An age-independent MASLD-related liver fibrosis index reflecting gut dysbiosis and hepatic stellate cells reprogramming

An age-independent MASLD-related liver fibrosis index reflecting gut dysbiosis and hepatic stellate cells reprogramming | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article An age-independent MASLD-related liver fibrosis index reflecting gut dysbiosis and hepatic stellate cells reprogramming Daniel Cicero, Serena Zampieri, Greta Petrella, Elisa Nagni, Laura Micheli, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5268526/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Background The burden of metabolic dysfunction-associated steatotic liver disease (MASLD) is of immediate concern, as its prevalence is increasing worldwide. MASLD often progresses to liver fibrosis, posing significant health risks. Age-independent non-invasive tools to evaluate fibrosis are needed to improve diagnostic accuracy across all age groups. Methods. 84 inflammatory, hematological, and metabolic variables were quantified in the blood of n = 63 individuals with MASLD with different degrees of fibrosis and n = 22 age-matched controls. Linear regression models were employed to identify markers strongly correlated with liver fibrosis but not influenced by age. Logistic regression models were used to evaluate the ability of various indexes to discriminate between no/mild and severe liver fibrosis. Results. Levels of glutamine and propionate were identified as strongly correlated to fibrosis but not age and combined to form the GP index. The GP index demonstrated superior predictive power for liver fibrosis compared to existing scores, like circulating creatinine. It showed higher discriminatory ability (AUC = 0.872) and better model fit, indicating its robustness and reliability across all age groups. Conclusions. The study introduces the GP index, an age-independent tool for diagnosing and monitoring liver fibrosis in MASLD patients. By excluding age-dependent markers, the GP index can potentially reduce false positives and improve diagnostic accuracy, particularly in older populations. The combination of glutamine and propionate in this index reflects a novel approach, capturing both intrinsic hepatic metabolic changes and extrinsic influences from gut microbiota, offering a simple yet effective solution for liver fibrosis staging. Biological sciences/Biochemistry/Metabolomics Biological sciences/Physiology/Metabolism Metabolic associated steatotic liver disease fibrosis glutamine propionate gut-liver axis glutaminolysis Figures Figure 1 Figure 2 Figure 3 Introduction The increase in the prevalence of Metabolic-associated steatotic liver disease (MASLD) constitutes a major public health problem. Historically, it was defined as non-alcoholic fatty liver disease (NAFLD) and diagnosed by exclusion of other causes of chronic liver disease. In 2023, a multi-society consensus statement introduced the term MASLD 1 , which includes adults with excessive fat accumulation in the liver (hepatic steatosis) in the presence of at least one cardiometabolic risk factor and in the absence of excessive alcoholic intake. Because of the global epidemic of metabolic disorders 2 MASLD has surged to be the most common cause of chronic liver diseases, posing significant challenges in healthcare management. Progression of MASLD leads to liver fibrosis, characterized by abnormal accumulation of extracellular matrix proteins, which triggers liver scarring and functional degradation 3 . Its escalation could lead to more severe complications, including cirrhosis, liver failure, and hepatocellular carcinoma 4 . Fibrosis caused by MASLD combines metabolic dysregulation and pathologic tissue remodeling; thus, timely intervention is needed to stop disease progression. The gold standard for diagnosing and monitoring liver fibrosis is liver biopsy. Although informative, it is an invasive procedure that carries risks such as pain, bleeding, and infection. For this reason, significant effort was made to discover accurate, non-invasive diagnostic tools for liver fibrosis to enhance patient safety and clinical management 5 . These methods, including blood-based biomarkers and elastography, provide valuable tools. Still, they have limitations, particularly in their efficiency for screening and early detection of liver fibrosis in the general population 6 . Their use is particularly challenging in the context of an aging population. Aging impacts metabolic homeostasis and complicates MASLD's pathophysiology, making treatment and management more difficult 7 . It was recently shown that existing fibrosis scores have lower accuracy in elderly patients, leading to the need for revised or new scoring systems that can more accurately predict fibrosis without being skewed by age-related factors 8 . Existing non-invasive fibrosis scores, such as the Fibrosis-4 (FIB-4) index 9 , tend to have lower specificity and higher false-positive rates in elderly patients, as they were primarily validated in younger populations. More recently, the Fibrosis-3 (FIB-3) index was developed to overcome this limitation by excluding age from the formula 10 . This index, together with others presented previously 11 , 12 , highlights the importance of creating scores that exclude age. This is crucial because age is not linearly correlated with the extent of liver fibrosis, leading to potential inaccuracies when using age-dependent indices across various age groups. In this context, we aimed to develop an age-independent MASLD-related liver fibrosis index using a comprehensive inflammatory, hematological, and metabolic marker panel. The analysis of hemogram features, such as platelet count and mean corpuscular volume, can provide valuable insights into the extent of liver damage and potential complications like thrombocytopenia 13 . Inflammation is important in advancing liver fibrosis, with inflammatory mediators triggering fibrogenesis 14 . Metabolic markers can potentially reveal disruptions in metabolic functions that commonly accompany liver fibrosis 15 . Using such a broad source of blood-based markers increases the chances of finding variables related to different physiological aspects of the disease that can be combined into a more efficient score that can be applied to patients regardless of age. Materials and Methods Study population The "Integrated phenotyping of the Gut-plAtelet-Liver AXIS in the progression of chronic liver disease" (iGAL-AXIS)" project is an observational, prospective study aimed at exploring the relationship between gut dysbiosis, metabolome composition, inflammation, and platelet activation in chronic liver disease. Individuals (> 18 years) diagnosed with MASLD based on EASL guidelines 16 were enrolled and classified according to the level of fibrosis. Specifically, MASLD was defined by the presence of hepatic steatosis, occurring in subjects with at least one cardiometabolic risk factor and the absence of significant alcohol intake (greater than 20 grams per day for women and 30 grams per day for men) 17 . The study accounted for several covariates: age, sex, body mass index (BMI), diabetes, hypertension, dyslipidemia, and medication use. The study was conducted in full conformance with the principles of the Declaration of Helsinki, and it was approved by the local Ethics Committee of Policlinico Umberto I, Sapienza University of Rome, Rome, Italy (reference 6804, 09/11/2022). Written informed consent has been obtained from all patients. Assessment of Liver steatosis and fibrosis Vibration-controlled transient elastography (FibroScan, Echosens, Paris, France) was used for liver assessment, employing both M and XL probes and an automatic probe selection tool that determines the most suitable probe based on real-time measurements of the skin-to-liver capsule distance. The procedure was conducted according to the manufacturer's guidelines and training. Controlled attenuation parameter (CAP) measurements for assessing steatosis and liver stiffness measurements (LSM) for evaluating fibrosis were taken until ten valid readings were achieved for each patient. The operator was blinded to all clinical data and patient diagnoses. The presence of steatosis was considered for a CAP higher than 275 dB/m 18 , and fibrosis staging by transient elastography was classified using the following thresholds: 8.2, 9.7, and 13.6 for diagnosing ≥ F2, ≥F3, and F4, respectively 19 . Hemogram and Inflammatory profiling At enrollment, venous blood samples were collected from study participants. EDTA-anticoagulated blood was used to obtain the hemogram profile on a Sysmex hematology analyzer. Blood without anticoagulants was allowed to clot, and serum was separated by centrifugation at 3400 g for 20 min. Coded samples were stored at -80°C until batch analysis. The concentration of inflammatory cytokines was assessed in serum samples by multiplex bead-based flow cytometric assay (Biolgend, Inflammation Panel I, catalog number 740809), according to the manufacturer's instructions. Briefly, after thawing, the serum samples were immediately centrifuged at maximum speed and transferred to new tubes. A small volume of each serum (25ul) was diluted 1:1 in the Assay buffer provided in the kit. Each serum was incubated with 13 bead populations distinguished by size and internal APC fluorescent dye, which bind to 13 distinct human inflammatory cytokines and chemokines, including IL-1β, IFN-α2, IFN-γ, TNF-α, MCP-1 (CCL2), IL-6, IL-8 (CXCL8), IL-10, IL-12p70, IL-17A, IL-18, IL-23, and IL-33. The following day, the beads were incubated first with cytokine-specific biotinylated antibodies and then with Streptavidin-phycoerythrin, and they were immediately acquired at a BD Accuri C6 Plus flow cytometer. Cytokine-specific populations were segregated based on the size and internal APC fluorescence intensity. The concentration of a particular cytokine was quantified based on the PE fluorescent signal according to a standard curve generated in the same assay. Measurements were ascertained while blinded to the sample origin. All samples were assayed in duplicate, and those showing values above the standard curve were retested with appropriate dilutions. 1 H-NMR spectroscopy-based metabolomics Thawed serum samples underwent protein removal using 3 kDa cut-off Amicon Ultra-0.5 centrifugal filter devices. The filters were washed four times with distilled water to eliminate glycerol (13800 g, 4°C for 20 min), followed by centrifugation at 13800 g for 90 min. NMR buffer (250 mM phosphate buffer KH 2 PO 4 /K 2 HPO 4 , pH 7.4, containing 0.82 mM sodium trimethylsilyl propanoate- d 4 , 10% D 2 O, and 2% NaN 3 ) was added to each filtered serum to reach a final volume of 600 µL. The resultant solution was then transferred into a 5 mm NMR tube. The 1 H-NMR spectra for each sample were obtained using a Bruker Avance 700 MHz spectrometer equipped with a triple resonance TXI probe and a SampleXpress Lite autosampler. The spectra were collected at 25°C using a noesypr1d sequence, with 1024 scans, four dummy scans, a spectral width of 16 ppm, an acquisition time of 2s, a relaxation delay of 3s, and a mixing time of 100 ms. Following the acquisition, the spectra were processed with 0.5 Hz of line-broadening, and then manual phase and baseline correction were performed. Chenomx NMRSuite 8.5 (Chenomx Inc.) was used to quantify metabolites, with TSP- d 4 serving as the internal standard. 52 metabolites were quantified using this method in almost all samples. Data analysis All statistical analyses were conducted using R v4.3.1 ( https://www.r-project.org ), and plots were created using Excel. All inferential tests are two-tailed with a nominal alpha level of 0.05. Due to the analyses' exploratory nature, no multiplicity adjustments were made. The metabolite concentrations were normalized before statistical analysis using probabilistic quotient normalization 20 , and the data from the inflammation panel were log-transformed. The combined data sets, including the hemogram, inflammation panel, and metabolic panel, comprised 84 variables. Additionally, variables describing the demographic and health conditions of the subjects, such as age, sex (1 for female), BMI, fibrosis, diabetes, hypertension, and dyslipidemia were included in the analysis. The fibrosis variable was created by assigning a value of 0 to the control group and values of 1 (F = 0,1) through 4 (F = 2,3,4) based on the fibrosis scale 21 . Table S1 shows all the data used in this analysis. Before calculating the interaction terms, the collinearity of the variables was tested. As age and fibrosis were found to be collinear, all the conditions were scaled using unit variation (UV) 22 . Then, we calculated and UV-scaled the interaction term between age and fibrosis. Finally, we UV-scaled all the measured variables. This ensures that the coefficients will be comparable and allows the determination of the relative importance of the effect. To compute the profile linked to individual conditions, each of the 84 measured variables ( M i ) was linearly correlated with the seven parameters ( C j ) describing the previously mentioned demographic and health conditions, along with the interaction term, following the equation [1] as previously described 23 : For each of the 84 equations, the values of the nine coefficients, their standard errors, t-values, p-values, and 95% CI were calculated, and the complete results are shown in Table S2 . The set of metabolites showing statistically significant association with a given condition represents its corresponding profile. Using this approach, we explored the correlation between variable levels and fibrosis stages across the entire F0-F4 spectrum. This model, described by equation [1], obviated the need to convert the fibrosis condition into a binary variable as necessitated by the more frequently employed logistic regression technique. In evaluating the predictive ability of various indexes for explaining the severity of liver fibrosis (F = 0–1 versus F = 2–4), an unmatched logistic regression model was employed with and without adjustments for age and sex. To address potential issues of bias and separation in logistic regression, particularly in small sample sizes or when events are rare, we utilized Firth's method, a penalized maximum likelihood logistic regression implemented through the logistf function. This same approach was used to construct a mixed model combining metabolite concentrations. The best mixed model was selected by comparing all the possible combinations, from single markers to the total number of variables considered. We applied different methods to compare the effectiveness of various indexes. Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) 24 , together with Cross-Validation Accuracy (CVA) 25 , were used to provide insights into the model fit and predictive performance. The pseudo-R 2 statistics measure the model's explanatory power, indicating how well the model explains the variance in the dependent variable 26 . Finally, DeLong's non-parametric test was used to compare the AUCs of two ROC curves to evaluate the discriminative ability of the models. To determine the best model considering all the metrics, we created a combined index that considers each metric's relative importance. Pseudo-R 2 , CVA, and AUC values were scaled between 0 and 1, where 1 represents the best performance. We used an inverted scale for AIC and BIC so that the lower AIC and BIC scores, which indicate models that best explain the data with the fewest parameters, correspond to higher normalized values. The final combined index was obtained by averaging the five scaled parameters. To test whether the inclusion of age or age and sex as covariants provides a better index, we used the Likelihood Ratio Test (LRT) and its associated p-value 27 . Results Study participants This study included 63 adults diagnosed with MASLD and 22 controls. Transient elastography analysis showed that out of 63 subjects with steatosis, 38 had no/mild fibrosis (F0-1), and 25 had significant fibrosis (consisting of F2 = 10, F3 = 7, F4 = 8). The characteristics of the study participants are summarized in Table 1 . There were no significant differences among the groups regarding triglyceride levels. Groups with substantial fibrosis (F2, F3, F4) were more likely males, which may impact the identification of potential markers associated with liver fibrosis, potentially leading to a higher proportion of biases toward male characteristics in the responses. Patients with advanced fibrosis (F3) had more frequent Type 2 diabetes,. Table 1 Characteristics of patients with different fibrosis stages and controls. CTR F0-1 F2 F3 F4 Subjects, n (%) 22 (26) 38 (45) 10 (12) 7 (8) 8 (9) Age (years), mean (min-max) 57 (28–75) 58 (33–75) 52 (19–66) 41 (19–57)* 62 (40–80) Males, n (%) 10 (45) 19 (50) 6 (60) 6 (86)* 6 (75) BMI (Kg/m 2 ), mean ± SD 26 ± 3 28 ± 4* 30 ± 9 29 ± 5 32 ± 2** Triglycerides (mg/dL), mean ± SD 109 ± 43 122 ± 60 105 ± 37 120 ± 38 131 ± 36 Comorbidities Diabetes, n (%) 1 (5) 12 (32)** 5 (50)* 1 (14) 5 (63)* Hypertension, n (%) 12 (55) 23 (61) 5 (50) 3 (43) 3 (38) Dyslipidemia, n (%) 14 (64) 23 (61) 3 (30) 3 (43) 5 (63)** Valvular Insufficiency, n (%) 2 (9) 5 (13) 1 (10) 1 (14) 0 (0) Drug Treatments ACE a Inhibitors, n (%) 5 (23) 11 (29) 2 (20) 2 (29) 0 (0)* ARB b , n (%) 2 (9) 6 (16) 0 (0) 0 (0) 0 (0) Metformin, n (%) 0 (0) 5 (13)* 1 (10) 0 (0) 2 (25) b-blockers, n (%) 2 (9) 3 (8) 0 (0) 1 (14) 0 (0) CCB c , n (%) 2 (9) 2 (5) 0 (0) 1 (14) 0 (0) a ACE: angiotensin-converting enzyme; b ARB: angiotensin receptor blockers; C CCB: calcium channel blockers. Student’s t-test was calculated for each fibrosis group with respect the controls: *0.05 < p-value < 0.01, **p-value < 0.01 A comprehensive Inflammatory and Hematological-Metabolic Panel (IHMP) We tracked the progression of liver fibrosis related to MASLD by measuring a wide range of potential markers. We gathered data on circulating cell types and physical characteristics through a comprehensive blood test analysis. In addition, we evaluated the levels of 13 inflammatory cytokines. Additionally, we identified and quantified 52 blood-circulating metabolites in a single experiment using NMR. They belong to different chemical classes, including hydroxy-, keto-, carboxylic-, amino-acids, amine derivatives, carbohydrates, and organic nitrogen compounds. Table 2 presents the complete list of variables investigated for their association with the level of liver fibrosis. These variables constitute an Inflammatory and Hematological-Metabolic Panel (IHMP) encompassing 84 variables. The complete dataset is available in Table S1 . Table 2 The 84 variables constituting the IHMP and used for the analysis. Metabolic panel Hemogram Inflammation panel Metabolite Abbr. Metabolite Abbr. Parameter Abbr. Protein Abbr. 2-Hydroxybutyrate 2-HB Histidine His White Blood Cells WBC Interleukin-1 beta IL-1β 2-Hydroxyisovalerate 2-HIV Hypoxanthine Hyp Red Blood Cells RBC Interleukin-6 IL-6 2-Oxoisocaproate 2-Oxo-IC Isobutyrate iBu Hemoglobin HGB Interleukin-8 IL-8 3-Hydroxybutyrate 3-HB Isoleucine Ile Hematocrit HCT Interleukin-10 IL-10 3-Hydroxyisobutyrate 3-HIB Lactate Lac Mean Corpuscular Volume MCV Interleukin-12p70 IL-12p70 3-Hydroxyisovalerate 3-HIV Leucine Leu Mean Corpuscular Hemoglobin MCH Interleukin-17A IL-17A 3-Methyl-2-oxovalerate 3-MOV Lysine Lys Mean Corpuscular Hemoglobin Concentration MCHC Interleukin-18 IL-18 Acetate Ac Methionine Met Platelets PLT Interleukin-23 IL-23 Alanine Ala Methylguanidine MG Percentage of Lymphocytes LYM % Interleukin-33 IL-33 Arginine Arg myo-Inositol mIns Percentage of Mixed White Blood Cells MXD % Interferon-alpha 2 IFN-α2 Asparagine Asn N,N-Dimethylglycine DMG Percentage of Neutrophiles NEUT % Interferon-gamma IFN-γ Aspartate Asp O-Acetylcarnitine OAC Lymphocytes LYM Tumor Necrosis Factor-alpha TNF-α Betaine Bet Ornithine Orn Mixed White Blood Cells Count MXD Monocyte Chemoattractant Protein-1 MCP-1 Butyrate But Phenylalanine Phe Neutrophils NEUT Carnitine Car Proline Pro Red Cell Distribution Width - Standard Deviation RDW-SD Choline Cho Propionate Prop Red Cell Distribution Width - Coefficient of Variation RDW-CV Citrate Cit Pyroglutamate PyrGlu Platelet Distribution Width PDW Creatine Cr Pyruvate Pyr Mean Platelet Volume MPV Creatinine Crn Sarcosine Sar Platelet Large Cell Ratio P_LCR Dimethylamine DMA Serine Ser Dimethyl sulfone DMS Taurine Tau Formate For Threonine Thr Glucose Glc Trimethylamine TMA Glutamate Glu Tyrosine Tyr Glutamine Gln Urea Ure Glycine Gly Valine Val The IHMP-based profile of liver fibrosis In the initial analysis stage, we utilized the levels of the 84 features in the IHMP as dependent variables to examine their correlation with liver fibrosis based on the F0-F4 scale, as described in Materials and Methods. According to equation [1], fibrosis, age, diabetes, hypertension, dyslipidemia, and the interaction term between age and fibrosis were considered the independent variables. With this approach, we sought the linear combination of conditions that best explains the distribution of each IHMP feature's levels. The analysis output included coefficients displaying the strength and direction of the relationship between each independent and dependent variable. Their associated p-values were utilized to determine the significance of these relationships. The comprehensive results of this analysis are listed in Table S2. Then, we generated plots 28 to concurrently evaluate the effect size, direction, and statistical significance of the association of each IHMP feature with liver fibrosis and the interaction term age x fibrosis (Figures 1A-1B). Figure 1C summarizes the findings of this evaluation. We could classify the variables into three groups according to their associations. The first group comprises 21 variables correlating only with fibrosis, i.e. sensitive to disease progression but not to age. Pathways analysis of the fibrosis-related metabolite changes reflects a broad involvement of energy production pathways (Pyr, Ac, For, 2-HB, Cit), increased amino acid turnover (Glu, Gln, Tyr, Ile, Asn, Asp, Met, 2-HIV), extracellular matrix remodeling (Hyp, PyrGlu), urea cycle function (Urea, Asn, Asp), sulfur metabolism (DMS, Met), alterations in gut microbiome activity (But, iBu, Prop), and systemic responses to liver injury (Crn). A second group comprises six variables correlating only with the interaction term age x fibrosis. The identity of these features suggests that the combined or synergistic effect of aging and fibrosis may exacerbate systemic inflammation (IL-8), impact amino acid metabolism (3-HIV), influence gut microbiota composition or function (TMA), and alter the RBC turnover (MCV). Finally, we found two variables (Phe and RDW-CV) associated with fibrosis and age x fibrosis that are related to amino acid metabolism (Phe) and dysfunctional RBC turnover (RDW-CV). To derive an index that scores liver fibrosis independent of age, we concentrated our attention only on variables of the first group. Based on Figure 1A, the metabolites that exhibit the most significant association within this group are Crn, Glu, Gln, and three Short-Chain Fatty Acids (SCFAs): But, iBu, and Prop. Figure 2 shows their concentration changes as a function of the liver fibrosis index. We observed an increase in circulating levels of Glu and SCFAs and a decrease in Gln and Crn as a function of fibrosis severity. A combined metabolic score to stage age-independently liver fibrosis associated with MASLD: the GP index The six metabolites chosen for their significant correlation with the fibrosis variable are strong candidates for creating a mixed model, increasing their predictive power when combined. We generated all potential combinations of these six variables to extract the best combination and employed various metrics, such as pseudo-R 2 , AIC, BIC, CVA, and AUC, for candidate comparison. Higher pseudo-R 2 values signify a more comprehensive explanation of outcome variance, while lower AIC and BIC values suggest improved model fit and simplicity. CVA estimates predictive capability, while AUC measures class distinguishability. We excluded the control group to perform these calculations and divided individuals with MASLD into two groups based on their fibrosis level: none/mild (F=0,1) and severe (F=2-4). We assessed all 63 possible combinations, with Table 3 showcasing the top 5 performers in terms of a combined index (CI) that merges all the metrics as defined in Materials and Methods. Table 3. Comparison of the top-five predictive models based on a combined index (CI) that accounts for Pseudo-R², AIC, BIC, AUC, and CVA Model Pseudo-R 2 AIC BIC CVA AUC CI Gln + Prop 0.359 60.2 66.6 0.823 0.872 0.982 Glu + Gln + Prop 0.368 61.5 70.1 0.785 0.873 0.917 Gln + iBu + Prop 0.361 62.1 70.6 0.767 0.871 0.887 Crn + Gln + Prop 0.363 61.9 70.5 0.749 0.875 0.881 Glu + Gln + But + Prop 0.374 63.0 73.7 0.774 0.875 0.870 This data suggests that Gln + Prop can be regarded as the best mixed model. It has the lowest AIC and BIC, indicating the best balance between model fit and complexity. It also has the highest CVA and CI, making it the most robust model overall. Although the Glu + Gln + But + Prop model has a similar AUC, indicating equal discriminative ability, its higher AIC and BIC suggest it might be a more complex model without providing a significantly better fit. The coefficients of this index, called GP, are shown in Table 3. Table 3 . Calculated mixed model coefficients and related parameters for GP index construction Variable b SE [0.025] [0.975] z-value p-value Gln -0.006 0.002 -0.010 -0.001 -2.521 0.012 Prop 0.266 0.099 0.072 0.460 2.690 0.007 We then investigated whether adding age or age and sex as covariates improves the predictive performance of the GP model for liver fibrosis. The analysis involved comparing the base model against two other models. Table 5 summarizes the metrics used for the comparisons. Table 5. Comparison of the Predictive Performance of the GP Model with and without Age and Sex as Covariates for Predicting Liver Fibrosis Model Pseudo-R 2 AIC BIC AUC LRT p-value GP 0.359 60.2 66.6 0.872 - GP + Age 0.385 60.1 68.6 0.877 0.142 GP + Age + Sex 0.401 60.7 71.4 0.884 0.173 Given the minimal improvement in metrics such as Pseudo-R 2 , AIC, and AUC, coupled with the non-significant LRT p-values, we can conclude that the inclusion of age as a covariate does not significantly enhance the performance of the GP model in distinguishing MASLD patients with different levels of fibrosis. In addition, while sex can be included in the model, it does not appear to be a crucial factor, and the GP model can perform effectively without it. We cannot consider this last result as definitive because of the low presence of females with advanced fibrosis or cirrhosis in our cohort (Table 1). Statistical evaluation of Crn, Glu/Gln, SCFAs, BTR, and GP performance in predicting liver fibrosis The performance of the GP index was compared with other scores from existing literature, such as serum Crn 29 , the Glu/Gln ratio 30 , and SCFA 31 . To perform the calculations, the SCFA score was defined as the sum of But, iBu, and Prop concentrations weighted with the coefficients that resulted in the best mixed model involving these metabolites in the previous calculations. Studies have also suggested that changes in plasma BCAAs may aid in diagnosing the degree of liver fibrosis through the BCAAs-to-Tyr ratio (BTR) 32–35 . Although only Ile exhibited a correlation with fibrosis in our cohort, and Tyr showed a less pronounced correlation, we decided to include it, as we found the expected decrease in Ile and increase in Tyr concentration. Using multiple criteria, these four scores were assessed and compared with the combined GP index. All the scores were scaled using unit variation to ensure a proper comparison of the Odd Rates (ORs). The detailed results are presented in Table 6. Table 6. League Table Showing Comparisons Among Each Fibrosis Index Cut-off value (sen/spe [%]) a AUC (95% CI) CVA OR b (95% CI) p-value (OR) BTR Crn Glu/Gln SCAFs p-value BTR 0.442 (36/87) 0.591 (0.436-0.745) 0.638 0.75 (0.45-1.26) 0.27 Crn 0.469 (48/82) 0.646 (0.505-0.788) 0.614 0.63 (0.37-1.06) 0.076 0.485 Glu/Gln 0.338 (88/76) 0.797 (0.677-0.917) 0.692 6.13 (1.12-33.59) 0.010 0.059 0.112 SCAFs 0.295 (88/68) 0.820 (0.722-0.933) 0.741 4.96 (2.17-11.30) <0.001 0.007 0.026 0.916 GP 0.315 (88/71) 0.872 (0.786-0.959) 0.823 7.28 (2.73-19.45) <0.001 <0.001 0.002 0.121 0.144 a Cut-off value and sensitivity to specificity ratio expressed in percentage. b To properly compare the Odds Ratios, all the indexes were scaled using unit variation. Odds Ratio values < 1 are associated with indexes negatively correlated with fibrosis. The results indicate that GP emerges as the most effective fibrosis index, with the highest AUC (0.873), demonstrating its ability to differentiate between patients with severe fibrosis and those with none or mild fibrosis. It shows the highest OR (7.28), with a significant associated p-value (<0.001), which further validates its predictive strength. GP also displays the highest CVA value (0.823), indicating that this index generalizes well to new, unseen data, reducing the risk of overfitting. Despite the lack of statistical significance in direct comparisons with SCAFs and Glu/Gln, their lower AUC, OR, and CVA values suggest slightly reduced diagnostic precision compared to GP. In contrast, BTR and Crn display much lower predictive power, with AUCs of 0.591 and 0.646 and non-significant p-values, making them less reliable for fibrosis prediction. An important advantage of GP over other indexes is its consistent and significant variation across the entire fibrosis scale utilized in this study. Figure 3 illustrates the behavior of four indexes in a scatter plot concerning the fibrosis scale, while the corresponding scatter plot for Crn is displayed in Figure 2. These graphs demonstrate that Crn, BTR, and Glu/Gln scores primarily differentiate between cirrhosis (F4) and all other fibrosis stages. The SCFAs index performs relatively well, but our dataset does not exhibit an increase between F2 and F3. In contrast, GP is the sole score that consistently increases across the entire scale, which presents a significant advantage, particularly when utilizing the index to monitor the complete progression of the disease. Discussion Our investigation sought to assess a range of potential biomarkers suitable for developing a composite index for the independent detection of severe fibrosis, irrespective of age-related factors. Recent guidelines recommend use of non-invasive blood-based biomarkers for fibrosis evaluation in metabolic liver disease patients 36 . To improve the chances of discovering reliable markers, we merged data from three sources: the hemogram, the inflammation panel, and the metabolic panel, collectively named the IHMP. At first, we utilized a multiple linear regression model similar to that we recently developed to investigate the metabolic crosstalk in multimorbidity 23 . It included the level of liver fibrosis, age, sex, BMI, diabetes, dyslipidemia, and hypertension as independent variables and the 84 feature levels from the IHMP as dependent variables. Including comorbidities such as diabetes, dyslipidemia, and hypertension in the linear regression model enhances the model's accuracy and specificity by accounting for the potential confounding effects of these metabolic conditions on liver health, thereby allowing for more precise isolation of the direct markers of fibrosis. We also incorporated the interaction term between age and fibrosis to identify markers that exhibit distinct changes in different age groups. In this way, we divided the variables into three groups. The first group comprises 21 variables correlating only with fibrosis, not with the interaction term age x fibrosis. These variables are sensitive to disease progression independently of age. The second group consists of variables correlating only with the interaction term, reflecting the combined effects of aging and fibrosis. The third group includes variables associated with both fibrosis and age x fibrosis and includes factors sensitive to disease progression but dependent on age. The last group of variables showed increased IL-8 concentration, a cytokine linked to liver fibrosis. Our findings suggest that aging and fibrosis together have a more significant effect on IL-8 levels, exacerbating inflammation and liver fibrosis in older individuals 37 , 38 . This interaction aligns with the concept of "inflammaging," contributing to age-related diseases 39 . Our study, together with others 40 – 42 , indicates that interleukin levels may serve as non-invasive markers for liver fibrosis, but their relationship with fibrosis is conditional upon age. The correlation between RDW-SD and the age-fibrosis interaction is due to a link between erythropoiesis and the inflammatory response. Inflammation increases oxidative stress and leads to dysregulation and increased iron and vitamin B12 utilization, resulting in reduced erythropoiesis and elevated RDW 43 . RDW has been proposed as an indicator for patients with MASLD and MASH, showing high specificity and sensitivity 44 , 45 . It is also independently associated with advanced fibrosis in patients with MASLD 46 . However, its increase is age-dependent, as indicated in our results by its correlation with the age x fibrosis interaction term. To develop a new age-independent index to detect liver fibrosis non-invasively, we focused on the first group, which included markers associated with fibrosis but not influenced by age. We selected six features (Crn, Glu, Gln, Prop, iBu, and But) based on their strong correlation with fibrosis regarding effect size and statistical significance. Despite the limited cohort size, our analysis has confirmed several connections between IHMP markers and liver