Gene Biomarkers Related to Th17 Cells in Macular Edema of Diabetic Retinopathy: Cutting-Edge Comprehensive Bioinformatics Analysis and In Vivo Validation

Machine Learning-Driven Identification of Hematological and Immunological Biomarkers for Predicting Proliferative Diabetic Retinopathy Progression

PURPOSE

Proliferative Diabetic Retinopathy (PDR) is a severe complication of diabetes characterized by neovascularization and retinal detachment, leading to significant vision loss. This study investigates the predictive power of hematological and immunological markers in PDR progression.

METHODS

Data from 126 patients were analyzed using advanced machine learning techniques, including LASSO regression, elastic net modeling, and backward stepwise regression.

RESULTS

The findings identified age, gender, IL-1, and lymphocyte count (LYM) as significant predictors of PDR, with a high AUC value of 0.839 from the ROC curve analysis. These markers, particularly cytokines in the aqueous humor and peripheral blood, offer a convenient and rapid method for early detection and risk assessment of PDR.

CONCLUSIONS

Despite the limitations of being a cross-sectional study with a relatively small sample size, the results highlight the clinical significance of these biomarkers and underscore the need for further validation in larger, more diverse populations. This study contributes to the development of targeted interventions and improved management strategies for diabetic retinopathy, emphasizing the importance of immunological health in disease progression.

Identification of Key Nucleotide Metabolism Genes in Diabetic Retinopathy Based on Bioinformatics Analysis and Experimental Verification

A dysregulated nucleotide metabolism has been implicated in the pathogenesis of diabetic retinopathy (DR). RNA sequencing datasets, GSE102485, GSE60436, and GSE165784, were downloaded from the GEO database. The differentially expressed genes (DEGs) between the DR and controls overlapped with nucleotide metabolism-related genes (NM-RGs), resulting in the differentially expressed NM-RGs (DE-NMRGs). Next, the core genes were identified by the five algorithms of the CytoHubba plugin. Receiver Operating Characteristic (ROC) curves and gene expression analysis were utilized to confirm the biomarkers. Then, the correlations between biomarker expression and the immune-related module were analyzed. The miRNA and transcription factor (TF) predictions, biomarker-targeting drugs, and molecular docking were implemented separately. The interaction between each subcluster of DR was elucidated through single-cell RNA (scRNA) analysis. Moreover, RT-PCR was applied to verify the expression of the biomarkers. In GSE102485, 48 DE-NMRGs were identified via the intersection of 1359 DEGs and 882 NM-RGs. Using the CytoHubba plugin, HMOX1, TLR4, and ACE were selected as core genes. As per the GSVA result, the interferon alpha response, IL6_JAK_STAT3 signaling, and apoptosis were activated in the DR group. The TF prediction identified TLR4 and HMOX1 as potential target genes of USF2. In conclusion, ACE and HMOX1 were possible diagnostic biomarkers related to nucleotide metabolism in DR.

Development and validation of potential molecular subtypes and signatures of thyroid eye disease based on angiogenesis-related gene analysis

BACKGROUND

Thyroid eye disease (TED) is an autoimmune inflammatory disorder of the orbit, associated with a range of potential clinical sequelae. Tumor cells in TED overexpress pro-angiogenic factors, driving the formation of heterogeneous and immature neovascularization. This dysregulated angiogenesis often leads to a hypoxic microenvironment due to insufficient perfusion. Despite its importance, the role of angiogenesis-related genes (ARGs) in TED pathophysiology remains poorly understood.

METHODS

To bridge this knowledge gap, our study aimed to identify and validate ARGs implicated in TED using a comprehensive bioinformatics strategy. By intersecting differential gene expression analyses with a curated list of 103 known ARGs, we aimed to pinpoint those with potential roles in TED. Advanced methodologies, including GSEA and GSVA, facilitated an in-depth exploration of the biological functions and pathways associated with these ARGs. Further refinement through Lasso regression and SVM-RFE enabled the identification of key hub genes and the evaluation of their diagnostic potential for TED. Additionally, we investigated the relationship between these hub ARGs and relevant clinical parameters. To corroborate our findings, we analyzed expression data from datasets GSE58331 and GSE105149, focusing on the six ARGs identified as potentially crucial to TED pathology.

