Cohort profile: OphtalmoLaus, an extension of the CoLaus|PsyCoLaus cohort to investigate the relationships between ocular, cardiovascular, and cognitive parameters

Cohort profile: OphtalmoLaus, an extension of the CoLaus|PsyCoLaus cohort to investigate the relationships between ocular, cardiovascular, and cognitive parameters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cohort profile: OphtalmoLaus, an extension of the CoLaus|PsyCoLaus cohort to investigate the relationships between ocular, cardiovascular, and cognitive parameters Ilenia Meloni, Adham Elwakil, Gryczka Aurélia, Torrecillos Flavie, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7660406/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Retinal imaging enables direct, non-invasive visualization of the human microvasculature, providing biomarkers of systemic cardiovascular, metabolic, and cognitive health.. The OphtalmoLaus study is an ophthalmic sub-study nested within the population-based CoLaus|PsyCoLaus cohort, aiming to investigate the relationships between ocular phenotypes and systemic health outcomes. A total of 2472 participants from the CoLaus|PsyCoLaus cohort underwent standardized ophthalmic examinations at the Jules Gonin Ophthalmic Hospital, including visual acuity, intraocular pressure, axial length measurements, and multimodal ophthalmic imaging using Optical Coherence Tomography (OCT), OCT angiography (OCT-A), fundus photography, and anterior segment/iris imaging. Additionally, sublingual microcirculation imaging was performed using a handheld CytoCam-IDF microscope in a subgroup. Findings based on clinical examination and imaging findings included glaucoma, macular drusen (19.3%), macular edema (1.5%), subretinal fluid (1.0%), and macular hemorrhage (0.2%), with prevalence calculated relative to the total number of participants. This manuscript provides a detailed overview of the study’s design, participant characteristics, imaging protocols, and clinical phenotyping methods, establishing the OphthalmoLaus dataset as a unique epidemiological resource for investigating links between ocular and systemic health, with potential for genetic, epidemiological, and longitudinal analyses. Ophthalmology Epidemiology Vascular Medicine Psychiatry Population-based cohort Ophthalmic epidemiology Multimodal imaging Retinal biomarkers Microvasculature 1. Introduction The retina offers a unique anatomical site for direct, non-invasive observation of the human microvasculature. Advances in retinal imaging technologies, particularly fundus imaging, optical coherence tomography (OCT) and OCT angiography (OCT-A), have facilitated precise and reproducible extraction of quantitative vascular and structural biomarkers. Such ocular biomarkers have demonstrated associations with systemic cardiovascular risk factors, metabolic disorders, and cognitive decline in various epidemiological studies, highlighting their potential for predictive and diagnostic utility (Ikram et al., 2013 ; Klein et al., 1992 ). The CoLaus|PsyCoLaus cohort, initiated in 2003–2004, is a prospective, population-based study of 6,734 adults aged 35–75 years randomly selected from Lausanne’s civil register. It was designed to investigate the epidemiological and genetic determinants of cardiovascular and psychiatric disorders, and provides comprehensive baseline and follow-up data on cardiovascular health, psychiatric assessments, and genetic information (Firmann et al., 2008 ; Preisig et al., 2009 ). To extend the rich phenotypic spectrum available through CoLaus|PsyCoLaus, several ancillary sub-studies were launched, including the OphtalmoLaus sub-study initiated in November 2015. This effort is supported by national initiatives such as the Swiss Ophthalmic Image Network (SOIN) (Bergin et al., 2022 ), which provides a collaborative, privacy-preserving environment for ophthalmic imaging research and machine learning–based biomarker discovery. OphtalmoLaus specifically integrates detailed ophthalmic assessments into the existing epidemiological framework, enabling comprehensive exploration of associations between ocular imaging biomarkers and cardiovascular, metabolic, and cognitive parameters. Participation in OphtalmoLaus is open to all individuals previously enrolled in the CoLaus|PsyCoLaus study, provided they consent to the ophthalmic examination, aligning with other successful population-based ophthalmic studies (Williams et al., 2015 ; Schram et al., 2014 ). In this manuscript, we describe the design, recruitment methods, detailed ophthalmic imaging protocols, clinical phenotyping strategies, and initial prevalence estimates of ocular conditions within the OphtalmoLaus sub-study. This detailed characterization aims to provide a robust foundation for future investigations into the utility of retinal and microvascular imaging as predictors of systemic health outcomes. Moreover, OphtalmoLaus contributes valuable data for genetic association studies and epidemiological assessments, facilitating novel insights into ocular-systemic disease relationships in aging populations. 2. Methods Study Design and Population OphtalmoLaus is an ancillary ophthalmic sub-study nested within the larger CoLaus|PsyCoLaus prospective, longitudinal cohort. Participants included in OphtalmoLaus were drawn from the original CoLaus study population, which consists of individuals randomly selected from the Lausanne civil register (Firmann et al., 2008; Preisig et al., 2009). Ophthalmic examinations for the OphtalmoLaus sub-study began in 2015 and are ongoing. The only eligibility criterion for inclusion in the OphtalmoLaus sub-study was prior participation in CoLaus, with no further exclusion criteria applied. Data Collection Procedures Participants underwent comprehensive ophthalmic assessments at the Jules Gonin Ophthalmic Hospital, Lausanne, Switzerland. Examinations were performed by trained optometrists using standardized and validated protocols, lasting approximately 50 minutes per participant. At each visit, participants completed a structured questionnaire documenting ocular history, systemic diseases, medication usage, visual habits (dominant eye, handedness), and previous ophthalmic interventions or surgeries. Ophthalmic Examination and Imaging Protocols Ophthalmic evaluation included visual acuity (VA) using decimal Snellen charts, intraocular pressure (IOP) measured with air-puff tonometry until 2019 and with the Icare HOME2 Tonometer thereafter (following COVID-19 guidelines), and axial length assessed by optical biometry. Autorefraction (sphere, cylinder, axis) was performed with a Nidek ARK-1 auto-refractometer. Retinal