Treffer: Photoreceptor layer thinning is an early biomarker for type 2 diabetes: a cohort study in UK Biobank.
Title:
Photoreceptor layer thinning is an early biomarker for type 2 diabetes: a cohort study in UK Biobank.
Authors:
Ma Y; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China., Wu Y; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China., Hu L; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China., Zhang X; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China., Zheng D; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China., Liu Z; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China. liuzhenzhen@gzzoc.com., Jin G; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China. jingm@mail2.sysu.edu.cn.
Source:
Eye (London, England) [Eye (Lond)] 2025 Dec; Vol. 39 (17), pp. 3135-3141. Date of Electronic Publication: 2025 Oct 03.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 8703986 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-5454 (Electronic) Linking ISSN: 0950222X NLM ISO Abbreviation: Eye (Lond) Subsets: MEDLINE
Imprint Name(s):
Publication: <2003->: London : Nature Publishing Group
Original Publication: [London : Ophthalmological Society of the United Kingdom, 1987-
Original Publication: [London : Ophthalmological Society of the United Kingdom, 1987-
MeSH Terms:
Diabetes Mellitus, Type 2*/diagnostic imaging , Diabetes Mellitus, Type 2*/epidemiology , Diabetes Mellitus, Type 2*/etiology , Predictive Learning Models*/classification , Predictive Learning Models*/statistics & numerical data , Tomography, Optical Coherence*/classification , Tomography, Optical Coherence*/statistics & numerical data , Retinal Photoreceptor Cell Outer Segment*/pathology, Proportional Hazards Models ; UK Biobank ; United Kingdom/epidemiology ; Biomarkers/analysis ; Humans ; Male ; Female ; Middle Aged ; Follow-Up Studies ; Prospective Studies ; Predictive Value of Tests ; Risk Factors ; Risk Assessment/methods
References:
Skyler JS, Bakris GL, Bonifacio E, Darsow T, Eckel RH, Groop L, et al. Differentiation of diabetes by pathophysiology, natural history, and prognosis. Diabetes. 2017;66:241–55. (PMID: 10.2337/db16-080627980006)
Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. (PMID: 10.1016/j.diabres.2021.10911934879977)
Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988–2012. JAMA. 2015;314:1021–9. (PMID: 10.1001/jama.2015.1002926348752)
Balkau B, Lange CL, Fezeu L, Tichet J, de Lauzon-Guillain B, Czernichow S, et al. Predicting Diabetes: clinical, biological, and genetic approaches. Diabetes Care. 2008;31:2056–61. (PMID: 10.2337/dc08-0368186896952551654)
Zhou X, Qiao Q, Ji L, Ning F, Yang W, Weng J, et al. Nonlaboratory-based risk assessment algorithm for undiagnosed type 2 diabetes developed on a nationwide diabetes survey. Diabetes Care. 2013;36:3944–52. (PMID: 10.2337/dc13-0593241446513836161)
Yu DY, Cringle SJ. Oxygen distribution and consumption within the retina in vascularised and avascular retinas and in animal models of retinal disease. Prog Retin Eye Res. 2001;20:175–208. (PMID: 10.1016/S1350-9462(00)00027-611173251)
Huru J, Leiviskä I, Saarela V, Liinamaa MJ. Prediabetes influences the structure of the macula: thinning of the macula in the Northern Finland birth cohort. Br J Ophthalmol. 2021;105:1731–7. (PMID: 10.1136/bjophthalmol-2020-31741433028576)
De Clerck EEB, Schouten JSAG, Berendschot TTJM, Goezinne F, Dagnelie PC, Schaper NC, et al. Macular thinning in prediabetes or type 2 diabetes without diabetic retinopathy: the Maastricht study. Acta Ophthalmol. 2018;96:174–82. (PMID: 10.1111/aos.1357029090852)
Şahin M, Şahin A, Kılınç F, Karaalp Ü, Yüksel H, Özkurt ZG, et al. Early detection of macular and peripapillary changes with spectralis optical coherence tomography in patients with prediabetes. Arch Physiol Biochem. 2018;124:75–79. (PMID: 10.1080/13813455.2017.136145028780883)
Yang S, Zhu Z, Chen S, Yuan Y, He M, Wang W. Metabolic fingerprinting on retinal pigment epithelium thickness for individualized risk stratification of type 2 diabetes mellitus. Nat Commun. 2023;14:6573. (PMID: 10.1038/s41467-023-42404-13785299510585002)