RESULTS

Our investigation unveiled six ARGs (CRIP2, DUSP1, CTSL, DOCK5, ERAP1, SCG2) as intimately connected to TED. Functional analyses highlighted their involvement in processes such as response to ameboidal-type cell migration, epithelial cell migration, epithelium migration. Importantly, the diagnostic capabilities of these ARGs demonstrated promising efficacy in distinguishing TED from non-affected states.

CONCLUSIONS

This study identifies six ARGs as novel biomarker candidates for TED, elucidating their potential roles in the disease's pathogenesis.

The Role of Th17/Treg Axis in Retinal Pathology Associated with Diabetes and Treatment Options

Diabetic retinopathy (DR) is a major complication of diabetes, leading to vision impairment and blindness. The pathogenesis of DR involves multiple factors, including hyperglycemia-induced vascular damage, hypertension, obesity, anemia, immune dysregulation, and disruption of the blood-retinal barrier (BRB). Th17 and Treg cells, two types of CD4+ T cells, play opposing roles in inflammation. Th17 cells are pro-inflammatory, producing cytokines such as IL-17A, while Treg cells help suppress immune responses and promote anti-inflammatory effects. Recent studies highlight the importance of the Th17/Treg balance in retinal inflammation and disease progression in DR. Our literature review reveals an imbalance in DR, with increased Th17 activity and reduced Treg function. This shift creates a pro-inflammatory environment in the retina, worsening vascular leakage, neovascularization, and vision loss. The limited infiltration of Treg cells suggests that Th17 cells may uniquely infiltrate the retina by overwhelming or outnumbering Tregs or increasing the expression of recruiting chemokines, rather than only taking advantage of a damaged BRB. Therapeutic strategies, such as neutralizing IL-17A and enhancing Treg function with compounds like IL-35 or curcumin, may reduce inflammation and retinal damage. Restoring the balance between Th17 and Treg cells could provide new approaches for treating DR by controlling inflammation and preventing further retinal damage.

Bioinformatics analysis and validation of novel biomarkers and competitive endogenous RNA networks involved in pyroptosis in diabetic nephropathy

Diabetic nephropathy (DN) is one of the major complications of diabetes mellitus. Pyroptosis is a type of programmed cell death that is closely related to the development of DN, however the molecular mechanism of pyroptosis in the development of DN is still unclear. The aim of this study is to identify pyroptosis-related potential biomarkers and competing endogenous RNA (ceRNA) networks in DN. The differentially expressed pyroptosis-related genes (DEPRGs) were identified using R software from Gene Expression Omnibus (GEO) database. In total, 4 significantly upregulated hub DEPRGs (CASP1, TXNIP, IRF9, and TRAF3) were selected and verified by machine learning techniques. Receiver Operating Curve (ROC) to assess the diagnostic value of pivotal DEPRGs. Immune infiltration was analysed using the CIBERSORT algorithm in R software. Then, differentially expressed miRNAs (DEmiRNAs) and lncRNAs (DElncRNAs) were obtained from the GEO database, respectively. The hub DEPRGs-associated ceRNA network was constructed. Finally, DN rats were induced by high-sugar and high-fat diet combined with an intraperitoneal injection of STZ. The expression of pyroptosis-related proteins and 4 hub DEPRGs were detected in rats' kidney tissues using Western blotting. The DN pyroptosis-related ceRNA networks constructed by hub genes were validated both in clinical samples and DN rat model using real-time PCR (qRT-PCR). Our results indicated that the ceRNA network consisting of key genes might be a potential regulatory axis for pyroptosis in DN.

