Application of ¹H NMR-based metabolomics in childhood leukemia. A preliminary study.

Application of ¹H NMR-based metabolomics in childhood leukemia. 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A preliminary study. Agata Serrafi, Małgorzata Pupek, Łukasz Lewandowski, Anna Janicka-Kłos, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5950449/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 Cancer cells undergo significant metabolic changes to support their rapid proliferation and growth. Leukemias, a diverse group of blood cancers, constitute a significant proportion of childhood cancers. This heterogeneity may result in distinct metabolic profiles, offering potential insights for improved diagnosis, prognosis, and treatment strategies. Cerebrospinal fluid (CSF) testing is essential to detect central nervous system involvement by leukemia and to counteract the effects of the involvement.. In preliminary study, a qualitative, untargeted metabolomics approach was used to analyze CSF and plasma samples from children with leukemia. A total of 20 pairs of CSF and plasma samples from patients were included in the analysis, along with 20 plasma samples from healthy children. ¹H NMR spectroscopy revealed more than 33 metabolites in the samples. Significant differences were observed between the metabolite profiles of CSF and plasma, with some metabolites being common to both fluids and others being unique to each. Our preliminary findings suggest that patients with leukemia exhibit distinct metabolic profiles between plasma and CSF and between plasma in leukemia and plasma from healthy controls. Further studies are warranted to investigate the potential diagnostic and prognostic value of metabolic profiling in childhood leukemia. Health sciences/Biomarkers Health sciences/Medical research Health sciences/Oncology acute leukemia metabolomics nuclear magnetic resonance pilot study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Leukemia is a malignant neoplasm that arises from the clonal proliferation of hematopoietic stem cells in the bone marrow [ 1 ]. Hematopoiesis, in turn, is the process of differentiation and maturation of stem cells into erythrocytes, megakaryocytes and immune cells (of myeloid, lymphoid or monocytic origin) [ 2 ]. Global estimates for all cancers are available in the GLOBOCAN database, compiled by the International Agency for Research on Cancer (IARC), which operates under the World Health Organization (WHO). In 2020, there were 474.519 new cases of leukemia and 311.594 deaths attributed to this disease. This reflects a 7.9% increase in new cases and a 0.83% increase in deaths compared to 2018. The incidence of leukemia is higher in men than in women, and it increases with age. The highest rates are observed in North America, with 13.3 cases per 100,000 men and 8.8 cases per 100,000 women. In Central and Eastern Europe, the incidence rates are 8.1 for men and 5.6 for women, respectively [ 2 – 5 ]. Leukemias are the most common malignancies in children. In the USA, approximately 3.800 children are diagnosed annually with acute lymphoblastic leukemia (ALL) or acute myeloid leukemia (AML) [ 6 ]. Among those under 19 years of age, ALL is the most prevalent type of leukemia, accounting for 25% of all childhood cancers [ 7 ]. Blasts are immature and dysfunctional cells that normally constitute between 1% and 5% of bone marrow cells. Acute leukemias are characterized by the presence of more than 20% blast cells in the peripheral blood, whereas chronic leukemias typically exhibit less than 20% blast cells in the peripheral blood. This distinction accounts for the more rapid onset of symptoms observed in acute leukemias [ 5 , 8 ]. There are four main subtypes of leukemias. Acute lymphoblastic leukemia is the most common leukemia in the pediatric population, with B-cell acute lymphoblastic leukemia (B-ALL) being the most prevalent form. It is driven by three primary types of genetic alterations: chromosomal aneuploidies, rearrangements, and point mutations. About 30% of affected children exhibit high hyperdiploidy, often associated with mutations in the Ras pathway [ 9 ]. The prognosis for B-ALL has significantly improved, with current cure rates between 75–85%, which are better than those for adults [ 10 ]. Acute myeloid leukemia is the most common type of acute leukemia in adults and is rarer in children [ 11 ]. It involves malignant cells of the myeloid lineage (excluding B and T lymphocytes) and is characterized by significant molecular complexity, complicating patient stratification and treatment selection. Studies have identified decreased expression levels of components of the NOX2 complex, as well as chromosomal losses and deletions [ 12 ]. Genetic conditions associated with AML include Klinefelter syndrome, neurofibromatosis, Fanconi anemia, and Li-Fraumeni syndrome [ 13 ]. The prognosis for AML is generally poor, with frequent relapses [ 14 ]. Chronic lymphoid leukemia arises from the proliferation of monoclonal lymphoid cells and predominantly affects the elderly, with a median age of incidence of 72 years. Given the slow progression of the disease, treatment focuses on controlling disease progression and prolonging patient life. Treatment is typically initiated only when there is rapid disease progression [ 15 ]. Chronic myeloid leukemia (CML) usually results from a reciprocal translocation between the BCR gene on chromosome 22 and the ABL1 gene on chromosome 9, forming the Philadelphia chromosome [ 11 ]. This translocation produces a constitutively activated tyrosine kinase, bcr-abl [ 16 ]. mainly affects older adults, with an average age of diagnosis at 65 years, and is extremely rare in children. With proper treatment, life expectancy for CML patients is comparable to that of the healthy population [ 17 ]. The diagnosis of acute leukaemias usually starts with the evaluation of the peripheral blood. Anemia, thrombocytopenia, leukopenia (but leukocytosis is also possible) are usually found [ 18 ]. An important step in diagnosis is the morphological evaluation of bone marrow cells. It allows differentiation of AML from ALL. The lymphoblast population (characteristic of ALL) is characterized by a homogeneous population of blasts, a predominantly round and centrally located nucleus, sparse cytoplasm, absent Auer rods and possible vacuolation. In contrast, myeloblasts (characteristic of AML) are usually characterized by a heterogeneous population, a nucleus that tends to be eccentric, a variable amount of cytoplasm, the possible presence of Auer rods and vacuolation [ 19 ]. Flow cytometry, cytogenetic and molecular studies on bone marrow aspirate should also be performed, allowing for a risk stratification. [ 18 ] A metabolic panel, determination of serum uric acid dehydrogenase and liver function tests are extremely important for proper patient profiling [ 18 ]. Cerebrospinal fluid (CSF) testing [ 20 ] is used primarily to evaluate for tumor markers indicative of leukemia in the central nervous system (CNS), primarily for the presence of leukemic cells in the cerebrospinal fluid, and secondarily for the brain damage associated biomarkers of following treatment such as chemotherapy and radiotherapy, to check for acute neurological toxicity or processes that may lead to long-term neurocognitive deficits. Evaluation of CSF cell counts, differential analysis, and the CSF cytology are mandatory for the initial diagnosis of central nervous system infiltration by ALL cells and for classification of the degree of CNS involvement as CNS1, CNS2, or CNS3, respectively. [ 19 , 20 ]. The most common methods of metabolomic analysis are the ¹H NMR spectroscopy and mass spectrometry. 1D ¹H NMR spectroscopy is a cornerstone of metabolomics, providing a foundation for understanding metabolic profiles. By enabling the identification and quantification of a wide range of metabolites, 1D NMR plays a crucial role in numerous biomedical studies and is routinely used to find out biomarkers in biofluids. However, as the NMR is a method with higher reproducibility and the ability to identify unknown substances [21,22], this method was selected as the analytical method of choice for this study and the in vitro results have been documented to translate into clinical application in vivo [22]. Moreover, ¹H NMR analysis is non-destructive and requires minimal sample preparation, so if necessary, the sample can be used for further testing, which is especially important in the case of such a rare and valuable body fluid from a seriously ill child. Metabolic changes are an indispensable part of cancerogenesis and a potential handle point for metabolomic methods, and the determination of the metabolome of a cancer cell can potentially provide information on the extent of cancerogenesis. As putative pathways that can form the basis for metabolomic profiling, one can highlight, for example, the pathways of cell bioenergetics, lipid metabolism, the Warburg effect or signaling pathways such as Myc, p53, HIF-1 or mTOR [ 23 ]. In the current trend of using ¹H NMR and MS as diagnostic methods with potential applications in microbiology [ 24 ] or inborn errors of metabolism [ 25 ], these methods do not exist in the diagnosis of leukemia. Our study aim was to investigate the metabolic differences between plasma and cerebrospinal fluid of children with leukemia and between plasma of children with leukemia and healthy children using ¹H NMR spectroscopy. By analyzing the metabolite composition of blood plasma and cerebrospinal fluid samples, we aimed to identify potential biomarker candidates. Our preliminary findings suggest significant differences in the metabolic profiles between the two groups. The conducted study is only the first step towards further metabolomic studies. The assiment have to be confirmed with 2D experiments in the next step before selected metabolites could be used to improve early diagnosis and disease monitoring. This study represents one of the first applications of ¹H NMR-based metabolomics to pediatric leukemia, offering a potential novel approach to understanding and managing this complex disease. Results Descriptive laboratory plasma parameters given as median values ​​in the study group with leukemia-type malignancies and in the control group of healthy children are shown in Table 1 . Compared to the control group, patients with leukemic tumors had usually lower white blood cell count (WBC, p = 0.002), red blood cell count (RBC, p < 0.001), hemoglobin level (HGB, p < 0.001), hematocrit (HTC, p < 0.001) and platelet count (PLT, p = 0.006). However, the percentage of neutrophils (p < 0.001), percentage of lymphocytes; (p < 0.001) and percentage of monocytes (p < 0.001) were usually higher in patients with leukemia. Table 1 Comparison of median values ​​(1st and 3rd quartile) of blood plasma laboratory variables in the group of children with leukemia-type malignancies and healthy controls. WBC, white blood cells; RBC, red blood cells; HGB, hemoglobin; HTC, hematocrit; MCV, mean corpuscular volume; MCH, mean content of hemoglobin; MCHC, mean cell hemoglobin concentration; RDW-CV, red blood cell distribution width, coefficient of variation; PLT, platelets; MPV, mean platelet volume; n/a – not applicable. Statistics were performed using the nonparametric Mann-Whitney U test to compare two independent groups of patients and healthy controls. *Ranges given for children from infancy to adolescence. Controls (n = 20) Leukemia (n = 20) Variable, unit n Median 1st Q 3rd Q n Median 1st Q 3rd Q p level Normal range Age, years 20 15.00 12.50 16.00 21 4.25 2.67 10.46 < 0.001 n/a WBC, ×10 9 /L 20 5.23 4.35 5.97 15 1.82 0.87 3.62 0.002 *5–10 RBC, ×10 12 /L 20 4.83 4.65 4.93 15 3.05 2.85 3.52 < 0.001 *4.0-5.5 HGB, g/dL 20 13.65 13.10 14.65 15 9.00 8.70 10.30 < 0.001 *9,5–15,5 HTC, vol% 20 40.95 38.65 43.75 15 26.90 24.00 29.20 < 0.001 *32–44 MCV, ×10 15 /L 20 86.55 81.85 88.85 15 84.30 83.00 90.70 0.856 80–97 MCH, ×10 12 /g 20 29.00 27.25 30.10 15 29.80 28.80 30.90 0.106 26–34 MCHC, g/dL 20 33.60 33.40 34.20 15 35.00 34.30 35.80 < 0.001 31–36 RDW-CV, % 20 13.55 13.05 15.00 15 14.00 12.60 15.20 0.987 11,5–14,5 PLT, ×10 9 /L 20 225.50 212.00 270.00 15 87.00 52.00 187.00 < 0.001 *150–400 MPV ×10 15 /L 20 9.40 9.20 9.85 15 10.60 9.60 11.20 0.004 7,0–12,0 Neutrophils, % 20 44.65 39.15 51.25 15 35.70 6.90 68.40 0.131 50–70 Lymphocytes, % 20 40.90 33.50 46.75 14 55.20 22.10 76.90 0.499 20–45 Monocytes, % 20 9.85 8.60 11.50 14 5.90 3.40 14.60 0.158 0,0–10,0 ¹H NMR analysis Chemical shift analysis performed using NMR profiling and metabolite determination allowed the identification of 26 compounds in the analyzed blood plasma samples. Among them, arginine, glucose α-H1, glucose β-H2, creatinine, alanine, lactate, valine, leucine, isoleucine, glycine, histamine, uridine diphosphate-glucose, guanosine monophosphate, phosphocholine, citrulline were identified with the same chemical shift values in both leukemic plasma and healthy control plasma. However, other metabolites showed small but significant differences in chemical shift values. Namely, chemical shift values of acetone, β-hydroxybutyrate, glutamine, phenylalanine, lysine, myoinositol, scyllo-inositol, also present in both plasma groups, showed significantly lower ​​in leukemic plasma, while chemical shift values of formate, citrate, acetoacetate, glycerophosphocholine (GPC) were significantly higher ​​in leukemic plasma compared to values of appropriate metabolites in healthy control plasma (each difference p < 0.001, respectively) (Table 2 ). Table 2 Metabolites in blood plasma of children with leukemia and control children identified on the basis of ¹H NMR chemical shift [ppm]. Leukemia vs. control differences in values of the analyzed variables were tested with the Mann-Whitney U test. Controls (n = 20) Leukemia (n = 20) Plasma variable n Median (ppm) 1st Q 3rd Q n Median (ppm) 1st Q 3rd Q p Formate 20 8.33 8.33 8.33 19 8.34 8.34 8.34 < 0.001 Arginine 20 1.58 1.58 1.58 19 1.58 1.58 1.59 0.425 α-H1 glucose 20 5.17 5.17 5.18 19 5.18 5.16 5.19 0.472 β-H2 glucose 20 3.28 3.28 3.28 19 3.28 3.28 3.29 0.567 Creatinine 20 3.02 3.02 3.03 19 3.03 2.99 3.03 0.930 Citrate 20 2.52 2.52 2.52 19 2.57 2.56 2.59 < 0.001 Acetone 20 2.24 2.24 2.24 19 2.22 2.21 2.22 < 0.001 Acetoacetate 20 1.91 1.91 1.92 19 1.97 1.96 2.00 < 0.001 Alanine 20 1.43 1.42 1.45 19 1.44 1.43 1.45 0.104 Lactate 20 4.17 4.16 4.17 19 4.15 4.14 4.17 0.045 β-hydroxybutyrate 20 1.21 1.21 1.21 19 1.20 1.20 1.21 < 0.001 Valine 20 1.05 1.04 1.05 19 1.05 1.04 1.07 0.076 Leucine 20 0.92 0.92 0.94 19 0.92 0.92 0.96 0.476 Isoleucine 20 0.91 0.91 0.92 19 0.91 0.90 0.94 0.662 Glycine 20 3.55 3.55 3.55 19 3.55 3.55 3.55 0.330 