Evidence map›Paper›PMID 40354653›Full record

ArticleJMIR research protocols2025

Integration of Digital Phenotyping and Genomics for Dry Eye Disease: Protocol for a Prospective Cohort Study.

Ken Nagino, Yasutsugu Akasaki, Nobuo Fuse, Soichi Ogishima, Atsushi Shimizu, Akira Uruno, Yoichi Sutoh, Yayoi Otsuka-Yamasaki, Fuji Nagami, Jun Seita and 13 more

Abstract read
In one paragraph

Article in JMIR research protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Ken Nagino *Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.ORCID 0000-0002-0317-6074
Yasutsugu Akasaki *Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.ORCID 0000-0001-9527-0115
Nobuo FuseTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.ORCID 0000-0003-0237-4746
Soichi OgishimaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.ORCID 0000-0001-8613-2562
Atsushi ShimizuDivision of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Yahaba, Japan.ORCID 0000-0001-8307-2461
Akira UrunoTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.ORCID 0000-0002-9224-0161
Yoichi SutohDivision of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Yahaba, Japan.ORCID 0000-0002-1607-9759
Yayoi Otsuka-YamasakiDivision of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Yahaba, Japan.ORCID 0000-0002-9464-0804
Fuji NagamiTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.ORCID 0000-0002-3652-9788
Jun SeitaAdvanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Tokyo, Japan.ORCID 0000-0002-3008-3615
Tomohiro NakamuraTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.ORCID 0000-0002-8349-2367
Satoshi NagaieTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.ORCID 0000-0002-8152-4556
Makiko TairaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.ORCID 0000-0001-5551-0786
Tomoko KobayashiTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.ORCID 0000-0002-8697-1996
Ritsuko ShimizuTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.ORCID 0000-0001-6672-7606
Atsushi HozawaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.ORCID 0000-0002-8031-8523
Shinichi KuriyamaTohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.ORCID 0000-0002-6445-0911
Atsuko EguchiDepartment of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.ORCID 0000-0002-5540-055X
Akie Midorikawa-InomataDepartment of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.ORCID 0000-0002-6054-5710
Masahiro NakamuraDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.ORCID 0000-0002-4363-069X
Akira MurakamiDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.ORCID 0000-0002-0022-2514
Shintaro NakaoDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.ORCID 0000-0003-4200-0052
Takenori InomataDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.ORCID 0000-0003-3435-1055

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Dry Eye SyndromesGenomicsMobile ApplicationsAdultFemaleHumansJapanMalePhenotypeProspective StudiesSmartphonebiobankdigital healthdry eye diseasedry eye syndromegenome-wide association studymobile healthocular surfacesmartphone

Identifiers

PMID40354653
PMCPMC12107206

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.