Exploring the Shared Pathophysiological Mechanisms Between Diabetic Retinopathy and High Myopia: Insights from OCTA and Bioinformatics Analysis

Exploring the Shared Pathophysiological Mechanisms Between Diabetic Retinopathy and High Myopia: Insights from OCTA and Bioinformatics Analysis | 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 Exploring the Shared Pathophysiological Mechanisms Between Diabetic Retinopathy and High Myopia: Insights from OCTA and Bioinformatics Analysis Guoxin Ding, Zhen Zhang, Zhou Zhang, Jing Wang, Suyun Qin, Xin Xiao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7482406/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective: To investigate the common pathophysiological mechanisms between diabetic retinopathy (DR) and high myopia (HM) using optical coherence tomography angiography (OCTA) and integrated bioinformatics approaches. Methods: A total of 72 patients with type 2 diabetes were enrolled and stratified based on axial length and DR severity. OCTA was performed to evaluate macular vascular density (VD), central macular thickness (CMT), and foveal avascular zone (FAZ). Gene expression profiles were retrieved from the GEO database (GSE160310 for DR; GSE138247 for HM). Differentially expressed genes (DEGs) were identified using the Limma package. Functional enrichment, Weighted Gene Co-expression Network Analysis (WGCNA), and transcription factor prediction were conducted to reveal shared molecular pathways and key regulatory genes. Results: Axial length (AL) was inversely associated with DR severity and positively correlated with FAZ area ( r = 0.576) and negatively with CMT ( r =–0.292). OCTA revealed reduced vascular density in eyes with longer AL. Bioinformatics analysis identified 757 DEGs in DR and 191 in HM. Functional enrichment suggested involvement of immune response, oxidative stress, and ECM remodeling. WGCNA revealed common gene modules, with three hub genes ( SBNO1 , GLTP , NUCKS1 ) potentially mediating ferroptosis, autophagy, and immune pathways. Conclusions: High myopia may exert a protective effect against DR via structural and hemodynamic remodeling. Shared molecular signatures between DR and HM suggest overlapping pathogenic pathways, providing potential therapeutic targets for both diseases. Diabetic Retinopathy High Myopia Optical Coherence Tomography Angiography Vascular Density Differentially Expressed Genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Diabetic retinopathy (DR) is one of the most common microvascular complications of diabetes mellitus and a leading cause of irreversible vision loss worldwide [1 , 2] . Its pathogenesis involves chronic hyperglycemia-induced damage to the retinal microvasculature, including breakdown of the blood-retinal barrier, ischemia, and neovascularization. Multiple molecular mechanisms have been implicated in DR progression, such as activation of the polyol pathway, oxidative stress, accumulation of advanced glycation end-products (AGEs), protein kinase C (PKC) activation, and dysregulated inflammatory cytokines [3 , 4] . High myopia (HM), in contrast, is primarily a refractive error caused by excessive elongation of the axial length (AL), often exceeding 26 mm. As the axial length of the eye increases, the anteroposterior diameter of the eyeball enlarges, leading to mechanical stretching and thinning of the retina. This, in turn, causes retinal blood vessels to straighten and their diameters to decrease, resulting in reduced retinal blood flow [5] . Such changes reduce the oxygen consumption of the retina, alleviating its hypoxic condition, and alter retinal blood flow and nutrient supply, which in turn affects the vascular density in the macula and reduces the risk of diabetic retinopathy (DR) [6 - 8] . These findings also support the theory proposed by Bazzazi [9] and Lin [10] et al. that an increase in axial length leads to mechanical stretching of the retina. This anatomical elongation is associated with structural changes in the retina. Although DR and HM arise from distinct etiologies—metabolic dysfunction versus biomechanical elongation—they both impact the integrity of retinal structure and function. Interestingly, accumulating clinical evidence suggests that high myopia may confer a protective effect against the development and progression of DR. In one study, the incidence of diabetic retinopathy was significantly lower in the high myopia group, with no proliferative changes observed. After adjusting for potential confounding factors, the odds ratio (OR) was 0.44, with a 95% CI: (0.20-0.96) and a P -value of 0.040 [11] .Another study also drew similar conclusions, suggesting that high myopia is an important protective factor, and the degree of protection increases with the increasing severity of myopia [12] . This indicates that as the degree of myopia increases, the risk of diabetic patients developing retinopathy decreases. It is worth noting that researchers believe this protective factor is associated with changes in both the structure and function of the retinal microvasculature in individuals with high myopia and axial elongation. For instance, several studies have suggested that in patients with diabetes, a longer axial length may reduce the risk of diabetic retinopathy (DR) by improving retinal blood supply and local hemodynamics [13 , 14] . Moreover, another study demonstrated a significant inverse relationship between axial length and vascular density measurements in the macular region, including perfusion density ( r ² = 0.186, P < 0.001) and vessel length density ( r ² = 0.102, P = 0.001) [15] . These findings indicate that axial elongation not only alters the structure and function of the retinal microvasculature but may also influence nutrient delivery and metabolic activity in the macular area by affecting blood flow dynamics and microvascular architecture, thereby playing a complex role in the onset and progression of DR. Despite these observations, the molecular basis underlying this protective association remains unclear. A key unanswered question is whether DR and HM share overlapping gene expression profiles or regulatory pathways that could explain their interaction at the molecular level. Given the recent advances in retinal imaging and transcriptomic analysis, it is now possible to integrate phenotypic imaging with bioinformatics to identify potential shared mechanisms. Therefore, in this study, we combine optical coherence tomography angiography (OCTA) with transcriptome-level bioinformatics to explore the shared structural and molecular characteristics between DR and HM. Specifically, we investigate how axial length affects retinal microvasculature in diabetic eyes and identify common differentially expressed genes (DEGs) and co-expression modules through microarray data analysis. Our aim is to provide novel insights into the intertwined pathophysiology of DR and HM, potentially revealing new therapeutic targets or diagnostic markers. Methods 1. Clinical Study Design and Participants This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University (Approval No. 2024-272). A total of 72 patients diagnosed with type 2 diabetes mellitus (T2DM) between October 2020 and October 2024 were enrolled. Informed consent was obtained from all participants. 2. Diabetic Retinopathy Staging According to the International Clinical Diabetic Retinopathy Disease Severity Scale, fundus findings were classified into three groups: no diabetic retinopathy (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR). 3. Ophthalmic Examinations (1) Axial Length Measurement Axial length (AL) was measured using the IOL Master biometry system (Carl Zeiss Meditec). Based on the measured AL, patients were divided into three groups: normal axial length (22 mm ≤ AL < 24 mm), long axial length (24 mm ≤ AL < 26 mm), and extremely long axial length (AL ≥ 26 mm). (2) OCTA Imaging and Analysis OCTA scans were performed using the CIRRUS HD-OCT AngioPlex system (Carl Zeiss, Germany). Scans were obtained in 3×3 mm and 6×6 mm fields centered on the fovea. The software quantified: Superficial capillary plexus vessel density (VD)、Foveal avascular zone (FAZ) area. (3) Optical Coherence Tomography (OCT) Examination The macular cube 512×128 scanning protocol was employed to measure central macular thickness (CMT). Full-thickness retinal measurements were obtained from the internal limiting membrane (ILM) to the retinal pigment epithelium (RPE). 4. Statistical Analysis All statistical analyses were performed using SPSS version 26. Continuous variables were first assessed for normality using the Shapir–Wilk test. For normally distributed data with homogeneity of variances, results were expressed as mean±standard deviation (SD). One-way analysis of variance (ANOVA) was used to compare differences among multiple groups, followed by least significant difference (LSD) post hoc tests for pairwise comparisons. For non-normally distributed data or data with unequal variances, values were expressed as median (Q1, Q3), and comparisons among multiple groups were performed using the Kruskal–Wallis H test. Correlation analyses were conducted using Pearson correlation coefficients for normally distributed data, and Spearman’s rank correlation for non-normally distributed data. A P-value < 0.05 was considered statistically significant. 5. Bioinformatics Analysis (1) Data Collection : We downloaded microarray data sets from the GEO database (GSE160310for DR and GSE138247 for HM). The data were processed using the Limma package to identify DEGs with a threshold of P 0.585. (2) Functional Annotation Utilizing the Metascape Database for GO and KEGG Pathway Analysis to Understand the Biological Functions and Pathways of DR and HM. GO and KEGG Function Analysis. The Metascape database (www.Metascape.org) is used for annotation and visualization. Differentially expressed genes (DEGs) are analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to obtain insights into the biological functions and signaling pathways involved in the occurrence and development of diseases. Enrichment is considered statistically significant when the overlap is ≥3 and the P-value is ≤0.01. (3) Network Analysis Network Analysis: Using Weighted Gene Co-expression Network Analysis (WGCNA) is used to identify co-expressed gene modules associated with diabetic retinopathy (DR) and high myopia (HM). Hub genes are identified by intersecting differentially expressed genes (DEGs) with key module genes. The WGCNA R package constructs a co-expression network for all genes, focusing on the top 10,000 genes with the highest variance. A weighted adjacency matrix is converted to a Topological Overlap Matrix (TOM) to estimate network connectivity, followed by hierarchical clustering to build a clustering tree. Branches of the tree represent different gene modules, with colors indicating various modules. Genes with similar expression patterns are grouped into modules based on their functional similarities and correlation coefficients. (4) Correlation and Regulation Analysis The relationships between hub genes and autophagy, iron deprivation, and immunity were explored. Transcriptional regulatory analysis was conducted to identify relevant transcription factors and motifs. In this study, the R package "RcisTarget" was utilized for transcription factor prediction. RcisTarget bases its predictions on the computational presence of certain motifs, with a Normalized Enrichment Score (NES) derived from the total number of motifs in the database. This approach was employed to predict the transcriptional regulatory relationships between our hub genes and their downstream targets. Results Impact of Axial Length on the Staging of Diabetic Retinopathy An analysis was conducted to examine the relationship between axial length (AL) and the severity of diabetic retinopathy (DR). The results indicated that AL was significantly longer in the NDR group compared to both the NPDR and PDR groups ( P 0.05). These findings suggest a potential inverse association between axial length and the severity of diabetic retinopathy (Figure 1). Correlation Analysis Between Axial Length and FAZ Area, CMT A correlation analysis was performed to evaluate the relationship between axial length (AL) and both the foveal avascular zone (FAZ) area and central macular thickness (CMT). The results showed a significant positive correlation between AL and FAZ area (r = 0.576, P < 0.001) (Figure 2A). In contrast, CMT was negatively correlated with AL, indicating a decrease in CMT with increasing axial length (r = –0.292, P < 0.001) (Figure 2B). Correlation Analysis Between Axial Length and Vascular Density in the 3×3 mm Macular Region A correlation analysis was conducted to assess the relationship between axial length and vascular density in the 3×3 mm macular region across all groups. The results revealed significant differences in vascular density across the four quadrants (superior, inferior, nasal, and temporal) among the three groups ( P < 0.05). Group A exhibited significantly lower vascular density in all quadrants compared to Groups B and C ( P 0.05) (Figure 3). Correlation Analysis Between Axial Length and Vascular Density in the 6×6 mm Macular Region A correlation analysis was performed to examine the relationship between axial length and vascular density in both the inner and outer rings of the 6×6 mm macular region (Figures 4 A and 4B). The results showed that vascular density differed significantly among the groups across all quadrants ( P < 0.05). Group A exhibited significantly lower vascular density compared to Groups B and C ( P < 0.001), and