Sarcosine

The objective of the present study is to analyze the urinary metabolome profile of patients with obesity and overweight and relate it to different obesity profiles. This is a prospective, cross-sectional study in which patients with a body mass index (BMI) ≥25 kg/m were selected. Anthropometric data were assessed by physical examination and body composition was obtained by bioimpedance (basal metabolic rate, body fat percentile, skeletal muscle mass, gross fat mass and visceral fat). Urine was collected for metabolomic analysis. Patients were classified according to abdominal circumference measurements between 81 and 93, 94 and 104, and >104 cm; visceral fat up to 16 kilos and less than; and fat percentiles of <36%, 36-46% and >46%. Spectral alignment of urinary metabolite signals and bioinformatic analysis were carried out to select the metabolites that stood out. NMR spectrometry was used to detect and quantify the main urinary metabolites and to compare the groups. Seventy-five patients were included, with a mean age of 38.3 years, and 72% females. The urinary metabolomic profile showed no differences in BMI, abdominal circumference and percentage of body fat. Higher concentrations of trigonelline (p = 0.0488), sarcosine (p = 0.0350) and phenylalanine (p = 0.0488) were associated with patients with visceral fat over 16 kg. The cutoff points obtained by the ROC curves were able to accurately differentiate between patients according to the amount of visceral fat: sarcosine 0.043 mg/mL; trigonelline 0.068 mg/mL and phenylalanine 0.204 mg/mL. In conclusion, higher visceral fat was associated with urinary levels of metabolites such as sarcosine, related to insulin resistance; trigonelline, related to muscle mass and strength; and phenylalanine, related to glucose metabolism and abdominal fat. Trigonelline, sarcosine and phenylalanine play significant roles in regulating energy balance and metabolic pathways essential for controlling obesity. Our findings could represent an interesting option for the non-invasive estimation of visceral fat through biomarkers related to alterations in metabolic pathways involved in the pathophysiology of obesity.

Cognitive impairment associated with schizophrenia (CIAS) and related deficits in learning (plasticity) are among the leading causes of disability in schizophrenia. Despite this, there are no Food and Drug Administration-approved treatments for CIAS, and the development of treatments has been limited by numerous phase 2/3 failures of compounds that showed initial promise in small-scale studies. NMDA-type glutamate receptors (NMDARs) have been proposed to play an important role in schizophrenia; moreover, the NMDAR has a well-characterized role in cognition, learning, and neuroplasticity. We review previously published clinical trials in CIAS that focused on NMDAR modulator treatments, focusing on published and recent developments of the use of novel NMDAR-modulating treatments for CIAS both alone and combined with plasticity/learning paradigms to enhance learning. We use this discussion of previous studies to highlight the importance of incorporating pharmacodynamic target engagement biomarkers early in treatment development, which can help predict which compounds will succeed or fail in phase 3. A range of direct and indirect NMDAR modulators are covered, including D-serine, D-cycloserine, memantine, and glycine and first-generation glycine transport inhibitors (e.g., sarcosine and bitopertin), as well as recent positive studies of iclepertin, a novel glycine transport inhibitor, and luvadaxistat, a D-amino acid oxidase inhibitor that increases brain D-serine levels, and indirect noninvasive brain stimulation NMDAR-modulating treatments. Several examples of successful use of pharmacodynamic target engagement biomarkers for dose/drug discovery are emphasized, including the mismatch negativity, auditory steady state, and time-frequency event-related potential approaches.