Imaging Structural OCT examinations included three-dimensional macular scans (7.0 × 7.0 mm, 512 × 256 px resolution, 256 b-scans), three-dimensional optic disc scans (6.0 × 6.0 mm, 512 × 256 px resolution, 256 b-scans), and line scans (9.0 mm scan length, 1024 horizontal/vertical, 799 × 400 px resolution). OCT angiography consisted of macula-centered scans (4.5 × 4.5 mm, 320 × 320 px resolution, 320 b-scans, four repetitions). Fundus photography was performed using the integrated camera module of the Topcon OCT systems to capture both macular and optic disk centered retinal images. High-resolution anterior segment and iris imaging was carried out with the anterior segment module of the Topcon Triton OCT. All imaging followed standardized protocols with auto-shot and auto-tracking activated. Image quality was inspected immediately by trained technicians, and scans with motion artifacts or insufficient signal strength were reacquired. Sublingual Microcirculation Imaging A subset of participants underwent non-invasive imaging of sublingual and inner lip microcirculation using a handheld CytoCam-IDF microscope (Braedius Medical). Incident Dark Field (IDF) illumination technology was employed to obtain high-resolution videos of capillary networks, which were subsequently analyzed offline using validated proprietary software to extract key microvascular parameters (e.g., total vessel density, perfused vessel density, microcirculatory flow index). Ethical Considerations Ethical approval for OphtalmoLaus was obtained from the Institutional Ethics Committee of the Canton of Vaud, Switzerland (see section “Ethics approval”). All participants provided written informed consent before their ophthalmic assessments. Data collected were pseudonymized and managed securely, leveraging resources of the SOIN initiative (Bergin et al., 2022),in accordance with the institutional guidelines of the Lausanne University Hospital (CHUV) and the CoLaus data-sharing governance framework. Data Management and Statistical Analyses Collected data were systematically verified for completeness and quality before inclusion in the analysis. The data quality was assessed with a 100-point scale provided by the Triton device. The determination of whether this quality is sufficient was based on the judgment of the optometrist, taking into account the patient’s clinical history and characteristics (such as cataract, age, or nystagmus). If the quality is deemed insufficient, image acquisition was repeated in order to obtain an optimal result. In the absence of improvement after several attempts, further repetitions were discontinued. Descriptive statistics were calculated with categorical variables reported as frequencies (%) and continuous variables as means, standard deviations, and ranges. Analyses were performed in Python using pandas, NumPy, SciPy, and statsmodels. 3. Results Baseline characteristics of the participants As of July 2025, a total of 2472 participants from the CoLaus|PsyCoLaus cohort had completed the ophthalmic examination within the OphtalmoLaus sub-study. Participants had a mean age of 64.2 years (range: 46–92) at the time of imaging. The sex distribution was 54.6% female and 45.4% male. The majority of participants self-identified as White/European, consistent with the demographic profile of the Lausanne population. Information on cardiovascular and metabolic comorbidities and psychological assessment were obtained from the parent CoLaus|PsyCoLaus dataset. The prevalence of glaucoma, macular drusen, macular hemorrhage, Subretinal fluid (SRF) and macular edema, at the time of the ophthalmic exam is summarized in Table 1. Table 1 | Prevalence of ocular conditions identified in the OphtalmoLaus cohort. Values represent the number of participants with each condition (N positive) and the corresponding percentage of the total study population (n = 2472). Condition N positive Percent of total participants (%) Macular drusen 476 19.3 Glaucoma 210 8.5 Macular edema 36 1.5 Subretinal fluid (SRF, any eye) 24 1 Macular hemorrhage 4 0.2 Clinical phenotypes The mean binocular visual acuity (VA) was 0.89 (decimal), with 9.5% of participants showing mild or moderate visual impairment in at least one eye. The mean intraocular pressure (IOP) across all eyes was 15.4 mmHg. The mean axial length was 23.8 mm, and 38.3% of eyes were classified as myopic (>24 mm). Refractive errors, including myopia, hyperopia, and astigmatism, were calculated from autorefraction. 26.6% of eyes were myopic, 44.9% were hyperopic, and 36.1% showed astigmatism greater than 0.75 D. Diagnoses based on ophthalmic examination findings included macular drusen, macular edema, SRF, and macular hemorrhage. The prevalence estimates of these diagnoses, reported in Table 1, were calculated using data from 2472 participants, with bilateral information OD (oculus dexter, right eye) / OS (oculus sinister, left eye), used to flag the presence of each condition in either eye. Image-derived phenotypes Quantitative image features were extracted from multimodal ophthalmic imaging using the Cohort Builder software pipeline (Mousavi et al., 2024), which enables automated integration of raw ophthalmic imaging data with clinical records. For OCT, measurements included retinal layer thicknesses—specifically the retinal nerve fibre layer (RNFL), ganglion cell layer and inner plexiform layer (GCL+IPL), inner nuclear layer and outer plexiform layer (INL+OPL), outer nuclear layer (ONL), photoreceptors and retinal pigment epithelium (PR+RPE), and the choriocapillaris and choroidal stroma (CC+CS). Volumetric parameters included intraretinal fluid (IRF), SRF, and pigment epithelial detachment (PED). In addition, probabilistic disease biomarkers were derived, including SRF, IRF, hyperreflective foci (HF), drusen, reticular pseudodrusen (RPD), epiretinal membrane (ERM), geographic atrophy (GA), outer retinal atrophy (ORA), and fibrovascular PED (FPED). From OCT angiography, a range of foveal avascular zone (FAZ) features was computed, including FAZ area, perimeter, axis ratio (major/minor), circularity, roundness, solidity, eccentricity, equivalent diameter, convex hull area, extent (area/bounding box), Feret diameters (maximum and minimum), and orientation. Additional descriptors such as acircularity, Hausdorff and Chamfer distances to an ideal ellipse, centroid coordinates, and related geometric parameters were also obtained. Arterial and venous features were derived, including vascular densities, median tortuosities, central retinal equivalents, diameters, diameter variabilities, and main temporal angles. Ratios