Association between asymmetric dimethylarginine, its change and subclinical carotid atherosclerosis in metabolic dysfunction-associated steatotic liver disease

Association between asymmetric dimethylarginine, its change and subclinical carotid atherosclerosis in metabolic dysfunction-associated steatotic liver disease | 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 Association between asymmetric dimethylarginine, its change and subclinical carotid atherosclerosis in metabolic dysfunction-associated steatotic liver disease Wei Wang, Xintian Ren, Pengfei Sun, Tingting LV, Yu Wang, Yanfang Tan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7078667/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Asymmetric dimethylarginine (ADMA) is a promising marker for assessing cardiovascular disease risk; however, it is unclear whether ADMA can predict subclinical carotid atherosclerosis (SCA) in individuals with metabolic dysfunction-associated steatotic liver disease (MASLD). This study aimed to investigate the association between baseline ADMA levels, changes in ADMA, and the risk of SCA in MASLD adults. Methods : A total of 600 MASLD adults were prospectively recruited. Serum ADMA concentrations were measured at baseline and one year of follow-up. The SCA was defined as the presence of carotid intima-media thickness and carotid plaque. Logistic regression and propensity score analysis was conducted to evaluate the association between baseline ADMA levels, changes in ADMA, and SCA risk. Results : The risk of SCA increased significantly with higher ADMA levels. Compared with participants in the lowest quartile of baseline ADMA levels, the multivariable-adjusted odds ratios were 0.989 (95% confidence interval, CI: 0.580-1.687), 1.853 (1.112-3.088) and 3.810 (2.055-7.063) for those in Quartile 2, Quartile 3 and Quartile 4, respectively. At 1-year follow-up, SCA progression occurred in 65 (12.45%) participants. Both absolute and relative changes in ADMA levels from baseline to one year were significantly associated with SCA progression, with adjusted relative risks being 1.056 (95% CI: 1.042-1.069) and 1.014 (95% CI: 1.011-1.018), respectively. Conclusion : ADMA levels associated with SCA in MASLD adults, while dynamic changes in ADMA could help predict the progression of SCA in these individuals. metabolic dysfunction-associated steatotic liver disease asymmetric dimethylarginine carotid intima-media thickness carotid plaque cohort Figures Figure 1 Figure 2 Figure 3 Introduction Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global public health priority, affecting approximately 30% of people worldwide [1]. It is well established that patients with MASLD present a higher risk of atherosclerosis and cardiovascular disease (CVD) compared with the general population [2-3]. In the progression of atherosclerosis and CVD, subclinical carotid atherosclerosis (SCA) typically develops at an early stage. Elucidating the mechanisms between MASLD and SCA is crucial for early atherosclerosis detection, cardiovascular risk assessment in MASLD adults. This is also informative for developing targeted prevention, optimal treatment strategies and ultimately, improving the prognosis of these patients. The vascular endothelium is crucial for maintaining cardiovascular health [4]. Asymmetric dimethylarginine (ADMA), a potent inhibitor of nitric oxide (NO) synthase, could interfere with the production of NO by the endothelium by acting as an analogue to L-arginine, the substrate for NO synthase. Growing evidence showed that patients with CVD often exhibited higher ADMA concentration than healthy controls [5]. Meanwhile, ADMA is recognized as an adipokine since the genes responsible for its synthesis and metabolism are also found in adipose tissue [6]. ADMA concentrations have been reported to increase as body mass index (BMI) rises [7]. Since the balance between ADMA production, metabolism, and excretion plays a crucial role in maintaining normal endothelial function [8], impaired metabolism of ADMA, as seen in conditions like MASLD, might lead to elevated levels of ADMA, contributing to endothelial dysfunction and an increased risk of atherosclerosis. Emerging preclinical and clinical data indeed observed ADMA accumulation in nonalcoholic fatty liver disease [9]. However, the role of ADMA in relation to SCA in MASLD adults remains unclear. Clarifying this correlation is essential for better understanding the pathogenesis and exploring the integration of this marker into clinical practice. Considering ADMA's identification as a cardiovascular risk factor, and its increased level in MASLD, the aim of this study was to assess the role of baseline ADMA levels, and its changes for monitoring SCA progression in MASLD adults. Methods Study population This was a prospective, single-center, cohort study that included MASLD adults between October 24, 2022 and April 28, 2023 at Beijing Friendship Hospital. The inclusion criteria were as follows: 1) individuals aged 18 years or older; 2) hepatic steatosis confirmed by abdominal ultrasound; 3) evidence of metabolic dysfunction, defined as the presence of type 2 diabetes mellitus, overweight/obesity (BMI ≥ 23 kg/m² for Asians), or at least two metabolic risk factors, including elevated waist circumference, hypertension, hypertriglyceridemia, reduced HDL cholesterol, prediabetes, insulin resistance, or elevated high-sensitivity C-reactive protein [10]; 4) alcohol consumption not exceeding 210 g/week in men and 140 g/week in women. Exclusion criteria comprised 1) established diagnosis of other chronic liver disease, including viral hepatitis, autoimmune liver disease, drug-induced liver disease, total parenteral nutrition, and Wilson's disease; 2) serious CVD event; 3) co-infection of cytomegalovirus, herpes simplex virus, or human immunodeficiency virus; 4) chronic wasting diseases, malignant tumors, hyperthyroidism, or hematological disorders; 5) pregnant and breastfeeding women. This study was approved by the Ethics Committee of Capital Medical University Affiliated Beijing Friendship Hospital (No. 2022-P2-188-01). All participants provided written informed consents. Measurement of serum ADMA levels Blood samples collected in EDTA tripotassium tubes were centrifuged at 2,500 × g (4°C, 15 minutes), and serum was stored in 1-mL aliquots at -80°C to preserve protein stability. Serum ADMA levels at baseline and one year follow-up were quantified using enzyme-linked immunosorbent assay (ELISA). The absolute change in ADMA (ΔADMA 1y-0y ) during the first year was determined by calculating the difference in ADMA levels between one year follow-up and baseline. The relative change in ADMA was calculated by dividing the absolute difference by baseline ADMA levels. Assessment of subclinical carotid atherosclerosis Carotid intima-media thickness and plaques were evaluated using high-resolution B-mode ultrasound with a linear-array transducer (7.5-10 MHz) at Beijing Friendship hospital. The common carotid arteries, carotid bulbs, and internal carotid arteries were scanned bilaterally in the longitudinal view. Carotid intima-media