A Metabolomic Severity Score for Airflow Obstruction and Emphysema.
A Metabolomic Severity Score for Airflow Obstruction and Emphysema.
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DOI：
10.3390/metabo12050368
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发表时间：
2022-04-19
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影响因子：
4.1
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慢性阻塞性肺疾病（COPD）是一种伴有明显代谢紊乱的疾病。先前的研究表明，单一代谢物与COPD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，从两个美国吸烟者队列中开发了代谢组学严重程度评分：COPD研究的亚群和中间结局测量（SPIROMICS） （n = 648；训练/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）模型来模拟一秒用力呼气量（FEV1）和mesa调整的肺密度，使用研究间常见的762种代谢物。通过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性评分（metSS）。共选择132种代谢物建立FEV1的代谢代谢指标。在训练组和验证组中，仅metss模型分别解释了64.8%和31.7%的FEV1变异。对于mesa调整的肺密度，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的模型解释了更多的可变性（53.4%比46.4%和31.6%）。精氨酸生物合成的代谢组学途径氨酰生物合成;adaLASSO代谢产物丰富了甘氨酸、丝氨酸和苏氨酸途径。这是呼吸代谢组学严重程度评分的首次证明，它显示了metSS如何为FEV1和mesa调整的肺密度的临床预测因子增加方差解释。综合代谢代谢谱的优势在于，它比单个代谢物能解释更多的疾病，并能解释代谢物类别之间的实质共线性。未来的研究应确定metss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。
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.