Metabolic Subtypes and Biomarkers in Preterm and Term Neonates via Targeted Screening

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We performed targeted metabolomics on dried blood spots from 448 preterm (32–36 weeks) and 351 term neonates using tandem mass spectrometry. Preterm infants showed elevated tyrosine, leucine/isoleucine, arginine, and hydroxyoctadecenoylcarnitine (C18:1-OH), with reduced glutamate (FDR < 0.05). Multivariate analyses (PCA, PLS-DA) revealed three metabolic clusters linked to gestational maturity and oxidative stress. Pathway analysis highlighted disruptions in the urea cycle, ammonia recycling, purine metabolism, and fatty acid oxidation. To our knowledge, this is the first study to identify C18:1-OH as a potential biomarker of mitochondrial dysfunction in preterm neonates. These findings suggest metabolic subtypes may guide precision interventions, though longitudinal validation is needed. Biological sciences/Biochemistry Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Preterm Neonates Metabolomics Biomarker Metabolic Subtypes Tandem Mass Spectrometry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Preterm birth, defined as delivery before 37 completed weeks of gestation, accounts for approximately 11% of live births worldwide and remains a leading cause of neonatal morbidity and mortality ( 1 , 2 ). Preterm infants face a unique set of physiological challenges due to the immaturity of multiple organ systems, particularly the liver, kidneys, and metabolic pathways essential for maintaining homeostasis and supporting rapid growth and development. Metabolic adaptation during the neonatal period is critical for survival and long-term health, yet preterm infants often exhibit dysregulated metabolism characterized by altered amino acid profiles, impaired fatty acid oxidation, and oxidative stress ( 3 , 4 ). These metabolic perturbations have been linked to adverse outcomes including neurodevelopmental delay, bronchopulmonary dysplasia, and metabolic syndrome later in life ( 5 , 6 ). The advent of high-throughput metabolomics techniques, particularly liquid chromatography-tandem mass spectrometry (LC-MS/MS), has revolutionized our ability to profile complex metabolic changes in biological samples with high sensitivity and specificity( 7 , 8 ). Antioxidant-related metabolites such as glutathione precursors are commonly reported, reflecting immature enzymatic systems and oxidative stress in preterm infants ( 9 ). While previous studies primarily focus on distinguishing preterm from term infants based on global metabolic signatures, recent evidence suggests significant heterogeneity within the preterm population itself, indicative of distinct metabolic subphenotypes that may have prognostic and therapeutic implications ( 9 ). Identifying these metabolic subgroups requires integrative analytical approaches combining multivariate statistics like principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) with pathway enrichment methods to unravel complex biochemical networks ( 10 , 11 ). We hypothesized that targeted metabolomics of newborn screening data could reveal distinct metabolic subtypes in preterm neonates and nominate novel biomarkers for clinical risk stratification. Methods Study Population This observational, cross-sectional study was conducted to compare the metabolic profiles of preterm and term neonates using dried blood spot (DBS) samples collected through the national newborn screening program. Participants were selected from neonates referred to the Metabolic Laboratory of the Growth and Development Research Center (GDRC), Tehran, Iran, between March 1, 2022, and March 20, 2024. Based on gestational age, infants were categorized into two groups: 448 preterm neonates (233 boys, 215 girls; 32–36 weeks) and 351 sex-matched full-term neonates (37–40 weeks). Feeding type (breast milk/formula) was recorded at the time of sampling. Only live-born infants with complete demographic data and confirmed gestational age were included. Exclusion criteria included major congenital anomalies, previously diagnosed inborn errors of metabolism, incomplete clinical data, maternal gestational diabetes, preeclampsia, or antenatal corticosteroid exposure. Ethics approval and consent The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the Growth and Development Research Center, Tehran University of Medical Sciences, Tehran, Iran (protocol code IR.TUMS.CHMC.REC.1404.105). Written informed consent was obtained from a parent and/or legal guardian of all neonates included in the study prior to the use of their anonymized dried blood spot samples for research purposes. Sample Collection Dried blood spot (DBS) samples were collected by heel prick 48–72 h after birth on Whatman 903 filter paper, air-dried for 3 h at room temperature, and transported to the laboratory for analysis. Targeted Metabolic Analysis by MS/MS Targeted Metabolic profiling of amino acids and acylcarnitines was performed using a triple quadrupole LC/MS system (Shimadzu-8045 LC/MS). A validated commercial kit for newborn screening, MassChrom® Newborn Screening Kit (57000F, non-derivatized; Chromsystems Instruments and Chemicals, Germany), was used according to the manufacturer’s protocol. Analytes corresponded to those routinely included in standard newborn screening panels. Potential confounders, including feeding type (breast milk, formula, or parenteral nutrition) and clinical interventions (e.g., corticosteroid use), were recorded. Sensitivity analyses showed no significant impact of feeding type on key metabolites in preterm