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Войти в исследования Journal of diabetes and its complications · 2025
Serum analysis of type 2 diabetes mellitus patients with low, moderate, and high risk of diabetic kidney disease using LC-MS metabolomics approach.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Pohan Salwa Dilfari, Sauriasari Rani, Maggadani Baitha Palanggatan, Rukmana Taufiq Indra, Mahdiyah Farah, Yusrina Fathia, Hayati Sri, James Richard Johari, Rofiee Mohd Salleh, Kek Teh Lay, Salleh Mohd Zaki
Аннотация
OBJECTIVES: This study investigated the relationship between serum metabolomic profiles and diabetic kidney disease (DKD) risk in patients with type 2 diabetes mellitus (T2DM) stratified into three KDIGO-defined risk categories.
METHODS: Serum samples from 48 patients were analyzed using untargeted liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS). Participants were classified into low (n = 16), moderate (n = 16), and high (n = 16) DKD risk groups according to KDIGO guidelines. Differential metabolites were identified based on p-value, log fold change, and variable importance in projection (VIP), and subsequently subjected to pathway enrichment analysis.
RESULTS: Comparative analysis revealed five differential metabolites between low- and moderate-risk groups, three between moderate- and high-risk groups, and three between low- and high-risk groups. Notably, sphinganine, arachidonic acid, and ornithine were progressively downregulated, whereas AFMK, L-arginine, lactosylceramide (LacCer), and lysophosphatidylcholine (lysoPC) were upregulated with increasing DKD risk. These metabolites mapped to disturbed pathways, including arginine and proline metabolism, sphingolipid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism, and tryptophan metabolism.
CONCLUSION: This study highlights progressive alterations in amino acid, lipid, and inflammatory pathways across DKD risk categories, suggesting that these stage-specific metabolomic signatures may serve as putative biomarkers for detection and monitoring of DKD progression in T2DM.
METHODS: Serum samples from 48 patients were analyzed using untargeted liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS). Participants were classified into low (n = 16), moderate (n = 16), and high (n = 16) DKD risk groups according to KDIGO guidelines. Differential metabolites were identified based on p-value, log fold change, and variable importance in projection (VIP), and subsequently subjected to pathway enrichment analysis.
RESULTS: Comparative analysis revealed five differential metabolites between low- and moderate-risk groups, three between moderate- and high-risk groups, and three between low- and high-risk groups. Notably, sphinganine, arachidonic acid, and ornithine were progressively downregulated, whereas AFMK, L-arginine, lactosylceramide (LacCer), and lysophosphatidylcholine (lysoPC) were upregulated with increasing DKD risk. These metabolites mapped to disturbed pathways, including arginine and proline metabolism, sphingolipid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism, and tryptophan metabolism.
CONCLUSION: This study highlights progressive alterations in amino acid, lipid, and inflammatory pathways across DKD risk categories, suggesting that these stage-specific metabolomic signatures may serve as putative biomarkers for detection and monitoring of DKD progression in T2DM.