Prediction of the risk of developing diabetic retinopathy based on blood molecules and retinal features 

Prediction of the risk of developing diabetic retinopathy based on blood molecules and retinal features | 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 Prediction of the risk of developing diabetic retinopathy based on blood molecules and retinal features Huiqian Kong, Jiahui Cao, Chunran Lai, Qinyi Li, Ying Fang, Huiyi Liao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6774565/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Diabetic retinopathy (DR) has rapidly become the leading blinding eye disease threatening the working population. We aimed to explore molecular biomarkers and retinal features and build prediction models of DR. Methods: Participants from UK Biobank were recruited from 2006 to 2010, and prospectively followed up until 2021. We divided the enrolled population according to the full-course DR into 5 groups: no diabetes mellitus (no DM), prediabetes (pre DM), diabetes mellitus (DM), non-proliferating diabetic retinopathy (NPDR), and proliferating diabetic retinopathy (PDR). The molecular biomarkers evaluated at baseline includes 7 lipids and 8 proteins, while the retinal features were measured by Optical coherence tomography (OCT). The associations between molecular biomarkers and retinal features were performed by correlation analysis. A predictive model of DR was constructed using both retinal features and molecular biomarkers. Result: The study included 3953 participants (2095 [53.0%] female), with a mean age (SD) of47.3 (5.8) years. Apo A, Apo B, HDL, LDL were associated with retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), and inner nuclear layer (INL) in full-course DR (all r range from 0.5 to 1 0.5, p< 0.05). Protein biomarkers, including albumin, total protein, creatine, showed significant correlation with GCL and RNFL in pre DM, DM, NPDR, and PDR groups compared to the no DM group (all p< 0.05). The Area Under Curve (AUC) of the DR prediction model based on the combination of molecular biomarkers and retinal features is 0.790 (95%CI:0.711-0.847), p <0.01, which is higher than the prediction models based on molecular biomarkers or retinal features alone. Conclusion: Molecular biomarkers were associated with retinal features during the full-course DR. DR prediction model based on the combination of molecular biomarkers and retinal features presented a higher AUC, suggesting a possible strategy for early diagnosis of DR. Diabetic retinopathy retinal features molecular markers prediction model Figures Figure 1 Figure 2 Figure 3 Introduction Diabetic retinopathy (DR) has become one of the blinding diseases that harm working population. At present, there are more than 100 million diabetes patients in the world, and the proportion of DR is increasing year by year[ 1 – 3 ]. DR which presents as a progressive eye disease, it will cause irreversible damage to the structure and function of the eye[ 4 – 6 ]. The diagnosis of DR often requires the use of ophthalmic imaging, such as fundus photography, OCT, angiography and so on[ 7 ]. At the same time, with the progression of DR, retinal images and protein molecules will also change, and the significant retinal features or molecular markers will also change in different stages of DR[ 8 , 9 ]. Accurately finding the retinal features and molecular markers of DR, especially in the early stage of the disease, and the prediction models of DR in the early onset, is of great significance to alleviate the threat of blindness caused by DR[ 10 – 12 ]. Previously, for the early diagnosis and treatment of DR, a number of studies used imaging markers or molecular markers to build models to predict the occurrence of DR in patients with diabetes[ 13 – 18 ]. Common imaging features such as hemangioma, venous abnormalities, and neovascularization have been found as imaging markers for the diagnosis of DR, providing as predictive signals[ 19 – 22 ]. In the exploration of molecular markers, previous studies paid more attention to the abnormality of intraocular fluid molecules such as VEGF, and made early prediction of the occurrence of DR based on the change of the concentration of these molecules in intraocular fluid[ 23 ]. However, the detecting of retinal and molecular markers are still limited[ 24 ]. Besides, the predictive models of DR in multi-center cohort are lacking, and the mechanism of the connection between retinal changes and molecules is left unclear. Few studies have explored the potential association between the retinal features and molecular markers, and the potential mechanisms of them. Therefore, in this study, we linked retinal features and molecular biomarkers of participants from UK Biobank, and grouped them according to the full-course DR, including no DM group, pre DM group, DM group, NPDR group and PDR group. We performed correlation analysis of retinal features and molecular biomarkers in the five groups. Predictive models of DR are constructed by combining retinal features and molecular biomarkers, aiming to provide new strategies for the early diagnosis and integrated management. Methods Study population This study was conducted using individual-level data from the UK biobank (UKB), which is a multi-center prospective cohort study that recruited more than 502,565 community citizens across the United Kingdom from 2006 to 2010, and followed up to 2021. All research program information (http://www.ukbiobank.ac.uk/resources/) and individual test program details (http://biobank.ctsu.ox.ac.uk/crystal/docs.cgi) were available[25]. The study was conducted under application number #86091 of the UK Biobank resource. Our study included participants from the UK Biobank who were with and without DM diagnostic information. The control group was obtained by randomly matching baseline data with people who were not diagnosed with DM and other chronic diseases. Those with serious fundus diseases and other diseases that seriously affect the intraocular fluid environment were excluded, such as age-related macular degeneration, polypoid choroidal vasculopathy, retinal artery/vein obstruction, central serous choroidal retinopathy, pore-derived retinal detachment, macular ischemia, glaucoma, etc. Patients with high myopia and lack of access to complete eye examinations for various reasons were also excluded. The flow chart of this study is shown in Figure 1. Grouping of DR We divided the population according to the DM status and the progression of DR into the following five groups: no DM group/control group whose participants without DM and other serious systemic diseases, pre DM group that the participants were no DM diagnosis and no medication to control blood glucose but with blood glucose or HbA1c exceeding the upper limit of normal range (Hba1c value 39~48 mmol/mol)[26], DM group that the patients with DM and/or taking drugs to control blood sugar had no DR changes in fundus photography, NPDR group whose diagnosis information included DM or taking drugs to control blood sugar showed DR changes but no PDR in fundus photography, and PDR group with DM diagnosis and/or taking diabetes related drugs r, with neovascularization in fundus photography. The diagnosis and classification of DR are judged by diagnosis code, medical history, and fundus image synthesis. Ophthalmic Examination and Image Acquisition Optical coherence tomography (OCT) imaging parameters were obtained from UKB data. Among them, we excluded parameters with missing data and quality control (QC) less than 6[27]. This study observed OCT parameters included overall macular thickness, macular thickness at central subfield, total macular volume, overall retinal pigment epithelium thickness, RNFL, GCL, INL, external limiting membrane (ELM). Molecular biomarkers Blood molecular samples were derived from the participants' peripheral blood. Baseline inclusion of molecular markers was from 2006 to 2010. In this study, 18 routine peripheral blood molecular were included for analysis and individuals with missing values were excluded. Covariates Baseline data were collected for age, sex, education, smoking status, high blood pressure, and level of education (college and non-college). Participants completed a detailed questionnaire on a touch-screen computer about their lifestyle and identified hypertension status based on blood pressure levels in the population. Lipid and protein measurements were obtained by examination of the peripheral blood of the enrolled population. Statistical analysis Intergroup analysis was used to compare the baseline difference of five groups in the full-course DR. Multiple regression analysis was conducted for screening the retinal features and molecular markers of DR. The follow-up cohort was used to stablished the predictive models of DR. We divided the data from UKB's six centers according to a 4:2 ratio of test sets to validation sets. We constructed predictive models of DR using regression screened molecules, images, lipids associated with the full-course DR, combining retinal features and molecular biomarkers. The prediction model of DR Was established based on ROC curve drawing and Cox regression analysis by SPSS (ver. 