Glutamine 20 2.44 2.44 2.44 19 2.43 2.42 2.43 < 0.001 Phenylalanine 20 7.46 7.46 7.46 19 7.40 7.40 7.44 < 0.001 Histamine 20 7.66 7.65 7.68 19 7.64 7.64 7.68 0.013 Uridine diphosphate-glucose 20 5.67 5.67 5.67 19 5.67 5.67 5.67 0.146 Glycerophosphocholine 20 3.23 3.23 3.24 19 3.24 3.24 3.24 < 0.001 Guanosine monophosphate 20 8.13 8.13 8.14 19 8.13 8.13 8.14 0.192 Phosphocholine 20 3.22 3.22 3.22 19 3.22 3.22 3.24 0.026 Citrulline 20 3.14 3.14 3.17 19 3.16 3.14 3.18 0.016 Lysine 20 1.79 1.79 1.79 19 1.71 1.71 1.72 < 0.001 Myo-inositol 20 4.06 4.05 4.06 19 4.05 4.04 4.05 < 0.001 Scyllo-inositol 20 3.37 3.37 3.37 19 3.36 3.36 3.36 < 0.001 Examples of typical 600 MHz ¹H NMR spectra and representative metabolite assays in CSF and plasma from children with leukemia and in control plasma samples are shown in Fig. 3 , Fig. 4 respectively. Chemical shift analysis performed using NMR profiling and metabolite determination allowed the identification of 16 compounds common to the pairs of analyzed CSF and plasma samples from children with leukemia. Among them, acetone, arginine, citrate, citrulline, creatinine, formate, glutamine, glycine, lactate, lysine, scyllo-inositol were identified in both CSF and plasma with the same chemical shift values. Other metabolites showed small but significant differences in chemical shift values. Namely, the chemical shift values ​​in CSF of leucine and myo-inositol were significantly lower, while the chemical shift values ​​in CSF of alanine, phenylalanine, and valine were significantly higher compared to the chemical shift values ​​of these metabolites in plasma (each difference p < 0.001, respectively) (Table 3 ). Table 3 Metabolites in the cerebrospinal fluid and blood plasma of children with leukemia, identified on the basis of ¹H NMR chemical shift [ppm]. Data were presented as medians. Statistical analysis was performed using the Wilcoxon signed-rank test for paired observations. Variable CSF median (ppm) Plasma median (ppm) Wilcoxon rank sum p Acetone 2.25 2.22 48 0.060 Alanine 1.35 1.44 0 < 0.001 Arginine 1.58 1.58 81.5 0.902 Citrate 2.57 2.57 37 0.031 Citrulline 3.17 3.16 70.5 0.382 Creatinine 3.04 3.03 60 0.177 Formate 8.34 8.34 35 1.000 Glutamine 2.43 2.43 88 0.822 Glycine 3.55 3.55 0 0.084 Lactate 4.02 4.15 19 0.002 Leucine 1.01 0.92 0 < 0.001 Lysine 1.71 1.71 72 0.708 Myo -inositol 4.07 4.05 2.5 < 0.001 Scyllo -inositol 3.36 3.36 0 0.157 Phenylalanine 7.2 7.4 0 < 0.001 Valine 1.02 1.05 0 < 0.001 Typical 600 MHz ¹H NMR spectra and representative metabolite assays in plasma from children with leukemia and in control plasma samples are shown in Fig. 4 and Table 2 and Table 4 . Table 4 Chemical shifts (ppm) of selected metabolites in the Human Metabolome Database ( http://www.hmdb.ca ) as a basis for metabolite identification in cerebrospinal fluid and blood plasma samples. Metabolites in blood plasma and CSF Chemical shift (ppm) Metabolites in blood plasma Chemical shift (ppm) Glucose 5.123 αH1-glucose 5.190 Alanine 1.455 βH2-glucose 3.280 Arginine 1.569 Phosphocholine (PC) 3.226 Glutamine 2.428 Glycerophosphocholine (GPC) 3.234 Glycine 3.556 Guanosine monophosphate (GMP) 8.220 Isolecine 0.992 Uridine diphosphate-glucose (UDP-gluc) 5.619 Leucine 0.998 Lysine 1.721 Phenylalanine 7.410 Metabolites in CSF Chemical shift (ppm) Valine 1.028 Acetate 1.910 Citrulline 3.161 2-hydroxybutyrate 0.893 Creatinine 3.041 3-hydroxyisovalerate 1.193 Acetone 2.223 GABA 1.841 Citrate 2.537 Histidine 7.722 Formate 8.450 Tyrosine 6.872 Lactate 4.107 Myo -inositol 4.070 Scyllo -inositol 3.360 Statistical analysis We used multivariate analysis to identify differences between groups. In this study, unsupervised principal component analysis (PCA) was initially used to explore the data. However, the first two main components of PCA do not allow a clear distinction to be made between the blood plasma of healthy individuals and that of patients with leukemia. To address this, we applied supervised orthogonal least squares discriminant analysis (OPLS-DA) to analyze the data. The OPLS-DA model effectively separated healthy controls from patients with leukemia along the t1 direction, achieving R2X = 51.3%, R2Y = 69.7%, and Q2 = 45.15%. The results of this analysis are shown in Fig. 1 . PLS-DA loading plots (Fig. 2 A) revealed several key metabolites (e.g., lysine; citrulline, lactate; glucose; uridine diphosphate-glucose; phosphocholine, isobutyrate) involved in distinguishing plasma ALL samples from those with other types of leukemia. In CSF (Fig. 2 B), lysine emerged as a significant marker differentiating ALL patients from patients with other types of leukemia. In contrast, CSF carnitine, lactate, choline, glutamine, glucose, creatine, 2-hydroxybutane, threonine, and valine showed potentially important contributions to distinguishing AML from other types of leukemia. Remarkably, none of the identified metabolites were able to effectively distinguish healthy children from children with leukemia. Discussion In this pilot study, ¹H NMR spectroscopy was used to investigate the metabolomic profiles of blood plasma and cerebrospinal fluid in children with leukemia in order to identify metabolites for further study as biomarkers useful in the diagnosis of childhood leukemia. The laboratory data are shown in Table 1 . Qualitative analysis of ¹H NMR spectra allowed the identification of over 33 metabolites, with 26 detected in blood plasma (Table 2 ). Notably, the spectra of leukemic plasma differed significantly from those of healthy children, showing subtle but significant variations in chemical shift values for some metabolites (Table 2 ). Additionally, the composition of metabolites in cerebrospinal fluid (CSF) from leukemia patients was distinct from that in plasma, with some metabolites common to both fluids and others unique to either plasma or CSF. Differences in chemical shift values of metabolites present in both fluids were also observed (Table 3 , Table 4 , Fig. 3 , Fig. 4 ). The strategy for identifying known and unknown metabolites involved several steps: first, collecting unassigned peaks in the ¹H NMR spectrum by comparing them with annotated spectra of identified metabolites; second, tentatively assigning these peaks using chemical shift databases and literature on blood and CSF metabolites; and third, comprehensively analyzing 1D ¹H NMR spectra to identify peaks corresponding to known metabolites. [ 21 – 25 , 27 – 29 ]. Methods such as ¹H NMR-based metabolomics have not been used in leukemia diagnostics so far, therefore our goal is to try to incorporate this method into diagnostics. This study is one of the first to use ¹H NMR-based metabolomics to analyze the metabolic profiles of children with leukemia. Our preliminary results revealed significant qualitative differences in the metabolomic fingerprints of blood plasma of patients with leukemia compared to healthy children, especially with respect to glucose, myo-inositol, amino acids and esterified cholesterol. The most pronounced changes were observed in metabolites of the glucose-alanine pathway [ 30 – 34 ]. The reproducibility of ¹H NMR, due to its high signal intensity and natural isotopic abundance of hydrogen, makes it a powerful tool in metabolomics, capable of rapid identification and quantification of up to 100 metabolites. With the right software, this technique could be effective in diagnostics, precisely because of its speed, automation and reliability. In this study, the use of the OPLS-DA model effectively separated healthy controls and patients with leukemia (Fig. 1 ). Several key metabolites, e.g., lysine; citrulline, lactate; glucose; uridine diphosphate-glucose; phosphocholine, isobutyrate, were revealed by using of PLS-DA loading plots (Fig. 2 A) as possibly involved in distinguishing plasma ALL samples from those with other types of leukemia. In cerebrospinal fluid (Fig. 2 B), lysine emerged as a significant marker differentiating ALL patients from those with other types of leukemia. In contrast, CSF carnitine, lactate, choline, glutamine, glucose, creatine, 2-hydroxybutane, threonine, and valine showed potential significant contribution to distinguishing AML from other types of leukemia. Cancer cells are characterized by metabolic reprogramming, such as the Warburg effect, where they rely on glycolysis for ATP production even in the presence of oxygen. This shift supports rapid proliferation by prioritizing the synthesis of nucleotides, amino acids, and lipids necessary for cell division. Leukemic cells, similar to activated lymphocytes, display unique metabolic traits, including increased glucose uptake and enhanced ribosome biogenesis. Unlike normal cells, leukemia cells often show growth factor independence and mutations in genes like IDH, as well as upregulated mTOR and PKM2 pathways [ 33 – 38 ]. Positron emission tomography (PET) scans using fluorodeoxyglucose (FDG) exploit the Warburg effect for cancer diagnosis, including leukemias. FDG [ 39 ], a glucose analog, accumulates in cancer cells, allowing tumor visualization based on elevated glucose metabolism. This knowledge is also applicable to brain tumors, especially since 5–10% of ALL patients have central nervous system involvement at diagnosis, and studies show differential expression of glucose transporters such as GLUT1 and GLUT3, which correlates with tumor stage and prognosis. (Fig. 5 ) [ 40 – 47 ]. Beyond glucose metabolism, cancer research has highlighted the importance of amino acids in sustaining tumor growth. Requirements for AAs is different between normal and tumor cells. Amino acids not only serve as building blocks but also regulate redox states, energy production, and immune responses [ 49 – 50 ]. Glutamine is a critical nutrient, fueling the TCA cycle and supporting biosynthesis, especially under glucose-limiting conditions. Asparagine, another key amino acid, plays a role in cell survival during glutamine deprivation. Cancer cells can also utilize branched-chain amino acids (BCAAs) for energy, challenging the assumption that cancer metabolism is strictly glucose-dependent. Elevated BCAA levels have been linked to early-stage cancers, such as pancreatic ductal adenocarcinoma driven by KRAS mutations, suggesting their role in nutrient acquisition and tumor progression [ 51 – 54 ]. Among the metabolites identified, inositols, particularly myo-inositol (MI), were of special interest. It is known that the concentration of scyllo inositol and myo inositol in the human brain can be measured by NMR. [ 55 ]. MI, a biologically active sugar alcohol, has been shown to inhibit carcinogenesis in various organs. Its status in biological systems is largely influenced by the enzyme myo-inositol-3-phosphate synthase (MIPS). MI seems to be a promising candidate for our further differential analyses, as metabolic auxotrophy of myo-inositol was found in AML [ 56 ]. Other identified metabolites in our study included amino acids (e.g., histidine, lysine, alanine, glutamine, valine, leucine, and phenylalanine) and endogenous compounds such as lactate, creatine, and pyruvate. ¹H NMR analysis also identified compounds such as UDP-glucose, glycerophosphocholine, and phosphocholine in the blood plasma metabolite profile. Our study aimed to identify metabolic alterations associated with acute lymphoblastic leukemia in children. Using ¹H NMR spectroscopy, we analyzed blood plasma and cerebrospinal fluid samples from pediatric patients with leukemia, as well as blood plasma from healthy children. Our results revealed significant differences in the metabolic composition of patients with leukemia, especially with respect to glucose and amino acid metabolism pathways. Interestingly, we observed changes in glutamine shift in leukemia, one of essential amino acids, and cancer cells are known to have an increased demand for this amino acid. Lysine may regulate AML cells’ survival by triggering redox metabolism reprogramming. Our study aim was to investigate the metabolic differences between plasma and cerebrospinal fluid of children with leukemia and between plasma of children with leukemia and healthy children using ¹H NMR spectroscopy. By analyzing the metabolite composition of blood plasma and cerebrospinal fluid samples, we aimed to identify potential biomarker candidates. Our preliminary findings suggest significant differences in the metabolic profiles between the two groups. The signal assignment that have been done is only the first step towards further metabolomic study. The assiment have to be confirmed with 2D experiments in the next step before selected metabolites could be used to improve early diagnosis and disease monitoring. In summary, this study demonstrates the utility of ¹H NMR spectroscopy in identifying metabolic changes in leukemia, highlighting its potential for noninvasive diagnostics and metabolic profiling. Future studies should investigate the integration of ¹H NMR in clinical practice and compare its performance with other metabolomic techniques to aid in pediatric leukemia diagnosis and treatment strategies. Conclusion Recent advances in cancer research have highlighted the importance of altered metabolism, particularly glucose metabolism, in cancer development. A deeper understanding of these metabolic changes offers promising opportunities for new therapeutic strategies. In the present study, we used ¹H NMR spectroscopy to perform metabolic profiling of blood plasma and cerebrospinal fluid in children with leukemia. By analyzing these biofluids, we aimed to identify distinct metabolic signatures according to the leukemia subtype and select metabolites for further study. Our preliminary findings suggest that patients with leukemia exhibit unique metabolic profiles in both blood plasma and cerebrospinal fluid, and the metabolic profiles of leukemia and healthy plasma were also different. This highlights the potential of NMR-based metabolomics as a rapid and noninvasive diagnostic tool in leukemia. Further studies should investigate the diagnostic and/or prognostic value of metabolic profiling in a much larger group of pediatric patients with leukemia. Materials and Methods Study population This preliminary study included twenty pediatric patients diagnosed with various hematological malignancies defined by the International Classification of Diseases (ICD-10) and were treated in compliance with their corresponding treatment protocols – specifically regarding this study the following treatment Protocols were used: Acute Lymphoblastic Leukemia: AIEOP-BFM-2017 treatment protocol, EudraCT Number: 2020-005017-41, Philadelphia chromosome-positive (Ph+) Acute Lymphoblastic Leukemia: EsPhALL2017/COGAALL1631 treatment protocol EudraCT Number: 2017-000705-20, Acute