the average vascular density followed the same pattern. However, no statistically significant differences were observed in vascular density between Groups B and C across all quadrants ( P > 0.05). Identification of DEGs IUsing data from the open NCBI GEO database, the analysis of the diabetic retinopathy (DR) dataset (GSE160310) identified differentially expressed genes (DEGs) between two groups using the limma package. A total of 757 DEGs were identified, among which 288 genes were found to be upregulated and 469 genes were downregulated (Figure 5A). Similarly, in the high myopia (HM) dataset (GSE138247), 191 DEGs were identified, including 86 upregulated genes and 105 downregulated genes (Figure 5B). These findings highlight distinct gene expression profiles in DR and HM, indicating specific molecular changes associated with each condition. Functional Enrichment Analysis The enrichment analysis of biological functions in diabetic retinopathy (DR) and high myopia (HM) reveals both distinct and overlapping molecular mechanisms. In DR, upregulated functions (Figure 5C, D) include cell shape regulation, cytokine response, protein ubiquitination, vasculature development, peptidyl-serine phosphorylation, and small GTPase signaling. These suggest that changes in cell morphology, inflammation, protein modifications, and signal transduction are crucial in DR progression. The downregulated functions (Figure 5E, F) involve processes like viral regulation, neuron apoptosis, temperature sensing, epithelial proliferation, and Wnt signaling, pointing to the suppression of immune, neuroprotective, and temperature-related responses in DR. In HM, the upregulated functions (Figure 5G, H) include tube morphogenesis, the KEAP1-NFE2L2 pathway, symbiont entry, organophosphate metabolism, and protein localization. These indicate that changes in cell shape, oxidative stress, and metabolism are key in HM development. The downregulated functions (Figure 5I, J) include protein complex formation, organelle localization, biotic stimulus response, bone morphogenesis, and extracellular matrix organization, suggesting impaired cellular and extracellular functions in HM. Comparing DR and HM, both conditions share common upregulated functions like cell shape regulation and protein modification. However, DR is more associated with vasculature and inflammatory responses, while HM is linked to morphogenesis and matrix organization. These differences highlight that while both diseases affect the retina, they have distinct molecular pathways: DR involves inflammation and vascular abnormalities, while HM is characterized by cell morphology changes and matrix remodeling. These insights help to further understand and treat these two conditions. Coexpression network construction and hub module identifcation The Weighted Gene Co-Expression Network Analysis (WGCNA) revealed key gene modules in high myopia (HM) and diabetic retinopathy (DR), shedding light on the underlying molecular mechanisms. In HM, several modules were identified (Figure 6A), with the turquoise module enriched in genes involved in cell structure and extracellular matrix regulation, like FN1 and COL5A1 . The blue module was linked to metabolism and oxidative stress response, containing genes such as HSPA5 and CPED1 . The yellow module, associated with signaling and morphogenesis, included genes like KIF26B and TGFB2 (Figure 6C). For DR, the turquoise module was enriched with genes related to inflammation and immune response, such as FN1 and HSPA5 (Figure 6E, G). The blue module was related to vascular development and hypoxia response, with genes like COL4A5 and PLAT (Figure 6F). The brown module, containing genes like TNNT2 and ROR1 , was linked to cellular adhesion and signaling (Figure 6H). A comparison between HM and DR showed both similarities and differences. Both conditions shared modules related to structural components and the extracellular matrix. For instance, FN1 and HSPA5 were present in the turquoise module of both conditions (Figures 6A, 6E). However, in HM, the blue module focused on metabolism and oxidative stress ( CPED1 and HSPA5 ), while in DR, it was associated with vascular development and hypoxia ( COL4A5 and PLAT ) (Figures 6B, 6F). Additionally, HM yellow module, involved in morphogenesis and signaling, was distinct from DR brown module, which was related to adhesion and immune signaling (Figures 6C, 6G). By intersecting genes from similar modules, three key hub genes were identified: SBNO1 , GTLP , and NUCKS1 . These findings provide insights into the distinct and shared pathways in HM and DR, offering potential targets for therapeutic intervention. Relationship between hub genes and genes related to key regulatory mechanisms The intersection of key hub genes in high myopia (HM) and diabetic retinopathy (DR) identified three critical genes: SBNO1 , GLTP , and NUCKS1 . To further explore their significance, a correlation analysis was performed with top genes from five gene sets: ferroptosis genes from datasets GSE29691 and GSE3365, autophagy genes from GSE29691 and GSE3365, and immunity genes from GSE29691. The results of the correlation analysis showed strong associations between SBNO1 , GLTP , and NUCKS1 and key ferroptosis-related genes in dataset GSE29691, indicating a possible role of ferroptosis in both HM and DR (Figure 7A). This finding was consistent in dataset GSE3365, reinforcing the link between ferroptosis and the identified hub genes (Figure 7B). Furthermore, SBNO1 , GLTP , and NUCKS1 were significantly correlated with autophagy-related genes in both datasets (GSE29691 and GSE3365), suggesting that autophagy pathways are involved in the molecular mechanisms underlying both conditions (Figures 7C, 7D). Additionally, the analysis with immunity-related genes in dataset GSE29691 revealed that these hub genes are significantly associated with several genes involved in immune responses, implying a potential role of immune mechanisms in HM and DR (Figure 7E). The downstream target genes of SBNO1 , GLTP , and NUCKS1 were analyzed using the RcisTarget package. The analysis showed that SBNO1 targets pathways related to cellular stress and inflammation, suggesting its involvement in these processes in both HM and DR (Figure 7F). GLTP was found to regulate pathways related to lipid metabolism and cellular homeostasis, indicating its role in maintaining cellular integrity under pathological conditions (Figure 7G). Lastly, NUCKS1 was associated with cell cycle regulation and DNA repair mechanisms, highlighting its role in genomic stability and cellular proliferation control (Figure 7H). These findings provide a comprehensive understanding of the roles of SBNO1 , GLTP , and NUCKS1 in HM and DR, linking them to ferroptosis, autophagy, and immunity processes. The downstream pathway analysis further underscores the complexity of these molecular mechanisms, offering insights for future therapeutic strategies targeting these diseases. Discussion 1. Protective Role of Axial Length in Diabetic Retinopathy: Clinical and Imaging Perspective This study investigated the interplay between axial length (AL) and diabetic retinopathy (DR) severity, while also exploring the shared molecular mechanisms between DR and high myopia (HM) using integrated OCTA imaging and bioinformatics analyses. Our findings provide new insights into how anatomical and molecular alterations in HM may influence DR progression and identify common regulatory pathways potentially linking the two conditions. In this study, axial length (AL) was found to be inversely associated with the severity of DR, suggesting that longer AL may exert a protective effect against disease progression. This finding is consistent with the results reported by Kim et al, who observed that eyes with longer AL had a lower risk of DR progression, supporting the hypothesis that AL may be an independent protective factor for DR [16] . However, AL itself is regulated by various physiological factors, such as genetic predisposition, intraocular pressure dynamics, and refractive status. Our study did not comprehensively account for potential confounding variables such as blood pressure and refractive parameters, which may influence the accuracy of the estimated effects. For example, retinal thinning associated with high myopia may alter the phenotypic expression of DR, while changes in intraocular pressure could indirectly affect DR progression by modulating retinal blood flow. These interactive effects were not fully captured in the current analytical model. Therefore, although existing evidence supports an independent protective role of AL in DR, the underlying mechanisms are likely multifactorial and complex. Future research should aim to construct causal mediation models incorporating multidimensional parameters—such as refractive status, dynamic intraocular pressure monitoring, and levels of vascular endothelial growth factor (VEGF)—to systematically elucidate the interaction network between AL and other biological markers. This approach may provide more precise insights into the pathophysiological pathways by which AL influences DR progression. In addition, this study utilized optical coherence tomography angiography (OCTA) to assess macular vascular density in DR patients under both 3×3 mm and 6×6 mm scanning protocols. The analysis revealed a negative correlation between AL and microvascular density in the macular region—that is, eyes with longer axial lengths exhibited lower macular vascular density. This observation aligns with the findings of Zhang [17] et al. and supports the theory proposed by Bazzazi and Lin et al [9 , 10] . that elongation of the axial length leads to mechanical stretching of the retina. Two major mechanisms may explain these findings: 1) Biomechanical remodeling: Increased AL results in mechanical stretching of the retina, leading to retinal thinning and geometric vascular remodeling, such as reduced vascular branching density. 2) Hemodynamic compensation: Axial elongation causes mechanical thinning of the retina and choroid, potentially reducing retinal metabolic demands, alleviating diabetes-related hypoxic stress, inhibiting neovascularization, and delaying the progression from non-proliferative diabetic retinopathy (NPDR) to proliferative diabetic retinopathy (PDR). However, the difference in vascular density (VD) between the long-AL and normal-AL groups did not reach statistical significance ( P > 0.05). Several factors may account for this observation. First, our data suggest that longer AL is often associated with mild to moderate DR, particularly at the NPDR stage, where retinal microcirculation may still exhibit compensatory capacity. The VD reduction induced by axial elongation may be partially offset by early compensatory mechanisms in diabetic microangiopathy, such as capillary dilation and increased blood flow velocity. Second, complex interactions may exist between AL and different DR stages. In NPDR, chronic microvascular damage and microcirculatory impairment may overshadow the effect of AL. In contrast, during PDR, neovascularization becomes a hallmark of disease progression. Although longer AL may reduce macular vascular density to some extent, the angiogenic process in PDR likely dominates the retinal vascular structure, thereby masking the impact of AL on VD. Beyond influencing overall DR progression and macular vascular density, AL may also affect localized retinal structure and function. Therefore, we further analyzed the relationship between AL and both the foveal avascular zone (FAZ) area and central macular thickness (CMT). The results showed a positive correlation between AL and FAZ area, and a negative correlation between AL and CMT. Specifically, as AL increased, the FAZ area expanded and CMT decreased. These findings are consistent with previous studies [18 , 19] . An enlarged FAZ indicates an extension of the avascular region in the macula, while reduced CMT generally reflects structural degeneration or microcirculatory disturbances, possibly resulting from diminished retinal metabolic demands. In summary, the influence of AL on macular structure and function extends beyond changes in vascular density. It may also modulate retinal metabolism and blood flow through its impact on FAZ and CMT. Collectively, these findings suggest that AL may confer a protective effect against the development and progression of DR, particularly in its early stages by altering ocular hemodynamics and reducing the likelihood of pathological neovascularization. 