thickness was measured on the far wall of the common carotid artery, approximately 1 cm below the bifurcation, during end-diastole. Carotid intima-media thickness between 1.0 mm and <1.5 mm was defined as increased intima-media thickening, while a carotid intima-media thickness≥1.5 mm without luminal narrowing was defined as carotid plaque. The primary outcomes of interest were: (1) presence of SCA, which was indicated by increased carotid intima-media thickness or the presence of carotid plaque; (2) occurrence of SCA progression at one year, which was defined as new onset of any SCA for participants who were free of SCA at baseline, new onset plaque for participants who already having increased carotid intima-media thickness at baseline, or new-onset of carotid artery stenosis. Covariates Trained research staffs, who were not involved in the patients' clinical care, reviewed the medical records for data collection. The collected information included age, gender, smoking, BMI, blood pressure, heart rate, and the presence of diabetes, hypertension, and dyslipidemia. Diabetes was identified based on self-reported or physician-diagnosed diabetes, or hemoglobin A1C % (HbA1c%) ≥6.5 at cohort recruitment. Hypertension was identified using self-reported information and blood pressure readings, with a systolic pressure of ≥140 mmHg or a diastolic pressure of ≥90 mmHg. Dyslipidemia was identified based on self-reported or physician-diagnosed. Serum levels of white cells, platelet counts, alanine aminotransferase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), γ-Glutamyl transferase (GGT), albumin (ALB), total bilirubin (TBIL), total cholesterol (TC), triglyceride, high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol (LDL), C-reactive protein (CRP), glucose, HbA1c, and creatinine were collected. Furthermore, we calculated the aspartate transaminase to platelet ratio (APRI) and fibrosis-4 (FIB-4) scores to evaluate non-invasive liver fibrosis [11, 12], along with the triglyceride-glucose index (TyG) as a surrogate marker for insulin resistance [13]. Statistical Analysis The study population was classified by quartiles of baseline ADMA levels (Q1, Q2, Q3 and Q4). Continuous variables were expressed as mean ± SD or median with interquartile range, depending on the data distribution. Categorical variables were presented as counts and percentages. For comparisons, analysis of variance or the Kruskal-Wallis test was applied as appropriate. Logistic regression and propensity score analysis was conducted to examine the relationship between baseline ADMA levels, changes in ADMA and the presence of SCA, adjusted for age, gender, smoke, disease history of diabetes and hypertension, BMI, platelet counts, ALT, AST, TC, triglyceride, HDL, LDL, and glucose. We calculated the odds ratios (ORs) and 95% confidence interval (CI) of the risk of SCA prevalence for each quartile in comparison with the lowest quartile (Q1). The trend of SCA prevalence with increased ADMA levels was tested using the same model with ADMA levels used as a continuous variable. The relative risks (RRs) and 95%CI were calculated to reveal the association of changes in ADMA (ΔADMA 1y-0y , Δ%ADMA 1y-0y ) and SCA progression. Considering the potential difference in the risk of SCA progression for participants who had SCA at baseline and those who were free of SCA at baseline, we conducted separate analyses for each subgroup of these participants, in addition to analyzing the overall participant group. Moreover, subgroup analyses were conducted to explore whether the relationship between baseline ADMA levels and changes in ADMA differed by age (<45, ≥45 years), sex (male, female), smoking status (current smoker or not), hypertension (present or absent), and diabetes (present or absent). Interaction terms for ADMA and these variables were included in the multivariable model to assess potential effect modification. In addition, restricted cubic spline analysis was performed to explore the potential non-linear relationship of baseline ADMA levels with the presence of SCA, and changes in ADMA with SCA progression, respectively, with median value of baseline ADAM level (23.30 ng/ml) and changes in ADAM (ΔADMA 1y-0y , 10.26 ng/ml; Δ%ADMA 1y-0y , 48.46%) as reference point. All statistical analyses were conducted using SAS version 9.4 (SAS Inc., Cary, NC, USA). P -value of 0.05 was considered as the threshold for statistical significance. Results Clinical characteristics of study population A total of 600 MASLD adults were enrolled in this study. Serum ADMA levels were measured for 600 and 522 participants at baseline and one year follow-up, respectively. The study population were predominantly male (59.83%), and the median age of participants was 46.00 (38.00, 54.00) years. About 12.17% of them were current smokers, 31.83% had a history of hypertension, and 10.17% had a history of diabetes. The overall prevalence of SCA in MASLD participants was 49.83%. The median baseline ADMA concentration across 600 MASLD adults was 23.30 (13.90, 40.95) ng/ml. Demographics and clinical characteristics of study participants stratified by baseline ADMA levels were presented in Table 1. Differences were observed in various factors such as age, sex, disease history of diabetes, platelet counts, ALT, AST, GGT, TyG, triglyceride, glucose, HbA1c, creatinine, FIB-4, and APRI among the ADMA quartiles. Association between baseline ADMA levels and SCA prevalence Table 2 listed the prevalence rates of SCA, carotid plaque, and their according ORs by baseline ADMA levels in quartiles. Both the prevalence rates and ORs were generally increasing with the quartiles went up. Compared to participants in the lowest quartile of baseline ADMA levels, the multivariable-adjusted ORs of SCA were 0.989 (95% CI: 0.580-1.687), 1.853 (1.112-3.088) and 3.810 (2.055-7.063) for those in Quartile 2, Quartile 3 and Quartile 4, respectively. For carotid plaque, participants in Quartile 4 had an increased risk compared to those in the lowest quartile, as indicated by the multivariable-adjusted model (OR=2.576, 95%CI: 1.361-4.874). However, participants in Quartile 2 and Quartile 3 exhibited a similar risk of carotid plaque as those in the lowest quartile. Additionally, the adjusted ORs (95%CIs) of SCA and carotid plaque were 1.035 (1.022-1.048), and 1.017 (1.007-1.027) per one-point increment of ADMA level increased (Table 2). However, the logistic regression model with restricted cubic spline analysis revealed a non-linear relationship between baseline ADMA (treated as a continuous variable) and the risk of SCA prevalence (P=0.004, Figure 1). We did pre-defined subgroup analysis for the association between baseline ADMA level and SCA prevalence, adjusting for multiple variables. Generally, the results showed that the associations were not altered by these characteristics, except that females and those with hypertension showed stronger association between baseline ADMA and SCA prevalence (Figure 2). Association between changes in ADMA and SCA progression Of these 522 (87%) MASLD adults who completed one year follow-up, SCA progression occurred