Myeloid Leukemia: AML-BFM 2019 treatment protocol, Diffuse Large B-Cell Lymphoma: Inter-B-NHL 2010 low/intermediate treatment group treatment protocol, Hemophagocytic Lymphocytosis: HLH 2004 Protocol. The patients included in this study were treated at the Department of Bone Marrow Transplantation, Oncology and Hematology of the Medical University of Wroclaw between October 19, 2021 and November 24, 2022. The legal guardians of each patient gave informed consent before being included in the study only after they were informed about what the procedure of collecting the cerebral spinal fluid entails, as well as its potential risks and complications. The study design was in accordance with the tenets of the Helsinki Declaration, and was approved by the Bioethics Committee of the Wroclaw Medical University in Poland, approval no. KB-525/2021. We planned a study population as diverse as possible to enable the selection of common and distinct metabolites to distinguish the population of children with leukemic-type cancers from the population of healthy children in this preliminary study. The study group (Leukemia-type, n = 20) consisted of patients aged 2–17 (6,8 ± 5,7) years, 8 boys (40%) and 12 (60%) girls who were admitted for diagnosis or planned oncological treatment, including the administration of chemostatic drugs and/or radiological treatment. In the study group the predominant type of hematological malignancy was ALL (n = 14, 70%) including 11 pre-B ALL (55%), 2 T-cell ALL (10%), 1 ALL (Ph+) (5%). The remaining children (n = 6, 30%) were diagnosed with: myelomonocytic leukemia with eosinophilia, AML M4/M5 (1 case), acute megakaryoblastic leukemia, AML M7 (1 case); diffuse large B cell lymphoma, DLBCL (1 case); T-Cell lymphoma (1 case); and hemophagocytic lymphohistiocytosis, HLH (1 case). Basic demographics such as age, gender, child's weight and height, previous medical histories, and clinical signs and symptoms of all patients were also obtained. Clinico-pathological parameters included histologically confirmed neoplasm, gender, and age. The laboratory data including a complete blood count and differential into neutrophils, lymphocytes, and monocytes, were recorded based on laboratory results from the plasma samples used in the study (Table 1 ). The study participants meeting the following criteria were included in the investigation: subject is ≤ 18 years old, acute leukemia diagnosis, first admission to hospital or coming back to the Clinic Hospital for planned oncological treatment, such as administration of chemostatics and radiological treatment. The exclusion criteria included the history of certain medical conditions e.g., chronic leukemia, Hodgkin's disease, multiple myeloma, non-Hodgkin's lymphoma, and amyloidosis, and others not belonging to the group of acute leukemia neoplasms. Also twenty healthy (free from blood malignancy) children aged 11 to 17 (14,3 ± 2,0) years, 8 females and 12 males, participating in the PICTURE study, were included as a control group. "Population Cohort Study of Wroclaw Citizens (PICTURE)" was established between 2019 and 2021” by both the Wrocław Medical University and Wroclaw Municipality [ 26 ]. The PICTURE study has been accepted by the Bioethics Committee of the Wroclaw Medical University in Poland (KB-667/2019). Sampling Paired matched cerebrospinal fluid and blood plasma samples were collected during routine diagnostic procedures from pediatric patients who required lumbar puncture because of suspected or previously diagnosed leukemia-type malignancies, whereas only plasma samples were collected from healthy children. Leukemia-type group. The standard diagnostic lumbar puncture was performed under regional anesthesia. The CSF was collected under aseptic conditions in sterile screw-capped tubes and immediately subjected to clinically required diagnostic tests. In this study, CSF samples were centrifuged at 1500 g for 15 min at a controlled temperature of 4°C, aliquoted into 0.1 ml and stored at − 76°C until analysis. Venous blood (2.0 ml) was taken from an antecubital vein into calcium-balanced lithium heparinized tubes (SARSTEDT Blood Gas Monovette®, SARSTEDT Ltd., Leicester, UK). Plasma samples were obtained by centrifugation of blood samples at 2000 g for 15 min. Samples were aliquoted into 0.1 ml and stored at − 76°C until analysis. Non-malignant control group. Venous blood was taken into EDTA-treated (K3-EDTA) tubes, and then centrifuged (2200 g, 15 min). Plasma samples were aliquoted into 0.4 ml and stored at − 76°C until analysis [ 26 ]. Frozen samples were thawed for 1 h at 20°C before use to allow complete dissolution of the plasma. Freeze-thaw cycles were avoided because they are detrimental to many serum components, and hemolyzed, icteric, or lipemic samples were discarded. Acquisition of NMR spectra Blood plasma, or cerebrospinal fluid, was thawed on ice and then centrifuged. 50 µl of clear sample was collected and mixed with 10 µl of deuterated water (D 2 O). The resulting mixture (total 60 µl) was transferred at room temperature to a 5 mm NMR tube for further analysis. All NMR experiments were performed on a Bruker NMR AVANCE III™ 600 MHz spectrometer equipped with a micro-cryoprobe (TCI, [¹H, 13C, 15N], 1.7 mm). Spectra were acquired at 298 K. One-dimensional proton spectra were acquired using zgesgp and noesygppr1d (1D NOESY) pulse sequences. An excitation sculpting pulse sequence or presaturation were applied to suppress water signals in the spectra, respectively. For both sequences, an exponential window function with a line broadening factor of 0.2 Hz was used for free induction decay (FID) before Fourier transform. For each FID spectrum, 32 scans were collected with a spectral width of 12 ppm, an acquisition time of 4.5 s, a relaxation delay of 5 s, and a mixing time of 10 ms. All NMR spectra were phased and baseline were corrected using TopSpin software (version 3.6.5, Bruker, BioSpin, Germany). Parameters were adjusted to enable quantification of metabolites using the MestReNova and TopSpin Bruker software. NMR profiling and metabolite determination Identification and qualitative assessment of the detected metabolites were performed using the MestReNova-8.1.4-12489 profiler and confirmed in TopSpin software. MestReNova-8.1.4-12489 can deconvolute metabolites in complex samples and can determine concentrations in overlapping regions of the spectrum. The software matches the peaks to a set of model spectra characterizing the chemical environments of each metabolite. An identical volume of D 2 O was added to all blood plasma and CSF samples, obtaining a mixture of D 2 O and H 2 O as the solvent. Characteristic bands for individual amino acid residues and other substances were assigned for all acquired spectra. Further information on proton peak assignments were obtained by comparing chemical shifts with those available in the Human Metabolome Database ( http://www.hmdb.ca ). Statistical analysis Preprocessing and statistical analysis were done in Python 3.10.7 (packages: NumPy, Pandas, SciPy). Due to rather low sample size, non-normality (tested with Shapiro-Wilk test) and the presence of outliers in the data (observed based on Q-Q plots), a non-parametric approach was selected. Leukemia vs. control differences in values of the analyzed variables were tested with the Mann-Whitney U test. Differences between the plasma and CSF of leukemia patients was tested with the Wilcoxon test, handling pairwise ties according to J.W. Pratt [ 27 ], with normal approximation described by E.E. Cureton [ 28 ]. The data are presented as percentages for qualitative variables (gender, disease) and as medians (1st and 3rd quartiles) for continuous variables, and a p-value of ˂ 0.05 was considered statistically significant. Data were analyzed using principal component analysis (PCA). To enhance group separation, partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were employed. Limitations Our preliminary study was based on a qualitative analysis of the metabolite composition of plasma and cerebrospinal fluid using ¹H NMR spectrometry. The study included a small number of samples from pediatric patients with different types of diagnosed leukemia. Therefore, the qualitative data obtained did not allow us to build a sophisticated statistical model to evaluate the results. Our studies should be considered preliminary and repeated with a quantitative determination of metabolites in a much larger group of patients. Declarations Declaration of Competing Interest : The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Informed consentstatement : Not applicable. Institutional review board statement Not applicable. Funding: This research was funded in whole or in part by the National Science Centre MINI.A070.22.004. Research was performed using biological material and data from Wrocław Medical University Biobank. Author Contribution Author Contributions: conceptualization, A.S. and M.P.; methodology, A.S., T.N, M.P, and AJ-K.; software, A.S., A.J-K.; validation, A.S., Ł.L., and A.J.-K.; formal analysis, A.S., T.N., A.J.-K., Ł.L. and B.K.; resources, B.K., T.B, T.Z .; data curation, A.S and M.P.; writing—original draft preparation, A.S., M.P. and A. J.-K..; writing—review and editing, A.W.; A.K..; visualization A.S., T.N..; supervision, A.S., M.P.; storage and portioning of samples, A.M-W., T.Z., K.P.-Z., M.Ś.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript. Data Availability All data generated or analysed during this study are included in this published article and its supplementary information files and are made available to the readers upon request from A.S. ( [email protected] ). References Davis, A. S., Viera, A. J. & Mead, M. D. Leukemia: an overview for primary care. Am Fam Physician. 1;89(9); pp.731-8. PMID: 24784336. (2014). Bispo, J. A. B. & Pinheiro, P. S. KobetzE.K. Epidemiology and Etiology of Leukemia and Lymphoma. Cold Spring Harb Perspect Med. 1;10(6):a034819. (2020). 10.1101/cshperspect.a034819 . PMID: 31727680; PMCID: PMC7263093. https://gco.iarc.fr/today/en https://gco.iarc.fr/overtime/en/dataviz/age_specific?populations=61600&sexes=1_2&cancers=28&multiple_populations=1&mode=cancer&group_populations=1&multiple_cancers=1&years=2012 Ciesielska, M., Orzechowska, B., Gamian, A. & Kazanowska, B. Epidemiology of childhood acute leukemias. Postępy Higieny i Medycyny Doświadczalnej . 78 , 22–36. https://doi.org/10.2478/ahem-2023-0023] (2024). Whitehead, T. P., Metayer, C., Wiemels, J. L., Singer, A. W. & Miller, M. D. Childhood Leukemia and Primary Prevention. Curr. Probl. Pediatr. Adolesc. Health Care . 46 (10), 317–352 (2016). PMID: 27968954; PMCID: PMC5161115. Bhojwani, D. & Yang, J. J. PuiCH. Biology of childhood acute lymphoblastic leukemia. Pediatr. Clin. North. Am. 62 (1), 47–60. 10.1016/j.pcl.2014.09.004 (2015). PMID: 25435111; PMCID: PMC4250840. Chennamadhavuni, A., Lyengar, V., Mukkamalla, S. K. R., Shimanovsky, A. & Leukemia In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024. PMID: 32809325. (2023). Inaba, H. & Mullighan, C. G. Pediatric acute lymphoblastic leukemia. Haematologica. pp: 2524–2539. (2020). 1;105(11) 10.3324/haematol.2020.247031 . PMID: 33054110; PMCID: PMC7604619. Malard, F. & Mohty, M. Acute lymphoblastic leukaemia. Lancet. ;395(10230), pp. 1146–1162. (2020). 10.1016/S0140-6736(19)33018-1 . PMID: 32247396. Jones, N. P. & Schulze, A. Targeting cancer metabolism-aiming at a tumor’s sweet-spot. Drug Discov . 17 , 232–241 (2012). Ijurko, C., González-García, N., Galindo-Villardón, P. & Hernández-Hernández, Á. A 29-gene signature associated with NOX2 discriminates acute myeloid leukemia prognosis and survival. Am. J. Hematol. 97 (4), 448–457. 10.1002/ajh.26477 (2022). Epub 2022 Feb 8. PMID: 35073432; PMCID: PMC9303675. Jemal, A. et al. Cancer statistics, 2006. CA Cancer J Clin. pp.106 – 30. (2006). Mar-Apr;56(2) 10.3322/canjclin.56.2.106 . PMID: 16514137. Estey, E. H. Acute myeloid leukemia: 2014 update on risk-stratification and management. Am J Hematol. ;89(11), pp. 1063-81. (2014). 10.1002/ajh.23834 . PMID: 25318680. Döhner, H. et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood 129 (4), 424–447. 10.1182/blood-2016-08-733196 (2017). Epub 2016. PMID: 27895058; PMCID: PMC5291965. Enright, H. & McGlave, P. B. Chronic myelogenous leukemia. Curr Opin Hematol. ;3(4),pp.303-9. (1996). 10.1097/00062752-199603040-00009 . PMID: 9372092. Cortes, J., Pavlovsky, C. & Saußele, S. Chronic myeloid leukaemia. Lancet. ;398(10314), pp. 1914–1926. (2021). 10.1016/S0140-6736(21)01204-6 . Epub 2021. PMID: 34425075. Devine, S. M. & Larson, R. A. Acute leukemia in adults: recent developments in diagnosis and treatment. CA Cancer J Clin. ;44(6), pp.326 – 52. (1994). 10.3322/canjclin.44.6.326 . PMID: 7953914. Chiaretti, S., Zini, G. & Bassan, R. Diagnosis and subclassification of acute lymphoblastic leukemia. Mediterr. J. Hematol. Infect. Dis. 6 (1), e2014073. 10.4084/MJHID.2014.073 (2014). PMID: 25408859; PMCID: PMC4235437. Crespo-Solis, E., López-Karpovitch, X., Higuera, J. & Vega-Ramos, B. Diagnosis of acute leukemia in cerebrospinal fluid (CSF-acute leukemia). Curr Oncol Rep. ;14(5), pp.369 – 78. (2012). 10.1007/s11912-012-0248-6 . PMID: 22639108. Altenbuchinger, M. et al.. Bucket Fuser: Statistical Signal Extraction for 1D ¹H NMR Metabolomic Data. Metabolites 29 ;12(9):812. 10.3390/metabo12090812 . PMID: 36144216; PMCID: PMC9501206. Markley, J. L. et al. The future of NMR-based metabolomics. Curr. Opin. Biotechnol. 43 , 34–40. 10.1016/j.copbio.2016.08.001 (2017). Epub 2016 Aug 28. PMID: 27580257; PMCID: PMC5305426. Dang, N. H., Singla, A. K., Mackay, E. M., Jirik, F. R. & Weljie, A. M. Targeted cancer therapeutics: biosynthetic and energetic pathways characterized by metabolomics and the interplay with key cancer regulatory factors. Curr Pharm Des. ;20(15), pp. 2637-47. (2014). 10.2174/13816128113199990489 . PMID: 23859615. Lussu, M. et al. ¹H NMR spectroscopy-based metabolomics analysis for the diagnosis of symptomatic E. coli-associated urinary tract infection (UTI). BMC Microbiol. ;17(1),pp.201. (2017). 10.1186/s12866-017-1108-1 . PMID: 28934947; PMCID: PMC5609053. Speyer, C. B. & Baleja, J. D. Use of nuclear magnetic resonance spectroscopy in diagnosis of inborn errors of metabolism. Emerg. Top. Life Sci. 5 (1), 39–48. 10.1042/ETLS20200259 (2021). PMID: 33522566; PMCID: PMC8630612. Zatońska, K. et al. Population cohort Study of Wroclaw citizens (PICTURE) – study protocol. J. Health Inequal. 8 (1), 37–43. https://doi.org/10.5114/jhi.2022.115930 (2022). Pratt, J. W. Remarks on Zeros and Ties in the Wilcoxon Signed Rank Procedures. J. Am. Stat. Assoc. 54 , 655–667 (1959). Cureton, E. E. The Normal Approximation to the Signed-Rank Sampling Distribution When Zero Differences are Present. J. Am. Stat. Assoc. 