Key Laboratory of Systems Biomedicine (Ministry of Education), School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Exercise Translational Medicine Center, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China; Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
Key Laboratory of Systems Biomedicine (Ministry of Education), School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Exercise Translational Medicine Center, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
EBioMedicine. 2021 Oct;72:103611. doi: 10.1016/j.ebiom.2021.103611. Epub 2021 Oct 7.
Cardiovascular diseases may originate in childhood. Biomarkers identifying individuals with increased risk for disease are needed to support early detection and to optimise prevention strategies.
In this prospective study, by applying a machine learning to high throughput NMR-based metabolomics data, we identified circulating childhood metabolic predictors of adult cardiovascular disease risk (MetS score) in a cohort of 396 females, followed from childhood (mean age 11·2 years) to early adulthood (mean age 18·1 years). The results obtained from the discovery cohort were validated in a large longitudinal birth cohort of females and males followed from puberty to adulthood (n = 2664) and in four cross-sectional data sets (n = 6341).
The identified childhood metabolic signature included three circulating biomarkers, glycoprotein acetyls (GlycA), large high-density lipoprotein phospholipids (L-HDL-PL), and the ratio of apolipoprotein B to apolipoprotein A-1 (ApoB/ApoA) that were associated with increased cardio-metabolic risk in early adulthood (AUC = 0·641‒0·802, all p<0·01). These associations were confirmed in all validation cohorts with similar effect estimates both in females (AUC = 0·667‒0·905, all p<0·01) and males (AUC = 0·734‒0·889, all p<0·01) as well as in elderly patients with and without type 2 diabetes (AUC = 0·517‒0·700, all p<0·01). We subsequently applied random intercept cross-lagged panel model analysis, which suggested bidirectional causal relationship between metabolic biomarkers and cardio-metabolic risk score from childhood to early adulthood.
These results provide evidence for the utility of a circulating metabolomics panel to identify children and adolescents at risk for future cardiovascular disease, to whom preventive measures and follow-up could be indicated.
This study was financially supported by the Academy of Finland, Ministry of Education of Finland and University of Jyv€askyl€a, the National Nature Science Foundation of China (Grant 31571219), the 111 Project (B17029), the Shanghai Jiao Tong University Zhiyuan Foundation (Grant CP2014013), China Postdoc Scholarship Council (201806230001), the Food and Health Bureau of Hong Kong SAR's Health and Medical Research Fund (HMRF grants 15162161 and 07181036) and the CUHK Direct Grants for Research (2016¢033 and 2018¢034), and a postdoctoral fellowship from K. Carole Ellison (to T.W.). The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. NFBC1966 received financial support from University of Oulu Grant no. 24000692, Oulu University Hospital Grant no. 24301140, ERDF European Regional Development Fund Grant no. 539/2010 A31592. This work was supported by European Union's Horizon 2020 research and innovation programme LongITools 874739.
心血管疾病可能起源于儿童期。需要识别出具有疾病风险增加的个体的生物标志物，以支持早期发现和优化预防策略。
在这项前瞻性研究中，我们通过应用机器学习对高通量基于 NMR 的代谢组学数据进行分析，确定了在一个 396 名女性队列中，从儿童期（平均年龄 11.2 岁）到成年早期（平均年龄 18.1 岁），成年心血管疾病风险（代谢综合征评分）的儿童期循环代谢预测因子。从发现队列中获得的结果在一个随访女性和男性从青春期到成年的大型纵向出生队列（n=2664）和四个横断面数据集（n=6341）中进行了验证。
确定的儿童代谢特征包括三种循环生物标志物，糖蛋白乙酰基（GlycA）、大高密度脂蛋白磷脂（L-HDL-PL）和载脂蛋白 B 与载脂蛋白 A-1 的比值（ApoB/ApoA），它们与成年早期的心血管代谢风险增加相关（AUC=0.641-0.802，均 p<0.01）。这些关联在所有验证队列中均得到了证实，在女性（AUC=0.667-0.905，均 p<0.01）和男性（AUC=0.734-0.889，均 p<0.01）以及患有和不患有 2 型糖尿病的老年患者中（AUC=0.517-0.700，均 p<0.01），均观察到了类似的效应估计值。随后我们应用随机截距交叉滞后面板模型分析，表明代谢生物标志物和心血管代谢风险评分之间存在从儿童期到成年早期的双向因果关系。
这些结果为使用循环代谢组学面板识别未来心血管疾病风险的儿童和青少年提供了证据，对这些患者可以进行预防性措施和随访。
本研究得到了芬兰科学院、芬兰教育部和于韦斯屈莱大学、中国国家自然科学基金（Grant 31571219）、111 项目（B17029）、上海交通大学致远基金会（Grant CP2014013）、中国博士后奖学金委员会（201806230001）、香港特别行政区食物及卫生局卫生及医疗研究基金（HMRF 拨款 15162161 和 07181036）和香港中文大学直接研究拨款（2016¢033 和 2018¢034）的支持。英国医学研究理事会和惠康基金会（Grant ref: 217065/Z/19/Z）和布里斯托大学为 ALSPAC 提供了核心支持。NFBC1966 得到了奥卢大学 24000692 号赠款、奥卢大学医院 24301140 号赠款和欧洲区域发展基金 539/2010 A31592 号赠款的支持。这项工作得到了欧盟地平线 2020 研究和创新计划 LongITools 874739 的支持。