versus term comparisons (p > 0.05 for interaction terms). Statistical and Metabolomic Data Analysis Data were analyzed using R software (version 4.2.1) and MetaboAnalyst 6.0. Metabolite concentrations were log-transformed and Pareto-scaled before analysis. Univariate comparisons (t-test, Mann–Whitney U) were adjusted using the false discovery rate (FDR) method (q < 0.05). Multivariate analyses, including principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA) with 5-fold cross-validation (Q² = 0.55, p < 0.001), were applied to identify metabolic patterns. Volcano plots, hierarchical heatmaps (Euclidean distance, Ward’s linkage), and pathway enrichment analyses (KEGG and SMPDB databases) were used to highlight key metabolites and pathways(12-14). All statistical tests were two-tailed, and an FDR-adjusted p-value < 0.05 was considered statistically significant. Results Metabolic profiling, conducted via Electrospray ionization–tandem mass spectrometry (ESI-MS/MS), identified significant metabolic differences between preterm and term infants, analyzed through integrated univariate, multivariate, and pathway enrichment approaches. Univariate Analysis and Volcano Plot Volcano plot analysis (Figure 1) highlighted key metabolites with FDR-adjusted p-values. Tyrosine, glycine, and leucine+isoleucine exhibited the most pronounced differences (-log₁₀(FDR-adjusted p ≈ 30), indicating major metabolic disruptions in preterm infants. Glutamic acid and C18:1OH followed with values around 20. Log₂ fold changes for these metabolites were: tyrosine (≈ 2.5), glycine (≈ 1.5), leucine+isoleucine (FC ≈ 2.0), glutamic acid (≈ -1.2), and C18:1OH (≈ 1.8), reflecting dysregulation in amino acid metabolism and fatty acid oxidation. These findings position C18:1OH as a potential biomarker for mitochondrial dysfunction, pending further validation (Table 1). Table 1: Summary of Key Metabolites and Associated Metabolic Pathways Metabolite Direction in Preterm Associated Pathways Tyrosine Elevated Thyroid hormone synthesis, Amino acid metabolism Glycine Elevated Glutathione biosynthesis, Purine metabolism Leucine + Isoleucine Elevated Branched-chain amino acid metabolism Glutamic Acid Reduced Ammonia recycling, Neurotransmitter balance Hydroxyoctadecenoylcarnitine (C18:1OH) Elevated Mitochondrial fatty acid oxidation Arginine Elevated Urea cycle, Nitric oxide production Supporting univariate significance, significance analysis of microarrays (SAM) and empirical bayes analysis of microarrays (EBAM) plots (Supplementary Figures S1 and S2) identified 27 and 35 significant metabolites (FDR = 0.004 and 0.006), confirming robust differential expression. Multivariate Analysis (PCA and OPLS-DA) Principal component analysis (PCA) (Figure 2) clearly separated preterm from term neonates, with PC1 (26.1%) and PC2 (21.9%) accounting for 48% of the total variance. Three distinct clusters were identified, validated by a silhouette score of 0.45: Cluster 1 (mostly preterm) showed elevated tyrosine, arginine, leucine+isoleucine, and C18:1OH, but reduced glycine and glutamate — suggestive of hepatic and mitochondrial immaturity. Cluster 2 (mainly term) displayed a mature metabolic phenotype with opposite trends. Cluster 3 represented an intermediate metabolic state, possibly reflecting transitional maturity or nutritional influences. These clusters hint at heterogeneity potentially linked to gestational age, nutritional status, or oxidative stress, warranting further exploration. Recursive SVM classification (Supplementary Figure S3) further optimized feature selection, showing error rates decreasing from 14.9% (26 variables) to 28.8% (5 variables), supporting the identification of key discriminatory metabolites. Partial least squares discriminant analysis (PLS-DA) (Figure 3), validated by 5-fold cross-validation (Q² = 0.55, p < 0.001), improved separation between groups. The presence of three metabolic subclusters (green, brown, pink ellipses) suggested structured biochemical heterogeneity. Variable importance in projection (VIP) scores and random forest analysis (Supplementary Figure S4) supported model robustness (error rate < 0.1). Hierarchical Clustering and Heatmap Heatmap analysis (Figure 4; Supplementary Figure S5) offered a detailed view of metabolite concentrations across individual samples and clusters, with a color gradient from green (low concentration) to red (high concentration). Key metabolites, including arginine, glutamate, tyrosine, citrulline, and carnitine derivatives, displayed differential patterns. Arginine was significantly elevated in preterm infants (FDR-adjusted p = 1.22×10⁻¹⁰), while glutamate and glycine were notably reduced (FDR-adjusted p < 0.05). Variable trends in tyrosine and C18:1OH suggest disruptions in thyroid hormone synthesis and fatty acid oxidation, respectively. Hierarchical clustering (Euclidean distance, Ward’s linkage) delineated two major groups, clearly separating term and preterm profiles with distinct upregulation and downregulation patterns. Metabolic Pathway and Enrichment Analysis Pathway enrichment analysis (Figure 5), leveraging MetaboAnalyst’s KEGG and SMPDB databases, identified significant alterations in the urea cycle, ammonia recycling, purine metabolism, porphyrin biosynthesis, and bile acid synthesis pathways. Elevated urea cycle intermediates (arginine, ornithine) and reduced activity in purine and bile acid pathways indicated hepatic immaturity and impaired detoxification in preterm neonates. Supplementary boxplots (Supplementary Figure S6) confirmed increased arginine and decreased glutamate levels with