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Series GSE215221 Query DataSets for GSE215221
Status Public on Oct 12, 2022
Title Discovery of a Trans-Omics Biomarker Signature that Predisposes High Risk Diabetic Patients to Diabetic Kidney Disease
Organism Homo sapiens
Experiment type Genome variation profiling by array
SNP genotyping by SNP array
Summary 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.
Overall design The genomic DNA was collected from peripheral white blood cells using the phenol/chloroform DNA extraction method after lysis of red blood cells. Each subject was genotyped using Axiom Genome-Wide TWB 2.0 array plates. SNPs were excluding with a minor allele frequency rate of 0 or SNPs with a missing rate of more than 10%.
Contributor(s) Sytwu H, Tsai T, Lai C
Citation(s) 36323795
Submission date Oct 11, 2022
Last update date Nov 18, 2022
Contact name Paul Wei-Che Hsu
Phone +886-37-206-166
Organization name National Health Research Institutes
Department National Health Research Institutes
Lab R2-3023
Street address Zhunan: 35, Keyan Road, Zhunan Town
City Miaoli County
State/province Taiwan
ZIP/Postal code 350
Country Taiwan
Platforms (1)
GPL32738 [Axiom_TWB_2] Affymetrix Axiom
Samples (616)
GSM6628149 control_1
GSM6628150 control_2
GSM6628151 control_3
BioProject PRJNA889394

Download family Format
SOFT formatted family file(s) SOFTHelp
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Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE215221_RAW.tar 7.1 Gb (http)(custom) TAR (of CEL)
GSE215221_process_data.txt.gz 100.3 Mb (ftp)(http) TXT
Processed data are available on Series record

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