Endometrial cancer (EC) is the most common cancer of the female reproductive tract in developed countries. At the moment, no effective screening system is available. Here, we evaluate the diagnostic performance of a serum metabolomic signature. Two enrollments were carried out, one consisting of 168 subjects: 88 with EC and 80 healthy women, was used for building the classification models. The second (used to establish the performance of the classification algorithm) was consisted of 120 subjects: 30 with EC, 30 with ovarian cancer, 10 with benign endometrial disease, and 50 healthy controls. Two ensemble models were built, one with all EC versus controls (Model I) and one in which EC patients were aggregated according to their histotype (Model II). Serum metabolomic analysis was conducted via gas chromatography-mass spectrometry, while classification was done by an ensemble learning machine. Accuracy ranged from 62% to 99% for the Model I and from 67% to 100% for the Model II. Ensemble model showed an accuracy of 100% both for Model I and II. The most important metabolites in class separation were lactic acid, progesterone, homocysteine, 3-hydroxybutyrate, linoleic acid, stearic acid, myristic acid, threonine, and valine. The serum metabolomics signature of endometrial cancer patients is peculiar because it differs from that of healthy controls and from that of benign endometrial disease and from other gynecological cancers (such as ovarian cancer).

To get insight into still elusive pathomechanisms of pediatric obesity and non-alcoholic fatty liver disease (NAFLD) we explored the interplay among GC-MS studied urinary metabolomic signature, gut liver axis (GLA) abnormalities, and food preferences (Kid-Med). Intestinal permeability (IP), small intestinal bacterial overgrowth (SIBO), and homeostatic model assessment-insulin resistance were investigated in forty children (mean age 9.8 years) categorized as normal weight (NW) or obese (body mass index <85th or >95th percentile, respectively) ± ultrasonographic bright liver and hypertransaminasemia (NAFLD). SIBO was increased in all obese children (p = 0.0022), IP preferentially in those with NAFLD (p = 0.0002). The partial least-square discriminant analysis of urinary metabolome correctly allocated children based on their obesity, NAFLD, visceral fat, pathological IP and SIBO. Compared to NW, obese children had (1) higher levels of glucose/1-methylhistidine, the latter more markedly in NAFLD patients; and (2) lower levels of xylitol, phenyl acetic acid and hydroquinone, the latter especially in children without NAFLD. The metabolic pathways of BCAA and/or their metabolites correlated with excess of visceral fat centimeters (leucine/oxo-valerate), and more deranged IP and SIBO (valine metabolites). Urinary metabolome analysis contributes to define a metabolic fingerprint of pediatric obesity and related NAFLD, by identifying metabolic pathways/metabolites reflecting typical obesity dietary habits and GLA perturbations.

Mutations in the PRRT2 (proline-rich transmembrane protein 2) gene have been identified as the main cause of an expanding spectrum of disorders, including paroxysmal kinesigenic dyskinesia and benign familial infantile epilepsy, which places this gene at the border between epilepsy and movement disorders. The clinical spectrum has largely expanded to include episodic ataxia, hemiplegic migraine, and complex neurodevelopmental disorders in cases with biallelic mutations. Prior to the discovery of PRRT2 as the causative gene for this spectrum of disorders, the sensitivity of paroxysmal kinesigenic dyskinesia to anticonvulsant drugs regulating ion channel function as well as the co-occurrence of epilepsy in some patients or families fostered the hypothesis this could represent a channelopathy. However, recent evidence implicates PRRT2 in synapse functioning, which disproves the “channel hypothesis” (although PRRT2 modulates ion channels at the presynaptic level), and justifies the classification of these conditions as synaptopathies, an emerging rubric of brain disorders. This review aims to provide an update of the clinical and pathophysiologic features of PRRT2-associated disorders.

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Objective Heart anomalies represent nearly one‐third of all congenital anomalies. They are currently diagnosed using ultrasound. However, there is a strong need for a more accurate and less operator‐dependent screening method. Here we report a metabolomics characterization of maternal serum in order to describe a metabolomic fingerprint representative of heart congenital anomalies. Methods Metabolomic profiles were obtained from serum of 350 mothers (280 controls and 70 cases). Nine classification models were built and optimized. An ensemble model was built based on the results from the individual models. Results The ensemble machine learning model correctly classified all cases and controls. Malonic, 3‐hydroxybutyric and methyl glutaric acid, urea, androstenedione, fructose, tocopherol, leucine, and putrescine were determined as the most relevant metabolites in class separation. Conclusion The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal heart anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the revelation of the associated metabolites and their respective biochemical pathways allows a better understanding of the overall pathophysiology of affected pregnancies.