inter-individual variability reflective of metabolic instability. Discussion Metabolic Alterations in Preterm Neonates This study demonstrated that preterm neonates exhibit distinct alterations in amino acid and acylcarnitine metabolism compared to term infants, reflecting hepatic and mitochondrial immaturity. Increased levels of tyrosine, branched-chain amino acids (leucine and isoleucine), and arginine, alongside decreased glycine and glutamate, characterize the metabolic profile of preterm neonates (3, 4, 11, 15-18). Elevated tyrosine may result from increased thyroid hormone synthesis demands and reduced hepatic tyrosine aminotransferase activity (15). Increased arginine and citrulline indicate incomplete urea cycle function and compensatory nitric oxide production under oxidative or inflammatory stress (16). Reduced glycine and glutamate may limit glutathione synthesis and disrupt neurotransmitter balance, increasing vulnerability to oxidative stress and adverse neurodevelopmental outcomes (15, 19). C18:1OH as a Candidate Biomarker Among acylcarnitines, hydroxyoctadecenoylcarnitine (C18:1OH) emerged as a potential biomarker for impaired mitochondrial fatty acid oxidation. While long-chain hydroxyl acylcarnitines have been previously associated with hepatic and mitochondrial immaturity, this study is the first to consistently identify C18:1OH as a differentiating metabolite in preterm neonates. Its elevation likely reflects incomplete β-oxidation and carnitine transport rather than inherited enzyme deficiencies. The increase in C18:1OH may contribute to false-positive newborn screening results, highlighting the importance of interpreting values based on gestational age and confirmatory tests (20–22). Identification of Metabolic Subtypes Multivariate analyses (PCA and PLS-DA) revealed three distinct metabolic clusters, indicating heterogeneity beyond simple gestational age classification. One cluster represented term neonates, possibly reflecting "metabolically mature" preterm infants with better nutritional adaptation. Cluster 1, mainly preterm, exhibited high tyrosine, arginine, leucine+isoleucine, and C18:1OH, along with low glycine and glutamate, consistent with hepatic and mitochondrial immaturity, urea cycle disruption, and oxidative stress vulnerability. Cluster 2 predominantly included term neonates, while Cluster 3 represented intermediate or transitional states, potentially influenced by gestational age, sampling time, or early nutrition (23–27, 26, 28). These findings emphasize that gestational age alone cannot explain metabolic heterogeneity, and nutrition, inflammation, and genetic factors are key modulators (26, 28). Pathway Dysregulation and Clinical Implications Pathway analysis indicated dysregulation in the urea cycle, purine metabolism, porphyrin biosynthesis, bile acid synthesis, and fatty acid oxidation. Elevated arginine and citrulline suggest incomplete urea cycle activity and susceptibility to hyperammonemia and neurotoxicity (38, 39). Reduced purine and porphyrin pathways may limit energy production and heme protein synthesis (40–45). Decreased glycine and glutamate links fatty acid oxidation to oxidative stress, potentially causing energy imbalance and neurodevelopmental vulnerability (29–32). These results provide potential strategies for targeted neonatal care. Targeted amino acid supplementation, such as citrulline or glycine, could improve urea cycle function and antioxidant capacity, and monitoring C18:1OH and related acylcarnitines could enable early detection of mitochondrial dysfunction. Integrating metabolomic signatures with clinical and neurodevelopmental parameters, along with multi-omics data, is essential to validate these metabolic subtypes and explore their clinical applications (28, 46, 47). Conclusion This study identified distinct metabolic subtypes in preterm neonates and highlighted C18:1OH as a potential biomarker of mitochondrial immaturity and oxidative stress. The findings underscore metabolic heterogeneity among preterm infants and provide a framework for targeted nutritional or pharmacological interventions. Longitudinal, multicenter, and multi-omics studies are warranted to validate these subtypes and assess their clinical utility ( 28 , 46 , 47 ). Declarations Competing interests The authors declare that they have no competing interests. Funding Declarations This research received no external funding. Author Contribution Conceptualization, Farzaneh Abbasi; Methodology, Maryam Gholami; Data Curation, Saeideh Abdolahpour, Maryam Gholami; Writing – Original Draft Preparation, Saeideh Abdolahpour; Writing – Review & Editing, Farzaneh Abbasi, Saeideh Abdolahpour, Maryam Gholami; Project Administration, Reihaneh Mohsenipour. All authors have read and agreed to the published version of the manuscript. Acknowledgments We thank the staff at the metabolic laboratory of the Growth and Development Research Center, Tehran University of Medical Sciences, Tehran, Iran, for their support in sample collection and analysis. Artificial intelligence–assisted tools were used for language polishing, with all content reviewed and finalized by the authors. 