N-lactoyl amino acids (Lac-AAs) are key players that regulate appetite and body weight. The most prominent and well-studied member is N-lactoyl phenylalanine (Lac-Phe), which can be induced by food intake, exercise and metformin treatment. However, its broader metabolic impact remains insufficiently characterized. This study investigates the effects of Lac-Phe on insulin signaling, inflammation, and mitochondrial respiration using HepG2 and differentiated C2C12 cell models, as well as isolated rat brain mitochondria and synaptosomes. Our results demonstrate that Lac-Phe significantly impairs insulin-stimulated phosphorylation of key proteins in the insulin signaling pathway, particularly in skeletal muscle cells, indicating disrupted insulin signaling. Additionally, Lac-Phe exposure increases the secretion of pro-inflammatory cytokines in C2C12 skeletal muscle cells and markedly impairs mitochondrial respiration in HepG2 liver cells and rat brain-derived synaptosomes, but not in isolated mitochondria. These findings highlight potential adverse metabolic effects of Lac-Phe, especially when administered at high concentrations, and underscore the necessity of conducting a comprehensive risk assessment and dose optimization before considering Lac-Phe or related Lac-AAs as therapeutic agents. Our work provides important insights into the molecular liabilities associated with Lac-Phe and calls for further studies to balance its therapeutic promise against possible metabolic risks.

Background: Newborn screening is an essential public health strategy that aims to detect a range of conditions, including inborn errors of metabolism, in neonates shortly after birth. The timely identification is crucial due to the asymptomatic nature of many conditions at birth, but which can lead to significant health complications if left untreated. Through this study, we aimed to investigate the incidence of IEMs screened by the Qatar National Newborn Screening Program. Methods: We retrospectively analyzed a total of 351,223 newborns screened from 2010 to 2023. The incidence for the studied IEMs was calculated and correlated with demographics, consanguinity, and family history. In addition, the diagnostic yield of different tests utilized was assessed. Results: Our study revealed a total of 318 positive cases with IEMs, and a significantly high incidence of 1:1105 for IEMs in Qatar. Classical Homocystinuria was the most frequently detected condition, with a cumulative incidence of 1:6754 live births, linked to the founder variant p. Arg336Cys in the CBS gene. Aminoacidopathies were the most prevalent category, followed by fatty acid oxidation disorders, organic acidurias, biotinidase deficiency, and urea cycle disorders. Genetic testing showed a high diagnostic yield of 90%. Of the 60 cases that underwent targeted variant testing, 98% were confirmed, while 90% of the 59 cases tested by single gene testing were confirmed. Conclusions: Our study provides the incidence rates of IEMs in Qatar and novel insights that could facilitate setting up/developing IEM incidence-reducing strategies and improving outcomes for affected newborns and their families.

Inborn errors of metabolism (IEMs) are a group of inherited genetic conditions that, in general, result from a specific enzyme defect. Clinical consequences caused by abnormal enzyme levels often disrupt affected metabolic pathways and their intermediary metabolites. Because diagnostic outcomes depend on early intervention, a timely and accurate diagnosis is essential. Quantitative targeted metabolomics (QTM) is an analytical approach that quantifies predefined metabolites and generates interpretable biochemical phenotypes. Instead of focusing solely on screening, expanded QTM methods enable higher coverage with multi-analyte profiling that can provide more comprehensive characterization of disease-associated metabolic perturbations, particularly in IEMs with overlapping biochemical profiles. This review summarizes diagnostic techniques for IEMs, outlines the principles and advantages of QTM, and evaluates its established role and emerging opportunities and limitations in advancing method development and deep metabolic phenotyping to support precision medicine.