Machine learning-based multi-omics models for diagnostic classification and risk stratification in diabetic kidney disease

Xian Shao; Suhua Gao; Pufei Bai; Qian Yang; Yao Lin; Mingzhen Pang; Weixi Wu; Lihua Wang; Ying Li; Saijun Zhou; Hongyan Liu; Pei Yu

doi:10.1002/ctm2.70133

Clinical and Translational Medicine ›› 2025, Vol. 15 ›› Issue (1) : e70133 DOI: 10.1002/ctm2.70133

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Machine learning-based multi-omics models for diagnostic classification and risk stratification in diabetic kidney disease

Xian Shao ¹^,²^,³
, Suhua Gao ¹^,²
, Pufei Bai ¹^,²
, Qian Yang ¹^,²
, Yao Lin ¹^,²
, Mingzhen Pang ³
, Weixi Wu ¹^,²
, Lihua Wang ⁴
, Ying Li ⁴
, Saijun Zhou ¹^,²
, Hongyan Liu ¹^,²
, Pei Yu ¹^,²^,⁴^,^†

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Xian Shao, Suhua Gao, Pufei Bai, Qian Yang, Yao Lin, Mingzhen Pang, Weixi Wu, Lihua Wang, Ying Li, Saijun Zhou, Hongyan Liu, Pei Yu. Machine learning-based multi-omics models for diagnostic classification and risk stratification in diabetic kidney disease. Clinical and Translational Medicine, 2025, 15(1): e70133 DOI:10.1002/ctm2.70133

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References

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[1]	Kovesdy CP. Epidemiology of chronic kidney disease: an update 2022. Kidney Int Suppl. 2022;12(1):7-11.

[2]	Kidney Disease: Improving Global Outcomes Diabetes Work G. KDIGO 2022 clinical practice guideline for diabetes management in chronic kidney disease. Kidney Int. 2022;102(5S):S1-S127.

[3]	Skolnik NS, Style AJ. Importance of early screening and diagnosis of chronic kidney disease in patients with type 2 diabetes. Diabetes Ther. 2021;12(6):1613-1630.

[4]	Eddy S, Mariani LH, Kretzler M. Integrated multi-omics approaches to improve classification of chronic kidney disease. Nat Rev Nephrol. 2020;16(11):657-668.

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2025 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.