Fernández-Duval Gonzalo, Razquin Cristina, Wang Fenglei, Yun Huan, Hu Jie, Guasch-Ferré Marta, Rexrode Kathryn, Balasubramanian Raji, García-Gavilán Jesús, Ruiz-Canela Miguel, Clish Clary B, Corella Dolores, Gómez-Gracia Enrique, Fiol Miquel, Estruch Ramón, Lapetra José, Fitó Montse, Serra-Majem Luis, Ros Emilio, Liang Liming, Dennis Courtney, Asensio Eva M, Castañer Olga, Planes Francisco J, Salas-Salvadó Jordi, Hu Frank B, Toledo Estefanía, Martínez-González Miguel A
Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IdiSNA), University of Navarra, Pamplona, Spain; Institute of Data Science and Artificial Intelligence (DATAI), University of Navarra, Pamplona, Spain.
Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IdiSNA), University of Navarra, Pamplona, Spain; Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
Metabolism. 2025 Sep;170:156195. doi: 10.1016/j.metabol.2025.156195. Epub 2025 Mar 17.
Metabolome-based biomarkers contribute to identify mechanisms of disease and to a better understanding of overall mortality. In a long-term follow-up subsample (n = 1878) of the PREDIMED trial, among 337 candidate baseline plasma metabolites repeatedly assessed at baseline and after 1 year, 38 plasma metabolites were identified as predictors of all-cause mortality. Gamma-amino-butyric acid (GABA), homoarginine, serine, creatine, 1-methylnicotinamide and a set of sphingomyelins, plasmalogens, phosphatidylethanolamines and cholesterol esters were inversely associated with all-cause mortality, whereas plasma dimethylguanidino valeric acid (DMGV), choline, short and long-chain acylcarnitines, 4-acetamidobutanoate, pseudouridine, 7-methylguanine, N6-acetyllysine, phenylacetylglutamine and creatinine were associated with higher mortality. The multi-metabolite signature created as a linear combination of these selected metabolites, also showed a strong association with all-cause mortality using plasma samples collected at 1-year follow-up in PREDIMED. This association was subsequently confirmed in 4 independent American cohorts, validating the signature as a consistent predictor of all-cause mortality across diverse populations.
基于代谢组的生物标志物有助于识别疾病机制并更好地理解全因死亡率。在PREDIMED试验的长期随访子样本(n = 1878)中,在基线和1年后反复评估的337种候选基线血浆代谢物中,有38种血浆代谢物被确定为全因死亡率的预测指标。γ-氨基丁酸(GABA)、高精氨酸、丝氨酸、肌酸、1-甲基烟酰胺以及一组鞘磷脂、缩醛磷脂、磷脂酰乙醇胺和胆固醇酯与全因死亡率呈负相关,而血浆二甲基胍基戊酸(DMGV)、胆碱、短链和长链酰基肉碱、4-乙酰氨基丁酸、假尿苷、7-甲基鸟嘌呤、N6-乙酰赖氨酸、苯乙酰谷氨酰胺和肌酐与较高的死亡率相关。作为这些选定代谢物的线性组合创建的多代谢物特征,在PREDIMED的1年随访中使用收集的血浆样本时,也显示出与全因死亡率有很强的相关性。这种关联随后在4个独立的美国队列中得到证实,验证了该特征作为不同人群全因死亡率的一致预测指标。