Zhao Qi, Shen Hui, Su Kuan-Jui, Zhang Ji-Gang, Tian Qing, Zhao Lan-Juan, Qiu Chuan, Zhang Qiang, Garrett Timothy J, Liu Jiawang, Deng Hong-Wen
1Department of Preventive Medicine, College of Medicine, University of Tennessee Health Science Center, 66 N, Memphis, TN 38163 USA.
2Tulane Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St., RM 1619F, New Orleans, LA 70112 USA.
Nutr Metab (Lond). 2018 Aug 10;15:57. doi: 10.1186/s12986-018-0296-5. eCollection 2018.
Individuals' peak bone mineral density (BMD) achieved and maintained at ages 20-40 years is the most powerful predictor of low bone mass and osteoporotic fractures later in life. The aim of this study was to identify metabolomic factors associated with peak BMD variation in US Caucasian women.
A total of 136 women aged 20-40 years, including 65 subjects with low and 71 with high hip BMD, were enrolled. The serum metabolites were assessed using a liquid chromatography-mass spectrometry (LC-MS) method. The partial least-squares discriminant analysis (PLS-DA) method and logistic regression models were used, respectively, to examine the associations of metabolomic profiles and individual metabolites with BMD.
The low and high BMD groups could be differentiated by the detected serum metabolites using PLS-DA ( = 0.008). A total of 14 metabolites, including seven amino acids and amino acid derivatives, five lipids (including three bile acids), and two organic acids, were significantly associated with the risk for low BMD. Most of these metabolites are novel in that they have never been linked with BMD in humans earlier. The prediction model including the newly identified metabolites significantly improved the classification of the groups with low and high BMD. The area under the receiver operating characteristic curve without and with metabolites were 0.88 (95% CI: 0.83-0.94) and 0.97 (95% CI: 0.94-0.99), respectively ( for the difference = 0.0004).
Metabolomic profiling may improve the risk prediction of osteoporosis among Caucasian women. Our findings also suggest the potential importance of the metabolism of amino acids and bile acids in bone health.
个体在20至40岁时达到并维持的峰值骨密度（BMD）是其晚年低骨量和骨质疏松性骨折最有力的预测指标。本研究的目的是确定与美国白人女性峰值骨密度变化相关的代谢组学因素。
共纳入136名20至40岁的女性，其中65名髋部骨密度低的受试者和71名髋部骨密度高的受试者。采用液相色谱 - 质谱（LC-MS）法评估血清代谢物。分别使用偏最小二乘判别分析（PLS-DA）方法和逻辑回归模型来检验代谢组学谱和单个代谢物与骨密度的关联。
使用PLS-DA（=0.008），通过检测到的血清代谢物可区分低骨密度组和高骨密度组。共有14种代谢物与低骨密度风险显著相关，包括7种氨基酸和氨基酸衍生物、5种脂质（包括3种胆汁酸）和2种有机酸。这些代谢物中的大多数都是新发现的，因为它们此前从未在人类中与骨密度相关联。包含新鉴定代谢物的预测模型显著改善了低骨密度组和高骨密度组的分类。在不使用和使用代谢物的情况下，受试者工作特征曲线下面积分别为0.88（95%CI：0.83 - 0.94）和0.97（95%CI：0.94 - 0.99）（差异的P值 = 0.0004）。
代谢组学分析可能会改善白人女性骨质疏松症的风险预测。我们的研究结果还表明氨基酸和胆汁酸代谢在骨骼健康中的潜在重要性。