25). The R (version 3.6.3) was used to performed the correlation analysis between retinal features and molecular biomarkers. The p value less than 0.05 is considered statistically significant. Results Our study enrolled 3,953 baseline patients with mean age of 47.3±5.8 and percentages of male and female were 47% and 53%, including 2120 no DM patients, 1220 pre DM patients, 425 DM patients, 155 NPDR patients and 33 PDR patients. The comparative analysis of baseline data for each group is shown in Table 1. Through regression analysis, we screened out meaningful retinal and molecular markers, as shown in Table 2. We carried out correlation analysis of the relationship between images and molecules for meaningful indicators in different groups, and the correlation visualization was shown in Figure 2. Apo A, Apo B, HDL, LDL were associated with RNFL, GCL, and INL in the full-course DR, suggesting that lipid metabolism biomarkers were closely related to nerve fiber layers and ganglion cells in the retina (all p <0.05). Compared with the no DM group, DM, DR and PDR group had a closer association, suggesting that there were abnormalities in the protein biomarkers (albumin, total protein, creatine) during the development from DM to DR. In DR (NPDR and PDR), there were associations with both lipid markers (Apo B, HDL, LDL, cholesterol, triglycerides) and protein (albumin) markers. According to the analysis of the correlation between different groups and molecular biomarkers at different levels, lipid biomarkers may reflect the progression of DM and DR to a certain extent, and serve as an understanding signals and supplement for the full-course DR. Based on the above retinal features and molecular biomarkers, we constructed predictions model of DR based on the UKB cohort population from 2010 to 2021. We constructed four models based on molecular biomarkers, retinal features, lipid biomarkers, and the combination of retinal features and molecular biomarkers respectively, as shown in Figure 3. The AUC values of the four models were 0.653 (95% Confidence Interval (CI): 0.590-0.721), 0.626 (95%CI:0.601-0.734), 0.644 (95%CI:0.532-0.697), and 0.790 (95%CI:0.711-0.847), all p <0.01. Discussion In this study, we explored the correlation between blood molecular biomarkers and retinal features measuring by OCT in five groups of the full-course DR, and found that lipid biomarkers were correlated with RNFL, GCL and INL during the full-course DR. Based on the retinal features and molecular biomarkers, we established prediction models of DR, suggesting that the model combining retinal features and molecular biomarkers show a higher AUC. Recently, there have been many studies on DR imaging or molecular markers[ 11 , 28 ]. In the exploration of imaging markers, common imaging features such as hemangioma, venous abnormalities, and neovascularization have been used as imaging markers for the diagnosis of DR to establish predictive or diagnostic models[ 19 , 23 ]. In the exploration of molecular markers, previous studies focused on the abnormalities of VEGF and other molecules in the intraocular fluid, and made early prediction of the occurrence of DR according to the changes in the concentration of these molecules in the intraocular fluid[ 29 – 32 ], which consistent with the molecular results we've screened. However, the extraction of vitreous or aqueous humor is an invasive procedure and cannot be used in the vast majority of diabetic patients. Besides, the acquisition and measurement of intraocular fluid is highly dependent on the collector and the level of detection, the leading that the acquisition and measurement of intraocular fluid are not widely used[ 33 ]. Therefore, in this study, the results of routine blood sampling were used as research materials to screen molecular markers related to DR, so as to achieve wider application value. In addition, the study not only conducted on patients with diabetes, but also used the grouping of the whole course of disease to describe the characteristics of blood molecular changes in patients from no DM group to PDR group in a more comprehensive and extensive way, filling the gap in the understanding and description of the disease. Previous studies have analyzed the DR processes corresponding to different levels of OCT[ 34 , 35 ], analyzed the image markers in the DR progression[ 36 , 37 ], and built models based on the markers to predict the occurrence of DR[ 14 , 38 ]. However, there were limited studies have explored the link between imaging and molecular markers on this basis or analyzed the two together. Through correlation visualization technology, this study conducted correlation mapping analysis on images and molecular markers of each group in the full-course DR, aiming to explore molecular biomarkers related to the OCT levels including RNFL, GCL, and INL in the progression of DR, so as to make more targeted diagnosis, treatment and intervention in the early stage of DR. At the same time, we found that lipid biomarkers were correlated with RNFL, GCL and INL in the full-course DR, suggesting that pre DM and early rise of lipid biomarkers may be the key to improve the corresponding retinal function and delay the progression of the disease to a certain extent[ 39 , 40 ]. As for the protein biomarkers, the DM group was significantly abnormal than the no DM group. Therefore, for patients with diabetes, simultaneous intervention of lipid and protein biomarkers could delay the progression of DR. It can be seen that this study describes the visual correlation between DR retinal features and molecular markers, which fills the gap in the understanding and development of diseases, and provides a new strategy for the comprehensive intervention of diseases[ 41 ]. We established four prediction models of DR using the images or/and molecular markers. The model combining retinal features and molecular biomarkers showed higher AUC value than the other three models (AUC = 0.790). A number of previous studies have used DR images or molecular markers to build predictive models for the onset or progression of DR, but their models are not all very efficient[ 14 , 15 , 19 ]. Moreover, there is a lack of study population support of big data and follow-up cohort[ 41 ]. In this study, the combined prediction method of image and molecular marker was adopted to achieve higher model efficiency and provide a more convenient, practical and reliable method for predicting the occurrence of DR. The study still has several limitations. First, this study takes the UK Biobank population as the research object, which may produce racial bias, and the results cannot be widely applied to all races in the world. Secondly, only the markers in OCT were considered as image markers in this study. However, in fact, there are many image markers in ophthalmology, such as fundus photography, eye ultrasound and so on. In the future, we will further explore the molecular mechanism of lipid indicators in the process of DR development. Conclusion Based on the UK population, the prediction model of DR combining retinal features and molecular biomarkers showed higher AUC comparing to use only retinal features or molecular biomarkers, which provides a new strategy for the early diagnosis and intervention of DR, and fills the gap for the understanding and management of the disease. Declarations Funding The present work was supported by the National Natural Science Foundation of China (82171075) and the Basic and Applied Basic Research foundation of Guangdong province (2023B1515120028). The sponsors or funding organizations had no role in the design or conduct of this research. Contributors Study concept and design: Yu HH. Acquisition, analysis, or interpretation: All authors. Drafting of the manuscript: Kong HQ, Zhang XY. Critical revision of the manuscript for important intellectual content: Yu HH, Zhang XY. Statistical analysis: Kong HQ, Zhang XY, Cao JH, Lai CR. Obtained funding: Yu HH, Zhang XY. Administrative, technical, or material support: Yu HH, Hu YY, Fang Y, and Liao HY. Study supervision: Yu HH. Acknowledgements Thanks to all the individuals and institutions who provide help during the research process, including financial support, technical support, data collection, academic advice, ‌ personal help and valuable comments from anonymous reviewers. ‌ Availability of data and material Data were obtained through the formal procedures from UK Biobank by the researcher. Any questions about the data analysis can be contacted by email to the researchers. The detailed code can be provided by contacting the mailing address. Competing interests None declared. Patient consent for publication Not applicable. Ethics approval The UK Biobank received ethics approval from the NHS National Research Ethics Service North West (16/NW/0274) and was performed in accordance with the Declaration of Helsinki. Informed consent was obtained for all UK Biobank participants. The present study was conducted under application number #86091 of the UK Biobank resource. References Jeng CJ, Hsieh YT, Yang CM, Yang CH, Lin CL, Wang IJ: Development of diabetic retinopathy after cataract surgery . PloS one 2018, 13 (8):e0202347. Tan TE, Wong TY: