Machine learning and semi-targeted lipidomics identify distinct serum lipid signatures in hospitalized COVID-19-positive and COVID-19-negative patients.
Machine learning and semi-targeted lipidomics identify distinct serum lipid signatures in hospitalized COVID-19-positive and COVID-19-negative patients.
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DOI：
10.1016/j.metabol.2022.155197
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发表时间：
2022-06
期刊：
影响因子：
--
通讯作者：
Joven J
中科院分区：
文献类型：
--
作者：
Castañé H;Iftimie S;Baiges-Gaya G;Rodríguez-Tomàs E;Jiménez-Franco A;López-Azcona AF;Garrido P;Castro A;Camps J;Joven J
脂质参与了病毒感染与宿主代谢和免疫反应之间的相互作用。已经报道了几项比较新冠肺炎阳性住院患者和健康受试者的脂质体的研究。然而，这些差异是否是这种疾病所特有的，在很大程度上尚不清楚。本研究比较了住院的新冠肺炎阳性患者与健康受试者以及因其他感染性/炎症性疾病住院的新冠肺炎阴性患者的血脂特征。我们分析了12 6例新冠肺炎阳性患者、45例新冠肺炎阴性的其他感染性/炎症性疾病患者和50名健康志愿者的血脂特征。半靶向脂组学分析是使用液-质联用进行的。对283种脂类进行了鉴定和定量。结果用机器学习工具进行解释。与健康志愿者相比，我们确定酰基肉碱、溶血磷脂酰乙醇胺、花生四烯酸和氧脂是新冠肺炎阳性患者中变化最大的物种。然而，我们在新冠肺炎阴性的有其他炎症原因的患者中发现了类似的变化。相反，溶血磷脂酰胆碱22：6-SN2、磷脂酰胆碱36：1和次级胆汁酸是区分新冠肺炎阳性和新冠肺炎阴性患者的最大能力的参数。这项研究表明，新冠肺炎感染与其他感染性/炎症性疾病具有许多共同的血脂变化，并与健康人群有所区别。在新冠肺炎阳性和新冠肺炎阴性患者中，观察到最显著的变化是氧磷脂，而胆汁酸和甘油磷脂的变化是最好的区分。我们的结果突出了将脂质组学与机器学习算法相结合来探索新冠肺炎的病理生理机制，从而改善临床决策的价值。
Lipids are involved in the interaction between viral infection and the host metabolic and immunological responses. Several studies comparing the lipidome of COVID-19-positive hospitalized patients vs. healthy subjects have already been reported. It is largely unknown, however, whether these differences are specific to this disease. The present study compared the lipidomic signature of hospitalized COVID-19-positive patients with that of healthy subjects, as well as with COVID-19-negative patients hospitalized for other infectious/inflammatory diseases. We analyzed the lipidomic signature of 126 COVID-19-positive patients, 45 COVID-19-negative patients hospitalized with other infectious/inflammatory diseases and 50 healthy volunteers. A semi-targeted lipidomics analysis was performed using liquid chromatography coupled to mass spectrometry. Two-hundred and eighty-three lipid species were identified and quantified. Results were interpreted by machine learning tools. We identified acylcarnitines, lysophosphatidylethanolamines, arachidonic acid and oxylipins as the most altered species in COVID-19-positive patients compared to healthy volunteers. However, we found similar alterations in COVID-19-negative patients who had other causes of inflammation. Conversely, lysophosphatidylcholine 22:6-sn2, phosphatidylcholine 36:1 and secondary bile acids were the parameters that had the greatest capacity to discriminate between COVID-19-positive and COVID-19-negative patients. This study shows that COVID-19 infection shares many lipid alterations with other infectious/inflammatory diseases, and which differentiate them from the healthy population. The most notable alterations were observed in oxylipins, while alterations in bile acids and glycerophospholipis best distinguished between COVID-19-positive and COVID-19-negative patients. Our results highlight the value of integrating lipidomics with machine learning algorithms to explore the pathophysiology of COVID-19 and, consequently, improve clinical decision making.
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影响因子：
5.5
作者：
Camps J;Castañé H;Rodríguez-Tomàs E;Baiges-Gaya G;Hernández-Aguilera A;Arenas M;Iftimie S;Joven J
通讯作者：
Joven J
影响因子：
7.4
作者：
Delafiori J;Navarro LC;Siciliano RF;de Melo GC;Busanello ENB;Nicolau JC;Sales GM;de Oliveira AN;Val FFA;de Oliveira DN;Eguti A;Dos Santos LA;Dalçóquio TF;Bertolin AJ;Abreu-Netto RL;Salsoso R;Baía-da-Silva D;Marcondes-Braga FG;Sampaio VS;Judice CC;Costa FTM;Durán N;Perroud MW;Sabino EC;Lacerda MVG;Reis LO;Fávaro WJ;Monteiro WM;Rocha AR;Catharino RR
通讯作者：
Catharino RR
影响因子：
3.8
作者：
Dissanayake, Thrimendra Kaushika;Yan, Bingpeng;To, Kelvin Kai-Wang
通讯作者：
To, Kelvin Kai-Wang
DOI：
10.1096/fj.202100540r
发表时间：
2021-06
期刊：
FASEB journal : official publication of the Federation of American Societies for Experimental Biology
影响因子：
--
作者：
Archambault AS;Zaid Y;Rakotoarivelo V;Turcotte C;Doré É;Dubuc I;Martin C;Flamand O;Amar Y;Cheikh A;Fares H;El Hassani A;Tijani Y;Côté A;Laviolette M;Boilard É;Flamand L;Flamand N
通讯作者：
Flamand N
影响因子：
3.8
作者：
Huang, Rui;Zhu, Li;Wu, Chao
通讯作者：
Wu, Chao