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Identification of ethanolamine as a biomarker and therapeutic potential for diabetic retinopathy in glucose well-controlled diabetic patients

ABSTRACT: Diabetic retinopathy (DR) is the leading cause of blindness in the working-age population, and there are no diagnostic biomarkers and intervention methods for DR developed from glucose well-controlled diabetic patients (GW-DR) to date. Here, we established a prospective cohort of 2126 diabetic Chinese adults (DM) including1134 subjects without ocular diseases at baseline. After 2-year follow-up, 42 participants (21 new-onset GW-DR and 21 matched control) with HbA1c persistently less than 7% were enrolled to profile the serum metabolic features of GW-DR through targeted metabolomics. Among different levels of metabolites, the lower level of ethanolamine with an AUC of 0.954 was significantly associated with early-stage GW-DR risk compared to HbA1c with a 0.506 AUC. Moreover, ethanolamine attenuated diabetic retinal inflammation in STZ-induced diabetic rats with inactivating microglia and DAG-PKC pathway remodeling. In conclusion, ethanolamine, as a potential metabolic biomarker for early diagnosis of GW-DR, may provide a biomarker-based treatment for DR prevention. We then performed gene expression profiling analysis using data obtained from RNA-seq of 4 different cells at two time points.

ORGANISM(S): Mus musculus

PROVIDER: GSE217573 | GEO | 2022/11/13

REPOSITORIES: GEO

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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.

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Identification of ethanolamine as a biomarker and therapeutic potential for diabetic retinopathy in glucose well-controlled diabetic patients

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