ApoL1 and ApoJ as Novel Determinants of MASH: A cross-sectional study

ApoL1 and ApoJ as Novel Determinants of MASH: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article ApoL1 and ApoJ as Novel Determinants of MASH: A cross-sectional study Zichun CAI, Souad Najib, María A. Nuñez-Sanchez, María A. Martinez-Sanchez, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6718852/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Lipids in Health and Disease → Version 1 posted 4 You are reading this latest preprint version Abstract Background Plasma apolipoproteins are linked to cardiometabolic dysfunctions, but their potential as biomarkers for metabolic dysfunction-associated steatohepatitis (MASH) remains underexplored. Methods Plasma levels of 14 apolipoproteins (apoA-I, A-II, A-IV, B100, C-I, C-II, C-III, D, E, F, H, J, L1, M) were measured using liquid chromatography-tandem mass spectrometry in a cross-sectional study of 148 individuals with obesity undergoing bariatric surgery. Participants were categorized based on liver histology into non-MASH (no liver alterations or simple steatosis, defined as ≥ 5% intrahepatic fat) and MASH (steatosis with ballooning and lobular inflammation, with or without fibrosis). Correlations with clinical and biochemical parameters were assessed via Spearman’s rank correlation, and associations with MASH were evaluated using logistic regression. Results Plasma levels of apoC-III and apoL1 were significantly higher in MASH participants compared to non-MASH participants, while the levels of other apolipoproteins did not differ significantly between the two groups. Higher plasma levels of apoE, apoL1 and apoJ were associated with increased odds of MASH, independently of age and sex. The associations for apoL1 and apoJ remained significant after adjusting for MASH risk factors, including insulin resistance, plasma triglycerides, waist circumference, and the AST/ALT ratio, as well as comorbidities such as diabetes, dyslipidemia, and hypertension. Conclusions Plasma apoJ and apoL1 may serve as potential biomarkers for diagnosing MASH in individuals with obesity, independent of traditional risk factors and comorbidities. Further validation in larger cohorts and exploration of the underlying biological mechanisms linking these apolipoproteins to MASH are warranted. Metabolic dysfunction-associated steatohepatitis Obesity Apolipoprotein Biomarkers Diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Metabolic dysfunction-associated steatotic liver disease (MASLD) is a chronic progressive condition primarily characterized by abnormal triglyceride accumulation in the liver, known as simple hepatic steatosis [ 1 ]. Over time, MASLD might progress to metabolic dysfunction-associated steatohepatitis (MASH), characterized by hepatocyte ballooning and lobular inflammation, and, in severe cases, liver fibrosis, significantly increasing the risk of hepatocellular carcinoma and cardiovascular disease [ 2 ]. MASH is particularly prevalent among individuals with obesity, whom can also present other metabolic treats such as type 2 diabetes (T2D), insulin resistance, and dyslipidemia. Despite the link between MASH and obesity, a subset of individuals with obesity appear metabolically healthy and have a lower prevalence of MASH, often limited to simple steatosis, even though progression to MASH can still occur [ 3 – 5 ]. Thus, identifying circulating markers for MASH in individuals with obesity, independent of their metabolic status, is clinically relevant. Notably, while non-invasive methods like transient elastography and biomarker-based scoring systems assist in screening and monitoring, liver biopsy remains the gold standard for MASH diagnosis when precise staging is required [ 6 ]. Thus, novel non-invasive biomarkers are crucial to improve diagnostic accuracy and reduce reliance on invasive procedures. Beyond the need for better biomarkers, pharmacological options for MASH remain limited. Resmetirom, a thyroid hormone receptor β-agonist, is the only specific approved therapy, yet only 30% of patients achieve MASH resolution without fibrosis progression [ 7 ]. Meanwhile, GLP-1 and GIP/GLP-1 receptor agonists, which show promising results in weight loss and MASH resolution [ 8 ], though their long-term effects remain under investigation [9]. Currently, the MASH management relies primarily on dietary and lifestyle interventions to promote weight loss through healthy eating, calorie reduction, and increasing physical activity. When these measures fail, bariatric surgery (BS) has been the primary option to treat non-cirrhotic MASLD/MASH patients [10]. This underscores the need for alternative treatments and precision medicine approaches, including the identification of novel biomarkers. Given the complex etiology of MASLD/MASH—shaped by genetic predisposition (e.g., PNPLA3 variants), metabolic dysregulation, lifestyle factors, and environmental influences—targeted approaches are essential for effective disease management. Apolipoproteins are multifunctional proteins involved in lipoprotein assembly, structure, and metabolism, influencing cell-surface receptor binding for lipid uptake as well as the regulation of lipase and lipid-transfer enzymes [11]. Although some studies have linked plasma levels of apoB100 (the main LDL apolipoprotein), apoA-I (the main HDL apolipoprotein), apoC-III (a natural inhibitor of lipoprotein lipase), and apoF to MASLD [12–16], other apolipoproteins with less understood functions have been poorly investigated in the context of MASH diagnosis. This cross-sectional study explored the relationship between plasma concentrations of 14 apolipoproteins and MASH status in individuals with obesity prior to bariatric surgery, with the aim of providing new insights into the role of apolipoproteins in MASH pathophysiology and highlighting their potential use as biomarker for early disease detection and risk stratification. Subjects and Methods Study participants The study workflow is illustrated in Fig. 1 . In the present study, we included a total of 148 individuals with obesity who underwent BS (Roux-en-Y gastric bypass) at the Virgen de la Arrixaca University Hospital (Murcia, Spain) between 2020 and 2022. Inclusion criteria included a signed informed consent, age between 18 and 65 years, a body mass index (BMI) of 30 kg/m² with significant obesity-related comorbidities, and a duration of obesity of 5 years or more. Exclusion criteria were evidence of liver disease other than MASLD (including viral hepatitis, medication-related disorders, autoimmune disease, hepatocellular carcinoma, hemochromatosis, Wilson’s disease, familial/genetic causes), a previous history of excessive alcohol consumption (> 30 g daily for men and > 20 g daily for women), treatment with any drugs potentially causing steatosis (e.g. tamoxifen, amiodarone, and valproic acid), or subjects who declined to participate. Liver sample collection and histological analysis Intraoperative wedge liver biopsies with a minimum of 1cm 2 were obtained from individuals who underwent BS. One section of the biopsy was rapidly frozen and stored at − 80℃, whereas the other section was fixed in formalin and embedded in paraffin for histopathological evaluation. 5-mm sections of paraffin-embedded liver biopsies were stained using hematoxylin and eosin, Masson trichrome, periodic acid-Schiff, Perls, and reticulin staining. Pathologists from the Virgen de la Arrixaca University Hospital and the Experimental Pathology Unit of the Biomedical Research Institute of Murcia reviewed and scored all biopsies to determine the steatosis, activity, and fibrosis (SAF) score, as previously described [17, 18]. Study design The study population was categorized into two groups based on the histopathological evaluation of liver biopsies using the SAF classification system [19]: (1) the non-MASH group, which included participants whose liver biopsies showed no histologic alterations, or at least grade 1 (5% or less) intrahepatic fat accumulation, and (2) the MASH group, which included participants whose liver biopsies demonstrated at least grade 1 steatosis, along with evidence of ballooning and lobular inflammation, with or without fibrosis. Lifestyle risk factors included smoking, where an individual is considered a non-smoker if they have never smoked or are a former smoker who has not smoked for at least 5–10 years, and alcohol consumption, where an individual is considered a non-alcohol consumer if no alcoholic habits have been reported. Comorbidities included hypertension (systolic blood pressure [SBP] ≥ 140 mmHg, and/or diastolic blood pressure [DBP] ≥ 90 mmHg at rest, and/or antihypertensive treatment), diabetes (fasting plasma glucose [FPG] ≥ 126 mg/dL, and/or hypoglycaemic treatment) and dyslipidemia (LDL-C ≥ 160 mg/dL, and/or triglyceride levels ≥ 150 mg/dL, and/or use of lipid-lowering treatment). Biochemical analysis Blood samples were obtained on the day of the surgery after a minimum 12-hour overnight fast, and serum and plasma were obtained by centrifugation. Samples were anonymized and blinded for MASH status. The following parameters were measured using the Cobas Analyzer c702 (Roche): glucose, total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) levels. Glycated hemoglobin (HbA 1C ) levels were determined using the glycohemoglobin analyzer HLC-723G8 (Tosoh Bioscience). Insulin levels were measured using the Cobas Analyzer e801 (Roche). Insulin resistance was assessed using the homeostasis model assessment of the insulin resistance index (HOMA-IR), calculated as insulin (µU/mL) x glucose (mmol/L)/22.5 [20]. Apolipoprotein measurements. Plasma concentrations of apolipoproteins (A-I, A-II, A-IV, B100, C-I, C-II, C-III, D, E, F, H, J, L1, and M) were determined by liquid chromatography-tandem mass spectrometry, as described previously [21]. Briefly, the apolipoproteins were quantified in 40-µL aliquots (EDTA plasma) using trypsin proteolysis and the subsequent analysis of proteotypic peptides. The intra- and inter-assay variabilities were measured and did not exceed 9.4% [21]. RNA purification and qPCR analysis Liver biopsies were collected during surgery and further preserved at − 80℃ in RNAlater (Sigma) until analysis. Total RNA was extracted and purified using TRIzol reagent (Life Technologies) and GeneJET RNA Purification kit (Thermo Scientific). One microgram of purified RNA was reverse transcribed using the High-Capacity RNA-to-cDNA kit (Applied Biosystems) according to the manufacturer’s instructions. For gene transcription quantification, qPCR amplification was performed using the Power SYBR Green Master mix (Applied Biosystems) on a Fast 7500 Real-Time instrument (Applied Biosystems), and relative transcription levels were performed using the 2^−ΔΔCt method, with 18S rRNA as a housekeeping gene. Gene and protein nomenclature Gene and protein names are based on gene names and were capitalized (Hugo Gene Nomenclature Committee, https://www.genenames.org/about/guidelines/ ). Statistical analysis All categorical parameters were expressed as the number (%) and tested by Pearson’s Chi-squared test. All quantitative parameters were expressed as the mean ± standard deviation (SD) and tested using Two-tailed unpaired Student’s t test unless otherwise specified. When the distribution was considered as skewed, parameters were expressed as the median (25th percentile; 75th percentile) and tested by Wilcoxon rank sum test. The correlation between the baseline characteristics was studied as a cross-sectional study. Spearman’s correlation coefficients ( r s ) were calculated between plasma apolipoprotein concentrations and the clinical characteristics, glucose homeostasis, and lipid values with p < 0.05 considered. Multivariable regression models were applied with the following adjustments: adjusted for age and sex (model 1), adjusted for age, sex, HOMA-IR, triglycerides, waist circumference, and AST/ALT ratio (model 2), and adjusted for age, sex, hypertension, diabetes and dyslipidemia (model 3). All analyses were performed using R software version 4.0.0 [22], and the R scripts are available upon request. Results Characteristics of the study population Baseline clinical and biochemical characteristics for the entire cohort, as well as for two groups categorized by MASH status (non-MASH vs MASH), are summarized in Table 1 . The overall population consisted of middle-aged individuals (46 ± 11 years), with severe obesity (BMI: 43.4 ± 6.0 kg/m²), predominantly women (75%), and exhibited insulin resistance (HOMA-IR: 3.0 [1.6; 4.8]) [23]. Other glucose metabolism parameters, including FPG, insulin and HbA 1c were mostly normal or near abnormal thresholds, while plasma lipids, such as triglycerides and LDL-C, were slightly elevated, likely reflecting partial treatment within the population. Accordingly, 72.3% had dyslipidemia, but only 29.1% received lipid-lowering drugs, while 45.9% had diabetes with 43.9% on anti-diabetic medications (Table 1 ). The prevalence of hypertension was high (72.3%), yet only 35.8% of affected individuals