Search Results (423)

Gestational diabetes mellitus (GDM), as one of the most common metabolic disorders occurring during pregnancy, represents a significant public health concern due to its rising prevalence and the numerous complications that can affect both the mother and the foetus. In recent years, there has been growing interest in the use of omics technologies, such as metabolomics and proteomics, in research on the pathogenesis and early detection of GDM. The aim of this paper was to summarise the current knowledge on metabolomic and proteomic changes observed in women with GDM and to assess the potential usefulness of these methods in identifying biomarkers of the disease. The narrative review was conducted in accordance with the PRISMA 2020 statement, using PubMed and Web of Science until 23 December 2025. The studies analysed show that GDM is associated with abnormalities in the metabolism of lipids, amino acids, carbohydrates and metabolites associated with the gut microbiota. The most commonly observed changes included: elevated levels of branched-chain amino acids, free fatty acids and purine metabolites, as well as changes in the metabolism of phospholipids and acylcarnitines. Multi-omics studies also indicate significant changes in plasma protein and lipid profiles. The data collected suggest that omics technologies may be a promising tool for identifying early biomarkers of GDM and for developing our understanding of the pathophysiological mechanisms of this condition. Nevertheless, further studies involving larger and more diverse patient populations are needed to confirm their diagnostic and clinical value. Full article

Background: Cardiovascular diseases (CVDs) remain the leading global cause of mortality, yet a critical “translational gap” persists: Conventional biomarkers often fail to detect subclinical stages or predict individual disease trajectories. While single-omics studies have proliferated, the field lacks a unified framework synthesizing these molecular layers with advanced computational intelligence. Aim: This review addresses this gap by evaluating the synergistic integration of multi-omics and Artificial Intelligence (AI) to transition from descriptive markers toward predictive precision cardiology. Scope: Evidence from non-coding RNA networks (miRNAs, lncRNAs) and exosomal trafficking is synthesized alongside a critical assessment of Machine Learning (ML) architectures, including supervised, unsupervised, and deep learning (DL) models. Findings: Unlike traditional reviews, this work delineates the specific pipelines required to deconvolute high-dimensional signatures—such as TMAO, acylcarnitines, and cardiac-enriched miRNAs—into actionable risk models for heart failure (HF) and post-infarction outcomes. The primary barrier to clinical translation is identified not as data scarcity but as the lack of standardized bioinformatic workflows and model interpretability. Conclusions: This review distinguishes itself by proposing an integrated molecular–computational framework that prioritizes Explainable AI (XAI) and standardized multi-omic protocols. Such a shift is essential to bridge the gap between high-dimensional biological insights and routine clinical decision-making. Full article

Serum and plasma are the most widely used matrices in metabolomics and human biomonitoring studies; however, the optimal matrix for integrated non-targeted analysis (NTA) workflows combining metabolomics and exposomics has not been systematically evaluated. This pilot study applied parallel NTA workflows to paired serum and plasma samples from five individuals to characterize matrix-dependent differences and provide an empirical basis for matrix selection in integrated studies. Three analytical methods were employed: one metabolomic method (Method 1) using Hydrophilic Interaction Liquid Chromatography (HILIC) and Reversed-Phase Liquid Chromatography (RPLC) columns and one exposomics (Method 2) method using an RPLC column, each analyzed in both electrospray ionization (ESI) positive and negative modes. Overall, serum and plasma showed broad similarity, with substantial overlap in detected features and strong linear correlations between paired samples (R² = 0.70–0.87). However, PCA revealed systematic differences between the two matrices along PC1 and PC2, likely attributable to matrix effects arising from coagulation-related compositional changes in serum. For metabolomics, glycerophospholipids, sphingolipids, and acylcarnitines were consistently enriched in serum, attributable to platelet activation and phospholipase release during blood coagulation, consistent with prior reports. In contrast, oxidized fatty acid species were predominantly elevated in plasma, warranting caution in oxylipin-focused studies using serum. For exogenous chemical profiling, the two matrices performed comparably, with 32 out of 36 annotated features showing no significant matrix-dependent differences (p > 0.05), including PFAS, pharmaceuticals, and diverse xenobiotics. These findings support the interchangeability of serum and plasma for broad exposomics studies. Overall, while both matrices provided broadly comparable global coverage, plasma may represent a more appropriate matrix for integrated NTA workflows, as it better preserves in vivo metabolite composition and minimizes coagulation-induced confounding, though validation in larger cohorts is needed. Full article

