N‐lactoyl amino acids (Lac‐AA) form an emerging class of metabolites that have gained significant attention in recent years due to their ubiquitous presence in different biological systems and potential roles in various biochemical processes. This narrative review aims to provide a comprehensive overview of the current understanding of Lac‐AA, emphasising their biosynthesis, physiological roles, and potential implications in various diseases. We discuss the discovery of Lac‐AA as signalling molecules, and their involvement in exercise‐induced appetite suppression, energy metabolism, and other pathways. This review explores the complex relationship between Lac‐AA and various pathological conditions, including mitochondrial disorders, type 2 diabetes, phenylketonuria, cancer, and rosacea. We also examine the interplay between Lac‐AA and the gut microbiota, as well as their association with metformin treatment. Furthermore, we address the ongoing debate regarding whether Lac‐AA are merely reflections of lactate and amino acid levels or independent signalling molecules. This review synthesises the latest research findings, highlights the significance of Lac‐AA in metabolic research, and identifies promising avenues for future investigation in this rapidly evolving field.

Background: Metabolic syndrome (MetS) is a major risk factor for cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM), especially in Middle Eastern populations with a high metabolic burden. This study aimed to evaluate the predictive utility of different lipid ratios, including triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C), total cholesterol (TC)/HDL-C, low-density lipoprotein (LDL-C)/HDL-C, and non-HDL-C/HDL-C, for identifying MetS. In addition, we aimed to characterise the underlying metabolic dysregulation using the most predictive lipid ratio by comparing metabolomic profiles between high-risk (T3) and low-risk (T1) groups. Method: We conducted a cross-sectional study using data from 2179 Qatari adults without CVD and/or T2DM. The predictive value of each lipid ratio for MetS was compared. Untargeted metabolomics was performed to profile metabolic changes between T3 and T1. Results: After adjustment for age, sex, and BMI, TG/HDL-C showed the highest discriminative ability for MetS (AUC = 0.896, 95% CI: 0.88–0.91; OR = 4.36, 95% CI: 3.63–5.28, p < 0.0001). In pairwise AUC comparisons, TG/HDL-C outperformed LDL-C/HDL-C (p = 2.6 × 10⁻⁴, after correction for multiple comparisons), with no significant differences versus other ratios. The high-risk group exhibited raised levels of phosphatidylethanolamines, phosphatidylinositols, and diacylglycerols, and lower levels of sphingomyelins and plasmalogens. These lipid classes have been suggested to be implicated in insulin resistance and metabolic dysfunction. Elevated monoacylglycerols were identified in high-TG/HDL-C groups, representing a previously underreported pattern. Conclusions: The TG/HDL-C ratio showed a better association with MetS compared with other lipid ratios and was linked to distinct metabolomic signatures. These findings suggest potential value for early risk evaluation, but longitudinal and mechanistic studies are needed to confirm clinical applicability.

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.

Obesity plays a crucial role in the progression of insulin resistance and type 2 diabetes which are related to inflammation and liver disease. GATA-3 is a transcription factor that is involved in adipogenesis and inflammation. Therefore, it could be a potential therapeutic target for obesity-associated metabolic disorders. This study aimed to examine the effects of GATA-3 suppression on body weight, fat depot redistribution, liver histopathology, and inflammatory markers in transgenic db/db obese mice. Male db/db mice received subcutaneous injections of GATA-3-specific DNAzyme (hgd40; 10 or 100 µg/mL), pioglitazone (as a positive control), or vehicle only (as a negative control), twice weekly for two weeks. Body weight, organ weights, liver histopathology, mRNA expression of selected genes and serum cytokine levels were assessed. GATA-3 expression was not region specific, and its suppression did not significantly affect fat depot distribution or organ weights. However, the low dose of hgd40 accelerated body weight gain transiently. It also increased Il10 mRNA expression in the liver and significantly increased IL-10 protein concentration in the serum. In addition, a high dose of hgd40 resulted in a marked decrease in hepatocyte ballooning degeneration. These findings suggest that GATA-3 suppression may modulate inflammation and liver injury in obesity, warranting further investigation into its therapeutic potential for obesity-related metabolic disorders.