received antihypertensive treatment. Overall, solely 15.5% of the study population had neither a diagnosis nor treatment for comorbidities. Table 1 Sociodemographic, clinical and biological characteristics of the study population according to MASH status. Characteristics Whole population (n = 148) Non-MASH (n 1 = 94) MASH (n 2 = 54) p-value N/A (n 1 ,n 2 ) Age 46 ± 11 45 ± 11 48 ± 10 0.078 1 (0,0) Gender 0.030 2 (0,0) Women, n (%) 111 (75.0) 76 (80.9) 35 (64.8) Men, n (%) 37 (25.0) 18 (19.1) 19 (35.2) Smoking 0.625 2 (1,0) No, n (%) 86 (58.5) 53 (57.0) 33 (61.1) Yes, n (%) 61 (41.5) 40 (43.0) 21 (38.9) Alcohol consumption 0.279 2 (1,0) No, n (%) 133 (90.5) 86 (92.5) 47 (87.0) Yes, n (%) 14 (9.5) 7 (7.5) 7 (13.0) Anthropometric measures BMI, kg/m 2 43.4 ± 6.0 43.5 ± 5.6 43.3 ± 6.8 0.849 1 (0,0) Waist circumference, cm 124 [117; 134] 124 [117; 132] 127 [118; 139] 0.230 3 (1,0) Glucose metabolism FPG , mg/dL 95 [84; 104] 92 [84; 100] 102 [89; 123] < 0.001 3 (0,0) Hb1Ac , % 5.70 [5.40; 6.10] 5.60 [5.30; 5.95] 5.80 [5.60; 6.70] 0.001 3 (3,0) Insulin , µUI/mL 12 [7; 19] 12 (7, 18) 15 [9; 27] 0.017 3 (1,0) HOMA-IR 3.0 [1.6; 4.8] 2.7 [1.5; 4.0] 3.7 [2.2; 7.7] 0.002 3 (0,0) Lipid metabolism Total cholesterol, mg/dL 163 ± 31 163 ± 31 163 ± 32 0.949 1 (0,0) Triglycerides , mg/dL 167 [125; 212] 155 [121; 206] 189 [154; 229] 0.035 3 (1,0) HDL-C , mg/dL 40 [34; 49] 42 [36; 50] 38 [33; 44] 0.050 3 (1,0) LDL-C, mg/dL 87 ± 29 88 ± 28 85 ± 31 0.617 1 (7,3) Transaminases AST , U/L 18 [15; 23] 17 [14; 20] 22 [18; 28] < 0.001 3 (5,5) ALT , U/L 19 [14; 28] 16 [12; 22] 24 [19; 40] < 0.001 3 (2,0) AST/ALT 0.95 [0.78; 1.16] 1.06 [0.87; 1.29] 0.90 [0.69; 1.00] < 0.001 3 (6,5) Blood pressure SBP, mmHg 140 [130; 150] 139 [126; 149] 141 [130; 150] 0.112 3 (0,0) DBP, mmHg 85 ± 12 86 ± 12 84 ± 11 0.553 1 (0,0) Comorbidities and treatments T2D , n (%) 4 68 (45.9) 33 (35.1) 35 (64.8) < 0.001 2 (0,0) Treatment T2D , n (%) 65 (43.9) 32 (34.0) 33 (61.1) 0.001 2 (0,0) Dyslipidemia , n (%) 5 107 (72.3) 59(62.8) 48 (88.9) 0.001 2 (2,0) Treatment dyslipidemia , n (%) 43 (29.1) 22 (23.4) 21 (38.9) 0.046 2 (0,0) Hypertension, n (%) 6 107 (72.3) 67 (71.3) 40 (74.1) 0.714 2 (0,0) Treatment hypertension, n (%) 53 (35.8) 32 (34.0) 21 (38.9) 0.554 2 (0,0) None, n (%) 23 (15.5) 17 (18.1) 6 (11.1) 0.260 2 (0,0) Categorical parameters are expressed as the number of individuals (%). Quantitative parameters are expressed as the mean ± SD for Gaussian distribution or as the median [25th percentile; 75th percentile] for non-Gaussian distribution. The difference between the two groups (non-MASH versus MASH) were analyzed using the following statistical test: 1 Two-tailed unpaired Student’s t test, 2 Pearson's Chi-squared test, or 3 Wilcoxon rank sum test. Comorbidities were defined as follows: 4 Diabetes: fasting plasma glucose ≥ 126 mg/dL or treatment; 5 Dyslipidemia: LDL-C ≥ 160 mg/dL and/or triglycerides (TG) ≥ 150 mg/dL or treatment; 6 Hypertension: systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg or treatment; 7 None: no diabetes, no dyslipidemia and no hypertension. ALT, alanine aminotransferase; AST, aspartate aminotransferase; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; N/A: not available; SBP, systolic blood pressure; T2D, type 2 diabetes. Significant differences were observed between the non-MASH and MASH groups. Specifically, the diagnosis of MASH was associated with a marked difference in sex distribution, with women being significantly less represented in the MASH group (64.8% versus 80.9% in non-MASH group, p = 0.03). Individuals with MASH were more insulin resistant, as documented by higher HOMA-IR values compared to the non-MASH (p = 0.002), along with significantly higher level of FPG, insulin, and HbA 1c . Additionally, triglycerides were significantly higher, and HDL-C levels were significantly lower in the MASH group compared to the non-MASH group. Consistent with these metabolic differences, individuals with MASH had a higher prevalence of diabetes and dyslipidemia, but showed no difference in the prevalence of hypertension. Furthermore, individuals with MASH had a higher level of transaminases (AST and ALT), accompanied by a lower AST/ALT ratio (p < 0.001). No significant differences were observed between groups in terms of anthropometric measures (BMI, waist circumference) and lifestyle risk factors such as smoking and alcohol consumption. Plasma concentration of apolipoproteins by MASH status The baseline concentrations of plasma apolipoproteins, categorized by patient MASH status, are presented in Table 2 . Among the apolipoproteins analyzed, plasma levels of apoC-III and apoL1 were significantly higher in the MASH group compared to the non-MASH group (p = 0.038 and 0.025, respectively). Additionally, levels of apoC-I, apoC-II, apoE and apoJ were marginally increased in the MASH group (p < 0.1). In contrast, apoA-I, apoA-II, apoA-IV, apoB100, apoD, apoF, apoH, and apoM did not significantly differ between groups. Table 2 Plasma concentration of apolipoproteins according to MASH status. Apolipoproteins (mg/dL) Whole population (n = 148) Non-MASH (n 1 = 94) MASH (n 2 = 54) p-value N/A (n 1 ,n 2 ) ApoA-I 120 ± 28 122 ± 30 115 ± 23 0.116 1 (0,0) ApoA-II 18.1 ± 5.3 18.2 ± 5.3 17.9 ± 5.5 0.768 1 (0,0) ApoA-IV 11.6 ± 5.6 11.5 ± 5.5 11.8 ± 5.9 0.720 1 (0,0) ApoB100 89 ± 42 88 ± 41 92 ± 43 0.548 1 (0,0) ApoC-I 2.22 ± 0.78 2.14 ± 0.77 2.37 ± 0.77 0.083 1 (0,0) ApoC-II 3.23 ± 2.86 2.91 ± 2.62 3.80 ± 3.18 0.083 1 (0,0) ApoC-III 5.41 ± 1.79 5.16 ± 1.58 5.85 ± 2.06 0.038 1 (0,0) ApoD 11.5 ± 4.0 11.3 ± 4.0 11.9 ± 4.1 0.391 1 (0,0) ApoE 5.54 ± 1.78 5.36 ± 1.90 5.85 ± 1.49 0.090 1 (0,0) ApoF 1.60 ± 0.38 1.57 ± 0.39 1.65 ± 0.37 0.189 1 (0,0) ApoH 4.70 ± 2.17 4.71 ± 2.32 4.67 ± 1.89 0.904 1 (0,0) ApoJ 9.8 ± 5.5 9.2 ± 4.8 10.9 ± 6.4 0.093 1 (0,0) ApoL1 2.40 ± 1.05 2.25 ± 0.98 2.67 ± 1.13 0.025 1 (0,0) ApoM 2.27 ± 0.62 2.22 ± 0.66 2.36 ± 0.54 0.163 1 (0,0) Data are expressed as the mean ± SD. 1 Two-tailed unpaired Student’s t test. N/A, not available. Association between plasma apolipoprotein levels and biochemical parameters For the study population, spearman’s rank correlation coefficients ( r s ) were calculated to evaluate the associations among plasma apolipoproteins (Fig. 2 and Supplementary Table 1) and between apolipoproteins and biochemical parameters at baseline (Fig. 3 and Supplementary Table 2). As anticipated, many apolipoproteins were intercorrelated due to their transport by lipoproteins [24] (Fig. 2 ). Specifically, apoA-I, the primary HDL apolipoprotein, showed positive correlations with apoA-II, apoE, apoL1 and apoM. Similarly, significant positive intercorrelations were observed among apoB100—a major protein constituent of VLDL and LDL—, apoE, apoC-I, apoC-II, and apoC-III. Among other apolipoproteins exhibiting multiple correlations, apoL1 positively correlated with apoA-I, apoC-I, apoC-III, apoE and apoM. ApoM displayed a similar correlation profile to apoL1, with additional correlations to apoA-II, apoC-II, and apoD, consistent with its transport by both HDL and LDL [25]. In contrast, apoA-IV was positively correlated only with apoA-I and negatively correlated with apoB100., while apoF was negatively correlated only with apoD and apoH. Additionally, apoD was positively correlated with both apoC-I and apoC-II, while apoH showed positive correlations with apoB100 and apoC-III. Notably, apoJ was the only apolipoprotein that did not show any correlation with the other apolipoproteins (Fig. 2 ). As expected, most plasma apolipoproteins were associated with lipid levels (Fig. 3 ). Half of the measured apolipoproteins, including apoA-I, apoA-II, apoB100, apoC-I, apoC-II, apoE, and apoM, showed a positive correlation with total cholesterol. ApoB100, apoC-I and apoE were positively associated with LDL-C, while apoC-III exhibited a negative correlation with HDL-C. In contrast, apoA-I, apoA-II and apoM were positively correlated with HDL-C. Additionally, apoC-I, apoC-II, apoC-III and apoE displayed a positive correlation with triglycerides. Regarding glycemic parameters (Fig. 3 ), apoA-IV, apoC-I, apoC-III, and apoF levels were positively correlated with FPG. Additionally, apoC-I was positively correlated with insulin, and both apoC-I and apoC-III showed significant positive correlations with HOMA-IR. ApoA-IV and apoC-III were also positively correlated with HbA 1c . ApoC-III, apoE and apoL1 exhibited positive correlations with plasma transaminase levels (AST and ALT). ApoA-IV and apoC-III were positively correlated with age, whereas apoA-I was negatively correlated with BMI and waist circumference. Furthermore, apoA-IV was positively associated with SBP, while apoC-I and apoL1 were positively associated with DBP. Noteworthily, no correlation was observed between apoH or apoJ and any of the measured biochemical parameters (Fig. 3 ). Plasma apolipoprotein levels and MASH diagnosis. The results of multivariable logistic regression analyses examining plasma apolipoproteins, HDL-C, and LDL-C levels in relation to MASH status are presented in Fig. 4 . In a model adjusted for age and sex (Model 1), plasma apoE, apoJ, and apoL1 were significantly associated with a diagnosis of MASH. The respective odds ratios (OR) per 1 SD increase were as follows: apoE (OR = 1.23, 95% CI [1.00–1.51], p = 0.046), apoJ (OR = 1.07, 95% CI [1.00–1.14], p = 0.040), and apoL1 (OR = 1.53, 95% CI [1.09–2.16], p = 0.015). These associations persisted for apoJ and apoL1 after further adjustment for classical MASH risk factors, including HOMA-IR, plasma triglyceride levels, waist circumference, and the AST/ALT ratio (Model 2: apoJ, OR = 1.08, 95% CI [1.00–1.16], p = 0.047; apoL1, OR = 1.54, 95% CI [1.00–2.37], p = 0.052), as well as for comorbidities such as diabetes, dyslipidemia, and hypertension (Model 3: apoJ, OR = 1.07, 95% CI [1.00–1.15], p = 0.055; apoL1, OR = 1.60, 95% CI [1.09–2.35], p = 0.017). Notably, among plasma apolipoproteins, those with differential concentration based on MASH status (apoC-III and apoL1, Table 2 ) and/or associated with MASH in multivariable logistic regression (apoE, apoJ, and apoL1, Fig. 4 ) were further analyzed at the hepatic level. As shown in Fig. 5 , hepatic mRNA levels of APOC3 , APOJ , and APOL1 —but not APOE —were significantly higher in individuals with obesity who were histologically diagnosed with MASH compared to those without MASH. Discussion MASH is a leading cause of liver transplantation and there are not specific biomarkers for the efficient diagnosis/prognosis of MASH. Obesity and metabolic status strongly drive MASH development [ 3 , 4 ]. However, metabolic status alone is unreliable for MASH assessment in individuals with obesity as metabolic health is often transient and deteriorates over time. In turn, MASH itself may exacerbate metabolic dysfunction, thereby displaying bidirectional interactions [ 4 ]. In this context, the early diagnosis of advanced MASH is becoming critical for timeline intervention in high-risk subjects with obesity [26]. Therefore, it becomes essential to identify non-invasive biomarkers for MASH diagnosis in individuals with obesity, independent of risk factors and comorbidities. The concentration of circulating apolipoproteins is sensitive to disturbances of the metabolic status and has become promising candidates to biomarkers of metabolic diseases. As such, altered concentrations of apolipoproteins are commonly related to alterations in lipoprotein metabolism and linked to adverse outcomes, including atherosclerosis and MASLD [11], highlighting their potential role in MASH pathophysiology. However, research investigating the relationship between plasma apolipoproteins and MASH is rather limited, with studies mainly focusing on apoA-I, apoB100 or apoC-III and MASLD [12–15, 27]. Moreover, the analysis of the relationship, if any, of other less studied apolipoproteins that structurally and functionally differ from classic apolipoproteins has remained elusive possibly due to the analytical technical issues. This study is the first to analyze plasma levels of 14 apolipoproteins in individuals with obesity undergoing BS and classified into non-MASH (simple steatosis/no disease) and MASH groups according to histological analysis. As expected, individuals with MASH were more likely to have T2D and dyslipidemia, with greater HOMA-IR, hypertriglyceridemia, and a lower AST/ALT ratio (< 1)—a recognized marker of MASH severity [28, 29]—despite comparable BMI and lifestyle factors. Our findings reveal that MASH status was significantly associated with the plasma concentrations of apoE, apoJ, and apoL1, independent of age and sex. This association remained significant for apoJ and apoL1 after further adjustment for traditional risk factors, including central obesity (waist circumference), insulin resistance (HOMA-IR), and hypertriglyceridemia (plasma triglyceride levels), as well as comorbidities such as diabetes, dyslipidemia, and hypertension. Notably, hepatic mRNA expression of APOJ and APOL1 was also higher in the MASH group, suggesting that their plasma protein levels may serve as proxies