fibrosis observed in recent investigations involving larger groups of subjects. A study of 296 patients with MASLD revealed that a reduced serum Crn predicted moderate to severe fibrosis in Chinese Americans 29 . Analysis of serum samples from 200 biopsy-proven MASLD patients across different fibrosis stages demonstrated that an elevation in Glu and a decrease in Gln were strongly associated with fibrosis progression 30 . In addition, targeted metabolomics on samples from 100 MASLD patients and 50 healthy controls exhibited increased SCAF plasma concentrations attributable to the disease compared to the control group 31 . These metabolites are associated with three distinct physiological aspects: the relationship between sarcopenia and liver fibrosis (Crn), the increased glutaminolysis in fibrogenic cells (Glu, Gln), and gut dysbiosis (Prop, But, iBu). In chronic liver diseases such as MASLD, common pathogenetic mechanisms, including insulin resistance, hormonal imbalance, systemic inflammation, and changes in physical activity, contribute to the development and exacerbation of both liver fibrosis and sarcopenia 47 . Research indicates that assessing skeletal muscle mass can serve as a valuable non-invasive method to monitor the progression of liver fibrosis 48 . Physiologically, muscular atrophy reduces insulin activity targets, leading to glucose intolerance and increased gluconeogenesis, subsequently accelerating muscle wasting and protein breakdown, resulting in decreased Crn levels. With the progression of insulin resistance, rates of lipolysis increase, leading to higher free fatty acid generation stored in muscle and liver tissues 29 . On the other hand, during the progression of MASLD, glutaminolysis is increased, particularly in hepatic stellate cells (HSCs) 49 . This metabolic shift is driven by the enhanced bioenergetic and biosynthetic demands of activated HSCs, which play a central role in fibrogenesis. Specifically, the liver experiences a transition from the expression of Gls2 (liver-type glutaminase) in healthy livers to Gls1 (kidney-type glutaminase) in fibrotic livers. The heightened glutaminase activity leads to increased conversion of glutamine to glutamate, which fuels the TCA cycle and supports succinate production. Succinate, in turn, activates its receptor GPR91 to promote fibrogenic pathways, including the upregulation of α-SMA and collagen type I, contributing to the progression of liver fibrosis 50 . Consequently, there is a change in the Glu/Gln ratio that can be used as a marker to assess liver fibrosis 30 . Finally, our results and those of Thing et al. 31 have indicated a positive correlation between liver fibrosis and the circulating levels of SCFAs. The mechanisms connecting SCFAs to liver fibrosis and MASLD progression might involve direct and systemic metabolic effects on the liver. The gut microbiota mainly produces SCFAs through the fermentation of dietary fibers. SCFAs have been shown to influence the activity of AMP-activated protein kinase (AMPK) 51 , a crucial regulator of cellular energy balance. Inhibition of AMPK by excessive SCFAs, particularly in conditions of dysbiosis where SCFA levels may be unusually high, can result in reduced fatty acid oxidation and increased lipogenesis, thereby contributing to hepatic fat accumulation 51 . The role of these SCFAs in fibrogenesis is still being investigated, and additional research is required to fully understand their impact on disease progression. Although these metabolite levels, when tested individually, showed promising performances, the true potential of a metabolic index lies in combining these changes into a unique score. We tested all the possible combinations of the six markers, showing the strongest association with fibrosis independent of age. Finally, we selected a combination of two of them, constituting the GP index: Gln and Prop. Such a simple index offers several advantages, including ease of use and interpretation, cost-effectiveness, and reduced analytical variability. The GP index demonstrated higher discriminatory power (AUC), association strength (OR), and predictive capability (CVA) than Crn, Glu/Gln, SCAFS., and another proposed fibrosis index, BTR, which correlates the circulating levels of BCAAs to Tyr 32 – 35 . This new combined index offers the possibility of monitoring intrinsic hepatic metabolic changes, like the shift towards an enhanced glutaminolysis in HSCs and extrinsic influences from the gut microbiota on liver conditions, as reflected by the increase in Prop. This may improve diagnostic accuracy by providing a dual perspective to distinguish liver fibrosis from other liver conditions. The GP index's simplicity makes it particularly suitable for developing non-invasive biosensors capable of measuring the two circulating metabolites with specificity and sensitivity. This future development may enable the affordable quantification of the GP index, facilitating a feasible precision medicine approach. Our study has limitations: a small number of subjects was included, and a sex imbalance was noted in the groups with advanced fibrosis. The small number of subjects limits the study's statistical power, even though the GP index is based on two known alterations proposed in previous studies 30 , 31 . However, larger cohorts will be necessary to determine the real accuracy and predictability of the GP index. The unbalanced sex ratio makes it difficult to assess its influence on the disease's pathophysiology, potentially leading to biased outcomes. In addition, the inclusion of only a small proportion of patients over 65 (10 out of 63 participants) represents a limitation in assessing the age-independent effectiveness of the proposed index. It is possible that studying larger and more diverse populations could help validate the GP index, and conducting prospective studies in real-world clinical settings could demonstrate its applicability in routine practice. Conclusions This study focused on identifying age-independent markers from a broad panel of variables to develop a novel non-invasive index for assessing liver fibrosis related to MASLD. While inflammation reporters like IL-8 and RDW-CV correlate with fibrosis severity, they were excluded from the final index to avoid age-related biases. The new GP index, composed of Gln and Prop, offers a robust, age-independent diagnostic tool. Its simplicity, cost-effectiveness, and high predictive power make it advantageous for accurately staging liver fibrosis across diverse age groups, potentially improving diagnostic accuracy and patient outcomes. Future studies will be needed to further validate and refine the index by including larger, sex-balanced, and ethnically diverse cohorts. Declarations Acknowledgments This study was supported by Ministero della Ricerca – PRIN 2022 (code: 2022C7ZR3W) to S.B. and D.O.C. and co-financed by the Next Generation EU (DM 1557 11.10.2022), in the context of the National Recovery and Resilience Plan, Investment PE8—Project Age-It: “Ageing Well in an Ageing Society”. The views and opinions expressed are only those of the authors and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them. Conflict of interest S.B. has received a research grant from MSD on a related topic. Precision Medicine Liver Group Adriano Pelliccelli Hospital San Camillo Forlanini [email protected] Paola Andreozzi Hospital Policlinico Umberto I [email protected] Antonella Cacciani Hospital Policlinico Umberto I [email protected] Marin Pecani Department of Experimental Medicine, Sapienza University [email protected] Nicolò Sperduti Department of Translational and Precision Medicine Sapienza University, Rome, Italy [email protected] Tania D'amico Department of Experimental Medicine, Sapienza University [email protected] Salvatore Fasano Department of Translational and Precision Medicine Sapienza University, Rome, Italy [email protected] Fabrizio Recchia Department of Translational and Precision Medicine Sapienza University, Rome, Italy [email protected] Roberto Cangemi Department of Translational and Precision Medicine Sapienza University, Rome, Italy [email protected] Luca Miele Policlinico Gemelli [email protected] Laura Stronati Department of Molecular Medicine, Sapienza University [email protected] Moris Sangineto Università di Foggia [email protected] Gaetano Serviddio Università di Foggia [email protected] Adriano Desantis Department of Translational and Precision Medicine Sapienza University, Rome, Italy [email protected] Luca Pizzichetti Hospital Policlinico Umberto I [email protected] Giulio Francesco Romiti Department of Translational and Precision Medicine Sapienza University, Rome, Italy [email protected] Alessandro Cincione Department of Translational and Precision Medicine Sapienza University, Rome, Italy [email protected] Giovanni Buoinfante Department of Translational and Precision Medicine Sapienza University, Rome, Italy [email protected] Lucas Rumbola Department of Translational and Precision Medicine Sapienza University, Rome, Italy [email protected] References Rinella, M. E. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. J Hepatol 79, 1542–1556 (2023). Boccatonda, A. et al. From NAFLD to MAFLD: Definition, Pathophysiological Basis and Cardiovascular Implications. Biomedicines 2023, Vol. 11, Page 883 11, 883 (2023). Kong, D., Zhang, F., Zhang, Z., Lu, Y. & Zheng, S. Clearance of activated stellate cells for hepatic fibrosis regression: Molecular basis and translational potential. Biomedicine & Pharmacotherapy 67, 246–250 (2013). Tsochatzis, E. A., Bosch, J. & Burroughs, A. K. Liver cirrhosis. The Lancet 383, 1749–1761 (2014). Elena, P., Raluca Ioana, A., Andrei Emilian, P. & Adorata Elena, C. Non-invasive Serological Markers of Hepatic Fibrosis – Mini Review. Archives of Surgery and Clinical Research 8, 032–038 (2024). Auricchio, P. & Finotti, M. From NAFLD to Chronic Liver Diseases. Assessment of Liver Fibrosis through Non-Invasive Methods before Liver Transplantation: Can We Rely on Them? Transplantology 2023, Vol. 4, Pages 71–84 4, 71–84 (2023). Zupo, R. et al. Liver Fibrosis and 8-Year All-Cause Mortality Trajectories in the Aging Cohort of the Salus in Apulia Study. Biomedicines 2021, Vol. 9, Page 1617 9, 1617 (2021). Pitisuttithum, P. et al. Predictors of advanced fibrosis in elderly patients with biopsy-confirmed nonalcoholic fatty liver disease: The GOASIA study. BMC Gastroenterol 20, 1–9 (2020). Nahon, P. et al. Assessment of liver fibrosis using transient elastography in patients with alcoholic liver disease. J Hepatol 49, 1062–1068 (2008). Kariyama, K. et al. Fibrosis-3 Index: A New Score to Predict Liver Fibrosis in Patients With Nonalcoholic Fatty Liver Disease Without Age as a Factor. Gastro Hep Advances 1, 1108–1113 (2022). Krishnasamy, N. et al. Non-Invasive Markers: Fibrometer, Fibroscan vs Liver Biopsy in Detecting Liver Fibrosis in HBV Patients. J Clin Exp Hepatol 6, S100–S101 (2016). Ogawa, Y. et al. Wisteria floribunda agglutinin-positive Mac-2-binding protein and type 4 collagen 7S: useful markers for the diagnosis of significant fibrosis in patients with non-alcoholic fatty liver disease. J Gastroenterol Hepatol 33, 1795–1803 (2018). Choe, E. K. & Kang, H. Y. The association between platelet-related parameters and nonalcoholic fatty liver disease in a metabolically healthy nonobese population. Scientific Reports 2024 14:1 14, 1–8 (2024). Seki, E. & Schwabe, R. F. Hepatic inflammation and fibrosis: Functional links and key pathways. Hepatology 61, 1066–1079 (2015). Semenovich, D. S. et al. Fibrosis Development Linked to Alterations in Glucose and Energy Metabolism and Prooxidant–Antioxidant Balance in Experimental Models of Liver Injury. Antioxidants 12, 1604 (2023). Tacke, F. et al. EASL–EASD–EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J Hepatol 81, 492–542 (2024). Rinella, M. E. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology 78, 1966–1986 (2023). Berzigotti, A. et al. EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis – 2021 update. J Hepatol 75, 659–689 (2021). Eddowes, P. J. et al. Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. Gastroenterology 156, 1717–1730 (2019). Dieterle, F., Ross, A., Schlotterbeck, G. & Senn, H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in1H NMR metabonomics. Anal Chem 78, 4281–4290 (2006). Bedossa, P. & Poynard, T. An algorithm for the grading of activity in chronic hepatitis C. Hepatology 24, 289–293 (1996). van den Berg, R. A., Hoefsloot, H. C. J. J., Westerhuis, J. A., Smilde, A. K. & van der Werf, M. J. Centering, scaling, and transformations: Improving the biological information content of metabolomics data. BMC Genomics 7, 1–15 (2006). Pitti, E. et al. Metabolic Crosstalk in Multimorbidity: Identifying Compensatory Effects Among Diabetes, Hypertension, and Dyslipidemia. J Endocr Soc 8, bvae152 (2024). Burnham, K. P. & Anderson, D. R. Multimodel Inference:Understanding AIC and BIC in Model Selection. Sociol Methods Res 33, 261–304 (2004). Wong, T. T. & Yeh, P. Y. Reliable Accuracy Estimates from k-Fold Cross Validation. IEEE Trans Knowl Data Eng 32, 1586–1594 (2020). Steyerberg, E. W. et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21, 128–138 (2010). Buse, A. The Likelihood Ratio, Wald, and Lagrange Multiplier Tests: An Expository Note. Am Stat 36, 153–157 (1982). Li, W. Volcano plots in analyzing differential expressions with mRNA microarrays. J Bioinform Comput Biol 10, (2012). Sun, M. et al. Serum Creatinine as an Independent Predictor of Moderate to Severe Fibrosis in Chinese American Non-obese Metabolic Dysfunction-Associated Steatotic Liver Disease. Cureus 16, (2024). Du, K. et al. Increased Glutaminolysis Marks Active Scarring in Nonalcoholic Steatohepatitis Progression. Cell Mol Gastroenterol Hepatol 10, 1–21 (2020). Thing, M. et al. Targeted metabolomics reveals plasma short-chain fatty acids are associated with metabolic dysfunction-associated steatotic liver disease. BMC Gastroenterol 24, 1–10 (2024). Enomoto, H. et al. A New Metabolism-Related Index Correlates with the Degree of Liver Fibrosis in Hepatitis C Virus-Positive Patients. Gastroenterol Res Pract 2015, 926169 (2015). Enomoto, H. et al. Association of amino acid imbalance with the severity of liver fibrosis and esophageal varices. Ann Hepatol 12, 471–478 (2013). Ishikawa, T. Branched-chain amino acids to tyrosine ratio value as a potential prognostic factor for hepatocellular carcinoma. World J Gastroenterol 18, 2005–2008 (2012). Michitaka, K. et al. Amino acid imbalance in patients with chronic liver diseases. Hepatology Research 40, 393–398 (2010). Sterling, R. K. et al. AASLD Practice Guideline on blood-based noninvasive liver disease assessment of hepatic fibrosis and steatosis. Hepatology (2024) doi: 10.1097/HEP.0000000000000845 . Godbole, N. et al. Prognostic and pathophysiologic significance of il-8 (Cxcl8) in biliary atresia. J Clin Med 10, 2705 (2021). Glass, O. et al. Serum Interleukin-8, Osteopontin, and Monocyte Chemoattractant Protein 1 Are Associated With Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. Hepatol Commun 2, 1344–1355 (2018). Franceschi, C., Garagnani, P., Parini, P., Giuliani, C. & Santoro, A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol 14, 576–590 (2018). Yadav, V. et al. Interleukin levels and non-alcoholic fatty liver disease in chronic plaque psoriasis: An analytical case control study. Indian J Dermatol 68, 377–384 (2023). Knorr, J. et al. Interleukin-18 signaling promotes activation of hepatic stellate cells in mouse liver fibrosis. Hepatology 77, 1968–1982 (2023). Wang, Y. Q., Cao, W. J., Gao, Y. F., Ye, J. & Zou, G. Z. Serum interleukin-34 level can be an indicator of liver fibrosis in patients with chronic hepatitis B virus infection. World J Gastroenterol 24, 1312–1320 (2018). Aslam, H. et al. The Role of Red Cell Distribution Width as a Prognostic Marker in Chronic Liver Disease: A Literature Review. International Journal of Molecular Sciences 2023, Vol. 24, Page 3487 24, 3487 (2023). Yang, W., Huang, H., Wang, Y., Yu, X. & Yang, Z. High red blood cell distribution width is closely associated with nonalcoholic fatty liver disease. Eur J Gastroenterol Hepatol 26, 174–178 (2014). Cengiz, M., Candir, B. A., Yilmaz, G., Akyol, G. & Ozenirler, S. Is increased red cell distribution width an indicating marker of nonalcoholic steatohepatitis and fibrotic stage? World J Gastroenterol 19, 7412–7418 (2013). Kim, H. M. ok et al. Elevated red cell distribution width is associated with advanced fibrosis in NAFLD. Clin Mol Hepatol 19, 258–265 (2013). Kumar, R., Prakash, S. S., Priyadarshi, R. N. & Anand, U. Sarcopenia in Chronic Liver Disease: A Metabolic Perspective. http://www.xiahepublishing. com/ 10, 1213–1222 (2022). Zhou, T. et al. Impact of skeletal muscle mass evaluating methods on severity of metabolic associated fatty liver disease in non-elderly adults. British Journal of Nutrition 130, 1373–1384 (2023). Rojas, Á., García-Lozano, M. R., Gil-Gómez, A., Romero-Gómez, M. & Ampuero, J. Glutaminolysis-ammonia-urea Cycle Axis, Non-alcoholic Fatty Liver Disease Progression and Development of Novel Therapies. http://www.xiahepublishing. com/ 10, 356–362 (2022). Cho, E. H. Succinate as a regulator of hepatic stellate cells in liver fibrosis. Front Endocrinol (Lausanne) 9, 383762 (2018). Hu, H. et al. Intestinal microbiome and NAFLD: molecular insights and therapeutic perspectives. J Gastroenterol 55, 142–158 (2019). Additional Declarations There is NO Competing Interest. Supplementary Files TableS1.xlsx TableS2.xlsx nrreportingsummary.pdf Reporting Summary Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5268526","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375780745,"identity":"e3e34748-bc17-49db-9001-0020e14b3177","order_by":0,"name":"Daniel Cicero","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACZjBpA2MgibDh15KGoSWNgQ2nHjA4jE0Ehxb+duaHjysqztsDGcce/qhgSNzOzvvw0Y0aZgY++QasWiQOsxkbnjlzO3HGYbZ0A4kzDIk7m9mNjXOOseF0mAEzD5tkY9vtBCDDTMKwjSFxw2E2NukcNh58Wth/Nradszdg5v8mkfgPpuWfBF5bGBvbDjBuADIkDjZAteS2GeDUAvKLZMOZZJBfzCQbjkkYA7UwG+f2JfCwsSVgD7H+ww8/NlTY2QMZzyR/1NjIbjh/jPFxzrf/cvLNB7Bbg24rnMVDlPpRMApGwSgYBVgBALHZS4O3J2EAAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-5012-1714","institution":"University of Rome Tor Vergata","correspondingAuthor":true,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Cicero","suffix":""},{"id":375780746,"identity":"47e3bb6e-0f5c-4436-a1a2-548f6a938263","order_by":1,"name":"Serena Zampieri","email":"","orcid":"","institution":"University of Rome \"Tor Vergata\"","correspondingAuthor":false,"prefix":"","firstName":"Serena","middleName":"","lastName":"Zampieri","suffix":""},{"id":375780747,"identity":"ea883c8a-6f4c-4db4-9220-9d22bdb96c33","order_by":2,"name":"Greta Petrella","email":"","orcid":"","institution":"University of Rome \"Tor Vergata\"","correspondingAuthor":false,"prefix":"","firstName":"Greta","middleName":"","lastName":"Petrella","suffix":""},{"id":375780748,"identity":"d3ea1204-1d25-4a77-b8ef-f45b0c3bf554","order_by":3,"name":"Elisa Nagni","email":"","orcid":"","institution":"University of Rome \"Tor Vergata\"","correspondingAuthor":false,"prefix":"","firstName":"Elisa","middleName":"","lastName":"Nagni","suffix":""},{"id":375780749,"identity":"ef730776-f91c-49f2-b732-0d3b03e74dd8","order_by":4,"name":"Laura Micheli","email":"","orcid":"","institution":"University of Rome \"Tor Vergata\"","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Micheli","suffix":""},{"id":375780750,"identity":"2484752f-72db-472b-8f4a-84a7fe853308","order_by":5,"name":"Francesca Maiorca","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Maiorca","suffix":""},{"id":375780751,"identity":"6176e912-f8c8-40ce-8391-8f0a6cca06e7","order_by":6,"name":"Lombardi Ludovica","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Lombardi","middleName":"","lastName":"Ludovica","suffix":""},{"id":375780752,"identity":"ae8eaf1f-3201-4db6-a557-bf0567d4ed4f","order_by":7,"name":"Marilena Carbone","email":"","orcid":"","institution":"University of Rome \"Tor Vergata\"","correspondingAuthor":false,"prefix":"","firstName":"Marilena","middleName":"","lastName":"Carbone","suffix":""},{"id":375780753,"identity":"49ee5006-3db7-424a-a430-0a0d46c4d009","order_by":8,"name":"Annamaria Sabetta","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Annamaria","middleName":"","lastName":"Sabetta","suffix":""},{"id":375780754,"identity":"5e27c4a2-a053-4daf-b346-d68000ea7db9","order_by":9,"name":"Marzia Miglionico","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Marzia","middleName":"","lastName":"Miglionico","suffix":""},{"id":375780755,"identity":"c6735055-8f79-412a-9d34-1158db70432a","order_by":10,"name":"Marin Pecani","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Marin","middleName":"","lastName":"Pecani","suffix":""},{"id":375780756,"identity":"53b1a17b-8039-4349-862e-da572635e9ca","order_by":11,"name":"Valeria Raparelli","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Valeria","middleName":"","lastName":"Raparelli","suffix":""},{"id":375780757,"identity":"9d45aa17-a983-4c48-96fb-6663899b59ba","order_by":12,"name":"Stefania Basili","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Stefania","middleName":"","lastName":"Basili","suffix":""},{"id":375780758,"identity":"6c9d72c0-e945-42a9-a310-b07643115235","order_by":13,"name":"Lucia Stefanini","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Lucia","middleName":"","lastName":"Stefanini","suffix":""}],"badges":[],"createdAt":"2024-10-15 12:00:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5268526/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5268526/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71481706,"identity":"c75562d3-d84d-4879-8f39-923ad9f588a6","added_by":"auto","created_at":"2024-12-16 06:06:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111956,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple linear regression coefficient (b) and regression p-value in logarithmic scale of the 83 variables with liver fibrosis (A) and age x fibrosis interaction (B). Variables with labels are the only ones that show a significant correlation. Venn diagram of the variables significantly correlated with the three conditions (C). The variables in red in (A) and (C) significantly correlate with fibrosis progression independently of age. Abbreviations are defined in Table 2.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5268526/v1/a82667be3640bdf2deac3bec.png"},{"id":71480740,"identity":"d3766f3b-a2e7-40ce-83c3-bcc5409a4a08","added_by":"auto","created_at":"2024-12-16 05:58:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62585,"visible":true,"origin":"","legend":"\u003cp\u003eConcentrations of metabolites significantly correlated with fibrosis progression independently of age and already proposed with their average and standard deviation values [mM] measured as a function of the fibrosis scale.\u003cstrong\u003e \u003c/strong\u003eThe change in metabolite concentrations was compared to the previous value on the scale to determine its significance. * p\u0026lt;0.05, ** p\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5268526/v1/b945b6f992ae5ebd6507eda9.png"},{"id":71480741,"identity":"83be2263-cbde-43e2-be41-403f7781965f","added_by":"auto","created_at":"2024-12-16 05:58:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42727,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of the measured values of three MASLD-related Liver Fibrosis prognostic indexes: BTR, Glu/Gln, SCFA\u003csup\u003e \u003c/sup\u003es, and the new proposed age-independent MASLD-related liver fibrosis index (GP) scatter plot. The change in index values was compared to the previous value on the scale to determine its significance. * p\u0026lt;0.05, ** p\u0026lt;0.01.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5268526/v1/36126acc2642333f018b45f5.png"},{"id":71481707,"identity":"9450c94d-8005-4ac1-91bb-af87baa74902","added_by":"auto","created_at":"2024-12-16 06:06:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1158957,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5268526/v1/4626623e-d6eb-4c16-acf3-b45fa5d07be3.pdf"},{"id":71480746,"identity":"13567359-7571-4a5f-9482-8e9b110b9257","added_by":"auto","created_at":"2024-12-16 05:58:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":85935,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5268526/v1/3587fddff4b08d25f1696d9e.xlsx"},{"id":71481705,"identity":"1f778ac5-6aa5-41af-993a-931cbe1049c5","added_by":"auto","created_at":"2024-12-16 06:06:43","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":70077,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5268526/v1/89dde48c1a44d723c1a944db.xlsx"},{"id":71480743,"identity":"9b7f0b05-d1db-4d3a-8d08-a46fcb7ef204","added_by":"auto","created_at":"2024-12-16 05:58:43","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":102082,"visible":true,"origin":"","legend":"\u003cp\u003eReporting Summary\u003c/p\u003e","description":"","filename":"nrreportingsummary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5268526/v1/903865c355b20429abab79e9.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"An age-independent MASLD-related liver fibrosis index reflecting gut dysbiosis and hepatic stellate cells reprogramming","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe increase in the prevalence of Metabolic-associated steatotic liver disease (MASLD) constitutes a major public health problem. Historically, it was defined as non-alcoholic fatty liver disease (NAFLD) and diagnosed by exclusion of other causes of chronic liver disease. In 2023, a multi-society consensus statement introduced the term MASLD \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, which includes adults with excessive fat accumulation in the liver (hepatic steatosis) in the presence of at least one cardiometabolic risk factor and in the absence of excessive alcoholic intake. Because of the global epidemic of metabolic disorders\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e MASLD has surged to be the most common cause of chronic liver diseases, posing significant challenges in healthcare management. Progression of MASLD leads to liver fibrosis, characterized by abnormal accumulation of extracellular matrix proteins, which triggers liver scarring and functional degradation \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Its escalation could lead to more severe complications, including cirrhosis, liver failure, and hepatocellular carcinoma \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Fibrosis caused by MASLD combines metabolic dysregulation and pathologic tissue remodeling; thus, timely intervention is needed to stop disease progression.\u003c/p\u003e \u003cp\u003eThe gold standard for diagnosing and monitoring liver fibrosis is liver biopsy. Although informative, it is an invasive procedure that carries risks such as pain, bleeding, and infection. For this reason, significant effort was made to discover accurate, non-invasive diagnostic tools for liver fibrosis to enhance patient safety and clinical management \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These methods, including blood-based biomarkers and elastography, provide valuable tools. Still, they have limitations, particularly in their efficiency for screening and early detection of liver fibrosis in the general population \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Their use is particularly challenging in the context of an aging population. Aging impacts metabolic homeostasis and complicates MASLD's pathophysiology, making treatment and management more difficult \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. It was recently shown that existing fibrosis scores have lower accuracy in elderly patients, leading to the need for revised or new scoring systems that can more accurately predict fibrosis without being skewed by age-related factors \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Existing non-invasive fibrosis scores, such as the Fibrosis-4 (FIB-4) index \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, tend to have lower specificity and higher false-positive rates in elderly patients, as they were primarily validated in younger populations. More recently, the Fibrosis-3 (FIB-3) index was developed to overcome this limitation by excluding age from the formula \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This index, together with others presented previously \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, highlights the importance of creating scores that exclude age. This is crucial because age is not linearly correlated with the extent of liver fibrosis, leading to potential inaccuracies when using age-dependent indices across various age groups.\u003c/p\u003e \u003cp\u003eIn this context, we aimed to develop an age-independent MASLD-related liver fibrosis index using a comprehensive inflammatory, hematological, and metabolic marker panel. The analysis of hemogram features, such as platelet count and mean corpuscular volume, can provide valuable insights into the extent of liver damage and potential complications like thrombocytopenia \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Inflammation is important in advancing liver fibrosis, with inflammatory mediators triggering fibrogenesis \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Metabolic markers can potentially reveal disruptions in metabolic functions that commonly accompany liver fibrosis \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Using such a broad source of blood-based markers increases the chances of finding variables related to different physiological aspects of the disease that can be combined into a more efficient score that can be applied to patients regardless of age.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eThe \u0026quot;Integrated phenotyping of the Gut-plAtelet-Liver AXIS in the progression of chronic liver disease\u0026quot; (iGAL-AXIS)\u0026quot; project is an observational, prospective study aimed at exploring the relationship between gut dysbiosis, metabolome composition, inflammation, and platelet activation in chronic liver disease. Individuals (\u0026gt;\u0026thinsp;18 years) diagnosed with MASLD based on EASL guidelines\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e were enrolled and classified according to the level of fibrosis. Specifically, MASLD was defined by the presence of hepatic steatosis, occurring in subjects with at least one cardiometabolic risk factor and the absence of significant alcohol intake (greater than 20 grams per day for women and 30 grams per day for men)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The study accounted for several covariates: age, sex, body mass index (BMI), diabetes, hypertension, dyslipidemia, and medication use. The study was conducted in full conformance with the principles of the Declaration of Helsinki, and it was approved by the local Ethics Committee of Policlinico Umberto I, Sapienza University of Rome, Rome, Italy (reference 6804, 09/11/2022). Written informed consent has been obtained from all patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssessment of Liver steatosis and fibrosis\u003c/h3\u003e\n\u003cp\u003eVibration-controlled transient elastography (FibroScan, Echosens, Paris, France) was used for liver assessment, employing both M and XL probes and an automatic probe selection tool that determines the most suitable probe based on real-time measurements of the skin-to-liver capsule distance. The procedure was conducted according to the manufacturer\u0026apos;s guidelines and training. Controlled attenuation parameter (CAP) measurements for assessing steatosis and liver stiffness measurements (LSM) for evaluating fibrosis were taken until ten valid readings were achieved for each patient. The operator was blinded to all clinical data and patient diagnoses. The presence of steatosis was considered for a CAP higher than 275 dB/m \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and fibrosis staging by transient elastography was classified using the following thresholds: 8.2, 9.7, and 13.6 for diagnosing\u0026thinsp;\u0026ge;\u0026thinsp;F2, \u0026ge;F3, and F4, respectively \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eHemogram and Inflammatory profiling\u003c/h3\u003e\n\u003cp\u003eAt enrollment, venous blood samples were collected from study participants. EDTA-anticoagulated blood was used to obtain the hemogram profile on a Sysmex hematology analyzer. Blood without anticoagulants was allowed to clot, and serum was separated by centrifugation at 3400 g for 20 min. Coded samples were stored at -80\u0026deg;C until batch analysis.\u003c/p\u003e\n\u003cp\u003eThe concentration of inflammatory cytokines was assessed in serum samples by multiplex bead-based flow cytometric assay (Biolgend, Inflammation Panel I, catalog number 740809), according to the manufacturer\u0026apos;s instructions. Briefly, after thawing, the serum samples were immediately centrifuged at maximum speed and transferred to new tubes. A small volume of each serum (25ul) was diluted 1:1 in the Assay buffer provided in the kit. Each serum was incubated with 13 bead populations distinguished by size and internal APC fluorescent dye, which bind to 13 distinct human inflammatory cytokines and chemokines, including IL-1\u0026beta;, IFN-\u0026alpha;2, IFN-\u0026gamma;, TNF-\u0026alpha;, MCP-1 (CCL2), IL-6, IL-8 (CXCL8), IL-10, IL-12p70, IL-17A, IL-18, IL-23, and IL-33. The following day, the beads were incubated first with cytokine-specific biotinylated antibodies and then with Streptavidin-phycoerythrin, and they were immediately acquired at a BD Accuri C6 Plus flow cytometer. Cytokine-specific populations were segregated based on the size and internal APC fluorescence intensity. The concentration of a particular cytokine was quantified based on the PE fluorescent signal according to a standard curve generated in the same assay. Measurements were ascertained while blinded to the sample origin. All samples were assayed in duplicate, and those showing values above the standard curve were retested with appropriate dilutions.