Sahabettin Selek

Growth retardation, defined by impaired anthropometric development relative to age-based population standards, may be accompanied by metabolic alterations. This study aimed to characterize serum and urine metabolomic profiles in children with growth retardation using exploratory ¹H-NMR spectroscopy. Thirty children with growth retardation and 24 controls were included in this cross-sectional study. Serum and urine samples were analyzed by ¹H-NMR spectroscopy, and annotated metabolites were evaluated using Chenomx, MetaboAnalyst, and R software. Clinical and laboratory variables were compared between groups. Metabolomic analysis included univariate comparisons, volcano plots, PCA, OPLS-DA, Spearman correlation analysis, age-adjusted regression, exploratory ROC analysis, and metabolite set enrichment analysis. The growth retardation group had lower age and BMI than controls, while serum iron and AST were higher. Volcano plot analysis showed a more distinct pattern of nominal metabolite alterations in serum than in urine. In serum, 3-hydroxybutyrate and glucuronate showed relative increases, whereas N-acetylgalactosamine, alanine, glutamate, and glucose showed relative decreases. In urine, metabolite differences were less pronounced. Multivariate analysis demonstrated a significant overall group effect in serum but not in urine. Within the growth retardation group, age was negatively correlated with serum 3-hydroxybutyrate and positively correlated with N-acetyl-L-aspartic acid, whereas several urinary metabolites showed negative correlations with age. After age adjustment, only 4-ethylbenzoic acid remained significant in serum after multiple testing correction. Exploratory ROC analysis identified N-acetylgalactosamine and 4-ethylbenzoic acid in serum and beta-alanine and creatinine in urine as the metabolites with the highest within cohort classification tendency. Children with growth retardation exhibited modest metabolic differences, with more consistent group-related alterations observed in serum than in urine. These findings mainly suggest changes related to energy metabolism, amino acid turnover, and intermediary metabolism. However, the results remain exploratory and require confirmation in larger, age-stratified and independently validated cohorts.

Background: Bipolar disorder (BD) is a chronic psychiatric illness characterized by alternating episodes of mania and depression. Diagnostic challenges arise from the subjective nature of clinical assessments, often leading to delayed or inaccurate treatment. The identification of objective biomarkers specific to BD may improve diagnostic accuracy and enable early intervention. This study aimed to analyze serum metabolomic profiles of BD patients during euthymic and manic states to identify potential diagnostic and predictive biomarkers. Methods: Serum samples were collected from 33 patients in the manic phase, 25 patients in the euthymic phase, and 40 healthy controls. Samples were analyzed using high-resolution liquid chromatography-mass spectrometry. Metabolite levels were determined semi-quantitatively. The identified metabolic features were analysed using univariate statistics, confounder-adjusted logistic regression, and multivariate and pathway enrichment approaches. Results: Comparisons between patients with BD and healthy controls revealed alterations primarily in lipid-related metabolites, including primary fatty acid amides, phosphatidylcholine derivatives, and acylcarnitines. Following adjustment, oleamide and 2-arachidonoylglycerol were found to be significantly associated with the euthymic state relative to the control group. In manic patients, several lipid metabolites exhibited consistent directional changes compared to the control group, although most of these associations did not survive multiple testing correction. Direct comparison between the manic and euthymic states identified a limited number of metabolites, suggesting metabolic differences related to the state. Conclusion: This exploratory serum metabolomics study has identified subtle yet biologically plausible metabolic alterations associated with bipolar disorder and its mood states. The findings emphasise the importance of lipid metabolism and mitochondrial energy pathways in the pathophysiology of BD, while also highlighting the influence of mood state. Although the results are preliminary and require validation in larger, longitudinal cohorts that are preferably drug-naïve, they lay the groundwork for future targeted metabolomic studies aimed at clarifying state- and disease-related metabolic signatures in BD. Graphical Abstract

