Kidney Week

Abstract: SA-PO313

Artificial Intelligence in the Identification of Key Metabolites Associated with Diabetic Kidney Disease

Session Information

• Diabetic Kidney Disease: Clinical Pathology, Diagnostic and Treatment Advances
  October 26, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Diabetic Kidney Disease

• 702 Diabetic Kidney Disease: Clinical

Authors

• Jung, Inha, Korea University Ansan Hospital, Ansan, Gyeonggi-do, Korea (the Republic of)

Inha Jung, MD, PhD

• Park, Sungjin, Korea University Anam Hospital, Seoul, Korea (the Republic of)

Sungjin Park

• Kwon, Soon hyo, Soonchunhyang University Hospital, Yongsan-gu, Seoul, Korea (the Republic of)

Soon hyo Kwon, MD

• Seo, Ji A, Korea University Ansan Hospital, Ansan, Gyeonggi-do, Korea (the Republic of)

Ji A Seo, MD

• Park, Hyeong-Kyu, Soonchunhyang University Hospital, Yongsan-gu, Seoul, Korea (the Republic of)

Hyeong-Kyu Park, MD

• Kim, Nan Hee, Korea University Ansan Hospital, Ansan, Gyeonggi-do, Korea (the Republic of)

Nan Hee Kim, MD

Background

Despite the increasing prevalence of DKD, reliable biomarkers for its early detection remain scarce. This study aimed to identify urine and serum metabolites that differentiate between DKD stages through bioinformatics analysis.

Methods

We analyzed 92 participants, categorizing them by eGFR and ACR combinations to identify criteria that best distinguish metabolites. Using these criteria, we trained and validated separate AI models. The criterion with the highest accuracy was selected for further investigation of metabolites that varied under this criterion. To understand the relationships and functions of these metabolites, we conducted a metabolic network analysis.

Results

The classification based on ACR achieved the highest prediction accuracy. We identified nine urine and thirteen serum metabolites common among the top 20 from each group. Four metabolites—Adenosine and 5-MTA in urine, and m2,2G and cis-Aconitic acid in serum—demonstrated significant differences across ACR groups (Fig 1). Metabolic network analysis revealed hub proteins and networks linking these metabolites, with significant mRNA expression differences in hub proteins between healthy controls and those with DKD in both urine and serum networks (Fig 2). Notably, IL4I1 mRNA was identified in both urine and serum.

Conclusion

The ACR-based classification demonstrated the highest accuracy. Urinary adenosine and 5-MTA, and serum m2,2G and cis-aconitic acid showed distinct patterns across ACR groups, highlighting their potential as biomarkers. Metabolic network analysis revealed hub proteins, including IL4I1, and networks connecting these differentially expressed metabolites. Further investigation into IL4I1's involvement in DKD is recommended.

Differentially expressed metabolites according to DKD stages in urine (A) and serum (B).

Urine (A) and serum (B) metabolic networks.

Funding

• Government Support – Non-U.S.