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/sup\u003e \u003cstrong\u003eH-NMR spectroscopy-based metabolomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThawed serum samples underwent protein removal using 3 kDa cut-off Amicon Ultra-0.5 centrifugal filter devices. The filters were washed four times with distilled water to eliminate glycerol (13800 g, 4\u0026deg;C for 20 min), followed by centrifugation at 13800 g for 90 min. NMR buffer (250 mM phosphate buffer KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e/K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, pH 7.4, containing 0.82 mM sodium trimethylsilyl propanoate-\u003cem\u003ed\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e, 10% D\u003csub\u003e2\u003c/sub\u003eO, and 2% NaN\u003csub\u003e3\u003c/sub\u003e) was added to each filtered serum to reach a final volume of 600 \u0026micro;L. The resultant solution was then transferred into a 5 mm NMR tube.\u003c/p\u003e\n\u003cp\u003eThe \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH-NMR spectra for each sample were obtained using a Bruker Avance 700 MHz spectrometer equipped with a triple resonance TXI probe and a SampleXpress Lite autosampler. The spectra were collected at 25\u0026deg;C using a noesypr1d sequence, with 1024 scans, four dummy scans, a spectral width of 16 ppm, an acquisition time of 2s, a relaxation delay of 3s, and a mixing time of 100 ms. Following the acquisition, the spectra were processed with 0.5 Hz of line-broadening, and then manual phase and baseline correction were performed. Chenomx NMRSuite 8.5 (Chenomx Inc.) was used to quantify metabolites, with TSP- \u003cem\u003ed\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e serving as the internal standard. 52 metabolites were quantified using this method in almost all samples.\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were conducted using R v4.3.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003c/span\u003e), and plots were created using Excel. All inferential tests are two-tailed with a nominal alpha level of 0.05. Due to the analyses\u0026apos; exploratory nature, no multiplicity adjustments were made. The metabolite concentrations were normalized before statistical analysis using probabilistic quotient normalization \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and the data from the inflammation panel were log-transformed. The combined data sets, including the hemogram, inflammation panel, and metabolic panel, comprised 84 variables.\u003c/p\u003e\n \u003cp\u003eAdditionally, variables describing the demographic and health conditions of the subjects, such as age, sex (1 for female), BMI, fibrosis, diabetes, hypertension, and dyslipidemia were included in the analysis. The fibrosis variable was created by assigning a value of 0 to the control group and values of 1 (F\u0026thinsp;=\u0026thinsp;0,1) through 4 (F\u0026thinsp;=\u0026thinsp;2,3,4) based on the fibrosis scale \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e shows all the data used in this analysis.\u003c/p\u003e\n \u003cp\u003eBefore calculating the interaction terms, the collinearity of the variables was tested. As age and fibrosis were found to be collinear, all the conditions were scaled using unit variation (UV) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Then, we calculated and UV-scaled the interaction term between age and fibrosis. Finally, we UV-scaled all the measured variables. This ensures that the coefficients will be comparable and allows the determination of the relative importance of the effect.\u003c/p\u003e\n \u003cp\u003eTo compute the profile linked to individual conditions, each of the 84 measured variables (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) was linearly correlated with the seven parameters (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e) describing the previously mentioned demographic and health conditions, along with the interaction term, following the equation [1] as previously described \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"382\" height=\"61\"\u003e\u003c/p\u003e\n \u003cp\u003eFor each of the 84 equations, the values of the nine coefficients, their standard errors, t-values, p-values, and 95% CI were calculated, and the complete results are shown in Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e. The set of metabolites showing statistically significant association with a given condition represents its corresponding profile. Using this approach, we explored the correlation between variable levels and fibrosis stages across the entire F0-F4 spectrum. This model, described by equation [1], obviated the need to convert the fibrosis condition into a binary variable as necessitated by the more frequently employed logistic regression technique.\u003c/p\u003e\n \u003cp\u003eIn evaluating the predictive ability of various indexes for explaining the severity of liver fibrosis (F\u0026thinsp;=\u0026thinsp;0\u0026ndash;1 versus F\u0026thinsp;=\u0026thinsp;2\u0026ndash;4), an unmatched logistic regression model was employed with and without adjustments for age and sex. To address potential issues of bias and separation in logistic regression, particularly in small sample sizes or when events are rare, we utilized Firth\u0026apos;s method, a penalized maximum likelihood logistic regression implemented through the \u003cem\u003elogistf\u003c/em\u003e function. This same approach was used to construct a mixed model combining metabolite concentrations.\u003c/p\u003e\n \u003cp\u003eThe best mixed model was selected by comparing all the possible combinations, from single markers to the total number of variables considered. We applied different methods to compare the effectiveness of various indexes. Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, together with Cross-Validation Accuracy (CVA) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, were used to provide insights into the model fit and predictive performance. The pseudo-R\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e statistics measure the model\u0026apos;s explanatory power, indicating how well the model explains the variance in the dependent variable \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Finally, DeLong\u0026apos;s non-parametric test was used to compare the AUCs of two ROC curves to evaluate the discriminative ability of the models. To determine the best model considering all the metrics, we created a combined index that considers each metric\u0026apos;s relative importance. Pseudo-R\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, CVA, and AUC values were scaled between 0 and 1, where 1 represents the best performance. We used an inverted scale for AIC and BIC so that the lower AIC and BIC scores, which indicate models that best explain the data with the fewest parameters, correspond to higher normalized values. The final combined index was obtained by averaging the five scaled parameters. To test whether the inclusion of age or age and sex as covariants provides a better index, we used the Likelihood Ratio Test (LRT) and its associated p-value \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eThis study included 63 adults diagnosed with MASLD and 22 controls. Transient elastography analysis showed that out of 63 subjects with steatosis, 38 had no/mild fibrosis (F0-1), and 25 had significant fibrosis (consisting of F2\u0026thinsp;=\u0026thinsp;10, F3\u0026thinsp;=\u0026thinsp;7, F4\u0026thinsp;=\u0026thinsp;8). The characteristics of the study participants are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no significant differences among the groups regarding triglyceride levels. Groups with substantial fibrosis (F2, F3, F4) were more likely males, which may impact the identification of potential markers associated with liver fibrosis, potentially leading to a higher proportion of biases toward male characteristics in the responses. Patients with advanced fibrosis (F3) had more frequent Type 2 diabetes,.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of patients with different fibrosis stages and controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF0-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjects, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean (min-max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (28\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (33\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (19\u0026ndash;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (19\u0026ndash;57)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62 (40\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (86)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u0026thinsp;\u0026plusmn;\u0026thinsp;4*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;2**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109\u0026thinsp;\u0026plusmn;\u0026thinsp;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122\u0026thinsp;\u0026plusmn;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105\u0026thinsp;\u0026plusmn;\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u0026thinsp;\u0026plusmn;\u0026thinsp;38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131\u0026thinsp;\u0026plusmn;\u0026thinsp;36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (32)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (50)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (63)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (63)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValvular Insufficiency, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrug Treatments\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE\u003csup\u003ea\u003c/sup\u003e Inhibitors, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARB\u003csup\u003eb\u003c/sup\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (13)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb-blockers, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCB\u003csup\u003ec\u003c/sup\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003eACE: angiotensin-converting enzyme; \u003csup\u003eb\u003c/sup\u003eARB: angiotensin receptor blockers; \u003csup\u003eC\u003c/sup\u003eCCB: calcium channel blockers. Student\u0026rsquo;s t-test was calculated for each fibrosis group with respect the controls: *0.05\u0026thinsp;\u0026lt;\u0026thinsp;p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eA comprehensive Inflammatory and Hematological-Metabolic Panel (IHMP)\u003c/h3\u003e\n\u003cp\u003eWe tracked the progression of liver fibrosis related to MASLD by measuring a wide range of potential markers. We gathered data on circulating cell types and physical characteristics through a comprehensive blood test analysis. In addition, we evaluated the levels of 13 inflammatory cytokines. Additionally, we identified and quantified 52 blood-circulating metabolites in a single experiment using NMR. They belong to different chemical classes, including hydroxy-, keto-, carboxylic-, amino-acids, amine derivatives, carbohydrates, and organic nitrogen compounds. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the complete list of variables investigated for their association with the level of liver fibrosis. These variables constitute an Inflammatory and Hematological-Metabolic Panel (IHMP) encompassing 84 variables. The complete dataset is available in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe 84 variables constituting the IHMP and used for the analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMetabolic panel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHemogram\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eInflammation panel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetabolite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbbr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAbbr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbbr.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-Hydroxybutyrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-HB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHistidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWhite Blood Cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterleukin-1 beta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIL-1β\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-Hydroxyisovalerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-HIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxanthine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRed Blood Cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterleukin-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIL-6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-Oxoisocaproate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-Oxo-IC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsobutyrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eiBu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterleukin-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIL-8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-Hydroxybutyrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-HB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsoleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHematocrit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterleukin-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIL-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-Hydroxyisobutyrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-HIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Corpuscular Volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterleukin-12p70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIL-12p70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-Hydroxyisovalerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-HIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLeu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Corpuscular Hemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterleukin-17A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIL-17A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-Methyl-2-oxovalerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-MOV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Corpuscular Hemoglobin Concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMCHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterleukin-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIL-18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlatelets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterleukin-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIL-23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethylguanidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage of Lymphocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLYM %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterleukin-33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIL-33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emyo-Inositol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emIns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage of Mixed White Blood Cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMXD %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterferon-alpha 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIFN-α2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsparagine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN,N-Dimethylglycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDMG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage of Neutrophiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNEUT %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterferon-gamma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIFN-γ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO-Acetylcarnitine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLymphocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLYM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTumor Necrosis Factor-alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetaine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrnithine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMixed White Blood Cells Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMXD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMonocyte Chemoattractant Protein-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMCP-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eButyrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBut\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeutrophils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNEUT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarnitine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRed Cell Distribution