Rotator cuff (RC) tears are a prevalent source of shoulder pain that restrict everyday activities and diminish quality of life. While MRI reveals structural damage in the tendon, it is uncertain if RC illness is also indicated in the body's circulating metabolites. The establishment of systemic signals may guide the future creation of blood‐based biomarker panels; however, existing evidence is currently insufficient. This exploratory, hypothesis‐generating case–control study investigated serum metabolites via proton nuclear magnetic resonance ( ¹ H‐NMR) in persons with full‐thickness RC injuries and age‐ and sex‐matched healthy controls (50 cases, 50 controls). Spectra underwent meticulous quality assessments, and predetermined statistical analyses were conducted with management of false discoveries. We also examined how well different metabolites discriminated RC tears from controls and explored which metabolic pathways were most represented. Numerous metabolites exhibited variations between the groups. Concentrations of N‐carbamoyl‐β‐alanine, taurine, erythritol, and N‐acetylaspartate were elevated in RC tears, but levels of 3‐methylhistidine and proline were diminished. Pathway enrichment revealed pyrimidine metabolism, bile‐acid biosynthesis, taurine/hypotaurine metabolism, β‐alanine metabolism, and histidine metabolism, indicating coordinated perturbations at the pathway level rather than isolated alterations. These data must be regarded as preliminary and hypothesis‐generating, lacking the establishment of causation or immediate clinical applicability. Although preliminary, these findings are scientifically significant because they suggest that full‐thickness RC tears may be associated with a coordinated systemic metabolic signature rather than only a focal tendon abnormality. This systems‐level perspective adds to the current understanding of RC pathology by generating biologically plausible hypotheses related to tissue remodeling, oxidative stress adaptation, and energy metabolism. Prospective longitudinal investigations with targeted quantification, external validation, and integration with imaging and clinical outcomes are required before clinical translation.

Vertigo is a type of dizziness that typically occurs without movement and may arise independently or as a symptom of an underlying condition. It is classified as either central or peripheral, with central vertigo often caused by brain tumors, hemorrhages, or vascular aneurysms. This study aims to identify functional metabolites associated with vertigo and explore their related metabolic pathways. Metabolic profiling of serum samples from 33 vertigo patients and 34 healthy controls was conducted using ¹ H‐QNMR spectroscopy. Following log ₁₀ transformation and normalization, univariate and multivariate analyses (FC, p ‐value, FDR, and VIP) were applied to identify significant metabolites. KEGG‐based pathway and disease enrichment analyses were performed to explore biological relevance and clinical associations. ROC analysis was performed to evaluate the ability of selected metabolites to discriminate between vertigo patients and healthy controls within this exploratory dataset, and inter‐metabolite relationships were assessed using Pearson correlation. Vertigo patients showed significantly different metabolomic profiles compared to healthy controls. Serum levels of capric acid, homocysteine, O‐phosphoethanolamine, methylmalonate, N‐carbamoylaspartate, and proline were significantly elevated, while glycylproline levels were decreased. These findings suggest a potential link between vertigo and these functionally relevant metabolites. Vertigo patients showed lower glycylproline and higher homocysteine and capric acid levels than healthy controls. These metabolites discriminated between groups in this exploratory cohort, but they should not be interpreted as clinically validated diagnostic markers. Further independent, longitudinal, and clinically well‐characterized studies are required.

Objective: Iron deficiency anemia is primarily evaluated using hematological and iron‐related parameters; however, these markers may not fully reflect the systemic metabolic consequences of impaired oxygen delivery and altered iron availability. This study aimed to characterize serum metabolomic alterations associated with iron deficiency anemia using an untargeted LC‐HRMS approach. Materials and Methods: Serum samples from 47 individuals with iron deficiency anemia and 50 healthy controls were analyzed by LC‐HRMS in positive and negative ionization modes. Data processing and metabolite annotation were performed using MassCube and MZmine workflows. Multivariate and univariate analyses, covariate‐adjusted models, metabolite set enrichment analysis, correlations with hematologic/iron indices, and internally cross‐validated exploratory ROC analyses were performed. Results: PCA showed partial separation between iron deficiency anemia and control groups, while OPLS‐DA demonstrated clearer group discrimination. Differential metabolites indicated prominent alterations in amino acid, central carbon, nitrogen‐related, and redox‐associated pathways. The principal amino‐acid/nitrogen‐related differences remained significant after adjustment for age, sex, albumin, and total protein. In the full cohort, several discriminatory metabolites correlated with hemoglobin, ferritin, and transferrin saturation, although these correlations did not remain FDR‐significant within the IDA group alone. Repeated stratified 5‐fold cross‐validation yielded a mean AUC of 0.964 for the three‐metabolite model comprising isoleucine, isonicotinic acid, and phenylalanine. Conclusion: Untargeted serum metabolomics revealed a distinct metabolic signature in iron deficiency anemia, mainly involving amino acid remodeling, central carbon metabolism, nitrogen balance, and redox‐associated pathways. These findings provide a basis for future targeted and externally validated metabolomics studies.