for their expression by the liver. Overall, these results suggest the potential of apoJ and apoL1 as independent determinants of MASH. Our results align with a previous study reporting that plasma apoJ and apoL1 are associated with liver fibrosis in patients with hepatitis C [30]. These findings pave the way for further research into the clinical utility of apoJ and apoL1 in early MASH detection, particularly in individuals with obesity. ApoJ, also known as clusterin, is a secreted sulfated glycoprotein that exists as a heterodimer (α ~ 35 kDa, β ~ 37 kDa) and is widely distributed across various tissues and fluids [31]. In human plasma, apoJ primarily originates from the liver and brain and is predominantly transported by HDL particles [32]. It has been proposed to exert an atheroprotective role through HDL-mediated reverse cholesterol transport and by enhancing paraoxonase 1 (PON1) activity, which protects against LDL oxidation [33]. Additionally, apoJ functions as an hepatokine, regulating muscle glucose metabolism via a low-density lipoprotein receptor-related protein-2 (LRP2)-dependent mechanism, thereby protecting against insulin resistance and glucose intolerance [34, 35]. However, in hyperlipidemia, HDL-bound apoJ decreases, while total circulating apoJ increases, correlating with triglycerides, cholesterol, and VLDL-C levels [36]. This redistribution from HDL-bound to unbound apoJ in metabolic syndrome may compromise its metabolic and cardiovascular protective functions. In our study, we measured total plasma apoJ without distinguishing its HDL association. However, based on the aforementioned report [36], non-HDL-bound apoJ would likely predominate in our study population of individuals with obesity, potentially explaining its association with MASH status. Notably, plasma apoJ was the only apolipoprotein in our dataset that showed no correlation with other apolipoproteins or bioclinical variables such as BMI and HDL-C. Nevertheless, it independently predicted MASH status in multivariable logistic regression analysis. Consistent with this observation, a recent study reported upregulation of apoJ in high-fat medium-fed hepatocytes and in the livers of patients with MAFLD [37]. Moreover, antagonizing apoJ was shown to promote proteasomal degradation of mTOR and alleviate hepatic lipid deposition [37]. ApoL1 is part of a six-member family (ApoL1–6) encoded by a 619 kb gene cluster on human chromosome 22 [38]. Among the six apoL family members, only apoL1 is secreted [38, 39] and it is the most extensively studied due its role in innate immunity against Trypanosoma brucei , the parasite responsible for African sleeping sickness [38]. Although apoL1 is widely expressed in human tissues [40–42], the liver is the primary source of circulating apoL1 [39, 43]. In the bloodstream, it is mainly associated with HDL 3 particles and, along with PON1 and PON3, may contribute to LDL protection from oxidation [44]. Supporting a potential atheroprotective function, reduced circulating apoL1 levels in patients with familial hypercholesterolemia have been linked to an increased risk of fatal cardiovascular events [45]. However, a separated study on coronary patients with low HDL-C levels found an association between apoL1 levels and hypertriglyceridemia as well as hyperglycemia [46], suggesting a role in impaired triglyceride and glucose metabolism—both key drivers of MASH. Consistently, a cross-sectional study showed that individuals with elevated apoL1 levels were more prone to insulin resistance, obesity, hypertriglyceridemia, and low HDL-C [47]. Furthermore, one longitudinal five-year studies reported that higher plasma levels of apoL1, along with apoJ, were associated with an increased risk of developing T2D [48]. In addition to apoJ and apoL1, plasma levels of apoC-III, apoE and apoF have been shown to predict T2D more accurately than traditional lipid markers, independent of classical risk factors [48]. Since T2D is a major MASH risk factor, these apolipoproteins are plausible MASH biomarkers. For instance, plasma apoC-III and apoE levels, and particularly their oxidized fragments, are elevated in MASLD patients (simple steatosis) and correlate with obesity, insulin resistance, dyslipidemia, and elevated transaminases [49]. However, in our study, we observe an independent positive relationship with MASH status solely with apoJ and apoL1 levels, suggesting that these two apolipoproteins may play more direct or prominent roles in the pathogenesis of MASH compared to the other evaluated apolipoproteins. For instance, it is likely that the relationship between apoC-III and MASH, with higher levels observed in MASH participants compared to non-MASH participants, is primarily driven by its role in the metabolism of triglyceride-rich lipoproteins (TRLs) —acting as an inhibitor of lipoprotein lipase and hepatic lipase activities—as well as its involvement in insulin resistance and inflammation [50–54], all of which are key contributors to MASH pathophysiology. Consistent with these functions of apoC-III, our study observed significant positive correlations between its plasma levels and triglycerides, FPG, insulin, and HOMA-IR, as well as AST and ALT. This mechanistic link may also explain why no significant association was found between apoC-III and MASH in the multivariable logistic regression analysis, which was adjusted for traditional risk factors and comorbidities. These adjustments likely account for the overlapping contributions of hypertriglyceridemia, insulin resistance, inflammation, and other pathways shared between apoC-III and the established risk factors for MASH. Regarding apoF, its hepatic expression has been linked to the metabolism of TRLs via SREBP2 pathway activation, with both hepatic and plasma levels negatively correlated with steatosis in individuals with obesity [16]. However, its role in MASH progression beyond simple steatosis remains unexplored. Our study did not find any correlation between plasma levels of apoF and MASH, suggesting that circulating apoF is unlikely to serve as a marker for advanced MASLD stages, as its function appears to be limited to liver lipid metabolism regulation. Based on available evidence, our work is the first to investigate the association between plasma levels of apolipoproteins and MASH in individuals with obesity. However, our study has certain limitations. First, the limited sample size may impact in multiple aspects of our findings as a relatively small sample size reduces statistical power, making it more challenging to detect potential effects. Additionally, it can amplify random error, leading to greater variability in results and wider confidence intervals. In the multivariable logistic regression analysis, a smaller sample size limits the ability to adjust for potential confounders. Since MASLD and MASH are influenced by various cardiometabolic factors, residual confounding variables may still be present in our analysis. Finally, MASH is a progressive disease, yet our data is cross-sectional, collected at a single time point. A longitudinal study with repeated measurements of apolipoproteins at different disease stages would provide deeper insights into their role in disease progression. Conclusions Our findings suggest that apolipoproteins, specifically apoJ and apoL1, may serve as diagnostic biomarkers for MASH in individuals with obesity, beyond traditional cardiometabolic risk factors. If validated in larger longitudinal studies, these biomarkers could improve noninvasive MASH screening in at-risk populations and facilitate the monitoring of disease progression. Future large-scale, population-based cohort studies are needed to confirm these associations and explore their clinical applications. Abbreviations Apo: apolipoprotein; MASH: metabolic dysfunction-associated steatohepatitis; MASLD, metabolic dysfunction-associated steatotic liver disease ; T2D, type 2 diabetes ; BS, bariatric surgery ; BMI, body mass index ; SBP, systolic blood pressure ; DBP, diastolic blood pressure ; FPG, fasting plasma glucose ; HDL-C, high-density lipoprotein cholesterol ; LDL-C, low-density lipoprotein cholesterol ; ALT, alanine aminotransferase ; AST, aspartate aminotransferase ; HOMA-IR, homeostasis model assessment of the insulin resistance index ; SD, standard deviation ; OR, odds ratios ; PON1, paraoxonase 1 ; LRP2, lipoprotein receptor-related protein-2 ; TRLs, triglyceride-rich lipoproteins Declarations Ethics approval and consent to participate The research protocol was approved by the Ethics and Clinical Research Committees of the Virgen de la Arrixaca University Hospital (ref. number 2020-2-4-HCUVA; date: 31/03/2020). Informed consent to participate was signed by all participants and all of the reported investigations were carried out in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and material The data supporting this study are available in the article, in Supplementary Information, or from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Fundings This work was supported by a grant from the Federation Française de Cardiologie (Dotation Recherche # FFC – MARTINEZ – Dotation 2022) and conducted in the context of IHU HealthAge, which has benefited from funding from the ANR under the France 2030 program (ANR-23-IAHU-0011). Z.C. was supported by the China Scholarship Council (CSC) at the University of Toulouse. We also acknowledge the support received from CIBERDEM (CB15/00071), a project of the ISCIIII, as well as the Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033) and the European Union’s “NextGeneration EU”/PRTR program, under the "Consolidación Investigadora 2022" action (CNS2022-135559), which provided support to J.J. The Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau is accredited by the Generalitat de Catalunya as Centre de Recerca de Catalunya (CERCA). J.J. and J.R. belong to the XARTEC Salut network, and is part of the coordinated consolidated group AGAUR (2021 SGR 00857, and 2021 SGR 01211). J.J. is member of the Quality Research Group 2017-SGR-1149 from Generalitat de Catalunya). This work was also partly funded by the Institute of Health “Carlos III” (ISCIII), and co-funded by the Fondo Europeo de Desarrollo Regional-FEDER (grant number PI23/00171). M.A.M-S is supported by a PFIS predoctoral fellowship from the ISCIII (FI21/00003, ISCIII, Spain; co-funded by the Fondo Europeo de Desarrollo Regional-FEDER); M.A.N.-S. is supported by the “Miguel Servet” program (CP23/00051, ISCIII, Spain; co-funded by the Fondo Europeo de Desarrollo Regional-FEDER). The funding organizations played no role in the design of the study, review, and interpretation of the data, or final approval of the manuscript. Authors’ contributions Z.C. contributed to methodology, data curation, formal analysis, and wrote the original draft. S.N., N.V., J.R., A.G., and J.J. were involved in writing, review, and editing. J.J. also secured funding. M.A.N.-S., M.A.M.-S., C.G.-M., C.M.M., and M.D.F. provided resources. M.C. contributed to methodology and supervision, and participated in writing, review, and editing. A.R.O. was involved in methodology, data curation, and formal analysis. 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The Janus-faced functions of Apolipoproteins L in membrane dynamics. Cell Mol Life Sci. 2024;81. Adeva-Andany MM, Funcasta-Calderón R, Fernández-Fernández C, Ameneiros-Rodríguez E, Vila-Altesor M, Castro-Quintela E. The metabolic effects of APOL1 in humans. Pflugers Archiv European Journal of Physiology. 2023;475:911–32. Page NM, Butlin DJ, Lomthaisong K, Lowry PJ. The human apolipoprotein L gene cluster: Identification, classification, and sites of distribution. Genomics. 2001;74:71–8. Duchateau PN, Pullinger CR, Orellana RE, Kunitake ST, Naya-Vigne J, O’Connor PM, et al. Apolipoprotein L, a new human high density lipoprotein apolipoprotein expressed by the pancreas. Identification, cloning, characterization, and plasma distribution of apolipoprotein L. J Biol Chem. 1997;272:25576–82. Shukha K, Mueller JL, Chung RT, Curry MP, Friedman DJ, Pollak MR, et al. Most ApoL1 is secreted by the liver. J Am Soc Nephrol. 2017;28:1079–83. 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Plasma apolipoprotein concentrations and incident diabetes in subjects with prediabetes. Cardiovasc Diabetol. 2022;21:21. Mocciaro G, George AL, Allison M, Frontini M, Huang-Doran I, Reiman F, et al. Oxidised Apolipoprotein Peptidome Characterises Metabolic Dysfunction-Associated Steatotic Liver Disease. Liver Int. 2025;45:e16200. Wang H, Eckel RH. Lipoprotein lipase: from gene to obesity. Am J Physiol Endocrinol Metab. 2009;297:E271-88. Gangabadage CS, Zdunek J, Tessari M, Nilsson S, Olivecrona G, Wijmenga SS. Structure and dynamics of human apolipoprotein CIII. J Biol Chem. 2008;283:17416–27. Rehues P, Girona J, Guardiola M, Ozcariz E, Amigó N, Rosales R, et al. ApoC-III proteoforms are associated with better lipid, inflammatory, and glucose profiles independent of total apoC-III. Cardiovasc Diabetol. 