Mohamed A. Elrayess

Glucagon like peptide-1 receptor agonists (GLP-1RAs) have revolutionized the management of T2D and obesity. T2D clustering highlights distinct phenotypes that differ in GLP-1RA benefit. While they have markedly improved quality of life for many individuals, converging evidence indicates that genetic variation, metabolomic remodeling, and gut microbiota composition contribute to substantial inter-individual variability in GLP-1RA efficacy and tolerability. GLP-1RAs, in return, can variably affect the metabolome and microbiome composition in treated individuals. This review focuses on human multi-omics GLP-1RA intervention studies, complemented by relevant mechanistic preclinical evidence. Genetic variation in GLP1R, TCF7L2 , and other loci has been associated with differences in glycaemic and weight outcomes, although most findings remain preliminary, inconsistent across populations, or not yet replicated. Pharmacometabolomic studies show recurrent of lipid-related pathways including, glycerophospholipids, sphingolipids, ceramides, amino acids, and bile acids, with drug-specific signatures. GLP-1RAs are also associated with changes in gut microbial composition, including taxa such as Akkermansia and Bacteroides , although the direction and clinical significance of these changes vary across studies due to methodological and population heterogeneity. Multi-omics and AI-based models show promise for predicting treatment response by integrating genetic, metabolic, microbial, and clinical data to supporting more effective and individualized therapy and advancing precision medicine.

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.

Background: Regular moderate physical activity helps prevent cardiometabolic diseases, yet the underlying molecular pathways, particularly in women in early to mid-adulthood, remain incompletely understood. Women aged 30–50 years undergo hormonal and metabolic transitions that may modify both cardiometabolic risk and responsiveness to exercise. This study investigated exercise-induced changes in serum metabolic signatures in women aged 30–50 years following 8 weeks of moderate-intensity aerobic training (MAT). Method: Thirty-nine healthy pre-menopause females (30–50 years) with a BMI of 26.5–33.5 kg/m ² completed an 8-week supervised moderate-intensity aerobic exercise program. Fasting blood samples were collected before and after the intervention. Serum targeted quantitative metabolic profiling and clinical traits were assessed. Results: Univariate analysis revealed a coordinated post-exercise upregulation of dihydroceramides, glycosylceramides, ceramides, acylcarnitines (e.g., C14:2, C3-DC/C4-OH), diacylglycerols(DG 16:0_20:4, DG O-18:2_18:2),triacylglycerols, selected cholesterol esters (e.g., CE 20:3), cystine, and the nucleobase-related metabolite xanthine, alongside decreases in specific phosphatidylcholines species (PC O-30:2, PC O-28:0, PC O-30:0, PC 42:2, LPC 26:0), the amino acid serine, and CE 14:0,decrease in DG 18:1_18:1, CE 14:1, CE 14:0 and the amino acid–related metabolite cystine. Post-exercise correlation analysis identified distinct associations between metabolic profiles and clinical traits, with higher physical activity (metabolic equivalent of task, MET) positively linked to selected ceramide species (Cer d18:1/16:0, Cer d18:2/18:0). Conclusion: Moderate aerobic exercise in midlife women elicits a coordinated serum metabolomic response characterized by remodeling of lipid and sphingolipid pathways, increased fatty acid related mitochondrial handling, and elevated energy/redox turnover. The upregulation of dihydroceramides, glycosylceramides, ceramides, acylcarnitines, glycerolipids, cholesterol esters, and xanthine, together with reductions in specific phosphatidylcholines, cystine and serine, supports an exercise-induced “lipid sphingolipid energy stress” signature. Distinct associations of ceramide, glyceride, and purine species with MET, insulin indices, BMI, HDL, and catalase further suggest that these metabolites are promising candidate markers of exercise adaptation in midlife women.