in 65 (12.45%) participants, including 18 who had SCA at baseline and 47 who were free of SCA at baseline. Table 3 showed the association of baseline ADMA level, changes in ADMA with SCA progression. No association was observed for baseline ADMA level and SCA progression. Regarding to the changes in ADMA, the median ΔADMA and Δ%ADMA was 10.26 (0.07, 40.95) ng/ml and 48.46% (0.32%, 123.88%), respectively. Both the univariable and multivariable analysis demonstrated that both the ΔADMA and Δ%ADMA were significantly associated with increased risk of SCA progression, with adjusted RRs being 1.056 (95% CI: 1.042-1.069) and 1.014 (95% CI: 1.011-1.018), respectively. This association was still significant in participants who had SCA at baseline and those who were free of SCA at baseline. The restricted cubic spline indicated non-linear association between ΔADMA, Δ%ADMA and SCA progression (P=0.003, P<0.001, Figure 1). Additionally, the subgroup analysis showed that the association between changes in ADMA and SCA progression were not altered by participants’ characteristics, except that Δ%ADMA showed a stronger association with increased risk of SCA progression in males (Figure 3). Discussion In this study, we conducted a prospective evaluation of the role of serum ADMA levels in MASLD adults, and found that SCA prevalence was significantly higher among individuals with elevated ADMA levels. Moreover, changes in ADMA were significantly associated with SCA progression at one year. This association was consistently observed for both participants with SCA at baseline and those who were free of SCA at baseline. This implied that ADMA could serve as a potential marker for SCA, as well as a short-term prognostic marker for MASLD individuals. Subjects with high ADMA levels showed higher proportion of SCA compared to those with low ADMA levels. Multivariable analysis further demonstrated that ADMA was associated with the presence of SCA, independent of traditional risk factors. We recognize its potential as an additional assessment tool, offering a straightforward, objective, and single laboratory measurement. Monitoring ADMA in MASLD patients can help detect early signs of atherosclerosis before clinical cardiovascular events occur, allowing for timely intervention. A potential link between ADMA and SCA may be attributed to ADMA's role in promoting endothelial dysfunction, which disrupts vascular homeostasis and triggers processes such as lipid accumulation, inflammation, smooth muscle cell proliferation, and thrombosis [14, 15]. However, this connection is likely to be complex, involving an intricate interplay of factors, including endothelial dysfunction, oxidative stress, inflammation, impaired nitric oxide signaling, fibrosis, vascular remodeling, hemodynamic alterations, and associated comorbid conditions. Since ADMA serves as a biomarker for endothelial dysfunction and atherosclerosis, a link between ADMA and the progression of SCA is anticipated. Previous research has indicated that ADMA is a strong independent predictor of cardiovascular events, with elevated levels being linked to a higher risk of such events [16, 17]. While baseline ADMA levels reflect the status of endothelial function at a single point in time, they may not fully capture the dynamic changes in vascular health. Although endothelial dysfunction plays an important role in the development of atherosclerosis, it can evolve over time due to various factors. And our results showed no association between baseline ADMA levels and SCA progression. The differences in results between this study and previous research may be attributed to the distinct populations studied. This study included individuals without pre-existing cardiovascular or cerebrovascular diseases, whereas many previous studies on ADMA primarily focused on patients with cardiovascular or cerebrovascular conditions. The presence of such conditions could significantly alter ADMA levels and their relationship with endothelial function, potentially explaining the variability in findings between the two groups. In fact, the baseline ADMA levels reported in this study are indeed slightly lower than those described in the literature [18]. Notably, our results demonstrated that changes in ADMA levels significantly correlated with SCA progression, indicating that changes in ADMA levels may be a valuable predictor of atherosclerotic progression and cardiovascular risk. An increase in ADMA levels during follow-up may indicate worsening endothelial function and a higher cardiovascular risk [19]. As a simple, single follow-up parameter, changes in ADMA could offer independent, additional predictive value [20]. Monitoring ADMA over time, rather than relying on baseline levels alone, provides a more accurate assessment of evolving factors contributing to atherosclerosis risk. Additional studies are required to explore how ADMA can be integrated as an independent marker into the existing risk assessment models. Nevertheless, the interpretation of our findings should be approached with caution due to the following limitations. Firstly, the relatively short follow-up period limited our ability to reveal the long-term prognostic significance of ADMA. Secondly, this was a single-center study with a cohort that lacked ethnic diversity, which highlighted the need for validation in an external population. Replication of these findings was essential before applying them to clinical practice. In conclusion, while baseline ADMA levels provided important insight into SCA prevalence, the changes in ADMA levels was found to be predictive of short-term SCA progression. As a straightforward, clear, and objective measure, dynamic monitoring of ADMA levels could serve as an additional independent marker of SCA progression, helping to assess cardiovascular risk and inform clinical decision-making. Declarations Author Contributions : LM and ZXY designed the study. WW and RXT drafted the manuscript. SPF performed the ultrasound examination. WW and LTT performed the laboratory test. WY analyzed the data. TYF validated the analysis. LM and ZXY revised the manuscript. Competing Interests : The authors have no conflict of interest to report. Ethics approval : This study was approved by the Ethics Committee of Capital Medical University Affiliated Beijing Friendship Hospital (No. 2022-P2-188-01). All participants provided written informed consents. Data Availability Statement: Data used in this study can be requested from the corresponding author. Funding : This work was supported by the National Natural Science Foundation of China under Grant [No. 82103902]. References Le MH, Le DM, Baez TC, et al. Global incidence of non-alcoholic fatty liver disease: A systematic review and meta-analysis of 63 studies and 1,201,807 persons. J Hepatol 2023;79:287-95. Chung NT, Hsu CY, Shih NC, et al. Elevated concurrent carotid atherosclerosis rates in patients with metabolic dysfunction-associated fatty liver disease (MAFLD) compared to non-alcoholic fatty liver disease (NAFLD): A cross-sectional observational study. Nutr Metab Cardiovasc Dis 2025;35:103767. Targher G, Byrne CD, Lonardo A, et al. Non-alcoholic fatty liver disease and risk of incident cardiovascular disease: A meta-analysis. J Hepatol 2016;65:589-600. Villanova N, Moscatiello S, Ramilli S, et al Endothelial dysfunction and cardiovascular risk profile in nonalcoholic fatty liver disease. Hepatology 2005;42:473-80. Valkonen VP, Päivä H, Salonen JT, et al. Risk of acute coronary events and serum concentration of asymmetrical dimethylarginine. Lancet 2001;358:2127-8. Spoto B, Parlongo RM, Parlongo G, et al. The enzymatic machinery for ADMA synthesis and degradation is fully expressed in human adipocytes. J Nephrol 2007;20:554-9. McLaughlin T, Stühlinger M, Lamendola C, et al. 