between arterial and venous values for the first four parameters were calculated, together with the total number of vessel bifurcations. These metrics were computed for all vessels as well as for a diameter-filtered subset (<50 μm), encompassing venules and arterioles. Fundus photographs centered on the macula and optic disc were also collected. From these images, vascular features analogous to those extracted from OCT-A—including vascular densities, tortuosity measures, vessel diameters, and bifurcation counts—were quantified. Anterior segment imaging was performed with the Topcon Triton to obtain high-resolution photographs of the iris. At present, these images are used for qualitative documentation only, and automated feature extraction has not yet been implemented. Finally, sublingual microcirculation imaging using the CytoCam-IDF system was added from July 2024 onward. In addition to sublingual recordings, imaging of the inner lower lip was performed to enhance visualization of the microvascular network. Videos were analyzed offline to extract parameters such as maximum capillary diameter (15.84 ± 0.23 μm), total capillary length (18.46 ± 6.72 mm), number of capillary segments (522.53 ± 189.17), average capillary length (35.86 ± 6.22 μm), and total capillary density (12.71 ± 4.21 mm/mm²). These analyses were carried out in 70 participants using dedicated Braedius software. 4. Discussion The OphtalmoLaus study represents a novel integration of ophthalmic phenotyping into a well-established, population-based epidemiological framework. By embedding advanced ocular imaging within the CoLaus|PsyCoLaus cohort, the study enables multimodal analyses of the relationships between retinal, systemic, and cognitive health in a general adult population. OL in comparison with other studies While several large-scale cohorts have incorporated retinal imaging—such as the UK Biobank, Rotterdam Study, and Beaver Dam Eye Study—OphtalmoLaus is unique in its deep linkage with cardiovascular, metabolic, and psychiatric data, all collected longitudinally over nearly two decades. The study’s use of swept-source OCT and OCT-A, alongside sublingual microcirculation imaging, further distinguishes it by enabling microvascular analyses across multiple anatomical compartments. The inclusion of both early imaging with Topcon 3D OCT-2000 and high-resolution Triton systems allows assessment of device harmonization over time, an under-addressed issue in longitudinal imaging studies. Key findings to date and further study plan Preliminary results confirm that image-derived ocular features—including FAZ area, vessel density, and macular thickness—can be reliably quantified across a wide age range. Early correlations with cardiovascular and cognitive parameters support the hypothesis that ocular microvasculature reflects systemic vascular health. The use of CytoCam-IDF to assess sublingual capillary perfusion provides an additional non-invasive microvascular endpoint, offering a unique opportunity for cross-tissue correlation studies. Integration with Genomic Data The OphtalmoLaus dataset is currently being used for genome-wide association studies (GWAS) of image-derived phenotypes such as retinal vessel tortuosity, FAZ area, and macular thickness. Preliminary analyses have replicated multiple loci associated with retinal vascular traits, and suggest links to systemic cardiovascular conditions including hypertension, stroke, and deep vein thrombosis (Tomasoni et al., 2023 ; Ortín Vela et al., 2024 ). In addition, AI-derived tortuosity and bifurcation features are being evaluated for their genetic architecture, leveraging the CoLaus multi-omics platform. Strengths and weaknesses The OphtalmoLaus study has several notable strengths. Its population-based design is enriched by linkage to deep systemic phenotyping, providing a uniquely comprehensive dataset. The study employs state-of-the-art ocular imaging technologies, including swept-source OCT and OCT-A, and incorporates the novel use of sublingual microvascular imaging. In addition, all imaging was performed in a single center using consistent, standardized acquisition protocols, ensuring methodological rigor and comparability across participants. Another methodological strength is the independent quality assessment of OCT imaging using PLUME-OCT, a novel metric for quantifying misalignments between B-scans, which has been successfully tested on OphtalmoLaus data (Milloz et al., 2024 ). At the same time, some limitations should be acknowledged. The generalizability of findings is restricted, as the cohort is composed predominantly of individuals of European descent. The transition from the Topcon 2000 to the Triton device may introduce batch effects, and not all participants underwent complete multimodal imaging or sublingual assessments. Finally, imaging to date has been cross-sectional, and longitudinal ocular follow-up—although planned—has not yet been completed. Collaboration The OphtalmoLaus dataset is hosted securely at the Lausanne University Hospital (CHUV) and managed under the governance framework of the CoLaus|PsyCoLaus study. Researchers interested in collaborative analyses may submit a proposal to the CoLaus Scientific Board. Data sharing is subject to ethical approval, data protection regulations, and participant consent limitations. Conclusion The OphtalmoLaus study provides a unique opportunity to investigate the relationship between ocular imaging biomarkers and systemic health outcomes within a well-established, population-based cohort. By embedding comprehensive ophthalmic assessments into the CoLaus|PsyCoLaus framework, the study enables detailed exploration of microvascular and structural changes in the retina in relation to cardiovascular, metabolic, and cognitive health. The integration of advanced multimodal imaging techniques, including swept-source OCT, OCT angiography, and sublingual microcirculation imaging, strengthens the dataset and supports novel cross-tissue analyses. These methodological innovations position OphtalmoLaus as a valuable resource for identifying and validating ocular biomarkers of systemic disease. Declarations Acknowledgements This work was supported by the Swiss Personalized Health Network (grant 2018DRI13), the Claire and Selma Kattenburg Foundation, and the Swiss National Science Foundation (grant CRSII5_209510). Ethics approval This study has obtained ethics approval from “Commission cantonale d'éthique de la recherche sur l'être humain (CER-VD)” (PB_2019-00168, Canton of Vaud, Switzerland). Data availability De-identified data from the OphthalmoLaus study are not publicly available. Access is only possible through the establishment of a formal scientific collaboration with the OphthalmoLaus investigators and approval by the CoLaus|PsyCoLaus Scientific Board, in accordance with participant consent and Swiss data protection regulations. Code availability Custom analysis code developed for this study is available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests References Bergin C, Mantel I, Schlingemann RO, Tomasoni M, Wolfensberger TJ. SOIN – MI Data Lab: Personalized Ophthalmology Through Collaborative Data Collection and Dynamic Patient Consent. 