Width - Standard Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRDW-SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePropionate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRed Cell Distribution Width - Coefficient of Variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRDW-CV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePyroglutamate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePyrGlu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlatelet Distribution Width\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePDW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePyruvate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePyr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Platelet Volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSarcosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlatelet Large Cell Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP_LCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimethylamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSerine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimethyl sulfone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTaurine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThreonine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrimethylamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTyrosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTyr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGln\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003ch3\u003eThe IHMP-based profile of liver fibrosis\u003c/h3\u003e\n\u003cp\u003eIn the initial analysis stage, we utilized the levels of the 84 features in the IHMP as dependent variables to examine their correlation with liver fibrosis based on the F0-F4 scale, as described in Materials and Methods. According to equation [1], fibrosis, age, diabetes, hypertension, dyslipidemia, and the interaction term between age and fibrosis were considered the independent variables. With this approach, we sought the linear combination of conditions that best explains the distribution of each IHMP feature\u0026apos;s levels. The analysis output included coefficients displaying the strength and direction of the relationship between each independent and dependent variable. Their associated p-values were utilized to determine the significance of these relationships. The comprehensive results of this analysis are listed in Table S2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThen, we generated plots\u003csup\u003e\u0026nbsp;28\u003c/sup\u003e\u0026nbsp; to concurrently evaluate the effect size, direction, and statistical significance of the association of each IHMP\u0026nbsp;feature with liver fibrosis and the interaction term age x fibrosis (Figures 1A-1B). Figure 1C summarizes the findings of this evaluation. We could classify the variables into three groups according to their associations.\u003c/p\u003e\n\u003cp\u003eThe first group comprises 21 variables correlating only with fibrosis, \u0026nbsp;i.e. sensitive to disease progression but not to age. Pathways analysis of the fibrosis-related metabolite changes reflects a broad involvement of energy production pathways (Pyr, Ac, For, 2-HB, Cit), increased amino acid turnover (Glu, Gln, Tyr, Ile, Asn, Asp, Met, 2-HIV), extracellular matrix remodeling (Hyp, PyrGlu), urea cycle function (Urea, Asn, Asp), sulfur metabolism (DMS, Met), alterations in gut microbiome activity (But, iBu, Prop), and systemic responses to liver injury (Crn).\u003c/p\u003e\n\u003cp\u003eA second group comprises six variables correlating only with the interaction term age x fibrosis. The identity of these features suggests that the combined or synergistic effect of aging and fibrosis may exacerbate systemic inflammation (IL-8), impact amino acid metabolism (3-HIV), influence gut microbiota composition or function (TMA), and alter the RBC turnover (MCV).\u003c/p\u003e\n\u003cp\u003eFinally, we found two variables (Phe and RDW-CV) associated with fibrosis and age x fibrosis that are related to amino acid metabolism (Phe) and dysfunctional RBC turnover (RDW-CV).\u003c/p\u003e\n\u003cp\u003eTo derive an index that scores liver fibrosis independent of age, we concentrated our attention only on variables of the first group. Based on Figure 1A, the metabolites that exhibit the most significant association within this group are Crn, Glu, Gln, and three Short-Chain Fatty Acids (SCFAs): But, iBu, and Prop. Figure 2 shows their concentration changes as a function of the liver fibrosis index. We observed an increase in circulating levels of Glu and SCFAs and a decrease in Gln and Crn as a function of fibrosis severity.\u003c/p\u003e\n\u003ch3\u003eA combined metabolic score to stage age-independently liver fibrosis associated with MASLD: the GP index\u003c/h3\u003e\n\u003cp\u003eThe six metabolites chosen for their significant correlation with the fibrosis variable are strong candidates for creating a mixed model, increasing their predictive power when combined. We generated all potential combinations of these six variables to extract the best combination and employed various metrics, such as pseudo-R\u003csup\u003e2\u003c/sup\u003e, AIC, BIC, CVA, and AUC, for candidate comparison. Higher pseudo-R\u003csup\u003e2\u003c/sup\u003e values signify a more comprehensive explanation of outcome variance, while lower AIC and BIC values suggest improved model fit and simplicity. CVA estimates predictive capability, while AUC measures class distinguishability. We excluded the control group to perform these calculations and divided individuals with MASLD into two groups based on their fibrosis level: none/mild (F=0,1) and severe (F=2-4). We assessed all 63 possible combinations, with Table 3 showcasing the top 5 performers in terms of a combined index (CI) that merges all the metrics as defined in Materials and Methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Comparison of the top-five predictive models based on a combined index (CI) that accounts for Pseudo-R\u0026sup2;, AIC, BIC, AUC, and CVA\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"398\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.6382%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0754%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0553%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0553%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5528%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.3166%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.3065%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.6382%;\"\u003e\n \u003cp\u003eGln + Prop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.0754%;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0553%;\"\u003e\n \u003cp\u003e60.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0553%;\"\u003e\n \u003cp\u003e66.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5528%;\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3166%;\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.3065%;\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.6382%;\"\u003e\n \u003cp\u003eGlu + Gln + Prop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.0754%;\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0553%;\"\u003e\n \u003cp\u003e61.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0553%;\"\u003e\n \u003cp\u003e70.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5528%;\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3166%;\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.3065%;\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.6382%;\"\u003e\n \u003cp\u003eGln + iBu + Prop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.0754%;\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0553%;\"\u003e\n \u003cp\u003e62.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0553%;\"\u003e\n \u003cp\u003e70.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5528%;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3166%;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.3065%;\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.6382%;\"\u003e\n \u003cp\u003eCrn + Gln + Prop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.0754%;\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0553%;\"\u003e\n \u003cp\u003e61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0553%;\"\u003e\n \u003cp\u003e70.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5528%;\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3166%;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.3065%;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.6382%;\"\u003e\n \u003cp\u003eGlu + Gln + But + Prop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.0754%;\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0553%;\"\u003e\n \u003cp\u003e63.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0553%;\"\u003e\n \u003cp\u003e73.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10.5528%;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3166%;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.3065%;\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis data suggests that Gln + Prop can be regarded as the best mixed model. It has the lowest AIC and BIC, indicating the best balance between model fit and complexity. It also has the highest CVA and CI, making it the most robust model overall. Although the Glu + Gln + But + Prop model has a similar AUC, indicating equal discriminative ability, its higher AIC and BIC suggest it might be a more complex model without providing a significantly better fit.\u003c/p\u003e\n\u003cp\u003eThe coefficients of this index, called GP, are shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Calculated mixed model coefficients and related parameters for GP index construction\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"376\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.7333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[0.025]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[0.975]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ez-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7333%;\"\u003e\n \u003cp\u003eGln\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4%;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.6667%;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.6667%;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.0667%;\"\u003e\n \u003cp\u003e-2.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.0667%;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.7333%;\"\u003e\n \u003cp\u003eProp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4%;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.4%;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.6667%;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.6667%;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.0667%;\"\u003e\n \u003cp\u003e2.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.0667%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe then investigated whether adding age or age and sex as covariates improves the predictive performance of the GP model for liver fibrosis. The analysis involved comparing the base model against two other models. Table 5 summarizes the metrics used for the comparisons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Comparison of the Predictive Performance of the GP Model with and without Age and Sex as Covariates for Predicting Liver Fibrosis\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"354\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.1638%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.1864%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4294%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.0169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4294%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.774%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRT p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.1638%;\"\u003e\n \u003cp\u003eGP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.1864%;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.4294%;\"\u003e\n \u003cp\u003e60.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0169%;\"\u003e\n \u003cp\u003e66.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.4294%;\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.774%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.1638%;\"\u003e\n \u003cp\u003eGP + Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.1864%;\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.4294%;\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0169%;\"\u003e\n \u003cp\u003e68.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.4294%;\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.774%;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.1638%;\"\u003e\n \u003cp\u003eGP + Age + Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21.1864%;\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.4294%;\"\u003e\n \u003cp\u003e60.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.0169%;\"\u003e\n \u003cp\u003e71.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.4294%;\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19.774%;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eGiven the minimal improvement in metrics such as Pseudo-R\u003csup\u003e2\u003c/sup\u003e, AIC, and AUC, coupled with the non-significant LRT p-values, we can conclude that the inclusion of age as a covariate does not significantly enhance the performance of the GP model in distinguishing MASLD patients with different levels of fibrosis. In addition, while sex can be included in the model, it does not appear to be a crucial factor, and the GP model can perform effectively without it. We cannot consider this last result as definitive because of the low presence of females with advanced fibrosis or cirrhosis in our cohort (Table 1).\u003c/p\u003e\n\u003ch3\u003eStatistical evaluation of Crn, Glu/Gln, SCFAs, BTR, and GP performance in predicting liver fibrosis\u003c/h3\u003e\n\u003cp\u003eThe performance of the GP index was compared with other scores from existing literature, such as serum Crn \u003csup\u003e29\u003c/sup\u003e,\u0026nbsp;the Glu/Gln ratio \u003csup\u003e30\u003c/sup\u003e, and SCFA \u003csup\u003e31\u003c/sup\u003e. To perform the calculations, the SCFA score was defined as the sum of But, iBu, and Prop concentrations weighted with the coefficients that resulted in the best mixed model involving these metabolites in the previous calculations. Studies have also suggested that changes in plasma BCAAs may aid in diagnosing the degree of liver fibrosis through the BCAAs-to-Tyr ratio (BTR) \u003csup\u003e32\u0026ndash;35\u003c/sup\u003e. Although only Ile exhibited a correlation with fibrosis in our cohort, and Tyr showed a less pronounced correlation, we decided to include it, as we found the expected decrease in Ile and increase in Tyr concentration. Using multiple criteria, these four scores were assessed and compared with the combined GP index. All the scores were scaled using unit variation to ensure a proper comparison of the Odd Rates (ORs). The detailed results are presented in Table 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u003c/strong\u003e League Table Showing Comparisons Among Each Fibrosis Index\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut-off value\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(sen/spe [%])\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(OR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlu/Gln\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCAFs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 231px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003cp\u003e(36/87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003cp\u003e(0.436-0.745)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003cp\u003e(0.45-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003cp\u003e(48/82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003cp\u003e(0.505-0.788)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003cp\u003e(0.37-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlu/Gln\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003cp\u003e(88/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003cp\u003e(0.677-0.917)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.13\u003c/p\u003e\n \u003cp\u003e(1.12-33.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSCAFs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003cp\u003e(88/68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003cp\u003e(0.722-0.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.96\u003c/p\u003e\n \u003cp\u003e(2.17-11.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003cp\u003e(88/71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003cp\u003e(0.786-0.959)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7.28\u003c/p\u003e\n \u003cp\u003e(2.73-19.