62 , 1068–1069 (1967). Mandal, R. et al. Multi-platform characterization of the human cerebrospinal fluid metabolome: A comprehensive and quantitative update. Genome Med. 4 (4), 1–11. https://doi.org/10.1186/gm337 (2012). Romeo, M. J. et al. CSF proteome: A protein repository for potential biomarker identification. Expert Rev. Proteomics . 2 (1), 57–70. https://doi.org/10.1586/14789450.2.1.57 (2005). Stoop, M. P. et al. Quantitative proteomics and metabolomics analysis of normal human cerebrospinal fluid samples. Mol. Cell. Proteomics . 9 (9), 2063–2075. https://doi.org/10.1074/mcp.M110.000877 (2010). Ellinger, J. J., Chylla, R. A., Ulrich, E. L. & Markley, J. L. Databases and software for NMR-based metabolomics James. Bone 72 (2), 132–135. https://doi.org/10.2174/2213235x11301010028 (2011). Gulino, F. A. et al. Effect of treatment with myo-inositol on semen parameters of patients undergoing an IVF cycle: in vivo study. Gynecol. Endocrinol. 32 , 65–68. 10.3109/09513590.2015.1080680 (2016). Dona, A. C. et al. A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. CSBJ 14 , 135–153. https://doi.org/10.1016/j.csbj.2016.02.005 (2016). ISSN 2001 – 0370. Emwas, A. H. M., Salek, R. M., Griffin, J. L. & Merzaban, J. NMR-based metabolomics in human disease diagnosis: Applications, limitations, and recommendations. Metabolomics 9 (5), 1048–1072. https://doi.org/10.1007/s11306-013-0524-y (2013). Soh, H., Wasa, M. & Fukuzawa, M. Hypoxia upregulates amino acid transport in a human neuroblastoma cell line. J. Pediatr. Surg. 42 , 608–612 (2007). Kobayashi, S. & Millhorn, D. E. Hypoxia regulates glutamate metabolism and membrane transport in rat PC12 cells. J. Neurochem . 76 , 1935–1948 (2001). Dang, C. V., Le, A. & Gao, P. MYC-induced cancer cell energy metabolism and therapeutic opportunities. Clin. Cancer Res. 15 , 6479–6483 (2009). Buzzai, M., Bauer, D. E. & Jones, R. G. at al. The glucose dependence of Akt-transformed cells can be reversed by pharmacologic activation of fatty acid beta-oxidation. Oncogene 24, pp.4165–4173. (2005). Levine, A. J. & Puzio-Kuter, A. M. The control of the metabolic switch in cancers by oncogenes and tumor suppressor genes. Science 330 , 1340–1344 (2010). Semenza, G. L. HIF-1: upstream and downstream of cancer metabolism. Curr. Opin. Genet. Dev. 20 , 51–56 (2010). DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G. & Thompson, C. B. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell. Metab. 7 , 11–20 (2008). Dang, C. V., Hamaker, M., Sun, P., Le, A. & Gao, P. Therapeutic targeting of cancer cell metabolism. J. Mol. Med. (Berl) . 89 , 205–212 (2011). Wood, I. S. & Trayhurn, P. Glucose transporters (GLUT and SGLT): expanded families of sugar transport proteins. Br. J. Nutr. 89 , 3–9 (2003). Wu, X. & Freeze, H. H. GLUT14, a duplicon of GLUT3, is specifically expressedin testis as alternative splice forms. Genomics 80 , 553–557 (2002). Joost, H. G. & Thorens, B. The extended GLUT-family of sugar/polyol transport facilitators: nomenclature, sequence characteristics, and potential function of its novel members (Review). Mo Membr. Biol. 18 , 247–256 (2001). Samih, N. et al. The impact of N- and O-glycosylation on the functions of Glut-1 transporter in human thyroid anaplastic cells. Biochim. Biophys. Acta . 1621 , 92–101 (2003). Macheda, M. L., Rogers, S. & Best, J. D. Molecular and cellular regulation of glucose transporter (GLUT) proteins in cancer. J. Cell. Physiol. 202 , 654–662 (2005). Chowdhury, R., Yeoh, K. K. & Tian, Y. M. The oncometabolite 2-hydroxyglutarate inhibits histone lysine demethylases. EMBO Rep. 12 , 463–469 (2011). Xu, W., Yang, H. & Liuy, I. at al. Oncometabolite 2-hydroxyglutarate is a competitive inhibitor of a-ketoglutarate-dependent dioxygenases. Cancer Cell ; 19, pp. 17–30. (2011). Wise, D. R., Ward, P. S. & Shay, J. E. at al. Hypoxia promotes isocitrate dehydrogenase -dependent carboxylation of a-ketoglutarate to citrate to support cell growth and viability. Proc Natl Acad Sci ; 108, pp. 19611–19616. (2011). Metallo, C. M., Gameiro, P. A. & Bell, E. L. Reductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia. Nature 481 , 380–384 (2012). Monti, S., Savage, K. J. & Kutok, J. L. at al. Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. Blood ; 105, pp. 1851–1861. (2005). Caro, P., Kishan, A. U. & Norberg, E. at al. Metabolic signatures uncover distinct targets in molecular subsets of diffuse large B-cell lymphoma. Cancer Cell ; 22, pp. 547–560. (2012). Michaelis, T. et al. Identification of scyllo-inositol in proton NMR spectra of human brain in vivo. NMR Biomed. 6 (1), 105–109 (1993). Wei, Y. et al. SLC5A3-Dependent myo-inositol auxotrophy in acute myeloid leukemia. Cancer Discov . 12 (2), 450–467. https://doi.org/10.1158/2159-8290.CD-20-1849 (2022). Additional Declarations No competing interests reported. Supplementary Files supplementaryinformation.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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University","correspondingAuthor":false,"prefix":"","firstName":"Tomasz","middleName":"","lastName":"Brutkowski","suffix":""},{"id":415543200,"identity":"5f0283c8-e5ce-45b5-a145-d99790a4103f","order_by":12,"name":"Bernarda Kazanowska","email":"","orcid":"","institution":"Wrocław Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bernarda","middleName":"","lastName":"Kazanowska","suffix":""}],"badges":[],"createdAt":"2025-02-03 10:53:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5950449/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5950449/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76660973,"identity":"a68a795e-2e24-4a97-975a-2ec1e34d455f","added_by":"auto","created_at":"2025-02-19 12:02:57","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20471,"visible":true,"origin":"","legend":"\u003cp\u003eThe OPLS-DA scores plot visualizes the results of multivariate analyses performed on data from healthy controls (circles, A) and leukemia patients (triangles, B).\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5950449/v1/4bbc76d6adbb1da1e0722e9b.jpg"},{"id":76660994,"identity":"911a0543-9212-466a-a524-b3e1bd790161","added_by":"auto","created_at":"2025-02-19 12:03:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37859,"visible":true,"origin":"","legend":"\u003cp\u003ePLS-DA loading plots of (A) blood plasma and (B) cerebrospinal fluid samples of leukemia patients. (1) Alanine; (2) Arginine; (3) Histidine; (4) Glutamine; (5) Lysine; (6) Tyrosine; (7) Valine; (8) Phenylalanine; (9) Citrulline; (10) Creatine; (11) Acetate; (12) Acetone; (13) Lactate; (14) 3hydroxyisovalerate; (15) 2hydroxybutyrate; (16) Glucose; (17) Myoinositol; (18) Uridine diphosphate-glucose; (19) Glycerophosphocholine; (20) Guanosine monophosphate; (21) Phosphocholine; (22) GABA; (23) Glycine; (24) Scyllo-inositol; (25) Formate; (26) Citrate; (27) Isobutyrate.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5950449/v1/95ea5759e13a0a922db47341.jpg"},{"id":76662891,"identity":"eada3ef2-8dee-428b-a77c-266479b09a7d","added_by":"auto","created_at":"2025-02-19 12:10:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":23888,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of typical 600 MHz ¹H NMR spectra and representative metabolite assays in the study samples of (A) plasma from a healthy control; (B) plasma from a leukemia patient; and (C) cerebrospinal fluid from a leukemia patient.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5950449/v1/9f32ed12ca2fff95d6745ad3.jpg"},{"id":76660976,"identity":"d6243fe2-0961-459a-88f8-48692347f424","added_by":"auto","created_at":"2025-02-19 12:02:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22213,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative 600 MHz ¹H NMR spectra for metabolite determination in plasma in selected diseases; (A) healthy control, (B) LL, (C) ALL, (D) DLBCL and (E) HLH. Explanation of abbreviations in the text (see: Materials and Methods).\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5950449/v1/2c47d8890a270f6f62b63f1e.jpg"},{"id":76663321,"identity":"cf6bacb9-a652-4091-8738-c8640c45c239","added_by":"auto","created_at":"2025-02-19 12:18:58","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":30624,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the structure of GLUT glucose transporters (description in the text – according to [47]; modified).\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5950449/v1/f3069bd230d1970138de70dd.jpg"},{"id":78169134,"identity":"6beb2159-db7d-48b7-a3cc-ba902bb5e66d","added_by":"auto","created_at":"2025-03-10 14:31:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1272251,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5950449/v1/10dd4d2f-d9cd-41b9-91f5-83d2fddd48ea.pdf"},{"id":76662892,"identity":"b4cbbea1-e207-41d4-9556-b8a26790909f","added_by":"auto","created_at":"2025-02-19 12:10:58","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":55247,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryinformation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5950449/v1/91a01fff6d020b0ce7ff69e0.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of ¹H NMR-based metabolomics in childhood leukemia. A preliminary study.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLeukemia is a malignant neoplasm that arises from the clonal proliferation of hematopoietic stem cells in the bone marrow [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Hematopoiesis, in turn, is the process of differentiation and maturation of stem cells into erythrocytes, megakaryocytes and immune cells (of myeloid, lymphoid or monocytic origin) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Global estimates for all cancers are available in the GLOBOCAN database, compiled by the International Agency for Research on Cancer (IARC), which operates under the World Health Organization (WHO). In 2020, there were 474.519 new cases of leukemia and 311.594 deaths attributed to this disease. This reflects a 7.9% increase in new cases and a 0.83% increase in deaths compared to 2018. The incidence of leukemia is higher in men than in women, and it increases with age. The highest rates are observed in North America, with 13.3 cases per 100,000 men and 8.8 cases per 100,000 women. In Central and Eastern Europe, the incidence rates are 8.1 for men and 5.6 for women, respectively [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Leukemias are the most common malignancies in children. In the USA, approximately 3.800 children are diagnosed annually with acute lymphoblastic leukemia (ALL) or acute myeloid leukemia (AML) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among those under 19 years of age, ALL is the most prevalent type of leukemia, accounting for 25% of all childhood cancers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Blasts are immature and dysfunctional cells that normally constitute between 1% and 5% of bone marrow cells. Acute leukemias are characterized by the presence of more than 20% blast cells in the peripheral blood, whereas chronic leukemias typically exhibit less than 20% blast cells in the peripheral blood. This distinction accounts for the more rapid onset of symptoms observed in acute leukemias [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are four main subtypes of leukemias. Acute lymphoblastic leukemia is the most common leukemia in the pediatric population, with B-cell acute lymphoblastic leukemia (B-ALL) being the most prevalent form. It is driven by three primary types of genetic alterations: chromosomal aneuploidies, rearrangements, and point mutations. About 30% of affected children exhibit high hyperdiploidy, often associated with mutations in the Ras pathway [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The prognosis for B-ALL has significantly improved, with current cure rates between 75\u0026ndash;85%, which are better than those for adults [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAcute myeloid leukemia is the most common type of acute leukemia in adults and is rarer in children [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It involves malignant cells of the myeloid lineage (excluding B and T lymphocytes) and is characterized by significant molecular complexity, complicating patient stratification and treatment selection. Studies have identified decreased expression levels of components of the NOX2 complex, as well as chromosomal losses and deletions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Genetic conditions associated with AML include Klinefelter syndrome, neurofibromatosis, Fanconi anemia, and Li-Fraumeni syndrome [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The prognosis for AML is generally poor, with frequent relapses [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChronic lymphoid leukemia arises from the proliferation of monoclonal lymphoid cells and predominantly affects the elderly, with a median age of incidence of 72 years. Given the slow progression of the disease, treatment focuses on controlling disease progression and prolonging patient life. Treatment is typically initiated only when there is rapid disease progression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChronic myeloid leukemia (CML) usually results from a reciprocal translocation between the BCR gene on chromosome 22 and the ABL1 gene on chromosome 9, forming the Philadelphia chromosome [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This translocation produces a constitutively activated tyrosine kinase, bcr-abl [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. mainly affects older adults, with an average age of diagnosis at 65 years, and is extremely rare in children. With proper treatment, life expectancy for CML patients is comparable to that of the healthy population [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe diagnosis of acute leukaemias usually starts with the evaluation of the peripheral blood. Anemia, thrombocytopenia, leukopenia (but leukocytosis is also possible) are usually found [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. An important step in diagnosis is the morphological evaluation of bone marrow cells. It allows differentiation of AML from ALL. The lymphoblast population (characteristic of ALL) is characterized by a homogeneous population of blasts, a predominantly round and centrally located nucleus, sparse cytoplasm, absent Auer rods and possible vacuolation. In contrast, myeloblasts (characteristic of AML) are usually characterized by a heterogeneous population, a nucleus that tends to be eccentric, a variable amount of cytoplasm, the possible presence of Auer rods and vacuolation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Flow cytometry, cytogenetic and molecular studies on bone marrow aspirate should also be performed, allowing for a risk stratification. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] A metabolic panel, determination of serum uric acid dehydrogenase and liver function tests are extremely important for proper patient profiling [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Cerebrospinal fluid (CSF) testing [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] is used primarily to evaluate for tumor markers indicative of leukemia in the central nervous system (CNS), primarily for the presence of leukemic cells in the cerebrospinal fluid, and secondarily for the brain damage associated biomarkers of following treatment such as chemotherapy and radiotherapy, to check for acute neurological toxicity or processes that may lead to long-term neurocognitive deficits. Evaluation of CSF cell counts, differential analysis, and the CSF cytology are mandatory for the initial diagnosis of central nervous system infiltration by ALL cells and for classification of the degree of CNS involvement as CNS1, CNS2, or CNS3, respectively. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe most common methods of metabolomic analysis are the \u0026sup1;H NMR spectroscopy and mass spectrometry. 1D \u0026sup1;H NMR spectroscopy is a cornerstone of metabolomics, providing a foundation for understanding metabolic profiles. By enabling the identification and quantification of a wide range of metabolites, 1D NMR plays a crucial role in numerous biomedical studies and is routinely used to find out biomarkers in biofluids. However, as the NMR is a method with higher reproducibility and the ability to identify unknown substances [21,22], this method was selected as the analytical method of choice for this study and the in vitro results have been documented to translate into clinical application in vivo [22]. Moreover, \u0026sup1;H NMR analysis is non-destructive and requires minimal sample preparation, so if necessary, the sample can be used for further testing, which is especially important in the case of such a rare and valuable body fluid from a seriously ill child. Metabolic changes are an indispensable part of cancerogenesis and a potential handle point for metabolomic methods, and the determination of the metabolome of a cancer cell can potentially provide information on the extent of cancerogenesis. As putative pathways that can form the basis for metabolomic profiling, one can highlight, for example, the pathways of cell bioenergetics, lipid metabolism, the Warburg effect or signaling pathways such as Myc, p53, HIF-1 or mTOR [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the current trend of using \u0026sup1;H NMR and MS as diagnostic methods with potential applications in microbiology [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] or inborn errors of metabolism [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], these methods do not exist in the diagnosis of leukemia.\u003c/p\u003e \u003cp\u003eOur study aim was to investigate the metabolic differences between plasma and cerebrospinal fluid of children with leukemia and between plasma of children with leukemia and healthy children using \u0026sup1;H NMR spectroscopy. By analyzing the metabolite composition of blood plasma and cerebrospinal fluid samples, we aimed to identify potential biomarker candidates. Our preliminary findings suggest significant differences in the metabolic profiles between the two groups. The conducted study is only the first step towards further metabolomic studies. The assiment have to be confirmed with 2D experiments in the next step before selected metabolites could be used to improve early diagnosis and disease monitoring. This study represents one of the first applications of \u0026sup1;H NMR-based metabolomics to pediatric leukemia, offering a potential novel approach to understanding and managing this complex disease.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive laboratory plasma parameters given as median values ​​in the study group with leukemia-type malignancies and in the control group of healthy children are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eCompared to the control group, patients with leukemic tumors had usually lower white blood cell count (WBC, p\u0026thinsp;=\u0026thinsp;0.002), red blood cell count (RBC, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hemoglobin level (HGB, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hematocrit (HTC, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and platelet count (PLT, p\u0026thinsp;=\u0026thinsp;0.006). However, the percentage of neutrophils (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), percentage of lymphocytes; (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and percentage of monocytes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were usually higher in patients with leukemia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of median values ​​(1st and 3rd quartile) of blood plasma laboratory variables in the group of children with leukemia-type malignancies and healthy controls. WBC, white blood cells; RBC, red blood cells; HGB, hemoglobin; HTC, hematocrit; MCV, mean corpuscular volume; MCH, mean content of hemoglobin; MCHC, mean cell hemoglobin concentration; RDW-CV, red blood cell distribution width, coefficient of variation; PLT, platelets; MPV, mean platelet volume; n/a \u0026ndash; not applicable. Statistics were performed using the nonparametric Mann-Whitney U test to compare two independent groups of patients and healthy controls. *Ranges given for children from infancy to adolescence.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eLeukemia (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable, unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1st Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3rd Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1st Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3rd Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNormal range\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e*5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC, \u0026times;10\u003csup\u003e12\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e*4.0-5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e*9,5\u0026ndash;15,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTC, vol%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e*32\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV, \u0026times;10\u003csup\u003e15\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e90.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e80\u0026ndash;97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCH, \u0026times;10\u003csup\u003e12\u003c/sup\u003e/g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCHC, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e31\u0026ndash;36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW-CV, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11,5\u0026ndash;14,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e187.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e*150\u0026ndash;400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPV \u0026times;10\u003csup\u003e15\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7,0\u0026ndash;12,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e76.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0,0\u0026ndash;10,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u0026sup1;H NMR analysis\u003c/h2\u003e \u003cp\u003eChemical shift analysis performed using NMR profiling and metabolite determination allowed the identification of 26 compounds in the analyzed blood plasma samples. Among them, arginine, glucose α-H1, glucose β-H2, creatinine, alanine, lactate, valine, leucine, isoleucine, glycine, histamine, uridine diphosphate-glucose, guanosine monophosphate, phosphocholine, citrulline were identified with the same chemical shift values in both leukemic plasma and healthy control plasma. However, other metabolites showed small but significant differences in chemical shift values. Namely, chemical shift values of acetone, β-hydroxybutyrate, glutamine, phenylalanine, lysine, myoinositol, scyllo-inositol, also present in both plasma groups, showed significantly lower ​​in leukemic plasma, while chemical shift values of formate, citrate, acetoacetate, glycerophosphocholine (GPC) were significantly higher ​​in leukemic plasma compared to values of appropriate metabolites in healthy control plasma (each difference p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMetabolites in blood plasma of children with leukemia and control children identified on the basis of \u0026sup1;H NMR chemical shift [ppm]. Leukemia vs. control differences in values of the analyzed variables were tested with the Mann-Whitney U test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eLeukemia (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1st Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3rd Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedian (ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1st Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3rd Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα-H1 glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-H2 glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetoacetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-hydroxybutyrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUridine diphosphate-glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerophosphocholine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuanosine monophosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphocholine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitrulline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyo-inositol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScyllo-inositol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eExamples of typical 600 MHz \u0026sup1;H NMR spectra and representative metabolite assays in CSF and plasma from children with leukemia and in control plasma samples are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e respectively. Chemical shift analysis performed using NMR profiling and metabolite determination allowed the identification of 16 compounds common to the pairs of analyzed CSF and plasma samples from children with leukemia. Among them, acetone, arginine, citrate, citrulline, creatinine, formate, glutamine, glycine, lactate, lysine, scyllo-inositol were identified in both CSF and plasma with the same chemical shift values. Other metabolites showed small but significant differences in chemical shift values. Namely, the chemical shift values ​​in CSF of leucine and myo-inositol were significantly lower, while the chemical shift values ​​in CSF of alanine, phenylalanine, and valine were significantly higher compared to the chemical shift values ​​of these metabolites in plasma (each difference p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMetabolites in the cerebrospinal fluid and blood plasma of children with leukemia, identified on the basis of \u0026sup1;H NMR chemical shift [ppm]. Data were presented as medians. Statistical analysis was performed using the Wilcoxon signed-rank test for paired observations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSF median (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlasma median (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWilcoxon rank sum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitrulline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMyo\u003c/em\u003e-inositol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eScyllo\u003c/em\u003e-inositol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTypical 600 MHz \u0026sup1;H NMR spectra and representative metabolite assays in plasma from children with leukemia and in control plasma samples are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChemical shifts (ppm) of selected metabolites in the Human Metabolome Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hmdb.ca\u003c/span\u003e\u003cspan address=\"http://www.hmdb.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as a basis for metabolite identification in cerebrospinal fluid and blood plasma samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolites in blood plasma and CSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChemical shift\u003c/p\u003e \u003cp\u003e(ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetabolites in blood plasma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChemical shift\u003c/p\u003e \u003cp\u003e(ppm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eαH1-glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβH2-glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhosphocholine (PC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlycerophosphocholine (GPC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGuanosine monophosphate (GMP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsolecine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUridine diphosphate-glucose (UDP-gluc)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetabolites in CSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChemical shift\u003c/p\u003e \u003cp\u003e(ppm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitrulline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2-hydroxybutyrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3-hydroxyisovalerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGABA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHistidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTyrosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMyo\u003c/em\u003e-inositol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eScyllo\u003c/em\u003e-inositol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe used multivariate analysis to identify differences between groups. In this study, unsupervised principal component analysis (PCA) was initially used to explore the data. However, the first two main components of PCA do not allow a clear distinction to be made between the blood plasma of healthy individuals and that of patients with leukemia. To address this, we applied supervised orthogonal least squares discriminant analysis (OPLS-DA) to analyze the data. The OPLS-DA model effectively separated healthy controls from patients with leukemia along the t1 direction, achieving R2X\u0026thinsp;=\u0026thinsp;51.3%, R2Y\u0026thinsp;=\u0026thinsp;69.7%, and Q2\u0026thinsp;=\u0026thinsp;45.15%. The results of this analysis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePLS-DA loading plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) revealed several key metabolites (e.g., lysine; citrulline, lactate; glucose; uridine diphosphate-glucose; phosphocholine, isobutyrate) involved in distinguishing plasma ALL samples from those with other types of leukemia. In CSF (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), lysine emerged as a significant marker differentiating ALL patients from patients with other types of leukemia. In contrast, CSF carnitine, lactate, choline, glutamine, glucose, creatine, 2-hydroxybutane, threonine, and valine showed potentially important contributions to distinguishing AML from other types of leukemia. Remarkably, none of the identified metabolites were able to effectively distinguish healthy children from children with leukemia.