Karaca C, Karaca Z. Beyond hyperglycemia, evidence for retinal neurodegeneration in metabolic syndrome. Investig Ophthalmol Vis Sci. 2018;59:1360–7. (PMID: 10.1167/iovs.17-23376)
Énzsöly A, Szabó A, Kántor O, Dávid C, Szalay P, Szabó K, et al. Pathologic alterations of the outer retina in streptozotocin-induced diabetes. Investig Ophthalmol Vis Sci. 2014;55:3686–99. (PMID: 10.1167/iovs.13-13562)
Hammoum I, Benlarbi M, Dellaa A, Szabó K, Dékány B, Csaba D, et al. Study of retinal neurodegeneration and maculopathy in diabetic Meriones shawi: a particular animal model with human-like macula. J Comp Neurol. 2017;525:2890–914. (PMID: 10.1002/cne.2424528542922)
Verma A, Rani PK, Raman R, Pal SS, Laxmi G, Gupta M, et al. Is neuronal dysfunction an early sign of diabetic retinopathy? Microperimetry and spectral domain optical coherence tomography (SD-OCT) study in individuals with diabetes, but no diabetic retinopathy. Eye. 2009;23:1824–30. (PMID: 10.1038/eye.2009.18419648899)
Zhu Z, Shi D, Liao H, Ha J, Shang X, Huang Y, et al. Visual impairment and risk of dementia: the UK Biobank study. Am J Ophthalmol. 2022;235:7–14. (PMID: 10.1016/j.ajo.2021.08.01034433084)
Ko F, Foster PJ, Strouthidis NG, Shweikh Y, Yang Q, Reisman CA, et al. Associations with retinal pigment epithelium thickness measures in a large cohort: results from the UK Biobank. Ophthalmology. 2017;124:105–17. (PMID: 10.1016/j.ophtha.2016.07.03327720551)
Foster HME, Celis-Morales CA, Nicholl BI, Petermann-Rocha F, Pell JP, Gill JMR, et al. The effect of socioeconomic deprivation on the association between an extended measurement of unhealthy lifestyle factors and health outcomes: a prospective analysis of the UK Biobank cohort. Lancet Public Health. 2018;3:e576–e585. (PMID: 10.1016/S2468-2667(18)30200-730467019)
Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i-xii, 1–253.
Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54:1451–62. (PMID: 10.1136/bjsports-2020-10295533239350)
Park S-Y, Freedman ND, Haiman CA, Le Marchand L, Wilkens LR, Setiawan VW. Association of coffee consumption with total and cause-specific mortality among nonwhite populations. Ann Intern Med. 2017;167:228–35. (PMID: 10.7326/M16-2472286930367494322)
Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15:361–87. (PMID: 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-48668867)
Pencina MJ, D’Agostino RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30:11–21. (PMID: 10.1002/sim.408521204120)
Leening MJG, Vedder MM, Witteman JCM, Pencina MJ, Steyerberg EW. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician’s guide. Ann Intern Med. 2014;160:122–31. (PMID: 10.7326/M13-152224592497)
Diehl T, Mullins R, Kapogiannis D. Insulin resistance in Alzheimer’s disease. Transl Res. 2017;183:26–40. (PMID: 10.1016/j.trsl.2016.12.00528034760)
Takeda S, Sato N, Ikimura K, Nishino H, Rakugi H, Morishita R. Increased blood-brain barrier vulnerability to systemic inflammation in an Alzheimer's disease mouse model. Neurobiol Aging. 2013;34:2064–70. (PMID: 10.1016/j.neurobiolaging.2013.02.01023561508)
Murphy MP. How mitochondria produce reactive oxygen species. Biochem J. 2009;417:1–13. (PMID: 10.1042/BJ2008138619061483)
Bhatti JS, Bhatti GK, Reddy PH. Mitochondrial dysfunction and oxidative stress in metabolic disorders—a step towards mitochondria based therapeutic strategies. Biochim Biophys Acta Mol Basis Dis. 2017;1863:1066–77. (PMID: 10.1016/j.bbadis.2016.11.01027836629)
Reddy PH. Mitochondrial medicine for aging and neurodegenerative diseases. Neuromol. Med. 2008;10:291–315. (PMID: 10.1007/s12017-008-8044-z)
Nieves-Moreno M, Martínez-de-la-Casa JM, Morales-Fernández L, Sánchez-Jean R, Sáenz-Francés F, García-Feijoó J. Impacts of age and sex on retinal layer thicknesses measured by spectral domain optical coherence tomography with Spectralis. PLoS ONE. 2018;13:e0194169. (PMID: 10.1371/journal.pone.0194169295225655844598)
Chua J, Tham YC, Tan B, Devarajan K, Schwarzhans F, Gan A, et al. Age-related changes of individual macular retinal layers among Asians. Sci Rep. 2019;9:20352. (PMID: 10.1038/s41598-019-56996-6318891436937292)