Integrated machine learning and Mendelian randomization reveal PALMD as a prognostic biomarker for nonspecific orbital inflammation

BACKGROUND

Nonspecific Orbital Inflammation (NSOI) remains a perplexing enigma among proliferative inflammatory disorders. Its etiology is idiopathic, characterized by distinctive and polymorphous lymphoid infiltration within the orbital region. Preliminary investigations suggest that PALMD localizes within the cytosol, potentially playing a crucial role in cellular processes, including plasma membrane dynamics and myogenic differentiation. The potential of PALMD as a biomarker for NSOI warrants meticulous exploration.

METHODS

PALMD was identified through the intersection analysis of common DEGs from datasets GSE58331 and GSE105149 from the GEO database, alongside immune-related gene lists from the ImmPort database, using Lasso regression and SVM-RFE analysis. GSEA and GSVA were conducted with gene sets co-expressed with PALMD. To further investigate the correlation between PALMD and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. The expression levels of PALMD were subsequently validated using GSE105149.

RESULTS

Among the 314 DEGs identified, several showed significant differences. Lasso and SVM-RFE algorithms pinpointed 15 hub genes. Functional analysis of PALMD emphasized its involvement in cell-cell adhesion, leukocyte migration, and leukocyte-mediated immunity. Enrichment analysis revealed that gene sets positively correlated with PALMD were enriched in immune-related pathways. Immune infiltration analysis indicated that resting dendritic cells, resting mast cells, activated NK cells, and plasma cells positively associate with PALMD expression. Conversely, naive B cells, activated dendritic cells, M0 and M1 macrophages, activated mast cells, activated CD4 memory T cells, and naive CD4 T cells showed a negative correlation with PALMD expression. PALMD demonstrated significant diagnostic potential in differentiating NSOI.

CONCLUSIONS

This study identifies PALMD as a potential biomarker linked to NSOI, providing insights into its pathogenesis and offering new avenues for tracking disease progression.

Impact of hyperglycemia on immune cell function: a comprehensive review

Hyperglycemia, a hallmark of diabetes and various metabolic disorders, has profound implications for immune cell function. The relationship between elevated blood glucose levels and immune cell function is a topic of significant medical interest. In this review, we aim to comprehensively review effects of hyperglycemia on various immune cell types and its clinical implications, particularly T cells, macrophages, natural killer cells, and neutrophils. It aims to consolidate current knowledge on the subject, with a focus on both type 1 and type 2 diabetes, as well as other pathological states where hyperglycemia is a concern. A comprehensive examination of recent studies and clinical data was conducted to assess effects of hyperglycemia on immune cell function. Evidence indicates that hyperglycemia can significantly alter immune cell function, with different diabetic conditions showing varied responses. Roles of key metabolic hormones in regulating T cell function highlight potential therapeutic targets for restoring immune balance. In addition, reprogramming of innate immune cells such as macrophages and natural killer cells under hyperglycemic conditions suggests a complex metabolic-immunological interface. This review will contribute to a better understanding of the link between diabetes, other metabolic disorders, and immune function. By examining recent research and clinical findings, this review will enhance our comprehension of the mechanisms at play and guide future medical strategies for managing and treating conditions associated with hyperglycemia.

Deciphering the role of HLF in idiopathic orbital inflammation: integrative analysis via bioinformatics and machine learning techniques