2024;23. Borén J, Packard CJ, Taskinen M-RR. The Roles of ApoC-III on the Metabolism of Triglyceride-Rich Lipoproteins in Humans. Front Endocrinol (Lausanne). 2020;11. Gerritsen G, Rensen PCN, Kypreos KE, Zannis VI, Havekes LM, Van Dijk KW. ApoC-III deficiency prevents hyperlipidemia induced by apoE overexpression. J Lipid Res. 2005;46:1466–73. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1LHD.docx SupplementaryTable2LHD.docx Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2025 Read the published version in Lipids in Health and Disease → Version 1 posted Editorial decision: Revision requested 22 May, 2025 Editor assigned by journal 22 May, 2025 Submission checks completed at journal 22 May, 2025 First submitted to journal 21 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Martinez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACPhReAhDzM0M57A3YtbBBKAMQwdgA0iLZDJXiOUCMFjATphKnFvYzZh8+7vjDoNt+9vmDhzts7I2P8x7+8HHPNgYeaex62HhyjGfOPGPAYHYm3bAh8Uwas9lhvjTJGc9uM/DwJeBwWI4xM28bUMuBNMaGxLbDbGaHecyYeQ7cZrDnweEw/jfGzH9BWs4/A2n5z2PczGP8+Q9QCw8uLRJAWxhBWm6AbTkgYcDMYyDNgFfLs2LG3jZjHrMbzxhnJLYlG0gAHSbZc+A2Dy4t/PzJmxl+tsnJmZ1PY/j4s83Onr//jPGHHwduy+HSAgOY0gQ0jIJRMApGwSjABwB0NVNcxG7PAQAAAABJRU5ErkJggg==","orcid":"","institution":"Institute of Cardiovascular and Metabolic Diseases","correspondingAuthor":true,"prefix":"","firstName":"Laurent","middleName":"O.","lastName":"Martinez","suffix":""}],"badges":[],"createdAt":"2025-05-21 18:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6718852/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6718852/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12944-025-02733-0","type":"published","date":"2025-10-14T15:58:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83439049,"identity":"fead30f6-afbc-4c4c-96d2-f1aca9e118ad","added_by":"auto","created_at":"2025-05-26 09:08:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110625,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flowchart.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population includes patients with obesity who underwent bariatric surgery and were classified as non-MASH (n = 94) or MASH (n = 54) based on liver histology. Plasma levels of 14 apolipoproteins were analyzed at baseline. Apolipoprotein levels were compared according to MASH status.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6718852/v1/21f3044b0e1387b0aaaba9cf.jpg"},{"id":83439048,"identity":"468cee78-bbce-4f98-bc19-53cc802f23ab","added_by":"auto","created_at":"2025-05-26 09:08:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51331,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman correlations between plasma apolipoprotein concentrations.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma apolipoproteins were measured in the whole study population (n = 148). Size and color, according to the color scale detailed on the right, represent the strength of the correlation intensity between variables, with Spearman’s correlation coefficient (\u003cem\u003ers\u003c/em\u003e) ranging from -1 to +1. Only correlations significant at the 5% threshold are shown. Non-significant correlations are indicated by empty boxes.\u003c/p\u003e\n\u003cp\u003eBlue dot, positive correlation; red dot, negative correlation.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6718852/v1/dfe8a9acf15e80366ca06ff8.jpg"},{"id":83439046,"identity":"8194e543-1e2c-48ff-a202-91447e48a519","added_by":"auto","created_at":"2025-05-26 09:08:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman correlations between plasma apolipoprotein concentrations and bioclinical variables.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma apolipoproteins were measured in the whole study population (n = 148). Size and color, according to the color scale detailed on the right, represent the strength of the correlation intensity between variables, with Spearman’s correlation coefficient (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e) ranging from -1 to +1. Only correlations significant at the 5% threshold are shown. Non-significant correlations are indicated by empty boxes.\u003c/p\u003e\n\u003cp\u003eBlue dot, positive correlation; red dot, negative correlation.\u003c/p\u003e\n\u003cp\u003eALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA\u003csub\u003e1c\u003c/sub\u003e, glycated hemoglobin; SBP, systolic blood pressure.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6718852/v1/d4040f608bceabee6df45130.jpg"},{"id":83439041,"identity":"768390bf-db14-4e7d-a0c3-94ea4a6ba5ff","added_by":"auto","created_at":"2025-05-26 09:08:33","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultiple Logistic regression models assessing MASH status with plasma apolipoprotein levels as independent variables.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel 1: Adjusted for age and sex.\u003c/p\u003e\n\u003cp\u003eModel 2: Model 1 + HOMA-IR, plasma triglyceride levels, waist circumference, and the AST/ALT ratio.\u003c/p\u003e\n\u003cp\u003eModel 3: Model 1 + diabetes, dyslipidemia, and hypertension.\u003c/p\u003e\n\u003cp\u003eThe 95% confidence interval (95% CI), and p-value associated with each odds ratio (OR) are reported.\u003c/p\u003e\n\u003cp\u003eDyslipidemia was defined as LDL-C ≥ 160 mg/dL and/or triglyceride levels ≥ 150 mg/dL and/or use of lipid-lowering treatment. Hypertension was defined as systolic blood pressure (SBP) ≥ 140mmHg and/or diastolic blood pressure (DBP) ≥ 90mmHg at rest, and/or antihypertensive treatment. Diabetes was defined as fasting plasma glucose (FPG) ≥ 126 mg/dL and/or hypoglycaemic treatment.\u003c/p\u003e\n\u003cp\u003eALT, alanine aminotransferase; AST, aspartate aminotransferase; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6718852/v1/a6e2f0b233d63833b61ecc85.jpg"},{"id":83439781,"identity":"7d2ee513-8b59-40fa-87d1-60323f04bd76","added_by":"auto","created_at":"2025-05-26 09:16:33","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":66086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAPOC3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, APOE, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAPOJ\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAPOL1 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003emRNA expression levels in liver based on the MASH status of individuals with obesity. \u003c/strong\u003eSubjects who underwent liver biopsy (n = 141) during bariatric surgery were stratified into two groups: those without liver disease or with simple steatosis (\u003cstrong\u003enon-MASH\u003c/strong\u003e, \u003cem\u003en\u003c/em\u003e = 87) and those with MASH (\u003cem\u003en\u003c/em\u003e = 54). Data are presented as \u003cstrong\u003emean ± SEM\u003c/strong\u003e. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001. \u003cstrong\u003eTwo-tailed unpaired Student’s \u003c/strong\u003e\u003cem\u003et\u003c/em\u003e\u003cstrong\u003e-test\u003c/strong\u003e was used for MASH status comparison.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6718852/v1/8f8b7fd477f16041aabb494a.jpg"},{"id":93956085,"identity":"2d0de029-e7e9-4837-9075-cfcac7b29a02","added_by":"auto","created_at":"2025-10-20 16:10:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1910377,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6718852/v1/10a3bd7c-8a21-44cf-acd0-e09005d9987d.pdf"},{"id":83439044,"identity":"102162c4-f3b0-45d5-a7a5-0292e55d9cd6","added_by":"auto","created_at":"2025-05-26 09:08:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17282,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1LHD.docx","url":"https://assets-eu.researchsquare.com/files/rs-6718852/v1/9aefbfc6ca0e448e6f1f8bd7.docx"},{"id":83439045,"identity":"56e1f2ea-bc22-4c55-98e2-8ba705aed180","added_by":"auto","created_at":"2025-05-26 09:08:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18796,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2LHD.docx","url":"https://assets-eu.researchsquare.com/files/rs-6718852/v1/f1eb6e8c3da7f72bb5ffc18d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"ApoL1 and ApoJ as Novel Determinants of MASH: A cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD) is a chronic progressive condition primarily characterized by abnormal triglyceride accumulation in the liver, known as simple hepatic steatosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Over time, MASLD might progress to metabolic dysfunction-associated steatohepatitis (MASH), characterized by hepatocyte ballooning and lobular inflammation, and, in severe cases, liver fibrosis, significantly increasing the risk of hepatocellular carcinoma and cardiovascular disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMASH is particularly prevalent among individuals with obesity, whom can also present other metabolic treats such as type 2 diabetes (T2D), insulin resistance, and dyslipidemia. Despite the link between MASH and obesity, a subset of individuals with obesity appear metabolically healthy and have a lower prevalence of MASH, often limited to simple steatosis, even though progression to MASH can still occur [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Thus, identifying circulating markers for MASH in individuals with obesity, independent of their metabolic status, is clinically relevant. Notably, while non-invasive methods like transient elastography and biomarker-based scoring systems assist in screening and monitoring, liver biopsy remains the gold standard for MASH diagnosis when precise staging is required [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Thus, novel non-invasive biomarkers are crucial to improve diagnostic accuracy and reduce reliance on invasive procedures.\u003c/p\u003e \u003cp\u003eBeyond the need for better biomarkers, pharmacological options for MASH remain limited. Resmetirom, a thyroid hormone receptor β-agonist, is the only specific approved therapy, yet only 30% of patients achieve MASH resolution without fibrosis progression [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Meanwhile, GLP-1 and GIP/GLP-1 receptor agonists, which show promising results in weight loss and MASH resolution [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], though their long-term effects remain under investigation [9]. Currently, the MASH management relies primarily on dietary and lifestyle interventions to promote weight loss through healthy eating, calorie reduction, and increasing physical activity. When these measures fail, bariatric surgery (BS) has been the primary option to treat non-cirrhotic MASLD/MASH patients [10].\u003c/p\u003e \u003cp\u003eThis underscores the need for alternative treatments and precision medicine approaches, including the identification of novel biomarkers. Given the complex etiology of MASLD/MASH\u0026mdash;shaped by genetic predisposition (e.g., PNPLA3 variants), metabolic dysregulation, lifestyle factors, and environmental influences\u0026mdash;targeted approaches are essential for effective disease management.\u003c/p\u003e \u003cp\u003eApolipoproteins are multifunctional proteins involved in lipoprotein assembly, structure, and metabolism, influencing cell-surface receptor binding for lipid uptake as well as the regulation of lipase and lipid-transfer enzymes [11]. Although some studies have linked plasma levels of apoB100 (the main LDL apolipoprotein), apoA-I (the main HDL apolipoprotein), apoC-III (a natural inhibitor of lipoprotein lipase), and apoF to MASLD [12\u0026ndash;16], other apolipoproteins with less understood functions have been poorly investigated in the context of MASH diagnosis. This cross-sectional study explored the relationship between plasma concentrations of 14 apolipoproteins and MASH status in individuals with obesity prior to bariatric surgery, with the aim of providing new insights into the role of apolipoproteins in MASH pathophysiology and highlighting their potential use as biomarker for early disease detection and risk stratification.\u003c/p\u003e"},{"header":"Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eThe study workflow is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the present study, we included a total of 148 individuals with obesity who underwent BS (Roux-en-Y gastric bypass) at the Virgen de la Arrixaca University Hospital (Murcia, Spain) between 2020 and 2022. Inclusion criteria included a signed informed consent, age between 18 and 65 years, a body mass index (BMI) of 30 kg/m\u0026sup2; with significant obesity-related comorbidities, and a duration of obesity of 5 years or more. Exclusion criteria were evidence of liver disease other than MASLD (including viral hepatitis, medication-related disorders, autoimmune disease, hepatocellular carcinoma, hemochromatosis, Wilson\u0026rsquo;s disease, familial/genetic causes), a previous history of excessive alcohol consumption (\u0026gt;\u0026thinsp;30 g daily for men and \u0026gt;\u0026thinsp;20 g daily for women), treatment with any drugs potentially causing steatosis (e.g. tamoxifen, amiodarone, and valproic acid), or subjects who declined to participate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLiver sample collection and histological analysis\u003c/h3\u003e\n\u003cp\u003eIntraoperative wedge liver biopsies with a minimum of 1cm\u003csup\u003e2\u003c/sup\u003e were obtained from individuals who underwent BS. One section of the biopsy was rapidly frozen and stored at \u0026minus;\u0026thinsp;80℃, whereas the other section was fixed in formalin and embedded in paraffin for histopathological evaluation. 