Targeted blood metabolomics in infants with bronchopulmonary dysplasia

Targeted blood metabolomics in infants with bronchopulmonary dysplasia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Targeted blood metabolomics in infants with bronchopulmonary dysplasia Huiqing Sun, Muchun Yu, Lu He, Ping Cheng, Yanxi Wang, Weiling Yan, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4544343/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Bronchopulmonary dysplasia (BPD) is associated with profound changes in lung microcirculation and metabolic status. This study aimed to investigate changes in significant blood metabolites and metabolic pathways in infants with BPD. Very preterm infants who underwent ultra-performance liquid chromatography-mass spectrometry testing at a corrected gestational age of 36 weeks were included. Infants with similar gestational ages were divided into two groups: those with BPD and those without BPD. Targeted metabolites were analyzed using the orthogonal partial least squares discriminant analysis model. Metabolic pathways were identified through Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The study included 170 infants in the BPD group and 177 infants in the control group. C6DC, C16OH, Met, Ala, C0, C5, C5DC, C4, C2, C14OH, C18:2, Orn, and Tyr were identified as significant and the top metabolites. Met, Ala, Leu, C0, and C2 levels were lower, and C6DC, C16OH, C5, C5DC, and C4 levels were higher in the BPD group than the control group (all p < 0.05). Correlation heat map analysis and Mantel test revealed relationships between specific metabolites and BPD grade. The Mantel test revealed that the BPD grade was related to C0, C2, C4, and C5DC, brain natriuretic peptide related to C0. KEGG enrichment analysis indicated the involvement of these metabolites in five metabolic pathways. The findings suggest that amino acid and carnitine metabolites may play a role in BPD development, providing valuable insights into the effects of these metabolites on the condition Health sciences/Health care/Paediatrics Health sciences/Diseases/Respiratory tract diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Bronchopulmonary dysplasia (BPD) is the most common chronic lung disease in infants. It is associated with increased mortality, respiratory morbidity, neurodevelopmental impairment, and increased healthcare costs 1 . Alveolar dysplasia, impaired vascularization, inflammatory responses, and fibrogenesis characterize BPD. Clinically, BPD is defined as a continued dependency on supplemental oxygen and respiratory support beyond 36-week-corrected gestation in premature infants 2 . The metabolism of nutrient substrates, such as glucose, glutamine, and fatty acids, provides acetyl-CoA for the tricarboxylic acid cycle to generate energy and metabolites for biomolecule biosynthesis, including nucleotides, proteins, and lipids. Glucose, fatty acid, and glutamine metabolism play crucial roles in modulating cellular proliferation, differentiation, apoptosis, autophagy, senescence, and inflammatory responses. These cellular processes contribute to the pathogenesis of chronic lung diseases, including BPD 3 . Resting metabolic expenditure is elevated in patients with BPD and growth failure 4 , suggesting impaired substrate utilization. This is corroborated by the finding that the sets of genes characteristic of oxidative stress phosphorylation were reduced in infants with BPD than control infants 5 . This agrees with the finding that L-type amino acid transporter-1 is reduced in patients with BPD 6 , suggesting abnormal amino acid metabolism. Nutrition promotes organ development, prevents stunting, and provides the primary nutrient substrates, including amino and fatty acids. Critical amino and fatty acids catabolism provide substrates for energy generation by oxidative phosphorylation in the mitochondria and a wealth of functional metabolites for cell structure and biosynthesis. Metabolic homeostasis is crucial in maintaining cellular activity under physiological and pathological conditions 3 . However, metabolic disorders that disrupt the energy of typical cellular organisms, such as proliferation, differentiation, and apoptosis, are thought to be associated with chronic lung diseases 7 . Recent research suggests that metabolic disorders may be involved in the etiology of chronic lung diseases, including BPD 8,9 . Metabolomics can distinguish metabolite profiles between infants with and without BPD and identify BPD-specific metabolic markers. This method is promising method for quantitatively analyzing low-molecular-weight metabolites, including amino acids and carnitine. Therefore, a metabolomic study was performed using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to identify significantly different metabolites and explore specific metabolic predictors of BPD. Materials and methods Patient population. This prospective cohort study focused on infants born before 32 weeks of gestation in five neonatal intensive care units (NICU) between January 1, 2021, and December 31, 2023 in Henan Province, China. The infants underwent Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS) testing with micro blood samples at a post-menstrual age (PMA) of 36 weeks. Infants diagnosed with BPD were placed in the BPD group, while infants of similar gestational age who underwent UPLC-MS/MS testing at a PMA of 36 weeks without BPD were placed in the control group. The infants were then categorized based on BPD severity and the highest mode of respiratory support they received at a PMA of 36 weeks. Infants who did not require any supplemental respiratory support were classified as having no BPD, those treated with a nasal cannula (at any flow rate) or non-invasive positive airway pressure were considered to have grade 1 or 2 BPD, and those treated with invasive mechanical ventilation were categorized as having grade 3 BPD 2 . Infants with genetic metabolic diseases, significant congenital abnormalities, congenital infections with obvious signs at birth, deceased infants, lack of parental consent, and those without UPLC-MS/MS analysis were excluded from the study. Metabolic Data Collection Blood spots with a diameter of 3.2 mm were prepared on dry blood filter paper using a hole punch and placed in a 96-well culture plate. To ensure accuracy, each test included four high- and low-quality controls and two blanks to ensure accuracy. Then, 100μl working liquid was added to each well. The sealing plate was sealed with a film and placed in a thermostatic oscillator at 45℃, 700 RPM /min, and 45 min. Next, 75 μl supernatant was extracted, added to the sample, wrapped with aluminum foil, and tested on the machine after machine balancing. Acetonitrile and pure water were used as mobile phase, and 2 μl was injected into each sample. A multi-reaction monitoring method was used for mass spectrometry. The non-derivatization kit (PerkinElmer) consisted of an internal standard isotope and high - and low-concentration blood tablets, including amino acid internal standard 11 and acylcarnitine internal standard 13. Before each experiment, the working liquid was prepared using an isotope internal standard and methanol in a ratio of 1:1:110. Methanol, acetonitrile, formamide, and formic acid used in the mobile phase were all high-performance liquid chromatographic grade purchased from Sigma. Instruments used for testing: Agilent High-performance Liquid chromatograph (Agilent 1600) and a 6460Triple Quad MS/MS series mass spectrometer. Metabolic Data Analysis Metabolic concentration was determined through quantitative analysis software based on the mass-to-charge ratio of the mass spectrum peak. Soft Independent Modelling by Class Analogy (v.14.1; Umetrics, Sweden) was utilized for multivariate data analysis. Additionally, orthogonal partial least squares-discriminant analysis (OPLS-DA) was employed to enhance classification separation, streamline the dataset, and pinpoint potential biomarkers 10 . Model quality was assessed using two key parameters: R2Ycum (goodness of fit) and cumulative Q2 (goodness of prediction). A threshold of 0.5 is commonly recognized in model classification to distinguish between strong (Q2cum ≥ 0.5) and weak (Q2cum < 0.5) predictive capabilities 10 . BPD was classified into grade 1, grade 2, or grade 3 based on the highest level of respiratory support given at a postmenstrual age (PMA) of 36 weeks. Premature babies with severe BPD are susceptible to developing pulmonary hypertension (PH). Serum levels of brain natriuretic peptide (BNP) can help predict PH in infants with severe BPD. The Mantel test demonstrated a correlation between BPD grade and BNP levels with metabolites. Statistical methods Statistical analysis and data management were carried out using the Statistical Package for the Social Sciences software (version 25.0; SPSS Inc., Chicago, Illinois, USA). T-tests were used for continuous variables, while chi-square tests were utilized for categorical variables. Multivariate logistic regression analyses were conducted to ascertain the impact of metabolites on grade 3 BPD. Potential metabolic biomarkers for predicting BPD were assessed through receiver operating