Background/Objectives: Very long-chain acyl-CoA dehydrogenase deficiency (VLCADD) is an autosomal recessive disorder of mitochondrial fatty acid β-oxidation caused by pathogenic variants in ACADVL. The clinical spectrum is highly heterogeneous, ranging from lethal neonatal cardiomyopathy to late-onset myopathy. This study aims to characterize the rare c.215C>T (p.Ser72Phe) variant, identified in compound heterozygosity with the common pathogenic allele c.848T>C (p.Val283Ala) in a male neonate detected by newborn screening (NBS). Methods: Genetic analysis was performed using Sanger sequencing on the proband and his family members. The pathogenicity of the p.Ser72Phe variant was evaluated through multiple bioinformatic predictors and interpreted according to ACMG/AMP guidelines. To understand the functional impact on the protein, structural modeling was conducted using FoldX 4.0 for energy calculations and UCSF ChimeraX for the visualization of conformational changes and cofactor-binding site perturbations in the VLCAD homodimer. Results: At the end of the first postnatal week, liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis of dried blood spots of the proband revealed a markedly abnormal acylcarnitine profile, with C14:1 levels (1.837 μmol/L) approximately five times above the reference range. Clinical reports documented hypoketotic hypoglycemia, consistent with VLCADD. Segregation analysis demonstrated transmission of both variants within the family, with additional heterozygous and homozygous carriers identified. Bioinformatic predictions uniformly classified p.Ser72Phe as deleterious. This variant has an extremely low allele frequency and affects a highly conserved residue in the FAD-binding domain. Structural modeling with FoldX yielded a mean ΔΔG of +22.63 ± 5.48 kcal/mol, indicating a significant localized thermodynamic burden. Inspection of the mutant model in ChimeraX showed perturbation of the side-chain orientation and attenuation of the local hydrogen-bonding network at the FAD-binding site, together with increased steric packing around residue 72. Taken together, the clinical, genetic, and structural evidence support reclassification of p. Ser72Phe as likely pathogenic according to ACMG criteria, specifically applying the ClinGen ACADVL VCEP specifications. Conclusions: This study expands the ACADVL mutational spectrum and underscores the value of integrating sequencing, segregation, and structural bioinformatics in interpreting rare variants detected through NBS. Full article

Ketosis is one of the most economically significant metabolic disorders affecting periparturient dairy cows, causing production losses and predisposing animals to secondary complications. Current blood-based diagnostics are invasive and provide limited insight into the underlying metabolic perturbations. This study employed an integrated three-platform metabolomics approach to characterize milk metabolite alterations in ketotic Holstein dairy cows and to evaluate milk-based biomarker panels for early ketosis detection. Milk samples from 20 healthy control (CON) cows and 6 ketotic cows were collected at 2 weeks postpartum and analyzed by direct injection/liquid chromatography–tandem mass spectrometry (DI/LC-MS/MS), proton nuclear magnetic resonance (¹H-NMR) spectroscopy, and inductively coupled plasma mass spectrometry (ICP-MS). Ketosis was confirmed by serum β-hydroxybutyrate concentrations ≥ 1400 μmol/L. Principal component analysis, partial least squares-discriminant analysis, and receiver operating characteristic (ROC) curve analyses were applied. All three platforms discriminated ketotic cows from healthy cows, with clear cluster separation validated by 2000 permutation tests (p < 0.05). DI/LC-MS/MS identified 16 significantly altered metabolites (p < 0.05), with butyrylcarnitine (C4), phosphatidylcholine 30:0 (PC 30:0), ether-linked phosphatidylcholine O-38:3 (PC O-38:3), and citrulline identified as the top discriminatory biomarkers (AUC = 0.920; 95% CI: 0.85–0.98; sensitivity = 91.7%; specificity = 93.3%). ICP-MS revealed significantly reduced selenium (Se, p = 0.017), manganese (Mn, p = 0.045), and chromium (Cr, p = 0.037), as well as elevated cobalt (Co, p = 0.014) in ketotic milk (AUC = 0.870). ¹H-NMR detected no individually significant metabolites; however, multivariate analysis distinguished groups (AUC = 0.890), with succinate (numerical fold change: +5.77×; p = 0.059), methanol (−1.94×; not significant), and acetate (+2.88×; not significant) as top VIP contributors. The combined multi-platform biomarker panel (joint classification using top VIP features from all three platforms, without formal data fusion) achieved superior diagnostic performance (AUC = 0.970; 95% CI: 0.93–1.00; sensitivity = 95.0%; specificity = 96.7%). These findings identify coordinated perturbations in glycerophospholipid metabolism, acylcarnitine profiles, amino acid homeostasis, antioxidant mineral status, and energy metabolism during early ketosis, and suggest that milk metabolomics is a promising non-invasive approach for precision dairy health monitoring, pending validation in independent cohorts. We acknowledge the small ketotic group size (n = 6) as a limitation; therefore, these findings should be considered discovery cohort observations requiring prospective validation before clinical translation. Full article