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this pilot study, \u0026sup1;H NMR spectroscopy was used to investigate the metabolomic profiles of blood plasma and cerebrospinal fluid in children with leukemia in order to identify metabolites for further study as biomarkers useful in the diagnosis of childhood leukemia.\u003c/p\u003e \u003cp\u003eThe laboratory data are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Qualitative analysis of \u0026sup1;H NMR spectra allowed the identification of over 33 metabolites, with 26 detected in blood plasma (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the spectra of leukemic plasma differed significantly from those of healthy children, showing subtle but significant variations in chemical shift values for some metabolites (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, the composition of metabolites in cerebrospinal fluid (CSF) from leukemia patients was distinct from that in plasma, with some metabolites common to both fluids and others unique to either plasma or CSF. Differences in chemical shift values of metabolites present in both fluids were also observed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe strategy for identifying known and unknown metabolites involved several steps: first, collecting unassigned peaks in the \u0026sup1;H NMR spectrum by comparing them with annotated spectra of identified metabolites; second, tentatively assigning these peaks using chemical shift databases and literature on blood and CSF metabolites; and third, comprehensively analyzing 1D \u0026sup1;H NMR spectra to identify peaks corresponding to known metabolites. [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMethods such as \u0026sup1;H NMR-based metabolomics have not been used in leukemia diagnostics so far, therefore our goal is to try to incorporate this method into diagnostics. This study is one of the first to use \u0026sup1;H NMR-based metabolomics to analyze the metabolic profiles of children with leukemia. Our preliminary results revealed significant qualitative differences in the metabolomic fingerprints of blood plasma of patients with leukemia compared to healthy children, especially with respect to glucose, myo-inositol, amino acids and esterified cholesterol. The most pronounced changes were observed in metabolites of the glucose-alanine pathway [\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The reproducibility of \u0026sup1;H NMR, due to its high signal intensity and natural isotopic abundance of hydrogen, makes it a powerful tool in metabolomics, capable of rapid identification and quantification of up to 100 metabolites. With the right software, this technique could be effective in diagnostics, precisely because of its speed, automation and reliability.\u003c/p\u003e \u003cp\u003eIn this study, the use of the OPLS-DA model effectively separated healthy controls and patients with leukemia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral key metabolites, e.g., lysine; citrulline, lactate; glucose; uridine diphosphate-glucose; phosphocholine, isobutyrate, were revealed by using of PLS-DA loading plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) as possibly involved in distinguishing plasma ALL samples from those with other types of leukemia. In cerebrospinal fluid (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), lysine emerged as a significant marker differentiating ALL patients from those with other types of leukemia. In contrast, CSF carnitine, lactate, choline, glutamine, glucose, creatine, 2-hydroxybutane, threonine, and valine showed potential significant contribution to distinguishing AML from other types of leukemia.\u003c/p\u003e \u003cp\u003eCancer cells are characterized by metabolic reprogramming, such as the Warburg effect, where they rely on glycolysis for ATP production even in the presence of oxygen. This shift supports rapid proliferation by prioritizing the synthesis of nucleotides, amino acids, and lipids necessary for cell division. Leukemic cells, similar to activated lymphocytes, display unique metabolic traits, including increased glucose uptake and enhanced ribosome biogenesis. Unlike normal cells, leukemia cells often show growth factor independence and mutations in genes like IDH, as well as upregulated mTOR and PKM2 pathways [\u003cspan additionalcitationids=\"CR34 CR35 CR36 CR37\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePositron emission tomography (PET) scans using fluorodeoxyglucose (FDG) exploit the Warburg effect for cancer diagnosis, including leukemias. FDG [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], a glucose analog, accumulates in cancer cells, allowing tumor visualization based on elevated glucose metabolism. This knowledge is also applicable to brain tumors, especially since 5\u0026ndash;10% of ALL patients have central nervous system involvement at diagnosis, and studies show differential expression of glucose transporters such as GLUT1 and GLUT3, which correlates with tumor stage and prognosis. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) [\u003cspan additionalcitationids=\"CR41 CR42 CR43 CR44 CR45 CR46\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBeyond glucose metabolism, cancer research has highlighted the importance of amino acids in sustaining tumor growth. Requirements for AAs is different between normal and tumor cells. Amino acids not only serve as building blocks but also regulate redox states, energy production, and immune responses [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Glutamine is a critical nutrient, fueling the TCA cycle and supporting biosynthesis, especially under glucose-limiting conditions. Asparagine, another key amino acid, plays a role in cell survival during glutamine deprivation. Cancer cells can also utilize branched-chain amino acids (BCAAs) for energy, challenging the assumption that cancer metabolism is strictly glucose-dependent. Elevated BCAA levels have been linked to early-stage cancers, such as pancreatic ductal adenocarcinoma driven by KRAS mutations, suggesting their role in nutrient acquisition and tumor progression [\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the metabolites identified, inositols, particularly myo-inositol (MI), were of special interest. It is known that the concentration of scyllo inositol and myo inositol in the human brain can be measured by NMR. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. MI, a biologically active sugar alcohol, has been shown to inhibit carcinogenesis in various organs. Its status in biological systems is largely influenced by the enzyme myo-inositol-3-phosphate synthase (MIPS). MI seems to be a promising candidate for our further differential analyses, as metabolic auxotrophy of myo-inositol was found in AML [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Other identified metabolites in our study included amino acids (e.g., histidine, lysine, alanine, glutamine, valine, leucine, and phenylalanine) and endogenous compounds such as lactate, creatine, and pyruvate. \u0026sup1;H NMR analysis also identified compounds such as UDP-glucose, glycerophosphocholine, and phosphocholine in the blood plasma metabolite profile.\u003c/p\u003e \u003cp\u003eOur study aimed to identify metabolic alterations associated with acute lymphoblastic leukemia in children. Using \u0026sup1;H NMR spectroscopy, we analyzed blood plasma and cerebrospinal fluid samples from pediatric patients with leukemia, as well as blood plasma from healthy children. Our results revealed significant differences in the metabolic composition of patients with leukemia, especially with respect to glucose and amino acid metabolism pathways. Interestingly, we observed changes in glutamine shift in leukemia, one of essential amino acids, and cancer cells are known to have an increased demand for this amino acid.\u003c/p\u003e \u003cp\u003eLysine may regulate AML cells\u0026rsquo; survival by triggering redox metabolism reprogramming. Our study aim was to investigate the metabolic differences between plasma and cerebrospinal fluid of children with leukemia and between plasma of children with leukemia and healthy children using \u0026sup1;H NMR spectroscopy. By analyzing the metabolite composition of blood plasma and cerebrospinal fluid samples, we aimed to identify potential biomarker candidates. Our preliminary findings suggest significant differences in the metabolic profiles between the two groups. The signal assignment that have been done is only the first step towards further metabolomic study. The assiment have to be confirmed with 2D experiments in the next step before selected metabolites could be used to improve early diagnosis and disease monitoring.\u003c/p\u003e \u003cp\u003eIn summary, this study demonstrates the utility of \u0026sup1;H NMR spectroscopy in identifying metabolic changes in leukemia, highlighting its potential for noninvasive diagnostics and metabolic profiling. Future studies should investigate the integration of \u0026sup1;H NMR in clinical practice and compare its performance with other metabolomic techniques to aid in pediatric leukemia diagnosis and treatment strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eRecent advances in cancer research have highlighted the importance of altered metabolism, particularly glucose metabolism, in cancer development. A deeper understanding of these metabolic changes offers promising opportunities for new therapeutic strategies. In the present study, we used \u0026sup1;H NMR spectroscopy to perform metabolic profiling of blood plasma and cerebrospinal fluid in children with leukemia. By analyzing these biofluids, we aimed to identify distinct metabolic signatures according to the leukemia subtype and select metabolites for further study. Our preliminary findings suggest that patients with leukemia exhibit unique metabolic profiles in both blood plasma and cerebrospinal fluid, and the metabolic profiles of leukemia and healthy plasma were also different. This highlights the potential of NMR-based metabolomics as a rapid and noninvasive diagnostic tool in leukemia. Further studies should investigate the diagnostic and/or prognostic value of metabolic profiling in a much larger group of pediatric patients with leukemia.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis preliminary study included twenty pediatric patients diagnosed with various hematological malignancies defined by the International Classification of Diseases (ICD-10) and were treated in compliance with their corresponding treatment protocols – specifically regarding this study the following treatment Protocols were used: Acute Lymphoblastic Leukemia: AIEOP-BFM-2017 treatment protocol, EudraCT Number: 2020-005017-41, Philadelphia chromosome-positive (Ph+) Acute Lymphoblastic Leukemia: EsPhALL2017/COGAALL1631 treatment protocol EudraCT Number: 2017-000705-20, Acute Myeloid Leukemia: AML-BFM 2019 treatment protocol, Diffuse Large B-Cell Lymphoma: Inter-B-NHL 2010 low/intermediate treatment group treatment protocol, Hemophagocytic Lymphocytosis: HLH 2004 Protocol. The patients included in this study were treated at the Department of Bone Marrow Transplantation, Oncology and Hematology of the Medical University of Wroclaw between October 19, 2021 and November 24, 2022. The legal guardians of each patient gave informed consent before being included in the study only after they were informed about what the procedure of collecting the cerebral spinal fluid entails, as well as its potential risks and complications. The study design was in accordance with the tenets of the Helsinki Declaration, and was approved by the Bioethics Committee of the Wroclaw Medical University in Poland, approval no. KB-525/2021.\u003c/p\u003e \u003cp\u003eWe planned a study population as diverse as possible to enable the selection of common and distinct metabolites to distinguish the population of children with leukemic-type cancers from the population of healthy children in this preliminary study. The study group (Leukemia-type, n = 20) consisted of patients aged 2–17 (6,8 ± 5,7) years, 8 boys (40%) and 12 (60%) girls who were admitted for diagnosis or planned oncological treatment, including the administration of chemostatic drugs and/or radiological treatment. In the study group the predominant type of hematological malignancy was ALL (n = 14, 70%) including 11 pre-B ALL (55%), 2 T-cell ALL (10%), 1 ALL (Ph+) (5%). The remaining children (n = 6, 30%) were diagnosed with: myelomonocytic leukemia with eosinophilia, AML M4/M5 (1 case), acute megakaryoblastic leukemia, AML M7 (1 case); diffuse large B cell lymphoma, DLBCL (1 case); T-Cell lymphoma (1 case); and hemophagocytic lymphohistiocytosis, HLH (1 case). Basic demographics such as age, gender, child's weight and height, previous medical histories, and clinical signs and symptoms of all patients were also obtained. Clinico-pathological parameters included histologically confirmed neoplasm, gender, and age. The laboratory data including a complete blood count and differential into neutrophils, lymphocytes, and monocytes, were recorded based on laboratory results from the plasma samples used in the study (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study participants meeting the following criteria were included in the investigation: subject is ≤ 18 years old, acute leukemia diagnosis, first admission to hospital or coming back to the Clinic Hospital for planned oncological treatment, such as administration of chemostatics and radiological treatment. The exclusion criteria included the history of certain medical conditions e.g., chronic leukemia, Hodgkin's disease, multiple myeloma, non-Hodgkin's lymphoma, and amyloidosis, and others not belonging to the group of acute leukemia neoplasms.