characteristic (ROC) analysis. A significance level of p<0.05 was used. Ethics approval and consent to participate All methods in this study were carried out in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The Life Science Ethics Committee of Children’s Hospital affiliated to Zhengzhou University approved the study. Written informed consent was obtained from all participants’ parents. Results Baseline Characteristics This study involved 347 very preterm infants, with 170 in the BPD group and 177 in the control group. Both groups exhibited similar baseline characteristics, including gestational age, length of rupture of membranes, clinical chorioamnionitis, gestational diabetes, antenatal corticosteroids, surfactant administration, and caffeine administration. However, infants in the BPD group had lower birth weight and Apgar Scores at 5 minutes compared to the control group. Additionally, the BPD group had higher rates of cesarean section, hypertension or pre-eclampsia, and sepsis than the control group (all p<0.05). Furthermore, the BPD group required longer durations of mechanical ventilation, continuous positive airway pressure (CPAP), and oxygen supplementation compared to the control group (all p<0.001) (Table 1). Metabolites Analysis OPLS-DA Model Building and Validation The OPLS-DA score plot depicted clear clustering between very preterm infants with and without BPD (Fig. 1 A). The OPLS-DA model exhibited R2Xcum (0.710) values greater than 0.5, indicating a strong fit of the model to the data. The first two principal components accounted for over 50% of the variation in the metabolites analyzed, further supporting the model's efficacy. Additionally, permutation analysis (Fig. 1B) confirmed the model's validity, as all permuted R2s and Q2s values were lower than the original values, suggesting that the model was not randomly generated. Contribution Analysis of All Metabolites to BPD Using a contribution plot, metabolites were ranked based on their contributions to the model (Fig. 1C). Fourteen metabolites were identified as potential discriminant metabolites for disease prediction (VIP > 1.0): methylglutarylcarnitine (C6DC) (VIP = 2.519), 3-hydroxylpalmitoylcarnitine (C16OH) (VIP = 2.174), Methionine (Met) (VIP = 1.957), Alanine (Ala) (VIP = 1.868), free carnitine (C0) (VIP = 1.686), isovalerylcarnitine (C5) (VIP = 1.650), glutarylcarnitine (C5DC) (VIP = 1.614), Leucine (Leu) (VIP = 1.461), butyrylcarnitine (C4) (VIP = 1.392), acetylcarnitine (C2) (VIP = 1.371), oleylcarnitine (C14OH) (VIP = 1.230), linoleoyl carnitine (C18:2) (VIP = 1.072), Ornithine (Orn) (VIP = 1.056), and Tyrosine (Tyr) (VIP = 1.054). S-plots indicated significant differences in C6DC, C16OH, Met, and Ala levels between the two groups. C6DC and C16OH showed a positive correlation, while Met and Ala exhibited a negative correlation (Fig. 1D). Univariate Analysis for Cross-Validation The univariate analysis of all metabolites (Tables 2 and 3) was complemented by multivariate analysis using OPLS-DA to identify changes in metabolites associated with the development of BPD. The top 14 potential discriminant metabolites were highlighted. The distribution pattern of each metabolite was evaluated to assess the significance of the tests. The scatter plot with bars illustrated a higher C6DC ratio in the BPD group compared to the control group (p<0.001). Met, Ala, Leu, C0, and C2 levels were lower in the BPD group than in the control group (all p<0.05), while C6DC, C16OH, C5, C5DC, and C4 levels were higher in the BPD group (all p<0.05) (Fig. 2). Multivariate logistic regression analyses indicated that changes in C6DC levels remained significant even after adjusting for factors such as birth weight, Apgar Scores, maternal hypertension, cesarean section, duration of Mechanical Ventilation, CPAP, and Oxygen, suggesting that C6DC is an independent risk factor for BPD. The predictive performance of potential metabolic biomarkers for BPD was evaluated using an ROC plot. The AUC values for all identified metabolites were > 0.5, with C6DC and C16OH having significantly higher AUC values (p=0.001 and p=0.003, respectively), indicating good predictive ability. The sensitivity and specificity were 51.8% and 63.3%, respectively, for C6DC, and 66.5% and 46.3%, respectively, for C16OH, suggesting that C6DC and C16OH could serve as potential biomarkers for diagnosing BPD. Differential analysis of metabolites The study identified 14 metabolites in both the BPD and control groups, which were selected based on criteria of p 1 for further evaluation of their impact on BPD. Analysis of correlation heat maps revealed significant negative correlations of Ala, Met, Orn, Tyr, C2, and C5 with C6DC, a positive correlation of C5DC and C2 with C0, and a positive correlation of C0, C2, and C4 with C5DC. The Mantel test indicated that the severity of BPD was associated with C0, C2, C4, and C5DC, while BNP levels were related to C0. (Fig. 3) Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that metabolites such as Ala, Leu, Met, Orn, C0, C18, C4, and C5DC were involved in metabolic pathways, secondary metabolites biosynthesis, D-Amino acid metabolism pathway, adenosine triphosphate-binding cassette transporter pathway, and amino acid pathway biosynthesis. (Fig. 4) Discussion BPD is characterized by alveolar dysplasia and impaired vascularization, inflammatory responses and fibrogenesis. Elevated resting metabolic expenditure has been reported in patients with BPD and growth failure 4 , indicating impaired substrate utilization. This study performed metabolic analysis, identified five significant amino acids and nine carnitines capable of distinguishing infants with BPD and identified C6DC and C16OH as potential BPD metabolic indicators. Metabolic homeostasis affects multiple cellular bioenergetic processes such as cell proliferation, differentiation, autotrophs, and apoptosis, which are involved in developing chronic lung diseases, including BPD 3,7,11,12 . In this study, Ala, Met, Leu, C0, and C2 levels were significantly lower, and C6DC, C16OH, C5, C5DC, and C4 levels were significantly higher in infants with BPD. Recent animal studies have shown that some blood metabolite levels are low in animal BPD models 6,13-15 , suggesting the presence of abnormal metabolism, which in turn has been associated with mitochondrial respiratory dysfunction in hyperoxia-induced lung injury in vitro or in a BPD animal 16 . Human umbilical vein endothelial cells from patients with BPD showed lower mitochondrial respiration than those from surviving infants without BPD. This finding suggests that maximal oxygen consumption is an essential predictor of BPD 17 .Exposure to hyperoxia (in a rodent model of BPD) led to reduced mitochondrial respiration and complex I activity in neonatal mice 18 . Furthermore, the complex I inhibitor pyrroloquinoline administration significantly delayed alveolar formation in mice compared to control mice 18 , suggesting that mitochondrial respiratory dysfunction contributes to hyperoxia-induced lung injury. In neonatal rat models and rat lung epithelial cells, another major nutrient, fatty acids, is dysregulated under hyperoxia exposures 19 . Carnitine, including C0 and C2, is a biomarker of fatty acids metabolism, where C0 assists fatty acids in generating C2 and transporting them to the mitochondria for further bioenergy conversion via b-oxidation. In this study, C0 and C2 levels were lower, and C6DC, C16OH, C5, C5DC, and C4 levels were higher than the control group. Assessing these patterns in infants with BPD may reveal changes in fatty acid metabolism associated with BPD pathogenesis. In animal models, L-carnitine supplementation attenuates hyperoxia-induced apoptosis and lung injury. Some studies have noted that neonatal hyperoxia reduces carnitine and C2 levels, especially long-chain carnitine and C2, in the lungs of mice at 7 days of age 20 . Resident lipofibroblasts provide neutral lipids to type II lung cells for surfactant phospholipid synthesis in immature fetal lungs. Exposure to high oxygen levels leads to the transdifferentiation of fat into myofibroblasts. This is accompanied by an increased synthesis of ribonucleic acid from glucose via the pentose phosphate pathway and a decreased nascent lipid synthesis after hyperoxygen exposure. This explains the decrease in fibrogenesis and surfactant protein synthesis in type II cells of patients with BPD 21 . To produce bioenergy, endothelial cells rely on glycolysis, whereas epithelial cells rely on mitochondrial glucose oxidation. Therefore, it is essential to identify the cell-specific changes during BPD development and their roles in metabolism. Lipid droplets are thought to be the cargo of tunneling nanotubes 22 . Thus, dysregulation of fatty acids metabolism in adipose fibroblasts or endothelial cells due to hyperoxygenation or ventilation will not provide surfactant phospholipids and protein synthesis for alveolar type II cells, which may lead to lung collapse in patients with BPD 23 . This study showed that BNP was much higher in infants in the BPD group than in the control group, and the degree of BNP was related to C0. A much higher BNP level indicates PH