Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease

Author:

Wu I-Wen,Tsai Tsung-Hsien,Lo Chi-Jen,Chou Yi-Ju,Yeh Chi-Hsiao,Chan Yun-Hsuan,Chen Jun-Hong,Hsu Paul Wei-Che,Pan Heng-Chih,Hsu Heng-Jung,Chen Chun-Yu,Lee Chin-Chan,Shyu Yu-Chiau,Lin Chih-Lang,Cheng Mei-Ling,Lai Chi-Chun,Sytwu Huey-Kang,Tsai Ting-FenORCID

Abstract

AbstractDiabetic 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 gives 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 gives 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.

Funder

Chang Gung Memorial Hospital

Ministry of Health and Welfare

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Computer Science Applications,Medicine (miscellaneous)

Reference45 articles.

1. WHO. Improving Health Outcomes of People with Diabetes Mellitus: Target Setting to Reduce the Global Burden of Diabetes Mellitus by 2030. https://www.who.int/publications/m/item/improving-health-outcomes-of-people-with-diabetes-mellitus (2021).

2. Federation, I. D. IDF Diabetes Atlas. 9th edn 2019 (accessed 13 September 2021) www.diabetesatlas.org.

3. Alicic, R. Z., Cox, E. J., Neumiller, J. J. & Tuttle, K. R. Incretin drugs in diabetic kidney disease: biological mechanisms and clinical evidence. Nat. Rev. Nephrol. 17, 227–244 (2021).

4. System, U. S. R. D. 2020 USRDS Annual Data Report: Epidemiology of kidney disease in the United States. (National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD).

5. Webster, A. C., Nagler, E. V., Morton, R. L. & Masson, P. Chronic kidney disease. Lancet 389, 1238–1252 (2017).

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3