Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort Study
BackgroundDry eye disease (DED) is a common ocular condition with diverse and heterogeneous symptoms. Current treatment standards of DED include the post facto management of associated symptoms through topical eye drops. However, there is a need for predictive, preventive, pe...
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JMIR Publications
2025-05-01
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| Series: | JMIR Research Protocols |
| Online Access: | https://www.researchprotocols.org/2025/1/e67862 |
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| author | Ken Nagino Yasutsugu Akasaki Nobuo Fuse Soichi Ogishima Atsushi Shimizu Akira Uruno Yoichi Sutoh Yayoi Otsuka-Yamasaki Fuji Nagami Jun Seita Tomohiro Nakamura Satoshi Nagaie Makiko Taira Tomoko Kobayashi Ritsuko Shimizu Atsushi Hozawa Shinichi Kuriyama Atsuko Eguchi Akie Midorikawa-Inomata Masahiro Nakamura Akira Murakami Shintaro Nakao Takenori Inomata |
| author_facet | Ken Nagino Yasutsugu Akasaki Nobuo Fuse Soichi Ogishima Atsushi Shimizu Akira Uruno Yoichi Sutoh Yayoi Otsuka-Yamasaki Fuji Nagami Jun Seita Tomohiro Nakamura Satoshi Nagaie Makiko Taira Tomoko Kobayashi Ritsuko Shimizu Atsushi Hozawa Shinichi Kuriyama Atsuko Eguchi Akie Midorikawa-Inomata Masahiro Nakamura Akira Murakami Shintaro Nakao Takenori Inomata |
| author_sort | Ken Nagino |
| collection | DOAJ |
| description |
BackgroundDry eye disease (DED) is a common ocular condition with diverse and heterogeneous symptoms. Current treatment standards of DED include the post facto management of associated symptoms through topical eye drops. However, there is a need for predictive, preventive, personalized, and participatory medicine. The DryEyeRhythm mobile health app enables real-time data collection on environmental, lifestyle, host, and digital factors in a patient’s daily environment. Combining these data with genetic information from biobanks could enhance our understanding of individual variations and facilitate the development of personalized treatment strategies for DED.
ObjectiveThis study aims to integrate digital data from the DryEyeRhythm smartphone app with the Tohoku Medical Megabank database to create a comprehensive database that elucidates the interplay between multifactorial factors and the onset and progression of DED.
MethodsThis prospective observational cohort study will include 1200 participants for the discovery stage and 1000 participants for the replication stage, all of whom have data available in the Tohoku Medical Megabank database. Participants will be recruited from the Community Support Center of Sendai, Miyagi Prefecture, Japan. Participant enrollment for the discovery stage was conducted from August 1, 2021, to June 30, 2022, and the replication stage will be conducted from August 31, 2024, to March 31, 2026. Participants will provide demographic data, medical history, lifestyle information, DED symptoms, and maximum blink interval measurements at baseline and after 30 days using the DryEyeRhythm smartphone app. Upon scanning a registration code, each participant’s cohort ID from the Tohoku Medical Megabank database will be linked to their smartphone app, enabling data integration between the Tohoku Medical Megabank and DryEyeRhythm database. The primary outcome will assess the association between genetic polymorphisms and DED using a genome-wide association study. Secondary outcomes will explore associations between DED and various factors, including sociodemographic characteristics, lifestyle habits, medical history, biospecimen analyses (eg, blood and urine), and physiological measurements (eg, height, weight, and eye examination results). Associations will be evaluated using logistic regression analysis, adjusting for potential confounding factors.
ResultsThe discovery stage of participant enrollment was conducted from August 1, 2021, to June 30, 2022. The replication stage will take place from August 31, 2024, to March 31, 2026. Data analysis is expected to be completed by September 2026, with results reported by March 2027.
ConclusionsThis study highlights the potential of smartphone apps in advancing biobank research and deepening the understanding of multifactorial DED, paving the way for personalized treatment strategies in the future.