Idiopathic orbital inflammation, formerly known as NSOI (nonspecific orbital inflammation), is characterized as a spectrum disorder distinguished by the polymorphic infiltration of lymphoid tissue, presenting a complex and poorly understood etiology. Recent advancements have shed light on the HLF (Human lactoferrin), proposing its critical involvement in the regulation of hematopoiesis and the maintenance of innate mucosal immunity. This revelation has generated significant interest in exploring HLF's utility as a biomarker for NSOI, despite the existing gaps in our understanding of its biosynthetic pathways and operational mechanisms. Intersecting multi-omic datasets-specifically, common differentially expressed genes between GSE58331 and GSE105149 from the Gene Expression Omnibus and immune-related gene compendiums from the ImmPort database-we employed sophisticated analytical methodologies, including Lasso regression and support vector machine-recursive feature elimination, to identify HLF. Gene set enrichment analysis and gene set variation analysis disclosed significant immune pathway enrichment within gene sets linked to HLF. The intricate relationship between HLF expression and immunological processes was further dissected through the utilization of CIBERSORT and ESTIMATE algorithms, which assess characteristics of the immune microenvironment, highlighting a noteworthy association between increased HLF expression and enhanced immune cell infiltration. The expression levels of HLF were corroborated using data from the GSE58331 dataset, reinforcing the validity of our findings. Analysis of 218 HLF-related differentially expressed genes revealed statistically significant discrepancies. Fifteen hub genes were distilled using LASSO and SVM-RFE algorithms. Biological functions connected with HLF, such as leukocyte migration, ossification, and the negative regulation of immune processes, were illuminated. Immune cell analysis depicted a positive correlation between HLF and various cells, including resting mast cells, activated NK cells, plasma cells, and CD8 T cells. Conversely, a negative association was observed with gamma delta T cells, naive B cells, M0 and M1 macrophages, and activated mast cells. Diagnostic assessments of HLF in distinguishing NSOI showed promising accuracy. Our investigation delineates HLF as intricately associated with NSOI, casting light on novel biomarkers for diagnosis and progression monitoring of this perplexing condition.

The roles of IRF8 in nonspecific orbital inflammation: an integrated analysis by bioinformatics and machine learning

BACKGROUND

Nonspecific Orbital Inflammation (NSOI) represents a persistent and idiopathic proliferative inflammatory disorder, characterized by polymorphous lymphoid infiltration within the orbit. The transcription factor Interferon Regulatory Factor 8 (IRF8), integral to the IRF protein family, was initially identified as a pivotal element for the commitment and differentiation of myeloid cell lineage. Serving as a central regulator of innate immune receptor signaling, IRF8 orchestrates a myriad of functions in hematopoietic cell development. However, the intricate mechanisms underlying IRF8 production remain to be elucidated, and its potential role as a biomarker for NSOI is yet to be resolved.

METHODS

IRF8 was extracted from the intersection analysis of common DEGs of GSE58331 and GSE105149 from the GEO and immune- related gene lists in the ImmPort database using The Lasso regression and SVM-RFE analysis. We performed GSEA and GSVA with gene sets coexpressed with IRF8, and observed that gene sets positively related to IRF8 were enriched in immune-related pathways. To further explore the correlation between IRF8 and immune-related biological process, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate TME characteristics of each sample and confirmed that high IRF8 expression might give rise to high immune cell infiltration. Finally, the GSE58331 was utilized to confirm the levels of expression of IRF8.

RESULTS

Among the 314 differentially expressed genes (DEGs), some DEGs were found to be significantly different. With LASSO and SVM-RFE algorithms, we obtained 15 hub genes. For biological function analysis in IRF8, leukocyte mediated immunity, leukocyte cell-cell adhesion, negative regulation of immune system process were emphasized. B cells naive, Macrophages M0, Macrophages M1, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD4 naive, and T cells gamma delta were shown to be positively associated with IRF8. While, Mast cells resting, Monocytes, NK cells activated, Plasma cells, T cells CD8, and T cells regulatory (Tregs) were shown to be negatively linked with IRF8. The diagnostic ability of the IRF8 in differentiating NSOI exhibited a good value.

CONCLUSIONS

This study discovered IRF8 that are linked to NSOI. IRF8 shed light on potential new biomarkers for NSOI and tracking its progression.