5-mm sections of paraffin-embedded liver biopsies were stained using hematoxylin and eosin, Masson trichrome, periodic acid-Schiff, Perls, and reticulin staining. Pathologists from the Virgen de la Arrixaca University Hospital and the Experimental Pathology Unit of the Biomedical Research Institute of Murcia reviewed and scored all biopsies to determine the steatosis, activity, and fibrosis (SAF) score, as previously described [17, 18].\u003c/p\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eThe study population was categorized into two groups based on the histopathological evaluation of liver biopsies using the SAF classification system [19]: (1) the non-MASH group, which included participants whose liver biopsies showed no histologic alterations, or at least grade 1 (5% or less) intrahepatic fat accumulation, and (2) the MASH group, which included participants whose liver biopsies demonstrated at least grade 1 steatosis, along with evidence of ballooning and lobular inflammation, with or without fibrosis.\u003c/p\u003e \u003cp\u003eLifestyle risk factors included smoking, where an individual is considered a non-smoker if they have never smoked or are a former smoker who has not smoked for at least 5\u0026ndash;10 years, and alcohol consumption, where an individual is considered a non-alcohol consumer if no alcoholic habits have been reported. Comorbidities included hypertension (systolic blood pressure [SBP]\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, and/or diastolic blood pressure [DBP]\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg at rest, and/or antihypertensive treatment), diabetes (fasting plasma glucose [FPG]\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, and/or hypoglycaemic treatment) and dyslipidemia (LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;160 mg/dL, and/or triglyceride levels\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL, and/or use of lipid-lowering treatment).\u003c/p\u003e\n\u003ch3\u003eBiochemical analysis\u003c/h3\u003e\n\u003cp\u003eBlood samples were obtained on the day of the surgery after a minimum 12-hour overnight fast, and serum and plasma were obtained by centrifugation. Samples were anonymized and blinded for MASH status. The following parameters were measured using the Cobas Analyzer c702 (Roche): glucose, total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) levels. Glycated hemoglobin (HbA\u003csub\u003e1C\u003c/sub\u003e) levels were determined using the glycohemoglobin analyzer HLC-723G8 (Tosoh Bioscience). Insulin levels were measured using the Cobas Analyzer e801 (Roche). Insulin resistance was assessed using the homeostasis model assessment of the insulin resistance index (HOMA-IR), calculated as insulin (\u0026micro;U/mL) x glucose (mmol/L)/22.5 [20].\u003c/p\u003e \u003cp\u003e \u003cb\u003eApolipoprotein measurements.\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePlasma concentrations of apolipoproteins (A-I, A-II, A-IV, B100, C-I, C-II, C-III, D, E, F, H, J, L1, and M) were determined by liquid chromatography-tandem mass spectrometry, as described previously [21]. Briefly, the apolipoproteins were quantified in 40-\u0026micro;L aliquots (EDTA plasma) using trypsin proteolysis and the subsequent analysis of proteotypic peptides. The intra- and inter-assay variabilities were measured and did not exceed 9.4% [21].\u003c/p\u003e\n\u003ch3\u003eRNA purification and qPCR analysis\u003c/h3\u003e\n\u003cp\u003eLiver biopsies were collected during surgery and further preserved at \u0026minus;\u0026thinsp;80℃ in RNAlater (Sigma) until analysis. Total RNA was extracted and purified using TRIzol reagent (Life Technologies) and GeneJET RNA Purification kit (Thermo Scientific). One microgram of purified RNA was reverse transcribed using the High-Capacity RNA-to-cDNA kit (Applied Biosystems) according to the manufacturer\u0026rsquo;s instructions. For gene transcription quantification, qPCR amplification was performed using the Power SYBR Green Master mix (Applied Biosystems) on a Fast 7500 Real-Time instrument (Applied Biosystems), and relative transcription levels were performed using the 2^\u0026minus;ΔΔCt method, with 18S rRNA as a housekeeping gene.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene and protein nomenclature\u003c/h2\u003e \u003cp\u003eGene and protein names are based on gene names and were capitalized (Hugo Gene Nomenclature Committee, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genenames.org/about/guidelines/\u003c/span\u003e\u003cspan address=\"https://www.genenames.org/about/guidelines/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll categorical parameters were expressed as the number (%) and tested by Pearson\u0026rsquo;s Chi-squared test. All quantitative parameters were expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and tested using Two-tailed unpaired Student\u0026rsquo;s t test unless otherwise specified. When the distribution was considered as skewed, parameters were expressed as the median (25th percentile; 75th percentile) and tested by Wilcoxon rank sum test. The correlation between the baseline characteristics was studied as a cross-sectional study. Spearman\u0026rsquo;s correlation coefficients (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e) were calculated between plasma apolipoprotein concentrations and the clinical characteristics, glucose homeostasis, and lipid values with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered. Multivariable regression models were applied with the following adjustments: adjusted for age and sex (model 1), adjusted for age, sex, HOMA-IR, triglycerides, waist circumference, and AST/ALT ratio (model 2), and adjusted for age, sex, hypertension, diabetes and dyslipidemia (model 3). All analyses were performed using R software version 4.0.0 [22], and the R scripts are available upon request.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study population\u003c/h2\u003e \u003cp\u003eBaseline clinical and biochemical characteristics for the entire cohort, as well as for two groups categorized by MASH status (non-MASH vs MASH), are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The overall population consisted of middle-aged individuals (46\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years), with severe obesity (BMI: 43.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 kg/m\u0026sup2;), predominantly women (75%), and exhibited insulin resistance (HOMA-IR: 3.0 [1.6; 4.8]) [23]. Other glucose metabolism parameters, including FPG, insulin and HbA\u003csub\u003e1c\u003c/sub\u003e were mostly normal or near abnormal thresholds, while plasma lipids, such as triglycerides and LDL-C, were slightly elevated, likely reflecting partial treatment within the population. Accordingly, 72.3% had dyslipidemia, but only 29.1% received lipid-lowering drugs, while 45.9% had diabetes with 43.9% on anti-diabetic medications (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The prevalence of hypertension was high (72.3%), yet only 35.8% of affected individuals received antihypertensive treatment. Overall, solely 15.5% of the study population had neither a diagnosis nor treatment for comorbidities.\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\u003eSociodemographic, clinical and biological characteristics of the study population according to MASH status.\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=\"char\" char=\".\" 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 \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhole population\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-MASH\u003c/p\u003e \u003cp\u003e(n\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMASH\u003c/p\u003e \u003cp\u003e(n\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003cp\u003e(n\u003csub\u003e1\u003c/sub\u003e,n\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWomen, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (64.8)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMen, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (35.2)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.625\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (61.1)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (38.9)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.279\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (92.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (87.0)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (13.0)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnthropometric measures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 [117; 134]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 [117; 132]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127 [118; 139]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.230\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose metabolism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFPG\u003c/b\u003e, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 [84; 104]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 [84; 100]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 [89; 123]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHb1Ac\u003c/b\u003e, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.70 [5.40; 6.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.60 [5.30; 5.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.80 [5.60; 6.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsulin\u003c/b\u003e, \u0026micro;UI/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 [7; 19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (7, 18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 [9; 27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHOMA-IR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0 [1.6; 4.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7 [1.5; 4.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7 [2.2; 7.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipid metabolism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163\u0026thinsp;\u0026plusmn;\u0026thinsp;31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163\u0026thinsp;\u0026plusmn;\u0026thinsp;31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163\u0026thinsp;\u0026plusmn;\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.949\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglycerides\u003c/b\u003e, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 [125; 212]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 [121; 206]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e189 [154; 229]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C\u003c/b\u003e, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 [34; 49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 [36; 50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 [33; 44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.050\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87\u0026thinsp;\u0026plusmn;\u0026thinsp;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u0026thinsp;\u0026plusmn;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u0026thinsp;\u0026plusmn;\u0026thinsp;31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.617\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(7,3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTransaminases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST\u003c/b\u003e, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 [15; 23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 [14; 20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 [18; 28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5,5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT\u003c/b\u003e, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 [14; 28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 [12; 22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 [19; 40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST/ALT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 [0.78; 1.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06 [0.87; 1.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 [0.69; 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(6,5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood pressure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140 [130; 150]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 [126; 149]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141 [130; 150]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.553\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities and treatments\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT2D\u003c/b\u003e, n (%)\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment T2D\u003c/b\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDyslipidemia\u003c/b\u003e, n (%)\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment dyslipidemia\u003c/b\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (74.