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Obesity is associated with insulin resistance and impaired glucose tolerance, which represent characteristic features of the metabolic syndrome. Development of obesity is also linked to changes in fatty acid and amino acid metabolism observed in animal models of obesity as well as in humans. The aim of this study was to explore whether plasma metabolome, namely the levels of various acylcarnitines and amino acids, could serve as a biomarker of propensity to obesity and impaired glucose metabolism. Taking advantage of a high phenotypic variation in diet-induced obesity in C57BL/6J mice, 12-week-old male and female mice (n = 155) were fed a high-fat diet (lipids ~32 wt%) for a period of 10 weeks, while body weight gain (BWG) and changes in insulin sensitivity (ΔHOMA-IR) were assessed. Plasma samples were collected before (week 4) and after (week 22) high-fat feeding. Both univariate and multivariate statistical analyses were then used to examine the relationships between plasma metabolome and selected phenotypes including BWG and ΔHOMA-IR. Partial least squares-discrimination analysis was able to distinguish between animals selected either for their low or high BWG (or ΔHOMA-IR) in male but not female mice. Among the metabolites that differentiated male mice with low and high BWG, and which also belonged to the major discriminating metabolites when analyzed in plasma collected before and after high-fat feeding, were amino acids Tyr and Orn, as well as acylcarnitines C16-DC and C18:1-OH. In general, the separation of groups selected for their low or high ΔHOMA-IR was less evident and the outcomes of a corresponding multivariate analysis were much weaker than in case of BWG. Thus, our results document that plasma acylcarnitines and amino acids could serve as a gender-specific complex biomarker of propensity to obesity, however with a limited predictive value in case of the associated impairment of insulin sensitivity.

Newborn screening (NBS) of inborn errors of metabolism (IEMs) is based on the reference ranges established on a healthy newborn population using quantile statistics of molar concentrations of biomarkers and their ratios. The aim of this paper is to investigate whether multivariate independent component analysis (ICA) is a useful tool for the analysis of NBS data, and also to address the structure of the calculated ICA scores. NBS data were obtained from a routine NBS program performed between 2013 and 2022. ICA was tested on 10,213/150 free-diseased controls and 77/20 patients (9/3 different IEMs) in the discovery/validation phases, respectively. The same model computed during the discovery phase was used in the validation phase to confirm its validity. The plots of ICA scores were constructed, and the results were evaluated based on 5sd levels. Patient samples from 7/3 different diseases were clearly identified as 5sd-outlying from control groups in both phases of the study. Two IEMs containing only one patient each were separated at the 3sd level in the discovery phase. Moreover, in one latent variable, the effect of neonatal birth weight was evident. The results strongly suggest that ICA, together with an interpretation derived from values of the “average member of the score structure”, is generally applicable and has the potential to be included in the decision process in the NBS program.

Abstract Background Tauopathies represent heterogeneous groups of neurodegenerative diseases that are characterised by abnormal deposition of the microtubule-associated protein tau. Alzheimer’s disease is the most prevalent tauopathy, affecting more than 35 million people worldwide. In this study we investigated changes in metabolic pathways associated with tau-induced neurodegeneration. Methods Cerebrospinal fluid (CSF), plasma and brain tissue were collected from a transgenic rat model for tauopathies and from age-matched control animals. The samples were analysed by targeted and untargeted metabolomic methods using high-performance liquid chromatography coupled to mass spectrometry. Unsupervised and supervised statistical analysis revealed biochemical changes associated with the tauopathy process. Results Energy deprivation and potentially neural apoptosis were reflected in increased purine nucleotide catabolism and decreased levels of citric acid cycle intermediates and glucose. However, in CSF, increased levels of citrate and aconitate that can be attributed to glial activation were observed. Other significant changes were found in arginine and phosphatidylcholine metabolism. Conclusions Despite an enormous effort invested in development of biomarkers for tauopathies during the last 20 years, there is no clinically used biomarker or assay on the market. One of the most promising strategies is to create a panel of markers (e.g., small molecules, proteins) that will be continuously monitored and correlated with patients’ clinical outcome. In this study, we identified several metabolic changes that are affected during the tauopathy process and may be considered as potential markers of tauopathies in humans.