Polycystic ovary syndrome (PCOS) is a complex endocrine and metabolic disorder, and its diagnosis remains controversial due to heterogeneous phenotypes and varying diagnostic criteria. Insulin resistance and its metabolic consequences are central features of PCOS management. Metabolomics has increasingly been applied to elucidate the pathophysiology of complex disorders. In this study, we sought to determine which serum metabolites and metabolic pathways are differentially altered in women with PCOS compared with healthy controls, thereby addressing whether metabolomic profiling can reveal candidate biomarkers for early diagnosis and potential therapeutic targets. Fifty patients diagnosed with PCOS and 50 healthy controls matched for age and body mass index (BMI) were included in the study. Blood samples were collected for metabolomic analysis and routine biochemical parameters. Metabolomic analysis was performed by UPLC-HRMS and data were processed using MZmine, TidyMass and MetaboAnalyst. Metabolite annotation was performed using databases such as HMDB, MassBank and MoNA. Metabolomic analysis revealed 49 compounds in the serum of PCOS patients, 39 of which were upregulated and 10 of which were downregulated. Compounds such as di(2-ethylhexyl) phthalate (DEHP), promethazine N-oxide, tetrahydromagnolol, 5-methyl-5-phenylhydantoin, valerenic acid, butylparaben, erucamide, DDAO, d-erythro-sphinganin-1-phosphate and 1-arachidoyl-2-hydroxy-sn-glycero-3-phosphocholine were significantly higher in the PCOS group. Compounds such as L-methyladenosine, cystine, glu-gln and 2,2’-methylene-bis(6-tert-butyl-4-methylphenol) were significantly lower. In the pathway analysis performed using KEGG database, sphygolipid metabolism, sphygolipid signaling pathway, neuroactive ligand-receptor interaction and phenylalanine metabolism were found to be the most affected pathways. This study demonstrates distinct alterations in lipid and amino acid metabolism in PCOS and highlights the accumulation of exogenous molecules, including endocrine disruptors, in patient serum. By integrating metabolomic profiling with clinical phenotyping, our findings provide novel insights into PCOS pathophysiology and suggest potential serum biomarkers that may support early diagnosis and personalized therapeutic approaches.

Fibromyalgia (FM) is a multifactorial syndrome with poorly understood pathophysiology, characterized by chronic widespread pain and fatigue affecting the central and peripheral nervous systems, as well as the endocrine and muscular systems. These characteristics create significant challenges in diagnosis and treatment. This study compared the metabolic profiles of FM patients and healthy controls using one‐dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy and multivariate statistical analysis to identify potential biomarkers associated with FM symptoms. Urine and serum samples from 50 FM patients and 50 healthy individuals underwent untargeted metabolic profiling. Quantitative ¹ H‐NMR spectroscopy was used to determine metabolic changes. Chemometric models and receiver operating characteristic (ROC) curves were generated using the MetaboAnalyst platform. Fold change (FC) analysis and t ‐tests were conducted to determine statistically significant differences between groups. Our findings showed significant differences at the metabolic level between FM patients and healthy controls. Increased urea, glutamate, valine, taurine, proline, glycine, and homoserine metabolites, and decreased benzoate, leucine, π‐methylhistidine, galactitol, τ‐methylhistidine, glutamine, 3‐hydroxykynurenine, and fructose levels were observed in FM serum; while increased malate, dimethylamine, trimethylamine N ‐oxide (TMAO), creatine phosphate, N ‐phenylacetylglycine, N ‐acetylglutamate (NAG), hippurate, and urea levels were observed in FM urine, and decreased guanidoacetate, creatine, malonate, serine, glucuronate, creatinine, and uracil levels were observed. Serum glutamate was positively correlated with waist/hip ratio (WHR) and negatively correlated with Fibromyalgia Impact Questionnaire (FIQ) and Visual Analog Scale (VAS); negative correlations existed between taurine and FIQ and VAS; a positive correlation existed between urea and WHR; there was a positive correlation between anserine and body mass index (BMI). Also, in urine, there were positive correlations between TMAO, N ‐phenylacetylglycine, glutamate, and xylose and WHR; negative correlations existed between glutamine and FIQ; and a positive correlation existed between glucuronate and FIQ. Our findings provide important information about potential biomarkers, associated different metabolites, and metabolic pathways of FM patients; we think that they will provide new insight into the pathogenesis of the disease and help expand our knowledge.