Increased 1-deoxySphingolipids caused by an altered plasma Alanine to Serine ratio are associated with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)

Increased 1-deoxySphingolipids caused by an altered plasma Alanine to Serine ratio are associated with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Increased 1-deoxySphingolipids caused by an altered plasma Alanine to Serine ratio are associated with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) Florine Wipfli, Museer Lone, Arnold von Eckardstein, An Verrijken, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7435578/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Nov, 2025 Read the published version in Metabolomics → Version 1 posted 7 You are reading this latest preprint version Abstract Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) (formerly Non-Alcoholic Fatty Liver Disease, NAFLD) encompasses a spectrum of metabolic disorders ranging from isolated steatosis to Metabolic Dysfunction-Associated Steatohepatitis (MASH), potentially progressing to cirrhosis and hepatocellular carcinoma (HCC). In this study, we investigated metabolic changes in MASLD by analysing plasma lipidomics and metabolomics profiles from 315 biopsy-characterized patients. We observed significant alterations in alanine/serine (ala/ser) ratio, 1-deoxysphingolipids, alanine aminotransferase (ALT), and waist/hip ratio (whr) between patients with and without MASH. These findings highlight a close interplay between amino acid metabolism and sphingolipid biosynthesis in MASLD progression. The shift in ala/ser ratio particularly distinguished non-MASH from borderline MASH, suggesting that early metabolic disruptions precede overt liver damage. Additionally, elevated branched-chain and aromatic amino acids correlated with steatosis severity, reinforcing the central role of amino acid dysregulation in MASLD. While a simple model combining ALT, ala/ser ratio, and whr showed some potential to support risk stratification, the primary significance of our findings lies in the mechanistic insights into metabolic dysfunction. In conclusion, this study emphasizes the importance of metabolic network alterations in MASLD and points toward future opportunities for both mechanistic research and the development of supportive diagnostic strategies Figures Figure 1 Introduction Non-Alcoholic Fatty Liver Disease (NAFLD), recently renamed Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), is characterized by abnormal lipid synthesis and accumulation in the liver, independent of significant alcohol consumption or other secondary causes. MASLD spans a spectrum from isolated steatosis (MASL) to Metabolic Dysfunction-Associated Steatohepatitis (MASH) and fibrosis, potentially progressing to cirrhosis and hepatocellular carcinoma (HCC) [ 1 – 3 ]. It is now the leading cause of chronic liver disease in the Western world, with the highest prevalence in the Middle East and South America [ 4 , 5 ], and a global prevalence projected to exceed 30% by 2030 [ 4 , 6 ]. The pathogenesis of MASLD remains incompletely understood. The original “two-hit” hypothesis has evolved into a “multiple-hit” model, involving increased adipose lipolysis and de novo lipogenesis, leading to excess free fatty acids (FFAs), free cholesterol, triglycerides (TGs), and other lipids in the liver. FFA accumulation drives mitochondrial dysfunction, reactive oxygen species (ROS) production, and hepatic inflammation [ 7 ]. Besides genetic predisposition [ 8 ], MASLD is strongly associated with components of the metabolic syndrome (MetS) such as obesity, insulin resistance (IR), diabetes mellitus type 2 (T2DM), hypertension, and hypertriglyceridemia [ 9 , 10 ]. Consequently, MASLD is often regarded as the hepatic manifestation of MetS [ 10 – 12 ]. Histologically, MASL is defined by steatosis in ≥ 5% of hepatocytes, while MASH additionally features lobular inflammation and hepatocellular ballooning [ 1 ]. MASH can be identified in approximately 30% of patients diagnosed with MASLD by ultrasound [ 13 ], and it carries an increased risk for cirrhosis, HCC, and cardiovascular events [ 14 , 15 ]. Although fibrosis is not a diagnostic criterion for MASH [ 16 ], it significantly worsens prognosis [ 1 ]. Given the high prevalence of MASLD, there is a critical need to monitor disease progression, particularly in high-risk groups. Early identification hinges on detecting specific biomarkers. Currently, liver biopsy remains the gold standard for diagnosing and staging MASLD [ 17 ], but its invasiveness, cost, and need for expert interpretation [ 1 , 17 ] highlight the urgent need for reliable, non-invasive alternatives. Traditional serum markers of liver injury, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT), show limited sensitivity and specificity in MASLD [ 9 ]. Over half of MASLD patients have normal GGT levels [ 18 ], and disease can be present despite normal ALT values [ 19 ]. Elevated ALT also does not reliably indicate more severe MASH or fibrosis [ 20 , 21 ]. Recent efforts have focused on identifying circulating proteins—hormones, cytokines, adipokines, and carrier proteins—as biomarkers for MASLD and MASH [ 22 ]. However, these candidates face challenges related to clinical implementation and limited stage specificity. Emerging OMICS technologies, particularly lipidomics and metabolomics, may overcome these limitations and improve early detection of disease progression. Given the overlapping pathophysiology of MASLD, MetS, and T2DM [ 23 ], biomarkers identified in these conditions may also reflect MASH risk. Among lipid classes, sphingolipids (SL), particularly ceramides, have been implicated in insulin resistance, T2DM, and MASLD [ 24 ]. SLs play critical roles in cellular processes [ 25 ], and elevated levels in liver and plasma are associated with liver dysfunction, IR, and steatosis [ 26 , 27 ]. Circulating ceramides may directly impair insulin signalling [ 28 ], and enzymes regulating SL metabolism are considered therapeutic targets [ 29 – 31 ]. Recent OMICS studies have also linked an atypical class of sphingolipids, 1-deoxysphingolipids (1-deoxySLs), to MASLD, MetS, and T2DM [ 32 – 35 ]. 1-DeoxySLs are formed through a side reaction of de novo sphingolipid synthesis when serine availability is reduced, leading serine palmitoyltransferase to utilize alanine instead [ 36 , 37 ]. These aberrant products lack the C1-hydroxyl group essential for canonical sphingolipid function and degradation, contributing to toxicity [ 37 ]. Building on these findings, we aimed to investigate whether canonical and non-canonical sphingolipids, together with metabolic parameters, could reflect histological stages of MASLD, offering new insights into disease mechanisms and potential non-invasive diagnostic strategies. Patients and methods Study Patients attending the obesity clinic of the Antwerp University Hospital were consecutively included in the study upon informed consent. A metabolic work-up, laboratory analyses and a liver specific-program (to exclude other liver diseases than MASLD) were performed. Patients presenting to the obesity clinic underwent a detailed metabolic and liver assessment, including an extensive panel of coagulation factors. If NAFLD was suspected, a liver biopsy was proposed. A series of 273 consecutive patients (65% female) with a liver biopsy were included (age, 44 ± 0.76 years; body mass index: 39.6 ± 0.40 kg/m2). Patients had to be 18 years or older, had no significant alcohol consumption and no history of diabetes (as medical treatment of the latter may substantially influence liver histology; de novo diagnosis of T2DM was, however, allowed). For more information, refer to a previous description of the patient cohort [ 38 ]. Pathological features like steatosis grade, ballooning, lobular inflammation, and fibrosis stage were scored according to the NASH Clinical Research Network Scoring System (NASH-CRN) [ 39 ]. For the classification of patients based on the severity of MASLD we considered two different approaches. On the one hand, we distinguished between No-MASH and MASH [ 40 ], with MASH being defined as the coexistence of steatosis, ballooning, and lobular inflammation (hence a score of 1 or more for each). On the other hand, we classified the patients more granularly according to the NAFLD/MASLD activity score (NAS/MAS), which is defined by the sum of the scores for steatosis, ballooning, and lobular inflammation. The criteria for the classification are as follows: MAS < 3 (No-MASH); MAS 3–4 (borderline-MASH); and MAS ≥ 5 (definite MASH) [ 39 ][ 41 ]. Sphingoid base analysis Sphingoid bases were extracted from fasting plasma samples. Fifty microliters of plasma were filled up with phosphate-buffered saline to a volume of 100 µL. After mixing by vortex at room temperature, the lipids were extracted in 500 µL extraction buffer (499.8 µL methanol + 0.2 µL D7-labelled C18 sphingosine). After another short mixing, the lipids were extracted under constant agitation (1400 rpm) for 1 h at 37°C. Subsequently samples were centrifuged (16100 rcf, 5 min) and 500 µL of the supernatant was transferred to a new tube. For acid and base hydrolysis, 75 µL of HCl (conc. 32%) was first added and mixed, and the samples were then stored at 65°C for 12–15 h. After that, samples were neutralised by adding 100 µL of KOH (10M) and subjected to base hydrolysis. Base hydrolysis was performed in the following steps: First, 125 µL of chloroform (CHCl 3 ) was added and mixed. Then another 500 µL of CHCl 3 was added and mixed again. Finally, 100 µL of 2N ammonia and 0.5 mL of alkaline water were added and samples were centrifuged (16000 rcf) for 5 minutes. The upper phase was removed, and the lower phase was washed twice again with 1 mL alkaline water. The lipids were then dried under N 2 . Dried lipids were resuspended in 70 µL of reconstitution buffer (70% methanol, 10 mM ammonium acetate) and subjected to LC-MRM-MS analysis. The separation of sphingoid bases has been described previously [ 42 ]. Quantification was performed using MultiQuant (2.1) software (SCIEX) and normalized to the internal standard D7-C18 sphingosine. Sphingoid base 1-phosphate analysis Fifty microliter of fasting plasma was filled up with ddH 2 O to a volume of 100 µL. The lipids were then extracted in 400 µL extraction solvent (50 nM D7-S1P in methanol), mixed and extracted under constant agitation (1400 rpm) for 1 h at 37°C. After that, samples were centrifuged at 14000 rcf for 10 minutes. Subsequently, the supernatant was removed, transferred to a new tube, and dried under N 2 . The dried samples were re-dissolved in 50 µL reconstitution solvent (95% methanol, 10mM ammonium acetate) and shaken for 30 minutes (950 rpm, 25°C). After centrifugation for 15 minutes at 14000 rcf the supernatants were transferred to a new tube and analysed by LC-MRM-MS. Sphingoid base 1-phosphates (S1P) were separated via a reverse-phase C18 column (Luna Omega Polar C18, 150 x 2.1 mm) connected to a QTRAP 6500 + LC-MS/MS-MS System (Sciex), operated in multiple reaction monitoring mass spectrometry mode. Sample ionisation was achieved via electrospray ionisation in positive and negative ion mode, alternating. For chromatography, a binary solvent system consisting of solvent A (5 mM ammonium formate in H 2 O/methanol/formic acid (20/80/0.5, v/v/v)) and solvent B (5 mM ammonium formate in methanol/acetonitrile/formic acid (60/40/0.5, v/v/v)) was used. The flow rate was maintained at 0.3 mL/min throughout. The column was equilibrated with 100% solvent A, and a linear gradient to 100% B was run over 4 min and washed with 100% B for 4 minutes before equilibration in 100% A for 6 minutes. The acquired data were integrated and analysed using the open source software tool Skyline (DOI: 10.1021/acs.jproteome.9b00640 ). For normalisation and quantification, the internal standard D7-S1P was added to the samples. Amino acid analysis To 10 µL of serum, 1 nmol of stable isotope labelled amino acids were added (Cambridge Isotope Laboratories). Proteins were precipitated by adding 15 volumes ice cold methanol at -20°C. After incubation for 30 minutes at -20°C, samples were centrifuged for 10 minutes at 4°C (14000rcf). The supernatant was dried under N 2 and the amino acids were re-constituted in 100 µL 0.1% acetic acid for 1 hour at 24°C shaking at 950 rpm. After centrifugation for 5 minutes at 16100 rcf the supernatant was analysed by LC-MRM-MS. Amino acids were separated via a reverse-phase C18 column (EC 250/2 NUCLEOSIL 100-3 C18HD, L = 250 mm, ID: 2 mm; Macherey-Nagel). 