Diabetic retinopathy: Looking forward to 2030 . Frontiers in endocrinology 2022, 13 :1077669. Ting DS, Cheung GC, Wong TY: Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review . Clinical & experimental ophthalmology 2016, 44 (4):260-277. Meng Z, Chen Y, Wu W, Yan B, Meng Y, Liang Y, Yao X, Luo J: Exploring the Immune Infiltration Landscape and M2 Macrophage-Related Biomarkers of Proliferative Diabetic Retinopathy . Frontiers in endocrinology 2022, 13 :841813. Teo ZL, Tham YC, Yu M, Chee ML, Rim TH, Cheung N, Bikbov MM, Wang YX, Tang Y, Lu Y et al : Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis . Ophthalmology 2021, 128 (11):1580-1591. Wu H, Wu H, Shi L, Yuan X, Yin Y, Yuan M, Zhou Y, Hu Q, Jiang K, Dong J: The Association of Haptoglobin Gene Variants and Retinopathy in Type 2 Diabetic Patients: A Meta-Analysis . Journal of diabetes research 2017, 2017 :2195059. Nanegrungsunk O, Patikulsila D, Sadda SR: Ophthalmic imaging in diabetic retinopathy: A review . Clinical & experimental ophthalmology 2022, 50 (9):1082-1096. Stitt AW, Curtis TM, Chen M, Medina RJ, McKay GJ, Jenkins A, Gardiner TA, Lyons TJ, Hammes HP, Simó R et al : The progress in understanding and treatment of diabetic retinopathy . Progress in retinal and eye research 2016, 51 :156-186. Jenkins AJ, Joglekar MV, Hardikar AA, Keech AC, O'Neal DN, Januszewski AS: Biomarkers in Diabetic Retinopathy . The review of diabetic studies : RDS 2015, 12 (1-2):159-195. Himasa FI, Singhal M, Ojha A, Kumar B: Prospective for Diagnosis and Treatment of Diabetic Retinopathy . Current pharmaceutical design 2022, 28 (7):560-569. Leley SP, Ciulla TA, Bhatwadekar AD: Diabetic Retinopathy in the Aging Population: A Perspective of Pathogenesis and Treatment . Clinical interventions in aging 2021, 16 :1367-1378. Grzybowski A, Brona P, Lim G, Ruamviboonsuk P, Tan GSW, Abramoff M, Ting DSW: Artificial intelligence for diabetic retinopathy screening: a review . Eye (London, England) 2020, 34 (3):451-460. ValdezGuerrero AS, Quintana-Pérez JC, Arellano-Mendoza MG, Castañeda-Ibarra FJ, Tamay-Cach F, Alemán-González-Duhart D: Diabetic Retinopathy: Important Biochemical Alterations and the Main Treatment Strategies . Canadian journal of diabetes 2021, 45 (6):504-511. Wang GX, Hu XY, Zhao HX, Li HL, Chu SF, Liu DL: Development and validation of a diabetic retinopathy risk prediction model for middle-aged patients with type 2 diabetes mellitus . Frontiers in endocrinology 2023, 14 :1132036. Shen Z, Wu Q, Wang Z, Chen G, Lin B: Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data . Sensors (Basel, Switzerland) 2021, 21 (11). Liu W, Luo Z, Zhang L, Wang Y, Yang J, You D, Cao X, Yang W: hsa-mir-(4328, 4422, 548z and -628-5p) in diabetic retinopathy: diagnosis, prediction and linking a new therapeutic target . Acta diabetologica 2023, 60 (7):929-942. Zhang G, Chen H, Chen W, Zhang M: Prevalence and risk factors for diabetic retinopathy in China: a multi-hospital-based cross-sectional study . The British journal of ophthalmology 2017, 101 (12):1591-1595. Yue T, Shi Y, Luo S, Weng J, Wu Y, Zheng X: The role of inflammation in immune system of diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications . Frontiers in immunology 2022, 13 :1055087. Zhu C, Zhu J, Wang L, Xiong S, Zou Y, Huang J, Xie H, Zhang W, Wu H, Liu Y: Development and validation of a risk prediction model for diabetic retinopathy in type 2 diabetic patients . Scientific reports 2023, 13 (1):5034. Mudie LI, Wang X, Friedman DS, Brady CJ: Crowdsourcing and Automated Retinal Image Analysis for Diabetic Retinopathy . Current diabetes reports 2017, 17 (11):106. Alyoubi WL, Abulkhair MF, Shalash WM: Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning . Sensors (Basel, Switzerland) 2021, 21 (11). Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, Kaski K: Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading . Scientific reports 2019, 9 (1):10750. Huang X, Wang H, She C, Feng J, Liu X, Hu X, Chen L, Tao Y: Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy . Frontiers in endocrinology 2022, 13 :946915. Vujosevic S, Aldington SJ, Silva P, Hernández C, Scanlon P, Peto T, Simó R: Screening for diabetic retinopathy: new perspectives and challenges . The lancet Diabetes & endocrinology 2020, 8 (4):337-347. Collins R: What makes UK Biobank special? Lancet (London, England) 2012, 379 (9822):1173-1174. Gabbay KH: Glycosylated hemoglobin and diabetes mellitus . The Medical clinics of North America 1982, 66 (6):1309-1315. Podoleanu AG: Optical coherence tomography . Journal of microscopy 2012, 247 (3):209-219. Schumacher M, Schmidt D, Jurklies B, Gall C, Wanke I, Schmoor C, Maier-Lenz H, Solymosi L, Brueckmann H, Neubauer AS et al : Central retinal artery occlusion: local intra-arterial fibrinolysis versus conservative treatment, a multicenter randomized trial . Ophthalmology 2010, 117 (7):1367-1375.e1361. Du X, Yang L, Kong L, Sun Y, Shen K, Cai Y, Sun H, Zhang B, Guo S, Zhang A et al : Metabolomics of various samples advancing biomarker discovery and pathogenesis elucidation for diabetic retinopathy . Frontiers in endocrinology 2022, 13 :1037164. Youngblood H, Robinson R, Sharma A, Sharma S: Proteomic Biomarkers of Retinal Inflammation in Diabetic Retinopathy . International journal of molecular sciences 2019, 20 (19). Weber SR, Zhao Y, Gates C, Ma J, da Veiga Leprevost F, Basrur V, Nesvizhskii AI, Gardner TW, Sundstrom JM: Proteomic Analyses of Vitreous in Proliferative Diabetic Retinopathy: Prior Studies and Future Outlook . Journal of clinical medicine 2021, 10 (11). You J, Guo Y, Zhang Y, Kang JJ, Wang LB, Feng JF, Cheng W, Yu JT: Plasma proteomic profiles predict individual future health risk . Nature communications 2023, 14 (1):7817. Midena E, Frizziero L, Midena G, Pilotto E: Intraocular fluid biomarkers (liquid biopsy) in human diabetic retinopathy . Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie 2021, 259 (12):3549-3560. Yao X, Son T, Kim TH, Lu Y: Functional optical coherence tomography of retinal photoreceptors . Experimental biology and medicine (Maywood, NJ) 2018, 243 (17-18):1256-1264. Borrelli E, Sacconi R, Querques G, Bandello F: Optical coherence tomography angiography in the management of diabetic retinopathy . Indian journal of ophthalmology 2021, 69 (11):3009-3014. Waheed NK, Rosen RB, Jia Y, Munk MR, Huang D, Fawzi A, Chong V, Nguyen QD, Sepah Y, Pearce E: Optical coherence tomography angiography in diabetic retinopathy . Progress in retinal and eye research 2023, 97 :101206. Chen Y, Qi Z, Hou X, Chen L: Optical coherence tomography angiography for assessment of changes of the retina and choroid in different stages of diabetic retinopathy and their relationship with diabetic nephropathy . Endokrynologia Polska 2023, 74 (2):135-139. Amato A, Nadin F, Borghesan F, Cicinelli MV, Chatziralli I, Sadiq S, Mirza R, Bandello F: Widefield Optical Coherence Tomography Angiography in Diabetic Retinopathy . Journal of diabetes research 2020, 2020 :8855709. Chait A, Montes VN: Apolipoproteins and diabetic retinopathy . Diabetes care 2011, 34 (2):529-531. Ankit BS, Mathur G, Agrawal RP, Mathur KC: Stronger relationship of serum apolipoprotein A-1 and B with diabetic retinopathy than traditional lipids . Indian journal of endocrinology and metabolism 2017, 21 (1):102-105. Sun X, Guo S: Association between diabetic retinopathy and interleukin-related gene polymorphisms: a machine learning aided meta-analysis . Ophthalmic genetics 2020, 41 (3):216-222. Tables Table 1. The baseline in each group. Baseline characters No DM group pre DM group DM group NPDR group PDR group p value Age, mean (SD), y 45.6(6.5) 45.7(4.3) 49.8(5.7) 49.5(5.4) 45.6(6.9) 0.41 Sex, No, % Male 952(44.9) 525(43.0) 263(61.9) 94(60.8) 16(48.5) 0.39 Female 1168(55.1) 695(57.0) 162(38.1) 61(39.2) 17(51.5) Education level, No, % College or university degree 1359(64.1) 823(67.5) 302(71.1) 110(70.8) 15(45.5) 0.08 Others 761(35.9) 397(32.5) 123(28.9) 45(29.2) 18(54.5) Smoking status, No. (%) Never 1191(56.2) 680(55.7) 212(49.8) 79(50.7) 20(60.6) 0.12 Former/current 929(43.8) 540(44.3) 213(50.2) 76(49.3) 13(39.4) Hypertension 498(23.5) 420(34.4) 266(50.7) 53(34.0) 12(36.4) 0.06 * The p values were obtained by comparing the three groups above. Continuous variables groups were analyzed by ANOVA. Categorical variables groups were analyzed by Chi-square test. *The p values have been corrected and verified. Abbreviations: SD = standard deviation. Table 2. Characteristics of molecular biomarkers and retinal features based on logistic regression analysis. Biomarkers Standard error Exp(B) p value LDL 0.778 3.695 0.031 Triglycerides 2.557 1.568 <0.001 Apo B 0.103 2.544 0.030 Apo A 0.964 2.512 0.014 BMI 0.550 1.012 0.329 Hba1c 2.952 1.263 0.331 cholesterol 0.452 2.760 0.004 HDL 3.608 0.699 <0.001 Direct bilirubin 0.004 1.234 <0.001 Cr 0.148 1.554 0.056 Ca 1.738 0.067 <0.001 Albumin 0.426 0.205 0.008 Urate 0.009 1.002 <0.001 Urea 0.050 1.045 0.468 Cystatin C 0.484 1.561 0.051 Total protein 0.077 0.037 0.308 Lip A 2.533 0.005 0.298 CRP 0.181 1.179 0.133 RNFL 1.165 0.512 0.043 INL 2.583 0.036 0.012 GCL 0.445 0.179 <0.001 ELM 0.377 1.003 0.050 *Adjusted by sex, age, hypertension, diabetes medication, insulin use, education level, smoking. Only indicators with p values less than 0.05 are recorded in the table. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers invited by journal 01 Jul, 2025 Editor invited by journal 03 Jun, 2025 Editor assigned by journal 02 Jun, 2025 Submission checks completed at journal 02 Jun, 2025 First submitted to journal 29 May, 2025 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-6774565","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":479191536,"identity":"cfb40d9e-9523-4fc3-a969-a52dbb22eda4","order_by":0,"name":"Huiqian Kong","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huiqian","middleName":"","lastName":"Kong","suffix":""},{"id":479191537,"identity":"3e0b364d-4502-4be2-8640-1663a8b9112c","order_by":1,"name":"Jiahui Cao","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Cao","suffix":""},{"id":479191538,"identity":"39c971d2-2543-4ad2-ae05-23e4e5e01732","order_by":2,"name":"Chunran Lai","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunran","middleName":"","lastName":"Lai","suffix":""},{"id":479191540,"identity":"3771d148-b812-4008-8153-a6f7da3e5031","order_by":3,"name":"Qinyi Li","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qinyi","middleName":"","lastName":"Li","suffix":""},{"id":479191542,"identity":"94370580-f960-46f8-8904-f74b7318d6fa","order_by":4,"name":"Ying Fang","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Fang","suffix":""},{"id":479191543,"identity":"e85ab8f9-bd27-4dd2-b094-955c04c30f3a","order_by":5,"name":"Huiyi Liao","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huiyi","middleName":"","lastName":"Liao","suffix":""},{"id":479191544,"identity":"b7722957-c0db-41f8-af00-1ba99e13fe27","order_by":6,"name":"Qiaowei Wu","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiaowei","middleName":"","lastName":"Wu","suffix":""},{"id":479191546,"identity":"133e3369-3585-4868-b37f-7b3b124602e2","order_by":7,"name":"Yijun Hu","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Hu","suffix":""},{"id":479191548,"identity":"c99c6c9f-72a8-472c-9f2c-6a49b1af3cf0","order_by":8,"name":"Xiayin Zhang","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiayin","middleName":"","lastName":"Zhang","suffix":""},{"id":479191549,"identity":"d4de3374-f26b-47a7-9fcf-23d3ab1b8edd","order_by":9,"name":"Honghua Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYFACHhBxgIGBvYFBgrGBJC08B0jWIpFApBaDG7kHHxf8upPYP/P5xduFOxjkzPsXMH4uwKslL9l4Zt+zxBm3c4qtZ55hMJa58YBZegZeLTlm0rw9hxMbbuekSfO2MSTOkDjAxsxDjJb5N8+QooXnx+HEDTfYj0G08Dfg1yJ55o2xMW/DYeONZ3KYrWe2SRhLSDA2S+PTwnc8x/Axz5/DsvOOH394u7DNRk6C//DBz/i0KBwAEoxtICaPATMwdoAosQGPBgYGebD0HxDB/oAZLMR/AK+OUTAKRsEoGHkAAIt5UpHDW4xOAAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Honghua","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-05-29 08:38:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6774565/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6774565/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85996347,"identity":"d7d7455c-796c-4767-ad04-80aa8c2d0725","added_by":"auto","created_at":"2025-07-04 06:18:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":230814,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of this study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FIG.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6774565/v1/c145838799f91cefb6c5f07d.png"},{"id":85994904,"identity":"7b1f6e13-09cd-460f-adea-010b1f91830f","added_by":"auto","created_at":"2025-07-04 05:54:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":439725,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis between blood molecular biomarkers and retinal features in each group.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDots blue for positive correlation while red for negative correlation; The size of dot represents the correlation coefficient.\u003c/p\u003e\n\u003cp\u003e*The \u003cem\u003ep \u003c/em\u003evalues have been corrected and verified.\u003c/p\u003e","description":"","filename":"FIG.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6774565/v1/811c3a96d0d3b3989435777a.png"},{"id":85995614,"identity":"619f7c19-179f-4012-8548-7ed7541c395b","added_by":"auto","created_at":"2025-07-04 06:02:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDR prediction model based on molecular biomarkers and retinal features in the pre DM group.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. Prediction model based on protein and lipid biomarkers, AUC=0.644 (95%CI:0.590-0.721), p\u0026lt;0.01.\u003c/p\u003e\n\u003cp\u003eb. Prediction model based on retinal features from OCT, AUC=0.653 (95%CI:0.601-0.734), p\u0026lt;0.01.\u003c/p\u003e\n\u003cp\u003ec. Prediction model based on lipid biomarkers (95%CI:0.532-0.697), p\u0026lt;0.01.\u003c/p\u003e\n\u003cp\u003ed. Prediction model based on based on the combination of molecular biomarkers and retinal features (95%CI:0.711-0.847), p\u0026lt;0.01.\u003c/p\u003e\n\u003cp\u003e*The p values have been corrected and verified.\u003c/p\u003e","description":"","filename":"FIG.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6774565/v1/4de26d67bc5a743073b0c663.png"},{"id":85997196,"identity":"487ca43a-c72d-45ae-9ed4-3e31fdb34fc5","added_by":"auto","created_at":"2025-07-04 06:26:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2904127,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6774565/v1/37f54cd4-5e5a-4bf9-8a46-9aeaa0e39f3b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of the risk of developing diabetic retinopathy based on blood molecules and retinal features ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic retinopathy (DR) has become one of the blinding diseases that harm working population. At present, there are more than 100\u0026nbsp;million diabetes patients in the world, and the proportion of DR is increasing year by year[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. DR which presents as a progressive eye disease, it will cause irreversible damage to the structure and function of the eye[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The diagnosis of DR often requires the use of ophthalmic imaging, such as fundus photography, OCT, angiography and so on[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. At the same time, with the progression of DR, retinal images and protein molecules will also change, and the significant retinal features or molecular markers will also change in different stages of DR[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Accurately finding the retinal features and molecular markers of DR, especially in the early stage of the disease, and the prediction models of DR in the early onset, is of great significance to alleviate the threat of blindness caused by DR[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePreviously, for the early diagnosis and treatment of DR, a number of studies used imaging markers or molecular markers to build models to predict the occurrence of DR in patients with diabetes[\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Common imaging features such as hemangioma, venous abnormalities, and neovascularization have been found as imaging markers for the diagnosis of DR, providing as predictive signals[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In the exploration of molecular markers, previous studies paid more attention to the abnormality of intraocular fluid molecules such as VEGF, and made early prediction of the occurrence of DR based on the change of the concentration of these molecules in intraocular fluid[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the detecting of retinal and molecular markers are still limited[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Besides, the predictive models of DR in multi-center cohort are lacking, and the mechanism of the connection between retinal changes and molecules is left unclear. Few studies have explored the potential association between the retinal features and molecular markers, and the potential mechanisms of them.\u003c/p\u003e \u003cp\u003e Therefore, in this study, we linked retinal features and molecular biomarkers of participants from UK Biobank, and grouped them according to the full-course DR, including no DM group, pre DM group, DM group, NPDR group and PDR group. We performed correlation analysis of retinal features and molecular biomarkers in the five groups. Predictive models of DR are constructed by combining retinal features and molecular biomarkers, aiming to provide new strategies for the early diagnosis and integrated management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy population\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted using individual-level data from the UK biobank (UKB), which is a multi-center prospective cohort study that recruited more than 502,565 community citizens across the United Kingdom from 2006 to 2010, and followed up to 2021. All research program information (http://www.ukbiobank.ac.uk/resources/) and individual test program details (http://biobank.ctsu.ox.ac.uk/crystal/docs.cgi) were available[25]. The study was conducted under application number #86091 of the UK Biobank resource.\u003c/p\u003e\n\u003cp\u003eOur study included participants from the UK Biobank who were with and without DM diagnostic information.