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Characteristics of 600 participants with MASLD at baseline Variables Q1 Q2 Q3 Q4 † P value Age, years 43.00(36.00,52.00) 43.50(37.00,52.00) 43.50(36.00,54.00) 51.00(44.00,56.00) <0.001 Sex 0.001 Male 76(50.67) 79(52.67) 105(70.00) 99(66.00) Female 74(49.33) 71(47.33) 45(30.00) 51(34.00) Current Smoker 0.152 Yes 14(9.33) 14(9.33) 25(16.67) 20(13.33) No 136(90.67) 136(90.67) 125(83.33) 130(86.67) BMI 26.60(24.71,29.05) 26.93(25.26,29.52) 26.85(24.91,29.04) 26.79(25.17,29.07) 0.699 Systolic pressure 126.00(118.00,138.00) 128.00(119.00,136.00) 129.00(121.00,139.00) 130.00(121.00,140.00) 0.274 Diastolic pressure 81.00(73.00,88.00) 82.00(74.00,89.00) 82.00(74.00,89.00) 83.00(77.00,92.00) 0.158 Heart rate 80.00(72.00,84.00) 78.00(70.00,86.00) 78.00(70.00,86.00) 78.00(72.00,86.00) 0.933 Disease history Hypertension 43(28.67) 45(30.00) 52(34.67) 51(34.00) 0.614 Diabetes 12(8.00) 10(6.67) 13(8.67) 26(17.33) 0.009 Dyslipidemia 9(6.00) 11(7.33) 12(8.00) 18(12.00) 0.269 Laboratory tests White cells, 10~9/L 6.75(5.53,7.63) 6.18(5.29,7.22) 6.29(5.33,7.28) 6.49(5.58,7.32) 0.055 Platelet counts, 10~9/L 267.00(225.00,306.00) 245.00(210.50,276.00) 244.00(208.00,287.00) 239.50(205.00,279.00) 0.001 ALT, U/L 20.00(15.00,29.00) 24.00(17.00,33.00) 29.00(20.00,42.00) 29.00(21.00,48.00) <0.001 AST, U/L 20.40(17.10,24.20) 21.30(18.00,25.80) 24.20(20.10,29.50) 25.75(21.30,33.50) <0.001 ALP, U/L 70.00(63.00,84.00) 70.50(63.00,87.00) 76.00(60.00,87.00) 73.00(63.00,80.00) 0.884 GGT, U/L 28.00(23.00,38.00) 33.00(20.00,43.00) 35.00(27.00,73.00) 37.00(27.00,64.00) 0.035 ALB, g/L 44.90(43.50,47.60) 46.70(44.10,48.40) 46.60(44.60,48.10) 46.00(44.90,48.60) 0.082 TBIL, umol/L 13.33(11.28,15.94) 13.07(9.57,18.24) 13.44(11.62,15.71) 14.89(12.42,18.75) 0.250 TyG 8.62(8.29,8.95) 8.71(8.38,9.09) 8.86(8.47,9.18) 8.95(8.58,9.41) <0.001 TC, mmol/L 5.20(4.34,5.85) 5.31(4.57,6.02) 5.16(4.65,5.83) 5.47(4.46,6.12) 0.443 Triglyceride, mmol/L 1.32(0.98,1.68) 1.43(1.03,1.96) 1.55(1.18,2.19) 1.64(1.17,2.55) <0.001 HDL, mmol/L 1.24(1.11,1.47) 1.29(1.11,1.45) 1.22(1.07,1.42) 1.24(1.13,1.44) 0.465 LDL, mmol/L 3.20(2.68,3.70) 3.27(2.70,3.71) 3.27(2.92,3.69) 3.43(2.70,3.89) 0.613 CRP, mg/L 0.85(0.31,2.45) 1.06(0.41,2.35) 0.90(0.47,1.72) 0.98(0.41,2.24) 0.958 Glucose, mmol/L 5.31(4.96,5.73) 5.24(4.91,5.77) 5.40(4.97,5.93) 5.48(5.09,6.13) 0.006 HbA1c, % 5.50(5.30,5.70) 5.50(5.20,5.75) 5.60(5.30,5.90) 5.60(5.40,6.10) 0.005 Creatinine, umol/L 68.00(58.20,80.30) 70.40(58.10,83.60) 75.10(65.75,81.90) 72.80(63.90,80.70) 0.028 FIB-4 0.70(0.51,1.07) 0.82(0.62,0.98) 0.76(0.61,1.11) 1.00(0.77,1.34) <0.001 APRI 0.24(0.18,0.31) 0.27(0.21,0.35) 0.30(0.23,0.38) 0.33(0.25,0.44) <0.001 † All participants were classified into 4 quartiles (Q1, ADMA level <13.90 ng/ml; Q2, 13.90 ≤ ADMA level< 23.30 ng/ml; Q3, 23.30 ≤ ADMA level < 40.95 ng/ml; Q4, ADMA level≥40.95 ng/ml). Abbreviation: MASLD, metabolic dysfunction-associated steatotic liver disease; BMI, body mass index; ALT, alanine aminotransferase; AST, aspartate transaminase; ALP, alkaline phosphatase; GGT, γ-Glutamyl transferase; ALB, albumin; TBIL, total bilirubin; TC, total cholesterol; HDL, high density lipoprotein cholesterol; LDL, low density lipoprotein cholesterol; CRP, C-reactive protein; HbA1c, hemoglobin A1C; FIB-4, Fibrosis 4 score; APRI, aspartate aminotransferase-to-platelet ratio index. Table 2. The association between baseline ADMA and subclinical carotid atherosclerosis in participants with MASLD Outcomes Events/participants, (%) Univariable model Multivariable model † OR (95%CI) P value OR (95%CI) P value Subclinical carotid atherosclerosis Continuous - 1.039 (1.029-1.049) <0.001 1.035 (1.022-1.048) <0.001 IQR Q1 52/150 (34.67) Reference - Reference - Q2 52/150 (34.67) 1.000 (0.622-1.609) 1.000 0.989 (0.580-1.687) 0.969 Q3 76/150 (50.67) 1.936 (1.217-3.079) 0.005 1.853 (1.112-3.088) 0.068 Q4 119/150 (79.33) 7.234 (4.306-12.155) <0.001 3.810 (2.055-7.063) <0.001 P for trend - <0.001 - <0.001 Carotid plaque Continuous - 1.022 (1.015-1.030) <0.001 1.017 (1.007-1.027) 0.001 IQR Q1 25/150 (16.67) Reference - Reference - Q2 21/150 (14.00) 0.814 (0.433-1.529) 0.522 0.739 (0.351-1.552) 0.424 Q3 36/150 (24.00) 1.579 (0.893-2.792) 0.116 1.528 (0.804-2.902) 0.195 Q4 69/150 (46.00) 4.259 (2.492-7.281) <0.001 2.576 (1.361-4.874) 0.004 P for trend - <0.001 - <0.001 † Adjusted for age, gender, smoke, disease history of hypertension and diabetes, body mass index, platelet counts, ALT, AST, TC, Triglyceride, HDL, LDL, and glucose. Abbreviation: ADMA, asymmetric dimethylarginine; MASLD, metabolic dysfunction-associated steatotic liver disease; OR, odd ratio; CI, confidence interval; IQR, interquartile range. Table 3. The association of baseline ADMA, changes in ADMA and the risk of SCA progression Variables Level, IQR Univariable model Multivariable model † RR (95%CI) P value RR (95%CI) P value Total Base ADMA 23.30 (13.90, 40.95) 0.997 (0.986-1.008) 0.603 0.995 (0.983-1.007) 0.442 ΔADMA 1y-0y 10.26 (0.07, 40.95) 1.052 (1.040-1.064) <0.001 1.056 (1.042-1.069) <0.001 Δ% ADMA 1y-0y 48.46 (0.32, 123.88) 1.013 (1.010-1.015) <0.001 1.014 (1.011-1.018) <0.001 MASLDs without SCA Base ADMA 18.51 (12.51, 26.88) 1.008 (0.992-1.026) 0.330 1.009 (0.988-1.030) 0.410 ΔADMA 1y-0y 11.07 (2.91, 31.59) 1.095 (1.068-1.123) <0.001 1.104 (1.068-1.142) <0.001 Δ% ADMA 1y-0y 64.23 (11.53, 163.75) 1.012 (1.008-1.016) <0.001 1.013 (1.008-1.018) <0.001 MASLDs with SCA Base ADMA 32.90 (19.45, 51.89) 1.003 (0.988-1.018) 0.719 0.998 (0.981-1.015) 0.832 ΔADMA 1y-0y 7.82 (-2.75, 34.63) 1.033 (1.020-1.046) <0.001 1.040 (1.022-1.058) <0.001 Δ% ADMA 1y-0y 38.12 (-11.61, 88.36) 1.013 (1.008-1.018) <0.001 1.018 (1.010-1.026) <0.001 † Adjusted for age, gender, smoke, disease history of hypertension and diabetes, body mass index, platelet counts, ALT, AST, TC, Triglyceride, HDL, LDL, and glucose. Abbreviations: ADMA, asymmetric dimethylarginine; IQR, interquartile range; RR, relative risk; CI, confidence interval; MASLD, metabolic dysfunction-associated steatotic liver disease; SCA, subclinical carotid atherosclerosis. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7078667","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482810886,"identity":"2a4e0ac1-874e-4c81-b47d-2cb3248f588e","order_by":0,"name":"Wei Wang","email":"","orcid":"","institution":"Beijing Friendship Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":482810887,"identity":"9fff91ce-7b7c-4f68-aa68-8b2434af31ce","order_by":1,"name":"Xintian Ren","email":"","orcid":"","institution":"Beijing Friendship Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xintian","middleName":"","lastName":"Ren","suffix":""},{"id":482810888,"identity":"35bdce72-18d3-431e-9ed8-d699585ee405","order_by":2,"name":"Pengfei Sun","email":"","orcid":"","institution":"Beijing Friendship Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Sun","suffix":""},{"id":482810889,"identity":"93bba66d-7db9-426c-85b6-b78f7ad682f5","order_by":3,"name":"Tingting LV","email":"","orcid":"","institution":"Beijing Friendship Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"LV","suffix":""},{"id":482810890,"identity":"f4e0daa0-7856-47c5-94e4-1c0af0d10b1f","order_by":4,"name":"Yu Wang","email":"","orcid":"","institution":"Beijing Friendship Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""},{"id":482810891,"identity":"244ef23f-a7c0-4cc6-94dc-1157b6a4f99f","order_by":5,"name":"Yanfang Tan","email":"","orcid":"","institution":"Beijing Friendship Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanfang","middleName":"","lastName":"Tan","suffix":""},{"id":482810892,"identity":"8e2540cb-425c-427a-959b-44fad7132157","order_by":6,"name":"Xinyan Zhao","email":"","orcid":"","institution":"Beijing Friendship Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinyan","middleName":"","lastName":"Zhao","suffix":""},{"id":482810893,"identity":"0f8283b3-5a67-41a7-80cf-3a75f6c04208","order_by":7,"name":"Min Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnElEQVRIiWNgGAWjYDACdsbGB1CmAZFamBmbYUqJ1sLAJkGaFt1m5raKHzXbEhvYm7dJMNTcIazF7DBj282eY7cTG3iOlUkwHHtGnJbbjA1ALRI5ZhKMDYeJ01IM1iL/hgQtzBBbeIjX0iwJ9ItxG09asUXCMWK0HG9/+OFHzW3ZfvbDG298qCFCCxywgYgEEjSMglEwCkbBKMADAEcnOFzE9Ex9AAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Friendship Hospital, Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-09 01:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7078667/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7078667/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86666572,"identity":"7c192d23-d7c7-47e9-8416-078e4284c36e","added_by":"auto","created_at":"2025-07-14 11:08:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135761,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic splines for the association between baseline ADMA levels and SCA prevalence, changes in ADMA and SCA progression\u003c/strong\u003e. A, association between baseline ADMA levels and SCA prevalence: reference point is the median value of baseline ADMA (23.30 ng/ml), adjusting for age and sex. B, association between absolute ADMA change (ΔADMA\u003csub\u003e1y-0y\u003c/sub\u003e) and SCA progression: reference point is the median value of absolute ADMA change (10.26 ng/ml), adjusting for age and sex. C, association between relative ADMA change (Δ%ADMA\u003csub\u003e1y-0y\u003c/sub\u003e) and SCA progression: reference point is the median value of relative ADMA change (48.46%), adjusting for age and sex. Abbreviation: ADMA, asymmetric dimethylarginine; SCA, subclinical carotid atherosclerosis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7078667/v1/4d30daeddf524089a90e2802.png"},{"id":86666612,"identity":"7ed1f601-f767-4d56-ba2f-fee1426221cb","added_by":"auto","created_at":"2025-07-14 11:09:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":348987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis for the association between baseline ADMA levels and SCA prevalence\u003c/strong\u003e. Abbreviation: ADMA, asymmetric dimethylarginine; SCA, subclinical carotid atherosclerosis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7078667/v1/763707bc38d80addd2f1d506.png"},{"id":86666598,"identity":"a8ecd744-e744-4702-9ccc-b852351dcb6e","added_by":"auto","created_at":"2025-07-14 11:09:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":653666,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis for the association between changes in ADMA and SCA progression\u003c/strong\u003e. Abbreviation: ADMA, asymmetric dimethylarginine; SCA, subclinical carotid atherosclerosis.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7078667/v1/2a180dc0b4d853e6339b9dd3.png"},{"id":86667843,"identity":"a0f1f7aa-05d8-4872-b49b-4756dea162bd","added_by":"auto","created_at":"2025-07-14 11:16:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1944203,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7078667/v1/e845c491-9e49-49bd-a1cb-914645fc9bb5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between asymmetric dimethylarginine, its change and subclinical carotid atherosclerosis in metabolic dysfunction-associated steatotic liver disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD) is a global public health priority, affecting approximately 30% of people worldwide [1]. It is well established that patients with MASLD present a higher risk of\u0026nbsp;atherosclerosis and cardiovascular disease (CVD) compared with the general population [2-3]. In the progression of atherosclerosis and CVD, subclinical carotid atherosclerosis (SCA)\u0026nbsp;typically develops at an early stage. Elucidating the mechanisms between MASLD and SCA is crucial for early atherosclerosis detection, cardiovascular risk assessment in MASLD adults. This is also informative for developing targeted prevention, optimal treatment strategies and ultimately, improving the prognosis of these patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe vascular endothelium is crucial for maintaining cardiovascular health [4]. Asymmetric dimethylarginine (ADMA), a potent inhibitor of nitric oxide (NO) synthase, could interfere with the production of NO by the endothelium by acting as an analogue to L-arginine, the substrate for NO synthase. Growing evidence showed that patients with CVD often exhibited higher ADMA concentration than healthy controls [5]. Meanwhile, ADMA is recognized as an adipokine since the genes responsible for its synthesis and metabolism are also found in adipose tissue [6]. ADMA concentrations have been reported to increase as body mass index (BMI) rises [7]. Since the balance between ADMA production, metabolism, and excretion plays a crucial role in maintaining normal endothelial function [8], impaired metabolism of ADMA, as seen in conditions like MASLD, might lead to elevated levels of ADMA, contributing to endothelial dysfunction and an increased risk of atherosclerosis. Emerging preclinical and clinical data indeed observed ADMA accumulation\u0026nbsp;in nonalcoholic fatty liver disease [9]. However, the role of ADMA in relation to SCA in MASLD adults remains unclear. Clarifying this correlation is essential for better understanding the pathogenesis and exploring the integration of this marker into clinical practice.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsidering ADMA\u0026apos;s identification as a cardiovascular risk factor, and its increased level in MASLD, the aim of this study was to assess the role of baseline ADMA levels, and its changes for monitoring SCA progression in MASLD adults.