in infants with BPD. Some survivors of BPD exhibit pulmonary and cardiovascular sequelae (e.g., Chronic Obstructive Pulmonary Disease and PH) in adolescence and adulthood, suggesting an early origin of chronic lung disease 24,25 . Therefore, it is essential to determine whether metabolic reprogramming during neonatal lung development has long-term effects on susceptibility to other lung diseases in adulthood. This study has some limitations. In this observational cohort study, the average gestational age was similar between the two groups; however, the birth weight was much lower in infants with BPD. Birth weight differences may influence metabolic patterns, and regression analysis verified the correlation between significant metabolites and BPD. Metabolic analysis revealed significant differences in amino acids and carnitines between both groups. The findings revealed that 14 metabolites could distinguish infants prone to BPD, and five major metabolic pathways were identified. In conclusion, this study investigated amino acid and carnitine metabolomic alterations in the blood of infants with and without BPD using UPLC-MS/MS. The data set was analyzed using OPLS-DA, five amino acids and nine carnitine metabolites that significantly contributed to the separation between BPD and non-BPD were identified. This study also demonstrated that the metabolic dysregulation of amino acid and carnitine profiles is involved in BPD development. Metabolic analysis may enhance our knowledge of BPD pathogenesis and provide a prospective therapy targeting BPD in metabolomics. Abbreviations BPD, bronchopulmonary dysplasia; ROC, receiver operating characteristics; KEGG, Kyoto encyclopedia of genes and genomes; C6DC; methylglutarylcarnitine; C0, free carnitine; C5, isovalerylcarnitine; C5DC, glutarylcarnitine; Leu,Leu-leucine; Met, methionine; Ala, alanine; C4; butyrylcarnitine; C2, acetylcarnitine; C14OH, Oleylcarnitine; C18:2, Linileoyl carnitine; Orn, Ornithine; Tyr, Tyrosine; C16OH, 3-hydroxylpalmitoylcarnitine; PH, pulmonary hypertension; CPAP, continuous positive airway pressure; BNP, brain natriuretic peptide; UPLC-MS/MS, ultra-performance liquid chromatography-tandem mass spectrometry; NICU, neonatal intensive care unit; PMA, post-menstrual age; VIP, projected variable importance; OPLS-DA, orthogonal partial least squares-discriminant analysis. Declarations Data availability Data is provided within the manuscript. Acknowledgements Thank you to Dr. Dongxiao Li for her metabolic data and metabolic analysis. Thank you also to Research Center of the Children’s Hospital affiliated to Zhengzhou University for their contribution. Authors’ contributions HS.MY. and LH conceptualized and designed the study, drafted the initial manuscript, and critically reviewed and revised the manuscript. LL and PC designed the data collection instruments and carried the initial analyses. YW. WY. JH.WY. HM. QH. WY. SL. NL. and WS collected the data. All authors reviewed the manuscript. Funding Funding was provided by Henan Provincial Health Commission(YXKC2021022), and and the Department of Science and Technology of Henan Province of China (162102310001). Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to H.S. References Gilfillan, M., Bhandari, A. & Bhandari, V. Diagnosis and management of bronchopulmonary dysplasia. BMJ (Clinical research ed.) 375, n1974, doi: 10.1136/bmj.n1974 (2021). Jensen, E. A. et al. 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Tables Table 1 Comparison of baseline data between the two groups BPD group (n=170) Control group (n=177) Statistical values P value Gestational age (weeks), mean±SD 28.2±1.3 28.4±1.5 1.33 0.186 Birth weight (gram), mean±SD 1167.2±179.8 1213.4±192.4 2.31 0.022 Length of ROM, more than 24 hours, n(%) 41(24) 34(19) 1.23 0.298 Caesarean section, n(%) 66(39) 44(25) 7.810 0.006 Clinical chorioamnionitis, n(%) 19 (11) 12 (7) 2.06 0.188 Hypertension or pre-eclampsia, n(%) 32(19) 19(11) 4.526 0.035 Gestational diabetes, n(%) 26 (15) 18(10) 2.057 0.196 Antenatal corticosteroid, n(%) 82 (48) 104(59) 3.86 0.053 Sepsis, no. (%) 56(33) 35(20) 7.77 0.007 Apgar Scores, Five minutes, mean± SD 7.3 ±2.1 8.5±1.8 5.72 <0.001 CPAP (days), mean ± SD 39.1± 19.4 19.1±8.5 12.52 <0.001 Mechanical Ventilation (days), mean± SD 15.9± 9.2 4.7± 4.4 14.56 <0.001 Oxygen (days), mean± SD 78.3±43.4 28.8±12.9 14.52 <0.001 Surfactant administration, n(%) 112(66) 122(69) 0.37 0.568 Caffeine administration, n(%) 156(92) 152(86) 3.02 0.091 Abbreviations: SD, standard deviation; ROM, Rupture of membrane. CPAP, Continuous Positive Airway Pressure Table 2. Comparison of amino acids values between the two groups BPD gruop (n=170) control group (n=177) Statistical values p value Alanine(Ala) 200.32±71.11 223.91±74.41 -3.017 0.003 Arginine(Alg) 18.72±18.06 18.95±19.32 -0.116 0.907 Citrulline(Cit) 15.13±6.35 15.52±6.71 -0.560 0.576 Glycine(Gly) 314.24±103.16 326.27±113.36 -1.033 0.303 Leucine(Leu) 168.30±51.19 182.86±63.16 -2.354 0.019 Methionine(Met) 25.54±9.39 29.04±10.95 -3.193 0.002 Ornithine(Orn) 147.47±57.74 158.37±61.88 -1.695 0.091 Phenylalanine(Phe) 54.68±17.51 56.08±18.65 -0.721 0.471 Proline(Pro) 139.35±56.87 136.08±43.52 0.603 0.547 Tyrosine(Tyr) 79.91±54.12 89.14±49.76 -1.655 0.099 Valine(Val) 115.60±38.83 114.14±40.03 0.344 0.731 Table 3. Comparison of carnitine values between the two groups BPD group (n=170) control group (n=177) Statistical values p value Free carnitine(C0) 18.71±9.07 24.13±24.67 -2.692 0.007 acetylcarnitine(C2) 8.67±5.90 10.52±9.26 -2.220 0.027 Propionylcarnitine(C3) 0.88±0.72 0.96±0.77 -0.988 0.324 malonylcarnitine(C3DC/C40H) 0.09±0.05 0.08±0.06 0.296 0.767 Butylcarnitine(C4) 0.18±0.10 0.15±0.09 2.235 0.026 3-hydroxy-isovalerylcarnitine (C50H/C4DC) 0.22±0.09 0.21±0.09 0.981 0.327 Isovalerylcarnitine(C5) 0.13±0.07 0.11±0.05 2.688 0.008 Isopentenylcarnitine(C5:1) 0.01±0.01 0.01±0.01 0.454 0.650 glutarylcarnitine(C5DC/C6OH) 0.06±0.04 0.05±0.03 2.630 0.009 Caproylcarnitine(C6) 0.04±0.04 0.04±0.04 0.933 0.352 adipylcarnitine(C6DC) 0.07±0.04 0.05±0.03 4.061 <0.001 Caprylyl carnitine(C8) 0.05±0.04 0.05±0.03 0.062 0.951 Octenyl carnitine(C8:1) 0.10±0.06 0.09±0.06 0.708 0.480 Decanoylcarnitine(C10) 0.04±0.03 0.04±0.03 -0.058 0.954 decenoylcarnitine(C10:1) 0.04±0.041 0.04±0.02 1.272 0.204 Sebacenoylcarnitine(C10:2) 0.02±0.01 0.01±0.01 0.209 0.835 dodecacylcarnitine(C12) 0.02±0.02 0.02±0.02 0.418 0.677 dodecenoylcarnitine(C12:1) 0.02±0.02 0.02±0.02 0.129 0.898 tetradecanoylcarnitine(C14) 0.07±0.05 0.07±0.06 0.282 0.778 3-oH-tetradecanoylcarnitine(C14OH) 0.01±0.01 0.01±0.01 1.941 0.053 tetradecenoylcarnitine(C14:1) 0.05±0.04 0.05±0.04 -0.142 0.887 Tetradecadienoyl carnitine(C14:2) 0.02±0.01 0.02±0.01 0.643 0.521 cetacylcarnitine(C16) 0.71±0.63 0.72±0.61 -0.166 0.869 3-oH-cetacylcarnitine(C16OH) 0.01±0.01 0.01±0.01 3.502 0.001 hexadecenylcarnitine(C16:1) 0.06±0.06 0.06±0.08 -0.086 0.932 3-oH-hexadecanoylcarnitine(C16:1OH) 0.01±0.01 0.01±0.01 0.480 0.632 octadecylcarnitine(C18) 0.29±0.19 0.29±0.19 0.349 0.727 3-oH-octadecylcarnitine(C18OH) 0.01±0.01 0.01±0.01 0.995 0.320 octadecenoylcarnitine(C18:1) 0.53±0.30 0.58±0.36 -1.246 0.213 3-oH-octadecenoylcarnitine(C18:1OH) 0.01±0.01 0.01±0.01 -0.606 0.545 octadecadienoylcarnitine(C18:2) 0.21±0.15 0.18±0.13 1.649 0.100 Additional Declarations No competing interests reported. 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Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYFACHgaGBAYJBgb2BoYDJGrhOUCKFjCQSCBSg3wD7zGJB2UWefKRzx8eLqhhkOcXI2CZwQG+NImEcxLFhrdzDA7POMZgOHM2AesMGHjMJBLbJBI3zs5hOMzDxpBgcJuAFvkGmJaZxx8c5vlHhBaGA1At8yUYDA7zthGhxeAAj7EF0C+JG3iAfuHtkyDsF6DDDG/+KKtLnN9+/PFnnm828vzShBwm/wBIsIGsA3MlCCiHAzaQdcQqHgWjYBSMghEHALWCP0HoLoidAAAAAElFTkSuQmCC","orcid":"","institution":"Children’s Hospital affiliated to Zhengzhou University, Henan Children’s Hospital Zhengzhou Children’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Huiqing","middleName":"","lastName":"Sun","suffix":""},{"id":330213737,"identity":"9ddbbd22-722d-43bf-8383-dd39d44973cb","order_by":1,"name":"Muchun Yu","email":"","orcid":"","institution":"Children’s Hospital affiliated to Zhengzhou University, Henan Children’s Hospital Zhengzhou Children’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Muchun","middleName":"","lastName":"Yu","suffix":""},{"id":330213738,"identity":"ae577805-5b69-467b-891c-fe3fa48317f3","order_by":2,"name":"Lu He","email":"","orcid":"","institution":"Children’s Hospital affiliated to Zhengzhou University, Henan Children’s Hospital Zhengzhou Children’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"He","suffix":""},{"id":330213739,"identity":"118b66c9-2e72-44fd-9095-a5825a1782f9","order_by":3,"name":"Ping Cheng","email":"","orcid":"","institution":"Children’s Hospital affiliated to Zhengzhou University, Henan Children’s Hospital Zhengzhou Children’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Cheng","suffix":""},{"id":330213740,"identity":"91e06096-3487-49b0-a61d-97f702d6a5ef","order_by":4,"name":"Yanxi Wang","email":"","orcid":"","institution":"Zhoukou Yongshan 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Yang","suffix":""},{"id":330213744,"identity":"ea17fc56-8f2a-4799-a424-9edb36eca60b","order_by":8,"name":"Huijuan