Pantophlet Andre J, Roelofsen Han, de Vries Marcel P, Gerrits Walter J J, van den Borne Joost J G C, Vonk Roel J
Department of Pediatrics; Center for Liver, Digestive and Metabolic Diseases, University Groningen, University Medical Centre Groningen, Groningen, The Netherlands.
Medical Biomics, University Groningen, University Medical Centre Groningen, Groningen, The Netherlands.
PLoS One. 2017 Jun 15;12(6):e0179612. doi: 10.1371/journal.pone.0179612. eCollection 2017.
Heavy veal calves (4-6 months old) are at risk of developing insulin resistance and disturbed glucose homeostasis. Prolonged insulin resistance could lead to metabolic disorders and impaired growth performance. Recently, we discovered that heavy Holstein-Friesian calves raised on a high-lactose or high-fat diet did not differ in insulin sensitivity, that insulin sensitivity was low and 50% of the calves could be considered insulin resistant. Understanding the patho-physiological mechanisms underlying insulin resistance and discovering biomarkers for early diagnosis would be useful for developing prevention strategies. Therefore, we explored plasma metabolic profiling techniques to build models and discover potential biomarkers and pathways that can distinguish between insulin resistant and moderately insulin sensitive veal calves. The calves (n = 14) were classified as insulin resistant (IR) or moderately insulin sensitive (MIS) based on results from a euglycemic-hyperinsulinemic clamp, using a cut-off value (M/I-value <4.4) to identify insulin resistance. Metabolic profiles of fasting plasma samples were analyzed using reversed phase (RP) and hydrophilic interaction (HILIC) liquid chromatography-mass spectrometry (LC-MS). Orthogonal partial least square discriminant analysis was performed to compare metabolic profiles. Insulin sensitivity was on average 2.3x higher (P <0.001) in MIS than IR group. For both RP-LC-MS and HILIC-LC-MS satisfactory models were build (R2Y >90% and Q2Y >66%), which allowed discrimination between MIS and IR calves. A total of 7 and 20 metabolic features (for RP-LC-MS and HILIC-LC-MS respectively) were most responsible for group separation. Of these, 7 metabolites could putatively be identified that differed (P <0.05) between groups (potential biomarkers). Pathway analysis indicated disturbances in glycerophospholipid and sphingolipid metabolism, the glycine, serine and threonine metabolism, and primary bile acid biosynthesis. These results demonstrate that plasma metabolic profiling can be used to identify insulin resistance in veal calves and can lead to underlying mechanisms.
体重较重的犊牛（4 - 6月龄）有发生胰岛素抵抗和葡萄糖稳态紊乱的风险。长期的胰岛素抵抗可能导致代谢紊乱和生长性能受损。最近，我们发现以高乳糖或高脂肪饮食饲养的体重较重的荷斯坦 - 弗里生犊牛在胰岛素敏感性方面没有差异，胰岛素敏感性较低，50%的犊牛可被认为存在胰岛素抵抗。了解胰岛素抵抗背后的病理生理机制并发现早期诊断的生物标志物将有助于制定预防策略。因此，我们探索了血浆代谢谱分析技术，以建立模型并发现能够区分胰岛素抵抗和中度胰岛素敏感犊牛的潜在生物标志物和途径。根据正常血糖 - 高胰岛素钳夹试验的结果，将犊牛（n = 14）分为胰岛素抵抗（IR）或中度胰岛素敏感（MIS）组，使用临界值（M/I值<4.4）来识别胰岛素抵抗。使用反相（RP）和亲水相互作用（HILIC）液相色谱 - 质谱联用（LC - MS）分析空腹血浆样本的代谢谱。进行正交偏最小二乘判别分析以比较代谢谱。MIS组的胰岛素敏感性平均比IR组高2.3倍（P <0.001）。对于RP - LC - MS和HILIC - LC - MS都建立了令人满意的模型（R2Y>90%且Q2Y>66%），这使得能够区分MIS和IR犊牛。总共7个和20个代谢特征（分别针对RP - LC - MS和HILIC - LC - MS）对组间分离起主要作用。其中，7种代谢物可以被推测性地鉴定出来，两组之间存在差异（P <0.05）（潜在生物标志物）。通路分析表明甘油磷脂和鞘脂代谢、甘氨酸、丝氨酸和苏氨酸代谢以及初级胆汁酸生物合成存在紊乱。这些结果表明，血浆代谢谱分析可用于识别犊牛的胰岛素抵抗，并能揭示其潜在机制。