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a prospective, single-center, cohort study that included MASLD adults between October 24, 2022 and April 28, 2023 at Beijing Friendship Hospital. The inclusion criteria were as follows: 1) individuals aged 18 years or older; 2) hepatic steatosis confirmed by abdominal ultrasound; 3) evidence of metabolic dysfunction, defined as the presence of type 2 diabetes mellitus, overweight/obesity (BMI \u0026ge; 23 kg/m\u0026sup2; for Asians), or at least two metabolic risk factors, including elevated waist circumference, hypertension, hypertriglyceridemia, reduced HDL cholesterol, prediabetes, insulin resistance, or elevated high-sensitivity C-reactive protein [10]; 4) alcohol consumption not exceeding 210 g/week in men and 140 g/week in women.\u0026nbsp;Exclusion criteria comprised 1) established diagnosis of other chronic liver disease, including viral hepatitis, autoimmune liver disease, drug-induced liver disease, total parenteral nutrition, and Wilson\u0026apos;s disease; 2) serious CVD event; 3) co-infection of cytomegalovirus, herpes simplex virus, or human immunodeficiency virus; 4) chronic wasting diseases, malignant tumors, hyperthyroidism, or hematological disorders; 5) pregnant and breastfeeding women.\u0026nbsp;This study was approved by the Ethics Committee of Capital Medical University Affiliated Beijing Friendship Hospital (No. 2022-P2-188-01). All participants provided written informed consents.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of serum ADMA levels\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBlood samples collected in EDTA tripotassium tubes were centrifuged at 2,500 \u0026times; g (4\u0026deg;C, 15 minutes), and serum was stored in 1-mL aliquots at -80\u0026deg;C to preserve protein stability. Serum ADMA levels at baseline and one year follow-up were quantified using enzyme-linked immunosorbent assay (ELISA).\u0026nbsp;The absolute change in ADMA (\u0026Delta;ADMA\u003csub\u003e1y-0y\u003c/sub\u003e) during the first year was determined by calculating the difference in ADMA levels between one year follow-up and baseline. The relative change in ADMA was calculated by dividing the absolute difference by baseline ADMA levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of subclinical carotid atherosclerosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCarotid intima-media thickness and plaques were evaluated using high-resolution B-mode ultrasound with a linear-array transducer (7.5-10 MHz) at Beijing Friendship hospital. The common carotid arteries, carotid bulbs, and internal carotid arteries were scanned bilaterally in the longitudinal view. Carotid intima-media thickness was measured on the far wall of the common carotid artery, approximately 1 cm below the bifurcation, during end-diastole. Carotid intima-media thickness between 1.0 mm and \u0026lt;1.5 mm was defined as increased intima-media thickening, while a carotid intima-media thickness\u0026ge;1.5 mm without luminal narrowing was defined as carotid plaque.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe primary outcomes of interest were: (1) presence of SCA, which was indicated by increased carotid intima-media thickness or the presence of carotid plaque; (2) occurrence of SCA progression at one year, which was defined as new onset of any SCA for participants who were free of SCA at baseline, new onset plaque for participants who already having increased carotid intima-media thickness at baseline, or new-onset of carotid artery stenosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrained research staffs, who were not involved in the patients\u0026apos; clinical care, reviewed the medical records for data collection. The collected information included age, gender, smoking, BMI, blood pressure, heart rate, and the presence of diabetes, hypertension, and dyslipidemia. Diabetes was identified based on self-reported or physician-diagnosed diabetes, or hemoglobin A1C % (HbA1c%) \u0026ge;6.5 at cohort recruitment. Hypertension was identified using self-reported information and blood pressure readings, with a systolic pressure of \u0026ge;140 mmHg or a diastolic pressure of \u0026ge;90 mmHg. Dyslipidemia was identified based on self-reported or physician-diagnosed. Serum levels of white cells, platelet counts, alanine aminotransferase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), \u0026gamma;-Glutamyl transferase (GGT), albumin (ALB), total bilirubin (TBIL), total cholesterol (TC), triglyceride, high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol (LDL), C-reactive protein (CRP), glucose, HbA1c, and creatinine were collected. Furthermore, we calculated the aspartate transaminase to platelet ratio (APRI) and fibrosis-4 (FIB-4) scores to evaluate non-invasive liver fibrosis [11, 12], along with the triglyceride-glucose index (TyG) as a surrogate marker for insulin resistance [13].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population was classified by quartiles of baseline ADMA levels (Q1, Q2, Q3 and Q4). Continuous variables were expressed as mean \u0026plusmn; SD or median with interquartile range, depending on the data distribution. Categorical variables were presented as counts and percentages. For comparisons, analysis of variance or the Kruskal-Wallis test was applied as appropriate. Logistic regression and propensity score analysis was conducted to examine the relationship between baseline ADMA levels, changes in ADMA and the presence of SCA, adjusted for age, gender, smoke, disease history of diabetes and hypertension, BMI, platelet counts, ALT, AST, TC, triglyceride, HDL, LDL, and glucose. We calculated the odds ratios (ORs) and 95% confidence interval (CI) of the risk of SCA prevalence for each quartile in comparison with the lowest quartile (Q1). The trend of SCA prevalence with increased ADMA levels was tested using the same model with ADMA levels used as a continuous variable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe relative risks (RRs) and 95%CI were calculated to reveal the association of changes in ADMA (\u0026Delta;ADMA\u003csub\u003e1y-0y\u003c/sub\u003e, \u0026Delta;%ADMA\u003csub\u003e1y-0y\u003c/sub\u003e) and SCA progression. Considering the potential difference in the risk of SCA progression for participants who had SCA at baseline and those who were free of SCA at baseline, we conducted separate analyses for each subgroup of these participants, in addition to analyzing the overall participant group. Moreover, subgroup analyses were conducted to explore whether the relationship between baseline ADMA levels and changes in ADMA differed by age (\u0026lt;45, \u0026ge;45 years), sex (male, female), smoking status (current smoker or not), hypertension (present or absent), and diabetes (present or absent). Interaction terms for ADMA and these variables were included in the multivariable model to assess potential effect modification.