OBJECTIVE Plasma protein N-glycan profiling integrates information on enzymatic protein glycosylation, which is a highly controlled ubiquitous posttranslational modification. Here we investigate the ability of the plasma N-glycome to predict incidence of type 2 diabetes and cardiovascular diseases (CVDs; i.e., myocardial infarction and stroke). RESEARCH DESIGN AND METHODS Based on the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort (n = 27,548), we constructed case-cohorts including a random subsample of 2,500 participants and all physician-verified incident cases of type 2 diabetes (n = 820; median follow-up time 6.5 years) and CVD (n = 508; median follow-up time 8.2 years). Information on the relative abundance of 39 N-glycan groups in baseline plasma samples was generated by chromatographic profiling. We selected predictive N-glycans for type 2 diabetes and CVD separately, based on cross-validated machine learning, nonlinear model building, and construction of weighted prediction scores. This workflow for CVD was applied separately in men and women. RESULTS The N-glycan-based type 2 diabetes score was strongly predictive for diabetes risk in an internal validation cohort (weighted C-index 0.83, 95% CI 0.78-0.88), and this finding was externally validated in the Finland Cardiovascular Risk Study (FINRISK) cohort. N-glycans were moderately predictive for CVD incidence (weighted C-indices 0.66, 95% CI 0.60-0.72, for men; 0.64, 95% CI 0.55-0.73, for women). Information on the selected N-glycans improved the accuracy of established and clinically applied risk prediction scores for type 2 diabetes and CVD. CONCLUSIONS Selected N-glycans improve type 2 diabetes and CVD prediction beyond established risk markers. Plasma protein N-glycan profiling may thus be useful for risk stratification in the context of precisely targeted primary prevention of cardiometabolic diseases.

中文翻译：

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血浆N-糖类作为心脏代谢风险的新兴生物标记：EPIC-Potsdam队列研究中的前瞻性研究。

目的血浆蛋白N-聚糖谱分析整合了有关酶蛋白糖基化的信息，这是高度受控的普遍存在的翻译后修饰。在这里，我们研究血浆N-糖蛋白预测2型糖尿病和心血管疾病（CVD；即，心肌梗塞和中风）的发生率的能力。研究设计和方法基于欧洲前瞻性癌症前瞻性调查（EPIC）-波茨坦队列（n = 27,548），我们构建了病例队列，其中包括2,500名参与者的随机子样本以及所有经医生验证的2型糖尿病（n = 820；中位随访时间为6.5年）和CVD（n = 508；中位随访时间为8.2年）。有关基线血浆样品中39个N-聚糖基团的相对丰度的信息是通过色谱分析得出的。基于交叉验证的机器学习，非线性模型构建和加权预测得分的构建，我们分别为2型糖尿病和CVD选择了预测性N-聚糖。CVD的这一工作流程分别适用于男性和女性。结果在内部验证队列中，基于N-聚糖的2型糖尿病评分强烈预测了糖尿病风险（加权C指数0.83，95％CI 0.78-0.88），这一发现在芬兰心血管风险研究中得到了外部验证（ FINRISK）队列。N-聚糖可适度预测CVD的发生率（男性加权C指数为0.66，95％CI为0.60-0.72；女性为0.64，95％CI为0.55-0.73）。有关所选N-聚糖的信息可提高2型糖尿病和CVD的既定和临床应用风险预测评分的准确性。结论选定的N-聚糖可改善2型糖尿病和CVD的预测，超越已建立的危险指标。因此，在精确定位心血管代谢疾病一级预防的背景下，血浆蛋白N-聚糖谱分析可用于风险分层。