5 µL per sample were subjected to liquid chromatography coupled multiple reaction monitoring mass spectrometry using a QTRAP 6500 + LC-MS/MS-MS System (Sciex). The amino acids were chromatographed isocratically with 0.1% formic acid in water for five minutes, followed by a linear gradient to 50% solvent B (acetonitrile) over two minutes. Then, the column was washed with 80% solvent B prior equilibration with 100% solvent A. The flow rate was held constant at 0.2 mL/min. Sample ionization was achieved via electrospray ionization in positive ion mode. Quantification was performed using MultiQuant (2.1) software (SCIEX) and normalised to internal standards. Statistical analysis Statistical tests were performed using IBM SPSS version 27. Mean values were compared using either independent t-test or one-way ANOVA, followed by Bonferroni correction. In case of unequal variances, comparisons were conducted by Welch test followed by Bonferroni correction and Welch-ANOVA with Games-Howell correction. Where data were skewed, log or square root transformation was used in statistical analysis. To investigate the association of anthropometrical features, metabolic parameters, values of liver tests, sphingoid bases and amino acids within different stages of MASLD, we compared the following groups: No-MASH versus MASH dichotomously; No-MASH versus borderline-MASH versus definite MASH according to MAS. The regression coefficient β of multivariate binary regression models was calculated using IBM SPSS. Z-Scores and prediction function was calculated in Excel. Receiver operating characteristic (ROC) curve analysis was performed using MetaboAnalyst 5.0. Results Cohort Characteristics This study is based on the HEPADIP cohort that was initiated by the Antwerp University Hospital (reference 6/25/125, Belgian registration number B30020071389) and established as part of the FP6-Lifescihealth program ( https://cordis.europa.eu/project/id/18734/de ). It consists of clinically very well characterised individuals and comprises > 180 baseline parameters including liver histology. A total of 315 individuals were included in this analysis (217 women and 98 men). Mean age was 43.7 (±12.5) years, and mean body mass index (BMI) was 39.3 (±5.9) kg/m 2 . Based on the definition of MASH, 45.1% (n = 142) patients were diagnosed with no-MASH and 54.9% (n = 173) with MASH. Based on the MAS score, 39% were No-MASH (n = 123), 26.7% borderline MASH (n = 84) and 34.3% definite MASH (n = 108). Differences between No-MASH and MASH Anthropometric features, clinical laboratory values, metabolic markers, the sphingoid base and amino acid profile were compared between the No-MASH and MASH dichotomous groups (Table 1 ). The most significant differences between the groups were seen for ALT ( p = 2.04E-10), followed by the alanine/serine (ala/ser) ratio ( p = 6.92E-09) and the waist/hip-ratio (whr) ( p = 3.46E-08). Although less pronounced, significant differences were also seen for AST ( p = 6.83E-07) and GGT ( p = 2.27E-02). No significant differences in sphingoid base levels including sgphingoid-1-phohsphate were observed except for 1-deoxySO that was significantly increased in MASH ( p = 3.27E-03), confirming earlier findings [ 33 ]. For the amino acids, we found that alanine ( p = 2.22E-05), tyrosine ( p = 3.02E-05), phenylalanine ( p = 5.70E-03) and the branched chain AA (BCAA) valine ( p = 6.31E-04), leucine ( p = 1.25E-03) and isoleucine ( p = 1.67E-03) were significantly higher in MASH. In addition, variables typically associated with MetS such as plasma triglycerides (TG) ( p = 8.73E-03), HbA1c ( p = 2.41E-02), fasting glucose (p = 2.91E-02) and fasting insulin ( p = 1.28E-02) were significantly elevated in the MASH group. However, no differences were seen for total cholesterol (total-C), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). The use of lipid lowering medication did not significantly differ between the two groups. As about 70 percent of the patients were female, we also compared the groups separated by gender (supplementary Table S1and S2). Table 1 Clinical and laboratory data according to the definition of MASH Total N = 315 (100%) No-MASH N = 142 (45.1%) MASH N = 173 (54.9%) P value Adjusted p value Significance level Age (years) 43.7 (±12.5) 41.8 (±12.1) 45.2 (±12.6) 1.63E-02 0.978 † NS Gender: men 98 (31.1%) 26 (18.3%) 72 (41.6%) 8.74E-06 # **** Weight (kg) 111.9 (±19.8) 109.5 (±17.9) 113.9 (±21.1) 6.66E-02 1.00 † NS BMI (kg/m 2 ) 39.3 (±5.9) 39.2 (±6.0) 39.4 (±5.8) 0.706 1.00 † NS Waist (cm) 118.3 (±13.1) 115.2 (±13.2) 120.8 (±12.4) 7.53E-05 4.52E-03 † ** Hip (cm) 122.6 (±10.4) 124.4 (±10.1) 121.1 (±10.6) 4.48E-03 0.269 † NS Waist/Hip-Ratio 1.0 (±0.1) 0.9 (±0.1) 1.0 (±0.1) 5.77E-10 3.46E-08 † **** Total-C (mg/dL) 203.3 (±40.1) 202.2 (±42.2) 204.1 (±38.3) 0.621 1.00 † NS HDL-C (mg/dL) 49.8 (±14.2) 52.5 (±14.6) 47.5 (±13.4) 9.01E-04 5.40E-02 † NS LDL-C (mg/dL) 123.7 (±36.1) 122.6 (±37.6) 124.6 (±34.9) 0.574 1.00 † NS TG (mg/dL) 146.9 (±67.4) 130.9 (±58.0) 160.1 (±71.8) 1.45E-04 8.73E-03 † ** HbA1c (%) 5.6 (±0.5) 5.5 (±0.3) 5.7 (±0.6) 4.02E-04 2.41E-02 ‡ * Glucose (mg/dL) 85.6 (±21.4) 81.8 (±9.6) 88.7 (±27.1) 4.85E-04 2.91E-02 ‡ * Insulin (µU/dL) 17.5 (±11.5) 15.3 (±10.8) 19.3 (±11.8) 2.13E-04 1.28E-02 † * Medication for diabetes 0 (0%) 0 (0%) 0 (0%) NS Lipid lowering medication 34 (10.8%) 11 (7.7%) 23 (13.3%) 0.114 # NS AST (U/L) 30.8 (±17.0) 25.3 (±7.9) 35.4 (±20.6) 1.14E-08 6.83E-07 ‡ **** ALT (U/L) 44.4 (±26.6) 34.6 (±15.7) 52.4 (±30.8) 3.41E-12 2.04E-10 ‡ **** GGT (U/L) 40.7 (±28.6) 36.1 (±26.5) 44.4 (±29.8) 3.79E-04 2.27E-02 † * Total bilirubin (mg/dL) 0.55 (±0.25) 0.50 (±0.21) 0.59 (±0.27) 1.18E-03 7.10E-02 † NS CRP (mg/dL) 0.8 (±0.9) 0.9 (±1.0) 0.7 (±0.8) 9.27E-02 1.00 † NS CDT 1.89 (±0.39) 1.91 (±0.44) 1.88 (±0.34) 0.513 1.00 † NS Sphingoid bases (µM) C16SO 17.013 (±5.223) 17.319 (±5.022) 16.762 (±5.384) 0.285 1.00 † NS C16SA 0.665 (±0.279) 0.620 (±0.226) 0.701 (±0.313) 1.19E-02 0.712 † NS C17SO 7.695 (±1.947) 7.817 (±1.890) 7.594 (±1.993) 0.250 1.00 † NS C17SA 0.102 (±0.040) 0.095 (±0.034) 0.108 (±0.043) 3.56E-03 0.214 † NS C18SAdiene 34.487 (±8.222) 35.613 (±8.209) 33.562 (±8.140) 2.21E-02 1.00 † NS C18SO 74.879 (±13.227 76.277 (±13.699) 73.730 (±12.751) 9.36E-02 1.00 † NS C18SA 3.804 (±1.189) 3.689 (±1.147) 3.897 (±1.218) 0.127 1.00 † NS C19SO 0.235 (±0.081) 0.228 (±0.080) 0.241 (±0.081) 0.129 1.00 † NS C19SA 0.010 (±0.006) 0.009 (±0.006) 0.010 (±0.006) 0.423 1.00 † NS MeC18SO 2.511 (±0.908) 2.492 (±0.856) 2.526 (±0.952) 0.869 1.00 † NS MeC18SA 0.113 (±0.052) 0.104 (±0.047) 0.120 (±0.054) 6.85E-03 0.411 † NS C18PhytoSO 0.866 (±0.331) 0.881 (±0.342) 0.853 (±0.321) 0.440 1.00 † NS C20SO 0.185 (±0.064) 0.184 (±0.064) 0.186 (±0.065) 0.846 1.00 † NS C20SA 0.022 (±0.014) 0.021 (±0.013) 0.023 (±0.015) 0.253 1.00 † NS 1-deoxySO 0.236 (±0.134) 0.204 (±0.113) 0.262 (±0.144) 5.45E-05 3.27E-03 † ** 1-deoxySA 0.135 (±0.069) 0.126 (±0.062) 0.143 (±0.074) 2.44E-02 1.00 † NS C16SO-1-phosphate 0.033 (±0.017) 0.034 (±0.016) 0.033 (±0.018) 0.300 1.00 † NS C17SO-1-phosphate 0.019 (±0.011) 0.019 (±0.009) 0.018 (±0.012) 0.408 1.00 † NS C18SAdiene-1-phosphate 0.382 (±0.158) 0.386 (±0.137) 0.378 (±0.174) 0.468 1.00 † NS C18SO-1-phosphate 0.580 (±0.127) 0.592 (±0.129) 0.570 (±0.125) 0.133 1.00 † NS C18SA-1-phosphate 0.115 (±0.045) 0.115 (±0.040) 0.114 (±0.048) 0.719 1.00 † NS MeC18SO-1-phosphate 0.010 (±0.006) 0.010 (±0.006) 0.010 (±0.007) 0.827 1.00 † NS C19SO-1-phosphate 0.004 (±0.003) 0.003 (±0.003) 0.004 (±0.003) 0.207 1.00 † NS Amino Acids (mM) Glycine 0.079 (±0.027) 0.082 (±0.034) 0.076 (±0.020) 0.231 1.00 ‡ NS Alanine 0.148 (±0.034) 0.137 (±0.032) 0.156 (±0.034) 3.71E-07 2.22E-05 † **** Serine 0.036 (±0.008) 0.037 (±0.008) 0.035 (±0.007) 7.24E-02 1.00 † NS Alanine/Serine-ratio 4.234 (±1.060) 3.8 (±0.9) 4.6 (±1.1) 1.15E-10 6.92E-09 † **** Proline 0.084 (±0.024) 0.079 (±0.024) 0.087 (±0.024) 2.13E-03 0.128 † NS Valine 0.122 (±0.026) 0.115 (±0.024) 0.128 (±0.026) 1.05E-05 6.31E-04 † *** Threonine 0.058 (±0.014) 0.059 (±0.014) 0.058 (±0.014) 0.597 1.00 † NS Leucine 0.029 (±0.007) 0.027 (±0.006) 0.031 (±0.007) 2.08E-05 1.25E-03 † ** Isoleucine 0.071 (±0.016) 0.067 (±0.014) 0.074 (±0.016) 2.79E-05 1.67E-03 † ** Asparagine 0.010 (±0.002) 0.009 (±0.002) 0.010 (±0.002) 0.154 1.00 † NS Lysine 0.100 (±0.021) 0.098 (±0.022) 0.102 (±0.019) 0.103 1.00 † NS Glutamine 0.090 (±0.027) 0.086 (±0.024) 0.093 (±0.029) 1.64E-02 0.986 † NS Methionine 0.011 (±0.003) 0.010 (±0.003) 0.011 (±0.003) 1.50E-02 0.900 † NS Histidine 0.046 (±0.009) 0.046 (±0.010) 0.046 (±0.008) 0.540 1.00 † NS Arginine 0.032 (±0.016) 0.033 (±0.018) 0.031 (±0.014) 0.210 1.00 † NS Tyrosine 0.037 (±0.010) 0.034 (±0.009) 0.040 (±0.010) 5.03E-07 3.02E-05 † **** Cysteine 0.004 (±0.002) 0.004 (±0.003) 0.004 (±0.002) 0.416 1.00 † NS Phenylalanine 0.050 (±0.007) 0.048 (±0.008) 0.051 (±0.007) 9.50E-05 5.70E-03 † ** No histological evidence of Metabolic Dysfunction-Associated Steatohepatitis (No-MASH), Metabolic Dysfunction-Associated Steatohepatitis (MASH) according to Brunt, body mass index (BMI), total cholesterol (Total-C), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), glycated haemoglobin A1 (HbA1c), aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyltransferase (GGT), C-reactive protein (CRP), carbohydrate-deficient transferrin (CDT), C16 sphingosine (C16SO), C16 sphinganine (C16SA), C17 sphingosine (C17SO), C17 sphinganine (C17SA), C18 sphingadiene (C18SAdiene), C18 sphingosine (C18SO), C18 sphinganine (C18SA), C19 sphingosine (C19SO), C19 sphinganine (C19SA), omega-3-methylated C18 sphingosine (meC18SO), omega-3-methylated C18 sphinganine (meC18SA), C18 phyto-sphingosine (C18PhytoSO), C20 sphingosine (C20SO), C20 sphinganine (C20SA), 1-deoxysphingosine (1-deoxySO), 1-deoxysphinganine (1-deoxySA). Data are expressed as mean ±SD or percentage. p values: † Unpaired t test followed by Bonferroni multiple correction, ‡ Welch test followed by Bonferroni multiple correction, # Pearson Chi-square. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Binary Logistic Regression Analysis of MASH versus no-MASH We calculated different multivariate binary regression models using MASH versus No-MASH as dependent variable and optimised independent variables by stepwise conditional forwarding. The diagnostic potential of the combined markers was analysed in an receiver operator curve (ROC) curve analysis (Fig. 1 A,B). Optimal cut-off was set on closest point to top-left corner. The most complex model (model 1) including whr, ALT, GGT, 1-deoxySO, 1-deoxySA, C17SA, MeC18SO, alanine, serine, asparagine and histidine as independent variables (Fig. 1 A) showed with 0.851 ( p = 8.18E-29) the highest area under the curve (AUC) resulting in a sensitivity of 70.0% and a specificity of 84.5% for the distinction between MASH and no-MASH. The model with the highest AUC in relation to the number of variables is shown in Fig. 1 B (model 2). The signature consists of whr, ALT, alanine and serine as independent variables and showed an AUC of 0.769 ( p = 6.79E-21). Overall percentage of accuracy in classification was 72.7%, with a sensitivity of 72.8% and a specificity of 72.5%. All three variables included in the model contributed significantly to the prediction of MASH. In contrast to the previous approach, the MAS score allows for a more graded classification of liver histology including the category borderline MASH. To verify the diagnostic potential of our biomarker signature, we applied the MAS score-based classification to Model 2 (whr, ALT, alanine and serine). Out of 84 patients with histologically boderline NASH, 34 (40.48%) were assigned to the no-MASH group and 50 (59.52%) assigned to the MASH group. Distinguishing between no-MASH and borderline MASH (excluding the group of definite MASH), Model 2 was able to allocate 69.57% correctly. Although it correctly classified 116 out of 123 No-MASH patients (94.31%), only 28 out of 84 borderline-Patients (33.33%) were classified correctly. Discussion Liver biopsy remains the gold standard for diagnosing MASH [ 40 ], yet its invasive nature and limitations in routine diagnostics underline the ongoing need for non-invasive strategies to better understand and monitor disease progression. While several biomarker-based approaches have been proposed to address this gap, it is becoming increasingly evident that a deeper understanding of the underlying metabolic pathways may offer more meaningful clinical insight—particularly in the context of the recently redefined term MASLD. In this study, we identified a strong association between MASLD and specific shifts in amino acid metabolism, most notably an elevated alanine to serine (ala/ser) ratio. This finding is of particular interest given its mechanistic connection to sphingolipid and particularly 1-deoxysphingolipid biosynthesis—an atypical pathway that is activated when serine becomes limiting and alanine is used as an alternative substrate by serine palmitoyltransferase. The observed increase in 1-deoxySL levels in MASH patients, therefore, not only supports a metabolic imbalance but may also reflect a broader dysregulation of the alanine and serine metabolism influencing sphingolipid homeostasis in MASLD. Hasegawa et al. [ 45 ] found that alanine was the only amino acid significantly different between MASLD patients and healthy controls after adjusting for insulin resistance, but they did not explore this further. Pietzner et al . [ 46 ] also observed a positive link between liver fat (measured by MRI) and alanine levels, mainly influenced by insulin resistance. They also reported an inverse relationship between liver fat and serine levels, which was linked to triglycerides. However, neither of these studies used liver biopsy to confirm MASLD stages. Only Gaggini et al . [ 47 ] used histological criteria and reported higher alanine levels in MASLD patients, while L-serine levels remained unchanged. Although we explored a logistic regression model combining simple clinical variables (whr, ALT, alanine, and serine), this was not intended as a definitive diagnostic tool. Rather, the aim was to demonstrate that even routine metabolic and anthropometric parameters reflect the metabolic shifts occurring with disease progression. These shifts—especially the altered alanine and serine levels—appear to precede more classical markers of liver damage and could serve as early indicators of metabolic dysfunction rather than standalone diagnostic biomarkers. When applying the MAS score classification, the ala/ser ratio distinguished No-MASH from