\u003c/p\u003e\n\u003cp\u003eIn addition, restricted cubic spline analysis was performed to explore the potential non-linear relationship of baseline ADMA levels with the presence of SCA, and changes in ADMA with SCA progression, respectively, with median value of baseline ADAM level (23.30 ng/ml) and changes in ADAM (\u0026Delta;ADMA\u003csub\u003e1y-0y\u003c/sub\u003e, 10.26 ng/ml; \u0026Delta;%ADMA\u003csub\u003e1y-0y\u003c/sub\u003e, 48.46%) as reference point. All statistical analyses were conducted using SAS version 9.4 (SAS Inc., Cary, NC, USA). \u003cem\u003eP\u003c/em\u003e-value of 0.05 was considered as the threshold for statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eClinical characteristics of study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 600 MASLD adults were enrolled in this study. Serum ADMA levels were measured for 600 and 522 participants at baseline and one year follow-up, respectively. The study population were predominantly male (59.83%), and the median age of participants was 46.00 (38.00, 54.00) years. About 12.17% of them were current smokers, 31.83% had a history of hypertension, and 10.17% had a history of diabetes. The overall prevalence of SCA in MASLD participants was 49.83%. The median baseline ADMA concentration across 600 MASLD adults was 23.30 (13.90, 40.95) ng/ml. Demographics and clinical characteristics of study participants stratified by baseline ADMA levels were presented in Table 1. Differences were observed in various factors such as age, sex, disease history of diabetes, platelet counts, ALT, AST, GGT, TyG, triglyceride, glucose, HbA1c, creatinine, FIB-4, and APRI among the ADMA quartiles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between baseline ADMA levels and SCA prevalence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 listed the prevalence rates of SCA, carotid plaque, and their according ORs by baseline ADMA levels in quartiles. Both the prevalence rates and ORs were generally increasing with the quartiles went up. Compared to participants in the lowest quartile of baseline ADMA levels, the multivariable-adjusted ORs of SCA were 0.989 (95% CI: 0.580-1.687), 1.853 (1.112-3.088) and 3.810 (2.055-7.063) for those in Quartile 2, Quartile 3 and Quartile 4, respectively. For carotid plaque, participants in Quartile 4 had an increased risk compared to those in the lowest quartile, as indicated by the multivariable-adjusted model (OR=2.576, 95%CI: 1.361-4.874).\u0026nbsp;However, participants in Quartile 2 and Quartile 3 exhibited a similar risk of carotid plaque as those in the lowest quartile.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, the adjusted ORs (95%CIs) of SCA and carotid plaque were 1.035 (1.022-1.048), and 1.017 (1.007-1.027) per one-point increment of ADMA level increased (Table 2). However, the logistic regression model with restricted cubic spline analysis revealed a non-linear relationship between baseline ADMA (treated as a continuous variable) and the risk of SCA prevalence (P=0.004, Figure 1). We did pre-defined subgroup analysis for the association between baseline ADMA level and SCA prevalence, adjusting for multiple variables. Generally, the results showed that the associations were not altered by these characteristics, except that females and those with hypertension showed stronger association between baseline ADMA and SCA prevalence (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between changes in ADMA and SCA progression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf these 522 (87%) MASLD adults who completed one year follow-up, SCA progression occurred in 65 (12.45%) participants, including 18 who had SCA at baseline and 47 who were free of SCA at baseline. Table 3 showed the association of baseline ADMA level, changes in ADMA with SCA progression. No association was observed for baseline ADMA level and SCA progression. Regarding to the changes in ADMA, the median \u0026Delta;ADMA and \u0026Delta;%ADMA was 10.26 (0.07, 40.95) ng/ml and 48.46% (0.32%, 123.88%), respectively. Both the univariable and multivariable analysis demonstrated that both the \u0026Delta;ADMA and \u0026Delta;%ADMA were significantly associated with increased risk of SCA progression, with adjusted RRs being 1.056 (95% CI: 1.042-1.069) and 1.014 (95% CI: 1.011-1.018), respectively. This association was still significant in participants who had SCA at baseline and those who were free of SCA at baseline.\u003c/p\u003e\n\u003cp\u003eThe restricted cubic spline indicated non-linear association between \u0026Delta;ADMA, \u0026Delta;%ADMA and SCA progression (P=0.003, P\u0026lt;0.001, Figure 1). Additionally, the subgroup analysis showed that the association between changes in ADMA and SCA progression were not altered by participants\u0026rsquo; characteristics, except that \u0026Delta;%ADMA showed a stronger association with increased risk of SCA progression in males (Figure 3). \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted a prospective evaluation of the role of serum ADMA levels in MASLD adults, and found that SCA prevalence was significantly higher among individuals with elevated ADMA levels. Moreover, changes in ADMA were significantly associated with SCA progression at one year. This association was consistently observed for both participants with SCA at baseline and those who were free of SCA at baseline. This implied that ADMA could serve as a potential marker for SCA, as well as a short-term prognostic marker for MASLD individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubjects with high ADMA levels showed higher proportion of SCA compared to those with low ADMA levels. Multivariable analysis further demonstrated that ADMA was associated with the presence of SCA, independent of traditional risk factors. We recognize its potential as an additional assessment tool, offering a straightforward, objective, and single laboratory measurement. Monitoring ADMA in MASLD patients can help detect early signs of atherosclerosis before clinical cardiovascular events occur, allowing for timely intervention. A potential link between ADMA and SCA may be attributed to ADMA\u0026apos;s role in promoting endothelial dysfunction, which disrupts vascular homeostasis and triggers processes such as lipid accumulation, inflammation, smooth muscle cell proliferation, and thrombosis [14, 15]. However, this connection is likely to be complex, involving an intricate interplay of factors, including endothelial dysfunction, oxidative stress, inflammation, impaired nitric oxide signaling, fibrosis, vascular remodeling, hemodynamic alterations, and associated comorbid conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince ADMA serves as a biomarker for endothelial dysfunction and atherosclerosis, a link between ADMA and the progression of SCA is anticipated. Previous research has indicated that ADMA is a strong independent predictor of cardiovascular events, with elevated levels being linked to a higher risk of such events [16, 17]. While baseline ADMA levels reflect the status of endothelial function at a single point in time, they may not fully capture the dynamic changes in vascular health. Although endothelial dysfunction plays an important role in the development of atherosclerosis, it can evolve over time due to various factors. And our results showed no association between baseline ADMA levels and SCA progression. The differences in results between this study and previous research may be attributed to the distinct populations studied. This study included individuals without pre-existing cardiovascular or cerebrovascular diseases, whereas many previous studies on ADMA primarily focused on patients with cardiovascular or cerebrovascular conditions. The presence of such conditions could significantly alter ADMA levels and their relationship with endothelial function, potentially explaining the variability in findings between the two groups. In fact, the baseline ADMA levels reported in this study are indeed slightly lower than those described in the literature [18].