Pujos-Guillot Estelle, Pétéra Mélanie, Jacquemin Jérémie, Centeno Delphine, Lyan Bernard, Montoliu Ivan, Madej Dawid, Pietruszka Barbara, Fabbri Cristina, Santoro Aurelia, Brzozowska Anna, Franceschi Claudio, Comte Blandine
Université Clermont Auvergne, Institut National de la Recherche Agronomique, Unité de Nutrition Humaine, Centre Auvergne Rhône Alpes, Clermont-Ferrand, France.
Université Clermont Auvergne, Institut National de la Recherche Agronomique, Unité de Nutrition Humaine, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France.
Front Physiol. 2019 Jan 24;9:1903. doi: 10.3389/fphys.2018.01903. eCollection 2018.
Aging is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases thus contributing to elderly morbidity and mortality. Pre-frailty is still not well understood but it has been associated with global imbalance in several physiological systems, including inflammation, and in nutrition. Due to the complex phenotypes and underlying pathophysiology, the need for robust and multidimensional biomarkers is essential to move toward more personalized care. The objective of the present study was to better characterize the complexity of pre-frailty phenotype using untargeted metabolomics, in order to identify specific biomarkers, and study their stability over time. The approach was based on the NU-AGE project (clinicaltrials.gov, NCT01754012) that regrouped 1,250 free-living elderly people (65-79 y.o., men and women), free of major diseases, recruited within five European centers. Half of the volunteers were randomly assigned to an intervention group (1-year Mediterranean type diet). Presence of frailty was assessed by the criteria proposed by Fried et al. (2001). In this study, a sub-cohort consisting in 212 subjects (pre-frail and non-frail) from the Italian and Polish centers were selected for untargeted serum metabolomics at T0 (baseline) and T1 (follow-up). Univariate statistical analyses were performed to identify discriminant metabolites regarding pre-frailty status. Predictive models were then built using linear logistic regression and ROC curve analyses were used to evaluate multivariate models. Metabolomics enabled to discriminate sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility. The best resulting models included four different metabolites for each gender. They showed very good prediction capacity with AUCs of 0.93 (95% CI = 0.87-1) and 0.94 (95% CI = 0.87-1) for men and women, respectively. Additionally, early and/or predictive markers of pre-frailty were identified for both genders and the gender specific models showed also good performance (three metabolites; AUC = 0.82; 95% CI = 0.72-0.93) for men and very good for women (three metabolites; AUC = 0.92; 95% CI = 0.86-0.99). These results open the door, through multivariate strategies, to a possibility of monitoring the disease progression over time at a very early stage.
衰老为一个动态过程，取决于内在和外在因素，其演变是一个连续的转变过程，涉及多个层面的多方面进程。人们认识到，衰弱和肌肉减少症在主要的年龄相关疾病中都存在，从而导致老年人发病和死亡。衰弱前期仍未得到充分理解，但它与包括炎症和营养在内的多个生理系统的整体失衡有关。由于其复杂的表型和潜在的病理生理学，需要强大的多维生物标志物才能朝着更个性化的护理方向发展。本研究的目的是使用非靶向代谢组学更好地表征衰弱前期表型的复杂性，以识别特定的生物标志物，并研究它们随时间的稳定性。该方法基于NU-AGE项目（clinicaltrials.gov，NCT01754012），该项目汇集了1250名无重大疾病的自由生活老年人（65 - 79岁，男女皆有），这些人是在五个欧洲中心招募的。一半的志愿者被随机分配到干预组（为期1年的地中海式饮食）。衰弱的存在通过Fried等人（2001年）提出的标准进行评估。在本研究中，从意大利和波兰中心选取了一个由212名受试者（衰弱前期和非衰弱）组成的亚队列，在T0（基线）和T1（随访）时进行非靶向血清代谢组学分析。进行单变量统计分析以识别与衰弱前期状态相关的判别代谢物。然后使用线性逻辑回归建立预测模型，并使用ROC曲线分析来评估多变量模型。代谢组学能够在性别层面以及根据衰弱前期的进展和可逆性来区分衰弱前期的亚表型。最佳的最终模型在男性和女性中各包含四种不同的代谢物。它们显示出非常好的预测能力，男性和女性的AUC分别为0.93（95%CI = 0.87 - 1）和0.94（95%CI = 0.87 - 1）。此外，还为男性和女性确定了衰弱前期的早期和/或预测性标志物，性别特异性模型在男性中也表现良好（三种代谢物；AUC = 0.82；95%CI = 0.72 - 0.93），在女性中表现非常好（三种代谢物；AUC = 0.92；95%CI = 0.86 - 0.99）。这些结果通过多变量策略为在极早期阶段监测疾病随时间的进展提供了可能性。