Background: Diabetic nephropathy (DN) is characterized by complex and region-specific metabolic dysregulation that is not captured by conventional biomarkers. However, the spatiotemporal organization of metabolic alterations across renal compartments in type 1 diabetes remains poorly understood. Methods: In this study, spatial metabolomics based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) was applied to investigate metabolic alterations in kidney tissues from alloxan-induced diabetic rats at 4 and 8 weeks post-induction. Complementary LC–MS/MS metabolite profiling and label-free proteomic analysis were performed to support pathway interpretation. Results: Spatial metabolomics revealed pronounced region- and time-dependent metabolic reprogramming in diabetic kidneys. Early-stage (DN-4w) changes were characterized by elevated glucose and activation of glucose-associated pathways, including the polyol pathway, accompanied by accumulation of acylcarnitines and lipid intermediates, indicating metabolic substrate overload. At later stages (DN-8w), glucose and related metabolites declined, reflecting impaired metabolic capacity and mitochondrial dysfunction. Broad remodeling of lipid metabolism, including glycerophospholipids, fatty acids, and hexosylceramide, was observed, along with dysregulation of amino acid metabolism and redox-related pathways. These alterations exhibited clear regional heterogeneity across renal cortex and medulla, highlighting compartment-specific metabolic vulnerability. Conclusions: This study provides a comprehensive spatial characterization of metabolic perturbations during DN progression, revealing coordinated alterations in glucose utilization, lipid metabolism, and mitochondrial function. The findings demonstrate the value of spatial metabolomics in uncovering region-specific metabolic mechanisms and provide new insights into the pathogenesis of diabetic nephropathy. Full article

Background and Objectives: Diabetic peripheral neuropathy (DPN) is a common and debilitating complication of obesity and type 2 diabetes (T2D) affecting up to 50% of patients with long-standing disease. While chronic hyperglycemia plays a central role in its pathogenesis, intensive glycemic control provides only partial protection, suggesting the involvement of additional metabolic pathways. The primary objective of this systematic review was to evaluate the role of lipid metabolism disturbances and advanced lipidomic alterations in the development and progression of DPN in patients with obesity and T2D. Secondary objectives included identifying specific lipid species associated with DPN and exploring their potential pathophysiological and clinical implications. Methods: This systematic review included 8 studies that met the inclusion criteria and was conducted according to PRISMA guidelines and registered in PROSPERO/2026/CRD420261288920. Study quality was assessed using the Newcastle–Ottawa Scale. Results: Large population-based cohorts reported a consistent association between hypertriglyceridemia and DPN prevalence, with triglyceride levels >204 mg/dL associated with an approximately 40% increased risk. Lipidomic analysis revealed alterations in acylcarnitine, sphingolipids, and phospholipids. However, the evidence remains limited and heterogeneous, and neuropathy-specific outcomes were insufficiently evaluated in interventional studies. Conclusions: Lipid metabolism disturbances, particularly hypertriglyceridemia and specific lipidomic alterations, may contribute to DPN beyond the effects of hyperglycemia. Although not yet clinically actionable, lipidomic alterations may represent promising future biomarkers and therapeutic targets in DPN. However, the current evidence is limited by heterogeneity and predominantly observational designs. Further well-designed longitudinal and interventional studies are needed to clarify causal relationships and clinical relevance. Full article