\u0026nbsp;The control group was obtained by randomly matching baseline data with people who were not diagnosed with DM and other chronic diseases. Those with serious fundus diseases and other diseases that seriously affect the intraocular fluid environment were excluded, such as age-related macular degeneration, polypoid choroidal vasculopathy, retinal artery/vein obstruction, central serous choroidal retinopathy, pore-derived retinal detachment, macular ischemia, glaucoma, etc. Patients with high myopia and lack of access to complete eye examinations for various reasons were also excluded. The flow chart of this study is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGrouping of DR\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe divided the population according to the DM status and the progression of DR into the following five groups: no DM group/control group whose participants without DM and other serious systemic diseases, pre DM group that the participants were no DM diagnosis and no medication to control blood glucose but with blood glucose or HbA1c exceeding the upper limit of normal range (Hba1c value 39~48 mmol/mol)[26], DM group that the patients with DM and/or taking drugs to control blood sugar had no DR changes in fundus photography, NPDR group whose diagnosis information included DM or taking drugs to control blood sugar showed DR changes but no PDR in fundus photography, and PDR group with DM diagnosis and/or taking diabetes related drugs r, with neovascularization in fundus photography. The diagnosis and classification of DR are judged by diagnosis code, medical history, and fundus image synthesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOphthalmic Examination and Image Acquisition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOptical coherence tomography (OCT) imaging parameters were obtained from UKB data. Among them, we excluded parameters with missing data and quality control (QC) less than 6[27]. This study observed OCT parameters included overall macular thickness, macular thickness at central subfield, total macular volume, overall retinal pigment epithelium thickness, RNFL, GCL, INL, external limiting membrane (ELM).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMolecular biomarkers\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBlood molecular samples were derived from the participants\u0026apos; peripheral blood. Baseline inclusion of molecular markers was from 2006 to 2010. In this study, 18 routine peripheral blood molecular were included for analysis and individuals with missing values were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCovariates\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBaseline data were collected for age, sex, education, smoking status, high blood pressure, and level of education (college and non-college). Participants completed a detailed questionnaire on a touch-screen computer about their lifestyle and identified hypertension status based on blood pressure levels in the population. Lipid and protein measurements were obtained by examination of the peripheral blood of the enrolled population.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIntergroup analysis was used to compare the baseline difference of five groups in the full-course DR. Multiple regression analysis was conducted for screening the retinal features and molecular markers of DR. The follow-up cohort was used to stablished the predictive models of DR. We divided the data from UKB\u0026apos;s six centers according to a 4:2 ratio of test sets to validation sets. We constructed predictive models of DR using regression screened molecules, images, lipids associated with the full-course DR, combining retinal features and molecular biomarkers. The prediction model of DR Was established based on ROC curve drawing and Cox regression analysis by SPSS (ver. 25). The R (version 3.6.3) was used to performed the correlation analysis between retinal features and molecular biomarkers. The \u003cem\u003ep\u003c/em\u003e value less than 0.05 is considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOur study enrolled 3,953 baseline patients with mean age of 47.3\u0026plusmn;5.8 and percentages of male and female were 47% and 53%, including 2120 no DM patients, 1220 pre DM patients, 425 DM patients, 155 NPDR patients and 33 PDR patients. The comparative analysis of baseline data for each group is shown in Table 1. Through regression analysis, we screened out meaningful retinal and molecular markers, as shown in Table 2. We carried out correlation analysis of the relationship between images and molecules for meaningful indicators in different groups, and the correlation visualization was shown in Figure 2. Apo A, Apo B, HDL, LDL were associated with RNFL, GCL, and INL in the full-course DR, suggesting that lipid metabolism biomarkers were closely related to nerve fiber layers and ganglion cells in the retina (all \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared with the no DM group, DM, DR and PDR group had a closer association, suggesting that there were abnormalities in the protein biomarkers (albumin, total protein, creatine) during the development from DM to DR. In DR (NPDR and PDR), there were associations with both lipid markers (Apo B, HDL, LDL, cholesterol, triglycerides) and protein (albumin) markers. According to the analysis of the correlation between different groups and molecular biomarkers at different levels, lipid biomarkers may reflect the progression of DM and DR to a certain extent, and serve as an understanding signals and supplement for the full-course DR.\u003c/p\u003e\n\u003cp\u003eBased on the above retinal features and molecular biomarkers, we constructed predictions model of DR based on the UKB cohort population from 2010 to 2021. We constructed four models based on molecular biomarkers, retinal features, lipid biomarkers, and the combination of retinal features and molecular biomarkers respectively, as shown in Figure 3. The AUC values of the four models were 0.653 (95%\u0026nbsp;Confidence Interval (CI):\u0026nbsp;0.590-0.721), 0.626 (95%CI:0.601-0.734), 0.644 (95%CI:0.532-0.697), and 0.790 (95%CI:0.711-0.847), all\u003cem\u003e\u0026nbsp;p\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we explored the correlation between blood molecular biomarkers and retinal features measuring by OCT in five groups of the full-course DR, and found that lipid biomarkers were correlated with RNFL, GCL and INL during the full-course DR. Based on the retinal features and molecular biomarkers, we established prediction models of DR, suggesting that the model combining retinal features and molecular biomarkers show a higher AUC.\u003c/p\u003e \u003cp\u003eRecently, there have been many studies on DR imaging or molecular markers[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In the exploration of imaging markers, common imaging features such as hemangioma, venous abnormalities, and neovascularization have been used as imaging markers for the diagnosis of DR to establish predictive or diagnostic models[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the exploration of molecular markers, previous studies focused on the abnormalities of VEGF and other molecules in the intraocular fluid, and made early prediction of the occurrence of DR according to the changes in the concentration of these molecules in the intraocular fluid[\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which consistent with the molecular results we've screened. However, the extraction of vitreous or aqueous humor is an invasive procedure and cannot be used in the vast majority of diabetic patients. Besides, the acquisition and measurement of intraocular fluid is highly dependent on the collector and the level of detection, the leading that the acquisition and measurement of intraocular fluid are not widely used[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, in this study, the results of routine blood sampling were used as research materials to screen molecular markers related to DR, so as to achieve wider application value. In addition, the study not only conducted on patients with diabetes, but also used the grouping of the whole course of disease to describe the characteristics of blood molecular changes in patients from no DM group to PDR group in a more comprehensive and extensive way, filling the gap in the understanding and description of the disease.