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.714\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment hypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.554\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.260\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eCategorical parameters are expressed as the number of individuals (%). Quantitative parameters are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for Gaussian distribution or as the median [25th percentile; 75th percentile] for non-Gaussian distribution. The difference between the two groups (non-MASH versus MASH) were analyzed using the following statistical test: \u003csup\u003e1\u003c/sup\u003eTwo-tailed unpaired Student\u0026rsquo;s t test, \u003csup\u003e2\u003c/sup\u003ePearson's Chi-squared test, or \u003csup\u003e3\u003c/sup\u003eWilcoxon rank sum test. Comorbidities were defined as follows: \u003csup\u003e4\u003c/sup\u003eDiabetes: fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL or treatment; \u003csup\u003e5\u003c/sup\u003eDyslipidemia: LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;160 mg/dL and/or triglycerides (TG)\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL or treatment; \u003csup\u003e6\u003c/sup\u003eHypertension: systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg or treatment; \u003csup\u003e7\u003c/sup\u003eNone: no diabetes, no dyslipidemia and no hypertension. ALT, alanine aminotransferase; AST, aspartate aminotransferase; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; N/A: not available; SBP, systolic blood pressure; T2D, type 2 diabetes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificant differences were observed between the non-MASH and MASH groups. Specifically, the diagnosis of MASH was associated with a marked difference in sex distribution, with women being significantly less represented in the MASH group (64.8% versus 80.9% in non-MASH group, p\u0026thinsp;=\u0026thinsp;0.03). Individuals with MASH were more insulin resistant, as documented by higher HOMA-IR values compared to the non-MASH (p\u0026thinsp;=\u0026thinsp;0.002), along with significantly higher level of FPG, insulin, and HbA\u003csub\u003e1c\u003c/sub\u003e. Additionally, triglycerides were significantly higher, and HDL-C levels were significantly lower in the MASH group compared to the non-MASH group. Consistent with these metabolic differences, individuals with MASH had a higher prevalence of diabetes and dyslipidemia, but showed no difference in the prevalence of hypertension. Furthermore, individuals with MASH had a higher level of transaminases (AST and ALT), accompanied by a lower AST/ALT ratio (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were observed between groups in terms of anthropometric measures (BMI, waist circumference) and lifestyle risk factors such as smoking and alcohol consumption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePlasma concentration of apolipoproteins by MASH status\u003c/h2\u003e \u003cp\u003eThe baseline concentrations of plasma apolipoproteins, categorized by patient MASH status, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among the apolipoproteins analyzed, plasma levels of apoC-III and apoL1 were significantly higher in the MASH group compared to the non-MASH group (p\u0026thinsp;=\u0026thinsp;0.038 and 0.025, respectively). Additionally, levels of apoC-I, apoC-II, apoE and apoJ were marginally increased in the MASH group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). In contrast, apoA-I, apoA-II, apoA-IV, apoB100, apoD, apoF, apoH, and apoM did not significantly differ between groups.\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\u003ePlasma concentration of apolipoproteins according to MASH status.\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cp\u003eApolipoproteins\u003c/p\u003e \u003cp\u003e(mg/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhole population\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-MASH\u003c/p\u003e \u003cp\u003e(n\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMASH\u003c/p\u003e \u003cp\u003e(n\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003cp\u003e(n\u003csub\u003e1\u003c/sub\u003e,n\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoA-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e120\u0026thinsp;\u0026plusmn;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e122\u0026thinsp;\u0026plusmn;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e115\u0026thinsp;\u0026plusmn;\u0026thinsp;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoA-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e18.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e18.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e17.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.768\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoA-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.720\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoB100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e89\u0026thinsp;\u0026plusmn;\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e88\u0026thinsp;\u0026plusmn;\u0026thinsp;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e92\u0026thinsp;\u0026plusmn;\u0026thinsp;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.548\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoC-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoC-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eApoC-III\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.904\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eApoL1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e(0,0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.163\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e1\u003c/sup\u003e Two-tailed unpaired Student\u0026rsquo;s t test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eN/A, not available.\u003c/td\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between plasma apolipoprotein levels and biochemical parameters\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor the study population, spearman\u0026rsquo;s rank correlation coefficients (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e) were calculated to evaluate the associations among plasma apolipoproteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;1) and between apolipoproteins and biochemical parameters at baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\u003cp\u003eAs anticipated, many apolipoproteins were intercorrelated due to their transport by lipoproteins [24] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, apoA-I, the primary HDL apolipoprotein, showed positive correlations with apoA-II, apoE, apoL1 and apoM. Similarly, significant positive intercorrelations were observed among apoB100\u0026mdash;a major protein constituent of VLDL and LDL\u0026mdash;, apoE, apoC-I, apoC-II, and apoC-III. Among other apolipoproteins exhibiting multiple correlations, apoL1 positively correlated with apoA-I, apoC-I, apoC-III, apoE and apoM. ApoM displayed a similar correlation profile to apoL1, with additional correlations to apoA-II, apoC-II, and apoD, consistent with its transport by both HDL and LDL [25]. In contrast, apoA-IV was positively correlated only with apoA-I and negatively correlated with apoB100., while apoF was negatively correlated only with apoD and apoH. Additionally, apoD was positively correlated with both apoC-I and apoC-II, while apoH showed positive correlations with apoB100 and apoC-III. Notably, apoJ was the only apolipoprotein that did not show any correlation with the other apolipoproteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs expected, most plasma apolipoproteins were associated with lipid levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Half of the measured apolipoproteins, including apoA-I, apoA-II, apoB100, apoC-I, apoC-II, apoE, and apoM, showed a positive correlation with total cholesterol. ApoB100, apoC-I and apoE were positively associated with LDL-C, while apoC-III exhibited a negative correlation with HDL-C. In contrast, apoA-I, apoA-II and apoM were positively correlated with HDL-C. Additionally, apoC-I, apoC-II, apoC-III and apoE displayed a positive correlation with triglycerides.\u003c/p\u003e \u003cp\u003eRegarding glycemic parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), apoA-IV, apoC-I, apoC-III, and apoF levels were positively correlated with FPG. Additionally, apoC-I was positively correlated with insulin, and both apoC-I and apoC-III showed significant positive correlations with HOMA-IR. ApoA-IV and apoC-III were also positively correlated with HbA\u003csub\u003e1c\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eApoC-III, apoE and apoL1 exhibited positive correlations with plasma transaminase levels (AST and ALT). ApoA-IV and apoC-III were positively correlated with age, whereas apoA-I was negatively correlated with BMI and waist circumference. Furthermore, apoA-IV was positively associated with SBP, while apoC-I and apoL1 were positively associated with DBP. Noteworthily, no correlation was observed between apoH or apoJ and any of the measured biochemical parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePlasma apolipoprotein levels and MASH diagnosis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results of multivariable logistic regression analyses examining plasma apolipoproteins, HDL-C, and LDL-C levels in relation to MASH status are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn a model adjusted for age and sex (Model 1), plasma apoE, apoJ, and apoL1 were significantly associated with a diagnosis of MASH. The respective odds ratios (OR) per 1 SD increase were as follows: apoE (OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI [1.00\u0026ndash;1.51], p\u0026thinsp;=\u0026thinsp;0.046), apoJ (OR\u0026thinsp;=\u0026thinsp;1.07, 95% CI [1.00\u0026ndash;1.14], p\u0026thinsp;=\u0026thinsp;0.040), and apoL1 (OR\u0026thinsp;=\u0026thinsp;1.53, 95% CI [1.09\u0026ndash;2.16], p\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e \u003cp\u003eThese associations persisted for apoJ and apoL1 after further adjustment for classical MASH risk factors, including HOMA-IR, plasma triglyceride levels, waist circumference, and the AST/ALT ratio (Model 2: apoJ, OR\u0026thinsp;=\u0026thinsp;1.08, 95% CI [1.00\u0026ndash;1.16], p\u0026thinsp;=\u0026thinsp;0.047; apoL1, OR\u0026thinsp;=\u0026thinsp;1.54, 95% CI [1.00\u0026ndash;2.37], p\u0026thinsp;=\u0026thinsp;0.052), as well as for comorbidities such as diabetes, dyslipidemia, and hypertension (Model 3: apoJ, OR\u0026thinsp;=\u0026thinsp;1.07, 95% CI [1.00\u0026ndash;1.15], p\u0026thinsp;=\u0026thinsp;0.055; apoL1, OR\u0026thinsp;=\u0026thinsp;1.60, 95% CI [1.09\u0026ndash;2.35], p\u0026thinsp;=\u0026thinsp;0.017). Notably, among plasma apolipoproteins, those with differential concentration based on MASH status (apoC-III and apoL1, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and/or associated with MASH in multivariable logistic regression (apoE, apoJ, and apoL1, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) were further analyzed at the hepatic level. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, hepatic mRNA levels of \u003cem\u003eAPOC3\u003c/em\u003e, \u003cem\u003eAPOJ\u003c/em\u003e, and \u003cem\u003eAPOL1\u003c/em\u003e\u0026mdash;but not \u003cem\u003eAPOE\u003c/em\u003e\u0026mdash;were significantly higher in individuals with obesity who were histologically diagnosed with MASH compared to those without MASH.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMASH is a leading cause of liver transplantation and there are not specific biomarkers for the efficient diagnosis/prognosis of MASH. Obesity and metabolic status strongly drive MASH development [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, metabolic status alone is unreliable for MASH assessment in individuals with obesity as metabolic health is often transient and deteriorates over time. In turn, MASH itself may exacerbate metabolic dysfunction, thereby displaying bidirectional interactions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In this context, the early diagnosis of advanced MASH is becoming critical for timeline intervention in high-risk subjects with obesity [26]. Therefore, it becomes essential to identify non-invasive biomarkers for MASH diagnosis in individuals with obesity, independent of risk factors and comorbidities.