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Supplementary Files SupplementaryMaterials1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Feb, 2026 Reviews received at journal 05 Feb, 2026 Reviewers agreed at journal 25 Jan, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers invited by journal 12 Jan, 2026 Editor assigned by journal 11 Jan, 2026 Editor invited by journal 27 Nov, 2025 Submission checks completed at journal 25 Nov, 2025 First submitted to journal 19 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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11:58:43","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":99253,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8064129/v1/beb5e3df9bab86d3541166bc.html"},{"id":100399455,"identity":"d6c9bfdc-e503-4c96-a011-975c4dc53d8b","added_by":"auto","created_at":"2026-01-16 11:57:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72307,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plot illustrating differential metabolite expression between preterm and term infants, with log2 fold change (x-axis) and -log10(p-value) (y-axis).\u003c/strong\u003e log\u003csub\u003e2\u003c/sub\u003eFC for key metabolites include tyrosine (2.5, -log10(p) ≈ 30), glycine (≈ 1.5, -log10(p) ≈ 28), and leucine+isoleucine (2.0, -log10(p) ≈ 31). FDR-adjusted p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8064129/v1/a9c247c2d58c10d47bf2ff86.jpg"},{"id":100421492,"identity":"d42a50dc-6a9f-4531-a730-26a501f40ae1","added_by":"auto","created_at":"2026-01-16 13:33:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88297,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA scatter plot of metabolomic profiles for preterm (red) and term (cyan) infants.\u003c/strong\u003e PC1 (26.1%) and PC2 (21.9%) explain 47.3% of the variance. Three clusters (green, brown, pink) indicate distinct metabolic phenotypes, validated by a Q² value of 0.55 (p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8064129/v1/7966ba3d88627eea68d05b32.jpg"},{"id":100401170,"identity":"69c41036-6390-45e3-b6e6-e92c22269ffb","added_by":"auto","created_at":"2026-01-16 11:58:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":94120,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScores plot of metabolomic profiles with enhanced discrimination.\u003c/strong\u003e Likely derived from PLS-DA or an optimized PCA, showing preterm (red) and term (green) infants. PC1 (26.1%) and PC2 (~21.2%) explain 47.3% of the variance. Term infants cluster mainly in the positive PC1 range (0–10), while preterm infants are concentrated in the negative to zero range (−10–0). Three regions (green, brown, pink ellipses) highlight improved metabolic cluster separation compared to standard PCA, reflecting enhanced discrimination between groups.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8064129/v1/bfaf691d4e4eff06e58bf972.jpg"},{"id":100400374,"identity":"ea1ebfd0-560b-4bb4-9aaf-f804eb165cc8","added_by":"auto","created_at":"2026-01-16 11:58:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161643,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of Metabolomic Profiles.\u003c/strong\u003e Heatmap generated from ESI-MS/MS data, showing relative concentrations of key metabolites (e.g., arginine, glutamate, tyrosine, citrulline, carnitine species) across preterm and term infants. Rows represent metabolites and columns represent individual samples or grouped categories. Color gradients from green (low) to red (high) highlight elevated arginine in preterm infants (p = 1.22×10⁻¹⁰) and reduced glutamate and glycine (p \u0026lt; 0.05). Tyrosine and C18:1OH show variable red trends in preterm samples, suggesting potential disruptions in thyroid hormone synthesis and fatty acid oxidation. Hierarchical clustering separates preterm and term profiles, revealing distinct upregulation and downregulation patterns.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8064129/v1/aed65525116a829f2dbf0bcf.jpg"},{"id":100401115,"identity":"80101a60-37e5-40a3-a488-030ee64e90e8","added_by":"auto","created_at":"2026-01-16 11:58:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":125767,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of metabolic pathways identified in the metabolomic profiling of preterm and term infants.\u003c/strong\u003e Key amino acids (e.g., glycine, serine, arginine, proline, phenylalanine, tyrosine), intermediates (e.g., glutamate, alanine, aspartate), and metabolic processes—including ammonia recycling, urea cycle, purine metabolism, glutathione synthesis, thyroid hormone synthesis, porphyrin biosynthesis, bile acid production, carnitine metabolism, and propanoate degradation—are illustrated. Additional pathways involve catecholamine synthesis, folate metabolism, nicotinamide biosynthesis, and branched-chain amino acids (valine, leucine, isoleucine), as well as beta-alanine, cysteine, lysine, and histidine. Significant alterations observed in pathway enrichment analysis are highlighted.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8064129/v1/38c862884b9bf19c8b67c5e3.jpg"},{"id":100422834,"identity":"490e3d6d-2745-4fa7-95a9-8d8e0e52e99a","added_by":"auto","created_at":"2026-01-16 14:11:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1354021,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8064129/v1/444129ff-75cc-46b3-86a0-3c3aa81bd05f.pdf"},{"id":100400571,"identity":"f9db149e-f8d4-414a-9640-1f5d46c678a0","added_by":"auto","created_at":"2026-01-16 11:58:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15788,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8064129/v1/f1fab6a04631fd6ca761c5c4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolic Subtypes and Biomarkers in Preterm and Term Neonates via Targeted Screening","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePreterm birth, defined as delivery before 37 completed weeks of gestation, accounts for approximately 11% of live births worldwide and remains a leading cause of neonatal morbidity and mortality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Preterm infants face a unique set of physiological challenges due to the immaturity of multiple organ systems, particularly the liver, kidneys, and metabolic pathways essential for maintaining homeostasis and supporting rapid growth and development.