borderline MASH more clearly than ALT, suggesting that disruptions in amino acid metabolism may occur early in the disease process, before overt liver injury. This metabolic signal, coupled with the downstream impact on sphingolipid profiles, aligns with prior reports linking elevated alanine and reduced serine availability with hepatic steatosis, insulin resistance, and oxidative stress [ 53 ]. Previous studies have reported altered alanine levels in MASLD, although many lacked histological confirmation or did not measure serine. Our findings expand on these by providing histology-based differentiation and linking the amino acid changes directly to sphingolipid metabolism. Other amino acids—including branched-chain and aromatic amino acids—were also elevated in MASH patients, consistent with earlier reports and reinforcing the idea that amino acid perturbations reflect systemic metabolic stress in MASLD. The connection between alanine, serine, and 1-deoxySLs is further supported by studies in glycogen storage diseases and metabolic syndrome, where similar patterns of metabolite shifts have been observed. In these contexts, altered substrate availability for sphingolipid synthesis led to increased production of cytotoxic 1-deoxySLs. Moreover, studies demonstrating the therapeutic potential of serine supplementation suggest that restoring amino acid balance could modify disease trajectory, further underlining the relevance of these pathways. A strength of our study is the well-characterized cohort with biopsy-confirmed MASLD stages, enabling us to explore these metabolic alterations across a continuum of disease severity. However, limitations include the gender imbalance in the cohort and the absence of a non-obese control group, which may influence generalizability. Additionally, while the statistical model illustrates the relationship between variables, it was not cross-validated in an independent dataset, and we emphasize that the model serves an illustrative rather than predictive purpose. In conclusion, our study highlights a potentially important metabolic axis—connecting amino acid imbalance to sphingolipid dysregulation—in the pathogenesis of MASLD. These findings emphasize the complexity of MASLD as a systemic metabolic disease and offer a framework for future studies focused on mechanistic validation and therapeutic targeting, rather than immediate clinical application as biomarkers. Declarations Ethical Approval This study complies with the Declaration of Helsinki and was performed according to ethics committee approval. All participant have given a written informed consent. The study approval was granted by the Ethics Committee of the Antwerp University Hospital (reference 6/25/125, Belgian registration number B30020071389). Funding This project was supported by the Swiss National Science Foundation (SNF 310030_215134) and by the SNF under the frame of the European Joint Program on Rare Diseases (EJP RD + SNF 32ER30_187505) to T.H and a clinical investigator fellowship from the Research Foundation Flanders (FWO) (1802154N) to S.F. This publication is based upon work from COST Action 19105-Pan-European Network in Lipidomics and EpiLipidomics (EpiLipidNET) supported by COST (European Cooperation in Science and Technology). Disclosures S.F. has received grants from Astellas, Falk Pharma, Genfit, Gilead Sciences, GlympsBio, Janssens Pharmaceutica, Inventiva, Merck Sharp & Dome, Pfizer, Roche. He has acted as consultant for Abbvie, Actelion, Aelin Therapeutics, AgomAb, Aligos Therapeutics, Allergan, Alnylam, Astellas, Astra Zeneca, Bayer, Boehringer Ingelheim, Bristoll-Meyers Squibb, CSL Behring, Coherus, Echosens, Dr. Falk Pharma, Eisai, Enyo, Galapagos, Galmed, Genetech, Genfit, Genflow Biosciences, Gilead Sciences, Intercept, Inventiva, Janssens Pharmaceutica, PRO.MED.CS Praha, Julius Clinical, Madrigal, Medimmune, Merck Sharp & Dome, Mursla Bio, NGM Bio, Novartis, Novo Nordisk, Promethera, Roche, Siemens Healthineers. SF has been lecturer for Abbvie, Allergan, Bayer, Eisai, Genfit, Gilead Sciences, Janssens Cilag, Intercept, Inventiva, Merck Sharp & Dome, Novo Nordisk, Promethera, Siemens. Author Contribution F.W, M.L generated and analysed the data. A.V., S.F., J.W. , L.G. and B.S. provided the study material. F.W. A.E and T.H. prepared the figures and wrote the manuscript. All authors reviewed the manuscript Data Availability Data are made available by the authors on request References Chalasani, N., Younossi, Z., Lavine, J. E., Charlton, M., Cusi, K., Rinella, M., Harrison, S. A., Brunt, E. M., & Sanyal, A. J. (2018). The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology , 67 , 328–357. Li, Z., Xue, J., Chen, P., Chen, L., Yan, S., & Liu, L. (2014). Prevalence of nonalcoholic fatty liver disease in mainland of China: A meta-analysis of published studies. J Gastroen Hepatol , 29 , 42–51. Eslam, M., Valenti, L., & Romeo, S. (2018). Genetics and epigenetics of NAFLD and NASH: Clinical impact. Journal Of Hepatology , 68 , 268–279. Younossi, Z. M., Koenig, A. B., Abdelatif, D., Fazel, Y., Henry, L., & Wymer, M. (2016). Global epidemiology of nonalcoholic fatty liver disease—Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology , 64 , 73–84. Alkhouri, N., & Feldstein, A. E. (2016). Noninvasive diagnosis of nonalcoholic fatty liver disease: Are we there yet? Metabolis , 65 , 1087–1095. Estes, C., Razavi, H., Loomba, R., Younossi, Z., & Sanyal, A. J. (2018). Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease. Hepatology , 67 , 123–133. Buzzetti, E., Pinzani, M., & Tsochatzis, E. A. (2016). The multiple-hit pathogenesis of non-alcoholic fatty liver disease (NAFLD). Metabolis , 65 , 1038–1048. Sookoian, S., & Pirola, C. J. (2017). Genetic predisposition in nonalcoholic fatty liver disease. Clin Mol Hepatology , 23 , 1–12. Leite, N. C., Salles, G. F., Araujo, A. L. E., Villela-Nogueira, C. A., & Cardoso, C. R. L. (2009). Prevalence and associated factors of non‐alcoholic fatty liver disease in patients with type‐2 diabetes mellitus. Liver International : Official Journal Of The International Association For The Study Of The Liver , 29 , 113–119. Caballería, L., Pera, G., Auladell, M. A., et al. (2010). Prevalence and factors associated with the presence of nonalcoholic fatty liver disease in an adult population in Spain. Eur J Gastroen Hepat , 22 , 24–32. Marchesini, G., Marzocchi, R., Agostini, F., & Bugianesi, E. (2005). Nonalcoholic fatty liver disease and the metabolic syndrome. Current Opinion In Lipidology , 16 , 421–427. Loria, P., Lonardo, A., & Carulli, N. (2005). Should Nonalcoholic Fatty Liver Disease Be Renamed? Digest Dis , 23 , 72–82. Williams, C. D., Stengel, J., Asike, M. I., Torres, D. M., Shaw, J., Contreras, M., Landt, C. L., & Harrison, S. A. (2011). Prevalence of Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis Among a Largely Middle-Aged Population Utilizing Ultrasound and Liver Biopsy: A Prospective Study. Gastroenterology , 140 , 124–131. Caligiuri, A., Gentilini, A., & Marra, F. (2016). Molecular Pathogenesis of NASH. International Journal Of Molecular Sciences , 17 , 1575. Lindenmeyer, C. C., & McCullough, A. J. (2018). The Natural History of Nonalcoholic Fatty Liver Disease—An Evolving View. Clinics In Liver Disease , 22 , 11–21. Brunt, E. M., & Tiniakos, D. G. (2010). Histopathology of nonalcoholic fatty liver disease. World journal of gastroenterology , 16 , 5286–5296. Cobbina, E., & Akhlaghi, F. (2017). Non-alcoholic fatty liver disease (NAFLD) – pathogenesis, classification, and effect on drug metabolizing enzymes and transporters. Drug Metabolism Reviews , 49 , 1–43. Sanyal, D., Mukherjee, P., Raychaudhuri, M., Ghosh, S., Mukherjee, S., & Chowdhury, S. (2015). Profile of liver enzymes in non-alcoholic fatty liver disease in patients with impaired glucose tolerance and newly detected untreated type 2 diabetes. Indian J Endocrinol Metabolism , 19 , 597–601. Mofrad, P., Contos, M. J., Haque, M., Sargeant, C., Fisher, R. A., Luketic, V. A., Sterling, R. K., Shiffman, M. L., Stravitz, R. T., & Sanyal, A. J. (2003). Clinical and histologic spectrum of nonalcoholic fatty liver disease associated with normal ALT values. Hepatology , 37 , 1286–1292. Maximos, M., Bril, F., Sanchez, P. P., Lomonaco, R., Orsak, B., Biernacki, D., Suman, A., Weber, M., & Cusi, K. (2015). The role of liver fat and insulin resistance as determinants of plasma aminotransferase elevation in nonalcoholic fatty liver disease. Hepatology , 61 , 153–160. Fracanzani, A. L., Valenti, L., Bugianesi, E., et al. (2008). Risk of severe liver disease in nonalcoholic fatty liver disease with normal aminotransferase levels: A role for insulin resistance and diabetes. Hepatology , 48 , 792–798. Wong, V. W. S., Adams, L. A., de Lédinghen, V., Wong, G. L. H., & Sookoian, S. (2018). Noninvasive biomarkers in NAFLD and NASH — current progress and future promise. Nat Rev Gastroentero , 15 , 461–478. Mikolasevic, I., Milic, S., Wensveen, T. T., Grgic, I., Jakopcic, I., Stimac, D., Wensveen, F., & Orlic, L. (2016). Nonalcoholic fatty liver disease - A multisystem disease? World J Gastroentero , 22 , 9488–9505. Chaurasia, B., Tippetts, T. S., Monibas, R. M., et al. (2019). Targeting a ceramide double bond improves insulin resistance and hepatic steatosis. Science , 365 , 386–392. Simons, K., & Ikonen, E. (1997). Functional rafts in cell membranes. Nature , 387 , 569–572. ICHI, I., NAKAHARA, K., FUJII, K., IIDA, C., MIYASHITA, Y., & KOJO, S. (2007). Increase of Ceramide in the Liver and Plasma after Carbon Tetrachloride Intoxication in the Rat. J Nutr Sci Vitaminol , 53 , 53–56. Xia, J. Y., Morley, T. S., & Scherer, P. E. (2014). The adipokine/ceramide axis: Key aspects of insulin sensitization. Biochimie , 96 , 130–139. Holland, W. L., Knotts, T. A., Chavez, J. A., Wang, L., Hoehn, K. L., & Summers, S. A. (2007). Lipid Mediators of Insulin Resistance. Nutrition Reviews , 65 , S39–S46. Hammerschmidt, P., Ostkotte, D., Nolte, H., et al. (2019). CerS6-Derived Sphingolipids Interact with Mff and Promote Mitochondrial Fragmentation in Obesity. Cell , 177 , 1536–1552e23. Raichur, S., Brunner, B., Bielohuby, M., Hansen, G., Pfenninger, A., Wang, B., Bruning, J. C., Larsen, P. J., & Tennagels, N. (2019). The role of C16:0 ceramide in the development of obesity and type 2 diabetes: CerS6 inhibition as a novel therapeutic approach. Mol Metab , 21 , 36–50. Turpin-Nolan, S. M., Hammerschmidt, P., Chen, W., Jais, A., Timper, K., Awazawa, M., Brodesser, S., & Brüning, J. C. (2019). CerS1-Derived C18:0 Ceramide in Skeletal Muscle Promotes Obesity-Induced Insulin Resistance. Cell Reports , 26 , 1–10e7. Gorden, D. L., Myers, D. S., Ivanova, P. T., et al. (2015). Biomarkers of NAFLD progression: a lipidomics approach to an epidemic. Journal of Lipid Research , 56 , 722–736. Weyler, J., Verrijken, A., Hornemann, T., et al. (2020). Association of 1-deoxy-sphingolipids with steatosis but not steatohepatitis nor fibrosis in non-alcoholic fatty liver disease. Acta diabetologica , 28 , 162–169. Othman, A., Rütti, M. F., Ernst, D., et al. (2011). Plasma deoxysphingolipids: a novel class of biomarkers for the metabolic syndrome? Diabetologia , 55 , 421–431. Bertea, M., Rütti, M. F., Othman, A., Marti-Jaun, J., Hersberger, M., von Eckardstein, A., & Hornemann, T. (2010). Deoxysphingoid bases as plasma markers in Diabetes mellitus. Lipids in health and disease , 9 , 84. Zitomer, N. C., Mitchell, T., Voss, K. A., et al. (2009). Ceramide Synthase Inhibition by Fumonisin B 1Causes Accumulation of 1-Deoxysphinganine. The Journal of biological chemistry , 284 , 4786–4795. Lone, M. A., Santos, T., Alecu, I., Silva, L. C., & Hornemann, T. (2019). 1-Deoxysphingolipids. Biochimica et biophysica acta Molecular and cell biology of lipids , 1864 , 512–521. Verrijken, A., Francque, S., Mertens, I., Prawitt, J., Caron, S., Hubens, G., Marck, E. V., Staels, B., Michielsen, P., & Gaal, L. V. (2014). Prothrombotic factors in histologically proven nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatology , 59 , 121–129. Kleiner, D. E., Brunt, E. M., Natta, M. V., et al. (2005). Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology , 41 , 1313–1321. Brunt, E. M., Janney, C. G., Bisceglie, A. M. D., Neuschwander-Tetri, B. A., & Bacon, B. R. (1999). Nonalcoholic Steatohepatitis: A Proposal for Grading and Staging The Histological Lesions. American Journal Of Gastroenterology , 94 , 2467–2474. Brunt, E. M., Kleiner, D. E., Wilson, L. A., Belt, P., Neuschwander-Tetri, B. A., & (CRN) NCRN. (2011). Nonalcoholic fatty liver disease (NAFLD) activity score and the histopathologic diagnosis in NAFLD: distinct clinicopathologic meanings. Hepatology , 53 , 810–820. Lone, M. A., Hülsmeier, A. J., Saied, E. M., Karsai, G., Arenz, C., von Eckardstein, A., & Hornemann, T. (2020). Subunit composition of the mammalian serine-palmitoyltransferase defines the spectrum of straight and methyl-branched long-chain bases. Proceedings of the National Academy of Sciences of the United States of America , 117 , 15591–15598. Kaikkonen, J. E., Würtz, P., Suomela, E., et al. (2017). Metabolic profiling of fatty liver in young and middle-aged adults: Cross‐sectional and prospective analyses of the Young Finns Study. Hepatology , 65 , 491–500. Yamakado, M., Tanaka, T., Nagao, K., et al. (2017). Plasma amino acid profile associated with fatty liver disease and co-occurrence of metabolic risk factors. Sci Rep-uk , 7 , 14485. Hasegawa, T., Iino, C., Endo, T., Mikami, K., Kimura, M., Sawada, N., Nakaji, S., & Fukuda, S. (2020). Changed Amino Acids in NAFLD and Liver Fibrosis: A Large Cross-Sectional Study without Influence of Insulin Resistance. Nutrients , 12 , 1450. Pietzner, M., Budde, K., Homuth, G., et al. (2018). Hepatic Steatosis Is Associated With Adverse Molecular Signatures in Subjects Without Diabetes. J Clin Endocrinol Metabolism , 103 , 3856–3868. Gaggini, M., Carli, F., Rosso, C., et al. (2018). Altered amino acid concentrations in NAFLD: Impact of obesity and insulin resistance. Hepatology , 67 , 145–158. Tidhar, R., Ben-Dor, S., Wang, E., Kelly, S., Merrill, A. H., & Futerman, A. H. (2011). Acyl Chain Specificity of Ceramide Synthases Is Determined within a Region of 150 Residues in the Tram-Lag-CLN8 (TLC) Domain. Journal of Biological Chemistry , 287 , 3197–3206. Chashmniam, S., Ghafourpour, M., Farimani, A. R., Gholami, A., & Ghoochani, B. F. N. M. (2019). Metabolomic Biomarkers in the Diagnosis of Non-Alcoholic Fatty Liver Disease. Hepat Mon. https://doi.org/10.5812/hepatmon.92244 Goffredo, M., Santoro, N., Tricò, D., et al. (2017). A Branched-Chain Amino Acid-Related Metabolic Signature Characterizes Obese Adolescents with Non-Alcoholic Fatty Liver Disease. Nutrients , 9 , 642. Hornemann, T., Alecu, I., Hagenbuch, N., et al. (2018). Disturbed sphingolipid metabolism with elevated 1-deoxysphingolipids in glycogen storage disease type I – A link to metabolic control. Molecular Genetics And Metabolism , 125 , 73–78. Melis, D., Rossi, A., Pivonello, R., et al. (2015). Glycogen storage disease type Ia (GSDIa) but not Glycogen storage disease type Ib (GSDIb) is associated to an increased risk of metabolic syndrome: possible role of microsomal glucose 6-phosphate accumulation. Orphanet Journal Of Rare Diseases , 10 , 91. Mardinoglu, A., Bjornson, E., Zhang, C., et al. (2017). Personal model-assisted identification of NAD + and glutathione metabolism as intervention target in NAFLD. Molecular Systems Biology , 13 , 916. Additional Declarations No competing interests reported. Supplementary Files floatimage1.jpeg Graphical Abstract: MAFLD is associated with an increased ala/ser ratio which results in a metabolic shift from canonical SL to toxic 1-deoxySL. SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 02 Nov, 2025 Read the published version in Metabolomics → Version 1 posted Editorial decision: Accepted 01 Oct, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 23 Aug, 2025 Reviewers invited by journal 23 Aug, 2025 Editor assigned by journal 23 Aug, 2025 Submission checks completed at journal 23 Aug, 2025 First submitted to journal 22 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7435578","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504629793,"identity":"a1b69239-9cf8-4787-99a7-a836c9a226a0","order_by":0,"name":"Florine Wipfli","email":"","orcid":"","institution":"University Hospital Zurich and University of Zurich","correspondingAuthor":false,"prefix":"","firstName":"Florine","middleName":"","lastName":"Wipfli","suffix":""},{"id":504629795,"identity":"bf048f6c-2b63-4a0d-b9c5-426fad10c6ec","order_by":1,"name":"Museer Lone","email":"","orcid":"","institution":"University Hospital Zurich and 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16:07:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1634996,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7435578/v1/a27e71ee-b2fe-471b-948e-39f93ada3c36.pdf"},{"id":90317021,"identity":"cc243e94-2836-4da3-b409-7c37b3955b2a","added_by":"auto","created_at":"2025-09-01 10:23:56","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":119115,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract:\u003c/p\u003e\n\u003cp\u003eMAFLD is associated with an increased ala/ser ratio which results in a metabolic shift from canonical SL to toxic 1-deoxySL.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7435578/v1/4be6d1d45c4fa8f03931358b.jpeg"},{"id":90317020,"identity":"b9379a13-4bee-4359-ad13-614e5cb60976","added_by":"auto","created_at":"2025-09-01 10:23:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":57174,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7435578/v1/47ee62f65aa8014342290de5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Increased 1-deoxySphingolipids caused by an altered plasma Alanine to Serine ratio are associated with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-Alcoholic Fatty Liver Disease (NAFLD), recently renamed Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), is characterized by abnormal lipid synthesis and accumulation in the liver, independent of significant alcohol consumption or other secondary causes. MASLD spans a spectrum from isolated steatosis (MASL) to Metabolic Dysfunction-Associated Steatohepatitis (MASH) and fibrosis, potentially progressing to cirrhosis and hepatocellular carcinoma (HCC) [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is now the leading cause of chronic liver disease in the Western world, with the highest prevalence in the Middle East and South America [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and a global prevalence projected to exceed 30% by 2030 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe pathogenesis of MASLD remains incompletely understood. The original \u0026ldquo;two-hit\u0026rdquo; hypothesis has evolved into a \u0026ldquo;multiple-hit\u0026rdquo; model, involving increased adipose lipolysis and de novo lipogenesis, leading to excess free fatty acids (FFAs), free cholesterol, triglycerides (TGs), and other lipids in the liver. FFA accumulation drives mitochondrial dysfunction, reactive oxygen species (ROS) production, and hepatic inflammation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Besides genetic predisposition [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], MASLD is strongly associated with components of the metabolic syndrome (MetS) such as obesity, insulin resistance (IR), diabetes mellitus type 2 (T2DM), hypertension, and hypertriglyceridemia [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, MASLD is often regarded as the hepatic manifestation of MetS [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHistologically, MASL is defined by steatosis in \u0026ge;\u0026thinsp;5% of hepatocytes, while MASH additionally features lobular inflammation and hepatocellular ballooning [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. MASH can be identified in approximately 30% of patients diagnosed with MASLD by ultrasound [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and it carries an increased risk for cirrhosis, HCC, and cardiovascular events [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although fibrosis is not a diagnostic criterion for MASH [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], it significantly worsens prognosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven the high prevalence of MASLD, there is a critical need to monitor disease progression, particularly in high-risk groups. Early identification hinges on detecting specific biomarkers. Currently, liver biopsy remains the gold standard for diagnosing and staging MASLD [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], but its invasiveness, cost, and need for expert interpretation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] highlight the urgent need for reliable, non-invasive alternatives.\u003c/p\u003e\u003cp\u003eTraditional serum markers of liver injury, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT), show limited sensitivity and specificity in MASLD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Over half of MASLD patients have normal GGT levels [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and disease can be present despite normal ALT values [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Elevated ALT also does not reliably indicate more severe MASH or fibrosis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent efforts have focused on identifying circulating proteins\u0026mdash;hormones, cytokines, adipokines, and carrier proteins\u0026mdash;as biomarkers for MASLD and MASH [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, these candidates face challenges related to clinical implementation and limited stage specificity. Emerging OMICS technologies, particularly lipidomics and metabolomics, may overcome these limitations and improve early detection of disease progression.\u003c/p\u003e\u003cp\u003eGiven the overlapping pathophysiology of MASLD, MetS, and T2DM [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], biomarkers identified in these conditions may also reflect MASH risk. Among lipid classes, sphingolipids (SL), particularly ceramides, have been implicated in insulin resistance, T2DM, and MASLD [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. SLs play critical roles in cellular processes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and elevated levels in liver and plasma are associated with liver dysfunction, IR, and steatosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Circulating ceramides may directly impair insulin signalling [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and enzymes regulating SL metabolism are considered therapeutic targets [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Recent OMICS studies have also linked an atypical class of sphingolipids, 1-deoxysphingolipids (1-deoxySLs), to MASLD, MetS, and T2DM [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e1-DeoxySLs are formed through a side reaction of de novo sphingolipid synthesis when serine availability is reduced, leading serine palmitoyltransferase to utilize alanine instead [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These aberrant products lack the C1-hydroxyl group essential for canonical sphingolipid function and degradation, contributing to toxicity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBuilding on these findings, we aimed to investigate whether canonical and non-canonical sphingolipids, together with metabolic parameters, could reflect histological stages of MASLD, offering new insights into disease mechanisms and potential non-invasive diagnostic strategies.\u003c/p\u003e"},{"header":"Patients and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy\u003c/h2\u003e\u003cp\u003ePatients attending the obesity clinic of the Antwerp University Hospital were consecutively included in the study upon informed consent. A metabolic work-up, laboratory analyses and a liver specific-program (to exclude other liver diseases than MASLD) were performed.\u003c/p\u003e\u003cp\u003ePatients presenting to the obesity clinic underwent a detailed metabolic and liver assessment, including an extensive panel of coagulation factors. If NAFLD was suspected, a liver biopsy was proposed. A series of 273 consecutive patients (65% female) with a liver biopsy were included (age, 44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76 years; body mass index: 39.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40 kg/m2). Patients had to be 18 years or older, had no significant alcohol consumption and no history of diabetes (as medical treatment of the latter may substantially influence liver histology; \u003cem\u003ede novo\u003c/em\u003e diagnosis of T2DM was, however, allowed). For more information, refer to a previous description of the patient cohort [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePathological features like steatosis grade, ballooning, lobular inflammation, and fibrosis stage were scored according to the NASH Clinical Research Network Scoring System (NASH-CRN) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. For the classification of patients based on the severity of MASLD we considered two different approaches. On the one hand, we distinguished between No-MASH and MASH [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], with MASH being defined as the coexistence of steatosis, ballooning, and lobular inflammation (hence a score of 1 or more for each). On the other hand, we classified the patients more granularly according to the NAFLD/MASLD activity score (NAS/MAS), which is defined by the sum of the scores for steatosis, ballooning, and lobular inflammation. The criteria for the classification are as follows: MAS\u0026thinsp;\u0026lt;\u0026thinsp;3 (No-MASH); MAS 3\u0026ndash;4 (borderline-MASH); and MAS\u0026thinsp;\u0026ge;\u0026thinsp;5 (definite MASH) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e][\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSphingoid base analysis\u003c/h3\u003e\n\u003cp\u003eSphingoid bases were extracted from fasting plasma samples. Fifty microliters of plasma were filled up with phosphate-buffered saline to a volume of 100 \u0026micro;L. After mixing by vortex at room temperature, the lipids were extracted in 500 \u0026micro;L extraction buffer (499.8 \u0026micro;L methanol\u0026thinsp;+\u0026thinsp;0.2 \u0026micro;L D7-labelled C18 sphingosine). After another short mixing, the lipids were extracted under constant agitation (1400 rpm) for 1 h at 37\u0026deg;C. Subsequently samples were centrifuged (16100 rcf, 5 min) and 500 \u0026micro;L of the supernatant was transferred to a new tube. For acid and base hydrolysis, 75 \u0026micro;L of HCl (conc. 32%) was first added and mixed, and the samples were then stored at 65\u0026deg;C for 12\u0026ndash;15 h. After that, samples were neutralised by adding 100 \u0026micro;L of KOH (10M) and subjected to base hydrolysis. Base hydrolysis was performed in the following steps: First, 125 \u0026micro;L of chloroform (CHCl\u003csub\u003e3\u003c/sub\u003e) was added and mixed. Then another 500 \u0026micro;L of CHCl\u003csub\u003e3\u003c/sub\u003e was added and mixed again. Finally, 100 \u0026micro;L of 2N ammonia and 0.5 mL of alkaline water were added and samples were centrifuged (16000 rcf) for 5 minutes. The upper phase was removed, and the lower phase was washed twice again with 1 mL alkaline water. The lipids were then dried under N\u003csub\u003e2\u003c/sub\u003e. Dried lipids were resuspended in 70 \u0026micro;L of reconstitution buffer (70% methanol, 10 mM ammonium acetate) and subjected to LC-MRM-MS analysis. The separation of sphingoid bases has been described previously [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Quantification was performed using MultiQuant (2.1) software (SCIEX) and normalized to the internal standard D7-C18 sphingosine.