Elucidating the multifaceted roles of GPR146 in non-specific orbital inflammation: a concerted analytical approach through the prisms of bioinformatics and machine learning

Background

Non-specific Orbital Inflammation (NSOI) is a chronic idiopathic condition marked by extensive polymorphic lymphoid infiltration in the orbital area. The integration of metabolic and immune pathways suggests potential therapeutic roles for C-peptide and G protein-coupled receptor 146 (GPR146) in diabetes and its sequelae. However, the specific mechanisms through which GPR146 modulates immune responses remain poorly understood. Furthermore, the utility of GPR146 as a diagnostic or prognostic marker for NSOI has not been conclusively demonstrated.

Methods

We adopted a comprehensive analytical strategy, merging differentially expressed genes (DEGs) from the Gene Expression Omnibus (GEO) datasets GSE58331 and GSE105149 with immune-related genes from the ImmPort database. Our methodology combined LASSO regression and support vector machine-recursive feature elimination (SVM-RFE) for feature selection, followed by Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to explore gene sets co-expressed with GPR146, identifying a significant enrichment in immune-related pathways. The tumor microenvironment's immune composition was quantified using the CIBERSORT algorithm and the ESTIMATE method, which confirmed a positive correlation between GPR146 expression and immune cell infiltration. Validation of GPR146 expression was performed using the GSE58331 dataset.

Results

Analysis identified 113 DEGs associated with GPR146, with a significant subset showing distinct expression patterns. Using LASSO and SVM-RFE, we pinpointed 15 key hub genes. Functionally, these genes and GPR146 were predominantly linked to receptor ligand activity, immune receptor activity, and cytokine-mediated signaling. Specific immune cells, such as memory B cells, M2 macrophages, resting mast cells, monocytes, activated NK cells, plasma cells, and CD8+ T cells, were positively associated with GPR146 expression. In contrast, M0 macrophages, naive B cells, M1 macrophages, activated mast cells, activated memory CD4+ T cells, naive CD4+ T cells, and gamma delta T cells showed inverse correlations. Notably, our findings underscore the potential diagnostic relevance of GPR146 in distinguishing NSOI.

Conclusion

Our study elucidates the immunological signatures associated with GPR146 in the context of NSOI, highlighting its prognostic and diagnostic potential. These insights pave the way for GPR146 to be a novel biomarker for monitoring the progression of NSOI, providing a foundation for future therapeutic strategies targeting immune-metabolic pathways.

hsa_circ_0087100/hsa-miR-6743-5p affects Th1 cell differentiation by regulating STAT1 in diabetic retinopathy

Objective: To elucidate the role of the competitive endogenous RNA (ceRNA) network in immune infiltration of diabetic retinopathy (DR). Methods: We obtained differentially expressed (DE) circRNAs, miRNAs and mRNAs from the Gene Expression Omnibus database. Then, we identified immune infiltration by CIBERSORT and single-sample gene set enrichment analysis and discovered co-expression genes by weighted gene co-expression network analysis. Furthermore, STAT1-mediated Th1 differentiation was determined in DR cell models, DR patients and DR mouse models. Results: hsa_circ_0087100/hsa-miR-6743-5p/STAT1 was involved in immune infiltration of Th1 cells. Aberrant expression of the ceRNA network and STAT1-mediated Th1 differentiation was thus verified in vitro and in vivo. Conclusion: hsa_circ_0087100/hsa-miR-6743-5p/STAT1 may affect Th1 cell differentiation in DR.

Glutamine metabolism-related genes and immunotherapy in nonspecific orbital inflammation were validated using bioinformatics and machine learning

BACKGROUND

Nonspecific orbital inflammation (NSOI) is an idiopathic, persistent, and proliferative inflammatory condition affecting the orbit, characterized by polymorphous lymphoid infiltration. Its pathogenesis and progression have been linked to imbalances in tumor metabolic pathways, with glutamine (Gln) metabolism emerging as a critical aspect in cancer. Metabolic reprogramming is known to influence clinical outcomes in various malignancies. However, comprehensive research on glutamine metabolism's significance in NSOI is lacking.