2. Shared Molecular Signatures Between DR and HM: Bioinformatics Insights To better understand the underlying biological overlap between diabetic retinopathy (DR) and high myopia (HM), we integrated publicly available transcriptomic datasets through a multi-step bioinformatics pipeline. Differential gene expression analysis revealed 757 differentially expressed genes (DEGs) in DR and 191 in HM. Functional enrichment via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed disease-specific and overlapping biological processes. In DR, the functions associated with upregulated genes include cell shape regulation, cytokine-mediated signaling, angiogenesis, and protein ubiquitination, indicating the important roles of inflammation, vascular abnormalities, and changes in cell morphology in the progression of DR. A case-control study shows that inflammatory factors play an important role in the pathogenesis of DR. Abnormal expression of factors such as TNF-α, IL-1, and IL-6 enhances retinal microvascular permeability, induces cell apoptosis and angiogenesis, and accelerates the progression of retinal lesions [20 , 21] . In contrast, in HM, the upregulated genes are primarily related to extracellular matrix (ECM) remodeling, oxidative stress response, and morphogenesis, suggesting that oxidative stress and ECM remodeling are key mechanisms in the development of HM. Previous studies have shown that the level of oxidative stress is positively correlated with axial length (AL) ( r = 0.70, P < 0.01), suggesting that oxidative stress may promote axial elongation, thereby leading to the occurrence of retinal lesions [22] . Similarly, Chen et al.'s research findings indicate that inflammation and oxidative stress also promote ECM abnormalities, such as the upregulation of the NLRP3 inflammasome, which accelerates the progression of myopia [23] . These findings are consistent with existing literature, indicating that DR and HM indeed share common pathways, such as extracellular matrix regulation and changes in cell morphology. These findings are consistent with previous transcriptomic studies. For example, Yu [24] and Li [ 25] , through cell experiments, validated the regulatory effects of transforming growth factor β (TGF-β) and miRNA on the expression of type I collagen in scleral fibroblasts, contributing to abnormal scleral remodeling and ECM degradation, thereby promoting the development of high myopia. Our data not only validate these findings but also demonstrate partial convergence of disease mechanisms. Using Weighted Gene Co-expression Network Analysis (WGCNA), we identified disease-relevant modules and extracted hub genes based on module connectivity and trait association. Notably, the turquoise module, present in both DR and HM networks, included genes such as FN1 (fibronectin 1) and HSPA5 (heat shock protein 5), suggesting a common involvement of cellular stress response and matrix structural adaptation in both diseases. Prior studies have shown that FN1 is upregulated in both DR and myopic eyes and is involved in pericyte detachment, fibrosis, and choroidal ECM disorganization [26 , 27] .Disease-specific modules were also informative. In DR, modules were dominated by angiogenesis and hypoxia response pathways, including COL4A5 and PLAT—genes linked to capillary basement membrane remodeling and fibrinolysis. In HM, modules such as the blue and yellow clusters contained genes involved in developmental morphogenesis, retinal ECM remodeling, and oxidative metabolism, such as TGFB2 and CPED1 . These findings echo recent discoveries by Gong and Jobling et al, who demonstrated that myopic scleral remodeling is driven by oxidative damage and reduced ECM gene expression [28 , 29] . Our results indicate that ferroptosis and autophagy may play crucial roles in both DR and HM. Ferroptosis, a form of regulated cell death driven by iron-dependent lipid peroxidation, may lead to retinal cell damage in DR, while autophagy might serve as a protective mechanism [30] . In high myopia, dysregulation of these pathways could affect scleral remodeling and retinal metabolic changes, revealing the complex interplay between cellular stress responses and retinal pathology, which underlies the shared mechanisms of both diseases [31 , 32] . By analyzing the intersections of these network modules, we identified three key hub genes: SBNO1 , GLTP , and NUCKS1 . Each of these genes showed strong correlations with hallmark gene sets associated with ferroptosis, autophagy, and immune regulation, suggesting that these pathways play critical roles in diabetic microvascular injury and myopia-related retinal remodeling. To further elucidate transcriptional regulation, we employed RcisTarget for transcription factor (TF) motif analysis. The results revealed that: SBNO1 is potentially regulated by NF-κB and CEBPB, both of which are key mediators of inflammation in diabetic retinal tissue [33] ; GLTP plays a crucial role in regulating autophagy, inflammation, and cell death [34] ; NUCKS1 was enriched for motifs associated with E2F and MYC, supporting its role in cell proliferation and stress-induced DNA repair [35] . Collectively, although there is currently no direct evidence linking these three core genes with DR and HM, these findings suggest that, despite their distinct clinical origins, DR and HM may converge at the molecular level through shared regulatory networks involving immune modulation, oxidative stress adaptation, and extracellular matrix remodeling. This convergence may help explain the clinical observations of how high myopia alters the progression of diabetic microvascular complications. 3. Limitations and Future Perspectives Despite the strengths of integrated imaging and transcriptomics, this study has several limitations. OCTA measurements are susceptible to projection artifacts and segmentation errors, particularly in highly myopic eyes. Although we employed both 3×3 mm and 6×6 mm scan protocols to ensure spatial coverage, axial elongation may still affect image reliability. Additionally, potential systemic confounders—such as glycemic control (HbA1c), blood pressure, lipid status, and refractive error—were not comprehensively included in the multivariate analysis. Their omission may limit the precision of the associations observed between AL and retinal microvascular parameters. Furthermore, gene expression datasets used in this study were derived from unmatched samples. The lack of direct pairing between clinical phenotypes and transcriptomic profiles limits mechanistic interpretation. To address this, future studies should consider combining OCTA imaging, axial length profiling, and patient-matched retinal transcriptomics or proteomics. Finally, although SBNO1 , GLTP , and NUCKS1 were identified as key regulatory genes, their roles in retinal physiology and pathology remain poorly understood. In vitro or in vivo experiments—such as CRISPR knockout models or gene overexpression in retinal endothelial cells—are needed to validate their functional relevance in DR and HM. Conclusion In this study, we demonstrated that increased axial length (AL), as seen in high myopia (HM), may exert a protective effect against the progression of diabetic retinopathy (DR) by inducing structural thinning and hemodynamic remodeling of the retina. OCTA-based imaging revealed that longer AL was associated with reduced macular vascular density, enlarged foveal avascular zone (FAZ), and decreased central macular thickness (CMT), suggesting decreased retinal metabolic demand and vascular stress. Through integrated bioinformatics analysis, we identified shared molecular pathways and hub genes— SBNO1 , GLTP , and NUCKS1 —that may link DR and HM through mechanisms involving ferroptosis, autophagy, and immune regulation. These findings support a novel conceptual framework in which biomechanical changes in myopia interact with diabetic vascular pathology through converging gene networks. Together, this study provides both clinical and molecular evidence for the interaction between HM and DR, offering potential targets for personalized screening, risk stratification, and therapeutic development in diabetic patients with high myopia. Declarations Acknowledgements Throughout the course of this study, the authors would like to thank all the patients who contributed. Author contributions GXD: Conceptualization, Investigation, Writing- Original draft preparation, ZZ and ZZ: collected, analyzed and interpreted the data, did review. JW: provide data. XX: Collect and organize data. XW: Supervision. YQL: Project administration, Writing- Reviewing , Editing and Funding acquisition. Funding This work was supported by the National Natural Science Foundation of China (82260204). This work was supported by the China Foundation for International Medical Exchange (Z-2017-26-2302); The Guizhou Provincial Science and Technology Project (Qiankehe Achievements-LC[2022]038); The Affiliated Hospital of Guizhou Medical University (gyfynsfc[2024]-25). Data availability The datasets analyzed during the current study were obtained from the public GEO database, while additional data are available from the corresponding author upon reasonable request. The clinical data contain sensitive patient information and are not publicly available due to restrictions imposed by the institutional ethics committee and the informed consent agreements. However, de-identified data may be provided upon reasonable request to the corresponding author and with approval from the relevant ethics committee. Declarations Ethics approval and consent to participate This study was approved by the The Affiliated Hospital of Guizhou Medical University Ethics Committee. Informed consent was obtained from all participants, and the study was conducted in accordance with the Declaration of Helsinki. Consent for publication Not Applicable. Competing interests The authors declare no competing interests. Author details 1+ Xuzhou Central Hospital (Affiliated Hospital of Southeast University), Xuzhou 221009, Jiangsu Province, China; The Affiliated Hospital of Guizhou Medical University, Guiyang 550004, Guizhou, China 2,4 Guizhou Medical University, Guiyang 550001, Guizhou Province, China 3 Xuzhou Central Hospital (Affiliated Hospital of Southeast University), Xuzhou 221009, Jiangsu Province, China 1* The Affiliated Hospital of Guizhou Medical University, Guiyang 550004, Guizhou, China References Teo ZL, Tham YC, Yu M, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis[J]. Ophthalmology. 2021;128(11):1580–91. Ejigu T, Tsegaw A. Prevalence of diabetic retinopathy and risk factors among diabetic patients at university of Gondar tertiary eye care and training center, North-West Ethiopia[J]. Middle East Afr J Ophthalmol. 2021;28(2):71–80. Biswas A, Choudhury AD, Agrawal S, et al. Recent insights into the etiopathogenesis of diabetic retinopathy and its management[J]. J Ocul Pharmacol Ther. 2024;40(1):13–33. Zhu H, Li B, Huang T et al. Update in the molecular mechanism and biomarkers of diabetic retinopathy[J]. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 2025, 1871(5): 167758. Li M, Yang Y, Jiang H, et al. Retinal microvascular network and microcirculation assessments in high myopia[J]. Am J Ophthalmol. 2017;174:56–67. Man REK, Lamoureux EL, Taouk Y, et al. Axial length, retinal function, and oxygen consumption: a potential mechanism for a lower risk of diabetic retinopathy in longer eyes[J]. Investig Ophthalmol Vis Sci. 2013;54(12):7691–8. Lim LS, Lamoureux E, Saw SM, et al. Are myopic eyes less likely to have diabetic retinopathy?[J]. Ophthalmology. 2010;117(3):524–30. Xiao G, Ge M, Qiao G, et al. Relationship Between Axial Length and Retinal Oxygen Dynamics in Adults With Myopia[J]. Volume 14. Translational Vision Science & Technology; 2025. pp. 18–18. 1. Bazzazi N, Akbarzadeh S, Yavarikia M, et al. High myopia and diabetic retinopathy: a contralateral eye study in diabetic patients with high myopic anisometropia[J]. Retina. 2017;37(7):1270–6. Lin Z, Li D, Zhai G, et al. High myopia is protective against diabetic retinopathy via thinning retinal vein: A report from Fushun Diabetic Retinopathy Cohort Study (FS-DIRECT)[J]. Diabetes Vascular Disease Res. 2020;17(4):1479164120940988. Ten W, Yuan Y, Zhang W, et al. High myopia is protective against diabetic retinopathy in the participants of the National Health and Nutrition Examination Survey[J]. BMC Ophthalmol. 2023;23(1):468. Kong H, Zang S, Hu Y, et al. Effect of High Myopia and Cataract Surgery on the Correlation Between Diabetic Retinopathy and Chronic Kidney Disease[J]. Front Med. 2022;9:788573. Wang Q, Wang YX, Wu SL, et al. Ocular axial length and diabetic retinopathy: the Kailuan eye study[J]. Volume 60. Investigative ophthalmology & visual science; 2019. pp. 3689–95. 10. Mouzaka AE, Chandrinos A, Chatziralli I, et al. Eye Axial Length: Is There a Protective Link to Diabetic Retinopathy?[J]. Cureus. 2024;16(12):e75712. Khan MH, Lam AKC, Armitage JA, et al. Impact of axial eye size on retinal microvasculature density in the macular region[J]. J Clin Med. 2020;9(8):2539. Kim DY, Song JH, Kim YJ, et al. Asymmetric diabetic retinopathy progression in patients with axial anisometropia[J]. Retina. 2018;38(9):1809–15. Zhang Q, Yang L, Xu X et al. The association of axial length with macular microvascular changes in Chinese diabetic retinopathy patients[J]. Int J Gen Med, 2022: 3895–902. Shiihara H, Terasaki H, Sonoda S, et al. Objective evaluation of size and shape of superficial foveal avascular zone in normal subjects by optical coherence tomography angiography[J]. Sci Rep. 2018;8(1):10143. Piao H, Guo Y, Zhang H, et al. Acircularity and circularity indexes of the foveal avascular zone in high myopia[J]. Sci Rep. 2021;11(1):16808. Ra H, Lee A, Lee J, et al. Cytokines associated with hemorrhage in proliferative diabetic retinopathy[J]. Int Ophthalmol. 2021;41(5):1845–53. Tang L, Xu GT, Zhang JF. Inflammation in diabetic retinopathy: possible roles in pathogenesis and potential implications for therapy[J]. Neural Regeneration Res. 2023;18(5):976–82. Mérida S, Villar VM, Navea A, et al. Imbalance between oxidative stress and growth factors in human high myopia[J]. Front Physiol. 2020;11:463. Chen Z, Xiao K, Long Q. Up-regulation of NLRP3 in the sclera correlates with myopia progression in a form-deprivation myopia mouse model[J]. Front Bioscience-Landmark. 2023;28(2):27. Yu Q, Zhou JB, Scleralremodelinginmyopiadevelopment. IntJ Ophthalmol. 2022;15(3):510–4. Li Q, Zheng Q, He J, et al. Hsa-miR-142-3p reduces collagen I in human scleral fibroblasts by targeting TGF-β1 in high myopia[J]. Exp Eye Res. 2022;219:109023. Liu D, Meng Z, Jin C, et al. Fibronectin Mediates Endothelial-to-Mesenchymal Transition in Retina Angiogenesis[J]. Volume 66. Investigative ophthalmology & visual science; 2025. pp. 10–10. 