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by complex and interconnected pathophysiological mechanisms, including mitochondrial dysfunction, oxidative stress, neuroinflammation, lysosomal impairment, and altered neurotransmitter metabolism. Unlike cerebrospinal fluid or blood, urine offers a truly non-invasive source of biomarkers, reflecting systemic metabolic changes and renal protein excretion linked to neurodegeneration. This review aims to critically synthesize current evidence on urinary biomarkers in PD and to organize this heterogeneous literature into pathophysiologically meaningful domains. Methods: A comprehensive literature search of human studies investigating urinary biomarkers in PD was performed. Eligible studies were comprehensively analyzed and classified according to dominant biological pathways. To facilitate interpretation, findings were organized into six thematic domains: genetic and protein-based biomarkers; metabolic pathways and mitochondrial dysfunction; oxidative stress and neuroinflammation; gut–brain-axis-related metabolites; hormonal and systemic biomarkers; and emerging exploratory markers. Results were summarized in domain-specific tables and integrated using a conceptual framework. Results: A total of 32 human studies met the inclusion criteria, revealing diverse urinary molecular signatures associated with PD across multiple biological domains. Genetic and protein-based markers, including LRRK2-related proteins, α-synuclein species, and lysosomal lipids, showed potential for disease stratification. Metabolomic studies consistently identified alterations in acylcarnitines, organic acids, and amino acid metabolism, reflecting mitochondrial dysfunction. Biomarkers related to oxidative stress, immune activation, gut microbiota metabolism, and hormonal regulation further highlighted the systemic nature of PD. However, most individual biomarkers lacked disease specificity and exhibited methodological heterogeneity. Conclusions: Current evidence supports urine as a valuable source of systemic biomarkers reflecting multiple pathophysiological processes in PD. While single urinary markers remain insufficient for clinical application, integrated omics-based approaches—particularly metabolomics and peptidomics/proteomics—hold promise for identifying combinatorial biomarker signatures. Future longitudinal and standardized studies are required to enhance specificity and translational potential for non-invasive diagnosis and disease monitoring in PD. Full article

The gut microbiota plays a crucial role in regulating fish growth. In this study, we performed 16S rRNA sequencing and metabolomics to compare the gut microbiota and metabolic profiles of fast-growing (FG) and slow-growing (SG) brass gudgeon (Coreius heterodon) from the same family, reared under identical conditions for 12 months. Our results revealed that there was no significant difference in the overall gut microbiota structure between FG and SG groups, but significant differences were observed at specific phylum and genus levels. The FG group harbored a greater abundance of potential probiotics (e.g., Prevotella, Lactobacillus, and Lachnospiraceae NK4A136_group), while opportunistic pathogens such as Klebsiella and Pseudomonas were less abundant. Metabolomics analysis identified 136 differential metabolites, among them, 61 were upregulated and 75 were downregulated in the FG group, with higher levels of phosphatidylcholine, acylcarnitine, and amino acid derivatives in the FG group. KEGG pathway analysis showed enrichment of butanoate metabolism, tryptophan metabolism, and pyrimidine metabolism in the FG group. Spearman correlation analysis indicated that specific gut microbiota was significantly correlated with metabolites involved in energy metabolism, gut homeostasis, and oxidative balance. These findings revealed associations between specific gut microbiota, gut metabolites, and growth performance in brass gudgeon. Although overall community structure did not differ significantly between groups, the compositional and metabolic shifts observed suggest that the gut microbiota–metabolite association might be linked to growth variation. This study provided new insights into the microbiota–metabolite–growth axis of brass gudgeon and offers valuable reference information for the development of specialized probiotic feeds for this species. Full article

Background/Objectives: Acylcarnitine has been linked to adiposity, yet evidence in healthy adults is scarce. Hence, we aim to investigate associations of circulating acylcarnitine levels with traditional and newer anthropometric markers cross-sectionally, and with future weight change prospectively. Methods: We used data from CoLaus|PsyCoLaus cohort of apparently healthy adults in Lausanne, Switzerland. Anthropometry markers include body mass index, waist circumference, waist–hip ratio, conicity index, body roundness index, body shape index, leptin, adiponectin and grip strength. Results: Cross-sectionally, free carnitine, short-chain acylcarnitines (SCACs: C0, C3:0, C4:0 C5:0 and C5:0-OH), medium-chain acylcarnitines (MCACs: C6:0 and C8:1) and long-chain acylcarnitines (LCACs: C16:0) were positively associated with most anthropometric markers. After multivariate adjustment, only free carnitine, SCACs (C3:0 and C5:0), and MCAC C8:1 retained their positive associations with multiple markers. SCACs showed the strongest associations (−log10 p-values up to 91), followed by free carnitine and Deoxycarnitine. When stratified by sex, C8:1 showed consistent positive associations with anthropometric markers only in females. Prospectively, a higher baseline level of SCAC (C5:0-OH) was associated with ≥5 kg weight gain at both 5- and 10-year follow-ups, whereas higher baseline levels of MCAC (C8:1) and LCACs (C16:0 and C18:2) were associated with weight gain only at 10 years. Conclusions: SCAC showed most consistent associations with multiple anthropometric markers. Prospectively, specific ACs were associated with weight gain, suggesting that baseline AC levels may reflect early metabolic alterations linked to adiposity. Full article