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Cohort profile: the China surgery and anesthesia cohort (CSAC). Eur J Epidemiol. 2024;39:207–218. https://doi.org/10.1007/s10654-023-01083-4 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7660406","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517784768,"identity":"e139d74d-383a-447b-8c08-71a7926461c7","order_by":0,"name":"Ilenia Meloni","email":"","orcid":"https://orcid.org/0000-0001-9078-0538","institution":"Platform for Research in Ocular Imaging, Fondation Asile des Aveugles, Jules Gonin Eye Hospital, Lausanne, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Ilenia","middleName":"","lastName":"Meloni","suffix":""},{"id":517784769,"identity":"4eb1c121-080f-4138-aed5-656630c8b3fb","order_by":1,"name":"Adham Elwakil","email":"","orcid":"https://orcid.org/0009-0009-4881-6643","institution":"Platform for Research in Ocular Imaging, Fondation Asile des Aveugles, Jules Gonin Eye Hospital, Lausanne, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Adham","middleName":"","lastName":"Elwakil","suffix":""},{"id":517784770,"identity":"3fe7afeb-5837-415d-9a7f-484ec3216ac0","order_by":2,"name":"Gryczka Aurélia","email":"","orcid":"","institution":"Department of Ophthalmology, University of Lausanne, Fondation Asile des Aveugles, Jules Gonin Eye Hospital, Lausanne, Switzerland","correspondingAuthor":false,"prefix":"","firstName":"Gryczka","middleName":"","lastName":"Aurélia","suffix":""},{"id":517784771,"identity":"a9018cd3-2360-4fca-9b6b-4d2ddbb196a4","order_by":3,"name":"Torrecillos Flavie","email":"","orcid":"","institution":"Department of Ophthalmology, University of Lausanne, Fondation Asile des Aveugles, Jules Gonin Eye Hospital, Lausanne, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Torrecillos","middleName":"","lastName":"Flavie","suffix":""},{"id":517784772,"identity":"21d7a74b-931b-491f-bbec-ef60aabab99f","order_by":4,"name":"Aurélie Navarro","email":"","orcid":"","institution":"Department of Ophthalmology, University of Lausanne, Fondation Asile des Aveugles, Jules Gonin Eye Hospital, Lausanne, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Aurélie","middleName":"","lastName":"Navarro","suffix":""},{"id":517784773,"identity":"06b83fc4-a7fd-45d2-85f7-0f5661af3b95","order_by":5,"name":"Lionel Bagatella","email":"","orcid":"","institution":"Department of Ophthalmology, University of Lausanne, Fondation Asile des Aveugles, Jules Gonin Eye Hospital, Lausanne, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Lionel","middleName":"","lastName":"Bagatella","suffix":""},{"id":517784774,"identity":"506644f4-66ef-47be-a12d-eb435aeaf305","order_by":6,"name":"Flore Racodon","email":"","orcid":"","institution":"Department of Ophthalmology, University of Lausanne, Fondation Asile des Aveugles, Jules Gonin Eye Hospital, Lausanne, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Flore","middleName":"","lastName":"Racodon","suffix":""},{"id":517784775,"identity":"8d80b9f3-cae5-490c-87e1-35d5da1abe4e","order_by":7,"name":"Fanny Jeunet","email":"","orcid":"","institution":"Department of Ophthalmology, University of Lausanne, Fondation Asile des Aveugles, Jules Gonin Eye Hospital, Lausanne, Switzerland.","correspondingAuthor":false,"prefix":"","firstName":"Fanny","middleName":"","lastName":"Jeunet","suffix":""},{"id":517784776,"identity":"779aa9a0-f455-442e-8469-275fd2063cc1","order_by":8,"name":"Thomas J. 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Tomasoni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie3OsYrCQBCA4QkBbVa2kwkBfQIhIgQLH2aDYBrF7qpDUq1NvPqCj2Fj58qBNgFbwSYSsDYIx9mIGzgJFm5ai/2rHdiPGQCd7l1jxRMb1cAUYASq/6YRSIIP0iGiwsoJFAS8UBBHSVrT3TZJPmFC6z/pOVt2/bAqHLguXxM37svDNoDWfOBaUYyjkDBmzGIFETmpADoH5po1jqMVMGEaXEF2qSS3nPiXiyQ+ocdATfZyi8dzMnRsSRjBPpSQtP3tfaEVzYcfVsSxHeIJ1jPlYV6S/f32KLX9xTnjkyah4yy5Ksh/+DyKUqDT6XQ6ZXdBj1CagO9JHQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8775-2384","institution":"Platform for Research in Ocular Imaging, Fondation Asile des Aveugles, Jules Gonin Eye Hospital, Lausanne, Switzerland.","correspondingAuthor":true,"prefix":"","firstName":"Mattia","middleName":"","lastName":"Tomasoni","suffix":""}],"badges":[],"createdAt":"2025-09-19 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06:32:07","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":59977,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7660406/v1/2e2da1176ce3944bbcc32909.html"},{"id":91816413,"identity":"7f8a3108-13d4-4406-b363-a7cee66d8829","added_by":"auto","created_at":"2025-09-22 06:40:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":685294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7660406/v1/354f1dc3-a3c6-4173-9339-be200881d054.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCohort profile: OphtalmoLaus, an extension of the CoLaus|PsyCoLaus cohort to investigate the relationships between ocular, cardiovascular, and cognitive parameters\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe retina offers a unique anatomical site for direct, non-invasive observation of the human microvasculature. Advances in retinal imaging technologies, particularly fundus imaging, optical coherence tomography (OCT) and OCT angiography (OCT-A), have facilitated precise and reproducible extraction of quantitative vascular and structural biomarkers. Such ocular biomarkers have demonstrated associations with systemic cardiovascular risk factors, metabolic disorders, and cognitive decline in various epidemiological studies, highlighting their potential for predictive and diagnostic utility (Ikram et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Klein et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe CoLaus|PsyCoLaus cohort, initiated in 2003\u0026ndash;2004, is a prospective, population-based study of 6,734 adults aged 35\u0026ndash;75 years randomly selected from Lausanne\u0026rsquo;s civil register. It was designed to investigate the epidemiological and genetic determinants of cardiovascular and psychiatric disorders, and provides comprehensive