Trinh M, Khou V, Zangerl B, Kalloniatis M, Nivison-Smith L. Modelling normal age-related changes in individual retinal layers using location-specific OCT analysis. Sci Rep. 2021;11:558. (PMID: 10.1038/s41598-020-79424-6334367157804110)
Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. (PMID: 10.1016/j.diabres.2021.10911934879977)
Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988–2012. JAMA. 2015;314:1021–9. (PMID: 10.1001/jama.2015.1002926348752)
Balkau B, Lange CL, Fezeu L, Tichet J, de Lauzon-Guillain B, Czernichow S, et al. Predicting Diabetes: clinical, biological, and genetic approaches. Diabetes Care. 2008;31:2056–61. (PMID: 10.2337/dc08-0368186896952551654)
Zhou X, Qiao Q, Ji L, Ning F, Yang W, Weng J, et al. Nonlaboratory-based risk assessment algorithm for undiagnosed type 2 diabetes developed on a nationwide diabetes survey. Diabetes Care. 2013;36:3944–52. (PMID: 10.2337/dc13-0593241446513836161)
Yu DY, Cringle SJ. Oxygen distribution and consumption within the retina in vascularised and avascular retinas and in animal models of retinal disease. Prog Retin Eye Res. 2001;20:175–208. (PMID: 10.1016/S1350-9462(00)00027-611173251)
Huru J, Leiviskä I, Saarela V, Liinamaa MJ. Prediabetes influences the structure of the macula: thinning of the macula in the Northern Finland birth cohort. Br J Ophthalmol. 2021;105:1731–7. (PMID: 10.1136/bjophthalmol-2020-31741433028576)
De Clerck EEB, Schouten JSAG, Berendschot TTJM, Goezinne F, Dagnelie PC, Schaper NC, et al. Macular thinning in prediabetes or type 2 diabetes without diabetic retinopathy: the Maastricht study. Acta Ophthalmol. 2018;96:174–82. (PMID: 10.1111/aos.1357029090852)
Şahin M, Şahin A, Kılınç F, Karaalp Ü, Yüksel H, Özkurt ZG, et al. Early detection of macular and peripapillary changes with spectralis optical coherence tomography in patients with prediabetes. Arch Physiol Biochem. 2018;124:75–79. (PMID: 10.1080/13813455.2017.136145028780883)
Yang S, Zhu Z, Chen S, Yuan Y, He M, Wang W. Metabolic fingerprinting on retinal pigment epithelium thickness for individualized risk stratification of type 2 diabetes mellitus. Nat Commun. 2023;14:6573. (PMID: 10.1038/s41467-023-42404-13785299510585002)
Karaca C, Karaca Z. Beyond hyperglycemia, evidence for retinal neurodegeneration in metabolic syndrome. Investig Ophthalmol Vis Sci. 2018;59:1360–7. (PMID: 10.1167/iovs.17-23376)
Énzsöly A, Szabó A, Kántor O, Dávid C, Szalay P, Szabó K, et al. Pathologic alterations of the outer retina in streptozotocin-induced diabetes. Investig Ophthalmol Vis Sci. 2014;55:3686–99. (PMID: 10.1167/iovs.13-13562)
Hammoum I, Benlarbi M, Dellaa A, Szabó K, Dékány B, Csaba D, et al. Study of retinal neurodegeneration and maculopathy in diabetic Meriones shawi: a particular animal model with human-like macula. J Comp Neurol. 2017;525:2890–914. (PMID: 10.1002/cne.2424528542922)
Verma A, Rani PK, Raman R, Pal SS, Laxmi G, Gupta M, et al. Is neuronal dysfunction an early sign of diabetic retinopathy? Microperimetry and spectral domain optical coherence tomography (SD-OCT) study in individuals with diabetes, but no diabetic retinopathy. Eye. 2009;23:1824–30. (PMID: 10.1038/eye.2009.18419648899)
Zhu Z, Shi D, Liao H, Ha J, Shang X, Huang Y, et al. Visual impairment and risk of dementia: the UK Biobank study. Am J Ophthalmol. 2022;235:7–14. (PMID: 10.1016/j.ajo.2021.08.01034433084)
Ko F, Foster PJ, Strouthidis NG, Shweikh Y, Yang Q, Reisman CA, et al. Associations with retinal pigment epithelium thickness measures in a large cohort: results from the UK Biobank. Ophthalmology. 2017;124:105–17. (PMID: 10.1016/j.ophtha.2016.07.03327720551)
Foster HME, Celis-Morales CA, Nicholl BI, Petermann-Rocha F, Pell JP, Gill JMR, et al. The effect of socioeconomic deprivation on the association between an extended measurement of unhealthy lifestyle factors and health outcomes: a prospective analysis of the UK Biobank cohort. Lancet Public Health. 2018;3:e576–e585. (PMID: 10.1016/S2468-2667(18)30200-730467019)
Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i-xii, 1–253.
Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54:1451–62. (PMID: 10.1136/bjsports-2020-10295533239350)
Park S-Y, Freedman ND, Haiman CA, Le Marchand L, Wilkens LR, Setiawan VW. Association of coffee consumption with total and cause-specific mortality among nonwhite populations. Ann Intern Med. 2017;167:228–35. (PMID: 10.7326/M16-2472286930367494322)
Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15:361–87. (PMID: 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-48668867)
Pencina MJ, D’Agostino RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30:11–21. (PMID: 10.1002/sim.408521204120)
Leening MJG, Vedder MM, Witteman JCM, Pencina MJ, Steyerberg EW. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician’s guide. Ann Intern Med. 2014;160:122–31. (PMID: 10.7326/M13-152224592497)
Diehl T, Mullins R, Kapogiannis D. Insulin resistance in Alzheimer’s disease. Transl Res. 2017;183:26–40. (PMID: 10.1016/j.trsl.2016.12.00528034760)
Takeda S, Sato N, Ikimura K, Nishino H, Rakugi H, Morishita R. Increased blood-brain barrier vulnerability to systemic inflammation in an Alzheimer's disease mouse model. Neurobiol Aging. 2013;34:2064–70. (PMID: 10.1016/j.neurobiolaging.2013.02.01023561508)
Murphy MP. How mitochondria produce reactive oxygen species. Biochem J. 2009;417:1–13. (PMID: 10.1042/BJ2008138619061483)
Bhatti JS, Bhatti GK, Reddy PH. Mitochondrial dysfunction and oxidative stress in metabolic disorders—a step towards mitochondria based therapeutic strategies. Biochim Biophys Acta Mol Basis Dis. 2017;1863:1066–77. (PMID: 10.1016/j.bbadis.2016.11.01027836629)
Reddy PH. Mitochondrial medicine for aging and neurodegenerative diseases. Neuromol. Med. 2008;10:291–315. (PMID: 10.1007/s12017-008-8044-z)
Nieves-Moreno M, Martínez-de-la-Casa JM, Morales-Fernández L, Sánchez-Jean R, Sáenz-Francés F, García-Feijoó J. Impacts of age and sex on retinal layer thicknesses measured by spectral domain optical coherence tomography with Spectralis. PLoS ONE. 2018;13:e0194169. (PMID: 10.1371/journal.pone.0194169295225655844598)
Chua J, Tham YC, Tan B, Devarajan K, Schwarzhans F, Gan A, et al. Age-related changes of individual macular retinal layers among Asians. Sci Rep. 2019;9:20352. (PMID: 10.1038/s41598-019-56996-6318891436937292)
Trinh M, Khou V, Zangerl B, Kalloniatis M, Nivison-Smith L. Modelling normal age-related changes in individual retinal layers using location-specific OCT analysis. Sci Rep. 2021;11:558. (PMID: 10.1038/s41598-020-79424-6334367157804110)
Substance Nomenclature:
0 (Biomarkers)
Entry Date(s):
Date Created: 20251003 Date Completed: 20251204 Latest Revision: 20251204
Update Code:
20251204
PubMed Central ID:
PMC12623438
DOI:
10.1038/s41433-025-04068-7
PMID:
41044166
Database:
MEDLINE
Weitere Informationen
Objectives: To investigate the association between photoreceptor outer segment (POS) thickness and the risk of T2D.
Methods: This prospective cohort study included 35,024 UK Biobank participants with high-quality optical coherence tomography (OCT) images, excluding individuals with neurological or ocular diseases. Cox proportional hazard models were used to analyse the association between baseline POS thickness and incident T2D. Risk model improvement was assessed using net reclassification improvement (NRI) and integrated discrimination improvement (IDI).
Results: Among 35,024 participants (mean age, 55.6 years; 54.0% women), 1187 (3.39%) developed T2D during a median follow-up of 13.18 years. A decrease of 5 μm in POS thickness was linked to a 10% higher risk of T2D (HR, 1.10; 95% CI, 1.02-1.18; P = 0.010). Participants in the lowest tertile of POS thickness had a 22% higher T2D risk compared to those in the highest tertile (HR, 1.22; 95% CI, 1.06-1.42; P = 0.007). Adding POS thickness to traditional risk models significantly improved predictive accuracy (NRI, 0.0356; P = 0.010; IDI, 0.0003; P = 0.010).
Conclusions: POS thinning detected by OCT is significantly associated with an increased risk of T2D and improves risk prediction when added to conventional models. These findings suggest that POS thickness may serve as a novel biomarker for early detection of T2D, supporting timely interventions to reduce risk.
(© 2025. The Author(s), under exclusive licence to The Royal College of Ophthalmologists.)
Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: The UK Biobank Study has received ethical approval from the National Information Governance Board for Health and Social Care and the NHS North West Multicenter Research Ethics Committee (REC reference: 16/NW/0274). All participants in the study provided informed consent through electronic signature during the baseline assessment. This present study was conducted under application number 87083 of the UK Biobank resource.