\u003c/p\u003e \u003cp\u003eMetabolic adaptation during the neonatal period is critical for survival and long-term health, yet preterm infants often exhibit dysregulated metabolism characterized by altered amino acid profiles, impaired fatty acid oxidation, and oxidative stress (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These metabolic perturbations have been linked to adverse outcomes including neurodevelopmental delay, bronchopulmonary dysplasia, and metabolic syndrome later in life (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe advent of high-throughput metabolomics techniques, particularly liquid chromatography-tandem mass spectrometry (LC-MS/MS), has revolutionized our ability to profile complex metabolic changes in biological samples with high sensitivity and specificity(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Antioxidant-related metabolites such as glutathione precursors are commonly reported, reflecting immature enzymatic systems and oxidative stress in preterm infants (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile previous studies primarily focus on distinguishing preterm from term infants based on global metabolic signatures, recent evidence suggests significant heterogeneity within the preterm population itself, indicative of distinct metabolic subphenotypes that may have prognostic and therapeutic implications (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Identifying these metabolic subgroups requires integrative analytical approaches combining multivariate statistics like principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) with pathway enrichment methods to unravel complex biochemical networks (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe hypothesized that targeted metabolomics of newborn screening data could reveal distinct metabolic subtypes in preterm neonates and nominate novel biomarkers for clinical risk stratification.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis observational, cross-sectional study was conducted to compare the metabolic profiles of preterm and term neonates using dried blood spot (DBS) samples collected through the national newborn screening program. Participants were selected from neonates referred to the Metabolic Laboratory of the Growth and Development Research Center (GDRC), Tehran, Iran, between March 1, 2022, and March 20, 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on gestational age, infants were categorized into two groups: 448 preterm neonates (233 boys, 215 girls; 32\u0026ndash;36 weeks) and 351 sex-matched full-term neonates (37\u0026ndash;40 weeks). Feeding type (breast milk/formula) was recorded at the time of sampling. Only live-born infants with complete demographic data and confirmed gestational age were included. Exclusion criteria included major congenital anomalies, previously diagnosed inborn errors of metabolism, incomplete clinical data, maternal gestational diabetes, preeclampsia, or antenatal corticosteroid exposure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the Growth and Development Research Center, Tehran University of Medical Sciences, Tehran, Iran (protocol code IR.TUMS.CHMC.REC.1404.105). Written informed consent was obtained from a parent and/or legal guardian of all neonates included in the study prior to the use of their anonymized dried blood spot samples for research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDried blood spot (DBS)\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003esamples were collected by heel prick 48\u0026ndash;72 h after birth on Whatman 903 filter paper, air-dried for 3 h at room temperature, and transported to the laboratory for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTargeted Metabolic Analysis by\u003c/strong\u003e \u003cstrong\u003eMS/MS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTargeted Metabolic profiling of amino acids and acylcarnitines was performed using a triple quadrupole LC/MS system (Shimadzu-8045 LC/MS). A validated commercial kit for newborn screening, MassChrom\u0026reg; Newborn Screening Kit (57000F, non-derivatized; Chromsystems Instruments and Chemicals, Germany), was used according to the manufacturer\u0026rsquo;s protocol. Analytes corresponded to those routinely included in standard newborn screening panels.\u003c/p\u003e\n\u003cp\u003ePotential confounders, including feeding type (breast milk, formula, or parenteral nutrition) and clinical interventions (e.g., corticosteroid use), were recorded. Sensitivity analyses showed no significant impact of feeding type on key metabolites in preterm versus term comparisons (p \u0026gt; 0.05 for interaction terms).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical and Metabolomic Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using R software (version 4.2.1) and MetaboAnalyst 6.0. Metabolite concentrations were log-transformed and Pareto-scaled before analysis. Univariate comparisons (t-test, Mann\u0026ndash;Whitney U) were adjusted using the false discovery rate (FDR) method (q \u0026lt; 0.05). Multivariate analyses, including principal component analysis (PCA) and partial least squares\u0026ndash;discriminant analysis (PLS-DA) with 5-fold cross-validation (Q\u0026sup2; = 0.55, p \u0026lt; 0.001), were applied to identify metabolic patterns. Volcano plots, hierarchical heatmaps (Euclidean distance, Ward\u0026rsquo;s linkage), and pathway enrichment analyses (KEGG and SMPDB databases) were used to highlight key metabolites and pathways(12-14).