Characterization of different fat depots in NAFLD using inflammation-associated proteome, lipidome and metabolome.

Characterization of different fat depots in NAFLD using inflammation-associated proteome, lipidome and metabolome.
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DOI:
10.1038/s41598-018-31865-w
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发表时间:
2018-09-21
期刊:
影响因子:
4.6
通讯作者:
Boren J
Boren J
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Lovric A;Granér M;Bjornson E;Arif M;Benfeitas R;Nyman K;Ståhlman M;Pentikäinen MO;Lundbom J;Hakkarainen A;Sirén R;Nieminen MS;Lundbom N;Lauerma K;Taskinen MR;Mardinoglu A;Boren J

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非酒精性脂肪性肝病(NAFLD)被认为是代谢综合征的肝脏表现,伴随着肝脏和其他重要器官中的过度脂肪积聚。异位脂肪堆积以前与人体全身和局部水平的负面影响有关。因此,我们的目的是确定和评估新的潜在的代谢生物标志物的预测能力异位脂肪沉积在非糖尿病男性NAFLD,使用炎症相关的蛋白质组,脂质组和代谢。采用磁共振成像测量左心室功能、心包和心外膜脂肪、皮下和内脏脂肪组织。使用随机森林算法并通过估计受试者工作特征曲线下的面积来分析和预测测量的异位脂肪库。我们已经确定了肝脏(TAG 50:1、谷氨酸、diSM 18:0和CE 20:3)、心包(N-棕榈酰-鞘氨醇、HGF、diSM 18:0、谷氨酸和TNFSF 14)、心外膜(鞘磷脂、CE 20:3、PC 38:3和TNFSF 14)和心肌(CE 20:3、LAPTGF-β1、谷氨酸和葡萄糖)中脂肪库的不同代谢特征。我们的分析强调了非侵入性生物标志物,这些生物标志物准确预测异位脂肪库,并反映了NAFLD受试者的独特代谢特征。
Non-alcoholic fatty liver disease (NAFLD) is recognized as a liver manifestation of metabolic syndrome, accompanied with excessive fat accumulation in the liver and other vital organs. Ectopic fat accumulation was previously associated with negative effects at the systemic and local level in the human body. Thus, we aimed to identify and assess the predictive capability of novel potential metabolic biomarkers for ectopic fat depots in non-diabetic men with NAFLD, using the inflammation-associated proteome, lipidome and metabolome. Myocardial and hepatic triglycerides were measured with magnetic spectroscopy while function of left ventricle, pericardial and epicardial fat, subcutaneous and visceral adipose tissue were measured with magnetic resonance imaging. Measured ectopic fat depots were profiled and predicted using a Random Forest algorithm, and by estimating the Area Under the Receiver Operating Characteristic curves. We have identified distinct metabolic signatures of fat depots in the liver (TAG50:1, glutamate, diSM18:0 and CE20:3), pericardium (N-palmitoyl-sphinganine, HGF, diSM18:0, glutamate, and TNFSF14), epicardium (sphingomyelin, CE20:3, PC38:3 and TNFSF14), and myocardium (CE20:3, LAPTGF-β1, glutamate and glucose). Our analyses highlighted non-invasive biomarkers that accurately predict ectopic fat depots, and reflect their distinct metabolic signatures in subjects with NAFLD.
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