baseline and follow-up data on cardiovascular health, psychiatric assessments, and genetic information (Firmann et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Preisig et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo extend the rich phenotypic spectrum available through CoLaus|PsyCoLaus, several ancillary sub-studies were launched, including the OphtalmoLaus sub-study initiated in November 2015. This effort is supported by national initiatives such as the Swiss Ophthalmic Image Network (SOIN) (Bergin et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which provides a collaborative, privacy-preserving environment for ophthalmic imaging research and machine learning\u0026ndash;based biomarker discovery. OphtalmoLaus specifically integrates detailed ophthalmic assessments into the existing epidemiological framework, enabling comprehensive exploration of associations between ocular imaging biomarkers and cardiovascular, metabolic, and cognitive parameters. Participation in OphtalmoLaus is open to all individuals previously enrolled in the CoLaus|PsyCoLaus study, provided they consent to the ophthalmic examination, aligning with other successful population-based ophthalmic studies (Williams et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Schram et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this manuscript, we describe the design, recruitment methods, detailed ophthalmic imaging protocols, clinical phenotyping strategies, and initial prevalence estimates of ocular conditions within the OphtalmoLaus sub-study. This detailed characterization aims to provide a robust foundation for future investigations into the utility of retinal and microvascular imaging as predictors of systemic health outcomes. Moreover, OphtalmoLaus contributes valuable data for genetic association studies and epidemiological assessments, facilitating novel insights into ocular-systemic disease relationships in aging populations.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003ch3\u003eStudy Design and Population\u003c/h3\u003e\n\u003cp\u003eOphtalmoLaus is an ancillary ophthalmic sub-study nested within the larger CoLaus|PsyCoLaus prospective, longitudinal cohort. Participants included in OphtalmoLaus were drawn from the original CoLaus study population, which consists of individuals randomly selected from the Lausanne civil register (Firmann et al., 2008; Preisig et al., 2009). Ophthalmic examinations for the OphtalmoLaus sub-study began in 2015 and are ongoing. The only eligibility criterion for inclusion in the OphtalmoLaus sub-study was prior participation in CoLaus, with no further exclusion criteria applied.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eData Collection Procedures\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eParticipants underwent comprehensive ophthalmic assessments at the Jules Gonin Ophthalmic Hospital, Lausanne, Switzerland. Examinations were performed by trained optometrists using standardized and validated protocols, lasting approximately 50 minutes per participant. At each visit, participants completed a structured questionnaire documenting ocular history, systemic diseases, medication usage, visual habits (dominant eye, handedness), and previous ophthalmic interventions or surgeries.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eOphthalmic Examination and Imaging Protocols\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eOphthalmic evaluation included visual acuity (VA) using decimal Snellen charts, intraocular pressure (IOP) measured with air-puff tonometry until 2019 and with the Icare HOME2 Tonometer thereafter (following COVID-19 guidelines), and axial length assessed by optical biometry. Autorefraction (sphere, cylinder, axis) was performed with a Nidek ARK-1 auto-refractometer.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eRetinal Imaging\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eStructural OCT examinations included three-dimensional macular scans (7.0 × 7.0 mm, 512 × 256 px resolution, 256 b-scans), three-dimensional optic disc scans (6.0 × 6.0 mm, 512 × 256 px resolution, 256 b-scans), and line scans (9.0 mm scan length, 1024 horizontal/vertical, 799 × 400 px resolution). OCT angiography consisted of macula-centered scans (4.5 × 4.5 mm, 320 × 320 px resolution, 320 b-scans, four repetitions). Fundus photography was performed using the integrated camera module of the Topcon OCT systems to capture both macular and optic disk centered retinal images. High-resolution anterior segment and iris imaging was carried out with the anterior segment module of the Topcon Triton OCT.\u003c/p\u003e\n\u003cp\u003eAll imaging followed standardized protocols with auto-shot and auto-tracking activated. Image quality was inspected immediately by trained technicians, and scans with motion artifacts or insufficient signal strength were reacquired.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eSublingual Microcirculation Imaging\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eA subset of participants underwent non-invasive imaging of sublingual and inner lip microcirculation using a handheld CytoCam-IDF microscope (Braedius Medical). Incident Dark Field (IDF) illumination technology was employed to obtain high-resolution videos of capillary networks, which were subsequently analyzed offline using validated proprietary software to extract key microvascular parameters (e.g., total vessel density, perfused vessel density, microcirculatory flow index).\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eEthical approval for OphtalmoLaus was obtained from the Institutional Ethics Committee of the Canton of Vaud, Switzerland (see section “Ethics approval”). All participants provided written informed consent before their ophthalmic assessments. Data collected were pseudonymized and managed securely, leveraging resources of the SOIN initiative (Bergin et al., 2022),in accordance with the institutional guidelines of the Lausanne University Hospital (CHUV) and the CoLaus data-sharing governance framework.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eData Management and Statistical Analyses\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eCollected data were systematically verified for completeness and quality before inclusion in the analysis. The data quality was assessed with a 100-point scale provided by the Triton device. The determination of whether this quality is sufficient was based on the judgment of the optometrist, taking into account the patient’s clinical history and characteristics (such as cataract, age, or nystagmus). If the quality is deemed insufficient, image acquisition was repeated in order to obtain an optimal result. In the absence of improvement after several attempts, further repetitions were discontinued.