\u003c/p\u003e\n\u003cp\u003eAll statistical tests were two-tailed, and an FDR-adjusted p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMetabolic profiling, conducted via Electrospray ionization\u0026ndash;tandem mass spectrometry (ESI-MS/MS), identified significant metabolic differences between preterm and term infants, analyzed through integrated univariate, multivariate, and pathway enrichment approaches.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate Analysis and Volcano Plot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVolcano plot analysis (Figure 1) highlighted key metabolites with FDR-adjusted p-values. Tyrosine, glycine, and leucine+isoleucine exhibited the most pronounced differences (-log₁₀(FDR-adjusted p \u0026asymp; 30), indicating major metabolic disruptions in preterm infants. Glutamic acid and C18:1OH followed with values around 20. Log₂ fold changes for these metabolites were: tyrosine (\u0026asymp; 2.5), glycine (\u0026asymp; 1.5), leucine+isoleucine (FC \u0026asymp; 2.0), glutamic acid (\u0026asymp; -1.2), and C18:1OH (\u0026asymp; 1.8), reflecting dysregulation in amino acid metabolism and fatty acid oxidation. These findings position C18:1OH as a potential biomarker for mitochondrial dysfunction, pending further validation\u0026nbsp;(Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Summary of Key Metabolites and Associated Metabolic Pathways\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDirection in\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ePreterm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 278px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssociated Pathways\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eTyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 278px;\"\u003e\n \u003cp\u003eThyroid hormone synthesis, Amino acid metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eGlycine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 278px;\"\u003e\n \u003cp\u003eGlutathione biosynthesis, Purine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eLeucine + Isoleucine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 278px;\"\u003e\n \u003cp\u003eBranched-chain amino acid metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eGlutamic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eReduced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 278px;\"\u003e\n \u003cp\u003eAmmonia recycling, Neurotransmitter balance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eHydroxyoctadecenoylcarnitine (C18:1OH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 278px;\"\u003e\n \u003cp\u003eMitochondrial fatty acid oxidation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eArginine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 278px;\"\u003e\n \u003cp\u003eUrea cycle, Nitric oxide production\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSupporting univariate significance, significance analysis of microarrays (SAM) and empirical bayes analysis of microarrays (EBAM) plots (Supplementary Figures S1 and S2) identified 27 and 35 significant metabolites (FDR = 0.004 and 0.006), confirming robust differential expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Analysis (PCA and OPLS-DA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis (PCA) (Figure 2) clearly separated preterm from term neonates, with PC1 (26.1%) and PC2 (21.9%) accounting for 48% of the total variance. Three distinct clusters were identified, validated by a silhouette score of 0.45: Cluster 1 (mostly preterm) showed elevated tyrosine, arginine, leucine+isoleucine, and C18:1OH, but reduced glycine and glutamate \u0026mdash; suggestive of hepatic and mitochondrial immaturity. Cluster 2 (mainly term) displayed a mature metabolic phenotype with opposite trends. Cluster 3 represented an intermediate metabolic state, possibly reflecting transitional maturity or nutritional influences.\u003c/p\u003e\n\u003cp\u003eThese clusters hint at heterogeneity potentially linked to gestational age, nutritional status, or oxidative stress, warranting further exploration. Recursive SVM classification (Supplementary Figure S3) further optimized feature selection, showing error rates decreasing from 14.9% (26 variables) to 28.8% (5 variables), supporting the identification of key discriminatory metabolites.\u003c/p\u003e\n\u003cp\u003ePartial least squares discriminant analysis (PLS-DA) (Figure 3), validated by 5-fold cross-validation (Q\u0026sup2; = 0.55, p \u0026lt; 0.001), improved separation between groups. The presence of three metabolic subclusters (green, brown, pink ellipses) suggested structured biochemical heterogeneity. Variable importance in projection (VIP) scores and random forest analysis (Supplementary Figure S4) supported model robustness (error rate \u0026lt; 0.1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHierarchical Clustering and Heatmap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmap analysis (Figure 4; Supplementary Figure S5) offered a detailed view of metabolite concentrations across individual samples and clusters, with a color gradient from green (low concentration) to red (high concentration). Key metabolites, including arginine, glutamate, tyrosine, citrulline, and carnitine derivatives, displayed differential patterns. Arginine was significantly elevated in preterm infants (FDR-adjusted p = 1.22\u0026times;10⁻\u0026sup1;⁰), while glutamate and glycine were