3. Shan Y, Gao X, Zhao K, et al. Liraglutide intervention improves high-glucose-induced reactive gliosis of Müller cells and ECM dysregulation[J]. Mol Cell Endocrinol. 2023;576:112013. Jobling AI, Nguyen M, Gentle A, et al. Isoform-specific changes in scleral transforming growth factor-β expression and the regulation of collagen synthesis during myopia progression[J]. J Biol Chem. 2004;279(18):18121–6. Gong Y, Li X, Xie L. Circ_0001897 regulates high glucose-induced angiogenesis and inflammation in retinal microvascular endothelial cells through miR-29c-3p/transforming growth factor beta 2 axis[J]. Bioengineered. 2022;13(5):11694–705. He W, Chang L, Li X, et al. Research progress on the mechanism of ferroptosis and its role in diabetic retinopathy[J]. Front Endocrinol. 2023;14:1155296. Zhang R, Wen Y, Liu J, et al. The miR-15b-5p/miR-379-3p-FOXO axis regulates cell cycle and apoptosis in scleral remodeling during experimental myopia[J]. J Translational Med. 2024;22:710. Yao Y, Chen Z, Wu Q et al. Single-cell RNA sequencing of retina revealed novel transcriptional landscape in high myopia and underlying cell‐type‐specific mechanisms[J]. Medcomm, 2023, 4(5). Zhang Y, Zhu Z, Li Z, et al. Sbno1 mediates cell–cell communication between neural stem cells and microglia through small extracellular vesicles[J]. Cell Bioscience. 2024;14(1):1–19. Mishra SK, Gao YG, Zou X, et al. Emerging roles for human glycolipid transfer protein superfamily members in the regulation of autophagy, inflammation, and cell death[J]. Prog Lipid Res. 2020;78:101031–101031. Maranon DG, Sharma N, Huang Y, et al. NUCKS1 promotes RAD54 activity in homologous recombination DNA repair[J]. J Cell Biol. 2020;219(10):e201911049. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor invited by journal 02 Sep, 2025 Editor assigned by journal 28 Aug, 2025 Submission checks completed at journal 28 Aug, 2025 First submitted to journal 28 Aug, 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-7482406","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530517763,"identity":"56f13bfd-3a5c-458a-addf-9c16c2614a9d","order_by":0,"name":"Guoxin Ding","email":"","orcid":"","institution":"Xuzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guoxin","middleName":"","lastName":"Ding","suffix":""},{"id":530517764,"identity":"78b89aaa-88e0-48c0-8a05-1df19255b720","order_by":1,"name":"Zhen Zhang","email":"","orcid":"","institution":"Xuzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Zhang","suffix":""},{"id":530517767,"identity":"281adab2-6d99-4089-bad8-ad4d33ef0ab2","order_by":2,"name":"Zhou Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Zhang","suffix":""},{"id":530517768,"identity":"e9ef505f-8601-4c22-8957-780f5a86f678","order_by":3,"name":"Jing Wang","email":"","orcid":"","institution":"Guiyang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""},{"id":530517775,"identity":"7e756d35-03b3-490a-9cc6-4f344e599c7b","order_by":4,"name":"Suyun Qin","email":"","orcid":"","institution":"Xuzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Suyun","middleName":"","lastName":"Qin","suffix":""},{"id":530517776,"identity":"63e8dcc7-146f-4101-8971-0ad68956391e","order_by":5,"name":"Xin Xiao","email":"","orcid":"","institution":"Guiyang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Xiao","suffix":""},{"id":530517777,"identity":"94aa5484-20b5-43b4-9fbd-3a03f8a253ed","order_by":6,"name":"Xian Wang","email":"","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xian","middleName":"","lastName":"Wang","suffix":""},{"id":530517778,"identity":"341fd380-8df3-4903-a005-946ed7f6ce81","order_by":7,"name":"Yingqi Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIie3SPQ6CMBTA8YdNiMMTNgNx8Ao1Lg4GrgLhAhwBd+Ms0UN4hJI3uBA9AA4mJk4OHRkcbBNdoW4m9j+94f2GfgDYbL+YYIUArgbGhJCtEXE+xE2rcm1I3hPOaegaCK+pVoT5JdocURIgTP2x6CbhKS0I+T0raXSgfAGzcpd0E147mlDGNdkiJLwxJ3gldL8gkSJgRkJFqj2nJCSXq0sO+s/i1Yzk40mxd6ablO1y6k96iG6AAKl6UF3Qv65z1DeJQZgt22w22x/2Al/ITpoK9K6WAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yingqi","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-08-28 17:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7482406/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7482406/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93794448,"identity":"b98a4542-ef3f-4627-920c-343780136b6b","added_by":"auto","created_at":"2025-10-17 15:38:53","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2826404,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptHM.docx","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/f7f7513d26c2192c4ff591b7.docx"},{"id":93794404,"identity":"c38206a2-50ea-4650-978e-65a048f7a463","added_by":"auto","created_at":"2025-10-17 15:38:48","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8791,"visible":true,"origin":"","legend":"","description":"","filename":"942db3f35396488297829b26b2b21df4.json","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/1c2c8e99b10f3be2c0e99638.json"},{"id":93794403,"identity":"f3bc9054-0b49-40cc-ba39-c6f593dfd700","added_by":"auto","created_at":"2025-10-17 15:38:48","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98510,"visible":true,"origin":"","legend":"","description":"","filename":"942db3f35396488297829b26b2b21df41enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/5f8e28c00c8446098c6b1b89.xml"},{"id":93794439,"identity":"3cd66aa8-8b87-4a6e-a4ab-03f6150b7e08","added_by":"auto","created_at":"2025-10-17 15:38:51","extension":"emf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":765400,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.emf","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/40ec43b4f1cbf95a69437f0d.emf"},{"id":93794401,"identity":"05ab71fe-03ad-4554-9d6c-e69dd80daaa9","added_by":"auto","created_at":"2025-10-17 15:38:48","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179338,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/a8bf9fd729ab19dfbed3ffcc.png"},{"id":93794355,"identity":"cebe0388-9a42-4173-8efc-e45df2e38320","added_by":"auto","created_at":"2025-10-17 15:38:47","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":60783,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/c09fbe1251a7fbfd550f0185.png"},{"id":93794437,"identity":"276de34c-1603-4f62-bf25-228898558cf4","added_by":"auto","created_at":"2025-10-17 15:38:51","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":579110,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/119cce63dfa71db3b88b77b7.png"},{"id":93794435,"identity":"d9b1c590-f18b-4a04-87a6-66a45728b687","added_by":"auto","created_at":"2025-10-17 15:38:50","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":827354,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/ff7fd063555886e238127db8.png"},{"id":93794527,"identity":"60c02d3f-8143-46d8-9cbf-c237187a8b08","added_by":"auto","created_at":"2025-10-17 15:38:55","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":741273,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/c7610fdbd65f461e27d720b2.png"},{"id":93794524,"identity":"7e3ad7ae-492e-42a9-b128-df72a8227044","added_by":"auto","created_at":"2025-10-17 15:38:54","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":351632,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/fbddf7964bd0dda89049f14f.png"},{"id":93794429,"identity":"aab0d8eb-6860-4126-a00b-fb2f31c13a3f","added_by":"auto","created_at":"2025-10-17 15:38:49","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42339,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/b2316d85f85b7dedaf3f90cd.png"},{"id":93794442,"identity":"1763c50e-8793-4e48-8d59-19ca9dafd4bb","added_by":"auto","created_at":"2025-10-17 15:38:52","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":19794,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/2c211320a2a3f8dca2e73fc4.png"},{"id":93795048,"identity":"da4afa79-52c2-4438-ac10-b1a89056fc30","added_by":"auto","created_at":"2025-10-17 15:46:49","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12449,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/f6cad367afee874b0de7ef3f.png"},{"id":93794438,"identity":"90462382-e054-4d51-993b-f2608cc36ed1","added_by":"auto","created_at":"2025-10-17 15:38:51","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63419,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/c00cb006a9f6bb01fa0d01cc.png"},{"id":93794522,"identity":"cb9cc7dc-be12-4cfb-b898-09c4a47d422f","added_by":"auto","created_at":"2025-10-17 15:38:53","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88544,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/d99c2a1766a5919bb7f81691.png"},{"id":93794445,"identity":"b30720c3-854b-46e0-b6ef-57485f1b1cdb","added_by":"auto","created_at":"2025-10-17 15:38:52","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73872,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/fc050dd4bb4df4ef6a07f10d.png"},{"id":93796915,"identity":"09efc505-2b5b-4b54-bc70-5eeda794c7f6","added_by":"auto","created_at":"2025-10-17 15:54:52","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73470,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/69e2e71d363b0b44884462cf.png"},{"id":93794523,"identity":"c7636860-cb1a-4888-a6e9-1623a5bd4553","added_by":"auto","created_at":"2025-10-17 15:38:53","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97112,"visible":true,"origin":"","legend":"","description":"","filename":"942db3f35396488297829b26b2b21df41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/e70e22b0b1d67005ed0ba17b.xml"},{"id":93794436,"identity":"07bc309a-638b-4638-858e-a227dd0b8f92","added_by":"auto","created_at":"2025-10-17 15:38:51","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":107754,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/aa975ed9f5ac4a48954a0f83.html"},{"id":93794434,"identity":"a782abd1-c4fe-4672-a38c-932ddef0ab18","added_by":"auto","created_at":"2025-10-17 15:38:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStratified Impact of Axial Length on Diabetic Retinopathy Progression\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/85fe61cebe1ce206bd204203.png"},{"id":93794400,"identity":"2eff6abd-139b-44cf-809f-245f7e66fe03","added_by":"auto","created_at":"2025-10-17 15:38:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Analysis Between Axial Length and Ocular Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Scatter plot showing the correlation between axial length and FAZ area; (B) Scatter plot showing the correlation between axial length and CMT.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/4a762d5780be48080a341798.png"},{"id":93794505,"identity":"5b3dea21-c0aa-4ac4-8cbb-80d2c7c54de6","added_by":"auto","created_at":"2025-10-17 15:38:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStatistical Visualization of Axial Length and 3mm×3mm Vessel Density Correlations\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/6b96bd302f5f32a24a0668f7.png"},{"id":93795049,"identity":"cf0ac609-6301-4972-9360-e6a862648e37","added_by":"auto","created_at":"2025-10-17 15:46:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":182670,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegion Statistical Visualization of Axial Length and 6mm×6mm Vessel Density Correlation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/392f1964537cdc6646d88265.png"},{"id":93794399,"identity":"4e911667-76fc-4b68-a952-98bb62a4af59","added_by":"auto","created_at":"2025-10-17 15:38:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":198165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and Functional Enrichment Analysis of Differentially Expressed Genes (DEGs) in Diabetic Retinopathy (DR) and High Myopia (HM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot showing the distribution of differentially expressed genes (DEGs) in the high myopia (HM) dataset, with upregulated genes (red) and downregulated genes (blue) identified. (B) Volcano plot depicting DEGs in the diabetic retinopathy (DR) dataset, with upregulated genes (red) and downregulated genes (blue). (C, D) Functional enrichment analysis of upregulated biological processes in DR, highlighting key processes such as cell shape regulation, cytokine response, protein ubiquitination, and vasculature development. (E, F) Downregulated biological functions in DR, involving processes such as viral regulation, neuron apoptosis, and Wnt signaling. (G, H) Upregulated biological functions in HM, including tube morphogenesis, oxidative stress pathways (KEAP1-NFE2L2), and symbiont entry. (I, J) Downregulated biological processes in HM, such as protein complex formation, organelle localization, and extracellular matrix organization.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/489371ba6252340ddc87c35a.png"},{"id":93794346,"identity":"42d49661-2907-430d-9780-8a34cfada414","added_by":"auto","created_at":"2025-10-17 15:38:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":192447,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted Gene Co-Expression Network Analysis (WGCNA) of Differentially Expressed Genes (DEGs) in High Myopia (HM) and Diabetic Retinopathy (DR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Soft power threshold determination for WGCNA in high myopia (HM), showing the relationship between the soft power threshold and network topology measures. (B) Module dendrogram for the high myopia (HM) dataset, highlighting distinct gene modules. (C) Network visualization of HM gene modules, with genes associated with cell structure and extracellular matrix regulation (turquoise), metabolism and oxidative stress (blue), and signaling and morphogenesis (yellow). (D) Detailed gene interaction network for the yellow module in HM, focusing on genes related to morphogenesis and signaling pathways. (E) Soft power threshold determination for WGCNA in diabetic retinopathy (DR). (F) Module dendrogram for the DR dataset, revealing key gene modules. (G) Network visualization of DR gene modules, emphasizing genes related to inflammation and immune response (turquoise), vascular development and hypoxia response (blue), and cellular adhesion and signaling (brown). (H) Detailed gene interaction network for the brown module in DR, highlighting genes involved in cellular adhesion and immune signaling.