Mao","email":"","orcid":"","institution":"Xinmi Maternal and Children’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huijuan","middleName":"","lastName":"Mao","suffix":""},{"id":330213745,"identity":"62affa42-d922-43ba-a6fb-ed508dbd9f50","order_by":9,"name":"Qingnan Hu","email":"","orcid":"","institution":"Dengfeng People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qingnan","middleName":"","lastName":"Hu","suffix":""},{"id":330213746,"identity":"6900d5cf-f243-4d59-96f0-b6a5f13273ae","order_by":10,"name":"Shaohua Li","email":"","orcid":"","institution":"Xingyang People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shaohua","middleName":"","lastName":"Li","suffix":""},{"id":330213747,"identity":"138d4df4-e79e-457f-afbc-ac625b3027ed","order_by":11,"name":"Na Li","email":"","orcid":"","institution":"Shengma Hospital","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Li","suffix":""},{"id":330213748,"identity":"84ca3a70-40c6-4f15-add7-b00f296eb7ca","order_by":12,"name":"Wangbao Song","email":"","orcid":"","institution":"Zhongmou Maternal and Children’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wangbao","middleName":"","lastName":"Song","suffix":""},{"id":330213749,"identity":"d02484bb-56a3-4e64-a62c-a8d268d11c73","order_by":13,"name":"Lifeng Li","email":"","orcid":"","institution":"Children's Hospital Affiliated to Zhengzhou University, Henan Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lifeng","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-06-07 07:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4544343/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4544343/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61005287,"identity":"fee17e14-486f-4209-97be-ecfc53650829","added_by":"auto","created_at":"2024-07-24 13:43:41","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114413,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e OPLS-DA score scatter plot of first two principal components (ROP and control groups). Clear separate clustering can be observed between ROP and control groups. R2Xcum = 0.710, R2Ycum = 0.261. Green dots represent ROP (n = 170); blue dots represent control (n = 177). \u003cstrong\u003e\u0026nbsp;B:\u003c/strong\u003eValidity tests for OPLS-DA model. Permutation analysis plotting R2 and Q2 from 200 permutation tests in the OPLS-DA model. The y-axis shows R2 and Q2, whereas the x-axis shows the correlation coefficient of permuted and observed data. The two points on the right represent the observed R2 and Q2. Cluster of points on the left represents 200 permuted R2s and Q2s. Green and blue dots represent R2 and Q2 values, respectively. Dashed lines denote corresponding fitted regression lines for observed and permutated R2 and Q2. \u003cstrong\u003eC: \u003c/strong\u003eContribution plot from the OPLS-DA model including all metabolites. \u003cstrong\u003eD: \u003c/strong\u003eS-PLOTS revealed that C6DC,C16OH, Met, and Ala had the most significant difference.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4544343/v1/d6edb305923cf81bbf804c23.jpg"},{"id":61006500,"identity":"2bee2533-a032-49b3-af19-e31652141777","added_by":"auto","created_at":"2024-07-24 13:51:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129690,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariant analysis of the top 14 VIP. Concentration of the top 14 VIPs identified from the OPLS-DA model were compared between ROP and non-ROP premature infants.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4544343/v1/f9b1f6e1ab1fbbb47f4aae3b.jpg"},{"id":61005288,"identity":"2f1f90b3-62df-4e37-bc54-320ab8a0f945","added_by":"auto","created_at":"2024-07-24 13:43:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153929,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heat map analysis and the Mantel test.\u003c/p\u003e\n\u003cp\u003eCorrelation heat map analysis revealed significant negative correlations of Ala, Met, Orn, Tyr, C2, and C5 with C6DC; a positive correlation of C5DC and C2 with C0; and a positive correlation of C0, C2, and C4 with C5DC. The Mantel test revealed that the BPD grade was related to C0, C2, C4, and C5DC. BNP was related to C0.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4544343/v1/c9b4237b1229f1bc12cfc0ff.jpg"},{"id":61006501,"identity":"16acbf14-6490-4a23-9102-3df17639c8a3","added_by":"auto","created_at":"2024-07-24 13:51:41","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":265771,"visible":true,"origin":"","legend":"\u003cp\u003eEnriched chordal diagram.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4544343/v1/eddd25ca8589117afd8097fc.jpg"},{"id":64158676,"identity":"efbf03db-26e1-4372-a3ff-ecd8f5c85308","added_by":"auto","created_at":"2024-09-09 06:31:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1267931,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4544343/v1/d99df941-d7b7-420f-941a-4dd2a62b8dc7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Targeted blood metabolomics in infants with bronchopulmonary dysplasia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBronchopulmonary dysplasia (BPD) is the most common chronic lung disease in infants. It is associated with increased mortality, respiratory morbidity, neurodevelopmental impairment, and increased healthcare costs\u003csup\u003e1\u003c/sup\u003e. Alveolar dysplasia, impaired vascularization, inflammatory responses, and fibrogenesis characterize BPD. Clinically, BPD is defined as\u0026nbsp;a continued dependency on supplemental oxygen and respiratory support beyond 36-week-corrected gestation in premature infants\u003csup\u003e2\u003c/sup\u003e. The metabolism of nutrient substrates, such as glucose, glutamine, and fatty acids, provides acetyl-CoA for the tricarboxylic acid cycle to generate energy and metabolites for biomolecule biosynthesis, including nucleotides, proteins, and lipids. Glucose, fatty acid, and glutamine metabolism play crucial roles in modulating cellular proliferation, differentiation, apoptosis, autophagy, senescence, and inflammatory responses. These cellular processes contribute to the pathogenesis of chronic lung diseases, including BPD\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResting metabolic expenditure is elevated in patients with BPD and growth failure\u003csup\u003e4\u003c/sup\u003e, suggesting impaired substrate utilization. This is corroborated by the finding that the sets of genes characteristic of oxidative stress phosphorylation were reduced in infants with BPD than control infants\u003csup\u003e5\u003c/sup\u003e. This agrees with the finding that L-type amino acid transporter-1 is reduced in patients with BPD\u0026nbsp;\u003csup\u003e6\u003c/sup\u003e, suggesting\u0026nbsp;abnormal amino acid metabolism.\u0026nbsp;Nutrition promotes organ development, prevents stunting, and provides the primary nutrient substrates, including amino and fatty acids. Critical\u0026nbsp;amino\u0026nbsp;and\u0026nbsp;fatty acids\u0026nbsp;catabolism provide substrates for energy generation by oxidative phosphorylation in\u0026nbsp;the mitochondria and a wealth of functional metabolites for cell structure and biosynthesis. Metabolic homeostasis is crucial in maintaining cellular activity under physiological and pathological conditions\u003csup\u003e3\u003c/sup\u003e. However, metabolic disorders that disrupt the energy of typical cellular organisms, such as proliferation, differentiation, and apoptosis, are thought to be associated with chronic lung diseases\u0026nbsp;\u003csup\u003e7\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent research\u0026nbsp;suggests\u0026nbsp;that metabolic disorders may be involved in the etiology of chronic lung diseases, including BPD\u003csup\u003e8,9\u003c/sup\u003e. Metabolomics can distinguish metabolite profiles between infants with and without BPD and identify BPD-specific metabolic markers. This method is promising method for quantitatively analyzing low-molecular-weight metabolites, including amino acids and carnitine. Therefore, a metabolomic study was performed using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to identify significantly different metabolites and explore specific metabolic predictors of BPD. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003ePatient population.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective cohort study focused on infants born before 32 weeks of gestation in five neonatal intensive care units (NICU)\u0026nbsp;between January 1, 2021, and December 31, 2023\u0026nbsp;in Henan Province, China. The infants underwent\u0026nbsp;Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS)\u0026nbsp;testing with\u0026nbsp;micro blood samples\u0026nbsp;at a post-menstrual age (PMA) of 36 weeks. Infants diagnosed with BPD were placed in the BPD group, while infants of similar gestational age who underwent UPLC-MS/MS testing at a PMA of 36 weeks without BPD were placed in the control group. The infants were then categorized based on BPD severity and the highest mode of respiratory support they received at a PMA of 36 weeks. Infants who did not require any supplemental respiratory support were classified as having no BPD, those treated with a nasal cannula (at any flow rate) or non-invasive positive airway pressure were considered to have grade 1 or 2 BPD, and those treated with invasive mechanical ventilation were categorized as having grade 3 BPD\u0026nbsp;\u003csup\u003e2\u003c/sup\u003e.