Dataset Information

Early Proteomic and Metabolomic Signatures in Diabetes Associated with Progression to Diabetic Retinopathy Over 1–2 Years

ABSTRACT: In this study, we leveraged 15 paired plasma samples collected from the same individuals who exhibited rapid progression from DM to DR within 1-2 years and conducted integrated proteomic and metabolomic analyses to investigate molecular changes associated with disease progression. Key proteins were validated by ELISA in an independent cohort of 22 DM and 22 NPDR patients and their predictive performance was assessed using receiver operating characteristic (ROC) curve analysis. The results revealed significant alterations at both the protein and metabolite levels during the progression from DM to DR. Among the validated proteins, three ((ANXA1, GPX1, and GSTM1) were previously reported in DR, while five (ALDH18A1, GALT, PRG2, UBA1, and GCA) represent newly identified candidates. ROC analysis demonstrated strong discriminative performance for these proteins, with area under the curve (AUC) values ranging from 0.853 to 0.945. These findings provide insight into the molecular mechanisms underlying early DR progre

ORGANISM(S): Homo Sapiens

SUBMITTER: Jing Zhao

PROVIDER: PXD078538 | iProX | Tue May 19 00:00:00 GMT+01:00 2026

REPOSITORIES: iProX

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Publications

Early proteomic and metabolomic signatures in diabetes associated with progression to diabetic retinopathy over 1-2 years.

Zhao Jing J Cao Yitao Y Chai Zhi Z Xie Hanghang H Gu Dandan D Zhang Yanchun Y Niu Dongling D Liu Hongli H Lei Ting T

Frontiers in endocrinology 20260610

<h4>Background</h4>The progression from diabetes mellitus (DM) to diabetic retinopathy (DR) represents a dynamic pathological process in which vascular and metabolic alterations gradually lead to retinal damage. Understanding the molecular changes during this transition is critical for timely identification and intervention.<h4>Methods</h4>We performed DIA-based proteomics and untargeted metabolomics on longitudinal paired plasma samples from 15 DM patients who developed DR during 1-2 years. Dif ...[more]

PMID: 42358678

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Project description:Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features give an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features give an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.

Dataset Information

Early Proteomic and Metabolomic Signatures in Diabetes Associated with Progression to Diabetic Retinopathy Over 1–2 Years

Publications

Early proteomic and metabolomic signatures in diabetes associated with progression to diabetic retinopathy over 1-2 years.

Similar Datasets

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