\u003c/p\u003e\n\u003ch3\u003eSphingoid base 1-phosphate analysis\u003c/h3\u003e\n\u003cp\u003eFifty microliter of fasting plasma was filled up with ddH\u003csub\u003e2\u003c/sub\u003eO to a volume of 100 \u0026micro;L. The lipids were then extracted in 400 \u0026micro;L extraction solvent (50 nM D7-S1P in methanol), mixed and extracted under constant agitation (1400 rpm) for 1 h at 37\u0026deg;C. After that, samples were centrifuged at 14000 rcf for 10 minutes. Subsequently, the supernatant was removed, transferred to a new tube, and dried under N\u003csub\u003e2\u003c/sub\u003e. The dried samples were re-dissolved in 50 \u0026micro;L reconstitution solvent (95% methanol, 10mM ammonium acetate) and shaken for 30 minutes (950 rpm, 25\u0026deg;C). After centrifugation for 15 minutes at 14000 rcf the supernatants were transferred to a new tube and analysed by LC-MRM-MS. Sphingoid base 1-phosphates (S1P) were separated via a reverse-phase C18 column (Luna Omega Polar C18, 150 x 2.1 mm) connected to a QTRAP 6500\u0026thinsp;+\u0026thinsp;LC-MS/MS-MS System (Sciex), operated in multiple reaction monitoring mass spectrometry mode. Sample ionisation was achieved via electrospray ionisation in positive and negative ion mode, alternating. For chromatography, a binary solvent system consisting of solvent A (5 mM ammonium formate in H\u003csub\u003e2\u003c/sub\u003eO/methanol/formic acid (20/80/0.5, v/v/v)) and solvent B (5 mM ammonium formate in methanol/acetonitrile/formic acid (60/40/0.5, v/v/v)) was used. The flow rate was maintained at 0.3 mL/min throughout. The column was equilibrated with 100% solvent A, and a linear gradient to 100% B was run over 4 min and washed with 100% B for 4 minutes before equilibration in 100% A for 6 minutes. The acquired data were integrated and analysed using the open source software tool Skyline (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.jproteome.9b00640\u003c/span\u003e\u003cspan address=\"10.1021/acs.jproteome.9b00640\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For normalisation and quantification, the internal standard D7-S1P was added to the samples.\u003c/p\u003e\n\u003ch3\u003eAmino acid analysis\u003c/h3\u003e\n\u003cp\u003eTo 10 \u0026micro;L of serum, 1 nmol of stable isotope labelled amino acids were added (Cambridge Isotope Laboratories). Proteins were precipitated by adding 15 volumes ice cold methanol at -20\u0026deg;C. After incubation for 30 minutes at -20\u0026deg;C, samples were centrifuged for 10 minutes at 4\u0026deg;C (14000rcf). The supernatant was dried under N\u003csub\u003e2\u003c/sub\u003e and the amino acids were re-constituted in 100 \u0026micro;L 0.1% acetic acid for 1 hour at 24\u0026deg;C shaking at 950 rpm. After centrifugation for 5 minutes at 16100 rcf the supernatant was analysed by LC-MRM-MS.\u003c/p\u003e\u003cp\u003eAmino acids were separated via a reverse-phase C18 column (EC 250/2 NUCLEOSIL 100-3 C18HD, L\u0026thinsp;=\u0026thinsp;250 mm, ID: 2 mm; Macherey-Nagel). 5 \u0026micro;L per sample were subjected to liquid chromatography coupled multiple reaction monitoring mass spectrometry using a QTRAP 6500\u0026thinsp;+\u0026thinsp;LC-MS/MS-MS System (Sciex). The amino acids were chromatographed isocratically with 0.1% formic acid in water for five minutes, followed by a linear gradient to 50% solvent B (acetonitrile) over two minutes. Then, the column was washed with 80% solvent B prior equilibration with 100% solvent A. The flow rate was held constant at 0.2 mL/min. Sample ionization was achieved via electrospray ionization in positive ion mode. Quantification was performed using MultiQuant (2.1) software (SCIEX) and normalised to internal standards.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical tests were performed using IBM SPSS version 27. Mean values were compared using either independent t-test or one-way ANOVA, followed by Bonferroni correction. In case of unequal variances, comparisons were conducted by Welch test followed by Bonferroni correction and Welch-ANOVA with Games-Howell correction. Where data were skewed, log or square root transformation was used in statistical analysis. To investigate the association of anthropometrical features, metabolic parameters, values of liver tests, sphingoid bases and amino acids within different stages of MASLD, we compared the following groups: No-MASH versus MASH dichotomously; No-MASH versus borderline-MASH versus definite MASH according to MAS.\u003c/p\u003e\u003cp\u003eThe regression coefficient β of multivariate binary regression models was calculated using IBM SPSS. Z-Scores and prediction function was calculated in Excel. Receiver operating characteristic (ROC) curve analysis was performed using MetaboAnalyst 5.0.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eCohort Characteristics\u003c/h2\u003e\n \u003cp\u003eThis study is based on the HEPADIP cohort that was initiated by the Antwerp University Hospital (reference 6/25/125, Belgian registration number B30020071389) and established as part of the FP6-Lifescihealth program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cordis.europa.eu/project/id/18734/de\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIt consists of clinically very well characterised individuals and comprises\u0026thinsp;\u0026gt;\u0026thinsp;180 baseline parameters including liver histology. A total of 315 individuals were included in this analysis (217 women and 98 men). Mean age was 43.7 (\u0026plusmn;12.5) years, and mean body mass index (BMI) was 39.3 (\u0026plusmn;5.9) kg/m\u003csup\u003e2\u003c/sup\u003e. Based on the definition of MASH, 45.1% (n\u0026thinsp;=\u0026thinsp;142) patients were diagnosed with no-MASH and 54.9% (n\u0026thinsp;=\u0026thinsp;173) with MASH. Based on the MAS score, 39% were No-MASH (n\u0026thinsp;=\u0026thinsp;123), 26.7% borderline MASH (n\u0026thinsp;=\u0026thinsp;84) and 34.3% definite MASH (n\u0026thinsp;=\u0026thinsp;108).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eDifferences between No-MASH and MASH\u003c/h3\u003e\n\u003cp\u003eAnthropometric features, clinical laboratory values, metabolic markers, the sphingoid base and amino acid profile were compared between the No-MASH and MASH dichotomous groups (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The most significant differences between the groups were seen for ALT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.04E-10), followed by the alanine/serine (ala/ser) ratio (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.92E-09) and the waist/hip-ratio (whr) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.46E-08). Although less pronounced, significant differences were also seen for AST (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.83E-07) and GGT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.27E-02). No significant differences in sphingoid base levels including sgphingoid-1-phohsphate were observed except for 1-deoxySO that was significantly increased in MASH (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.27E-03), confirming earlier findings [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eFor the amino acids, we found that alanine (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.22E-05), tyrosine (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.02E-05), phenylalanine (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.70E-03) and the branched chain AA (BCAA) valine (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.31E-04), leucine (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.25E-03) and isoleucine (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.67E-03) were significantly higher in MASH. In addition, variables typically associated with MetS such as plasma triglycerides (TG) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.73E-03), HbA1c (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.41E-02), fasting glucose \u003cem\u003e(p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.91E-02) and fasting insulin (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.28E-02) were significantly elevated in the MASH group. However, no differences were seen for total cholesterol (total-C), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). The use of lipid lowering medication did not significantly differ between the two groups.\u003c/p\u003e\n\u003cp\u003eAs about 70 percent of the patients were female, we also compared the groups separated by gender (supplementary Table S1and S2).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical and laboratory data according to the definition of MASH\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;315 (100%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo-MASH\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;142 (45.1%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMASH\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;173 (54.9%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted \u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificance level\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.978\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender: men\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e98\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(31.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(18.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e72\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(41.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.74E-06\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e****\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.66E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e118.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;13.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e115.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;13.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e120.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;12.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.53E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.52E-03\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHip (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.48E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.269\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist/Hip-Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.77E-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.46E-08\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e****\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e202.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.01E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.40E-02\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e146.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;67.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e130.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;58.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e160.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;71.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.45E-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.73E-03\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.02E-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.41E-02\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026Dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlucose (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e85.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;21.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e81.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;9.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e88.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;27.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.85E-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.91E-02\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026Dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsulin (\u0026micro;U/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e17.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;11.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;10.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;11.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.13E-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.28E-02\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedication for diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipid lowering medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.114\u003csup\u003e\u003cem\u003e#\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST (U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e30.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;17.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e25.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;7.