\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u003c/strong\u003eInfants with genetic metabolic diseases, significant congenital abnormalities, congenital infections with obvious signs at birth, deceased infants, lack of parental consent, and those without UPLC-MS/MS analysis were excluded from the study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolic Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood spots with a diameter of 3.2 mm were prepared on dry blood filter paper using a hole punch and placed in a 96-well culture plate. To ensure accuracy, each test included four high- and low-quality controls and two blanks to ensure accuracy. Then, 100\u0026mu;l working liquid was added to each well. The sealing plate was sealed with a film and placed in a thermostatic oscillator at 45℃, 700 RPM /min, and 45 min. Next, 75 \u0026mu;l supernatant was extracted, added to the sample, wrapped with aluminum foil, and tested on the machine after machine balancing. Acetonitrile and pure water were used as mobile phase, and 2 \u0026mu;l was injected into each sample.\u0026nbsp;A multi-reaction monitoring method was used for mass spectrometry.\u003c/p\u003e\n\u003cp\u003eThe non-derivatization kit (PerkinElmer) consisted of an internal standard isotope and high - and low-concentration blood tablets, including amino acid internal standard 11\u0026nbsp;and acylcarnitine internal standard 13. Before each experiment, the working liquid was prepared using an isotope internal standard and methanol in a ratio of 1:1:110. Methanol, acetonitrile, formamide, and formic acid used in the mobile phase were all high-performance liquid chromatographic grade purchased from Sigma. Instruments used for testing: Agilent High-performance Liquid chromatograph (Agilent 1600) and\u0026nbsp;a 6460Triple Quad MS/MS series mass spectrometer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolic Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetabolic concentration was determined through quantitative analysis software based on the mass-to-charge ratio of the mass spectrum peak. Soft Independent Modelling by Class Analogy (v.14.1; Umetrics, Sweden) was utilized for multivariate data analysis. Additionally, orthogonal partial least squares-discriminant analysis (OPLS-DA) was employed to enhance classification separation, streamline the dataset, and pinpoint potential biomarkers\u0026nbsp;\u003csup\u003e10\u003c/sup\u003e.\u0026nbsp;Model quality was assessed using two key parameters: R2Ycum (goodness of fit) and cumulative Q2 (goodness of prediction). A threshold of 0.5 is commonly recognized in model classification to distinguish between strong (Q2cum\u0026nbsp;\u0026ge;\u0026nbsp;0.5) and weak (Q2cum \u0026lt; 0.5) predictive capabilities\u0026nbsp;\u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBPD was classified into grade 1, grade 2, or grade 3 based on the highest level of respiratory support given at a postmenstrual age (PMA) of 36 weeks. Premature babies with severe BPD are susceptible to developing pulmonary hypertension (PH). Serum levels of brain natriuretic peptide (BNP) can help predict PH in infants with severe BPD. The Mantel test demonstrated a correlation between BPD grade and BNP levels with metabolites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis and data management were carried out using the Statistical Package for the Social Sciences software (version 25.0; SPSS Inc., Chicago, Illinois, USA). T-tests were used for continuous variables, while chi-square tests were utilized for categorical variables. Multivariate logistic regression analyses were conducted to ascertain the impact of metabolites on grade 3 BPD. Potential metabolic biomarkers for predicting BPD were assessed through receiver operating characteristic (ROC) analysis. A significance level of p\u0026lt;0.05 was used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods in this study were carried out in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The Life Science Ethics Committee of Children\u0026rsquo;s Hospital affiliated to Zhengzhou University approved the study. Written informed consent was obtained from all participants\u0026rsquo; parents.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Characteristics\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study involved 347 very preterm infants, with 170 in the BPD group and 177 in the control group. Both groups exhibited similar baseline characteristics, including gestational age, length of rupture of membranes, clinical chorioamnionitis, gestational diabetes, antenatal corticosteroids, surfactant administration, and caffeine administration. However, infants in the BPD group had lower birth weight and Apgar Scores at 5 minutes compared to the control group. Additionally, the BPD group had higher rates of cesarean section, hypertension or pre-eclampsia, and sepsis than the control group (all p\u0026lt;0.05). Furthermore, the BPD group required longer durations of mechanical ventilation, continuous positive airway pressure (CPAP), and oxygen supplementation compared to the control group (all p\u0026lt;0.001)\u0026nbsp;(Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolites Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOPLS-DA Model Building and Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe OPLS-DA score plot depicted clear clustering between very preterm infants with and without BPD\u0026nbsp;(Fig. 1 A). The OPLS-DA model exhibited R2Xcum (0.710) values greater than 0.5, indicating a strong fit of the model to the data. The first two principal components accounted for over 50% of the variation in the metabolites analyzed, further supporting the model\u0026apos;s efficacy. Additionally, permutation analysis\u0026nbsp;(Fig. 1B)\u0026nbsp;confirmed the model\u0026apos;s validity, as all permuted R2s and Q2s values were lower than the original values, suggesting that the model was not randomly generated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution Analysis of All Metabolites to BPD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing a contribution plot, metabolites were ranked based on their contributions to the model\u0026nbsp;(Fig. 1C). Fourteen metabolites were identified as potential discriminant metabolites for disease prediction (VIP \u0026gt; 1.0): methylglutarylcarnitine (C6DC) (VIP = 2.519), 3-hydroxylpalmitoylcarnitine (C16OH) (VIP = 2.174), Methionine (Met) (VIP = 1.957), Alanine (Ala) (VIP = 1.868), free carnitine (C0) (VIP = 1.686), isovalerylcarnitine (C5) (VIP = 1.650), glutarylcarnitine (C5DC) (VIP = 1.614), Leucine (Leu) (VIP = 1.461), butyrylcarnitine (C4) (VIP = 1.392), acetylcarnitine (C2) (VIP = 1.371), oleylcarnitine (C14OH) (VIP = 1.230), linoleoyl carnitine (C18:2) (VIP = 1.072), Ornithine (Orn) (VIP = 1.056), and Tyrosine (Tyr) (VIP = 1.054). S-plots indicated significant differences in C6DC, C16OH, Met, and Ala levels between the two groups. C6DC and C16OH showed a positive correlation, while Met and Ala exhibited a negative correlation\u0026nbsp;(Fig. 1D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate Analysis for Cross-Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe univariate analysis of all metabolites\u0026nbsp;(Tables 2 and 3)\u0026nbsp;was complemented by multivariate analysis using OPLS-DA to identify changes in metabolites associated with the development of BPD. The top 14 potential discriminant metabolites were highlighted. The distribution pattern of each metabolite was evaluated to assess the significance of the tests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe scatter plot with bars illustrated a higher C6DC ratio in the BPD group compared to the control group (p\u0026lt;0.001). Met, Ala, Leu, C0, and C2 levels were lower in the BPD group than in the control group (all p\u0026lt;0.05), while C6DC, C16OH, C5, C5DC, and C4 levels were higher in the BPD group (all p\u0026lt;0.05)\u0026nbsp;(Fig. 2). Multivariate logistic regression analyses indicated that changes in C6DC levels remained significant even after adjusting for factors such as birth weight, Apgar Scores, maternal hypertension, cesarean section, duration of Mechanical Ventilation, CPAP, and Oxygen, suggesting that C6DC is an independent risk factor for BPD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe predictive performance of potential metabolic biomarkers for BPD was evaluated using an ROC plot. The AUC values for all identified metabolites were \u0026gt; 0.5, with C6DC and C16OH having significantly higher AUC values (p=0.001 and p=0.003, respectively), indicating good predictive ability. The sensitivity and specificity were 51.8% and 63.3%, respectively, for C6DC, and 66.5% and 46.3%, respectively, for C16OH, suggesting that C6DC and C16OH could serve as potential biomarkers for diagnosing BPD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential analysis of metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study identified 14 metabolites in both the BPD and control groups, which were selected based on criteria of p\u0026lt;0.05 or VIP \u0026gt; 1 for further evaluation of their impact on BPD. Analysis of correlation heat maps revealed significant negative correlations of Ala, Met, Orn, Tyr, C2, and C5 with C6DC, a positive correlation of C5DC and C2 with C0, and a positive correlation of C0, C2, and C4 with C5DC. The Mantel test indicated that the severity of BPD was associated with C0, C2, C4, and C5DC, while BNP levels were related to C0.