Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension.
Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension.
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DOI：
10.3390/metabo12080679
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发表时间：
2022-07-24
期刊：
影响因子：
4.1
通讯作者：
中科院分区：
文献类型：
--
作者：
尽管发病率和死亡率相当高，但许多内分泌高血压（EHT）形式的病例，包括原发性醛固酮增多症（PA）、嗜铬细胞瘤和功能性副神经节瘤（PPGL）以及库欣综合征（CS），仍未被发现。我们的目标是建立不同形式EHT的特征，调查潜在的混淆效应，并建立无偏倚的疾病生物标志物。血浆样本来自7个国家的13个生物银行，并使用非靶向核磁共振代谢组学进行分析。我们比较了106例PHT患者和231例EHT患者的未分层样本，包括104例PA， 94例PPGL和33例CS患者。对PHT和EHT形式的光谱进行了多元统计比较，并获得了相关的特征。采用了三种方法来调查和纠正混淆效应。虽然我们发现了可以区分PHT和EHT形式的特征，但与样品起源中心和样品年龄的特征也有关键的相似之处。研究设计限制了所采用的校正方法的适用性。在现有的样本中，无法识别PHT与EHT的生物标志物。通过校正方法的稳健性，证明了混杂效应的复杂性，强调了在多中心回顾性代谢组学研究中，需要就如何处理可能归因于分析前因素的变异性达成共识。
Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing’s syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.
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影响因子：
3.7
作者：
Breier M;Wahl S;Prehn C;Fugmann M;Ferrari U;Weise M;Banning F;Seissler J;Grallert H;Adamski J;Lechner A
通讯作者：
Lechner A
影响因子：
4.2
作者：
Coene, Karlien L. M.;Kluijtmans, Leo A. J.;Wevers, Ron A.
通讯作者：
Wevers, Ron A.
影响因子：
3.6
作者：
Bervoets, Liene;Louis, Evelyne;Linsen, Loes
通讯作者：
Linsen, Loes
影响因子：
4.6
作者：
Ameta K;Gupta A;Kumar S;Sethi R;Kumar D;Mahdi AA
通讯作者：
Mahdi AA
DOI：
10.1210/clinem/dgaa954
发表时间：
2021-03-25
期刊：
The Journal of clinical endocrinology and metabolism
影响因子：
--
作者：
Erlic Z;Reel P;Reel S;Amar L;Pecori A;Larsen CK;Tetti M;Pamporaki C;Prehn C;Adamski J;Prejbisz A;Ceccato F;Scaroni C;Kroiss M;Dennedy MC;Deinum J;Langton K;Mulatero P;Reincke M;Lenzini L;Gimenez-Roqueplo AP;Assié G;Blanchard A;Zennaro MC;Jefferson E;Beuschlein F
通讯作者：
Beuschlein F