Identification of serum metabolome signatures associated with retinal and renal complications of type 2 diabetes

鉴定与 2 型糖尿病视网膜和肾脏并发症相关的血清代谢物特征

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作者:Yoshihiko Tomofuji #, Ken Suzuki #, Toshihiro Kishikawa, Nobuhiro Shojima, Jun Hosoe, Kyoko Inagaki, Sunao Matsubayashi, Hisamitsu Ishihara, Hirotaka Watada, Yasushi Ishigaki; BioBank Japan Project; Hidenori Inohara, Yoshinori Murakami, Koichi Matsuda, Yukinori Okada, Toshimasa Yamauchi, Takashi Kad

Background

Type 2 diabetes is a common disease around the world and its major complications are diabetic retinopathy (DR) and diabetic kidney disease (DKD). Persons with type 2 diabetes with complications, especially who have both DR and DKD, have poorer prognoses than those without complications. Therefore, prevention and early identification of the complications of type 2 diabetes are necessary to improve the prognosis of persons with type 2 diabetes. The

Conclusions

Our metabolome analysis identifies the serum metabolite features of the persons with type 2 diabetes with multiple complications, which could potentially be used as biomarkers.

Methods

We profiled serum metabolites of persons with type 2 diabetes with both DR and DKD (N = 141) and without complications (N = 159) using a comprehensive non-targeted metabolomics approach with mass spectrometry. Based on the serum metabolite profiles, case-control comparisons and metabolite set enrichment analysis (MSEA) were performed.

Results

Here we show that five metabolites (cyclohexylamine, P = 4.5 × 10-6; 1,2-distearoyl-glycero-3-phosphocholine, P = 7.3 × 10-6; piperidine, P = 4.8 × 10-4; N-acetylneuraminic acid, P = 5.1 × 10-4; stearoyl ethanolamide, P = 6.8 × 10-4) are significantly increased in those with the complications. MSEA identifies fatty acid biosynthesis as the type 2 diabetes complications-associated biological pathway (P = 0.0020). Conclusions: Our metabolome analysis identifies the serum metabolite features of the persons with type 2 diabetes with multiple complications, which could potentially be used as biomarkers.

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