Discovery of potential biomarkers for osteoporosis using LC-MS/MS metabolomic methods
Discovery of potential biomarkers for osteoporosis using LC-MS/MS metabolomic methods
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使用 LC-MS/MS 代谢组学方法发现骨质疏松症的潜在生物标志物
DOI：
10.1007/s00198-019-04892-0
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发表时间：
2019-02
影响因子：
4
通讯作者：
Jia W
中科院分区：
文献类型：
--
作者：
Wang J;Yan D;Zhao A;Hou X;Zheng X;Chen P;Bao Y;Jia W;Hu C;Zhang Z L;Jia W
本研究关注代谢物与骨密度和骨质疏松症的关系，发现几种代谢物与骨密度有关，代谢物结合骨转换标志物对男性和绝经后女性骨质疏松症的鉴别更敏感，这可能对骨质疏松症的早期诊断有意义。我们的研究旨在评估代谢物与骨的关系，试图找到新的代谢标志物，以区分低骨密度（BMD）。方法本研究从上海地区招募320名受试者，其中男性138人，绝经后女性182人。检测骨转换标志物（BTMs），包括骨钙素、PINP和β-CTX等生化指标。采用双能x线骨密度仪测定腰椎（L1-4）、股骨颈和全髋的骨密度值，并采用质谱法测定血清代谢组谱，包括5组（酰基肉碱、氨基酸、生物胺、甘油磷脂、鞘脂和己糖）的221种代谢物。结果在主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）模型中，不同BMD组的代谢谱无明显差异。我们分别比较了男性和绝经后女性三组不同BMD水平的代谢物，并通过随机森林特征选择进一步过滤这些代谢物，随机森林特征选择是一种常用的机器学习算法，可以选择对骨质疏松影响最大的特征，然后选择最重要的代谢物（≥5%）（男性5个，绝经后女性9个）构建更好的骨质疏松分类模型。在模型中加入这些选定的代谢物后，受试者工作特征曲线（ROC）曲线下面积（AUC）明显高于纯btm模型的男性AUC （btm: AUC 0.729, 95% CI 0.647-0.802,p< 0.0001，模型1:AUC = 0.828, 95% CI 0.754-0.888,p< 0.0001；模型1与btm模型相比：p= 0.0158）。在绝经后女性中也观察到类似的结果（BTMs: AUC = 0.638, 95% CI 0.562-0.708,p= 0.0025；模型2:AUC = 0.741, 95% CI 0.669-0.803,p< 0.0001；模型1与BTMs模型：p= 0.0182）。结论代谢产物联合传统BTMs对男性和绝经后女性骨质疏松的鉴别指标优于单独使用BTMs。
SummaryOur study focused on the associations of metabolites with BMD and osteoporosis, finding that several metabolites are associated with BMD, and metabolites combined with bone turnover markers tend to be more sensitive in distinguishing osteoporosis in both males and postmenopausal females, which might be meaningful for the early diagnosis of osteoporosis.IntroductionOur study aimed to evaluate the association of metabolites with bone, trying to find new metabolic markers that are distinguishing for low bone mineral density (BMD).MethodsOur study recruited 320 participants, including 138 males and 182 postmenopausal females from the Shanghai area. Bone turnover markers (BTMs), including osteocalcin, PINP and β-CTX, and other biochemical traits were tested. BMD values of the lumber spine (L1–4), femoral neck and total hip were determined using dual-energy X-ray absorptiometry and the serum metabolome profiles including 221 metabolites from five groups (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids and hexose) were assessed by mass spectrometry.ResultsNo visual separation in the metabolic profiles between different BMD groups was observed in principal component analysis (PCA) or partial least squares discriminant analysis (PLS-DA) models. We compared metabolites in three groups with different BMD levels in males and postmenopausal females separately and further filtering these metabolites via random forest-based feature selection, a commonly applied machine learning algorithm which could select the features with the greatest impact on osteoporosis, then metabolites with the highest importance (≥ 5%) (5 in males and 9 in postmenopausal females) were selected to construct better models for osteoporosis classification. After adding these selected metabolites to the model, the area under the curve (AUC) of receiver operating characteristic (ROC) curves increased significantly (BTMs: AUC 0.729, 95% CI 0.647–0.802,p< 0.0001, model 1: AUC = 0.828, 95% CI 0.754–0.888,p< 0.0001; model 1 versus model of BTMs:p= 0.0158) compared to the AUC of the BTM-only model in males. Similar results were also observed in postmenopausal females (BTMs: AUC = 0.638, 95% CI 0.562–0.708,p= 0.0025; model 2: AUC = 0.741, 95% CI 0.669–0.803,p< 0.0001; model 1 versus model of BTMs:p= 0.0182).ConclusionMetabolites combined with traditional BTMs tend to better markers for distinguishing osteoporosis in both males and postmenopausal females than BTMs alone.
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影响因子：
3.5
作者：
Abraham A;Pedregosa F;Eickenberg M;Gervais P;Mueller A;Kossaifi J;Gramfort A;Thirion B;Varoquaux G
通讯作者：
Varoquaux G
影响因子：
4
作者：
Qu, B.;Ma, Y.;Hong, Z.
通讯作者：
Hong, Z.
影响因子：
4.2
作者：
Toru Yamaguchi;M. Kanatani;M. Yamauchi;H. Kaji;T. Sugishita;D. Baylink;S. Mohan;Kazuo Chihara;T. Sugimoto
通讯作者：
Toru Yamaguchi;M. Kanatani;M. Yamauchi;H. Kaji;T. Sugishita;D. Baylink;S. Mohan;Kazuo Chihara;T. Sugimoto
代谢组学及其在骨质疏松症研究生物标志物开发中的应用。
影响因子：
5.6
作者：
Lv H;Jiang F;Guan D;Lu C;Guo B;Chan C;Peng S;Liu B;Guo W;Zhu H;Xu X;Lu A;Zhang G
通讯作者：
Zhang G
影响因子：
6.3
作者：
Engelen, M. P. K. J.;Com, G.;Deutz, N. E. P.
通讯作者：
Deutz, N. E. P.