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/e0a49ea0a9572f41a6ba68d7.png"},{"id":93794431,"identity":"859b796b-4697-4a9d-8114-75e15fee01c0","added_by":"auto","created_at":"2025-10-17 15:38:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":144646,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship Between Hub Genes and Key Regulatory Mechanisms in High Myopia (HM) and Diabetic Retinopathy (DR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Heatmap showing the correlation between hub genes (SBNO1, GLTP, NUCKS1) and ferroptosis-related genes from the GSE29691 dataset, highlighting a strong association with key ferroptosis genes. (B) Correlation analysis between hub genes and ferroptosis-related genes from the GSE3365 dataset, reinforcing the link between ferroptosis and the identified hub genes. (C) Heatmap displaying the correlation between hub genes and autophagy-related genes from the GSE29691 dataset, suggesting the involvement of autophagy in both HM and DR. (D) Correlation analysis between hub genes and autophagy-related genes from the GSE3365 dataset, further supporting the role of autophagy pathways. (E) Correlation of hub genes with immunity-related genes from the GSE29691 dataset, showing significant associations with genes involved in immune responses. (F) Downstream pathway analysis of SBNO1 using the RcisTarget package, indicating its regulation of pathways related to cellular stress and inflammation. (G) Downstream pathway analysis of GLTP, highlighting its regulation of lipid metabolism and cellular homeostasis. (H) Downstream pathway analysis of NUCKS1, showing its involvement in cell cycle regulation and DNA repair mechanisms.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/e233fd7c739c3320cdd35b9b.png"},{"id":93796918,"identity":"cfe8b5c5-c241-40cd-90e4-bbab0e19e4e9","added_by":"auto","created_at":"2025-10-17 15:54:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1904589,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7482406/v1/be04a40c-152c-452a-a6e6-d3cffccd204b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Shared Pathophysiological Mechanisms Between Diabetic Retinopathy and High Myopia: Insights from OCTA and Bioinformatics Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic retinopathy (DR) is one of the most common microvascular complications of diabetes mellitus and a leading cause of irreversible vision loss worldwide\u003csup\u003e[1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e2]\u003c/sup\u003e. Its pathogenesis involves chronic hyperglycemia-induced damage to the retinal microvasculature, including breakdown of the blood-retinal barrier, ischemia, and neovascularization. Multiple molecular mechanisms have been implicated in DR progression, such as activation of the polyol pathway, oxidative stress, accumulation of advanced glycation end-products (AGEs), protein kinase C (PKC) activation, and dysregulated inflammatory cytokines\u003csup\u003e[3\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e4]\u003c/sup\u003e. High myopia (HM), in contrast, is primarily a refractive error caused by excessive elongation of the axial length (AL), often exceeding 26 mm. As the axial length of the eye increases, the anteroposterior diameter of the eyeball enlarges, leading to mechanical stretching and thinning of the retina. This, in turn, causes retinal blood vessels to straighten and their diameters to decrease, resulting in reduced retinal blood flow\u003csup\u003e[5]\u003c/sup\u003e. Such changes reduce the oxygen consumption of the retina, alleviating its hypoxic condition, and alter retinal blood flow and nutrient supply, which in turn affects the vascular density in the macula and reduces the risk of diabetic retinopathy (DR)\u003csup\u003e[6\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e8]\u003c/sup\u003e. These findings also support the theory proposed by Bazzazi\u003csup\u003e[9]\u003c/sup\u003e and Lin\u003csup\u003e[10]\u003c/sup\u003e et al. that an increase in axial length leads to mechanical stretching of the retina. This anatomical elongation is associated with structural changes in the retina. Although DR and HM arise from distinct etiologies—metabolic dysfunction versus biomechanical elongation—they both impact the integrity of retinal structure and function.\u003c/p\u003e\n\u003cp\u003eInterestingly, accumulating clinical evidence suggests that high myopia may confer a protective effect against the development and progression of DR. In one study, the incidence of diabetic retinopathy was significantly lower in the high myopia group, with no proliferative changes observed. After adjusting for potential confounding factors, the odds ratio (OR) was 0.44, with a 95% CI: (0.20-0.96) and a \u003cem\u003eP\u003c/em\u003e-value of 0.040\u003csup\u003e[11]\u003c/sup\u003e.Another study also drew similar conclusions, suggesting that high myopia is an important protective factor, and the degree of protection increases with the increasing severity of myopia\u003csup\u003e[12]\u003c/sup\u003e. This indicates that as the degree of myopia increases, the risk of diabetic patients developing retinopathy decreases. It is worth noting that researchers believe this protective factor is associated with changes in both the structure and function of the retinal microvasculature in individuals with high myopia and axial elongation. For instance, several studies have suggested that in patients with diabetes, a longer axial length may reduce the risk of diabetic retinopathy (DR) by improving retinal blood supply and local hemodynamics\u003csup\u003e[13\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e14]\u003c/sup\u003e. Moreover, another study demonstrated a significant inverse relationship between axial length and vascular density measurements in the macular region, including perfusion density (\u003cem\u003er\u003c/em\u003e\u003cem\u003e²\u003c/em\u003e = 0.186, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and vessel length density (\u003cem\u003er\u003c/em\u003e\u003cem\u003e²\u003c/em\u003e = 0.102, \u003cem\u003eP\u003c/em\u003e = 0.001)\u003csup\u003e[15]\u003c/sup\u003e. These findings indicate that axial elongation not only alters the structure and function of the retinal microvasculature but may also influence nutrient delivery and metabolic activity in the macular area by affecting blood flow dynamics and microvascular architecture, thereby playing a complex role in the onset and progression of DR.\u003c/p\u003e\n\u003cp\u003eDespite these observations, the molecular basis underlying this protective association remains unclear. A key unanswered question is whether DR and HM share overlapping gene expression profiles or regulatory pathways that could explain their interaction at the molecular level. Given the recent advances in retinal imaging and transcriptomic analysis, it is now possible to integrate phenotypic imaging with bioinformatics to identify potential shared mechanisms. Therefore, in this study, we combine optical coherence tomography angiography (OCTA) with transcriptome-level bioinformatics to explore the shared structural and molecular characteristics between DR and HM. Specifically, we investigate how axial length affects retinal microvasculature in diabetic eyes and identify common differentially expressed genes (DEGs) and co-expression modules through microarray data analysis. Our aim is to provide novel insights into the intertwined pathophysiology of DR and HM, potentially revealing new therapeutic targets or diagnostic markers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e1. Clinical Study Design and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University (Approval No. 2024-272). A total of 72 patients diagnosed with type 2 diabetes mellitus (T2DM) between October 2020 and October 2024 were enrolled. Informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Diabetic Retinopathy Staging\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;According to the International Clinical Diabetic Retinopathy Disease Severity Scale, fundus findings were classified into three groups: no diabetic retinopathy (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Ophthalmic Examinations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(1) Axial Length Measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAxial length (AL) was measured using the IOL Master biometry system (Carl Zeiss Meditec). Based on the measured AL, patients were divided into three groups: normal axial length (22 mm ≤ AL \u0026lt; 24 mm), long axial length (24 mm ≤ AL \u0026lt; 26 mm), and extremely long axial length (AL ≥ 26 mm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(2) OCTA Imaging and Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOCTA scans were performed using the CIRRUS HD-OCT AngioPlex system (Carl Zeiss, Germany). Scans were obtained in 3×3 mm and 6×6 mm fields centered on the fovea. The software quantified: Superficial capillary plexus vessel density (VD)、Foveal avascular zone (FAZ) area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(3) Optical Coherence Tomography (OCT) Examination\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The macular cube 512×128 scanning protocol was employed to measure central macular thickness (CMT). Full-thickness retinal measurements were obtained from the internal limiting membrane (ILM) to the retinal pigment epithelium (RPE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using SPSS version 26. Continuous variables were first assessed for normality using the Shapir–Wilk test. For normally distributed data with homogeneity of variances, results were expressed as mean±standard deviation (SD). One-way analysis of variance (ANOVA) was used to compare differences among multiple groups, followed by least significant difference (LSD) post hoc tests for pairwise comparisons. For non-normally distributed data or data with unequal variances, values were expressed as median (Q1, Q3), and comparisons among multiple groups were performed using the Kruskal–Wallis H test. Correlation analyses were conducted using Pearson correlation coefficients for normally distributed data, and Spearman’s rank correlation for non-normally distributed data. A P-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Bioinformatics Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(1) Data Collection\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eWe downloaded microarray data sets from the GEO database (GSE160310for DR and GSE138247 for HM). The data were processed using the Limma package to identify DEGs with a threshold of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 and |logFC| \u0026gt; 0.585.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(2) Functional Annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUtilizing the Metascape Database for GO and KEGG Pathway Analysis to Understand the Biological Functions and Pathways of DR and HM. GO and KEGG Function Analysis. The Metascape database (www.Metascape.org) is used for annotation and visualization. Differentially expressed genes (DEGs) are analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to obtain insights into the biological functions and signaling pathways involved in the occurrence and development of diseases. Enrichment is considered statistically significant when the overlap is\u0026nbsp;≥3 and the P-value is\u0026nbsp;≤0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(3) Network Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNetwork Analysis: Using Weighted Gene Co-expression Network Analysis (WGCNA) is used to identify co-expressed gene modules associated with diabetic retinopathy (DR) and high myopia (HM). Hub genes are identified by intersecting differentially expressed genes (DEGs) with key module genes. The WGCNA R package constructs a co-expression network for all genes, focusing on the top 10,000 genes with the highest variance. A weighted adjacency matrix is converted to a Topological Overlap Matrix (TOM) to estimate network connectivity, followed by hierarchical clustering to build a clustering tree. Branches of the tree represent different gene modules, with colors indicating various modules. Genes with similar expression patterns are grouped into modules based on their functional similarities and correlation coefficients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(4) Correlation and Regulation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationships between hub genes and autophagy, iron deprivation, and immunity were explored. Transcriptional regulatory analysis was conducted to identify relevant transcription factors and motifs. In this study, the R package \"RcisTarget\" was utilized for transcription factor prediction. RcisTarget bases its predictions on the computational presence of certain motifs, with a Normalized Enrichment Score (NES) derived from the total number of motifs in the database. This approach was employed to predict the transcriptional regulatory relationships between our hub genes and their downstream targets.