Deciphering disease-specific glycosylation: unraveling diabetes subtypes through serum glycopattern.

IF 3.8 2区 化学 Q1 BIOCHEMICAL RESEARCH METHODS
Rumeng Zhang, Yu Zhou, Shengye Wen, Yan Chen, Jing Du, Junfeng Ma, Jun Xia, Shuang Yang
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Abstract

Latent autoimmune diabetes in adults (LADA) is a slowly progressing form of diabetes that develops in adulthood, characterized by autoimmune destruction of pancreatic β-cells and subsequent insulin deficiency, akin to type 1 diabetes (T1D). Due to its shared genetic, immunological, and metabolic features with both T1D and type 2 diabetes (T2D), LADA is frequently misdiagnosed and inappropriately treated as T2D. To address this, we developed the A.NG algorithm, which identifies serum glycopatterns by calculating the ratio of upregulated to downregulated N-glycans, thereby facilitating the detection of subtle glycan alterations specific to each diabetes subtype. Our method, which utilizes matrix-assisted laser desorption ionization (MALDI) for N-glycan profiling, revealed distinct glycan patterns across T1D, T2D, and LADA, with observed correlations achieving an AUC of 0.918 in this cohort. While these findings demonstrate the technical feasibility of detecting subtype-associated glycosylation changes, their clinical utility for subtype differentiation requires validation in larger studies with refined quantification approaches. Furthermore, complementary ELISA and intact glycopeptide analyses showed that enzymes like FUT8 and FUCA1 contribute to altered glycan expression patterns on specific glycoproteins, which could serve as potential biomarkers for LADA. In conclusion, the A.NG algorithm represents a promising novel approach for distinguishing between LADA and T1D or T2D, with the potential to significantly improve the diagnosis and management of these diabetes subtypes.

解读疾病特异性糖基化:通过血清血糖模式揭示糖尿病亚型。
成人潜伏性自身免疫性糖尿病(LADA)是一种进展缓慢的糖尿病,发生于成年期,特点是自身免疫破坏胰腺β细胞,随后出现胰岛素缺乏,类似于1型糖尿病(T1D)。由于其与T1D和2型糖尿病(T2D)具有相同的遗传、免疫和代谢特征,LADA经常被误诊为T2D,并被不恰当地治疗。为了解决这个问题,我们开发了a.g.算法,该算法通过计算n -聚糖上调与下调的比例来识别血清血糖模式,从而促进检测每种糖尿病亚型特异性的细微聚糖改变。我们的方法利用基质辅助激光解吸电离(MALDI)进行n -聚糖谱分析,揭示了T1D、T2D和LADA中不同的聚糖模式,在该队列中观察到的相关性达到了0.918。虽然这些发现证明了检测亚型相关糖基化变化的技术可行性,但它们在亚型分化方面的临床应用需要在更大规模的研究中进行验证,并采用更精确的量化方法。此外,互补ELISA和完整糖肽分析表明,FUT8和FUCA1等酶有助于改变特定糖蛋白上的聚糖表达模式,这可能是LADA的潜在生物标志物。总之,A.NG算法代表了一种很有前途的区分LADA和T1D或T2D的新方法,具有显著改善这些糖尿病亚型的诊断和管理的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.00
自引率
4.70%
发文量
638
审稿时长
2.1 months
期刊介绍: Analytical and Bioanalytical Chemistry’s mission is the rapid publication of excellent and high-impact research articles on fundamental and applied topics of analytical and bioanalytical measurement science. Its scope is broad, and ranges from novel measurement platforms and their characterization to multidisciplinary approaches that effectively address important scientific problems. The Editors encourage submissions presenting innovative analytical research in concept, instrumentation, methods, and/or applications, including: mass spectrometry, spectroscopy, and electroanalysis; advanced separations; analytical strategies in “-omics” and imaging, bioanalysis, and sampling; miniaturized devices, medical diagnostics, sensors; analytical characterization of nano- and biomaterials; chemometrics and advanced data analysis.
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