\u003c/p\u003e \u003cp\u003eAlso twenty healthy (free from blood malignancy) children aged 11 to 17 (14,3 ± 2,0) years, 8 females and 12 males, participating in the PICTURE study, were included as a control group. \"Population Cohort Study of Wroclaw Citizens (PICTURE)\" was established between 2019 and 2021” by both the Wrocław Medical University and Wroclaw Municipality [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The PICTURE study has been accepted by the Bioethics Committee of the Wroclaw Medical University in Poland (KB-667/2019).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling\u003c/h3\u003e\n\u003cp\u003ePaired matched cerebrospinal fluid and blood plasma samples were collected during routine diagnostic procedures from pediatric patients who required lumbar puncture because of suspected or previously diagnosed leukemia-type malignancies, whereas only plasma samples were collected from healthy children.\u003c/p\u003e \u003cp\u003eLeukemia-type group. The standard diagnostic lumbar puncture was performed under regional anesthesia. The CSF was collected under aseptic conditions in sterile screw-capped tubes and immediately subjected to clinically required diagnostic tests. In this study, CSF samples were centrifuged at 1500 g for 15 min at a controlled temperature of 4°C, aliquoted into 0.1 ml and stored at − 76°C until analysis. Venous blood (2.0 ml) was taken from an antecubital vein into calcium-balanced lithium heparinized tubes (SARSTEDT Blood Gas Monovette®, SARSTEDT Ltd., Leicester, UK). Plasma samples were obtained by centrifugation of blood samples at 2000 g for 15 min. Samples were aliquoted into 0.1 ml and stored at − 76°C until analysis.\u003c/p\u003e \u003cp\u003eNon-malignant control group. Venous blood was taken into EDTA-treated (K3-EDTA) tubes, and then centrifuged (2200 g, 15 min). Plasma samples were aliquoted into 0.4 ml and stored at − 76°C until analysis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrozen samples were thawed for 1 h at 20°C before use to allow complete dissolution of the plasma. Freeze-thaw cycles were avoided because they are detrimental to many serum components, and hemolyzed, icteric, or lipemic samples were discarded.\u003c/p\u003e\n\u003ch3\u003eAcquisition of NMR spectra\u003c/h3\u003e\n\u003cp\u003eBlood plasma, or cerebrospinal fluid, was thawed on ice and then centrifuged. 50 µl of clear sample was collected and mixed with 10 µl of deuterated water (D\u003csub\u003e2\u003c/sub\u003eO). The resulting mixture (total 60 µl) was transferred at room temperature to a 5 mm NMR tube for further analysis.\u003c/p\u003e \u003cp\u003eAll NMR experiments were performed on a Bruker NMR AVANCE III™ 600 MHz spectrometer equipped with a micro-cryoprobe (TCI, [¹H, 13C, 15N], 1.7 mm). Spectra were acquired at 298 K. One-dimensional proton spectra were acquired using zgesgp and noesygppr1d (1D NOESY) pulse sequences. An excitation sculpting pulse sequence or presaturation were applied to suppress water signals in the spectra, respectively. For both sequences, an exponential window function with a line broadening factor of 0.2 Hz was used for free induction decay (FID) before Fourier transform. For each FID spectrum, 32 scans were collected with a spectral width of 12 ppm, an acquisition time of 4.5 s, a relaxation delay of 5 s, and a mixing time of 10 ms. All NMR spectra were phased and baseline were corrected using TopSpin software (version 3.6.5, Bruker, BioSpin, Germany). Parameters were adjusted to enable quantification of metabolites using the MestReNova and TopSpin Bruker software.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNMR profiling and metabolite determination\u003c/h2\u003e \u003cp\u003eIdentification and qualitative assessment of the detected metabolites were performed using the MestReNova-8.1.4-12489 profiler and confirmed in TopSpin software. MestReNova-8.1.4-12489 can deconvolute metabolites in complex samples and can determine concentrations in overlapping regions of the spectrum. The software matches the peaks to a set of model spectra characterizing the chemical environments of each metabolite. An identical volume of D\u003csub\u003e2\u003c/sub\u003eO was added to all blood plasma and CSF samples, obtaining a mixture of D\u003csub\u003e2\u003c/sub\u003eO and H\u003csub\u003e2\u003c/sub\u003eO as the solvent. Characteristic bands for individual amino acid residues and other substances were assigned for all acquired spectra. Further information on proton peak assignments were obtained by comparing chemical shifts with those available in the Human Metabolome Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hmdb.ca\u003c/span\u003e\u003cspan address=\"http://www.hmdb.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePreprocessing and statistical analysis were done in Python 3.10.7 (packages: NumPy, Pandas, SciPy). Due to rather low sample size, non-normality (tested with Shapiro-Wilk test) and the presence of outliers in the data (observed based on Q-Q plots), a non-parametric approach was selected. Leukemia vs. control differences in values of the analyzed variables were tested with the Mann-Whitney U test. Differences between the plasma and CSF of leukemia patients was tested with the Wilcoxon test, handling pairwise ties according to J.W. Pratt [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], with normal approximation described by E.E. Cureton [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The data are presented as percentages for qualitative variables (gender, disease) and as medians (1st and 3rd quartiles) for continuous variables, and a p-value of ˂ 0.05 was considered statistically significant. Data were analyzed using principal component analysis (PCA). To enhance group separation, partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were employed.\u003c/p\u003e \u003c/div\u003e "},{"header":"Limitations","content":"\u003cp\u003eOur preliminary study was based on a qualitative analysis of the metabolite composition of plasma and cerebrospinal fluid using ¹H NMR spectrometry. The study included a small number of samples from pediatric patients with different types of diagnosed leukemia. Therefore, the qualitative data obtained did not allow us to build a sophisticated statistical model to evaluate the results. Our studies should be considered preliminary and repeated with a quantitative determination of metabolites in a much larger group of patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cb\u003eDeclaration of Competing Interest\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003cp\u003e \u003cb\u003eInformed consentstatement\u003c/b\u003e: Not applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInstitutional review board statement\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded in whole or in part by the National Science Centre MINI.A070.22.004. Research was performed using biological material and data from Wrocław Medical University Biobank.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: conceptualization, A.S. and M.P.; methodology, A.S., T.N, M.P, and AJ-K.; software, A.S., A.J-K.; validation, A.S., Ł.L., and A.J.-K.; formal analysis, A.S., T.N., A.J.-K., Ł.L. and B.K.; resources, B.K., T.B, T.Z .; data curation, A.S and M.P.; writing\u0026mdash;original draft preparation, A.S., M.P. and A. J.-K..; writing\u0026mdash;review and editing, A.W.; A.K..; visualization A.S., T.N..; supervision, A.S., M.P.; storage and portioning of samples, A.M-W., T.Z., K.P.-Z., M.Ś.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files and are made available to the readers upon request from A.S. ([email protected]).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDavis, A. S., Viera, A. J. \u0026amp; Mead, M. D. Leukemia: an overview for primary care. Am Fam Physician. 1;89(9); pp.731-8. PMID: 24784336. (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBispo, J. A. B. \u0026amp; Pinheiro, P. S. KobetzE.K. Epidemiology and Etiology of Leukemia and Lymphoma. Cold Spring Harb Perspect Med. 1;10(6):a034819. (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/cshperspect.a034819\u003c/span\u003e\u003cspan address=\"10.1101/cshperspect.a034819\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 31727680; PMCID: PMC7263093.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gco.iarc.fr/today/en\u003c/span\u003e\u003cspan address=\"https://gco.iarc.fr/today/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ehttps://gco.iarc.fr/overtime/en/dataviz/age_specific?populations=61600\u0026amp;sexes=1_2\u0026amp;cancers=28\u0026amp;multiple_populations=1\u0026amp;mode=cancer\u0026amp;group_populations=1\u0026amp;multiple_cancers=1\u0026amp;years=2012\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCiesielska, M., Orzechowska, B., Gamian, A. \u0026amp; Kazanowska, B. Epidemiology of childhood acute leukemias. \u003cem\u003ePostępy Higieny i Medycyny Doświadczalnej\u003c/em\u003e. \u003cb\u003e78\u003c/b\u003e, 22\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2478/ahem-2023-0023]\u003c/span\u003e\u003cspan address=\"10.2478/ahem-2023-0023]\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhitehead, T. P., Metayer, C., Wiemels, J. L., Singer, A. W. \u0026amp; Miller, M. D. Childhood Leukemia and Primary Prevention. \u003cem\u003eCurr. Probl. Pediatr. Adolesc. Health Care\u003c/em\u003e. \u003cb\u003e46\u003c/b\u003e (10), 317\u0026ndash;352 (2016). PMID: 27968954; PMCID: PMC5161115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhojwani, D. \u0026amp; Yang, J. J. PuiCH. Biology of childhood acute lymphoblastic leukemia. \u003cem\u003ePediatr. Clin. North. Am.\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e (1), 47\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pcl.2014.09.004\u003c/span\u003e\u003cspan address=\"10.1016/j.pcl.2014.09.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015). PMID: 25435111; PMCID: PMC4250840.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChennamadhavuni, A., Lyengar, V., Mukkamalla, S. K. R., Shimanovsky, A. \u0026amp; Leukemia In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024. PMID: 32809325. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInaba, H. \u0026amp; Mullighan, C. G. Pediatric acute lymphoblastic leukemia. Haematologica. pp: 2524\u0026ndash;2539. (2020). 1;105(11) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3324/haematol.2020.247031\u003c/span\u003e\u003cspan address=\"10.3324/haematol.2020.247031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 33054110; PMCID: PMC7604619.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalard, F. \u0026amp; Mohty, M. Acute lymphoblastic leukaemia. Lancet. ;395(10230), pp. 1146\u0026ndash;1162. (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(19)33018-1\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(19)33018-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 32247396.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones, N. P. \u0026amp; Schulze, A. Targeting cancer metabolism-aiming at a tumor\u0026rsquo;s sweet-spot. \u003cem\u003eDrug Discov\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e, 232\u0026ndash;241 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIjurko, C., Gonz\u0026aacute;lez-Garc\u0026iacute;a, N., Galindo-Villard\u0026oacute;n, P. \u0026amp; Hern\u0026aacute;ndez-Hern\u0026aacute;ndez, \u0026Aacute;. A 29-gene signature associated with NOX2 discriminates acute myeloid leukemia prognosis and survival. \u003cem\u003eAm. J. Hematol.\u003c/em\u003e \u003cb\u003e97\u003c/b\u003e (4), 448\u0026ndash;457. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ajh.26477\u003c/span\u003e\u003cspan address=\"10.1002/ajh.26477\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022). Epub 2022 Feb 8. PMID: 35073432; PMCID: PMC9303675.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJemal, A. et al. Cancer statistics, 2006. CA Cancer J Clin. pp.106\u0026thinsp;\u0026ndash;\u0026thinsp;30. (2006). Mar-Apr;56(2) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/canjclin.56.2.106\u003c/span\u003e\u003cspan address=\"10.3322/canjclin.56.2.106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 16514137.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEstey, E. H. Acute myeloid leukemia: 2014 update on risk-stratification and management. Am J Hematol. ;89(11), pp. 1063-81. (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ajh.23834\u003c/span\u003e\u003cspan address=\"10.1002/ajh.23834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 25318680.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026ouml;hner, H. et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. \u003cem\u003eBlood\u003c/em\u003e \u003cb\u003e129\u003c/b\u003e (4), 424\u0026ndash;447. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood-2016-08-733196\u003c/span\u003e\u003cspan address=\"10.1182/blood-2016-08-733196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017). Epub 2016. PMID: 27895058; PMCID: PMC5291965.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnright, H. \u0026amp; McGlave, P. B. Chronic myelogenous leukemia. Curr Opin Hematol. ;3(4),pp.303-9. (1996). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00062752-199603040-00009\u003c/span\u003e\u003cspan address=\"10.1097/00062752-199603040-00009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 9372092.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortes, J., Pavlovsky, C. \u0026amp; Sau\u0026szlig;ele, S. Chronic myeloid leukaemia. Lancet. ;398(10314), pp. 1914\u0026ndash;1926. (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(21)01204-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(21)01204-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2021. PMID: 34425075.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevine, S. M. \u0026amp; Larson, R. A. Acute leukemia in adults: recent developments in diagnosis and treatment. CA Cancer J Clin. ;44(6), pp.326\u0026thinsp;\u0026ndash;\u0026thinsp;52. (1994). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/canjclin.44.6.326\u003c/span\u003e\u003cspan address=\"10.3322/canjclin.44.6.326\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 7953914.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiaretti, S., Zini, G. \u0026amp; Bassan, R. Diagnosis and subclassification of acute lymphoblastic leukemia. \u003cem\u003eMediterr. J. Hematol. Infect. Dis.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (1), e2014073. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4084/MJHID.2014.073\u003c/span\u003e\u003cspan address=\"10.4084/MJHID.2014.073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014). PMID: 25408859; PMCID: PMC4235437.