The plasma membrane plays essential roles in cellular transport and signaling. One of its fundamental structural features is the asymmetric distribution of lipids between the inner and outer leaflets. This asymmetry is actively maintained by lipid transport systems, including flippases, floppases, and scramblases, and is critical for membrane integrity and signaling regulation. Accumulating evidence indicates that membrane lipid asymmetry is frequently altered in cancer cells, leading to the externalization of normally inner-leaflet phospholipids such as phosphatidylserine and phosphatidylethanolamine. These alterations can influence tumor signaling, immune interactions, and membrane-associated biological processes. Recent studies further suggest that metabolic reprogramming in cancer may play an important role in regulating membrane lipid asymmetry. Changes in cellular energy status, oxidative stress, calcium signaling, and lipid metabolism can modulate lipid transport systems and membrane organization. In addition, tumor metabolism generates diverse circulating metabolites, including lactate, lysophospholipids, and acylcarnitines, which may influence membrane properties and lipid redistribution. These observations raise the possibility that membrane lipid asymmetry functions as a metabolically responsive interface linking intracellular metabolic state to cell surface signaling and tumor–microenvironment interactions. In this review, we propose a conceptual framework in which cancer-associated metabolic reprogramming influences lipid transport systems and membrane organization, thereby reshaping phospholipid distribution across the plasma membrane. We discuss how metabolic perturbations—including changes in energy metabolism, redox balance, calcium signaling, and lipid remodeling—may regulate membrane lipid asymmetry and explore the implications of these processes for tumor signaling, immune interactions, and emerging membrane-targeted therapeutic strategies. Full article

Background/Objectives: Colorectal cancer (CRC) is a leading cause of cancer-related mortality, with colorectal liver metastasis (CRLM) being the major determinant of poor prognosis. Tumor metabolic reprogramming and spatial heterogeneity complicate biomarker discovery and clinical management. This study aimed to characterize the spatial metabolomic landscape of CRC and identify progression-associated metabolic alterations and potential metabolic signatures for liver metastasis. Methods: A total of 23 tissue samples were collected from patients with CRC, with and without liver metastasis. Air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) was used to map the spatial metabolite distributions. Region-of-interest analysis guided by histopathology enabled comparative metabolomic profiling across different tissue types. Multivariate statistical analysis, pathway enrichment, and receiver operating characteristic (ROC) curve analyses were performed to identify key metabolic alterations and evaluate potential biomarker performance. Results: Distinct spatial metabolomic profiles were observed across normal mucosa, primary tumors, liver metastases, and normal liver tissues. In primary colorectal tumors, amino acid, purine, and choline metabolism were significantly upregulated, whereas liver metastases were characterized by elevated levels of triglycerides, diglycerides, cholesteryl esters, and acylcarnitines, indicating enhanced lipid synthesis, incomplete fatty acid oxidation, and/or mitochondrial dysfunction. Progression-associated analyses across tissue types revealed consistently increasing trends in glycerides and acylcarnitines, along with heterogeneous alterations in amino acids and phospholipids. Furthermore, 122 differential metabolites were identified between metastatic and non-metastatic CRC, and a four-lipid panel demonstrated strong discriminatory performance. Conclusions: This study provides a spatially resolved characterization of metabolic reprogramming during CRC progression and liver metastasis, highlighting lipid and amino acid metabolism as key features and revealing the metabolic signatures of CRLM. Full article