METHODS

This study conducted a bioinformatics analysis to identify and validate potential glutamine-related molecules (GlnMgs) associated with NSOI. The discovery of GlnMgs involved the intersection of differential expression analysis with a set of 42 candidate GlnMgs. The biological functions and pathways of the identified GlnMgs were analyzed using GSEA and GSVA. Lasso regression and SVM-RFE methods identified hub genes and assessed the diagnostic efficacy of fourteen GlnMgs in NSOI. The correlation between hub GlnMgs and clinical characteristics was also examined. The expression levels of the fourteen GlnMgs were validated using datasets GSE58331 and GSE105149.

RESULTS

Fourteen GlnMgs related to NSOI were identified, including FTCD, CPS1, CTPS1, NAGS, DDAH2, PHGDH, GGT1, GCLM, GLUD1, ART4, AADAT, ASNSD1, SLC38A1, and GFPT2. Biological function analysis indicated their involvement in responses to extracellular stimulus, mitochondrial matrix, and lipid transport. The diagnostic performance of these GlnMgs in distinguishing NSOI showed promising results.

CONCLUSIONS

This study successfully identified fourteen GlnMgs associated with NSOI, providing insights into potential novel biomarkers for NSOI and avenues for monitoring disease progression.

Purine metabolism-related genes and immunization in thyroid eye disease were validated using bioinformatics and machine learning

Thyroid eye disease (TED), an autoimmune inflammatory disorder affecting the orbit, exhibits a range of clinical manifestations. While the disease presentation can vary, cases that adhere to a prototypical pattern typically commence with mild symptoms that subsequently escalate in severity before entering a phase of stabilization. Notably, the metabolic activity of cells implicated in the disease substantially deviates from that of healthy cells, with purine metabolism representing a critical facet of cellular material metabolism by supplying components essential for DNA and RNA synthesis. Nevertheless, the precise involvement of Purine Metabolism Genes (PMGs) in the defensive mechanism against TED remains largely unexplored. The present study employed a bioinformatics approach to identify and validate potential PMGs associated with TED. A curated set of 65 candidate PMGs was utilized to uncover novel PMGs through a combination of differential expression analysis and a PMG dataset. Furthermore, GSEA and GSVA were employed to explore the biological functions and pathways associated with the newly identified PMGs. Subsequently, the Lasso regression and SVM-RFE algorithms were applied to identify hub genes and assess the diagnostic efficacy of the top 10 PMGs in distinguishing TED. Additionally, the relationship between hub PMGs and clinical characteristics was investigated. Finally, the expression levels of the identified ten PMGs were validated using the GSE58331 and GSE105149 datasets. This study revealed ten PMGs related with TED. PRPS2, PFAS, ATIC, NT5C1A, POLR2E, POLR2F, POLR3B, PDE3A, ADSS, and NTPCR are among the PMGs. The biological function investigation revealed their participation in processes such as RNA splicing, purine-containing chemical metabolism, and purine nucleotide metabolism. Furthermore, the diagnostic performance of the 10 PMGs in differentiating TED was encouraging. This study was effective in identifying ten PMGs linked to TED. These findings provide light on potential new biomarkers for TED and open up possibilities for tracking disease development.

CD44 Drives M1 Macrophage Polarization in Diabetic Retinopathy

PURPOSE

Diabetic retinopathy is a typical complication of diabetes, which can facilitate the risk of blindness in severe cases. We sought to determine the function of CD44 in inflammatory responses of human retinal microvascular endothelial cells (HRMECs) and macrophage polarization during diabetic retinopathy (DR).

METHODS

The hub genes were tested based on two datasets from the Gene Expression Omnibus database. Gene Ontology and pathway enrichment analysis was conducted on the base of differentially expressed genes (DEGs). The infiltration score and infiltration of the immune cells were assessed, and the link between key genes and macrophages was analyzed. The role of CD44 in HRMECs and macrophage polarization was determined by quantitative reverse transcription polymerase chain reaction, western blot, cell counting kit-8, Enzyme-linked immunosorbent assay, flow cytometry, and immunofluorescence.