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eImpact of Axial Length on the Staging of Diabetic Retinopathy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn analysis was conducted to examine the relationship between axial length (AL) and the severity of diabetic retinopathy (DR). The results indicated that AL was significantly longer in the NDR group compared to both the NPDR and PDR groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). However, no statistically significant difference in AL was observed between the NPDR and PDR groups (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). These findings suggest a potential inverse association between axial length and the severity of diabetic retinopathy (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Analysis Between Axial Length and FAZ Area, CMT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA correlation analysis was performed to evaluate the relationship between axial length (AL) and both the foveal avascular zone (FAZ) area and central macular thickness (CMT). The results showed a significant positive correlation between AL and FAZ area (r = 0.576, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Figure 2A). In contrast, CMT was negatively correlated with AL, indicating a decrease in CMT with increasing axial length (r = \u0026ndash;0.292, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Figure 2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Analysis Between Axial Length and Vascular Density in the 3\u0026times;3 mm Macular Region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA correlation analysis was conducted to assess the relationship between axial length and vascular density in the 3\u0026times;3 mm macular region across all groups. The results revealed significant differences in vascular density across the four quadrants (superior, inferior, nasal, and temporal) among the three groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Group A exhibited significantly lower vascular density in all quadrants compared to Groups B and C (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). However, no statistically significant differences in vascular density were observed between Groups B and C in any of the four quadrants (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05) (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Analysis Between Axial Length and Vascular Density in the 6\u0026times;6 mm Macular Region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA correlation analysis was performed to examine the relationship between axial length and vascular density in both the inner and outer rings of the 6\u0026times;6 mm macular region (Figures 4 A and 4B). The results showed that vascular density differed significantly among the groups across all quadrants (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Group A exhibited significantly lower vascular density compared to Groups B and C (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and the average vascular density followed the same pattern. However, no statistically significant differences were observed in vascular density between Groups B and C across all quadrants (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIUsing data from the open NCBI GEO database, the analysis of the diabetic retinopathy (DR) dataset (GSE160310) identified differentially expressed genes (DEGs) between two groups using the limma package. A total of 757 DEGs were identified, among which 288 genes were found to be upregulated and 469 genes were downregulated (Figure 5A). Similarly, in the high myopia (HM) dataset (GSE138247), 191 DEGs were identified, including 86 upregulated genes and 105 downregulated genes (Figure 5B). These findings highlight distinct gene expression profiles in DR and HM, indicating specific molecular changes associated with each condition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe enrichment analysis of biological functions in diabetic retinopathy (DR) and high myopia (HM) reveals both distinct and overlapping molecular mechanisms. In DR, upregulated functions (Figure 5C, D) include cell shape regulation, cytokine response, protein ubiquitination, vasculature development, peptidyl-serine phosphorylation, and small GTPase signaling. These suggest that changes in cell morphology, inflammation, protein modifications, and signal transduction are crucial in DR progression. The downregulated functions (Figure 5E, F) involve processes like viral regulation, neuron apoptosis, temperature sensing, epithelial proliferation, and Wnt signaling, pointing to the suppression of immune, neuroprotective, and temperature-related responses in DR. In HM, the upregulated functions (Figure 5G, H) include tube morphogenesis, the KEAP1-NFE2L2 pathway, symbiont entry, organophosphate metabolism, and protein localization. These indicate that changes in cell shape, oxidative stress, and metabolism are key in HM development. The downregulated functions (Figure 5I, J) include protein complex formation, organelle localization, biotic stimulus response, bone morphogenesis, and extracellular matrix organization, suggesting impaired cellular and extracellular functions in HM. Comparing DR and HM, both conditions share common upregulated functions like cell shape regulation and protein modification. However, DR is more associated with vasculature and inflammatory responses, while HM is linked to morphogenesis and matrix organization. These differences highlight that while both diseases affect the retina, they have distinct molecular pathways: DR involves inflammation and vascular abnormalities, while HM is characterized by cell morphology changes and matrix remodeling. These insights help to further understand and treat these two conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCoexpression network construction and\u0026nbsp;hub module identifcation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Weighted Gene Co-Expression Network Analysis (WGCNA) revealed key gene modules in high myopia (HM) and diabetic retinopathy (DR), shedding light on the underlying molecular mechanisms. In HM, several modules were identified (Figure 6A), with the turquoise module enriched in genes involved in cell structure and extracellular matrix regulation, like \u003cem\u003eFN1\u003c/em\u003e and \u003cem\u003eCOL5A1\u003c/em\u003e. The blue module was linked to metabolism and oxidative stress response, containing genes such as \u003cem\u003eHSPA5\u003c/em\u003e and \u003cem\u003eCPED1\u003c/em\u003e. The yellow module, associated with signaling and morphogenesis, included genes like \u003cem\u003eKIF26B\u003c/em\u003e and \u003cem\u003eTGFB2\u003c/em\u003e (Figure 6C). For DR, the turquoise module was enriched with genes related to inflammation and immune response, such as \u003cem\u003eFN1\u003c/em\u003e and \u003cem\u003eHSPA5\u003c/em\u003e (Figure 6E, G). The blue module was related to vascular development and hypoxia response, with genes like \u003cem\u003eCOL4A5\u003c/em\u003e and \u003cem\u003ePLAT\u003c/em\u003e (Figure 6F). The brown module, containing genes like \u003cem\u003eTNNT2\u003c/em\u003e and \u003cem\u003eROR1\u003c/em\u003e, was linked to cellular adhesion and signaling (Figure 6H). A comparison between HM and DR showed both similarities and differences. Both conditions shared modules related to structural components and the extracellular matrix. For instance, \u003cem\u003eFN1\u003c/em\u003e and \u003cem\u003eHSPA5\u003c/em\u003e were present in the turquoise module of both conditions (Figures 6A, 6E). However, in HM, the blue module focused on metabolism and oxidative stress (\u003cem\u003eCPED1\u003c/em\u003e and \u003cem\u003eHSPA5\u003c/em\u003e), while in DR, it was associated with vascular development and hypoxia (\u003cem\u003eCOL4A5\u003c/em\u003e and \u003cem\u003ePLAT\u003c/em\u003e) (Figures 6B, 6F). Additionally, HM yellow module, involved in morphogenesis and signaling, was distinct from DR brown module, which was related to adhesion and immune signaling (Figures 6C, 6G). By intersecting genes from similar modules, three key hub genes were identified: \u003cem\u003eSBNO1\u003c/em\u003e, \u003cem\u003eGTLP\u003c/em\u003e, and \u003cem\u003eNUCKS1\u003c/em\u003e. These findings provide insights into the distinct and shared pathways in HM and DR, offering potential targets for therapeutic intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between\u0026nbsp;hub genes and\u0026nbsp;genes related to\u0026nbsp;key regulatory mechanisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intersection of key hub genes in high myopia (HM) and diabetic retinopathy (DR) identified three critical genes: \u003cem\u003eSBNO1\u003c/em\u003e, \u003cem\u003eGLTP\u003c/em\u003e, and \u003cem\u003eNUCKS1\u003c/em\u003e. To further explore their significance, a correlation analysis was performed with top genes from five gene sets: ferroptosis genes from datasets GSE29691 and GSE3365, autophagy genes from GSE29691 and GSE3365, and immunity genes from GSE29691. The results of the correlation analysis showed strong associations between \u003cem\u003eSBNO1\u003c/em\u003e, \u003cem\u003eGLTP\u003c/em\u003e, and \u003cem\u003eNUCKS1\u003c/em\u003e and key ferroptosis-related genes in dataset GSE29691, indicating a possible role of ferroptosis in both HM and DR (Figure 7A). This finding was consistent in dataset GSE3365, reinforcing the link between ferroptosis and the identified hub genes (Figure 7B). Furthermore, \u003cem\u003eSBNO1\u003c/em\u003e, \u003cem\u003eGLTP\u003c/em\u003e, and \u003cem\u003eNUCKS1\u003c/em\u003e were significantly correlated with autophagy-related genes in both datasets (GSE29691 and GSE3365), suggesting that autophagy pathways are involved in the molecular mechanisms underlying both conditions (Figures 7C, 7D). Additionally, the analysis with immunity-related genes in dataset GSE29691 revealed that these hub genes are significantly associated with several genes involved in immune responses, implying a potential role of immune mechanisms in HM and DR (Figure 7E).\u003c/p\u003e\n\u003cp\u003eThe downstream target genes of \u003cem\u003eSBNO1\u003c/em\u003e, \u003cem\u003eGLTP\u003c/em\u003e, and \u003cem\u003eNUCKS1\u003c/em\u003e were analyzed using the RcisTarget package. The analysis showed that \u003cem\u003eSBNO1\u003c/em\u003e targets pathways related to cellular stress and inflammation, suggesting its involvement in these processes in both HM and DR (Figure 7F). \u003cem\u003eGLTP\u003c/em\u003e was found to regulate pathways related to lipid metabolism and cellular homeostasis, indicating its role in maintaining cellular integrity under pathological conditions (Figure 7G). Lastly, \u003cem\u003eNUCKS1\u0026nbsp;\u003c/em\u003ewas associated with cell cycle regulation and DNA repair mechanisms, highlighting its role in genomic stability and cellular proliferation control (Figure 7H). These findings provide a comprehensive understanding of the roles of \u003cem\u003eSBNO1\u003c/em\u003e, \u003cem\u003eGLTP\u003c/em\u003e, and \u003cem\u003eNUCKS1\u003c/em\u003e in HM and DR, linking them to ferroptosis, autophagy, and immunity processes. The downstream pathway analysis further underscores the complexity of these molecular mechanisms, offering insights for future therapeutic strategies targeting these diseases.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e1. \u0026nbsp; Protective Role of Axial Length in Diabetic Retinopathy: Clinical and Imaging Perspective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study investigated the interplay between axial length (AL) and diabetic retinopathy (DR) severity, while also exploring the shared molecular mechanisms between DR and high myopia (HM) using integrated OCTA imaging and bioinformatics analyses. Our findings provide new insights into how anatomical and molecular alterations in HM may influence DR progression and identify common regulatory pathways potentially linking the two conditions.\u003c/p\u003e\n\u003cp\u003eIn this study, axial length (AL) was found to be inversely associated with the severity of DR, suggesting that longer AL may exert a protective effect against disease progression. This finding is consistent with the results reported by Kim et al, who observed that eyes with longer AL had a lower risk of DR progression, supporting the hypothesis that AL may be an independent protective factor for DR\u003csup\u003e[16]\u003c/sup\u003e. However, AL itself is regulated by various physiological factors, such as genetic predisposition, intraocular pressure dynamics, and refractive status. Our study did not comprehensively account for potential confounding variables such as blood pressure and refractive parameters, which may influence the accuracy of the estimated effects. For example, retinal thinning associated with high myopia may alter the phenotypic expression of DR, while changes in intraocular pressure could indirectly affect DR progression by modulating retinal blood flow. These interactive effects were not fully captured in the current analytical model. Therefore, although existing evidence supports an independent protective role of AL in DR, the underlying mechanisms are likely multifactorial and complex. Future research should aim to construct causal mediation models incorporating multidimensional parameters\u0026mdash;such as refractive status, dynamic intraocular pressure monitoring, and levels of vascular endothelial growth factor (VEGF)\u0026mdash;to systematically elucidate the interaction network between AL and other biological markers. This approach may provide more precise insights into the pathophysiological pathways by which AL influences DR progression.