Circulating Lysophosphatidylcholines Are Markers of a Metabolically Benign Nonalcoholic Fatty Liver

Abstract

OBJECTIVENonalcoholic fatty liver (NAFL) is thought to contribute to insulin resistance and its metabolic complications. However, some individuals with NAFL remain insulin sensitive. Mechanisms involved in the susceptibility to develop insulin resistance in humans with NAFL are largely unknown. We investigated circulating markers and mechanisms of a metabolically benign and malignant NAFL by applying a metabolomic approach.RESEARCH DESIGN AND METHODSA total of 265 metabolites were analyzed before and after a 9-month lifestyle intervention in plasma from 20 insulin-sensitive and 20 insulin-resistant subjects with NAFL. The relevant plasma metabolites were then tested for relationships with insulin sensitivity in 17 subjects without NAFL and in plasma from 29 subjects with liver tissue samples.RESULTSThe best separation of the insulin-sensitive from the insulin-resistant NAFL group was achieved by a metabolite pattern including the branched-chain amino acids leucine and isoleucine, ornithine, the acylcarnitines C3:0-, C16:0-, and C18:0-carnitine, and lysophosphatidylcholine (lyso-PC) C16:0 (area under the ROC curve, 0.77 [P = 0.00023] at baseline and 0.80 [P = 0.000019] at follow-up). Among the individual metabolites, predominantly higher levels of lyso-PC C16:0, both at baseline (P = 0.0039) and at follow-up (P = 0.001), were found in the insulin-sensitive compared with the insulin-resistant subjects. In the non-NAFL groups, no differences in lyso-PC C16:0 levels were found between the insulin-sensitive and insulin-resistant subjects, and these relationships were replicated in plasma from subjects with liver tissue samples.CONCLUSIONSFrom a plasma metabolomic pattern, particularly lyso-PCs are able to separate metabolically benign from malignant NAFL in humans and may highlight important pathways in the pathogenesis of fatty liver–induced insulin resistance.

Type 2 diabetes alters metabolic and transcriptional signatures of glucose and amino acid metabolism during exercise and recovery

“…The increase in glycine in that study was associated with an improvement in insulin sensitivity. Plasma amino acid data, which are available for 75 participants at baseline and after 9 months of combined exercise and diet intervention in the Tübingen lifestyle intervention programme, also showed a slight but significant correlation for increased glycine (p=0.037, R 2 =0.06) and serine plasma levels (p=0.005, R 2 =0.10) with increased insulin sensitivity [40]. Thus, the exercise-induced regulation of genes involved in amino acid synthesis and metabolism in the muscles of the diabetic participants may be important in normalising plasma glycine concentrations but may also be involved in the amelioration of insulin resistance.…”

Tipping the inflammatory balance: inflammasome activation distinguishes metabolically unhealthy from healthy obesity

“…One such condition is characterised by high levels of circulating lysophosphatidylcholines that are mainly derived from the liver [21], and this difference may be used to distinguish MUO from MHO people. Interestingly, lysophosphatidylcholines were recently found to enhance the suppressive function of human T regs on macrophages [22].…”

Diabetes Subphenotypes and Metabolomics: The Key to Discovering Laboratory Markers for Personalized Medicine?

“…However, the metabolic profiles obtained are often very complex, and bioinformatics tools such as feature subset selection approaches are needed to find the relevant pattern in the hundreds or thousands of detected metabolite signals (7 ). It is worth noting that bioinformatically elucidated patterns found suitable to separate subphenotypes may include a mixture of metabolites, some of which, when considered univariately, are not significantly different between phenotypes, and others which are significantly different (7 ).…”

“…However, the metabolic profiles obtained are often very complex, and bioinformatics tools such as feature subset selection approaches are needed to find the relevant pattern in the hundreds or thousands of detected metabolite signals (7 ). It is worth noting that bioinformatically elucidated patterns found suitable to separate subphenotypes may include a mixture of metabolites, some of which, when considered univariately, are not significantly different between phenotypes, and others which are significantly different (7 ). The advantage of metabolite patterns over single parameters can be seen in the ability of a multivariate pattern to predict future development of T2Dm by use of fasting plasma concentrations of 5 amino acids (8 ) and by the differentiation of prediabetic subphenotypes (metabolically benign vs malignant fatty liver) by a pattern of 7 plasma metabolites (7 ).…”

“…The metabolites most consistently found are amino acids, in particular, branched-chain amino acids (8,9 ), lyso-phosphatidylcholines (6,7,10,11 ), fatty acids, and acyl carnitines (6,7,9 ). In this context, it is important to note that these diabetes-associated markers do not preclude the utility of other metabolic biomarkers.…”