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eCut-off value and sensitivity to specificity ratio expressed in percentage. \u003csup\u003eb\u003c/sup\u003eTo properly compare the Odds Ratios, all the indexes were scaled using unit variation. Odds Ratio values \u0026lt; 1 are associated with indexes negatively correlated with fibrosis.\u003c/p\u003e\n\u003cp\u003eThe results indicate that GP emerges as the most effective fibrosis index, with the highest AUC (0.873), demonstrating its ability to differentiate between patients with severe fibrosis and those with none or mild fibrosis. It shows the highest OR (7.28), with a significant associated p-value (\u0026lt;0.001), which further validates its predictive strength. GP also displays the highest CVA value (0.823), indicating that this index generalizes well to new, unseen data, reducing the risk of overfitting. Despite the lack of statistical significance in direct comparisons with SCAFs and Glu/Gln, their lower AUC, OR, and CVA values suggest slightly reduced diagnostic precision compared to GP. In contrast, BTR and Crn display much lower predictive power, with AUCs of 0.591 and 0.646 and non-significant p-values, making them less reliable for fibrosis prediction.\u003c/p\u003e\n\u003cp\u003eAn important advantage of GP over other indexes is its consistent and significant variation across the entire fibrosis scale utilized in this study. Figure 3 illustrates the behavior of four indexes in a scatter plot concerning the fibrosis scale, while the corresponding scatter plot for Crn is displayed in Figure 2. These graphs demonstrate that Crn, BTR, and Glu/Gln scores primarily differentiate between cirrhosis (F4) and all other fibrosis stages. The SCFAs index performs relatively well, but our dataset does not exhibit an increase between F2 and F3. In contrast, GP is the sole score that consistently increases across the entire scale, which presents a significant advantage, particularly when utilizing the index to monitor the complete progression of the disease.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur investigation sought to assess a range of potential biomarkers suitable for developing a composite index for the independent detection of severe fibrosis, irrespective of age-related factors. Recent guidelines recommend use of non-invasive blood-based biomarkers for fibrosis evaluation in metabolic liver disease patients \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. To improve the chances of discovering reliable markers, we merged data from three sources: the hemogram, the inflammation panel, and the metabolic panel, collectively named the IHMP. At first, we utilized a multiple linear regression model similar to that we recently developed to investigate the metabolic crosstalk in multimorbidity \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. It included the level of liver fibrosis, age, sex, BMI, diabetes, dyslipidemia, and hypertension as independent variables and the 84 feature levels from the IHMP as dependent variables. Including comorbidities such as diabetes, dyslipidemia, and hypertension in the linear regression model enhances the model's accuracy and specificity by accounting for the potential confounding effects of these metabolic conditions on liver health, thereby allowing for more precise isolation of the direct markers of fibrosis. We also incorporated the interaction term between age and fibrosis to identify markers that exhibit distinct changes in different age groups.\u003c/p\u003e \u003cp\u003eIn this way, we divided the variables into three groups. The first group comprises 21 variables correlating only with fibrosis, not with the interaction term age x fibrosis. These variables are sensitive to disease progression independently of age. The second group consists of variables correlating only with the interaction term, reflecting the combined effects of aging and fibrosis. The third group includes variables associated with both fibrosis and age x fibrosis and includes factors sensitive to disease progression but dependent on age.\u003c/p\u003e \u003cp\u003eThe last group of variables showed increased IL-8 concentration, a cytokine linked to liver fibrosis. Our findings suggest that aging and fibrosis together have a more significant effect on IL-8 levels, exacerbating inflammation and liver fibrosis in older individuals \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. This interaction aligns with the concept of \"inflammaging,\" contributing to age-related diseases \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Our study, together with others \u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, indicates that interleukin levels may serve as non-invasive markers for liver fibrosis, but their relationship with fibrosis is conditional upon age.\u003c/p\u003e \u003cp\u003eThe correlation between RDW-SD and the age-fibrosis interaction is due to a link between erythropoiesis and the inflammatory response. Inflammation increases oxidative stress and leads to dysregulation and increased iron and vitamin B12 utilization, resulting in reduced erythropoiesis and elevated RDW \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. RDW has been proposed as an indicator for patients with MASLD and MASH, showing high specificity and sensitivity \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. It is also independently associated with advanced fibrosis in patients with MASLD \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. However, its increase is age-dependent, as indicated in our results by its correlation with the age x fibrosis interaction term.\u003c/p\u003e \u003cp\u003eTo develop a new age-independent index to detect liver fibrosis non-invasively, we focused on the first group, which included markers associated with fibrosis but not influenced by age. We selected six features (Crn, Glu, Gln, Prop, iBu, and But) based on their strong correlation with fibrosis regarding effect size and statistical significance. Despite the limited cohort size, our analysis has confirmed several connections between IHMP markers and liver fibrosis observed in recent investigations involving larger groups of subjects. A study of 296 patients with MASLD revealed that a reduced serum Crn predicted moderate to severe fibrosis in Chinese Americans \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Analysis of serum samples from 200 biopsy-proven MASLD patients across different fibrosis stages demonstrated that an elevation in Glu and a decrease in Gln were strongly associated with fibrosis progression \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In addition, targeted metabolomics on samples from 100 MASLD patients and 50 healthy controls exhibited increased SCAF plasma concentrations attributable to the disease compared to the control group \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These metabolites are associated with three distinct physiological aspects: the relationship between sarcopenia and liver fibrosis (Crn), the increased glutaminolysis in fibrogenic cells (Glu, Gln), and gut dysbiosis (Prop, But, iBu).\u003c/p\u003e \u003cp\u003eIn chronic liver diseases such as MASLD, common pathogenetic mechanisms, including insulin resistance, hormonal imbalance, systemic inflammation, and changes in physical activity, contribute to the development and exacerbation of both liver fibrosis and sarcopenia \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Research indicates that assessing skeletal muscle mass can serve as a valuable non-invasive method to monitor the progression of liver fibrosis \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Physiologically, muscular atrophy reduces insulin activity targets, leading to glucose intolerance and increased gluconeogenesis, subsequently accelerating muscle wasting and protein breakdown, resulting in decreased Crn levels. With the progression of insulin resistance, rates of lipolysis increase, leading to higher free fatty acid generation stored in muscle and liver tissues \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn the other hand, during the progression of MASLD, glutaminolysis is increased, particularly in hepatic stellate cells (HSCs) \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. This metabolic shift is driven by the enhanced bioenergetic and biosynthetic demands of activated HSCs, which play a central role in fibrogenesis. Specifically, the liver experiences a transition from the expression of Gls2 (liver-type glutaminase) in healthy livers to Gls1 (kidney-type glutaminase) in fibrotic livers. The heightened glutaminase activity leads to increased conversion of glutamine to glutamate, which fuels the TCA cycle and supports succinate production. Succinate, in turn, activates its receptor GPR91 to promote fibrogenic pathways, including the upregulation of α-SMA and collagen type I, contributing to the progression of liver fibrosis \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Consequently, there is a change in the Glu/Gln ratio that can be used as a marker to assess liver fibrosis \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFinally, our results and those of Thing et al.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e have indicated a positive correlation between liver fibrosis and the circulating levels of SCFAs. The mechanisms connecting SCFAs to liver fibrosis and MASLD progression might involve direct and systemic metabolic effects on the liver. The gut microbiota mainly produces SCFAs through the fermentation of dietary fibers. SCFAs have been shown to influence the activity of AMP-activated protein kinase (AMPK) \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, a crucial regulator of cellular energy balance. Inhibition of AMPK by excessive SCFAs, particularly in conditions of dysbiosis where SCFA levels may be unusually high, can result in reduced fatty acid oxidation and increased lipogenesis, thereby contributing to hepatic fat accumulation \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The role of these SCFAs in fibrogenesis is still being investigated, and additional research is required to fully understand their impact on disease progression.\u003c/p\u003e \u003cp\u003eAlthough these metabolite levels, when tested individually, showed promising performances, the true potential of a metabolic index lies in combining these changes into a unique score. We tested all the possible combinations of the six markers, showing the strongest association with fibrosis independent of age. Finally, we selected a combination of two of them, constituting the GP index: Gln and Prop. Such a simple index offers several advantages, including ease of use and interpretation, cost-effectiveness, and reduced analytical variability. The GP index demonstrated higher discriminatory power (AUC), association strength (OR), and predictive capability (CVA) than Crn, Glu/Gln, SCAFS., and another proposed fibrosis index, BTR, which correlates the circulating levels of BCAAs to Tyr \u003csup\u003e\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This new combined index offers the possibility of monitoring intrinsic hepatic metabolic changes, like the shift towards an enhanced glutaminolysis in HSCs and extrinsic influences from the gut microbiota on liver conditions, as reflected by the increase in Prop. This may improve diagnostic accuracy by providing a dual perspective to distinguish liver fibrosis from other liver conditions. The GP index's simplicity makes it particularly suitable for developing non-invasive biosensors capable of measuring the two circulating metabolites with specificity and sensitivity. This future development may enable the affordable quantification of the GP index, facilitating a feasible precision medicine approach.\u003c/p\u003e \u003cp\u003eOur study has limitations: a small number of subjects was included, and a sex imbalance was noted in the groups with advanced fibrosis. The small number of subjects limits the study's statistical power, even though the GP index is based on two known alterations proposed in previous studies \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. However, larger cohorts will be necessary to determine the real accuracy and predictability of the GP index. The unbalanced sex ratio makes it difficult to assess its influence on the disease's pathophysiology, potentially leading to biased outcomes. In addition, the inclusion of only a small proportion of patients over 65 (10 out of 63 participants) represents a limitation in assessing the age-independent effectiveness of the proposed index. It is possible that studying larger and more diverse populations could help validate the GP index, and conducting prospective studies in real-world clinical settings could demonstrate its applicability in routine practice.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study focused on identifying age-independent markers from a broad panel of variables to develop a novel non-invasive index for assessing liver fibrosis related to MASLD. While inflammation reporters like IL-8 and RDW-CV correlate with fibrosis severity, they were excluded from the final index to avoid age-related biases. The new GP index, composed of Gln and Prop, offers a robust, age-independent diagnostic tool. Its simplicity, cost-effectiveness, and high predictive power make it advantageous for accurately staging liver fibrosis across diverse age groups, potentially improving diagnostic accuracy and patient outcomes. Future studies will be needed to further validate and refine the index by including larger, sex-balanced, and ethnically diverse cohorts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Ministero della Ricerca \u0026ndash; PRIN 2022 (code: 2022C7ZR3W) to S.B. and D.O.C. and co-financed by the Next Generation EU (DM 1557 11.10.2022), in the context of the National Recovery and Resilience Plan, Investment PE8\u0026mdash;Project Age-It: \u0026ldquo;Ageing Well in an Ageing Society\u0026rdquo;. \u0026nbsp;The views and opinions expressed are only those of the authors and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.B. has received a research grant from MSD on a related topic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrecision Medicine \u0026nbsp; Liver Group\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eAdriano Pelliccelli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eHospital San Camillo Forlanini\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003ePaola Andreozzi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eHospital Policlinico Umberto I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eAntonella Cacciani\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eHospital Policlinico Umberto I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eMarin Pecani\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003e\u003cem\u003eDepartment of Experimental Medicine, Sapienza University\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eNicol\u0026ograve; Sperduti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eDepartment of Translational and Precision Medicine Sapienza University, Rome, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eTania D\u0026apos;amico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003e\u003cem\u003eDepartment of Experimental Medicine, Sapienza University\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eSalvatore Fasano\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eDepartment of Translational and Precision Medicine Sapienza University, Rome, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eFabrizio Recchia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eDepartment of Translational and Precision Medicine Sapienza University, Rome, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eRoberto Cangemi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eDepartment of Translational and Precision Medicine Sapienza University, Rome, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eLuca Miele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003ePoliclinico Gemelli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eLaura Stronati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eDepartment of Molecular Medicine, Sapienza University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eMoris Sangineto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eUniversit\u0026agrave; di Foggia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eGaetano Serviddio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eUniversit\u0026agrave; di Foggia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eAdriano Desantis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eDepartment of Translational and Precision Medicine Sapienza University, Rome, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eLuca Pizzichetti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eHospital Policlinico Umberto I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eGiulio Francesco Romiti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eDepartment of Translational and Precision Medicine Sapienza University, Rome, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eAlessandro Cincione\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eDepartment of Translational and Precision Medicine Sapienza University, Rome, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eGiovanni Buoinfante\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eDepartment of Translational and Precision Medicine Sapienza University, Rome, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5513%;\"\u003e\n \u003cp\u003eLucas Rumbola\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.0449%;\"\u003e\n \u003cp\u003eDepartment of Translational and Precision Medicine Sapienza University, Rome, Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.4038%;\"\u003e\n \u003cp\[email protected]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRinella, M. E. \u003cem\u003eet al.