International Registered Report Identifier (IRRID)DERR1-10.2196/67862 |
| format | Article |
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| institution | OA Journals |
| issn | 1929-0748 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | JMIR Publications |
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| series | JMIR Research Protocols |
| spelling | doaj-art-ef30cef7b7a94efa9bdb0ff795cfe3492025-08-20T01:50:31ZengJMIR PublicationsJMIR Research Protocols1929-07482025-05-0114e6786210.2196/67862Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort StudyKen Naginohttps://orcid.org/0000-0002-0317-6074Yasutsugu Akasakihttps://orcid.org/0000-0001-9527-0115Nobuo Fusehttps://orcid.org/0000-0003-0237-4746Soichi Ogishimahttps://orcid.org/0000-0001-8613-2562Atsushi Shimizuhttps://orcid.org/0000-0001-8307-2461Akira Urunohttps://orcid.org/0000-0002-9224-0161Yoichi Sutohhttps://orcid.org/0000-0002-1607-9759Yayoi Otsuka-Yamasakihttps://orcid.org/0000-0002-9464-0804Fuji Nagamihttps://orcid.org/0000-0002-3652-9788Jun Seitahttps://orcid.org/0000-0002-3008-3615Tomohiro Nakamurahttps://orcid.org/0000-0002-8349-2367Satoshi Nagaiehttps://orcid.org/0000-0002-8152-4556Makiko Tairahttps://orcid.org/0000-0001-5551-0786Tomoko Kobayashihttps://orcid.org/0000-0002-8697-1996Ritsuko Shimizuhttps://orcid.org/0000-0001-6672-7606Atsushi Hozawahttps://orcid.org/0000-0002-8031-8523Shinichi Kuriyamahttps://orcid.org/0000-0002-6445-0911Atsuko Eguchihttps://orcid.org/0000-0002-5540-055XAkie Midorikawa-Inomatahttps://orcid.org/0000-0002-6054-5710Masahiro Nakamurahttps://orcid.org/0000-0002-4363-069XAkira Murakamihttps://orcid.org/0000-0002-0022-2514Shintaro Nakaohttps://orcid.org/0000-0003-4200-0052Takenori Inomatahttps://orcid.org/0000-0003-3435-1055 BackgroundDry eye disease (DED) is a common ocular condition with diverse and heterogeneous symptoms. Current treatment standards of DED include the post facto management of associated symptoms through topical eye drops. However, there is a need for predictive, preventive, personalized, and participatory medicine. The DryEyeRhythm mobile health app enables real-time data collection on environmental, lifestyle, host, and digital factors in a patient’s daily environment. Combining these data with genetic information from biobanks could enhance our understanding of individual variations and facilitate the development of personalized treatment strategies for DED. ObjectiveThis study aims to integrate digital data from the DryEyeRhythm smartphone app with the Tohoku Medical Megabank database to create a comprehensive database that elucidates the interplay between multifactorial factors and the onset and progression of DED. MethodsThis prospective observational cohort study will include 1200 participants for the discovery stage and 1000 participants for the replication stage, all of whom have data available in the Tohoku Medical Megabank database. Participants will be recruited from the Community Support Center of Sendai, Miyagi Prefecture, Japan. Participant enrollment for the discovery stage was conducted from August 1, 2021, to June 30, 2022, and the replication stage will be conducted from August 31, 2024, to March 31, 2026. Participants will provide demographic data, medical history, lifestyle information, DED symptoms, and maximum blink interval measurements at baseline and after 30 days using the DryEyeRhythm smartphone app. Upon scanning a registration code, each participant’s cohort ID from the Tohoku Medical Megabank database will be linked to their smartphone app, enabling data integration between the Tohoku Medical Megabank and DryEyeRhythm database. The primary outcome will assess the association between genetic polymorphisms and DED using a genome-wide association study. Secondary outcomes will explore associations between DED and various factors, including sociodemographic characteristics, lifestyle habits, medical history, biospecimen analyses (eg, blood and urine), and physiological measurements (eg, height, weight, and eye examination results). Associations will be evaluated using logistic regression analysis, adjusting for potential confounding factors. ResultsThe discovery stage of participant enrollment was conducted from August 1, 2021, to June 30, 2022. The replication stage will take place from August 31, 2024, to March 31, 2026. Data analysis is expected to be completed by September 2026, with results reported by March 2027. ConclusionsThis study highlights the potential of smartphone apps in advancing biobank research and deepening the understanding of multifactorial DED, paving the way for personalized treatment strategies in the future. International Registered Report Identifier (IRRID)DERR1-10.2196/67862https://www.researchprotocols.org/2025/1/e67862 |
| spellingShingle | Ken Nagino Yasutsugu Akasaki Nobuo Fuse Soichi Ogishima Atsushi Shimizu Akira Uruno Yoichi Sutoh Yayoi Otsuka-Yamasaki Fuji Nagami Jun Seita Tomohiro Nakamura Satoshi Nagaie Makiko Taira Tomoko Kobayashi Ritsuko Shimizu Atsushi Hozawa Shinichi Kuriyama Atsuko Eguchi Akie Midorikawa-Inomata Masahiro Nakamura Akira Murakami Shintaro Nakao Takenori Inomata Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort Study JMIR Research Protocols |
| title | Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort Study |
| title_full | Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort Study |
| title_fullStr | Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort Study |
| title_full_unstemmed | Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort Study |
| title_short | Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort Study |
| title_sort | integration of digital phenotyping and genomics for dry eye disease protocol for a prospective cohort study |
| url | https://www.researchprotocols.org/2025/1/e67862 |
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