\u003c/p\u003e \u003cp\u003eThe concentration of circulating apolipoproteins is sensitive to disturbances of the metabolic status and has become promising candidates to biomarkers of metabolic diseases. As such, altered concentrations of apolipoproteins are commonly related to alterations in lipoprotein metabolism and linked to adverse outcomes, including atherosclerosis and MASLD [11], highlighting their potential role in MASH pathophysiology. However, research investigating the relationship between plasma apolipoproteins and MASH is rather limited, with studies mainly focusing on apoA-I, apoB100 or apoC-III and MASLD [12\u0026ndash;15, 27]. Moreover, the analysis of the relationship, if any, of other less studied apolipoproteins that structurally and functionally differ from classic apolipoproteins has remained elusive possibly due to the analytical technical issues.\u003c/p\u003e \u003cp\u003e This study is the first to analyze plasma levels of 14 apolipoproteins in individuals with obesity undergoing BS and classified into non-MASH (simple steatosis/no disease) and MASH groups according to histological analysis. As expected, individuals with MASH were more likely to have T2D and dyslipidemia, with greater HOMA-IR, hypertriglyceridemia, and a lower AST/ALT ratio (\u0026lt;\u0026thinsp;1)\u0026mdash;a recognized marker of MASH severity [28, 29]\u0026mdash;despite comparable BMI and lifestyle factors. Our findings reveal that MASH status was significantly associated with the plasma concentrations of apoE, apoJ, and apoL1, independent of age and sex. This association remained significant for apoJ and apoL1 after further adjustment for traditional risk factors, including central obesity (waist circumference), insulin resistance (HOMA-IR), and hypertriglyceridemia (plasma triglyceride levels), as well as comorbidities such as diabetes, dyslipidemia, and hypertension. Notably, hepatic mRNA expression of \u003cem\u003eAPOJ\u003c/em\u003e and \u003cem\u003eAPOL1\u003c/em\u003e was also higher in the MASH group, suggesting that their plasma protein levels may serve as proxies for their expression by the liver. Overall, these results suggest the potential of apoJ and apoL1 as independent determinants of MASH. Our results align with a previous study reporting that plasma apoJ and apoL1 are associated with liver fibrosis in patients with hepatitis C [30]. These findings pave the way for further research into the clinical utility of apoJ and apoL1 in early MASH detection, particularly in individuals with obesity.\u003c/p\u003e \u003cp\u003eApoJ, also known as clusterin, is a secreted sulfated glycoprotein that exists as a heterodimer (α\u0026thinsp;~\u0026thinsp;35 kDa, β\u0026thinsp;~\u0026thinsp;37 kDa) and is widely distributed across various tissues and fluids [31]. In human plasma, apoJ primarily originates from the liver and brain and is predominantly transported by HDL particles [32]. It has been proposed to exert an atheroprotective role through HDL-mediated reverse cholesterol transport and by enhancing paraoxonase 1 (PON1) activity, which protects against LDL oxidation [33]. Additionally, apoJ functions as an hepatokine, regulating muscle glucose metabolism \u003cem\u003evia\u003c/em\u003e a low-density lipoprotein receptor-related protein-2 (LRP2)-dependent mechanism, thereby protecting against insulin resistance and glucose intolerance [34, 35]. However, in hyperlipidemia, HDL-bound apoJ decreases, while total circulating apoJ increases, correlating with triglycerides, cholesterol, and VLDL-C levels [36]. This redistribution from HDL-bound to unbound apoJ in metabolic syndrome may compromise its metabolic and cardiovascular protective functions. In our study, we measured total plasma apoJ without distinguishing its HDL association. However, based on the aforementioned report [36], non-HDL-bound apoJ would likely predominate in our study population of individuals with obesity, potentially explaining its association with MASH status. Notably, plasma apoJ was the only apolipoprotein in our dataset that showed no correlation with other apolipoproteins or bioclinical variables such as BMI and HDL-C. Nevertheless, it independently predicted MASH status in multivariable logistic regression analysis. Consistent with this observation, a recent study reported upregulation of apoJ in high-fat medium-fed hepatocytes and in the livers of patients with MAFLD [37]. Moreover, antagonizing apoJ was shown to promote proteasomal degradation of mTOR and alleviate hepatic lipid deposition [37].\u003c/p\u003e \u003cp\u003eApoL1 is part of a six-member family (ApoL1\u0026ndash;6) encoded by a 619 kb gene cluster on human chromosome 22 [38]. Among the six apoL family members, only apoL1 is secreted [38, 39] and it is the most extensively studied due its role in innate immunity against \u003cem\u003eTrypanosoma brucei\u003c/em\u003e, the parasite responsible for African sleeping sickness [38]. Although apoL1 is widely expressed in human tissues [40\u0026ndash;42], the liver is the primary source of circulating apoL1 [39, 43]. In the bloodstream, it is mainly associated with HDL\u003csub\u003e3\u003c/sub\u003e particles and, along with PON1 and PON3, may contribute to LDL protection from oxidation [44]. Supporting a potential atheroprotective function, reduced circulating apoL1 levels in patients with familial hypercholesterolemia have been linked to an increased risk of fatal cardiovascular events [45]. However, a separated study on coronary patients with low HDL-C levels found an association between apoL1 levels and hypertriglyceridemia as well as hyperglycemia [46], suggesting a role in impaired triglyceride and glucose metabolism\u0026mdash;both key drivers of MASH. Consistently, a cross-sectional study showed that individuals with elevated apoL1 levels were more prone to insulin resistance, obesity, hypertriglyceridemia, and low HDL-C [47]. Furthermore, one longitudinal five-year studies reported that higher plasma levels of apoL1, along with apoJ, were associated with an increased risk of developing T2D [48].\u003c/p\u003e \u003cp\u003eIn addition to apoJ and apoL1, plasma levels of apoC-III, apoE and apoF have been shown to predict T2D more accurately than traditional lipid markers, independent of classical risk factors [48]. Since T2D is a major MASH risk factor, these apolipoproteins are plausible MASH biomarkers. For instance, plasma apoC-III and apoE levels, and particularly their oxidized fragments, are elevated in MASLD patients (simple steatosis) and correlate with obesity, insulin resistance, dyslipidemia, and elevated transaminases [49]. However, in our study, we observe an independent positive relationship with MASH status solely with apoJ and apoL1 levels, suggesting that these two apolipoproteins may play more direct or prominent roles in the pathogenesis of MASH compared to the other evaluated apolipoproteins. For instance, it is likely that the relationship between apoC-III and MASH, with higher levels observed in MASH participants compared to non-MASH participants, is primarily driven by its role in the metabolism of triglyceride-rich lipoproteins (TRLs) \u0026mdash;acting as an inhibitor of lipoprotein lipase and hepatic lipase activities\u0026mdash;as well as its involvement in insulin resistance and inflammation [50\u0026ndash;54], all of which are key contributors to MASH pathophysiology. Consistent with these functions of apoC-III, our study observed significant positive correlations between its plasma levels and triglycerides, FPG, insulin, and HOMA-IR, as well as AST and ALT. This mechanistic link may also explain why no significant association was found between apoC-III and MASH in the multivariable logistic regression analysis, which was adjusted for traditional risk factors and comorbidities. These adjustments likely account for the overlapping contributions of hypertriglyceridemia, insulin resistance, inflammation, and other pathways shared between apoC-III and the established risk factors for MASH. Regarding apoF, its hepatic expression has been linked to the metabolism of TRLs via SREBP2 pathway activation, with both hepatic and plasma levels negatively correlated with steatosis in individuals with obesity [16]. However, its role in MASH progression beyond simple steatosis remains unexplored. Our study did not find any correlation between plasma levels of apoF and MASH, suggesting that circulating apoF is unlikely to serve as a marker for advanced MASLD stages, as its function appears to be limited to liver lipid metabolism regulation.\u003c/p\u003e \u003cp\u003eBased on available evidence, our work is the first to investigate the association between plasma levels of apolipoproteins and MASH in individuals with obesity. However, our study has certain limitations. First, the limited sample size may impact in multiple aspects of our findings as a relatively small sample size reduces statistical power, making it more challenging to detect potential effects. Additionally, it can amplify random error, leading to greater variability in results and wider confidence intervals. In the multivariable logistic regression analysis, a smaller sample size limits the ability to adjust for potential confounders. Since MASLD and MASH are influenced by various cardiometabolic factors, residual confounding variables may still be present in our analysis. Finally, MASH is a progressive disease, yet our data is cross-sectional, collected at a single time point. A longitudinal study with repeated measurements of apolipoproteins at different disease stages would provide deeper insights into their role in disease progression.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings suggest that apolipoproteins, specifically apoJ and apoL1, may serve as diagnostic biomarkers for MASH in individuals with obesity, beyond traditional cardiometabolic risk factors. If validated in larger longitudinal studies, these biomarkers could improve noninvasive MASH screening in at-risk populations and facilitate the monitoring of disease progression. Future large-scale, population-based cohort studies are needed to confirm these associations and explore their clinical applications.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eApo: apolipoprotein; MASH: metabolic dysfunction-associated steatohepatitis; MASLD, metabolic dysfunction-associated steatotic liver disease ; T2D, type 2 diabetes ; BS, bariatric surgery ; BMI, body mass index ; SBP, systolic blood pressure ; DBP, diastolic blood pressure ; FPG, fasting plasma glucose ; HDL-C, high-density lipoprotein cholesterol ; LDL-C, low-density lipoprotein cholesterol ; ALT, alanine aminotransferase ; AST, aspartate aminotransferase ; HOMA-IR, homeostasis model assessment of the insulin resistance index ; SD, standard deviation ; OR, odds ratios ; PON1, paraoxonase 1 ; LRP2, lipoprotein receptor-related protein-2 ; TRLs, triglyceride-rich lipoproteins\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research protocol was approved by the Ethics and Clinical Research Committees of the Virgen de la Arrixaca University Hospital (ref. number 2020-2-4-HCUVA; date: 31/03/2020). Informed consent to participate was signed by all participants and all of the reported investigations were carried out in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study are available in the article, in Supplementary Information, or from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from the Federation Française de Cardiologie (Dotation Recherche # FFC – MARTINEZ – Dotation 2022) and conducted\u0026nbsp;in the context of IHU HealthAge, which has benefited from funding from the ANR under the France 2030 program (ANR-23-IAHU-0011). Z.C. was supported by the China Scholarship Council (CSC) at the University of Toulouse.\u0026nbsp;We also acknowledge the support received from CIBERDEM (CB15/00071), a project of the ISCIIII, as well as the Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033) and the European Union’s “NextGeneration EU”/PRTR program, under the \"Consolidación Investigadora 2022\" action (CNS2022-135559), which provided support to J.J. The Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau is accredited by the Generalitat de Catalunya as Centre de Recerca de Catalunya (CERCA). J.J. and J.R. belong to the XARTEC Salut network, and is part of the coordinated consolidated group AGAUR (2021 SGR 00857, and 2021 SGR 01211). J.J. is member of the Quality Research Group 2017-SGR-1149 from Generalitat de Catalunya).\u0026nbsp;This work was also partly funded by the Institute of Health “Carlos III” (ISCIII), and co-funded by the Fondo Europeo de Desarrollo Regional-FEDER (grant number PI23/00171). M.A.M-S is supported by a PFIS predoctoral fellowship from the ISCIII (FI21/00003, ISCIII, Spain; co-funded by the Fondo Europeo de Desarrollo Regional-FEDER); M.A.N.-S. is supported by the “Miguel Servet” program (CP23/00051, ISCIII, Spain; co-funded by the Fondo Europeo de Desarrollo Regional-FEDER). The funding organizations played no role in the design of the study, review, and interpretation of the data, or final approval of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZ.C. contributed to methodology, data curation, formal analysis, and wrote the original draft. S.N., N.V., J.R., A.G., and J.J. were involved in writing, review, and editing. J.J. also secured funding. M.A.N.-S., M.A.M.-S., C.G.