\u003c/em\u003e A multisociety Delphi consensus statement on new fatty liver disease nomenclature. J Hepatol 79, 1542\u0026ndash;1556 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoccatonda, A. \u003cem\u003eet al.\u003c/em\u003e From NAFLD to MAFLD: Definition, Pathophysiological Basis and Cardiovascular Implications. Biomedicines 2023, \u003cem\u003eVol. 11, Page 883\u003c/em\u003e 11, 883 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong, D., Zhang, F., Zhang, Z., Lu, Y. \u0026amp; Zheng, S. Clearance of activated stellate cells for hepatic fibrosis regression: Molecular basis and translational potential. Biomedicine \u0026amp; Pharmacotherapy 67, 246\u0026ndash;250 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsochatzis, E. A., Bosch, J. \u0026amp; Burroughs, A. K. Liver cirrhosis. The Lancet 383, 1749\u0026ndash;1761 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElena, P., Raluca Ioana, A., Andrei Emilian, P. \u0026amp; Adorata Elena, C. Non-invasive Serological Markers of Hepatic Fibrosis \u0026ndash; Mini Review. Archives of Surgery and Clinical Research 8, 032\u0026ndash;038 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuricchio, P. \u0026amp; Finotti, M. From NAFLD to Chronic Liver Diseases. Assessment of Liver Fibrosis through Non-Invasive Methods before Liver Transplantation: Can We Rely on Them? \u003cem\u003eTransplantology\u003c/em\u003e 2023, \u003cem\u003eVol. 4, Pages 71\u0026ndash;84\u003c/em\u003e 4, 71\u0026ndash;84 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZupo, R. \u003cem\u003eet al.\u003c/em\u003e Liver Fibrosis and 8-Year All-Cause Mortality Trajectories in the Aging Cohort of the Salus in Apulia Study. Biomedicines 2021, \u003cem\u003eVol. 9, Page 1617\u003c/em\u003e 9, 1617 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePitisuttithum, P. \u003cem\u003eet al.\u003c/em\u003e Predictors of advanced fibrosis in elderly patients with biopsy-confirmed nonalcoholic fatty liver disease: The GOASIA study. BMC Gastroenterol 20, 1\u0026ndash;9 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNahon, P. \u003cem\u003eet al.\u003c/em\u003e Assessment of liver fibrosis using transient elastography in patients with alcoholic liver disease. J Hepatol 49, 1062\u0026ndash;1068 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKariyama, K. \u003cem\u003eet al.\u003c/em\u003e Fibrosis-3 Index: A New Score to Predict Liver Fibrosis in Patients With Nonalcoholic Fatty Liver Disease Without Age as a Factor. Gastro Hep Advances 1, 1108\u0026ndash;1113 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrishnasamy, N. \u003cem\u003eet al.\u003c/em\u003e Non-Invasive Markers: Fibrometer, Fibroscan vs Liver Biopsy in Detecting Liver Fibrosis in HBV Patients. J Clin Exp Hepatol 6, S100\u0026ndash;S101 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgawa, Y. \u003cem\u003eet al.\u003c/em\u003e Wisteria floribunda agglutinin-positive Mac-2-binding protein and type 4 collagen 7S: useful markers for the diagnosis of significant fibrosis in patients with non-alcoholic fatty liver disease. J Gastroenterol Hepatol 33, 1795\u0026ndash;1803 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoe, E. K. \u0026amp; Kang, H. Y. The association between platelet-related parameters and nonalcoholic fatty liver disease in a metabolically healthy nonobese population. \u003cem\u003eScientific Reports 2024 14:1\u003c/em\u003e 14, 1\u0026ndash;8 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeki, E. \u0026amp; Schwabe, R. F. Hepatic inflammation and fibrosis: Functional links and key pathways. Hepatology 61, 1066\u0026ndash;1079 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSemenovich, D. S. \u003cem\u003eet al.\u003c/em\u003e Fibrosis Development Linked to Alterations in Glucose and Energy Metabolism and Prooxidant\u0026ndash;Antioxidant Balance in Experimental Models of Liver Injury. Antioxidants 12, 1604 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTacke, F. \u003cem\u003eet al.\u003c/em\u003e EASL\u0026ndash;EASD\u0026ndash;EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J Hepatol 81, 492\u0026ndash;542 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRinella, M. E. \u003cem\u003eet al.\u003c/em\u003e A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology 78, 1966\u0026ndash;1986 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerzigotti, A. \u003cem\u003eet al.\u003c/em\u003e EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis \u0026ndash;\u0026thinsp;2021 update. J Hepatol 75, 659\u0026ndash;689 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEddowes, P. J. \u003cem\u003eet al.\u003c/em\u003e Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. Gastroenterology 156, 1717\u0026ndash;1730 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDieterle, F., Ross, A., Schlotterbeck, G. \u0026amp; Senn, H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in1H NMR metabonomics. Anal Chem 78, 4281\u0026ndash;4290 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBedossa, P. \u0026amp; Poynard, T. An algorithm for the grading of activity in chronic hepatitis C. Hepatology 24, 289\u0026ndash;293 (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan den Berg, R. A., Hoefsloot, H. C. J. J., Westerhuis, J. A., Smilde, A. K. \u0026amp; van der Werf, M. J. Centering, scaling, and transformations: Improving the biological information content of metabolomics data. BMC Genomics 7, 1\u0026ndash;15 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePitti, E. \u003cem\u003eet al.\u003c/em\u003e Metabolic Crosstalk in Multimorbidity: Identifying Compensatory Effects Among Diabetes, Hypertension, and Dyslipidemia. J Endocr Soc 8, bvae152 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurnham, K. P. \u0026amp; Anderson, D. R. Multimodel Inference:Understanding AIC and BIC in Model Selection. Sociol Methods Res 33, 261\u0026ndash;304 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong, T. T. \u0026amp; Yeh, P. Y. Reliable Accuracy Estimates from k-Fold Cross Validation. IEEE Trans Knowl Data Eng 32, 1586\u0026ndash;1594 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteyerberg, E. W. \u003cem\u003eet al.\u003c/em\u003e Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21, 128\u0026ndash;138 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuse, A. The Likelihood Ratio, Wald, and Lagrange Multiplier Tests: An Expository Note. Am Stat 36, 153\u0026ndash;157 (1982).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, W. Volcano plots in analyzing differential expressions with mRNA microarrays. J Bioinform Comput Biol 10, (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, M. \u003cem\u003eet al.\u003c/em\u003e Serum Creatinine as an Independent Predictor of Moderate to Severe Fibrosis in Chinese American Non-obese Metabolic Dysfunction-Associated Steatotic Liver Disease. \u003cem\u003eCureus\u003c/em\u003e 16, (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu, K. \u003cem\u003eet al.\u003c/em\u003e Increased Glutaminolysis Marks Active Scarring in Nonalcoholic Steatohepatitis Progression. Cell Mol Gastroenterol Hepatol 10, 1\u0026ndash;21 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThing, M. \u003cem\u003eet al.\u003c/em\u003e Targeted metabolomics reveals plasma short-chain fatty acids are associated with metabolic dysfunction-associated steatotic liver disease. BMC Gastroenterol 24, 1\u0026ndash;10 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnomoto, H. \u003cem\u003eet al.\u003c/em\u003e A New Metabolism-Related Index Correlates with the Degree of Liver Fibrosis in Hepatitis C Virus-Positive Patients. \u003cem\u003eGastroenterol Res Pract\u003c/em\u003e 2015, 926169 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnomoto, H. \u003cem\u003eet al.\u003c/em\u003e Association of amino acid imbalance with the severity of liver fibrosis and esophageal varices. Ann Hepatol 12, 471\u0026ndash;478 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshikawa, T. Branched-chain amino acids to tyrosine ratio value as a potential prognostic factor for hepatocellular carcinoma. World J Gastroenterol 18, 2005\u0026ndash;2008 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichitaka, K. \u003cem\u003eet al.\u003c/em\u003e Amino acid imbalance in patients with chronic liver diseases. Hepatology Research 40, 393\u0026ndash;398 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSterling, R. K. \u003cem\u003eet al.\u003c/em\u003e AASLD Practice Guideline on blood-based noninvasive liver disease assessment of hepatic fibrosis and steatosis. Hepatology (2024) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/HEP.0000000000000845\u003c/span\u003e\u003cspan address=\"10.1097/HEP.0000000000000845\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGodbole, N. \u003cem\u003eet al.\u003c/em\u003e Prognostic and pathophysiologic significance of il-8 (Cxcl8) in biliary atresia. J Clin Med 10, 2705 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlass, O. \u003cem\u003eet al.\u003c/em\u003e Serum Interleukin-8, Osteopontin, and Monocyte Chemoattractant Protein 1 Are Associated With Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. Hepatol Commun 2, 1344\u0026ndash;1355 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranceschi, C., Garagnani, P., Parini, P., Giuliani, C. \u0026amp; Santoro, A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol 14, 576\u0026ndash;590 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYadav, V. \u003cem\u003eet al.\u003c/em\u003e Interleukin levels and non-alcoholic fatty liver disease in chronic plaque psoriasis: An analytical case control study. Indian J Dermatol 68, 377\u0026ndash;384 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnorr, J. \u003cem\u003eet al.\u003c/em\u003e Interleukin-18 signaling promotes activation of hepatic stellate cells in mouse liver fibrosis. Hepatology 77, 1968\u0026ndash;1982 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y. Q., Cao, W. J., Gao, Y. F., Ye, J. \u0026amp; Zou, G. Z. Serum interleukin-34 level can be an indicator of liver fibrosis in patients with chronic hepatitis B virus infection. World J Gastroenterol 24, 1312\u0026ndash;1320 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAslam, H. \u003cem\u003eet al.\u003c/em\u003e The Role of Red Cell Distribution Width as a Prognostic Marker in Chronic Liver Disease: A Literature Review. International Journal of Molecular Sciences 2023, \u003cem\u003eVol. 24, Page 3487\u003c/em\u003e 24, 3487 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, W., Huang, H., Wang, Y., Yu, X. \u0026amp; Yang, Z. High red blood cell distribution width is closely associated with nonalcoholic fatty liver disease. Eur J Gastroenterol Hepatol 26, 174\u0026ndash;178 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCengiz, M., Candir, B. A., Yilmaz, G., Akyol, G. \u0026amp; Ozenirler, S. Is increased red cell distribution width an indicating marker of nonalcoholic steatohepatitis and fibrotic stage? World J Gastroenterol 19, 7412\u0026ndash;7418 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, H. M. ok \u003cem\u003eet al.\u003c/em\u003e Elevated red cell distribution width is associated with advanced fibrosis in NAFLD. Clin Mol Hepatol 19, 258\u0026ndash;265 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, R., Prakash, S. S., Priyadarshi, R. N. \u0026amp; Anand, U. Sarcopenia in Chronic Liver Disease: A Metabolic Perspective. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.xiahepublishing.\u003c/span\u003e\u003cspan address=\"http://www.xiahepublishing.\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003ecom/\u003c/em\u003e 10, 1213\u0026ndash;1222 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, T. \u003cem\u003eet al.\u003c/em\u003e Impact of skeletal muscle mass evaluating methods on severity of metabolic associated fatty liver disease in non-elderly adults. British Journal of Nutrition 130, 1373\u0026ndash;1384 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRojas, \u0026Aacute;., Garc\u0026iacute;a-Lozano, M. R., Gil-G\u0026oacute;mez, A., Romero-G\u0026oacute;mez, M. \u0026amp; Ampuero, J. Glutaminolysis-ammonia-urea Cycle Axis, Non-alcoholic Fatty Liver Disease Progression and Development of Novel Therapies. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.xiahepublishing.\u003c/span\u003e\u003cspan address=\"http://www.xiahepublishing.\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003ecom/\u003c/em\u003e 10, 356\u0026ndash;362 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho, E. H. Succinate as a regulator of hepatic stellate cells in liver fibrosis. Front Endocrinol (Lausanne) 9, 383762 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, H. \u003cem\u003eet al.\u003c/em\u003e Intestinal microbiome and NAFLD: molecular insights and therapeutic perspectives. J Gastroenterol 55, 142\u0026ndash;158 (2019).\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Metabolic associated steatotic liver disease, fibrosis, glutamine, propionate, gut-liver axis, glutaminolysis","lastPublishedDoi":"10.21203/rs.3.rs-5268526/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5268526/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe burden of metabolic dysfunction-associated steatotic liver disease (MASLD) is of immediate concern, as its prevalence is increasing worldwide. MASLD often progresses to liver fibrosis, posing significant health risks. Age-independent non-invasive tools to evaluate fibrosis are needed to improve diagnostic accuracy across all age groups.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003e84 inflammatory, hematological, and metabolic variables were quantified in the blood of n\u0026thinsp;=\u0026thinsp;63 individuals with MASLD with different degrees of fibrosis and n\u0026thinsp;=\u0026thinsp;22 age-matched controls. Linear regression models were employed to identify markers strongly correlated with liver fibrosis but not influenced by age. Logistic regression models were used to evaluate the ability of various indexes to discriminate between no/mild and severe liver fibrosis.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eLevels of glutamine and propionate were identified as strongly correlated to fibrosis but not age and combined to form the GP index. The GP index demonstrated superior predictive power for liver fibrosis compared to existing scores, like circulating creatinine. It showed higher discriminatory ability (AUC\u0026thinsp;=\u0026thinsp;0.872) and better model fit, indicating its robustness and reliability across all age groups.\u003c/p\u003e\u003ch2\u003eConclusions.\u003c/h2\u003e \u003cp\u003eThe study introduces the GP index, an age-independent tool for diagnosing and monitoring liver fibrosis in MASLD patients. By excluding age-dependent markers, the GP index can potentially reduce false positives and improve diagnostic accuracy, particularly in older populations. The combination of glutamine and propionate in this index reflects a novel approach, capturing both intrinsic hepatic metabolic changes and extrinsic influences from gut microbiota, offering a simple yet effective solution for liver fibrosis staging.\u003c/p\u003e","manuscriptTitle":"An age-independent MASLD-related liver fibrosis index reflecting gut dysbiosis and hepatic stellate cells reprogramming","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 05:58:35","doi":"10.21203/rs.3.rs-5268526/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d61d4265-4960-4a71-9655-e197b24f9c29","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":40008648,"name":"Biological sciences/Biochemistry/Metabolomics"},{"id":40008649,"name":"Biological sciences/Physiology/Metabolism"}],"tags":[],"updatedAt":"2025-04-03T10:45:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 05:58:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5268526","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5268526","identity":"rs-5268526","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}