Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
Diabetes Care. 2018 Aug;41(8):1765-1775. doi: 10.2337/dc18-0030. Epub 2018 Jun 21.
Because quantifying glycans with complex structures is technically challenging, little is known about the association of glycosylation profiles with the renal prognosis in diabetic kidney disease (DKD).
In 675 patients with type 2 diabetes, we assessed the baseline urinary glycan signals binding to 45 lectins with different specificities. The end point was a decrease of estimated glomerular filtration rate (eGFR) by ≥30% from baseline or dialysis for end-stage renal disease.
During a median follow-up of 4.0 years, 63 patients reached the end point. Cox proportional hazards analysis revealed that urinary levels of glycans binding to six lectins were significantly associated with the outcome after adjustment for known indicators of DKD, although these urinary glycans, except that for DBA, were highly correlated with baseline albuminuria and eGFR. Hazard ratios for these lectins were (+1 SD for the glycan index) as follows: SNA (recognizing glycan Siaα2-6Gal/GalNAc), 1.42 (95% CI 1.14-1.76); RCA120 (Galβ4GlcNAc), 1.28 (1.01-1.64); DBA (GalNAcα3GalNAc), 0.80 (0.64-0.997); ABA (Galβ3GalNAc), 1.29 (1.02-1.64); Jacalin (Galβ3GalNAc), 1.30 (1.02-1.67); and ACA (Galβ3GalNAc), 1.32 (1.04-1.67). Adding these glycan indexes to a model containing known indicators of progression improved prediction of the outcome (net reclassification improvement increased by 0.51 [0.22-0.80], relative integrated discrimination improvement increased by 0.18 [0.01-0.35], and the Akaike information criterion decreased from 296 to 287).
The urinary glycan profile identified in this study may be useful for predicting renal prognosis in patients with type 2 diabetes. Additional investigation of glycosylation changes and urinary glycan excretion in DKD is needed.
由于对具有复杂结构的糖进行定量具有技术挑战性，因此，人们对糖基化谱与糖尿病肾病（DKD）的肾脏预后之间的关联知之甚少。
在 675 例 2 型糖尿病患者中，我们评估了基线时与 45 种具有不同特异性的凝集素结合的尿糖信号。终点是估计肾小球滤过率（eGFR）从基线下降≥30％或因终末期肾脏疾病进行透析。
在中位随访 4.0 年期间，有 63 例患者达到了终点。Cox 比例风险分析显示，在调整 DKD 的已知指标后，与结果相关的六种凝集素结合的尿糖水平具有统计学意义，但除 DBA 外，这些尿糖与基线白蛋白尿和 eGFR 高度相关。这些凝集素的危险比（糖基指数增加 1 个标准差）如下：SNA（识别糖 Siaα2-6Gal/GalNAc），1.42（95％CI 1.14-1.76）；RCA120（Galβ4GlcNAc），1.28（1.01-1.64）；DBA（GalNAcα3GalNAc），0.80（0.64-0.997）；ABA（Galβ3GalNAc），1.29（1.02-1.64）；Jacalin（Galβ3GalNAc），1.30（1.02-1.67）；和 ACA（Galβ3GalNAc），1.32（1.04-1.67）。将这些糖基指数添加到包含进展指标的模型中，改善了对结果的预测（净重新分类改善增加了 0.51 [0.22-0.80]，综合判别改善增加了 0.18 [0.01-0.35]，Akaike 信息准则从 296 降低至 287）。
本研究中鉴定的尿糖谱可能有助于预测 2 型糖尿病患者的肾脏预后。需要进一步研究 DKD 中的糖基化变化和尿糖排泄。