Cardiovascular disease involves complex molecular, cellular, and physiological derangements that present challenges for traditional diagnostic and therapeutic approaches. Precision medicine is an evolving field that seeks to tailor interventions according to an individual's genetic, molecular, and physiological characteristics. By leveraging advanced omics approaches, including genomics, proteomics, metabolomics, and microbiomics, precision medicine may enable more comprehensive patient phenotyping and support the development of more individualized strategies. The emerging concept of precision cardiac rehabilitation (CR) could provide a more tailored approach to cardiac care by integrating artificial intelligence (AI) with multimodal data, including clinical, imaging, physiological, and omics information. In this context, AI has been explored as a tool to support risk assessment, exercise prescription, and monitoring during CR. However, current evidence remains limited and heterogeneous. Omics-based approaches are primarily confined to research and translational settings, and their role in guiding individualized exercise prescription or clinical decision making in CR remains unestablished. Similarly, while AI shows promise for improving adherence, remote monitoring, and data integration, its effectiveness for optimizing clinical outcomes in CR requires further validation. Overall, precision CR represents a promising but still evolving paradigm. Future progress will depend on well-designed clinical trials, real-world data integration, standardized methodologies, and interdisciplinary collaboration to determine whether these technologies can be safely and effectively integrated into routine clinical practice.

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.

Background: Visceral adipose tissue (VAT) is a key determinant of cardiometabolic risk, yet its underlying molecular mechanisms remain incompletely characterized. Metabolomics offers an opportunity to identify circulating biomarkers that capture VAT-related biology beyond conventional clinical measures. Methods: We conducted a cross-sectional analysis of 2526 participants from the Qatar Biobank using untargeted serum metabolomics profiling. VAT was quantified using DXA-derived estimates and analyzed both as a continuous variable and by comparing individuals in the highest quartile to the remainder quartiles. Associations between metabolites and VAT were assessed using multivariate partial least squares discriminant analysis and adjusted linear regression models controlling for age, sex, and BMI, with Bonferroni correction for multiple testing. Results: Continuous VAT was associated with 106 metabolites, while the Q4 versus Q1–Q3 contrast identified 23 metabolites, with overlapping metabolites defining a robust core VAT signature. Higher VAT was characterized by coordinated elevation of branched-chain amino acids and their keto/hydroxy acid derivatives, glutamate, and central carbon intermediates, consistent with impaired mitochondrial oxidative decarboxylation. We further identified 4-hydroxyglutamate as a novel collagen-derived metabolite positively associated with VAT, suggesting a potential link between extracellular matrix remodeling and glutamate-centered metabolism. Additionally, greater VAT was associated with lower concentrations of glycine and glycine conjugates and reduced levels of unsaturated sphingomyelins and plasmalogens. Conclusions: These findings provide potential mechanistic insights into VAT-related metabolic dysfunction and identify candidate circulating biomarkers that may enable non-invasive assessment of visceral fat-associated cardiometabolic risk. Longitudinal and mechanistic studies are warranted to establish causality and clinical utility.

Background: Children with obesity-related asthma exhibit poorer symptom control and more frequent exacerbations than their normal-weight peers, but the underlying metabolic mechanisms are unclear. This study aimed to identify drivers of obesity-related asthma through untargeted plasma metabolomic and lipidomic profiling. Methods: Plasma was obtained from normal weight (NW) asthmatic (n = 95) and non-asthmatic (n = 67) and overweight/obese (OO) asthmatic (n = 99) and non-asthmatic (n = 100) children (6–17 years). We assessed metabolic and lipidomic differences between asthmatics and controls within each BMI group using orthogonal partial least squares discriminant analysis (OPLS-DA), examined overlap with the adult Qatar Biobank cohort, and mapped metabolic–clinical interactions using Gaussian Graphical Models. Results: In the fitted OPLS-DA models, separation between asthmatic and control groups was stronger in the NW group (R²Y = 0.72/0.52) than in OO (R²Y = 0.65/0.63) children. Asthma was associated with altered tricarboxylic acid (TCA) intermediates, ether-linked phosphatidylethanolamines, and sphingomyelins (SM) in NW, and with phosphatidylcholines, lysophosphatidylcholines, and phosphatidylethanolamines in OO. Integrating metabolomic, lipidomic, and clinical data revealed connections between altered SMs and interleukins, and TCA intermediates and electrolytes, all associated with elevated leptin in NW. An increased residual volume to total lung capacity ratio in OO was associated with phospholipid shifts. The overall dynamics in lipid metabolism with asthma, conditioned on BMI, was also observed in the adult Qatar Biobank cohort. Conclusions: Among NW children with asthma, we found enhanced TCA cycle activity and inflammation linked to altered SM metabolism, whereas in OO, the findings suggest oxidative stress arising from chronic obesity-related inflammation. These data reveal BMI-specific metabolic mechanisms of pediatric asthma that might inform precision approaches to disease management.