\u003c/p\u003e \u003cp\u003ePrevious studies have analyzed the DR processes corresponding to different levels of OCT[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], analyzed the image markers in the DR progression[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and built models based on the markers to predict the occurrence of DR[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, there were limited studies have explored the link between imaging and molecular markers on this basis or analyzed the two together. Through correlation visualization technology, this study conducted correlation mapping analysis on images and molecular markers of each group in the full-course DR, aiming to explore molecular biomarkers related to the OCT levels including RNFL, GCL, and INL in the progression of DR, so as to make more targeted diagnosis, treatment and intervention in the early stage of DR. At the same time, we found that lipid biomarkers were correlated with RNFL, GCL and INL in the full-course DR, suggesting that pre DM and early rise of lipid biomarkers may be the key to improve the corresponding retinal function and delay the progression of the disease to a certain extent[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. As for the protein biomarkers, the DM group was significantly abnormal than the no DM group. Therefore, for patients with diabetes, simultaneous intervention of lipid and protein biomarkers could delay the progression of DR. It can be seen that this study describes the visual correlation between DR retinal features and molecular markers, which fills the gap in the understanding and development of diseases, and provides a new strategy for the comprehensive intervention of diseases[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe established four prediction models of DR using the images or/and molecular markers. The model combining retinal features and molecular biomarkers showed higher AUC value than the other three models (AUC\u0026thinsp;=\u0026thinsp;0.790). A number of previous studies have used DR images or molecular markers to build predictive models for the onset or progression of DR, but their models are not all very efficient[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, there is a lack of study population support of big data and follow-up cohort[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In this study, the combined prediction method of image and molecular marker was adopted to achieve higher model efficiency and provide a more convenient, practical and reliable method for predicting the occurrence of DR.\u003c/p\u003e \u003cp\u003eThe study still has several limitations. First, this study takes the UK Biobank population as the research object, which may produce racial bias, and the results cannot be widely applied to all races in the world. Secondly, only the markers in OCT were considered as image markers in this study. However, in fact, there are many image markers in ophthalmology, such as fundus photography, eye ultrasound and so on. In the future, we will further explore the molecular mechanism of lipid indicators in the process of DR development.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on the UK population, the prediction model of DR combining retinal features and molecular biomarkers showed higher AUC comparing to use only retinal features or molecular biomarkers, which provides a new strategy for the early diagnosis and intervention of DR, and fills the gap for the understanding and management of the disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present work was supported by the National Natural Science Foundation of China (82171075) and the Basic and Applied Basic Research foundation of Guangdong province (2023B1515120028). The sponsors or funding organizations had no role in the design or conduct of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: Yu HH.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcquisition, analysis, or interpretation: All authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: Kong HQ, Zhang XY.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCritical revision of the manuscript for important intellectual content: Yu HH, Zhang XY.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Kong HQ, Zhang XY, Cao JH, Lai CR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObtained funding: Yu HH, Zhang XY.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdministrative, technical, or material support: Yu HH, Hu YY, Fang Y, and Liao HY.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy supervision: Yu HH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to all the individuals and institutions who provide help during the research process, including financial support, technical support, data collection, academic advice, \u0026zwnj; personal help and valuable comments from anonymous reviewers. \u0026zwnj;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were obtained through the formal procedures from UK Biobank by the researcher. Any questions about the data analysis can be contacted by email to the researchers. The detailed code can be provided by contacting the mailing address.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UK Biobank received ethics approval from the NHS National Research Ethics Service North West (16/NW/0274) and was performed in accordance with the Declaration of Helsinki. Informed consent was obtained for all UK Biobank participants. The present study was conducted under application number #86091 of the UK Biobank resource.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJeng CJ, Hsieh YT, Yang CM, Yang CH, Lin CL, Wang IJ: \u003cstrong\u003eDevelopment of diabetic retinopathy after cataract surgery\u003c/strong\u003e. \u003cem\u003ePloS one \u003c/em\u003e2018, \u003cstrong\u003e13\u003c/strong\u003e(8):e0202347.\u003c/li\u003e\n\u003cli\u003eTan TE, Wong TY: \u003cstrong\u003eDiabetic retinopathy: Looking forward to 2030\u003c/strong\u003e. \u003cem\u003eFrontiers in endocrinology \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:1077669.\u003c/li\u003e\n\u003cli\u003eTing DS, Cheung GC, Wong TY: \u003cstrong\u003eDiabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review\u003c/strong\u003e. \u003cem\u003eClinical \u0026amp; experimental ophthalmology \u003c/em\u003e2016, \u003cstrong\u003e44\u003c/strong\u003e(4):260-277.\u003c/li\u003e\n\u003cli\u003eMeng Z, Chen Y, Wu W, Yan B, Meng Y, Liang Y, Yao X, Luo J: \u003cstrong\u003eExploring the Immune Infiltration Landscape and M2 Macrophage-Related Biomarkers of Proliferative Diabetic Retinopathy\u003c/strong\u003e. \u003cem\u003eFrontiers in endocrinology \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:841813.\u003c/li\u003e\n\u003cli\u003eTeo ZL, Tham YC, Yu M, Chee ML, Rim TH, Cheung N, Bikbov MM, Wang YX, Tang Y, Lu Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGlobal Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis\u003c/strong\u003e. \u003cem\u003eOphthalmology \u003c/em\u003e2021, \u003cstrong\u003e128\u003c/strong\u003e(11):1580-1591.\u003c/li\u003e\n\u003cli\u003eWu H, Wu H, Shi L, Yuan X, Yin Y, Yuan M, Zhou Y, Hu Q, Jiang K, Dong J: \u003cstrong\u003eThe Association of Haptoglobin Gene Variants and Retinopathy in Type 2 Diabetic Patients: A Meta-Analysis\u003c/strong\u003e. \u003cem\u003eJournal of diabetes research \u003c/em\u003e2017, \u003cstrong\u003e2017\u003c/strong\u003e:2195059.\u003c/li\u003e\n\u003cli\u003eNanegrungsunk O, Patikulsila D, Sadda SR: \u003cstrong\u003eOphthalmic imaging in diabetic retinopathy: A review\u003c/strong\u003e. \u003cem\u003eClinical \u0026amp; experimental ophthalmology \u003c/em\u003e2022, \u003cstrong\u003e50\u003c/strong\u003e(9):1082-1096.\u003c/li\u003e\n\u003cli\u003eStitt AW, Curtis TM, Chen M, Medina RJ, McKay GJ, Jenkins A, Gardiner TA, Lyons TJ, Hammes HP, Sim\u0026oacute; R\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe progress in understanding and treatment of diabetic retinopathy\u003c/strong\u003e. \u003cem\u003eProgress in retinal and eye research \u003c/em\u003e2016, \u003cstrong\u003e51\u003c/strong\u003e:156-186.\u003c/li\u003e\n\u003cli\u003eJenkins AJ, Joglekar MV, Hardikar AA, Keech AC, O\u0026apos;Neal DN, Januszewski AS: \u003cstrong\u003eBiomarkers in Diabetic Retinopathy\u003c/strong\u003e. \u003cem\u003eThe review of diabetic studies : RDS \u003c/em\u003e2015, \u003cstrong\u003e12\u003c/strong\u003e(1-2):159-195.\u003c/li\u003e\n\u003cli\u003eHimasa FI, Singhal M, Ojha A, Kumar B: \u003cstrong\u003eProspective for Diagnosis and Treatment of Diabetic Retinopathy\u003c/strong\u003e. \u003cem\u003eCurrent pharmaceutical design \u003c/em\u003e2022, \u003cstrong\u003e28\u003c/strong\u003e(7):560-569.\u003c/li\u003e\n\u003cli\u003eLeley SP, Ciulla TA, Bhatwadekar AD: \u003cstrong\u003eDiabetic Retinopathy in the Aging Population: A Perspective of Pathogenesis and Treatment\u003c/strong\u003e. \u003cem\u003eClinical interventions in aging \u003c/em\u003e2021, \u003cstrong\u003e16\u003c/strong\u003e:1367-1378.\u003c/li\u003e\n\u003cli\u003eGrzybowski A, Brona P, Lim G, Ruamviboonsuk P, Tan GSW, Abramoff M, Ting DSW: \u003cstrong\u003eArtificial intelligence for diabetic retinopathy screening: a review\u003c/strong\u003e. \u003cem\u003eEye (London, England) \u003c/em\u003e2020, \u003cstrong\u003e34\u003c/strong\u003e(3):451-460.\u003c/li\u003e\n\u003cli\u003eValdezGuerrero AS, Quintana-P\u0026eacute;rez JC, Arellano-Mendoza MG, Casta\u0026ntilde;eda-Ibarra FJ, Tamay-Cach F, Alem\u0026aacute;n-Gonz\u0026aacute;lez-Duhart D: \u003cstrong\u003eDiabetic Retinopathy: Important Biochemical Alterations and the Main Treatment Strategies\u003c/strong\u003e. \u003cem\u003eCanadian journal of diabetes \u003c/em\u003e2021, \u003cstrong\u003e45\u003c/strong\u003e(6):504-511.