\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were calculated with categorical variables reported as frequencies (%) and continuous variables as means, standard deviations, and ranges. Analyses were performed in Python using pandas, NumPy, SciPy, and statsmodels.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch3\u003eBaseline characteristics of the participants\u003c/h3\u003e\n\u003cp\u003eAs of July 2025, a total of 2472 participants from the CoLaus|PsyCoLaus cohort had completed the ophthalmic examination within the OphtalmoLaus sub-study. Participants had a mean age of 64.2 years (range: 46\u0026ndash;92) at the time of imaging. The sex distribution was 54.6% female and 45.4% male.\u003c/p\u003e\n\u003cp\u003eThe majority of participants self-identified as White/European, consistent with the demographic profile of the Lausanne population. Information on cardiovascular and metabolic comorbidities and psychological assessment were obtained from the parent CoLaus|PsyCoLaus dataset. The prevalence of glaucoma, macular drusen, macular hemorrhage, Subretinal fluid (SRF) and macular edema, at the time of the ophthalmic exam is summarized in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;|\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Prevalence of ocular conditions identified in the OphtalmoLaus cohort.\u0026nbsp;\u003c/strong\u003eValues represent the number of participants with each condition (N positive) and the corresponding percentage of the total study population (n = 2472).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eN positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003ePercent of total participants (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003eMacular drusen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003eGlaucoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003eMacular edema\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003eSubretinal fluid (SRF, any eye)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003eMacular hemorrhage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eClinical phenotypes\u003c/h3\u003e\n\u003cp\u003eThe mean binocular visual acuity (VA) was 0.89 (decimal), with 9.5% of participants showing mild or moderate visual impairment in at least one eye. The mean intraocular pressure (IOP) across all eyes was 15.4 mmHg. The mean axial length was 23.8 mm, and 38.3% of eyes were classified as myopic (\u0026gt;24 mm). Refractive errors, including myopia, hyperopia, and astigmatism, were calculated from autorefraction. 26.6% of eyes were myopic, 44.9% were hyperopic, and 36.1% showed astigmatism greater than 0.75 D.\u003c/p\u003e\n\u003cp\u003eDiagnoses based on ophthalmic examination findings included macular drusen, macular edema, SRF, and macular hemorrhage. The prevalence estimates of these diagnoses, reported in Table 1, were calculated using data from 2472 participants, with bilateral information OD (oculus dexter, right eye) / OS (oculus sinister, left eye), used to flag the presence of each condition in either eye.\u003c/p\u003e\n\u003ch3\u003eImage-derived phenotypes\u003c/h3\u003e\n\u003cp\u003eQuantitative image features were extracted from multimodal ophthalmic imaging using the Cohort Builder software pipeline (Mousavi et al., 2024), which enables automated integration of raw ophthalmic imaging data with clinical records. For OCT, measurements included retinal layer thicknesses\u0026mdash;specifically the retinal nerve fibre layer (RNFL), ganglion cell layer and inner plexiform layer (GCL+IPL), inner nuclear layer and outer plexiform layer (INL+OPL), outer nuclear layer (ONL), photoreceptors and retinal pigment epithelium (PR+RPE), and the choriocapillaris and choroidal stroma (CC+CS). Volumetric parameters included intraretinal fluid (IRF), SRF, and pigment epithelial detachment (PED). In addition, probabilistic disease biomarkers were derived, including SRF, IRF, hyperreflective foci (HF), drusen, reticular pseudodrusen (RPD), epiretinal membrane (ERM), geographic atrophy (GA), outer retinal atrophy (ORA), and fibrovascular PED (FPED).\u003c/p\u003e\n\u003cp\u003eFrom OCT angiography, a range of foveal avascular zone (FAZ) features was computed, including FAZ area, perimeter, axis ratio (major/minor), circularity, roundness, solidity, eccentricity, equivalent diameter, convex hull area, extent (area/bounding box), Feret diameters (maximum and minimum), and orientation. Additional descriptors such as acircularity, Hausdorff and Chamfer distances to an ideal ellipse, centroid coordinates, and related geometric parameters were also obtained. Arterial and venous features were derived, including vascular densities, median tortuosities, central retinal equivalents, diameters, diameter variabilities, and main temporal angles. Ratios between arterial and venous values for the first four parameters were calculated, together with the total number of vessel bifurcations. These metrics were computed for all vessels as well as for a diameter-filtered subset (\u0026lt;50 \u0026mu;m), encompassing venules and arterioles.\u003c/p\u003e\n\u003cp\u003eFundus photographs centered on the macula and optic disc were also collected. From these images, vascular features analogous to those extracted from OCT-A\u0026mdash;including vascular densities, tortuosity measures, vessel diameters, and bifurcation counts\u0026mdash;were quantified.\u003c/p\u003e\n\u003cp\u003eAnterior segment imaging was performed with the Topcon Triton to obtain high-resolution photographs of the iris. At present, these images are used for qualitative documentation only, and automated feature extraction has not yet been implemented.\u003c/p\u003e\n\u003cp\u003eFinally, sublingual microcirculation imaging using the CytoCam-IDF system was added from July 2024 onward. In addition to sublingual recordings, imaging of the inner lower lip was performed to enhance visualization of the microvascular network. Videos were analyzed offline to extract parameters such as maximum capillary diameter (15.84 \u0026plusmn; 0.23 \u0026mu;m), total capillary length (18.46 \u0026plusmn; 6.72 mm), number of capillary segments (522.53 \u0026plusmn; 189.17), average capillary length (35.86 \u0026plusmn; 6.22 \u0026mu;m), and total capillary density (12.71 \u0026plusmn; 4.21 mm/mm\u0026sup2;). These analyses were carried out in 70 participants using dedicated Braedius software.