notably reduced (FDR-adjusted p \u0026lt; 0.05). Variable trends in tyrosine and C18:1OH suggest disruptions in thyroid hormone synthesis and fatty acid oxidation, respectively. Hierarchical clustering (Euclidean distance, Ward\u0026rsquo;s linkage) delineated two major groups, clearly separating term and preterm profiles with distinct upregulation and downregulation patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolic Pathway and Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePathway enrichment analysis (Figure 5), leveraging MetaboAnalyst\u0026rsquo;s KEGG and SMPDB databases, identified significant alterations in the urea cycle, ammonia recycling, purine metabolism, porphyrin biosynthesis, and bile acid synthesis pathways. Elevated urea cycle intermediates (arginine, ornithine) and reduced activity in purine and bile acid pathways indicated hepatic immaturity and impaired detoxification in preterm neonates. Supplementary boxplots (Supplementary Figure S6) confirmed increased arginine and decreased glutamate levels with inter-individual variability reflective of metabolic instability.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eMetabolic Alterations in Preterm Neonates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study demonstrated that preterm neonates exhibit distinct alterations in amino acid and acylcarnitine metabolism compared to term infants, reflecting hepatic and mitochondrial immaturity. Increased levels of tyrosine, branched-chain amino acids (leucine and isoleucine), and arginine, alongside decreased glycine and glutamate, characterize the metabolic profile of preterm neonates (3, 4, 11, 15-18). Elevated tyrosine may result from increased thyroid hormone synthesis demands and reduced hepatic tyrosine aminotransferase activity (15). Increased arginine and citrulline indicate incomplete urea cycle function and compensatory nitric oxide production under oxidative or inflammatory stress (16). Reduced glycine and glutamate may limit glutathione synthesis and disrupt neurotransmitter balance, increasing vulnerability to oxidative stress and adverse neurodevelopmental outcomes (15, 19).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC18:1OH as a Candidate Biomarker\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong acylcarnitines, hydroxyoctadecenoylcarnitine (C18:1OH) emerged as a potential biomarker for impaired mitochondrial fatty acid oxidation. While long-chain hydroxyl acylcarnitines have been previously associated with hepatic and mitochondrial immaturity, this study is the first to consistently identify C18:1OH as a differentiating metabolite in preterm neonates. Its elevation likely reflects incomplete \u0026beta;-oxidation and carnitine transport rather than inherited enzyme deficiencies. The increase in C18:1OH may contribute to false-positive newborn screening results, highlighting the importance of interpreting values based on gestational age and confirmatory tests (20\u0026ndash;22).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of Metabolic Subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate analyses (PCA and PLS-DA) revealed three distinct metabolic clusters, indicating heterogeneity beyond simple gestational age classification. One cluster represented term neonates, possibly reflecting \u0026quot;metabolically mature\u0026quot; preterm infants with better nutritional adaptation. Cluster 1, mainly preterm, exhibited high tyrosine, arginine, leucine+isoleucine, and C18:1OH, along with low glycine and glutamate, consistent with hepatic and mitochondrial immaturity, urea cycle disruption, and oxidative stress vulnerability. Cluster 2 predominantly included term neonates, while Cluster 3 represented intermediate or transitional states, potentially influenced by gestational age, sampling time, or early nutrition (23\u0026ndash;27, 26, 28). These findings emphasize that gestational age alone cannot explain metabolic heterogeneity, and nutrition, inflammation, and genetic factors are key modulators (26, 28).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathway Dysregulation and Clinical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePathway analysis indicated dysregulation in the urea cycle, purine metabolism, porphyrin biosynthesis, bile acid synthesis, and fatty acid oxidation. Elevated arginine and citrulline suggest incomplete urea cycle activity and susceptibility to hyperammonemia and neurotoxicity (38, 39). Reduced purine and porphyrin pathways may limit energy production and heme protein synthesis (40\u0026ndash;45). Decreased glycine and glutamate links fatty acid oxidation to oxidative stress, potentially causing energy imbalance and neurodevelopmental vulnerability (29\u0026ndash;32).\u003c/p\u003e\n\u003cp\u003eThese results provide potential strategies for targeted neonatal care. Targeted amino acid supplementation, such as citrulline or glycine, could improve urea cycle function and antioxidant capacity, and monitoring C18:1OH and related acylcarnitines could enable early detection of mitochondrial dysfunction. Integrating metabolomic signatures with clinical and neurodevelopmental parameters, along with multi-omics data, is essential to validate these metabolic subtypes and explore their clinical applications (28, 46, 47).