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;20.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.14E-08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.83E-07\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026Dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e****\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT (U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e44.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;26.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e34.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;15.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e52.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;30.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.41E-12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.04E-10\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026Dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e****\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGGT (U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e40.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;28.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;26.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e44.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;29.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.79E-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.27E-02\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal bilirubin (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.10E-02\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.27E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSphingoid bases\u003c/em\u003e (\u0026micro;M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC16SO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;5.223)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;5.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;5.384)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC16SA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.313)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.712\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC17SO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;1.947)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;1.890)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;1.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC17SA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.56E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.214\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18SAdiene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;8.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;8.209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;8.140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18SO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;13.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;13.699)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;12.751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.36E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18SA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;1.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;1.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;1.218)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC19SO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC19SA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeC18SO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.908)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.952)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeC18SA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.85E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.411\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18PhytoSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.331)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.342)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.321)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC20SO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC20SA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1-deoxySO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.236\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.134)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.204\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.113)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.262\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.144)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.45E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.27E-03\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-deoxySA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.44E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC16SO-1-phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC17SO-1-phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18SAdiene-1-phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18SO-1-phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18SA-1-phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMeC18SO-1-phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC19SO-1-phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAmino Acids (mM)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n 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\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.034)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.71E-07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.22E-05\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e****\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n 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\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.026)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.05E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.31E-04\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThreonine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeucine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.007)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.006)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.007)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.08E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.25E-03\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsoleucine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.071\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.016)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.067\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.014)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.074\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.016)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.79E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.67E-03\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsparagine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLysine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlutamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.986\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethionine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.900\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArginine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTyrosine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.010)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.009)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.010)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.03E-07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.02E-05\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e****\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCysteine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026plusmn;0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhenylalanine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.007)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.008)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.051\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026plusmn;0.007)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.50E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.70E-03\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eNo histological evidence of Metabolic Dysfunction-Associated Steatohepatitis (No-MASH), Metabolic Dysfunction-Associated Steatohepatitis (MASH) according to Brunt, body mass index (BMI), total cholesterol (Total-C), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), glycated haemoglobin A1 (HbA1c), aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyltransferase (GGT), C-reactive protein (CRP), carbohydrate-deficient transferrin (CDT), C16 sphingosine (C16SO), C16 sphinganine (C16SA), C17 sphingosine (C17SO), C17 sphinganine (C17SA), C18 sphingadiene (C18SAdiene), C18 sphingosine (C18SO), C18 sphinganine (C18SA), C19 sphingosine (C19SO), C19 sphinganine (C19SA), omega-3-methylated C18 sphingosine (meC18SO), omega-3-methylated C18 sphinganine (meC18SA), C18 phyto-sphingosine (C18PhytoSO), C20 sphingosine (C20SO), C20 sphinganine (C20SA), 1-deoxysphingosine (1-deoxySO), 1-deoxysphinganine (1-deoxySA). Data are expressed as mean \u0026plusmn;SD or percentage. \u003cem\u003ep\u003c/em\u003e values: \u003csup\u003e\u003cem\u003e\u0026dagger;\u003c/em\u003e\u003c/sup\u003e Unpaired t test followed by Bonferroni multiple correction, \u003csup\u003e\u003cem\u003e\u0026Dagger;\u003c/em\u003e\u003c/sup\u003e Welch test followed by Bonferroni multiple correction, \u003csup\u003e#\u003c/sup\u003ePearson Chi-square. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBinary Logistic Regression Analysis of MASH versus no-MASH\u003c/h2\u003e\n \u003cp\u003eWe calculated different multivariate binary regression models using MASH versus No-MASH as dependent variable and optimised independent variables by stepwise conditional forwarding. The diagnostic potential of the combined markers was analysed in an receiver operator curve (ROC) curve analysis (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA,B). Optimal cut-off was set on closest point to top-left corner.\u003c/p\u003e\n \u003cp\u003eThe most complex model (model 1) including whr, ALT, GGT, 1-deoxySO, 1-deoxySA, C17SA, MeC18SO, alanine, serine, asparagine and histidine as independent variables (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA) showed with 0.851 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.18E-29) the highest area under the curve (AUC) resulting in a sensitivity of 70.0% and a specificity of 84.5% for the distinction between MASH and no-MASH.\u003c/p\u003e\n \u003cp\u003eThe model with the highest AUC in relation to the number of variables is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB (model 2). The signature consists of whr, ALT, alanine and serine as independent variables and showed an AUC of 0.769 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.79E-21). Overall percentage of accuracy in classification was 72.7%, with a sensitivity of 72.8% and a specificity of 72.5%. All three variables included in the model contributed significantly to the prediction of MASH.\u003c/p\u003e\n \u003cp\u003eIn contrast to the previous approach, the MAS score allows for a more graded classification of liver histology including the category borderline MASH. To verify the diagnostic potential of our biomarker signature, we applied the MAS score-based classification to Model 2 (whr, ALT, alanine and serine). Out of 84 patients with histologically boderline NASH, 34 (40.48%) were assigned to the no-MASH group and 50 (59.52%) assigned to the MASH group. Distinguishing between no-MASH and borderline MASH (excluding the group of definite MASH), Model 2 was able to allocate 69.57% correctly. Although it correctly classified 116 out of 123 No-MASH patients (94.31%), only 28 out of 84 borderline-Patients (33.33%) were classified correctly.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLiver biopsy remains the gold standard for diagnosing MASH [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], yet its invasive nature and limitations in routine diagnostics underline the ongoing need for non-invasive strategies to better understand and monitor disease progression. While several biomarker-based approaches have been proposed to address this gap, it is becoming increasingly evident that a deeper understanding of the underlying metabolic pathways may offer more meaningful clinical insight\u0026mdash;particularly in the context of the recently redefined term MASLD.\u003c/p\u003e\u003cp\u003eIn this study, we identified a strong association between MASLD and specific shifts in amino acid metabolism, most notably an elevated alanine to serine (ala/ser) ratio. This finding is of particular interest given its mechanistic connection to sphingolipid and particularly 1-deoxysphingolipid biosynthesis\u0026mdash;an atypical pathway that is activated when serine becomes limiting and alanine is used as an alternative substrate by serine palmitoyltransferase. The observed increase in 1-deoxySL levels in MASH patients, therefore, not only supports a metabolic imbalance but may also reflect a broader dysregulation of the alanine and serine metabolism influencing sphingolipid homeostasis in MASLD.