-M., C.M.M., and M.D.F. provided resources. M.C. contributed to methodology and supervision, and participated in writing, review, and editing. A.R.O. was involved in methodology, data curation, and formal analysis. B.R.-M. and L.O.M. contributed to the conceptualization, supervision, writing, review, and editing, and also secured funding. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHaas JT, Francque S, Staels B. Pathophysiology and Mechanisms of Nonalcoholic Fatty Liver Disease. Annual Review of Physiology. 2016;78:181\u0026ndash;205.\u003c/li\u003e\n\u003cli\u003eYounossi ZM. Non-alcoholic fatty liver disease \u0026ndash; A global public health perspective. Journal of Hepatology. 2019;70:531\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eLonardo A, Mantovani A, Lugari S, Targher G. Epidemiology and pathophysiology of the association between NAFLD and metabolically healthy or metabolically unhealthy obesity. Ann Hepatol. 2020;19:359\u0026ndash;66.\u003c/li\u003e\n\u003cli\u003eFrey S, Patouraux S, Debs T, Gugenheim J, Anty R, Iannelli A. Prevalence of NASH/NAFLD in people with obesity who are currently classified as metabolically healthy. Surg Obes Relat Dis. 2020;16:2050\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eSmith GI, Mittendorfer B, Klein S. Metabolically healthy obesity: Facts and fantasies. Journal of Clinical Investigation. 2019;129:3978\u0026ndash;89.\u003c/li\u003e\n\u003cli\u003eBerger D, Desai V, Janardhan S. Con: Liver Biopsy Remains the Gold Standard to Evaluate Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. 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Diabetologia. 1985;28:412\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eBlanchard V, Gar\u0026ccedil;on D, Jaunet C, Chemello K, Billon-Crossouard SS, Aguesse A, et al. A high-throughput mass spectrometry\u0026ndash;based assay for large-scale profiling of circulating human apolipoproteins. J Lipid Res. 2020;61:jlr.D120000835.\u003c/li\u003e\n\u003cli\u003eWilson A, Norden N. The R Project for Statistical Computing The R Project for Statistical Computing. URL: http://www. r-project. org/254. 2015;3:1\u0026ndash;9. https://www.r-project.org/. Accessed 17 Dec 2024.\u003c/li\u003e\n\u003cli\u003eWallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004;27:1487\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003ePoljak A, Duncan MW, Jayasena T, Sachdev PS. Quantitative Assays of Plasma Apolipoproteins. Methods Mol Biol. 2020;2138:49\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eChristoffersen C, Benn M, Christensen PM, Gordts PLSM, Roebroek AJM, Frikke-Schmidt R, et al. The plasma concentration of HDL-associated apoM is influenced by LDL receptor-mediated clearance of apoB-containing particles. J Lipid Res. 2012;53:2198\u0026ndash;204.\u003c/li\u003e\n\u003cli\u003eFabbrini E, Sullivan S, Klein S. Obesity and nonalcoholic fatty liver disease: Biochemical, metabolic, and clinical implications. Hepatology. 2010;51:679\u0026ndash;89.\u003c/li\u003e\n\u003cli\u003eCharlton M, Sreekumar R, Rasmussen D, Lindor K, Nair KS. Apolipoprotein synthesis in nonalcoholic steatohepatitis. Hepatology. 2002;35:898\u0026ndash;904.\u003c/li\u003e\n\u003cli\u003eZou Y, Zhong L, Hu C, Sheng G. Association between the alanine aminotransferase/aspartate aminotransferase ratio and new-onset non-alcoholic fatty liver disease in a nonobese Chinese population: a population-based longitudinal study. Lipids Health Dis. 2020;19.\u003c/li\u003e\n\u003cli\u003eXuan Y, Wu D, Zhang Q, Yu Z, Yu J, Zhou D. Elevated ALT/AST ratio as a marker for NAFLD risk and severity: insights from a cross-sectional analysis in the United States. Front Endocrinol (Lausanne). 2024;15:1457598.\u003c/li\u003e\n\u003cli\u003eGangadharan B, Bapat M, Rossa J, Antrobus R, Chittenden D, Kampa B, et al. Discovery of novel biomarker candidates for liver Fibrosis in Hepatitis C patients: A preliminary study. PLoS One. 2012;7.\u003c/li\u003e\n\u003cli\u003eAronow BJ, Lund SD, Brown TL, Harmony JAK, Witte DP. Apolipoprotein J expression at fluid-tissue interfaces: Potential role in barrier cytoprotection. Proc Natl Acad Sci U S A. 1993;90:725\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eKujiraoka T, Hattori H, Miwa Y, Ishihara M, Ueno T, Ishii J, et al. Serum Apolipoprotein J in Health, Coronary Heart Disease and Type 2 Diabetes Mellitus. 2006.\u003c/li\u003e\n\u003cli\u003eYang N, Qin Q. Apolipoprotein J: A new predictor and therapeutic target in cardiovascular disease? Chin Med J (Engl). 2015;128:2530\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eGil SY, Youn BS, Byun K, Huang H, Namkoong C, Jang PG, et al. Clusterin and LRP2 are critical components of the hypothalamic feeding regulatory pathway. Nat Commun. 2013;4.\u003c/li\u003e\n\u003cli\u003eSeo JA, Kang MC, Yang WM, Hwang WM, Kim SS, Hong SH, et al. Apolipoprotein J is a hepatokine regulating muscle glucose metabolism and insulin sensitivity. Nat Commun. 2020;11:1\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eRull A, Mart\u0026iacute;nez-Bujidos M, P\u0026eacute;rez-Cuellar M, P\u0026eacute;rez A, Ord\u0026oacute;\u0026ntilde;ez-Llanos J, S\u0026aacute;nchez-Quesada JL. Increased concentration of clusterin/apolipoprotein J (apoJ) in hyperlipemic serum is paradoxically associated with decreased apoJ content in lipoproteins. Atherosclerosis. 2015;241:463\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eDuan S, Qin N, Pi J, Sun P, Gao Y, Liu L, et al. Antagonizing apolipoprotein J chaperone promotes proteasomal degradation of mTOR and relieves hepatic lipid deposition. Hepatology. 2023;78:1182\u0026ndash;99.\u003c/li\u003e\n\u003cli\u003eSmith EE, Malik HS. The apolipoprotein L family of programmed cell death and immunity genes rapidly evolved in primates at discrete sites of host-pathogen interactions. Genome Res. 2009;19:850\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003ePays E. The Janus-faced functions of Apolipoproteins L in membrane dynamics. Cell Mol Life Sci. 2024;81.\u003c/li\u003e\n\u003cli\u003eAdeva-Andany MM, Funcasta-Calder\u0026oacute;n R, Fern\u0026aacute;ndez-Fern\u0026aacute;ndez C, Ameneiros-Rodr\u0026iacute;guez E, Vila-Altesor M, Castro-Quintela E. The metabolic effects of APOL1 in humans. Pflugers Archiv European Journal of Physiology. 2023;475:911\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003ePage NM, Butlin DJ, Lomthaisong K, Lowry PJ. The human apolipoprotein L gene cluster: Identification, classification, and sites of distribution. Genomics. 2001;74:71\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eDuchateau PN, Pullinger CR, Orellana RE, Kunitake ST, Naya-Vigne J, O\u0026rsquo;Connor PM, et al. Apolipoprotein L, a new human high density lipoprotein apolipoprotein expressed by the pancreas. Identification, cloning, characterization, and plasma distribution of apolipoprotein L. J Biol Chem. 1997;272:25576\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eShukha K, Mueller JL, Chung RT, Curry MP, Friedman DJ, Pollak MR, et al. Most ApoL1 is secreted by the liver. J Am Soc Nephrol. 2017;28:1079\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eDavidson WS, Silva RAGD, Chantepie S, Lagor WR, Chapman MJ, Kontush A. Proteomic analysis of defined hdl subpopulations reveals particle-specific protein clusters: Relevance to antioxidative function. Arterioscler Thromb Vasc Biol. 2009;29:870\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eCubedo J, Padr\u0026oacute; T, Alonso R, Mata P, Badimon L. ApoL1 levels in high density lipoprotein and cardiovascular event presentation in patients with familial hypercholesterolemia. J Lipid Res. 2016;57:1059\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eAlbert TSE, Duchateau PN, Deeb SS, Pullinger CR, Cho MH, Heilbron DC, et al. Apolipoprotein L-I is positively associated with hyperglycemia and plasma triglycerides in CAD patients with low HDL. J Lipid Res. 2005;46:469\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eNishimura K, Murakami T, Sakurai T, Miyoshi M, Kurahashi K, Kishi S, et al. Circulating Apolipoprotein L1 is associated with insulin resistance-induced abnormal lipid metabolism. Sci Rep. 2019;9.\u003c/li\u003e\n\u003cli\u003eCroyal M, Wargny M, Chemello K, Chevalier C, Blanchard V, Bigot-Corbel E, et al. Plasma apolipoprotein concentrations and incident diabetes in subjects with prediabetes. Cardiovasc Diabetol. 2022;21:21.\u003c/li\u003e\n\u003cli\u003eMocciaro G, George AL, Allison M, Frontini M, Huang-Doran I, Reiman F, et al. Oxidised Apolipoprotein Peptidome Characterises Metabolic Dysfunction-Associated Steatotic Liver Disease. Liver Int. 2025;45:e16200.\u003c/li\u003e\n\u003cli\u003eWang H, Eckel RH. Lipoprotein lipase: from gene to obesity. Am J Physiol Endocrinol Metab. 2009;297:E271-88.\u003c/li\u003e\n\u003cli\u003eGangabadage CS, Zdunek J, Tessari M, Nilsson S, Olivecrona G, Wijmenga SS. Structure and dynamics of human apolipoprotein CIII. J Biol Chem. 2008;283:17416\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003eRehues P, Girona J, Guardiola M, Ozcariz E, Amig\u0026oacute; N, Rosales R, et al. ApoC-III proteoforms are associated with better lipid, inflammatory, and glucose profiles independent of total apoC-III. Cardiovasc Diabetol. 2024;23.\u003c/li\u003e\n\u003cli\u003eBor\u0026eacute;n J, Packard CJ, Taskinen M-RR. The Roles of ApoC-III on the Metabolism of Triglyceride-Rich Lipoproteins in Humans. Front Endocrinol (Lausanne). 2020;11.\u003c/li\u003e\n\u003cli\u003eGerritsen G, Rensen PCN, Kypreos KE, Zannis VI, Havekes LM, Van Dijk KW. ApoC-III deficiency prevents hyperlipidemia induced by apoE overexpression. J Lipid Res. 2005;46:1466\u0026ndash;73.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metabolic dysfunction-associated steatohepatitis, Obesity, Apolipoprotein, Biomarkers, Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-6718852/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6718852/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePlasma apolipoproteins are linked to cardiometabolic dysfunctions, but their potential as biomarkers for metabolic dysfunction-associated steatohepatitis (MASH) remains underexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePlasma levels of 14 apolipoproteins (apoA-I, A-II, A-IV, B100, C-I, C-II, C-III, D, E, F, H, J, L1, M) were measured using liquid chromatography-tandem mass spectrometry in a cross-sectional study of 148 individuals with obesity undergoing bariatric surgery. Participants were categorized based on liver histology into non-MASH (no liver alterations or simple steatosis, defined as \u0026ge;\u0026thinsp;5% intrahepatic fat) and MASH (steatosis with ballooning and lobular inflammation, with or without fibrosis). Correlations with clinical and biochemical parameters were assessed via Spearman\u0026rsquo;s rank correlation, and associations with MASH were evaluated using logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e Plasma levels of apoC-III and apoL1 were significantly higher in MASH participants compared to non-MASH participants, while the levels of other apolipoproteins did not differ significantly between the two groups. Higher plasma levels of apoE, apoL1 and apoJ were associated with increased odds of MASH, independently of age and sex. The associations for apoL1 and apoJ remained significant after adjusting for MASH risk factors, including insulin resistance, plasma triglycerides, waist circumference, and the AST/ALT ratio, as well as comorbidities such as diabetes, dyslipidemia, and hypertension.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePlasma apoJ and apoL1 may serve as potential biomarkers for diagnosing MASH in individuals with obesity, independent of traditional risk factors and comorbidities. Further validation in larger cohorts and exploration of the underlying biological mechanisms linking these apolipoproteins to MASH are warranted.\u003c/p\u003e","manuscriptTitle":"ApoL1 and ApoJ as Novel Determinants of MASH: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-26 09:08:28","doi":"10.21203/rs.3.rs-6718852/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-22T10:47:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-22T07:19:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-22T06:18:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Lipids in Health and Disease","date":"2025-05-21T17:57:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a27ac68-c860-4fc8-bc65-709ee5f3356c","owner":[],"postedDate":"May 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:04:29+00:00","versionOfRecord":{"articleIdentity":"rs-6718852","link":"https://doi.org/10.1186/s12944-025-02733-0","journal":{"identity":"lipids-in-health-and-disease","isVorOnly":false,"title":"Lipids in Health and Disease"},"publishedOn":"2025-10-14 15:58:24","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2025-05-26 09:08:28","video":"","vorDoi":"10.1186/s12944-025-02733-0","vorDoiUrl":"https://doi.org/10.1186/s12944-025-02733-0","workflowStages":[]},"version":"v1","identity":"rs-6718852","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6718852","identity":"rs-6718852","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}