\u003c/li\u003e\n\u003cli\u003eWang GX, Hu XY, Zhao HX, Li HL, Chu SF, Liu DL: \u003cstrong\u003eDevelopment and validation of a diabetic retinopathy risk prediction model for middle-aged patients with type 2 diabetes mellitus\u003c/strong\u003e. \u003cem\u003eFrontiers in endocrinology \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e:1132036.\u003c/li\u003e\n\u003cli\u003eShen Z, Wu Q, Wang Z, Chen G, Lin B: \u003cstrong\u003eDiabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data\u003c/strong\u003e. \u003cem\u003eSensors (Basel, Switzerland) \u003c/em\u003e2021, \u003cstrong\u003e21\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003eLiu W, Luo Z, Zhang L, Wang Y, Yang J, You D, Cao X, Yang W: \u003cstrong\u003ehsa-mir-(4328, 4422, 548z and -628-5p) in diabetic retinopathy: diagnosis, prediction and linking a new therapeutic target\u003c/strong\u003e. \u003cem\u003eActa diabetologica \u003c/em\u003e2023, \u003cstrong\u003e60\u003c/strong\u003e(7):929-942.\u003c/li\u003e\n\u003cli\u003eZhang G, Chen H, Chen W, Zhang M: \u003cstrong\u003ePrevalence and risk factors for diabetic retinopathy in China: a multi-hospital-based cross-sectional study\u003c/strong\u003e. \u003cem\u003eThe British journal of ophthalmology \u003c/em\u003e2017, \u003cstrong\u003e101\u003c/strong\u003e(12):1591-1595.\u003c/li\u003e\n\u003cli\u003eYue T, Shi Y, Luo S, Weng J, Wu Y, Zheng X: \u003cstrong\u003eThe role of inflammation in immune system of diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications\u003c/strong\u003e. \u003cem\u003eFrontiers in immunology \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:1055087.\u003c/li\u003e\n\u003cli\u003eZhu C, Zhu J, Wang L, Xiong S, Zou Y, Huang J, Xie H, Zhang W, Wu H, Liu Y: \u003cstrong\u003eDevelopment and validation of a risk prediction model for diabetic retinopathy in type 2 diabetic patients\u003c/strong\u003e. \u003cem\u003eScientific reports \u003c/em\u003e2023, \u003cstrong\u003e13\u003c/strong\u003e(1):5034.\u003c/li\u003e\n\u003cli\u003eMudie LI, Wang X, Friedman DS, Brady CJ: \u003cstrong\u003eCrowdsourcing and Automated Retinal Image Analysis for Diabetic Retinopathy\u003c/strong\u003e. \u003cem\u003eCurrent diabetes reports \u003c/em\u003e2017, \u003cstrong\u003e17\u003c/strong\u003e(11):106.\u003c/li\u003e\n\u003cli\u003eAlyoubi WL, Abulkhair MF, Shalash WM: \u003cstrong\u003eDiabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning\u003c/strong\u003e. \u003cem\u003eSensors (Basel, Switzerland) \u003c/em\u003e2021, \u003cstrong\u003e21\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003eSahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, Kaski K: \u003cstrong\u003eDeep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading\u003c/strong\u003e. \u003cem\u003eScientific reports \u003c/em\u003e2019, \u003cstrong\u003e9\u003c/strong\u003e(1):10750.\u003c/li\u003e\n\u003cli\u003eHuang X, Wang H, She C, Feng J, Liu X, Hu X, Chen L, Tao Y: \u003cstrong\u003eArtificial intelligence promotes the diagnosis and screening of diabetic retinopathy\u003c/strong\u003e. \u003cem\u003eFrontiers in endocrinology \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:946915.\u003c/li\u003e\n\u003cli\u003eVujosevic S, Aldington SJ, Silva P, Hern\u0026aacute;ndez C, Scanlon P, Peto T, Sim\u0026oacute; R: \u003cstrong\u003eScreening for diabetic retinopathy: new perspectives and challenges\u003c/strong\u003e. \u003cem\u003eThe lancet Diabetes \u0026amp; endocrinology \u003c/em\u003e2020, \u003cstrong\u003e8\u003c/strong\u003e(4):337-347.\u003c/li\u003e\n\u003cli\u003eCollins R: \u003cstrong\u003eWhat makes UK Biobank special?\u003c/strong\u003e \u003cem\u003eLancet (London, England) \u003c/em\u003e2012, \u003cstrong\u003e379\u003c/strong\u003e(9822):1173-1174.\u003c/li\u003e\n\u003cli\u003eGabbay KH: \u003cstrong\u003eGlycosylated hemoglobin and diabetes mellitus\u003c/strong\u003e. \u003cem\u003eThe Medical clinics of North America \u003c/em\u003e1982, \u003cstrong\u003e66\u003c/strong\u003e(6):1309-1315.\u003c/li\u003e\n\u003cli\u003ePodoleanu AG: \u003cstrong\u003eOptical coherence tomography\u003c/strong\u003e. \u003cem\u003eJournal of microscopy \u003c/em\u003e2012, \u003cstrong\u003e247\u003c/strong\u003e(3):209-219.\u003c/li\u003e\n\u003cli\u003eSchumacher M, Schmidt D, Jurklies B, Gall C, Wanke I, Schmoor C, Maier-Lenz H, Solymosi L, Brueckmann H, Neubauer AS\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCentral retinal artery occlusion: local intra-arterial fibrinolysis versus conservative treatment, a multicenter randomized trial\u003c/strong\u003e. \u003cem\u003eOphthalmology \u003c/em\u003e2010, \u003cstrong\u003e117\u003c/strong\u003e(7):1367-1375.e1361.\u003c/li\u003e\n\u003cli\u003eDu X, Yang L, Kong L, Sun Y, Shen K, Cai Y, Sun H, Zhang B, Guo S, Zhang A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMetabolomics of various samples advancing biomarker discovery and pathogenesis elucidation for diabetic retinopathy\u003c/strong\u003e. \u003cem\u003eFrontiers in endocrinology \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:1037164.\u003c/li\u003e\n\u003cli\u003eYoungblood H, Robinson R, Sharma A, Sharma S: \u003cstrong\u003eProteomic Biomarkers of Retinal Inflammation in Diabetic Retinopathy\u003c/strong\u003e. \u003cem\u003eInternational journal of molecular sciences \u003c/em\u003e2019, \u003cstrong\u003e20\u003c/strong\u003e(19).\u003c/li\u003e\n\u003cli\u003eWeber SR, Zhao Y, Gates C, Ma J, da Veiga Leprevost F, Basrur V, Nesvizhskii AI, Gardner TW, Sundstrom JM: \u003cstrong\u003eProteomic Analyses of Vitreous in Proliferative Diabetic Retinopathy: Prior Studies and Future Outlook\u003c/strong\u003e. \u003cem\u003eJournal of clinical medicine \u003c/em\u003e2021, \u003cstrong\u003e10\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003eYou J, Guo Y, Zhang Y, Kang JJ, Wang LB, Feng JF, Cheng W, Yu JT: \u003cstrong\u003ePlasma proteomic profiles predict individual future health risk\u003c/strong\u003e. \u003cem\u003eNature communications \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e(1):7817.\u003c/li\u003e\n\u003cli\u003eMidena E, Frizziero L, Midena G, Pilotto E: \u003cstrong\u003eIntraocular fluid biomarkers (liquid biopsy) in human diabetic retinopathy\u003c/strong\u003e. \u003cem\u003eGraefe\u0026apos;s archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie \u003c/em\u003e2021, \u003cstrong\u003e259\u003c/strong\u003e(12):3549-3560.\u003c/li\u003e\n\u003cli\u003eYao X, Son T, Kim TH, Lu Y: \u003cstrong\u003eFunctional optical coherence tomography of retinal photoreceptors\u003c/strong\u003e. \u003cem\u003eExperimental biology and medicine (Maywood, NJ) \u003c/em\u003e2018, \u003cstrong\u003e243\u003c/strong\u003e(17-18):1256-1264.\u003c/li\u003e\n\u003cli\u003eBorrelli E, Sacconi R, Querques G, Bandello F: \u003cstrong\u003eOptical coherence tomography angiography in the management of diabetic retinopathy\u003c/strong\u003e. \u003cem\u003eIndian journal of ophthalmology \u003c/em\u003e2021, \u003cstrong\u003e69\u003c/strong\u003e(11):3009-3014.\u003c/li\u003e\n\u003cli\u003eWaheed NK, Rosen RB, Jia Y, Munk MR, Huang D, Fawzi A, Chong V, Nguyen QD, Sepah Y, Pearce E: \u003cstrong\u003eOptical coherence tomography angiography in diabetic retinopathy\u003c/strong\u003e. \u003cem\u003eProgress in retinal and eye research \u003c/em\u003e2023, \u003cstrong\u003e97\u003c/strong\u003e:101206.\u003c/li\u003e\n\u003cli\u003eChen Y, Qi Z, Hou X, Chen L: \u003cstrong\u003eOptical coherence tomography angiography for assessment of changes of the retina and choroid in different stages of diabetic retinopathy and their relationship with diabetic nephropathy\u003c/strong\u003e. \u003cem\u003eEndokrynologia Polska \u003c/em\u003e2023, \u003cstrong\u003e74\u003c/strong\u003e(2):135-139.\u003c/li\u003e\n\u003cli\u003eAmato A, Nadin F, Borghesan F, Cicinelli MV, Chatziralli I, Sadiq S, Mirza R, Bandello F: \u003cstrong\u003eWidefield Optical Coherence Tomography Angiography in Diabetic Retinopathy\u003c/strong\u003e. \u003cem\u003eJournal of diabetes research \u003c/em\u003e2020, \u003cstrong\u003e2020\u003c/strong\u003e:8855709.\u003c/li\u003e\n\u003cli\u003eChait A, Montes VN: \u003cstrong\u003eApolipoproteins and diabetic retinopathy\u003c/strong\u003e. \u003cem\u003eDiabetes care \u003c/em\u003e2011, \u003cstrong\u003e34\u003c/strong\u003e(2):529-531.\u003c/li\u003e\n\u003cli\u003eAnkit BS, Mathur G, Agrawal RP, Mathur KC: \u003cstrong\u003eStronger relationship of serum apolipoprotein A-1 and B with diabetic retinopathy than traditional lipids\u003c/strong\u003e. \u003cem\u003eIndian journal of endocrinology and metabolism \u003c/em\u003e2017, \u003cstrong\u003e21\u003c/strong\u003e(1):102-105.\u003c/li\u003e\n\u003cli\u003eSun X, Guo S: \u003cstrong\u003eAssociation between diabetic retinopathy and interleukin-related gene polymorphisms: a machine learning aided meta-analysis\u003c/strong\u003e. \u003cem\u003eOphthalmic genetics \u003c/em\u003e2020, \u003cstrong\u003e41\u003c/strong\u003e(3):216-222.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe baseline in each group.