\u003c/p\u003e\u003cp\u003eHasegawa \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] found that alanine was the only amino acid significantly different between MASLD patients and healthy controls after adjusting for insulin resistance, but they did not explore this further. Pietzner \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] also observed a positive link between liver fat (measured by MRI) and alanine levels, mainly influenced by insulin resistance. They also reported an inverse relationship between liver fat and serine levels, which was linked to triglycerides. However, neither of these studies used liver biopsy to confirm MASLD stages. Only Gaggini \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] used histological criteria and reported higher alanine levels in MASLD patients, while L-serine levels remained unchanged.\u003c/p\u003e\u003cp\u003eAlthough we explored a logistic regression model combining simple clinical variables (whr, ALT, alanine, and serine), this was not intended as a definitive diagnostic tool. Rather, the aim was to demonstrate that even routine metabolic and anthropometric parameters reflect the metabolic shifts occurring with disease progression. These shifts\u0026mdash;especially the altered alanine and serine levels\u0026mdash;appear to precede more classical markers of liver damage and could serve as early indicators of metabolic dysfunction rather than standalone diagnostic biomarkers.\u003c/p\u003e\u003cp\u003eWhen applying the MAS score classification, the ala/ser ratio distinguished No-MASH from borderline MASH more clearly than ALT, suggesting that disruptions in amino acid metabolism may occur early in the disease process, before overt liver injury. This metabolic signal, coupled with the downstream impact on sphingolipid profiles, aligns with prior reports linking elevated alanine and reduced serine availability with hepatic steatosis, insulin resistance, and oxidative stress [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies have reported altered alanine levels in MASLD, although many lacked histological confirmation or did not measure serine. Our findings expand on these by providing histology-based differentiation and linking the amino acid changes directly to sphingolipid metabolism. Other amino acids\u0026mdash;including branched-chain and aromatic amino acids\u0026mdash;were also elevated in MASH patients, consistent with earlier reports and reinforcing the idea that amino acid perturbations reflect systemic metabolic stress in MASLD.\u003c/p\u003e\u003cp\u003eThe connection between alanine, serine, and 1-deoxySLs is further supported by studies in glycogen storage diseases and metabolic syndrome, where similar patterns of metabolite shifts have been observed. In these contexts, altered substrate availability for sphingolipid synthesis led to increased production of cytotoxic 1-deoxySLs. Moreover, studies demonstrating the therapeutic potential of serine supplementation suggest that restoring amino acid balance could modify disease trajectory, further underlining the relevance of these pathways.\u003c/p\u003e\u003cp\u003eA strength of our study is the well-characterized cohort with biopsy-confirmed MASLD stages, enabling us to explore these metabolic alterations across a continuum of disease severity. However, limitations include the gender imbalance in the cohort and the absence of a non-obese control group, which may influence generalizability. Additionally, while the statistical model illustrates the relationship between variables, it was not cross-validated in an independent dataset, and we emphasize that the model serves an illustrative rather than predictive purpose.\u003c/p\u003e\u003cp\u003eIn conclusion, our study highlights a potentially important metabolic axis\u0026mdash;connecting amino acid imbalance to sphingolipid dysregulation\u0026mdash;in the pathogenesis of MASLD. These findings emphasize the complexity of MASLD as a systemic metabolic disease and offer a framework for future studies focused on mechanistic validation and therapeutic targeting, rather than immediate clinical application as biomarkers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003cp\u003e This study complies with the Declaration of Helsinki and was performed according to ethics committee approval. All participant have given a written informed consent. The study approval was granted by the Ethics Committee of the Antwerp University Hospital (reference 6/25/125, Belgian registration number B30020071389).\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis project was supported by the Swiss National Science Foundation (SNF 310030_215134) and by the SNF under the frame of the European Joint Program on Rare Diseases (EJP RD\u0026thinsp;+\u0026thinsp;SNF 32ER30_187505) to T.H and a clinical investigator fellowship from the Research Foundation Flanders (FWO) (1802154N) to S.F. This publication is based upon work from COST Action 19105-Pan-European Network in Lipidomics and EpiLipidomics (EpiLipidNET) supported by COST (European Cooperation in Science and Technology).\u003c/p\u003e\u003cp\u003eDisclosures\u003c/p\u003e\u003cp\u003eS.F. has received grants from Astellas, Falk Pharma, Genfit, Gilead Sciences, GlympsBio, Janssens Pharmaceutica, Inventiva, Merck Sharp \u0026amp; Dome, Pfizer, Roche.\u003c/p\u003e\u003cp\u003eHe has acted as consultant for Abbvie, Actelion, Aelin Therapeutics, AgomAb, Aligos Therapeutics, Allergan, Alnylam, Astellas, Astra Zeneca, Bayer, Boehringer Ingelheim, Bristoll-Meyers Squibb, CSL Behring, Coherus, Echosens, Dr. Falk Pharma, Eisai, Enyo, Galapagos, Galmed, Genetech, Genfit, Genflow Biosciences, Gilead Sciences, Intercept, Inventiva, Janssens Pharmaceutica, PRO.MED.CS Praha, Julius Clinical, Madrigal, Medimmune, Merck Sharp \u0026amp; Dome, Mursla Bio, NGM Bio, Novartis, Novo Nordisk, Promethera, Roche, Siemens Healthineers.\u003c/p\u003e\u003cp\u003eSF has been lecturer for Abbvie, Allergan, Bayer, Eisai, Genfit, Gilead Sciences, Janssens Cilag, Intercept, Inventiva, Merck Sharp \u0026amp; Dome, Novo Nordisk, Promethera, Siemens.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eF.W, M.L generated and analysed the data. A.V., S.F., J.W. , L.G. and B.S. provided the study material. F.W. A.E and T.H. prepared the figures and wrote the manuscript. All authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData are made available by the authors on request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChalasani, N., Younossi, Z., Lavine, J. E., Charlton, M., Cusi, K., Rinella, M., Harrison, S. A., Brunt, E. M., \u0026amp; Sanyal, A. J. (2018). The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. \u003cem\u003eHepatology\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e, 328\u0026ndash;357.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, Z., Xue, J., Chen, P., Chen, L., Yan, S., \u0026amp; Liu, L. (2014). Prevalence of nonalcoholic fatty liver disease in mainland of China: A meta-analysis of published studies. \u003cem\u003eJ Gastroen Hepatol\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e, 42\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEslam, M., Valenti, L., \u0026amp; Romeo, S. (2018). Genetics and epigenetics of NAFLD and NASH: Clinical impact. \u003cem\u003eJournal Of Hepatology\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e, 268\u0026ndash;279.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYounossi, Z. M., Koenig, A. B., Abdelatif, D., Fazel, Y., Henry, L., \u0026amp; Wymer, M. (2016). Global epidemiology of nonalcoholic fatty liver disease\u0026mdash;Meta-analytic assessment of prevalence, incidence, and outcomes. \u003cem\u003eHepatology\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e, 73\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlkhouri, N., \u0026amp; Feldstein, A. E. (2016). Noninvasive diagnosis of nonalcoholic fatty liver disease: Are we there yet? \u003cem\u003eMetabolis\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e, 1087\u0026ndash;1095.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEstes, C., Razavi, H., Loomba, R., Younossi, Z., \u0026amp; Sanyal, A. J. (2018). Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease. \u003cem\u003eHepatology\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e, 123\u0026ndash;133.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuzzetti, E., Pinzani, M., \u0026amp; Tsochatzis, E. A. (2016). The multiple-hit pathogenesis of non-alcoholic fatty liver disease (NAFLD). \u003cem\u003eMetabolis\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e, 1038\u0026ndash;1048.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSookoian, S., \u0026amp; Pirola, C. J. (2017). Genetic predisposition in nonalcoholic fatty liver disease. \u003cem\u003eClin Mol Hepatology\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e, 1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeite, N. C., Salles, G. F., Araujo, A. L. E., Villela-Nogueira, C. A., \u0026amp; Cardoso, C. R. L. (2009). Prevalence and associated factors of non‐alcoholic fatty liver disease in patients with type‐2 diabetes mellitus. \u003cem\u003eLiver International : Official Journal Of The International Association For The Study Of The Liver\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e, 113\u0026ndash;119.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaballer\u0026iacute;a, L., Pera, G., Auladell, M. A., et al. (2010). Prevalence and factors associated with the presence of nonalcoholic fatty liver disease in an adult population in Spain. \u003cem\u003eEur J Gastroen Hepat\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 24\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarchesini, G., Marzocchi, R., Agostini, F., \u0026amp; Bugianesi, E. (2005). Nonalcoholic fatty liver disease and the metabolic syndrome. \u003cem\u003eCurrent Opinion In Lipidology\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 421\u0026ndash;427.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoria, P., Lonardo, A., \u0026amp; Carulli, N. (2005). Should Nonalcoholic Fatty Liver Disease Be Renamed? \u003cem\u003eDigest Dis\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e, 72\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliams, C. D., Stengel, J., Asike, M. I., Torres, D. M., Shaw, J., Contreras, M., Landt, C. L., \u0026amp; Harrison, S. A. (2011). Prevalence of Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis Among a Largely Middle-Aged Population Utilizing Ultrasound and Liver Biopsy: A Prospective Study. \u003cem\u003eGastroenterology\u003c/em\u003e, \u003cem\u003e140\u003c/em\u003e, 124\u0026ndash;131.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaligiuri, A., Gentilini, A., \u0026amp; Marra, F. (2016). Molecular Pathogenesis of NASH. \u003cem\u003eInternational Journal Of Molecular Sciences\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, 1575.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindenmeyer, C. C., \u0026amp; McCullough, A. J. (2018). The Natural History of Nonalcoholic Fatty Liver Disease\u0026mdash;An Evolving View. \u003cem\u003eClinics In Liver Disease\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 11\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrunt, E. M., \u0026amp; Tiniakos, D. G. (2010). Histopathology of nonalcoholic fatty liver disease. \u003cem\u003eWorld journal of gastroenterology\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 5286\u0026ndash;5296.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCobbina, E., \u0026amp; Akhlaghi, F. (2017). Non-alcoholic fatty liver disease (NAFLD) \u0026ndash; pathogenesis, classification, and effect on drug metabolizing enzymes and transporters. \u003cem\u003eDrug Metabolism Reviews\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e, 1\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSanyal, D., Mukherjee, P., Raychaudhuri, M., Ghosh, S., Mukherjee, S., \u0026amp; Chowdhury, S. (2015). Profile of liver enzymes in non-alcoholic fatty liver disease in patients with impaired glucose tolerance and newly detected untreated type 2 diabetes. \u003cem\u003eIndian J Endocrinol Metabolism\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e, 597\u0026ndash;601.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMofrad, P., Contos, M. J., Haque, M., Sargeant, C., Fisher, R. A., Luketic, V. A., Sterling, R. K., Shiffman, M. L., Stravitz, R. T., \u0026amp; Sanyal, A. J. (2003). Clinical and histologic spectrum of nonalcoholic fatty liver disease associated with normal ALT values. \u003cem\u003eHepatology\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e, 1286\u0026ndash;1292.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaximos, M., Bril, F., Sanchez, P. P., Lomonaco, R., Orsak, B., Biernacki, D., Suman, A., Weber, M., \u0026amp; Cusi, K. (2015). The role of liver fat and insulin resistance as determinants of plasma aminotransferase elevation in nonalcoholic fatty liver disease. \u003cem\u003eHepatology\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e, 153\u0026ndash;160.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFracanzani, A. L., Valenti, L., Bugianesi, E., et al. (2008). Risk of severe liver disease in nonalcoholic fatty liver disease with normal aminotransferase levels: A role for insulin resistance and diabetes. \u003cem\u003eHepatology\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e, 792\u0026ndash;798.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWong, V. W. S., Adams, L. A., de L\u0026eacute;dinghen, V., Wong, G. L. H., \u0026amp; Sookoian, S. (2018). Noninvasive biomarkers in NAFLD and NASH \u0026mdash; current progress and future promise. \u003cem\u003eNat Rev Gastroentero\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 461\u0026ndash;478.