\u0026nbsp;(Fig. 3)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that metabolites such as Ala, Leu, Met, Orn, C0, C18, C4, and C5DC were involved in metabolic pathways, secondary metabolites biosynthesis, D-Amino acid metabolism pathway, adenosine triphosphate-binding cassette transporter pathway, and amino acid pathway biosynthesis. (Fig. 4)\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eBPD is characterized by alveolar dysplasia and impaired vascularization, inflammatory responses and fibrogenesis. Elevated resting metabolic expenditure has been reported in patients with\u0026nbsp;BPD and growth failure\u0026nbsp;\u003csup\u003e4\u003c/sup\u003e, indicating impaired substrate utilization. This study performed metabolic analysis, identified five significant amino acids and nine carnitines capable of distinguishing infants with BPD and identified C6DC and C16OH as potential BPD metabolic indicators. Metabolic homeostasis affects multiple cellular bioenergetic processes such as cell proliferation, differentiation, autotrophs, and apoptosis, which\u0026nbsp;are involved in developing chronic lung diseases, including BPD\u003csup\u003e3,7,11,12\u003c/sup\u003e. In this study, Ala, Met, Leu, C0, and C2\u0026nbsp;levels were significantly lower, and\u0026nbsp;C6DC, C16OH,\u0026nbsp;C5, C5DC, and C4\u0026nbsp;levels\u0026nbsp;were significantly higher in infants with BPD. Recent animal studies have shown that some blood metabolite levels are low in animal BPD models\u0026nbsp;\u003csup\u003e6,13-15\u003c/sup\u003e, suggesting the presence of abnormal metabolism, which in turn has been associated with mitochondrial respiratory dysfunction in hyperoxia-induced lung injury in vitro or in a BPD animal\u0026nbsp;\u003csup\u003e16\u003c/sup\u003e. Human umbilical vein endothelial cells from patients with BPD showed lower mitochondrial respiration than those from surviving infants without BPD. This\u0026nbsp;finding suggests that maximal oxygen consumption is an essential predictor of BPD\u0026nbsp;\u003csup\u003e17\u003c/sup\u003e.Exposure to hyperoxia\u0026nbsp;(in a rodent model of BPD) led to reduced mitochondrial respiration and complex I activity in neonatal mice\u003csup\u003e18\u003c/sup\u003e. Furthermore, the complex I inhibitor pyrroloquinoline administration significantly delayed alveolar formation in mice compared to control mice\u003csup\u003e18\u003c/sup\u003e, suggesting that mitochondrial respiratory dysfunction contributes to hyperoxia-induced lung injury.\u003c/p\u003e\n\u003cp\u003eIn neonatal rat models and rat lung epithelial cells, another major nutrient, fatty acids, is dysregulated under hyperoxia exposures\u003csup\u003e19\u003c/sup\u003e. Carnitine, including C0 and C2, is a biomarker of fatty acids metabolism, where C0 assists fatty acids in generating C2 and transporting them to the mitochondria for further bioenergy conversion via b-oxidation. In this study, C0 and C2 levels were lower, and\u0026nbsp;C6DC, C16OH,\u0026nbsp;C5, C5DC, and C4 levels were higher than\u0026nbsp;the control group.\u0026nbsp;Assessing these patterns in infants\u0026nbsp;with BPD may reveal changes in fatty acid metabolism associated with BPD pathogenesis. In animal models, L-carnitine supplementation attenuates hyperoxia-induced apoptosis and lung injury. Some studies have noted that neonatal hyperoxia reduces carnitine and\u0026nbsp;C2\u0026nbsp;levels, especially long-chain carnitine and\u0026nbsp;C2,\u0026nbsp;in the lungs of mice at 7 days of age\u0026nbsp;\u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eResident lipofibroblasts provide neutral lipids to type II lung cells for surfactant phospholipid synthesis in immature fetal lungs. Exposure to high oxygen levels leads to the transdifferentiation of fat into myofibroblasts. This is accompanied by an increased synthesis of ribonucleic acid from glucose via\u0026nbsp;the pentose phosphate pathway and a decreased nascent lipid synthesis after hyperoxygen exposure. This explains the decrease in fibrogenesis and surfactant protein\u0026nbsp;synthesis in type II cells of patients with BPD\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e. To produce bioenergy, endothelial cells rely on glycolysis, whereas epithelial cells rely on mitochondrial glucose oxidation. Therefore, it is essential to identify\u0026nbsp;the cell-specific changes during BPD development and their roles in metabolism. Lipid droplets are thought to be the cargo of tunneling nanotubes\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e. Thus, dysregulation of fatty acids metabolism in adipose fibroblasts or endothelial cells due to hyperoxygenation or ventilation will not\u0026nbsp;provide surfactant phospholipids and protein synthesis for alveolar type II cells, which may lead to lung collapse in patients with BPD\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis study showed\u0026nbsp;that BNP was much higher in infants in the BPD group than in the\u0026nbsp;control group, and\u0026nbsp;the degree of BNP was related to C0.\u0026nbsp;A much higher BNP\u0026nbsp;level indicates PH in infants with BPD. Some survivors of BPD exhibit pulmonary and cardiovascular sequelae (e.g., Chronic Obstructive Pulmonary Disease and PH) in adolescence and adulthood, suggesting an early origin of chronic lung disease\u0026nbsp;\u003csup\u003e24,25\u003c/sup\u003e. Therefore, it is essential to determine whether metabolic reprogramming during neonatal lung development has long-term effects on susceptibility to other lung diseases in adulthood.\u003c/p\u003e\n\u003cp\u003eThis study has some limitations. In this observational cohort study, the average gestational age was similar between\u0026nbsp;the two groups; however, the birth weight was much lower in infants\u0026nbsp;with BPD.\u0026nbsp;Birth weight differences may influence metabolic patterns, and regression analysis verified the correlation between significant metabolites and BPD. Metabolic analysis revealed significant\u0026nbsp;differences in amino acids and carnitines\u0026nbsp;between both groups. The findings revealed that 14 metabolites could distinguish infants prone to BPD, and five major metabolic pathways were identified.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study investigated amino acid and carnitine metabolomic alterations in the blood of infants with and without BPD using UPLC-MS/MS. The data set was analyzed using OPLS-DA, five amino acids and nine carnitine metabolites that significantly contributed to the separation between BPD and non-BPD were identified. This study also demonstrated that the metabolic dysregulation of amino acid and carnitine profiles is involved in BPD development. Metabolic analysis may enhance our knowledge of BPD pathogenesis and provide a prospective therapy targeting BPD in metabolomics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBPD, bronchopulmonary dysplasia;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC, receiver operating characteristics;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKEGG, Kyoto encyclopedia of genes and genomes;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC6DC; methylglutarylcarnitine; C0, free carnitine;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC5, isovalerylcarnitine;\u003c/p\u003e\n\u003cp\u003eC5DC, glutarylcarnitine;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLeu,Leu-leucine;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMet, methionine;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAla, alanine;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC4; butyrylcarnitine;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC2, acetylcarnitine;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC14OH, Oleylcarnitine;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC18:2, Linileoyl carnitine;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOrn, Ornithine;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTyr, Tyrosine;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC16OH, 3-hydroxylpalmitoylcarnitine;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePH, pulmonary hypertension;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCPAP, continuous positive airway pressure;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBNP, brain natriuretic peptide;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUPLC-MS/MS, ultra-performance liquid chromatography-tandem mass spectrometry; NICU, neonatal intensive care unit;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePMA, post-menstrual age;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVIP, projected variable importance;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOPLS-DA, orthogonal partial least squares-discriminant analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank you to Dr. Dongxiao Li for her metabolic data and metabolic analysis. Thank you also to Research Center of the Children’s Hospital affiliated to Zhengzhou University for their contribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHS.MY. and LH conceptualized and designed the study, drafted the initial manuscript, and critically reviewed and revised the manuscript. LL and PC designed the data collection instruments and carried the initial analyses. YW. WY. JH.WY. HM. QH. WY. SL. NL. and WS collected the data. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding was provided by Henan Provincial Health Commission(YXKC2021022), and and the Department of Science and Technology of Henan Province of China\u0026nbsp;(162102310001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to H.S.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGilfillan, M., Bhandari, A. \u0026amp; Bhandari, V. 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American journal of respiratory and critical care medicine 193, 362\u0026ndash;375, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.201508-1518PP\u003c/span\u003e\u003cspan address=\"10.1164/rccm.201508-1518PP\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 \u0026nbsp;Comparison of baseline data between the two groups\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003eBPD group\u003c/p\u003e\n \u003cp\u003e(n=170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003cp\u003e(n=177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003eStatistical values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eGestational age (weeks), mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e28.2\u0026plusmn;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e28.4\u0026plusmn;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eBirth weight (gram), mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e1167.2\u0026plusmn;179.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e1213.4\u0026plusmn;192.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eLength of ROM, more than 24 hours, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;41(24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;34(19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eCaesarean section, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;66(39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;44(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e7.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eClinical chorioamnionitis, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e19 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e12 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension or pre-eclampsia, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;32(19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;19(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e4.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eGestational diabetes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e26 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;18(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e2.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eAntenatal corticosteroid, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e82 (48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;104(59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eSepsis, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;56(33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;35(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eApgar Scores, Five minutes, mean\u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e7.3 \u0026plusmn;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e8.5\u0026plusmn;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eCPAP (days), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e39.1\u0026plusmn; 19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e19.1\u0026plusmn;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e12.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eMechanical Ventilation (days), mean\u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e15.9\u0026plusmn; 9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e4.7\u0026plusmn; 4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e14.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eOxygen (days), mean\u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e78.3\u0026plusmn;43.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e28.8\u0026plusmn;12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e14.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eSurfactant administration, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e112(66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;122(69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.46376811594203%\" valign=\"top\"\u003e\n \u003cp\u003eCaffeine administration, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3768115942029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;156(92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.82608695652174%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;152(86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.318840579710145%\" valign=\"top\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.014492753623188%\" valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SD, standard deviation; ROM, Rupture of membrane. CPAP, Continuous Positive Airway Pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Comparison of amino acids values between the two groups\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003eBPD gruop\u003c/p\u003e\n \u003cp\u003e(n=170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003econtrol group\u003c/p\u003e\n \u003cp\u003e(n=177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\" valign=\"top\"\u003e\n \u003cp\u003eStatistical values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.669014084507042%\" valign=\"top\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003eAlanine(Ala)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e200.32\u0026plusmn;71.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e223.91\u0026plusmn;74.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\" valign=\"top\"\u003e\n \u003cp\u003e-3.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.669014084507042%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003eArginine(Alg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e18.72\u0026plusmn;18.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e18.95\u0026plusmn;19.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\" valign=\"top\"\u003e\n \u003cp\u003e-0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.669014084507042%\" valign=\"top\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003eCitrulline(Cit)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e15.13\u0026plusmn;6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e15.52\u0026plusmn;6.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\" valign=\"top\"\u003e\n \u003cp\u003e-0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.669014084507042%\" valign=\"top\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003eGlycine(Gly)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e314.24\u0026plusmn;103.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e326.27\u0026plusmn;113.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\" valign=\"top\"\u003e\n \u003cp\u003e-1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.669014084507042%\" valign=\"top\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003eLeucine(Leu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e168.30\u0026plusmn;51.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e182.86\u0026plusmn;63.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\" valign=\"top\"\u003e\n \u003cp\u003e-2.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.669014084507042%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003eMethionine(Met)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e25.54\u0026plusmn;9.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e29.04\u0026plusmn;10.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\" valign=\"top\"\u003e\n \u003cp\u003e-3.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.669014084507042%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003eOrnithine(Orn)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e147.47\u0026plusmn;57.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e158.37\u0026plusmn;61.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\" valign=\"top\"\u003e\n \u003cp\u003e-1.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.669014084507042%\" valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003ePhenylalanine(Phe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e54.68\u0026plusmn;17.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e56.08\u0026plusmn;18.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\" valign=\"top\"\u003e\n \u003cp\u003e-0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.669014084507042%\" valign=\"top\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003eProline(Pro)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e139.35\u0026plusmn;56.