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe OphtalmoLaus study represents a novel integration of ophthalmic phenotyping into a well-established, population-based epidemiological framework. By embedding advanced ocular imaging within the CoLaus|PsyCoLaus cohort, the study enables multimodal analyses of the relationships between retinal, systemic, and cognitive health in a general adult population.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOL in comparison with other studies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhile several large-scale cohorts have incorporated retinal imaging\u0026mdash;such as the UK Biobank, Rotterdam Study, and Beaver Dam Eye Study\u0026mdash;OphtalmoLaus is unique in its deep linkage with cardiovascular, metabolic, and psychiatric data, all collected longitudinally over nearly two decades. The study\u0026rsquo;s use of swept-source OCT and OCT-A, alongside sublingual microcirculation imaging, further distinguishes it by enabling microvascular analyses across multiple anatomical compartments. The inclusion of both early imaging with Topcon 3D OCT-2000 and high-resolution Triton systems allows assessment of device harmonization over time, an under-addressed issue in longitudinal imaging studies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eKey findings to date and further study plan\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePreliminary results confirm that image-derived ocular features\u0026mdash;including FAZ area, vessel density, and macular thickness\u0026mdash;can be reliably quantified across a wide age range. Early correlations with cardiovascular and cognitive parameters support the hypothesis that ocular microvasculature reflects systemic vascular health. The use of CytoCam-IDF to assess sublingual capillary perfusion provides an additional non-invasive microvascular endpoint, offering a unique opportunity for cross-tissue correlation studies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntegration with Genomic Data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe OphtalmoLaus dataset is currently being used for genome-wide association studies (GWAS) of image-derived phenotypes such as retinal vessel tortuosity, FAZ area, and macular thickness. Preliminary analyses have replicated multiple loci associated with retinal vascular traits, and suggest links to systemic cardiovascular conditions including hypertension, stroke, and deep vein thrombosis (Tomasoni et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ort\u0026iacute;n Vela et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, AI-derived tortuosity and bifurcation features are being evaluated for their genetic architecture, leveraging the CoLaus multi-omics platform.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and weaknesses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe OphtalmoLaus study has several notable strengths. Its population-based design is enriched by linkage to deep systemic phenotyping, providing a uniquely comprehensive dataset. The study employs state-of-the-art ocular imaging technologies, including swept-source OCT and OCT-A, and incorporates the novel use of sublingual microvascular imaging. In addition, all imaging was performed in a single center using consistent, standardized acquisition protocols, ensuring methodological rigor and comparability across participants. Another methodological strength is the independent quality assessment of OCT imaging using PLUME-OCT, a novel metric for quantifying misalignments between B-scans, which has been successfully tested on OphtalmoLaus data (Milloz et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAt the same time, some limitations should be acknowledged. The generalizability of findings is restricted, as the cohort is composed predominantly of individuals of European descent. The transition from the Topcon 2000 to the Triton device may introduce batch effects, and not all participants underwent complete multimodal imaging or sublingual assessments. Finally, imaging to date has been cross-sectional, and longitudinal ocular follow-up\u0026mdash;although planned\u0026mdash;has not yet been completed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCollaboration\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe OphtalmoLaus dataset is hosted securely at the Lausanne University Hospital (CHUV) and managed under the governance framework of the CoLaus|PsyCoLaus study. Researchers interested in collaborative analyses may submit a proposal to the CoLaus Scientific Board. Data sharing is subject to ethical approval, data protection regulations, and participant consent limitations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe OphtalmoLaus study provides a unique opportunity to investigate the relationship between ocular imaging biomarkers and systemic health outcomes within a well-established, population-based cohort. By embedding comprehensive ophthalmic assessments into the CoLaus|PsyCoLaus framework, the study enables detailed exploration of microvascular and structural changes in the retina in relation to cardiovascular, metabolic, and cognitive health.\u003c/p\u003e\u003cp\u003eThe integration of advanced multimodal imaging techniques, including swept-source OCT, OCT angiography, and sublingual microcirculation imaging, strengthens the dataset and supports novel cross-tissue analyses. These methodological innovations position OphtalmoLaus as a valuable resource for identifying and validating ocular biomarkers of systemic disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Swiss Personalized Health Network (grant 2018DRI13), the Claire and Selma Kattenburg Foundation, and the Swiss National Science Foundation (grant CRSII5_209510).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has obtained ethics approval from “Commission cantonale d'éthique de la recherche sur l'être humain (CER-VD)” (PB_2019-00168, Canton of Vaud, Switzerland).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDe-identified data from the OphthalmoLaus study are not publicly available. Access is only possible through the establishment of a formal scientific collaboration with the OphthalmoLaus investigators and approval by the CoLaus|PsyCoLaus Scientific Board, in accordance with participant consent and Swiss data protection regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCustom analysis code developed for this study is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBergin C, Mantel I, Schlingemann RO, Tomasoni M, Wolfensberger TJ. SOIN \u0026ndash; MI Data Lab: Personalized Ophthalmology Through Collaborative Data Collection and Dynamic Patient Consent. \u003cem\u003eStud Health Technol Inform.