\u003c/p\u003e\n\u003cp\u003eIn addition, this study utilized optical coherence tomography angiography (OCTA) to assess macular vascular density in DR patients under both 3\u0026times;3 mm and 6\u0026times;6 mm scanning protocols. The analysis revealed a negative correlation between AL and microvascular density in the macular region\u0026mdash;that is, eyes with longer axial lengths exhibited lower macular vascular density. This observation aligns with the findings of Zhang\u003csup\u003e[17]\u003c/sup\u003eet al. and supports the theory proposed by Bazzazi and Lin et al\u003csup\u003e[9\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e10]\u003c/sup\u003e. that elongation of the axial length leads to mechanical stretching of the retina. Two major mechanisms may explain these findings: 1) Biomechanical remodeling: Increased AL results in mechanical stretching of the retina, leading to retinal thinning and geometric vascular remodeling, such as reduced vascular branching density. 2) Hemodynamic compensation: Axial elongation causes mechanical thinning of the retina and choroid, potentially reducing retinal metabolic demands, alleviating diabetes-related hypoxic stress, inhibiting neovascularization, and delaying the progression from non-proliferative diabetic retinopathy (NPDR) to proliferative diabetic retinopathy (PDR). However, the difference in vascular density (VD) between the long-AL and normal-AL groups did not reach statistical significance (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05). Several factors may account for this observation. First, our data suggest that longer AL is often associated with mild to moderate DR, particularly at the NPDR stage, where retinal microcirculation may still exhibit compensatory capacity. The VD reduction induced by axial elongation may be partially offset by early compensatory mechanisms in diabetic microangiopathy, such as capillary dilation and increased blood flow velocity. Second, complex interactions may exist between AL and different DR stages. In NPDR, chronic microvascular damage and microcirculatory impairment may overshadow the effect of AL. In contrast, during PDR, neovascularization becomes a hallmark of disease progression. Although longer AL may reduce macular vascular density to some extent, the angiogenic process in PDR likely dominates the retinal vascular structure, thereby masking the impact of AL on VD.\u003c/p\u003e\n\u003cp\u003eBeyond influencing overall DR progression and macular vascular density, AL may also affect localized retinal structure and function. Therefore, we further analyzed the relationship between AL and both the foveal avascular zone (FAZ) area and central macular thickness (CMT). The results showed a positive correlation between AL and FAZ area, and a negative correlation between AL and CMT. Specifically, as AL increased, the FAZ area expanded and CMT decreased. These findings are consistent with previous studies\u003csup\u003e[18\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e19]\u003c/sup\u003e. An enlarged FAZ indicates an extension of the avascular region in the macula, while reduced CMT generally reflects structural degeneration or microcirculatory disturbances, possibly resulting from diminished retinal metabolic demands. In summary, the influence of AL on macular structure and function extends beyond changes in vascular density. It may also modulate retinal metabolism and blood flow through its impact on FAZ and CMT. Collectively, these findings suggest that AL may confer a protective effect against the development and progression of DR, particularly in its early stages by altering ocular hemodynamics and reducing the likelihood of pathological neovascularization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Shared Molecular Signatures Between DR and HM: Bioinformatics Insights\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better understand the underlying biological overlap between diabetic retinopathy (DR) and high myopia (HM), we integrated publicly available transcriptomic datasets through a multi-step bioinformatics pipeline. Differential gene expression analysis revealed 757 differentially expressed genes (DEGs) in DR and 191 in HM. Functional enrichment via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed disease-specific and overlapping biological processes. In DR, the functions associated with upregulated genes include cell shape regulation, cytokine-mediated signaling, angiogenesis, and protein ubiquitination, indicating the important roles of inflammation, vascular abnormalities, and changes in cell morphology in the progression of DR. A case-control study shows that inflammatory factors play an important role in the pathogenesis of DR. Abnormal expression of factors such as TNF-\u0026alpha;, IL-1, and IL-6 enhances retinal microvascular permeability, induces cell apoptosis and angiogenesis, and accelerates the progression of retinal lesions\u003csup\u003e[20\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e21]\u003c/sup\u003e. In contrast, in HM, the upregulated genes are primarily related to extracellular matrix (ECM) remodeling, oxidative stress response, and morphogenesis, suggesting that oxidative stress and ECM remodeling are key mechanisms in the development of HM. Previous studies have shown that the level of oxidative stress is positively correlated with axial length (AL) (\u003cem\u003er\u003c/em\u003e = 0.70, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01), suggesting that oxidative stress may promote axial elongation, thereby leading to the occurrence of retinal lesions\u003csup\u003e[22]\u003c/sup\u003e. Similarly, Chen et al.\u0026apos;s research findings indicate that inflammation and oxidative stress also promote ECM abnormalities, such as the upregulation of the NLRP3 inflammasome, which accelerates the progression of myopia\u003csup\u003e[23]\u003c/sup\u003e. These findings are consistent with existing literature, indicating that DR and HM indeed share common pathways, such as extracellular matrix regulation and changes in cell morphology. These findings are consistent with previous transcriptomic studies. For example, Yu \u003csup\u003e[24]\u003c/sup\u003eand Li\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e25]\u003c/sup\u003e, through cell experiments, validated the regulatory effects of transforming growth factor \u0026beta; (TGF-\u0026beta;) and miRNA on the expression of type I collagen in scleral fibroblasts, contributing to abnormal scleral remodeling and ECM degradation, thereby promoting the development of high myopia. Our data not only validate these findings but also demonstrate partial convergence of disease mechanisms.\u003c/p\u003e\n\u003cp\u003eUsing Weighted Gene Co-expression Network Analysis (WGCNA), we identified disease-relevant modules and extracted hub genes based on module connectivity and trait association. Notably, the turquoise module, present in both DR and HM networks, included genes such as \u003cem\u003eFN1\u003c/em\u003e (fibronectin 1) and \u003cem\u003eHSPA5\u003c/em\u003e (heat shock protein 5), suggesting a common involvement of cellular stress response and matrix structural adaptation in both diseases. Prior studies have shown that \u003cem\u003eFN1\u003c/em\u003e is upregulated in both DR and myopic eyes and is involved in pericyte detachment, fibrosis, and choroidal ECM disorganization\u003csup\u003e[26\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e27]\u003c/sup\u003e.Disease-specific modules were also informative. In DR, modules were dominated by angiogenesis and hypoxia response pathways, including \u003cem\u003eCOL4A5\u003c/em\u003e and PLAT\u0026mdash;genes linked to capillary basement membrane remodeling and fibrinolysis. In HM, modules such as the blue and yellow clusters contained genes involved in developmental morphogenesis, retinal ECM remodeling, and oxidative metabolism, such as \u003cem\u003eTGFB2\u003c/em\u003e and \u003cem\u003eCPED1\u003c/em\u003e. These findings echo recent discoveries by Gong and Jobling et al, who demonstrated that myopic scleral remodeling is driven by oxidative damage and reduced ECM gene expression\u003csup\u003e[28\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e29]\u003c/sup\u003e.\u0026nbsp;Our results indicate that ferroptosis and autophagy may play crucial roles in both DR and HM. Ferroptosis, a form of regulated cell death driven by iron-dependent lipid peroxidation, may lead to retinal cell damage in DR, while autophagy might serve as a protective mechanism\u003csup\u003e[30]\u003c/sup\u003e. In high myopia, dysregulation of these pathways could affect scleral remodeling and retinal metabolic changes, revealing the complex interplay between cellular stress responses and retinal pathology, which underlies the shared mechanisms of both diseases\u003csup\u003e[31\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e32]\u003c/sup\u003e. By analyzing the intersections of these network modules, we identified three key hub genes: \u003cem\u003eSBNO1\u003c/em\u003e, \u003cem\u003eGLTP\u003c/em\u003e, and \u003cem\u003eNUCKS1\u003c/em\u003e. Each of these genes showed strong correlations with hallmark gene sets associated with ferroptosis, autophagy, and immune regulation, suggesting that these pathways play critical roles in diabetic microvascular injury and myopia-related retinal remodeling.\u003c/p\u003e\n\u003cp\u003eTo further elucidate transcriptional regulation, we employed RcisTarget for transcription factor (TF) motif analysis. The results revealed that: \u003cem\u003eSBNO1\u003c/em\u003e is potentially regulated by NF-\u0026kappa;B and CEBPB, both of which are key mediators of inflammation in diabetic retinal tissue\u003csup\u003e[33]\u003c/sup\u003e; \u003cem\u003eGLTP\u003c/em\u003e plays a crucial role in regulating autophagy, inflammation, and cell death\u003csup\u003e[34]\u003c/sup\u003e; \u003cem\u003eNUCKS1\u003c/em\u003e was enriched for motifs associated with E2F and MYC, supporting its role in cell proliferation and stress-induced DNA repair\u003csup\u003e[35]\u003c/sup\u003e. Collectively, although there is currently no direct evidence linking these three core genes with DR and HM, these findings suggest that, despite their distinct clinical origins, DR and HM may converge at the molecular level through shared regulatory networks involving immune modulation, oxidative stress adaptation, and extracellular matrix remodeling. This convergence may help explain the clinical observations of how high myopia alters the progression of diabetic microvascular complications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Limitations and Future Perspectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite the strengths of integrated imaging and transcriptomics, this study has several limitations. OCTA measurements are susceptible to projection artifacts and segmentation errors, particularly in highly myopic eyes. Although we employed both 3\u0026times;3 mm and 6\u0026times;6 mm scan protocols to ensure spatial coverage, axial elongation may still affect image reliability. Additionally, potential systemic confounders\u0026mdash;such as glycemic control (HbA1c), blood pressure, lipid status, and refractive error\u0026mdash;were not comprehensively included in the multivariate analysis. Their omission may limit the precision of the associations observed between AL and retinal microvascular parameters. Furthermore, gene expression datasets used in this study were derived from unmatched samples. The lack of direct pairing between clinical phenotypes and transcriptomic profiles limits mechanistic interpretation. To address this, future studies should consider combining OCTA imaging, axial length profiling, and patient-matched retinal transcriptomics or proteomics. Finally, although \u003cem\u003eSBNO1\u003c/em\u003e, \u003cem\u003eGLTP\u003c/em\u003e, and \u003cem\u003eNUCKS1\u0026nbsp;\u003c/em\u003ewere identified as key regulatory genes, their roles in retinal physiology and pathology remain poorly understood. In vitro or in vivo experiments\u0026mdash;such as CRISPR knockout models or gene overexpression in retinal endothelial cells\u0026mdash;are needed to validate their functional relevance in DR and HM.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we demonstrated that increased axial length (AL), as seen in high myopia (HM), may exert a protective effect against the progression of diabetic retinopathy (DR) by inducing structural thinning and hemodynamic remodeling of the retina. OCTA-based imaging revealed that longer AL was associated with reduced macular vascular density, enlarged foveal avascular zone (FAZ), and decreased central macular thickness (CMT), suggesting decreased retinal metabolic demand and vascular stress. Through integrated bioinformatics analysis, we identified shared molecular pathways and hub genes\u0026mdash;\u003cem\u003eSBNO1\u003c/em\u003e, \u003cem\u003eGLTP\u003c/em\u003e, and \u003cem\u003eNUCKS1\u003c/em\u003e\u0026mdash;that may link DR and HM through mechanisms involving ferroptosis, autophagy, and immune regulation. These findings support a novel conceptual framework in which biomechanical changes in myopia interact with diabetic vascular pathology through converging gene networks. Together, this study provides both clinical and molecular evidence for the interaction between HM and DR, offering potential targets for personalized screening, risk stratification, and therapeutic development in diabetic patients with high myopia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThroughout the course of this study, the authors would like to thank all the patients who contributed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGXD: Conceptualization, Investigation, Writing- Original draft preparation, ZZ and ZZ: collected, analyzed and interpreted the data, did review. JW: provide data. XX: Collect and organize data. XW: Supervision. YQL: Project administration, Writing- Reviewing , Editing and Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82260204). This work was supported by the China Foundation for International Medical Exchange (Z-2017-26-2302); The Guizhou Provincial Science and Technology Project (Qiankehe Achievements-LC[2022]038); The Affiliated Hospital of Guizhou Medical University (gyfynsfc[2024]-25).