Design Clinic-based cross-sectional study. Methods The ONH from one eye of 228 subjects (114 subjects with HTG (pre-treatment IOP=21 mm Hg) and 114 with NTG (pre-treatment IOP<21 mm Hg)) was imaged with optical coherence tomography (OCT) under the following conditions: (1) OCT primary gaze, (2) 20° adduction from OCT primary gaze, (3) 20° abduction from OCT primary gaze and (4) OCT primary gaze with acute IOP elevation (to approximately 33 mm Hg). We then performed digital volume correlation analysis to quantify IOP-induced and gaze-induced ONH tissue deformations and strains. Results Across all subjects, adduction generated high effective strain (4.4%±2.3%) in the LC tissue with no significant difference (p>0.05) with those induced by IOP elevation (4.5%±2.4%); while abduction generated significantly lower (p=0.01) effective strain (3.1%±1.9%). The lamina cribrosa (LC) of HTG subjects exhibited significantly higher effective strain than those of NTG subjects under IOP elevation (HTG: 4.6%±1.7% vs NTG: 4.1%±1.5%, p<0.05). Conversely, the LC of NTG subjects exhibited significantly higher effective strain than those of HTG subjects under adduction (NTG: 4.9%±1.9% vs HTG: 4.0%±1.4%, p<0.05). Conclusion We found that NTG subjects experienced higher strains due to adduction than HTG subjects, while HTG subjects experienced higher strain due to IOP elevation than NTG subjects--and that these differences were most pronounced in the LC tissue. [ABSTRACT FROM AUTHOR]

Published
2024
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44. AI-based clinical assessment of optic nerve head robustness superseding biomechanical testing.

Author

Braeu, Fabian A., Chuangsuwanich, Thanadet, Tun, Tin A., Perera, Shamira, Husain, Rahat, Thiery, Alexandre H., Tin Aung, Barbastathis, George, and Girard, Michaël J. A.

Abstract

Background/aims To use artificial intelligence (AI) to: (1) exploit biomechanical knowledge of the optic nerve head (ONH) from a relatively large population; (2) assess ONH robustness (ie, sensitivity of the ONH to changes in intraocular pressure (IOP)) from a single optical coherence tomography (OCT) volume scan of the ONH without the need for biomechanical testing and (3) identify what critical three-dimensional (3D) structural features dictate ONH robustness. Methods 316 subjects had their ONHs imaged with OCT before and after acute IOP elevation through ophthalmo-dynamometry. IOP-induced lamina cribrosa (LC) deformations were then mapped in 3D and used to classify ONHs. Those with an average effective LC strain superior to 4% were considered fragile, while those with a strain inferior to 4% robust. Learning from these data, we compared three AI algorithms to predict ONH robustness strictly from a baseline (undeformed) OCT volume: (1) a random forest classifier; (2) an autoencoder and (3) a dynamic graph convolutional neural network (DGCNN). The latter algorithm also allowed us to identify what critical 3D structural features make a given ONH robust. Results All three methods were able to predict ONH robustness from a single OCT volume scan alone and without the need to perform biomechanical testing. The DGCNN (area under the curve (AUC): 0.76±0.08) outperformed the autoencoder (AUC: 0.72±0.09) and the random forest classifier (AUC: 0.69±0.05). Interestingly, to assess ONH robustness, the DGCNN mainly used information from the scleral canal and the LC insertion sites. Conclusions We propose an AI-driven approach that can assess the robustness of a given ONH solely from a single OCT volume scan of the ONH, and without the need to perform biomechanical testing. Longitudinal studies should establish whether ONH robustness could help us identify fast visual field loss progressors. Precis Using geometric deep learning, we can assess optic nerve head robustness (ie, sensitivity to a change in IOP) from a standard OCT scan that might help to identify fast visual field loss progressors. [ABSTRACT FROM AUTHOR]

Published
2024
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