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrespo-Solis, E., L\u0026oacute;pez-Karpovitch, X., Higuera, J. \u0026amp; Vega-Ramos, B. Diagnosis of acute leukemia in cerebrospinal fluid (CSF-acute leukemia). Curr Oncol Rep. ;14(5), pp.369\u0026thinsp;\u0026ndash;\u0026thinsp;78. (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11912-012-0248-6\u003c/span\u003e\u003cspan address=\"10.1007/s11912-012-0248-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 22639108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltenbuchinger, M. et al.. Bucket Fuser: Statistical Signal Extraction for 1D \u0026sup1;H NMR Metabolomic Data. \u003cem\u003eMetabolites\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e;12(9):812. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/metabo12090812\u003c/span\u003e\u003cspan address=\"10.3390/metabo12090812\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 36144216; PMCID: PMC9501206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarkley, J. L. et al. The future of NMR-based metabolomics. \u003cem\u003eCurr. Opin. Biotechnol.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, 34\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.copbio.2016.08.001\u003c/span\u003e\u003cspan address=\"10.1016/j.copbio.2016.08.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017). Epub 2016 Aug 28. PMID: 27580257; PMCID: PMC5305426.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDang, N. H., Singla, A. K., Mackay, E. M., Jirik, F. R. \u0026amp; Weljie, A. M. Targeted cancer therapeutics: biosynthetic and energetic pathways characterized by metabolomics and the interplay with key cancer regulatory factors. Curr Pharm Des. ;20(15), pp. 2637-47. (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/13816128113199990489\u003c/span\u003e\u003cspan address=\"10.2174/13816128113199990489\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 23859615.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLussu, M. et al. \u0026sup1;H NMR spectroscopy-based metabolomics analysis for the diagnosis of symptomatic E. coli-associated urinary tract infection (UTI). BMC Microbiol. ;17(1),pp.201. (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12866-017-1108-1\u003c/span\u003e\u003cspan address=\"10.1186/s12866-017-1108-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 28934947; PMCID: PMC5609053.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpeyer, C. B. \u0026amp; Baleja, J. D. Use of nuclear magnetic resonance spectroscopy in diagnosis of inborn errors of metabolism. \u003cem\u003eEmerg. Top. Life Sci.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (1), 39\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1042/ETLS20200259\u003c/span\u003e\u003cspan address=\"10.1042/ETLS20200259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021). PMID: 33522566; PMCID: PMC8630612.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZatońska, K. et al. Population cohort Study of Wroclaw citizens (PICTURE) \u0026ndash; study protocol. \u003cem\u003eJ. Health Inequal.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (1), 37\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5114/jhi.2022.115930\u003c/span\u003e\u003cspan address=\"10.5114/jhi.2022.115930\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePratt, J. W. Remarks on Zeros and Ties in the Wilcoxon Signed Rank Procedures. \u003cem\u003eJ. Am. Stat. Assoc.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e, 655\u0026ndash;667 (1959).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCureton, E. E. The Normal Approximation to the Signed-Rank Sampling Distribution When Zero Differences are Present. \u003cem\u003eJ. Am. Stat. Assoc.\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e, 1068\u0026ndash;1069 (1967).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandal, R. et al. Multi-platform characterization of the human cerebrospinal fluid metabolome: A comprehensive and quantitative update. \u003cem\u003eGenome Med.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e (4), 1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/gm337\u003c/span\u003e\u003cspan address=\"10.1186/gm337\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRomeo, M. J. et al. CSF proteome: A protein repository for potential biomarker identification. \u003cem\u003eExpert Rev. Proteomics\u003c/em\u003e. \u003cb\u003e2\u003c/b\u003e (1), 57\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1586/14789450.2.1.57\u003c/span\u003e\u003cspan address=\"10.1586/14789450.2.1.57\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoop, M. P. et al. Quantitative proteomics and metabolomics analysis of normal human cerebrospinal fluid samples. \u003cem\u003eMol. Cell. Proteomics\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e (9), 2063\u0026ndash;2075. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/mcp.M110.000877\u003c/span\u003e\u003cspan address=\"10.1074/mcp.M110.000877\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllinger, J. J., Chylla, R. A., Ulrich, E. L. \u0026amp; Markley, J. L. Databases and software for NMR-based metabolomics James. \u003cem\u003eBone\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e (2), 132\u0026ndash;135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/2213235x11301010028\u003c/span\u003e\u003cspan address=\"10.2174/2213235x11301010028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGulino, F. A. et al. Effect of treatment with myo-inositol on semen parameters of patients undergoing an IVF cycle: in vivo study. \u003cem\u003eGynecol. Endocrinol.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 65\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3109/09513590.2015.1080680\u003c/span\u003e\u003cspan address=\"10.3109/09513590.2015.1080680\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDona, A. C. et al. A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. \u003cem\u003eCSBJ\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 135\u0026ndash;153. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.csbj.2016.02.005\u003c/span\u003e\u003cspan address=\"10.1016/j.csbj.2016.02.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016). ISSN 2001\u0026thinsp;\u0026ndash;\u0026thinsp;0370.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmwas, A. H. M., Salek, R. M., Griffin, J. L. \u0026amp; Merzaban, J. NMR-based metabolomics in human disease diagnosis: Applications, limitations, and recommendations. \u003cem\u003eMetabolomics\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (5), 1048\u0026ndash;1072. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11306-013-0524-y\u003c/span\u003e\u003cspan address=\"10.1007/s11306-013-0524-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoh, H., Wasa, M. \u0026amp; Fukuzawa, M. Hypoxia upregulates amino acid transport in a human neuroblastoma cell line. \u003cem\u003eJ. Pediatr. Surg.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e, 608\u0026ndash;612 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi, S. \u0026amp; Millhorn, D. E. Hypoxia regulates glutamate metabolism and membrane transport in rat PC12 cells. \u003cem\u003eJ. Neurochem\u003c/em\u003e. \u003cb\u003e76\u003c/b\u003e, 1935\u0026ndash;1948 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDang, C. V., Le, A. \u0026amp; Gao, P. MYC-induced cancer cell energy metabolism and therapeutic opportunities. \u003cem\u003eClin. Cancer Res.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 6479\u0026ndash;6483 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuzzai, M., Bauer, D. E. \u0026amp; Jones, R. G. at al. The glucose dependence of Akt-transformed cells can be reversed by pharmacologic activation of fatty acid beta-oxidation. Oncogene 24, pp.4165\u0026ndash;4173. (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevine, A. J. \u0026amp; Puzio-Kuter, A. M. The control of the metabolic switch in cancers by oncogenes and tumor suppressor genes. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e330\u003c/b\u003e, 1340\u0026ndash;1344 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSemenza, G. L. HIF-1: upstream and downstream of cancer metabolism. \u003cem\u003eCurr. Opin. Genet. Dev.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 51\u0026ndash;56 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G. \u0026amp; Thompson, C. B. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. \u003cem\u003eCell. Metab.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 11\u0026ndash;20 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDang, C. V., Hamaker, M., Sun, P., Le, A. \u0026amp; Gao, P. Therapeutic targeting of cancer cell metabolism. \u003cem\u003eJ. Mol. Med. (Berl)\u003c/em\u003e. \u003cb\u003e89\u003c/b\u003e, 205\u0026ndash;212 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWood, I. S. \u0026amp; Trayhurn, P. Glucose transporters (GLUT and SGLT): expanded families of sugar transport proteins. \u003cem\u003eBr. J. Nutr.\u003c/em\u003e \u003cb\u003e89\u003c/b\u003e, 3\u0026ndash;9 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, X. \u0026amp; Freeze, H. H. GLUT14, a duplicon of GLUT3, is specifically expressedin testis as alternative splice forms. \u003cem\u003eGenomics\u003c/em\u003e \u003cb\u003e80\u003c/b\u003e, 553\u0026ndash;557 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoost, H. G. \u0026amp; Thorens, B. The extended GLUT-family of sugar/polyol transport facilitators: nomenclature, sequence characteristics, and potential function of its novel members (Review). \u003cem\u003eMo Membr. Biol.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 247\u0026ndash;256 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamih, N. et al. The impact of N- and O-glycosylation on the functions of Glut-1 transporter in human thyroid anaplastic cells. \u003cem\u003eBiochim. Biophys. Acta\u003c/em\u003e. \u003cb\u003e1621\u003c/b\u003e, 92\u0026ndash;101 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacheda, M. L., Rogers, S. \u0026amp; Best, J. D. Molecular and cellular regulation of glucose transporter (GLUT) proteins in cancer. \u003cem\u003eJ. Cell. Physiol.\u003c/em\u003e \u003cb\u003e202\u003c/b\u003e, 654\u0026ndash;662 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdhury, R., Yeoh, K. K. \u0026amp; Tian, Y. M. The oncometabolite 2-hydroxyglutarate inhibits histone lysine demethylases. \u003cem\u003eEMBO Rep.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 463\u0026ndash;469 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, W., Yang, H. \u0026amp; Liuy, I. at al. Oncometabolite 2-hydroxyglutarate is a competitive inhibitor of a-ketoglutarate-dependent dioxygenases. Cancer Cell ; 19, pp. 17\u0026ndash;30. (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWise, D. R., Ward, P. S. \u0026amp; Shay, J. E. at al. Hypoxia promotes isocitrate dehydrogenase -dependent carboxylation of a-ketoglutarate to citrate to support cell growth and viability. Proc Natl Acad Sci ; 108, pp. 19611\u0026ndash;19616. (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMetallo, C. M., Gameiro, P. A. \u0026amp; Bell, E. L. Reductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e481\u003c/b\u003e, 380\u0026ndash;384 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonti, S., Savage, K. J. \u0026amp; Kutok, J. L. at al. Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. Blood ; 105, pp. 1851\u0026ndash;1861. (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaro, P., Kishan, A. U. \u0026amp; Norberg, E. at al. Metabolic signatures uncover distinct targets in molecular subsets of diffuse large B-cell lymphoma. Cancer Cell ; 22, pp. 547\u0026ndash;560. (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichaelis, T. et al. Identification of scyllo-inositol in proton NMR spectra of human brain in vivo. \u003cem\u003eNMR Biomed.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (1), 105\u0026ndash;109 (1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei, Y. et al. SLC5A3-Dependent myo-inositol auxotrophy in acute myeloid leukemia. \u003cem\u003eCancer Discov\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (2), 450\u0026ndash;467. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/2159-8290.CD-20-1849\u003c/span\u003e\u003cspan address=\"10.1158/2159-8290.CD-20-1849\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"acute leukemia, metabolomics, nuclear magnetic resonance, pilot study","lastPublishedDoi":"10.21203/rs.3.rs-5950449/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5950449/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCancer cells undergo significant metabolic changes to support their rapid proliferation and growth. Leukemias, a diverse group of blood cancers, constitute a significant proportion of childhood cancers. This heterogeneity may result in distinct metabolic profiles, offering potential insights for improved diagnosis, prognosis, and treatment strategies. Cerebrospinal fluid (CSF) testing is essential to detect central nervous system involvement by leukemia and to counteract the effects of the involvement..\u003c/p\u003e \u003cp\u003eIn preliminary study, a qualitative, untargeted metabolomics approach was used to analyze CSF and plasma samples from children with leukemia. A total of 20 pairs of CSF and plasma samples from patients were included in the analysis, along with 20 plasma samples from healthy children. \u0026sup1;H NMR spectroscopy revealed more than 33 metabolites in the samples. Significant differences were observed between the metabolite profiles of CSF and plasma, with some metabolites being common to both fluids and others being unique to each. Our preliminary findings suggest that patients with leukemia exhibit distinct metabolic profiles between plasma and CSF and between plasma in leukemia and plasma from healthy controls. Further studies are warranted to investigate the potential diagnostic and prognostic value of metabolic profiling in childhood leukemia.\u003c/p\u003e","manuscriptTitle":"Application of ¹H NMR-based metabolomics in childhood leukemia. A preliminary study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-19 12:02:53","doi":"10.21203/rs.3.rs-5950449/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":"3171fbed-c84f-4bbd-beaa-ab25a999f3e8","owner":[],"postedDate":"February 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44312676,"name":"Health sciences/Biomarkers"},{"id":44312677,"name":"Health sciences/Medical research"},{"id":44312678,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-03-10T14:23:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-19 12:02:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5950449","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5950449","identity":"rs-5950449","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}