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study were obtained from the public GEO database, while additional data are available from the corresponding author upon reasonable request. The clinical data contain sensitive patient information and are not publicly available due to restrictions imposed by the institutional ethics committee and the informed consent agreements. However, de-identified data may be provided upon reasonable request to the corresponding author and with approval from the relevant ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the The Affiliated Hospital of Guizhou Medical University Ethics Committee. Informed consent was obtained from all participants, and the study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1+\u003c/sup\u003eXuzhou Central Hospital (Affiliated Hospital of Southeast University), Xuzhou 221009, Jiangsu Province, China; The Affiliated Hospital of Guizhou Medical University, Guiyang 550004, Guizhou, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2,4\u003c/sup\u003eGuizhou Medical University, Guiyang 550001, Guizhou Province, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eXuzhou Central Hospital (Affiliated Hospital of Southeast University), Xuzhou 221009, Jiangsu Province, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1*\u0026nbsp;\u003c/sup\u003eThe Affiliated Hospital of Guizhou Medical University, Guiyang 550004, Guizhou, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTeo ZL, Tham YC, Yu M, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis[J]. Ophthalmology. 2021;128(11):1580\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEjigu T, Tsegaw A. Prevalence of diabetic retinopathy and risk factors among diabetic patients at university of Gondar tertiary eye care and training center, North-West Ethiopia[J]. Middle East Afr J Ophthalmol. 2021;28(2):71\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBiswas A, Choudhury AD, Agrawal S, et al. Recent insights into the etiopathogenesis of diabetic retinopathy and its management[J]. J Ocul Pharmacol Ther. 2024;40(1):13\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu H, Li B, Huang T et al. Update in the molecular mechanism and biomarkers of diabetic retinopathy[J]. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 2025, 1871(5): 167758.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi M, Yang Y, Jiang H, et al. Retinal microvascular network and microcirculation assessments in high myopia[J]. Am J Ophthalmol. 2017;174:56\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMan REK, Lamoureux EL, Taouk Y, et al. Axial length, retinal function, and oxygen consumption: a potential mechanism for a lower risk of diabetic retinopathy in longer eyes[J]. Investig Ophthalmol Vis Sci. 2013;54(12):7691\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLim LS, Lamoureux E, Saw SM, et al. Are myopic eyes less likely to have diabetic retinopathy?[J]. Ophthalmology. 2010;117(3):524\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao G, Ge M, Qiao G, et al. Relationship Between Axial Length and Retinal Oxygen Dynamics in Adults With Myopia[J]. Volume 14. Translational Vision Science \u0026amp; Technology; 2025. pp. 18\u0026ndash;18. 1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBazzazi N, Akbarzadeh S, Yavarikia M, et al. High myopia and diabetic retinopathy: a contralateral eye study in diabetic patients with high myopic anisometropia[J]. Retina. 2017;37(7):1270\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin Z, Li D, Zhai G, et al. High myopia is protective against diabetic retinopathy via thinning retinal vein: A report from Fushun Diabetic Retinopathy Cohort Study (FS-DIRECT)[J]. Diabetes Vascular Disease Res. 2020;17(4):1479164120940988.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTen W, Yuan Y, Zhang W, et al. High myopia is protective against diabetic retinopathy in the participants of the National Health and Nutrition Examination Survey[J]. BMC Ophthalmol. 2023;23(1):468.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKong H, Zang S, Hu Y, et al. Effect of High Myopia and Cataract Surgery on the Correlation Between Diabetic Retinopathy and Chronic Kidney Disease[J]. Front Med. 2022;9:788573.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Q, Wang YX, Wu SL, et al. Ocular axial length and diabetic retinopathy: the Kailuan eye study[J]. Volume 60. Investigative ophthalmology \u0026amp; visual science; 2019. pp. 3689\u0026ndash;95. 10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMouzaka AE, Chandrinos A, Chatziralli I, et al. Eye Axial Length: Is There a Protective Link to Diabetic Retinopathy?[J]. Cureus. 2024;16(12):e75712.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan MH, Lam AKC, Armitage JA, et al. Impact of axial eye size on retinal microvasculature density in the macular region[J]. J Clin Med. 2020;9(8):2539.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim DY, Song JH, Kim YJ, et al. Asymmetric diabetic retinopathy progression in patients with axial anisometropia[J]. Retina. 2018;38(9):1809\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Q, Yang L, Xu X et al. The association of axial length with macular microvascular changes in Chinese diabetic retinopathy patients[J]. Int J Gen Med, 2022: 3895\u0026ndash;902.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShiihara H, Terasaki H, Sonoda S, et al. Objective evaluation of size and shape of superficial foveal avascular zone in normal subjects by optical coherence tomography angiography[J]. Sci Rep. 2018;8(1):10143.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePiao H, Guo Y, Zhang H, et al. Acircularity and circularity indexes of the foveal avascular zone in high myopia[J]. Sci Rep. 2021;11(1):16808.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRa H, Lee A, Lee J, et al. Cytokines associated with hemorrhage in proliferative diabetic retinopathy[J]. Int Ophthalmol. 2021;41(5):1845\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang L, Xu GT, Zhang JF. Inflammation in diabetic retinopathy: possible roles in pathogenesis and potential implications for therapy[J]. Neural Regeneration Res. 2023;18(5):976\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM\u0026eacute;rida S, Villar VM, Navea A, et al. Imbalance between oxidative stress and growth factors in human high myopia[J]. Front Physiol. 2020;11:463.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Z, Xiao K, Long Q. Up-regulation of NLRP3 in the sclera correlates with myopia progression in a form-deprivation myopia mouse model[J]. Front Bioscience-Landmark. 2023;28(2):27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu Q, Zhou JB, Scleralremodelinginmyopiadevelopment. IntJ Ophthalmol. 2022;15(3):510\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Q, Zheng Q, He J, et al. Hsa-miR-142-3p reduces collagen I in human scleral fibroblasts by targeting TGF-β1 in high myopia[J]. Exp Eye Res. 2022;219:109023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu D, Meng Z, Jin C, et al. Fibronectin Mediates Endothelial-to-Mesenchymal Transition in Retina Angiogenesis[J]. Volume 66. Investigative ophthalmology \u0026amp; visual science; 2025. pp. 10\u0026ndash;10. 3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShan Y, Gao X, Zhao K, et al. Liraglutide intervention improves high-glucose-induced reactive gliosis of M\u0026uuml;ller cells and ECM dysregulation[J]. Mol Cell Endocrinol. 2023;576:112013.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJobling AI, Nguyen M, Gentle A, et al. Isoform-specific changes in scleral transforming growth factor-β expression and the regulation of collagen synthesis during myopia progression[J]. J Biol Chem. 2004;279(18):18121\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGong Y, Li X, Xie L. Circ_0001897 regulates high glucose-induced angiogenesis and inflammation in retinal microvascular endothelial cells through miR-29c-3p/transforming growth factor beta 2 axis[J]. Bioengineered. 2022;13(5):11694\u0026ndash;705.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe W, Chang L, Li X, et al. Research progress on the mechanism of ferroptosis and its role in diabetic retinopathy[J]. Front Endocrinol. 2023;14:1155296.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang R, Wen Y, Liu J, et al. The miR-15b-5p/miR-379-3p-FOXO axis regulates cell cycle and apoptosis in scleral remodeling during experimental myopia[J]. J Translational Med. 2024;22:710.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao Y, Chen Z, Wu Q et al. Single-cell RNA sequencing of retina revealed novel transcriptional landscape in high myopia and underlying cell‐type‐specific mechanisms[J]. Medcomm, 2023, 4(5).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Zhu Z, Li Z, et al. Sbno1 mediates cell\u0026ndash;cell communication between neural stem cells and microglia through small extracellular vesicles[J]. Cell Bioscience. 2024;14(1):1\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMishra SK, Gao YG, Zou X, et al. Emerging roles for human glycolipid transfer protein superfamily members in the regulation of autophagy, inflammation, and cell death[J]. Prog Lipid Res. 2020;78:101031\u0026ndash;101031.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaranon DG, Sharma N, Huang Y, et al. NUCKS1 promotes RAD54 activity in homologous recombination DNA repair[J]. J Cell Biol. 2020;219(10):e201911049.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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, High Myopia, Optical Coherence Tomography Angiography, Vascular Density, Differentially Expressed Genes","lastPublishedDoi":"10.21203/rs.3.rs-7482406/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7482406/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo investigate the common pathophysiological mechanisms between diabetic retinopathy (DR) and high myopia (HM) using optical coherence tomography angiography (OCTA) and integrated bioinformatics approaches.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 72 patients with type 2 diabetes were enrolled and stratified based on axial length and DR severity. OCTA was performed to evaluate macular vascular density (VD), central macular thickness (CMT), and foveal avascular zone (FAZ). Gene expression profiles were retrieved from the GEO database (GSE160310 for DR; GSE138247 for HM). Differentially expressed genes (DEGs) were identified using the Limma package. Functional enrichment, Weighted Gene Co-expression Network Analysis (WGCNA), and transcription factor prediction were conducted to reveal shared molecular pathways and key regulatory genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Axial length (AL) was inversely associated with DR severity and positively correlated with FAZ area (\u003cem\u003er\u003c/em\u003e = 0.576) and negatively with CMT (\u003cem\u003er\u003c/em\u003e =–0.292). OCTA revealed reduced vascular density in eyes with longer AL. Bioinformatics analysis identified 757 DEGs in DR and 191 in HM. Functional enrichment suggested involvement of immune response, oxidative stress, and ECM remodeling. WGCNA revealed common gene modules, with three hub genes (\u003cem\u003eSBNO1\u003c/em\u003e, \u003cem\u003eGLTP\u003c/em\u003e, \u003cem\u003eNUCKS1\u003c/em\u003e) potentially mediating ferroptosis, autophagy, and immune pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e High myopia may exert a protective effect against DR via structural and hemodynamic remodeling. Shared molecular signatures between DR and HM suggest overlapping pathogenic pathways, providing potential therapeutic targets for both diseases.\u003c/p\u003e","manuscriptTitle":"Exploring the Shared Pathophysiological Mechanisms Between Diabetic Retinopathy and High Myopia: Insights from OCTA and Bioinformatics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 15:38:29","doi":"10.21203/rs.3.rs-7482406/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-25T18:05:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238067833854630077571256632841009388843","date":"2025-10-17T09:25:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-06T08:52:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-02T08:16:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-28T23:14:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-28T23:13:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Ophthalmology","date":"2025-08-28T17:00:10+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":"4c3b4f8f-5951-4ad3-a948-67252a930282","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T15:38:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 15:38:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7482406","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7482406","identity":"rs-7482406","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}