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMikolasevic, I., Milic, S., Wensveen, T. T., Grgic, I., Jakopcic, I., Stimac, D., Wensveen, F., \u0026amp; Orlic, L. (2016). Nonalcoholic fatty liver disease - A multisystem disease? \u003cem\u003eWorld J Gastroentero\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 9488\u0026ndash;9505.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChaurasia, B., Tippetts, T. S., Monibas, R. M., et al. (2019). Targeting a ceramide double bond improves insulin resistance and hepatic steatosis. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e365\u003c/em\u003e, 386\u0026ndash;392.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimons, K., \u0026amp; Ikonen, E. (1997). Functional rafts in cell membranes. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e387\u003c/em\u003e, 569\u0026ndash;572.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eICHI, I., NAKAHARA, K., FUJII, K., IIDA, C., MIYASHITA, Y., \u0026amp; KOJO, S. (2007). Increase of Ceramide in the Liver and Plasma after Carbon Tetrachloride Intoxication in the Rat. \u003cem\u003eJ Nutr Sci Vitaminol\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e, 53\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXia, J. Y., Morley, T. S., \u0026amp; Scherer, P. E. (2014). The adipokine/ceramide axis: Key aspects of insulin sensitization. \u003cem\u003eBiochimie\u003c/em\u003e, \u003cem\u003e96\u003c/em\u003e, 130\u0026ndash;139.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolland, W. L., Knotts, T. A., Chavez, J. A., Wang, L., Hoehn, K. L., \u0026amp; Summers, S. A. (2007). Lipid Mediators of Insulin Resistance. \u003cem\u003eNutrition Reviews\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e, S39\u0026ndash;S46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHammerschmidt, P., Ostkotte, D., Nolte, H., et al. (2019). CerS6-Derived Sphingolipids Interact with Mff and Promote Mitochondrial Fragmentation in Obesity. \u003cem\u003eCell\u003c/em\u003e, \u003cem\u003e177\u003c/em\u003e, 1536\u0026ndash;1552e23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaichur, S., Brunner, B., Bielohuby, M., Hansen, G., Pfenninger, A., Wang, B., Bruning, J. C., Larsen, P. J., \u0026amp; Tennagels, N. (2019). The role of C16:0 ceramide in the development of obesity and type 2 diabetes: CerS6 inhibition as a novel therapeutic approach. \u003cem\u003eMol Metab\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e, 36\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurpin-Nolan, S. M., Hammerschmidt, P., Chen, W., Jais, A., Timper, K., Awazawa, M., Brodesser, S., \u0026amp; Br\u0026uuml;ning, J. C. (2019). CerS1-Derived C18:0 Ceramide in Skeletal Muscle Promotes Obesity-Induced Insulin Resistance. \u003cem\u003eCell Reports\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e, 1\u0026ndash;10e7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGorden, D. L., Myers, D. S., Ivanova, P. T., et al. (2015). Biomarkers of NAFLD progression: a lipidomics approach to an epidemic. \u003cem\u003eJournal of Lipid Research\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e, 722\u0026ndash;736.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeyler, J., Verrijken, A., Hornemann, T., et al. (2020). Association of 1-deoxy-sphingolipids with steatosis but not steatohepatitis nor fibrosis in non-alcoholic fatty liver disease. \u003cem\u003eActa diabetologica\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e, 162\u0026ndash;169.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOthman, A., R\u0026uuml;tti, M. F., Ernst, D., et al. (2011). Plasma deoxysphingolipids: a novel class of biomarkers for the metabolic syndrome? \u003cem\u003eDiabetologia\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e, 421\u0026ndash;431.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBertea, M., R\u0026uuml;tti, M. F., Othman, A., Marti-Jaun, J., Hersberger, M., von Eckardstein, A., \u0026amp; Hornemann, T. (2010). Deoxysphingoid bases as plasma markers in Diabetes mellitus. \u003cem\u003eLipids in health and disease\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZitomer, N. C., Mitchell, T., Voss, K. A., et al. (2009). Ceramide Synthase Inhibition by Fumonisin B 1Causes Accumulation of 1-Deoxysphinganine. \u003cem\u003eThe Journal of biological chemistry\u003c/em\u003e, \u003cem\u003e284\u003c/em\u003e, 4786\u0026ndash;4795.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLone, M. A., Santos, T., Alecu, I., Silva, L. C., \u0026amp; Hornemann, T. (2019). 1-Deoxysphingolipids. \u003cem\u003eBiochimica et biophysica acta Molecular and cell biology of lipids\u003c/em\u003e, \u003cem\u003e1864\u003c/em\u003e, 512\u0026ndash;521.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerrijken, A., Francque, S., Mertens, I., Prawitt, J., Caron, S., Hubens, G., Marck, E. V., Staels, B., Michielsen, P., \u0026amp; Gaal, L. V. (2014). Prothrombotic factors in histologically proven nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. \u003cem\u003eHepatology\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e, 121\u0026ndash;129.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKleiner, D. E., Brunt, E. M., Natta, M. V., et al. (2005). Design and validation of a histological scoring system for nonalcoholic fatty liver disease. \u003cem\u003eHepatology\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e, 1313\u0026ndash;1321.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrunt, E. M., Janney, C. G., Bisceglie, A. M. D., Neuschwander-Tetri, B. A., \u0026amp; Bacon, B. R. (1999). Nonalcoholic Steatohepatitis: A Proposal for Grading and Staging The Histological Lesions. \u003cem\u003eAmerican Journal Of Gastroenterology\u003c/em\u003e, \u003cem\u003e94\u003c/em\u003e, 2467\u0026ndash;2474.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrunt, E. M., Kleiner, D. E., Wilson, L. A., Belt, P., Neuschwander-Tetri, B. A., \u0026amp; (CRN) NCRN. (2011). Nonalcoholic fatty liver disease (NAFLD) activity score and the histopathologic diagnosis in NAFLD: distinct clinicopathologic meanings. \u003cem\u003eHepatology\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e, 810\u0026ndash;820.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLone, M. A., H\u0026uuml;lsmeier, A. J., Saied, E. M., Karsai, G., Arenz, C., von Eckardstein, A., \u0026amp; Hornemann, T. (2020). Subunit composition of the mammalian serine-palmitoyltransferase defines the spectrum of straight and methyl-branched long-chain bases. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e, \u003cem\u003e117\u003c/em\u003e, 15591\u0026ndash;15598.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaikkonen, J. E., W\u0026uuml;rtz, P., Suomela, E., et al. (2017). Metabolic profiling of fatty liver in young and middle-aged adults: Cross‐sectional and prospective analyses of the Young Finns Study. \u003cem\u003eHepatology\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e, 491\u0026ndash;500.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYamakado, M., Tanaka, T., Nagao, K., et al. (2017). Plasma amino acid profile associated with fatty liver disease and co-occurrence of metabolic risk factors. \u003cem\u003eSci Rep-uk\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 14485.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHasegawa, T., Iino, C., Endo, T., Mikami, K., Kimura, M., Sawada, N., Nakaji, S., \u0026amp; Fukuda, S. (2020). Changed Amino Acids in NAFLD and Liver Fibrosis: A Large Cross-Sectional Study without Influence of Insulin Resistance. \u003cem\u003eNutrients\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 1450.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePietzner, M., Budde, K., Homuth, G., et al. (2018). Hepatic Steatosis Is Associated With Adverse Molecular Signatures in Subjects Without Diabetes. \u003cem\u003eJ Clin Endocrinol Metabolism\u003c/em\u003e, \u003cem\u003e103\u003c/em\u003e, 3856\u0026ndash;3868.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGaggini, M., Carli, F., Rosso, C., et al. (2018). Altered amino acid concentrations in NAFLD: Impact of obesity and insulin resistance. \u003cem\u003eHepatology\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e, 145\u0026ndash;158.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTidhar, R., Ben-Dor, S., Wang, E., Kelly, S., Merrill, A. H., \u0026amp; Futerman, A. H. (2011). Acyl Chain Specificity of Ceramide Synthases Is Determined within a Region of 150 Residues in the Tram-Lag-CLN8 (TLC) Domain. \u003cem\u003eJournal of Biological Chemistry\u003c/em\u003e, \u003cem\u003e287\u003c/em\u003e, 3197\u0026ndash;3206.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChashmniam, S., Ghafourpour, M., Farimani, A. R., Gholami, A., \u0026amp; Ghoochani, B. F. N. M. (2019). Metabolomic Biomarkers in the Diagnosis of Non-Alcoholic Fatty Liver Disease. Hepat Mon. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5812/hepatmon.92244\u003c/span\u003e\u003cspan address=\"10.5812/hepatmon.92244\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoffredo, M., Santoro, N., Tric\u0026ograve;, D., et al. (2017). A Branched-Chain Amino Acid-Related Metabolic Signature Characterizes Obese Adolescents with Non-Alcoholic Fatty Liver Disease. \u003cem\u003eNutrients\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 642.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHornemann, T., Alecu, I., Hagenbuch, N., et al. (2018). Disturbed sphingolipid metabolism with elevated 1-deoxysphingolipids in glycogen storage disease type I \u0026ndash; A link to metabolic control. \u003cem\u003eMolecular Genetics And Metabolism\u003c/em\u003e, \u003cem\u003e125\u003c/em\u003e, 73\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMelis, D., Rossi, A., Pivonello, R., et al. (2015). Glycogen storage disease type Ia (GSDIa) but not Glycogen storage disease type Ib (GSDIb) is associated to an increased risk of metabolic syndrome: possible role of microsomal glucose 6-phosphate accumulation. \u003cem\u003eOrphanet Journal Of Rare Diseases\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMardinoglu, A., Bjornson, E., Zhang, C., et al. (2017). Personal model-assisted identification of NAD\u0026thinsp;+\u0026thinsp;and glutathione metabolism as intervention target in NAFLD. \u003cem\u003eMolecular Systems Biology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 916.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7435578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7435578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) (formerly Non-Alcoholic Fatty Liver Disease, NAFLD) encompasses a spectrum of metabolic disorders ranging from isolated steatosis to Metabolic Dysfunction-Associated Steatohepatitis (MASH), potentially progressing to cirrhosis and hepatocellular carcinoma (HCC).\u003c/p\u003e\u003cp\u003eIn this study, we investigated metabolic changes in MASLD by analysing plasma lipidomics and metabolomics profiles from 315 biopsy-characterized patients. We observed significant alterations in alanine/serine (ala/ser) ratio, 1-deoxysphingolipids, alanine aminotransferase (ALT), and waist/hip ratio (whr) between patients with and without MASH. These findings highlight a close interplay between amino acid metabolism and sphingolipid biosynthesis in MASLD progression. The shift in ala/ser ratio particularly distinguished non-MASH from borderline MASH, suggesting that early metabolic disruptions precede overt liver damage.\u003c/p\u003e\u003cp\u003eAdditionally, elevated branched-chain and aromatic amino acids correlated with steatosis severity, reinforcing the central role of amino acid dysregulation in MASLD.\u003c/p\u003e\u003cp\u003eWhile a simple model combining ALT, ala/ser ratio, and whr showed some potential to support risk stratification, the primary significance of our findings lies in the mechanistic insights into metabolic dysfunction. In conclusion, this study emphasizes the importance of metabolic network alterations in MASLD and points toward future opportunities for both mechanistic research and the development of supportive diagnostic strategies\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Increased 1-deoxySphingolipids caused by an altered plasma Alanine to Serine ratio are associated with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:23:21","doi":"10.21203/rs.3.rs-7435578/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-10-01T10:30:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T12:01:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332460680237764577876719015155423147406","date":"2025-08-23T16:10:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-23T15:26:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-23T09:19:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-23T09:18:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Metabolomics","date":"2025-08-22T14:51:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"23462ffc-d598-4bb2-802f-3cb7c885b50a","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:02:39+00:00","versionOfRecord":{"articleIdentity":"rs-7435578","link":"https://doi.org/10.1007/s11306-025-02359-4","journal":{"identity":"metabolomics","isVorOnly":false,"title":"Metabolomics"},"publishedOn":"2025-11-02 15:58:15","publishedOnDateReadable":"November 2nd, 2025"},"versionCreatedAt":"2025-09-01 10:23:21","video":"","vorDoi":"10.1007/s11306-025-02359-4","vorDoiUrl":"https://doi.org/10.1007/s11306-025-02359-4","workflowStages":[]},"version":"v1","identity":"rs-7435578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7435578","identity":"rs-7435578","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}