COASY-related disorders (CRDs) are a spectrum of autosomal recessive conditions caused by the dysfunction of CoA synthase, an enzyme responsible for the final steps of CoA synthesis. Clinical manifestations of CRDs are highly variable, ranging from perinatal lethal pontocerebellar hypoplasia to childhood-onset neurodegenerative brain iron accumulation, which is often recognized after clinical regression. Recent reports have described a few individuals with CRD who screened positive for carnitine palmitoyltransferase-I deficiency by newborn screening (NBS). However, heterogeneous clinical presentations, conflicting biochemical/molecular sequencing of CPT1A, and a lack of metabolic characterization have led to lengthy, costly diagnostic journeys. To address some of these aspects, this investigation retrospectively evaluated NBS acylcarnitine patterns in five CRD cases using Collaborative Laboratory Integrated Reports (CLIR). A total of 25 metabolites/ratios were identified to deviate significantly from reference ranges and were primarily composed of elevated free carnitine and reduced long-chain acylcarnitine levels. While low acylcarnitine concentrations are often not reported due to a lack of lower reference cutoffs, ratios involving these metabolites relative to short-chain acylcarnitines could aid in identifying CRD cases via NBS. When comparing this pattern to CPT-Ia cases, we confirmed a nearly identical acylcarnitine pattern between these, and thus support the need to consider CRD in cases with NBS results suggestive of CPT-Ia. This study is the first case series to characterize NBS patterns in patients with CRD and highlights the unique opportunity for early detection, particularly in cases that are neonatally asymptomatic and have unremarkable confirmatory biochemical results. Full article

Blood metabolism in dairy cows is crucial for milk quality, functioning primarily through the “blood–milk” metabolic axis. Allium mongolicum Regel (AMR), a functional Allium herb, has been shown to regulate on ruminant lipid metabolism. This study investigated the impact of AMR ethanol extract (AME) on lactation performance, blood lipid parameters, and blood–milk metabolomes. Twelve mid-lactation Holsteins (606 ± 11 kg; milk yield 33.14 ± 2.08 kg/d) of parity 2–3 were assigned to either a basal diet (CON) or a diet supplemented with 54 g/d of AME (AEE). Results indicated that AME significantly decreased plasma triglycerides (TG), C15:0, C16:1, C18:1 n-9 c, C18:3 n-6, monounsaturated fatty acids (p < 0.05) and significantly increased C18:2 n-6 c, polyunsaturated fatty acids (p < 0.05). Lactation performance, including the average daily dry matter intake, daily yields of milk fat, protein and lactose, remained unaffected by the AME addition (p > 0.05). Metabolomic profiling revealed that AME significantly enriched the glycerophospholipid metabolism pathway in plasma, upregulating key phospholipid precursors such as L-serine and Sphinganine. Concurrently, milk metabolomics showed an upregulation of short-chain Acylcarnitines. Plasma TG correlated negatively with both plasma L-serine and milk Acylcarnitines, whereas low-density lipoprotein correlated positively with these energy-driven milk metabolites. These findings suggest that AME may contribute to remodeling the plasma lipid metabolic profile in a manner that could facilitate plasma-to-milk lipid flux. This appears to occur through enhanced hepatic lipid processing and increased mammary lipid utilization, offering preliminary insights into potential nutritional strategies for supporting lipid metabolism in dairy cows. Full article

Background and Objectives: This study investigated whether capillary and intravenous sampling affect acylcarnitine and amino acid profile results analyzed by tandem mass spectrometry. Methods: The study included 120 patients either diagnosed with an inherited metabolic disease or undergoing evaluation for a suspected metabolic disorder at the Department of Pediatric Nutrition and Metabolism, Selçuk University Faculty of Medicine. Paired capillary and intravenous blood samples were collected simultaneously, applied to filter paper, and analyzed by LC-MS/MS to determine acylcarnitine and amino acid profiles. Results: Significant differences were observed between capillary and intravenous samples for several acylcarnitines, including C0, C2, C8, C8.1, C10, C10.1, C14.1, C16, and C18.1 (p < 0.05). In the amino acid profile, arginine, aspartic acid, citrulline, glutamic acid, glycine, leucine + isoleucine, methionine, tyrosine, and the methionine/phenylalanine ratio differed significantly between sampling methods (p < 0.05). Despite these differences, Cohen’s kappa analysis showed high agreement between capillary and venous samples for most parameters (78.3–100%) when categorized as low, normal, or high based on reference ranges. Additionally, no significant discrepancies were found in key diagnostic parameters among patients with specific inherited metabolic diseases. Conclusions: Although certain acylcarnitine and amino acid levels differed between capillary and intravenous samples, overall diagnostic agreement was high. However, since the study group did not include any patients with fatty acid oxidation disorders, a separate confirmatory study is needed for this condition. Larger multicenter studies involving more patients and a wider range of metabolic disorders are needed to better understand the clinical impact of sampling method on dried blood spot analyses. Full article