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003echaracters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo DM group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epre DM group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPDR group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDR group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eAge, mean (SD), y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e45.6(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e45.7(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e49.8(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e49.5(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e45.6(6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 555px;\"\u003e\n \u003cp\u003eSex, No, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e952(44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e525(43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e263(61.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e94(60.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e16(48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1168(55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e695(57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e162(38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e61(39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e17(51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 555px;\"\u003e\n \u003cp\u003eEducation level, No, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eCollege or university degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1359(64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e823(67.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e302(71.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e110(70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e15(45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e761(35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e397(32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e123(28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e45(29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e18(54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 555px;\"\u003e\n \u003cp\u003eSmoking status, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1191(56.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e680(55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e212(49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e79(50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e20(60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eFormer/current\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e929(43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e540(44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e213(50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e76(49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e13(39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e498(23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e420(34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e266(50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e53(34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e12(36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eThe \u003cem\u003ep\u003c/em\u003e values were obtained by comparing the three groups above. Continuous variables groups were analyzed by ANOVA. Categorical variables groups were analyzed by Chi-square test.\u003c/p\u003e\n\u003cp\u003e*The \u003cem\u003ep\u0026nbsp;\u003c/em\u003evalues have been corrected and verified.\u003c/p\u003e\n\u003cp\u003eAbbreviations: SD = standard deviation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Characteristics of molecular biomarkers and retinal features based on logistic regression analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarkers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp(B)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e3.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eApo B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eApo A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eHba1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003echolesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e3.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eDirect bilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eCa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eUrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eUrea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eTotal protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eLip A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eRNFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eINL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eGCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eELM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Adjusted by sex, age, hypertension, diabetes medication, insulin use, education level, smoking. Only indicators with \u003cem\u003ep\u0026nbsp;\u003c/em\u003evalues less than 0.05 are recorded in the table.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-ophthalmology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"boph","sideBox":"Learn more about [BMC Ophthalmology](http://bmcophthalmol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/boph","title":"BMC Ophthalmology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetic retinopathy, retinal features, molecular markers, prediction model","lastPublishedDoi":"10.21203/rs.3.rs-6774565/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6774565/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eDiabetic retinopathy (DR) has rapidly become the leading blinding eye disease threatening the working population. We aimed to explore molecular biomarkers and retinal features and build prediction models of DR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eParticipants from UK Biobank were recruited from 2006 to 2010, and prospectively followed up until 2021. We divided the enrolled population according to the full-course DR into 5 groups: no diabetes mellitus (no DM), prediabetes (pre DM), diabetes mellitus (DM), non-proliferating diabetic retinopathy (NPDR), and proliferating diabetic retinopathy (PDR). The molecular biomarkers evaluated at baseline includes 7 lipids and 8 proteins, while the retinal features were measured by Optical coherence tomography (OCT). The associations between molecular biomarkers and retinal features were performed by correlation analysis. A predictive model of DR was constructed using both retinal features and molecular biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003eThe study included 3953 participants (2095 [53.0%] female), with a mean age (SD) of47.3 (5.8) years. Apo A, Apo B, HDL, LDL were associated with retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), and inner nuclear layer (INL) in full-course DR (all r range from 0.5 to 1 0.5, \u003cem\u003ep\u0026lt;\u003c/em\u003e0.05). Protein biomarkers, including albumin, total protein, creatine, showed significant correlation with GCL and RNFL in pre DM, DM, NPDR, and PDR groups compared to the no DM group (all \u003cem\u003ep\u0026lt;\u003c/em\u003e0.05). The Area Under Curve (AUC) of the DR prediction model based on the combination of molecular biomarkers and retinal features is 0.790 (95%CI:0.711-0.847), \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, which is higher than the prediction models based on molecular biomarkers or retinal features alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eMolecular biomarkers were associated with retinal features during the full-course DR. DR prediction model based on the combination of molecular biomarkers and retinal features presented a higher AUC, suggesting a possible strategy for early diagnosis of DR.\u003c/p\u003e","manuscriptTitle":"Prediction of the risk of developing diabetic retinopathy based on blood molecules and retinal features ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-04 05:54:25","doi":"10.21203/rs.3.rs-6774565/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-07-13T17:48:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302452223838320133403650150373976927975","date":"2025-07-13T17:14:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322103301427576520083395958790877693908","date":"2025-07-11T12:55:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-01T05:14:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-03T13:35:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-03T03:06:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-03T03:03:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Ophthalmology","date":"2025-05-29T08:34:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-ophthalmology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"boph","sideBox":"Learn more about [BMC Ophthalmology](http://bmcophthalmol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/boph","title":"BMC Ophthalmology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d508ef03-1ec5-439c-a820-9368a51ae33d","owner":[],"postedDate":"July 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-04T05:54:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-04 05:54:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6774565","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6774565","identity":"rs-6774565","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}