Effect of L-Carnitine on Amino Acid Metabolism in Elderly Patients Undergoing Regular Hemodialysis

Improved sensitivity and specificity for citrin deficiency using selected amino acids and acylcarnitines in the newborn screening

Citrin deficiency is an autosomal recessive disorder caused by a defect of citrin resulting from mutations in the SLC25A13 gene. Intrahepatic cholestasis and various metabolic abnormalities, including hypoglycemia, galactosemia, citrullinemia, and hyperammonemia may be present in neonates or infants in the "neonatal intrahepatic cholestasis caused by citrin deficiency" (NICCD) form of the disease. Because at present, newborn screening (NBS) for citrin deficiency using citrulline levels in dried blood spots (DBS) can only detect some of the patients, we tried to develop a new evaluation system to more reliably detect newborns with citrin deficiency utilizing parameters already in place in present NBS methods. To achieve this goal, we re-analyzed NBS profiles of amino acids and acylcarnitines in 96 NICCD patients, who were diagnosed through selective screening or positive family history. Hereby, we identified the combined evaluation of arginine (Arg), citrulline (Cit), isoleucine+leucine (Ile + Leu), tyrosine (Tyr), free carnitine (C0) / glutarylcarnitine (C5-DC) ratio in DBS as potentially sensitive to diagnose citrin deficiency in pre-symptomatic newborns. In particular, a scoring system using threshold levels for Arg (≥9 μmol/L), Cit (≥ 39 μmol/L), Ile + Leu (≥ 99 μmol/L), Tyr (≥ 96 μmol/L) and C0/C5-DC ratio (≥327) was significantly effective to detect newborns who later developed NICCD, and could thus be implemented in existing NBS programs at no extra analytical costs whenever citrin deficiency is considered to become a novel target disease.

Machine learning based on metabolomics reveals potential targets and biomarkers for primary Sjogren’s syndrome

Background: Using machine learning based on metabolomics, this study aimed to construct an effective primary Sjogren’s syndrome (pSS) diagnostics model and reveal the potential targets and biomarkers of pSS.

Methods: From a total of 39 patients with pSS and 38 healthy controls (HCs), serum specimens were collected. The samples were analyzed by ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry. Three machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost), were used to build the pSS diagnosis models. Afterward, four machine learning methods were used to reduce the dimensionality of the metabolomics data. Finally, metabolites with significant differences were screened and pathway analysis was conducted.

Results: The area under the curve (AUC), sensitivity, and specificity of LASSO, RF and XGBoost test set all reached 1.00. Orthogonal partial least squares discriminant analysis was used to classify the metabolomics data. By combining the results of the univariate false discovery rate and the importance of the variable in projection, we identified 21 significantly different metabolites. Using these 21 metabolites for diagnostic modeling, the AUC, sensitivity, and specificity of LASSO, RF, and XGBoost all reached 1.00. Metabolic pathway analysis revealed that these 21 metabolites are highly correlated with amino acid and lipid metabolisms. On the basis of 21 metabolites, we screened the important variables in the models. Further, five common variables were obtained by intersecting the important variables of three models. Based on these five common variables, the AUC, sensitivity, and specificity of LASSO, RF, and XGBoost all reached 1.00.2-Hydroxypalmitic acid, L-carnitine and cyclic AMP were found to be potential targets and specific biomarkers for pSS.

Conclusion: The combination of machine learning and metabolomics can accurately distinguish between patients with pSS and HCs. 2-Hydroxypalmitic acid, L-carnitine and cyclic AMP were potential targets and biomarkers for pSS.

The influence of pregnancy-induced hypertension syndrome on the metabolism of newborns

BACKGROUND

Pregnancy-induced hypertension (PIH) is associated with an increased number of neonatal complications, but its impact on neonatal metabolism remains unclear. This study aimed to investigate the differential metabolomics of infants born to mothers with and without PIH.

METHODS

Blood samples of a total of 115 infants born to mothers with (n=56) and without (n=59) PIH were collected and assigned to two groups, respectively, from the neonatal department of Sixth Affiliated Hospital of Sun Yat-Sen University. A tandem mass spectrometer was used to generate metabolic profiling of amino acid, free carnitine, and acyl-carnitines. The resulting data were analyzed using orthogonal partial least squares discriminant analysis based on the difference between infants born to mothers with or without PIH.

RESULTS

A significant relationship was observed between the two groups (with and without PIH) in the metabolic fingerprint. According to the pattern recognition analysis combined with variance importance, 25 metabolites with high importance were found. The top ten substances were selected for analysis. Compared with infants born to mothers without PIH, glycine levels increased, and C14DC, C22, C4DC, C5:1, C6DC, C5-OH, proline, C14-OH, and C20 decreased in infants born to mothers with PIH.

CONCLUSIONS

Using liquid chromatography (LC)-MS/MS metabolomics, a significant relationship was detected between neonatal metabolism and maternal hypertension. It is important to correct the subsequent infantile metabolic disorder by balancing the biomarker metabolites and suppling adequate nutrition to improve the health and growth of newborns of PIH mothers.

The Nutraceutical Value of Carnitine and Its Use in Dietary Supplements

Carnitine can be considered a conditionally essential nutrient for its importance in human physiology. This paper provides an updated picture of the main features of carnitine outlining its interest and possible use. Particular attention has been addressed to its beneficial properties, exploiting carnitine's properties and possible use by considering the main in vitro, in animal, and human studies. Moreover, the main aspects of carnitine-based dietary supplements have been indicated and defined with reference to their possible beneficial health properties.