The identification of metabolomic biomarkers relies on the analysis of large cohorts of patients compared to healthy controls followed by the validation of markers in an independent sample set.... Show moreThe identification of metabolomic biomarkers relies on the analysis of large cohorts of patients compared to healthy controls followed by the validation of markers in an independent sample set. Indeed, circulating biomarkers should be causally linked to pathology to ensure that changes in the marker precede changes in the disease. However, this approach becomes unfeasible in rare diseases due to the paucity of samples, necessitating the development of new methods for biomarker identification. The present study describes a novel approach that combines samples from both mouse models and human patients to identify biomarkers of OPMD. We initially identified a pathology-specific metabolic fingerprint in murine dystrophic muscle. This metabolic fingerprint was then translated into (paired) murine serum samples and then to human plasma samples. This study identified a panel of nine candidate biomarkers that could predict muscle pathology with a sensitivity of 74.3% and specificity of 100% in a random forest model. These findings demonstrate that the proposed approach can identify biomarkers with good predictive performance and a higher degree of confidence in their relevance to pathology than markers identified in a small cohort of human samples alone. Therefore, this approach has a high potential utility for identifying circulating biomarkers in rare diseases. Show less
Olga, L.; Diepen, J.A. van; Bobeldijk-Pastorova, I.; Gross, G.; Prentice, P.M.; Snowden, S.G.; ... ; Koulman, A. 2021
Background: Altered lipid metabolism in early life has been associated with subsequent weight gain and predicting this could aid in obesity prevention and risk management. Here, a lipidomic... Show moreBackground: Altered lipid metabolism in early life has been associated with subsequent weight gain and predicting this could aid in obesity prevention and risk management. Here, a lipidomic approach was used to identify circulating markers for future obesity risk in translational murine models and validate in a human infant cohort.Methods: Lipidomics was performed on the plasma of APOE*3 Leiden, Ldlr-/-.Leiden, and the wild-type C57BL/6J mice to capture candidate biomarkers predicting subsequent obesity parameters after exposure to high-fat diet. The identified candidate biomarkers were mapped onto corresponding lipid metabolism pathways and were investigated in the Cambridge Baby Growth Study. Infants' growth and adiposity were measured at 0-24 months. Capillary dried blood spots were sampled at 3 months for lipid profiling analysis.Findings: From the mouse models, cholesteryl esters were correlated with subsequent weight gain and other obesity parameters after HFD period (Spearman's r >= 0.5, FDR p values <0.05) among APOE*3 Leiden and Ldlr-/-.Leiden mice, but not among the wild-type C57BL/6J. Pathway analysis showed that those identified cholesteryl esters were educts or products of desaturases activities: stearoyl-CoA desaturase-1 (SCD1) and fatty acid desaturase (FADS) 1 and 2. In the human cohort, lipid ratios affected by SCD1 at 3 months was inversely associated with 3-12 months weight gain (BSE=-0.31 +/- 0.14, p=0.027), but positively with 12-24 months weight and adiposity gains (0.17 +/- 0.07, p=0.02 and 0.17 +/- 0.07, 0.53 +/- 0.26, p=0.04, respectively). Lipid ratios affected by SCD1 and FADS2 were inversely associated with adiposity gain but positively with height gain between 3-12 months.Interpretation: From murine models to human setting, the ratios of circulating lipid species indicating key desaturase activities in lipid metabolism were associated with subsequent body size increase, providing a potential tool to predict early life weight gain. (C) 2021 The Authors. Published by Elsevier B.V. Show less