RESULTS

DEGs were enriched in several pathways linked to DR, such as cellular response to retinoic acid, retinol metabolic process, retina homeostasis, PI3K-AKT signaling pathway, and leukocyte transendothelial migration. A total of 144 DEGs were identified by up-regulation both in GSE102485 and GSE160306. Moreover, the infiltration of macrophages was greater in the DR group than that in the control group. We highlighted an obvious increase in the expression of CD44 and CD86 in patients with DR, and distinct positive associations were found between levels of macrophages and levels of CD44 and CD86. Furthermore, CD44 expression was substantially increased in HRMECs under high glucose (HG) conditions and CD44 knockdown markedly inhibited HG-induced inflammatory responses of HRMECs. HG-induced HRMECs remarkably influenced M1 polarization of macrophages, but CD44 knockdown significantly nullified this effect.

CONCLUSIONS

CD44 influenced the advancement of DR via meditating M1 polarization of macrophages. Our findings could enhance the understanding of the mechanism of DR, which might offer a therapeutic target for DR patients.

Maintenance of Enteral ACE2 Prevents Diabetic Retinopathy in Type 1 Diabetes

BACKGROUND

We examined components of systemic and intestinal renin-angiotensin system on gut barrier permeability, glucose homeostasis, systemic inflammation, and progression of diabetic retinopathy (DR) in human subjects and mice with type 1 diabetes (T1D).

METHODS

T1D individual with (n=18) and without (n=20) DR and controls (n=34) were examined for changes in gut-regulated components of the immune system, gut leakage markers (FABP2 [fatty acid binding protein 2] and peptidoglycan), and Ang II (angiotensin II); Akita mice were orally administered a Lactobacillus paracasei (LP) probiotic expressing humanized ACE2 (angiotensin-converting enzyme 2) protein (LP-ACE2) as either a prevention or an intervention. Akita mice with genetic overexpression of humanAce2 by small intestine epithelial cells (Vil-Cre.hAce2KI-Akita) were similarly examined. After 9 months of T1D, circulatory, enteral, and ocular end points were assessed.

RESULTS

T1D subjects exhibit elevations in gut-derived circulating immune cells (ILC1 cells) and higher gut leakage markers, which were positively correlated with plasma Ang II and DR severity. The LP-ACE2 prevention cohort and genetic overexpression of intestinal ACE2 preserved barrier integrity, reduced inflammatory response, improved hyperglycemia, and delayed development of DR. Improvements in glucose homeostasis were due to intestinal MasR activation, resulting in a GSK-3β (glycogen synthase kinase-3 beta)/c-Myc (cellular myelocytomatosis oncogene)-mediated decrease in intestinal glucose transporter expression. In the LP-ACE2 intervention cohort, gut barrier integrity was improved and DR reversed, but no improvement in hyperglycemia was observed. These data support that the beneficial effects of LP-ACE2 on DR are due to the action of ACE2, not improved glucose homeostasis.

CONCLUSIONS

Dysregulated systemic and intestinal renin-angiotensin system was associated with worsening gut barrier permeability, gut-derived immune cell activation, systemic inflammation, and progression of DR in human subjects. In Akita mice, maintaining intestinal ACE2 expression prevented and reversed DR, emphasizing the multifaceted role of the intestinal renin-angiotensin system in diabetes and DR.

Novel biomarkers and therapeutic approaches for diabetic retinopathy and nephropathy: Recent progress and future perspectives

The global burden due to microvascular complications in patients with diabetes mellitus persists and even increases alarmingly, the intervention and management are now encountering many difficulties and challenges. This paper reviews the recent advancement and progress in novel biomarkers, artificial intelligence technology, therapeutic agents and approaches of diabetic retinopathy and nephropathy, providing more insights into the management of microvascular complications.