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified distinct metabolic subtypes in preterm neonates and highlighted C18:1OH as a potential biomarker of mitochondrial immaturity and oxidative stress. The findings underscore metabolic heterogeneity among preterm infants and provide a framework for targeted nutritional or pharmacological interventions. Longitudinal, multicenter, and multi-omics studies are warranted to validate these subtypes and assess their clinical utility (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding Declarations\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization, Farzaneh Abbasi; Methodology, Maryam Gholami; Data Curation, Saeideh Abdolahpour, Maryam Gholami; Writing \u0026ndash; Original Draft Preparation, Saeideh Abdolahpour; Writing \u0026ndash; Review \u0026amp; Editing, Farzaneh Abbasi, Saeideh Abdolahpour, Maryam Gholami; Project Administration, Reihaneh Mohsenipour. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe thank the staff at the metabolic laboratory of the Growth and Development Research Center, Tehran University of Medical Sciences, Tehran, Iran, for their support in sample collection and analysis. Artificial intelligence\u0026ndash;assisted tools were used for language polishing, with all content reviewed and finalized by the authors.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. All data will be shared for non-commercial research purposes, in compliance with participant confidentiality and ethical guidelines.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eBlencowe H, Cousens S, Oestergaard MZ, Chou D, Moller A-B, Narwal R, et al. National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. The lancet. 2012;379(9832):2162-72.\u003c/li\u003e\n \u003cli\u003eOhuma EO, Moller A-B, Bradley E, Chakwera S, Hussain-Alkhateeb L, Lewin A, et al. National, regional, and global estimates of preterm birth in 2020, with trends from 2010: a systematic analysis. The Lancet. 2023;402(10409):1261-71.\u003c/li\u003e\n \u003cli\u003eNilsson AK, Tebani A, Malmodin D, Pedersen A, Hellgren G, L\u0026ouml;fqvist C, et al. 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Oxidative stress after preterm birth: origins, biomarkers, and possible therapeutic approaches. \u003cem\u003eArch Pediatr\u003c/em\u003e. 2015;22(10):1047-1055.\u003c/li\u003e\n \u003cli\u003eFalsaperla R, Lombardo F, Filosco F, et al. Oxidative stress in preterm infants: overview of current evidence and future prospects. \u003cem\u003ePharmaceuticals (Basel)\u003c/em\u003e. 2020;13(7):145.\u003c/li\u003e\n \u003cli\u003eBosco A, Arru F, Abis A, et al. Application of multiomics in perinatology: a metabolomics integration-focused review. \u003cem\u003eInt J Mol Sci\u003c/em\u003e. 2025;26(9):4678.\u003c/li\u003e\n \u003cli\u003ePammi M, Aghaeepour N, Neu J. Multiomics, artificial intelligence, and precision medicine in perinatology. \u003cem\u003ePediatr Res\u003c/em\u003e. 2023;93(2):308-315.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Preterm Neonates, Metabolomics, Biomarker, Metabolic Subtypes, Tandem Mass Spectrometry","lastPublishedDoi":"10.21203/rs.3.rs-8064129/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8064129/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePreterm infants exhibit metabolic immaturity, but heterogeneity within this population remains underexplored. We performed targeted metabolomics on dried blood spots from 448 preterm (32\u0026ndash;36 weeks) and 351 term neonates using tandem mass spectrometry. Preterm infants showed elevated tyrosine, leucine/isoleucine, arginine, and hydroxyoctadecenoylcarnitine (C18:1-OH), with reduced glutamate (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate analyses (PCA, PLS-DA) revealed three metabolic clusters linked to gestational maturity and oxidative stress. Pathway analysis highlighted disruptions in the urea cycle, ammonia recycling, purine metabolism, and fatty acid oxidation. To our knowledge, this is the first study to identify C18:1-OH as a potential biomarker of mitochondrial dysfunction in preterm neonates. These findings suggest metabolic subtypes may guide precision interventions, though longitudinal validation is needed.\u003c/p\u003e","manuscriptTitle":"Metabolic Subtypes and Biomarkers in Preterm and Term Neonates via Targeted Screening","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 08:54:19","doi":"10.21203/rs.3.rs-8064129/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-09T03:46:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-05T16:24:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148786186498018321333584535292753203622","date":"2026-01-25T16:12:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328228244087649299140735991278075304144","date":"2026-01-23T16:02:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T12:25:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337000677588768252522314822077786570927","date":"2026-01-14T07:47:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-12T06:04:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-12T04:16:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-27T15:01:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-25T10:07:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-19T09:23:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb271baf-1b54-455e-9c1b-0d03988dffb5","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":60961345,"name":"Biological sciences/Biochemistry"},{"id":60961346,"name":"Health sciences/Biomarkers"},{"id":60961347,"name":"Health sciences/Diseases"},{"id":60961348,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-04-24T10:24:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 08:54:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8064129","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8064129","identity":"rs-8064129","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}