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e136.08\u0026plusmn;43.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\" valign=\"top\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.669014084507042%\" valign=\"top\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003eTyrosine(Tyr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e79.91\u0026plusmn;54.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e89.14\u0026plusmn;49.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\" valign=\"top\"\u003e\n \u003cp\u003e-1.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.669014084507042%\" valign=\"top\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.64788732394366%\" valign=\"top\"\u003e\n \u003cp\u003eValine(Val)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e115.60\u0026plusmn;38.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.654929577464788%\" valign=\"top\"\u003e\n \u003cp\u003e114.14\u0026plusmn;40.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37323943661972%\" valign=\"top\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.669014084507042%\" valign=\"top\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3. Comparison of carnitine values between the two groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"673\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003eBPD group\u003c/p\u003e\n \u003cp\u003e(n=170)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003econtrol group\u003c/p\u003e\n \u003cp\u003e(n=177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003eStatistical values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eFree carnitine(C0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e18.71\u0026plusmn;9.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e24.13\u0026plusmn;24.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e-2.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eacetylcarnitine(C2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e8.67\u0026plusmn;5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e10.52\u0026plusmn;9.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e-2.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003ePropionylcarnitine(C3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.88\u0026plusmn;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u0026plusmn;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e-0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003emalonylcarnitine(C3DC/C40H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eButylcarnitine(C4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.18\u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e2.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003e3-hydroxy-isovalerylcarnitine\u0026nbsp;(C50H/C4DC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.22\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.21\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eIsovalerylcarnitine(C5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e2.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eIsopentenylcarnitine(C5:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eglutarylcarnitine(C5DC/C6OH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.06\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e2.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eCaproylcarnitine(C6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eadipylcarnitine(C6DC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e4.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eCaprylyl carnitine(C8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eOctenyl carnitine(C8:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eDecanoylcarnitine(C10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003edecenoylcarnitine(C10:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u0026plusmn;0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eSebacenoylcarnitine(C10:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003edodecacylcarnitine(C12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003edodecenoylcarnitine(C12:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003etetradecanoylcarnitine(C14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003e3-oH-tetradecanoylcarnitine(C14OH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e1.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003etetradecenoylcarnitine(C14:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e-0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eTetradecadienoyl carnitine(C14:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003ecetacylcarnitine(C16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.71\u0026plusmn;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.72\u0026plusmn;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e-0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003e3-oH-cetacylcarnitine(C16OH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e3.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003ehexadecenylcarnitine(C16:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.06\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.06\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e-0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003e3-oH-hexadecanoylcarnitine(C16:1OH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eoctadecylcarnitine(C18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.29\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.29\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003e3-oH-octadecylcarnitine(C18OH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eoctadecenoylcarnitine(C18:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.53\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.58\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e-1.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003e3-oH-octadecenoylcarnitine(C18:1OH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e-0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.89004457652303%\" valign=\"top\"\u003e\n \u003cp\u003eoctadecadienoylcarnitine(C18:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.099554234769688%\" valign=\"top\"\u003e\n \u003cp\u003e0.21\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.790490341753344%\" valign=\"top\"\u003e\n \u003cp\u003e0.18\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.589895988112927%\" valign=\"top\"\u003e\n \u003cp\u003e1.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.63001485884101%\" valign=\"top\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4544343/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4544343/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBronchopulmonary dysplasia (BPD) is associated with profound changes in lung microcirculation and metabolic status. This study aimed to investigate changes in significant blood metabolites and metabolic pathways in infants with BPD. Very preterm infants who underwent ultra-performance liquid chromatography-mass spectrometry testing at a corrected gestational age of 36 weeks were included. Infants with similar gestational ages were divided into two groups: those with BPD and those without BPD. Targeted metabolites were analyzed using the orthogonal partial least squares discriminant analysis model. Metabolic pathways were identified through Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The study included 170 infants in the BPD group and 177 infants in the control group. C6DC, C16OH, Met, Ala, C0, C5, C5DC, C4, C2, C14OH, C18:2, Orn, and Tyr were identified as significant and the top metabolites. Met, Ala, Leu, C0, and C2 levels were lower, and C6DC, C16OH, C5, C5DC, and C4 levels were higher in the BPD group than the control group (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Correlation heat map analysis and Mantel test revealed relationships between specific metabolites and BPD grade. The Mantel test revealed that the BPD grade was related to C0, C2, C4, and C5DC, brain natriuretic peptide related to C0. KEGG enrichment analysis indicated the involvement of these metabolites in five metabolic pathways. The findings suggest that amino acid and carnitine metabolites may play a role in BPD development, providing valuable insights into the effects of these metabolites on the condition\u003c/p\u003e","manuscriptTitle":"Targeted blood metabolomics in infants with bronchopulmonary dysplasia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 13:43:37","doi":"10.21203/rs.3.rs-4544343/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a0f96b6-2c0b-4f01-b78c-3ecf425d5b49","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34953238,"name":"Health sciences/Health care/Paediatrics"},{"id":34953239,"name":"Health sciences/Diseases/Respiratory tract diseases"}],"tags":[],"updatedAt":"2024-09-09T06:23:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-24 13:43:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4544343","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4544343","identity":"rs-4544343","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}