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A Metabolomic Severity Score for Airflow Obstruction and Emphysema

机译:气流阻塞和肺气肿的代谢组学严重程度评分

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摘要

Chronic obstructive pulmonary disease (COPD) is a disease with marked metabolic disturbance. Previous studies have shown the association between single metabolites and lung function for COPD, but whether a combination of metabolites could predict phenotype is unknown. We developed metabolomic severity scores using plasma metabolomics from the Metabolon platform from two US cohorts of ever-smokers: the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) (n = 648; training/testing cohort; 72% non-Hispanic, white; average age 63 years) and the COPDGene Study (n = 1120; validation cohort; 92% non-Hispanic, white; average age 67 years). Separate adaptive LASSO (adaLASSO) models were used to model forced expiratory volume at one second (FEV1) and MESA-adjusted lung density using 762 metabolites common between studies. Metabolite coefficients selected by the adaLASSO procedure were used to create a metabolomic severity score (metSS) for each outcome. A total of 132 metabolites were selected to create a metSS for FEV1. The metSS-only models explained 64.8% and 31.7% of the variability in FEV1 in the training and validation cohorts, respectively. For MESA-adjusted lung density, 129 metabolites were selected, and metSS-only models explained 59.0% of the variability in the training cohort and 17.4% in the validation cohort. Regression models including both clinical covariates and the metSS explained more variability than either the clinical covariate or metSS-only models (53.4% vs. 46.4% and 31.6%) in the validation dataset. The metabolomic pathways for arginine biosynthesis; aminoacyl-tRNA biosynthesis; and glycine, serine, and threonine pathway were enriched by adaLASSO metabolites for FEV1. This is the first demonstration of a respiratory metabolomic severity score, which shows how a metSS can add explanation of variance to clinical predictors of FEV1 and MESA-adjusted lung density. The advantage of a comprehensive metSS is that it explains more disease than individual metabolites and can account for substantial collinearity among classes of metabolites. Future studies should be performed to determine whether metSSs are similar in younger, and more racially and ethnically diverse populations as well as whether a metabolomic severity score can predict disease development in individuals who do not yet have COPD.
机译:慢性阻塞性肺病 (COPD) 是一种具有明显代谢紊乱的疾病。先前的研究表明 COPD 的单一代谢物与肺功能之间存在关联,但代谢物的组合是否可以预测表型尚不清楚。我们使用来自 Metabolon 平台的血浆代谢组学从两个美国常吸烟者队列中开发了代谢组学严重程度评分:COPD 研究中的亚群和中间结果测量 (SPIROMICS) (n = 648;培训/测试队列;72% 非西班牙裔,白人;平均年龄 63 岁)和 COPDGene 研究 (n = 1120;验证队列;92% 非西班牙裔,白人;平均年龄 67 岁)。使用单独的自适应 LASSO (adaLASSO) 模型对一秒用力呼气容积 (FEV1) 和 MESA 调整后的肺密度进行建模,使用研究之间常见的 762 种代谢物。由 adaLASSO 程序选择的代谢物系数用于为每个结果创建代谢组学严重程度评分 (metSS)。总共选择了 132 种代谢物来产生 FEV1 的 metSS。仅 metSS 模型分别解释了训练队列和验证队列中 FEV1 变异性的 64.8% 和 31.7%。对于 MESA 调整后的肺密度,选择了 129 种代谢物,仅 metSS 模型解释了训练队列中 59.0% 的变异性和验证队列中 17.4% 的变异性。在验证数据集中,包括临床协变量和 metSS 的回归模型比临床协变量或仅 metSS 模型(53.4% 对 46.4% 和 31.6%)解释了更多的变异性。精氨酸生物合成的代谢组学途径;氨酰基-tRNA 生物合成;甘氨酸、丝氨酸和苏氨酸途径被 FEV1 的 adaLASSO 代谢物富集。这是呼吸代谢组学严重程度评分的首次演示,它显示了 metSS 如何为 FEV1 和 MESA 调整后的肺密度的临床预测因子添加方差解释。全面的 metSS 的优势在于,它比单个代谢物解释了更多的疾病,并且可以解释代谢物类别之间的大量共线性。应进行未来的研究,以确定 metSSs 在更年轻、种族和民族更多样化的人群中是否相似,以及代谢组学严重程度评分是否可以预测尚未患有 COPD 的个体的疾病发展。

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