\u003c/em\u003e 2022;294:281-282. https://doi.org/10.3233/SHTI220457\u003c/li\u003e\n\u003cli\u003eFirmann M, Mayor V, Vidal PM, Bochud M, P\u0026eacute;coud A, Hayoz D, et al. The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. \u003cem\u003eBMC Cardiovasc Disord.\u003c/em\u003e 2008;8:6. https://doi.org/10.1186/1471-2261-8-6\u003c/li\u003e\n\u003cli\u003eIkram MK, Ong YT, Cheung CY, Wong TY. Retinal vascular caliber measurements: clinical significance, current knowledge and future perspectives. \u003cem\u003eOphthalmologica.\u003c/em\u003e 2013;229(3):125-136. https://doi.org/10.1159/000342158\u003c/li\u003e\n\u003cli\u003eKlein R, Klein BEK, Linton KLP, DeMets DL. The Beaver Dam Eye Study: visual acuity. \u003cem\u003eOphthalmology.\u003c/em\u003e 1992;99(8):1310-1315. https://doi.org/10.1016/s0161-6420(91)32137-7\u003c/li\u003e\n\u003cli\u003eMilloz A, Molas G, Paych\u0026egrave;re Y, Bouillon A, et al. Estimating Quality of OCT Cubes using Phase-Level Unified Metric Evaluation (PLUME-OCT). \u003cem\u003eResearch Square Preprint.\u003c/em\u003e March 2024. https://doi.org/10.21203/rs.3.rs-4171462/v1\u003c/li\u003e\n\u003cli\u003eMousavi S, Garjani A, Elwakil A, Brock LP, Dherse A, Forestier E, et al. Cohort Builder: A Software Pipeline for Generating Patient Cohorts with Predetermined Baseline Characteristics from Medical Records and Raw Ophthalmic Imaging Data. \u003cem\u003eStud Health Technol Inform.\u003c/em\u003e 2024;316:1151-1155. https://doi.org/10.3233/SHTI240613\u003c/li\u003e\n\u003cli\u003eOrt\u0026iacute;n Vela S, Beyeler MJ, Trofimova O, Iuliani I, Vargas Quiros JD, de Vries VA, et al. Phenotypic and genetic characteristics of retinal vascular parameters and their association with diseases. \u003cem\u003eNat Commun.\u003c/em\u003e 2024;15:9593. https://doi.org/10.1038/s41467-024-45903-2\u003c/li\u003e\n\u003cli\u003ePreisig M, Waeber G, Vollenweider P, Bovet P, Rothen S, Vandeleur C, et al. The PsyCoLaus study: methodology and characteristics of the sample of a population-based survey on psychiatric disorders and their association with genetic and cardiovascular risk factors. \u003cem\u003eBMC Psychiatry.\u003c/em\u003e 2009;9:9. https://doi.org/10.1186/1471-244X-9-9\u003c/li\u003e\n\u003cli\u003eSchram MT, Sep SJ, van der Kallen CJ, Dagnelie PC, Koster A, Schaper N, et al. The Maastricht Study: an extensive phenotyping study on determinants of type 2 diabetes, its complications and its comorbidities. \u003cem\u003eEur J Epidemiol.\u003c/em\u003e 2014;29(6):439-451. https://doi.org/10.1007/s10654-014-9889-0\u003c/li\u003e\n\u003cli\u003eTomasoni M, Beyeler MJ, Ort\u0026iacute;n Vela S, Mounier N, Porcu E, Corre T, et al. Genome-wide Association Studies of Retinal Vessel Tortuosity Identify Numerous Novel Loci Revealing Genes and Pathways Associated With Ocular and Cardiometabolic Diseases. \u003cem\u003eOphthalmology Science.\u003c/em\u003e 2023;3(3):100288. https://doi.org/10.1016/j.xops.2023.100288\u003c/li\u003e\n\u003cli\u003eWilliams KM, Verhoeven VJM, Cumberland P, Bertelsen G, Wolfram C, Buitendijk GH, et al. Prevalence of refractive error in Europe: the European Eye Epidemiology (E\u0026sup3;) Consortium. \u003cem\u003eEur J Epidemiol.\u003c/em\u003e 2015;30(4):305-315. https://doi.org/10.1007/s10654-015-0010-0\u003c/li\u003e\n\u003cli\u003eYang L, Chen W, Chen D, et al. Cohort profile: the China surgery and anesthesia cohort (CSAC). \u003cem\u003eEur J Epidemiol.\u003c/em\u003e 2024;39:207\u0026ndash;218. https://doi.org/10.1007/s10654-023-01083-4\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"77d66b34-e7d2-4198-975c-3f684f01cd46","identifier":"10.13039/501100001711","name":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","awardNumber":"CRSII5_209510","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Hôpital Ophtalmique Jules-Gonin","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Population-based cohort, Ophthalmic epidemiology, Multimodal imaging, Retinal biomarkers, Microvasculature","lastPublishedDoi":"10.21203/rs.3.rs-7660406/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7660406/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRetinal imaging enables direct, non-invasive visualization of the human microvasculature, providing biomarkers of systemic cardiovascular, metabolic, and cognitive health.. The OphtalmoLaus study is an ophthalmic sub-study nested within the population-based CoLaus|PsyCoLaus cohort, aiming to investigate the relationships between ocular phenotypes and systemic health outcomes.\u003c/p\u003e\u003cp\u003eA total of 2472 participants from the CoLaus|PsyCoLaus cohort underwent standardized ophthalmic examinations at the Jules Gonin Ophthalmic Hospital, including visual acuity, intraocular pressure, axial length measurements, and multimodal ophthalmic imaging using Optical Coherence Tomography (OCT), OCT angiography (OCT-A), fundus photography, and anterior segment/iris imaging. Additionally, sublingual microcirculation imaging was performed using a handheld CytoCam-IDF microscope in a subgroup. Findings based on clinical examination and imaging findings included glaucoma, macular drusen (19.3%), macular edema (1.5%), subretinal fluid (1.0%), and macular hemorrhage (0.2%), with prevalence calculated relative to the total number of participants.\u003c/p\u003e\u003cp\u003e This manuscript provides a detailed overview of the study\u0026rsquo;s design, participant characteristics, imaging protocols, and clinical phenotyping methods, establishing the OphthalmoLaus dataset as a unique epidemiological resource for investigating links between ocular and systemic health, with potential for genetic, epidemiological, and longitudinal analyses.\u003c/p\u003e","manuscriptTitle":"Cohort profile: OphtalmoLaus, an extension of the CoLaus|PsyCoLaus cohort to investigate the relationships between ocular, cardiovascular, and cognitive parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 06:32:02","doi":"10.21203/rs.3.rs-7660406/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14d63ade-d141-4b81-ae8c-b98871122bf7","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55030246,"name":"Ophthalmology"},{"id":55030247,"name":"Epidemiology"},{"id":55030248,"name":"Vascular Medicine"},{"id":55030249,"name":"Psychiatry"}],"tags":[],"updatedAt":"2025-09-22T06:32:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 06:32:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7660406","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7660406","identity":"rs-7660406","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}