Introduction/Background: Metformin is the first-line therapy for type 2 diabetes (T2D); however, a considerable inter-individual variability in glycemic response is observed among patients. This heterogeneity suggests that metformin’s effects depend not only on drug exposure but also on the underlying metabolic and genetic factors. Methods: We applied a Gene–Environment interaction Mendelian Randomization (MR-G×E) in a cohort of 2743 individuals to investigate whether genetically influenced metabolite-HbA1c associations differ by metformin use. Metabolites associated with metformin response were used to establish metabolite-specific polygenic risk scores (PRSs) using metabolome-wide association study (mGWAS) variants. Generated PRS were used as genetic instruments within a one-sample, modified two-stage least squares model. An interaction term between PRS and metformin use was included to assess treatment-dependent genetic effects, adjusting for age, sex, body mass index, and genetic ancestry (principal components). Results: Metformin use significantly modified genetically influenced associations between 18 metabolites and HbA1c. Positive and negative PRS-metformin interaction effects indicated attenuation, strengthening or reversal of baseline genetic associations under treatment. Several amino acid metabolites, palmitoyl sphingomyelin (d18:1/16:0), and carbohydrate-related metabolite 1,5-anhydroglucitol showed specific patterns under metformin use. Interestingly, several metabolites (creatinine, gamma glutamylcitrulline, N-acetylthreonine, 3-methyl-2-oxovalerate, glycerol-3-phosphate, 1-(1-enyl-palmitoyl)-GPC (P-16:0), 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2), sphingomyelin (d18:1/22:1, d18:2/22:0, d16:1/24:1), fructose, and methyl-glucopyranoside (alpha + beta)) showed no basal causal association with HbA1c but exhibited significant interaction effect with metformin use, suggesting metabolic association only in the presence of metformin. Conclusions: These findings indicate that metformin modifies the genetically influenced metabolite-HbA1c relationships, exhibiting treatment-dependent metabolic effects that are not detectable with standard MR approaches. Incorporating pharmacological context into causal inference provides new insights into the metabolic basis for the variable metformin response and helps inform precision strategies for T2D management.

Introduction/Background: Observational links between circulating metabolites and lipid traits are often confounded by environmental factors. This study applied Mendelian randomization (MR) and gene–environment interaction MR (MR-G×E) to assess the average causal effects of genetically predicted metabolites on lipid traits and their modification by statin use. Methods: Participants (n = 2810) from Qatar Biobank were analyzed including genetics, metabolomics and lipid profiles. The cohort was divided into discovery (n = 1968) and validation (n = 842). One-sample MR using two-stage least squares regression was applied to estimate the average causal effect of metabolites on LDL, HDL, and Triglycerides, while MR-G×E analysis was also applied to investigate whether genetically predicted metabolite–LDL/HDL associations differ by statin use. Genetic variants associated with metabolites were used to generate a polygenic risk score (PRS). The interaction term between the PRS and statin use was incorporated in MR-G×E, while adjusting for confounders. Results: A total of 167 metabolites were commonly associated with LDL, HDL, and Triglycerides. One-sample MR showed no significant average causal effect of genetically predicted metabolites on lipid traits in the overall population. However, MR-G×E analysis revealed significant statin-dependent effects. Genetically predicted 2-amino-octanoate and cysteine–glutathione disulfide demonstrated strong and reproducible positive interaction effects with LDL levels in statin users in both discovery and validation cohorts. While no overall average causal effects were observed, MR-G×E revealed that genetically predicted metabolites can influence LDL levels in the context of statin use. Conclusions: These findings highlight the importance of incorporating pharmacological exposures in MR analyses and support the role of drug–metabolite interactions in precision lipid management.