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotably, our results demonstrated that changes in ADMA levels significantly correlated with SCA progression, indicating that changes in ADMA levels may be a valuable predictor of atherosclerotic progression and cardiovascular risk. An increase in ADMA levels during follow-up may indicate worsening endothelial function and a higher cardiovascular risk [19]. As a simple, single follow-up parameter, changes in ADMA could offer independent, additional predictive value [20]. Monitoring ADMA over time, rather than relying on baseline levels alone, provides a more accurate assessment of evolving factors contributing to atherosclerosis risk. Additional studies are required to explore how ADMA can be integrated as an independent marker into the existing risk assessment models.\u003c/p\u003e\n\u003cp\u003eNevertheless, the interpretation of our findings should be approached with caution due to the following limitations. Firstly, the relatively short follow-up period limited our ability to reveal the long-term prognostic significance of ADMA. Secondly, this was a single-center study with a cohort that lacked ethnic diversity, which highlighted the need for validation in an external population. Replication of these findings was essential before applying them to clinical practice.\u003c/p\u003e\n\u003cp\u003eIn conclusion, while baseline ADMA levels provided important insight into SCA prevalence, the changes in ADMA levels was found to be predictive of short-term SCA progression. As a straightforward, clear, and objective measure, dynamic monitoring of ADMA levels could serve as an additional independent marker of SCA progression, helping to assess cardiovascular risk and inform clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: LM and ZXY designed the study. WW and RXT drafted the manuscript. SPF performed the ultrasound examination. WW and LTT performed the laboratory test. WY analyzed the data. TYF validated the analysis. LM and ZXY revised the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e: The authors have no conflict of interest to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e: This study was approved by the Ethics Committee of Capital Medical University Affiliated Beijing Friendship Hospital (No. 2022-P2-188-01). All participants provided written informed consents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eData used in this study can be requested from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This work was supported by the National Natural Science Foundation of China under Grant [No. 82103902].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLe MH, Le DM, Baez TC, et al. Global incidence of non-alcoholic fatty liver disease: A systematic review and meta-analysis of 63 studies and 1,201,807 persons. J Hepatol 2023;79:287-95. \u003c/li\u003e\n\u003cli\u003eChung NT, Hsu CY, Shih NC, et al. Elevated concurrent carotid atherosclerosis rates in patients with metabolic dysfunction-associated fatty liver disease (MAFLD) compared to non-alcoholic fatty liver disease (NAFLD): A cross-sectional observational study. Nutr Metab Cardiovasc Dis 2025;35:103767. \u003c/li\u003e\n\u003cli\u003eTargher G, Byrne CD, Lonardo A, et al. Non-alcoholic fatty liver disease and risk of incident cardiovascular disease: A meta-analysis. J Hepatol 2016;65:589-600. \u003c/li\u003e\n\u003cli\u003eVillanova N, Moscatiello S, Ramilli S, et al Endothelial dysfunction and cardiovascular risk profile in nonalcoholic fatty liver disease. Hepatology 2005;42:473-80. \u003c/li\u003e\n\u003cli\u003eValkonen VP, P\u0026auml;iv\u0026auml; H, Salonen JT, et al. Risk of acute coronary events and serum concentration of asymmetrical dimethylarginine. Lancet 2001;358:2127-8. \u003c/li\u003e\n\u003cli\u003eSpoto B, Parlongo RM, Parlongo G, et al. The enzymatic machinery for ADMA synthesis and degradation is fully expressed in human adipocytes. J Nephrol 2007;20:554-9. \u003c/li\u003e\n\u003cli\u003eMcLaughlin T, St\u0026uuml;hlinger M, Lamendola C, et al. Plasma asymmetric dimethylarginine concentrations are elevated in obese insulin-resistant women and fall with weight loss. J Clin Endocrinol Metab 2006;91:1896-900. \u003c/li\u003e\n\u003cli\u003eB\u0026ouml;ger RH, Maas R, Schulze F, et al. Asymmetric dimethylarginine (ADMA) as a prospective marker of cardiovascular disease and mortality--an update on patient populations with a wide range of cardiovascular risk. Pharmacol Res 2009;60:481-7. \u003c/li\u003e\n\u003cli\u003eKasumov T, Edmison JM, Dasarathy S, et al. Plasma levels of asymmetric dimethylarginine in patients with biopsy-proven nonalcoholic fatty liver disease. Metabolism 2011;60:776-81. \u003c/li\u003e\n\u003cli\u003eRinella ME, Lazarus JV, Ratziu V, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023;78:1966-86. \u003c/li\u003e\n\u003cli\u003eDing R, Zhou X, Huang D, et al. Nomogram for predicting advanced liver fibrosis and cirrhosis in patients with chronic liver disease. BMC Gastroenterol 2021;21:190. \u003c/li\u003e\n\u003cli\u003eEuropean Association for the Study of the Liver (EASL); European Association for the Study of Diabetes (EASD); European Association for the Study of Obesity (EASO). EASL-EASD-EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease. Diabetologia 2016;59:1121-40. \u003c/li\u003e\n\u003cli\u003eSun Y, Ji H, Sun W, An X, Lian F. Triglyceride glucose (TyG) index: A promising biomarker for diagnosis and treatment of different diseases. Eur J Intern Med 2025;131:3-14. \u003c/li\u003e\n\u003cli\u003eCyr AR, Huckaby LV, Shiva SS, et al. Nitric Oxide and Endothelial Dysfunction. Crit Care Clin 2020;36:307-21. \u003c/li\u003e\n\u003cli\u003eSu\u0026scaron;ić L, Maričić L, \u0026Scaron;ahinović I, et al. The Relationship of Left Ventricular Diastolic Dysfunction and Asymmetrical Dimethylarginine as a Biomarker of Endothelial Dysfunction with Cardiovascular Risk Assessed by Systematic Coronary Risk Evaluation2 Algorithm and Heart Failure-A Cross-Sectional Study. Int J Environ Res Public Health 2023;20:4433. \u003c/li\u003e\n\u003cli\u003eBima C, Parasiliti-Caprino M, Rumbolo F, et al. Asymmetric and symmetric dimethylarginine as markers of endothelial dysfunction in cerebrovascular disease: A prospective study. Nutr Metab Cardiovasc Dis 2024;34:1639-48. \u003c/li\u003e\n\u003cli\u003eMu Y, Wang Y, Wang S, et al. Associations of plasma arginine, homoarginine, and ADMA/SDMA levels with risk of ischemic stroke: A nested case-control study. Nutr Metab Cardiovasc Dis 2025;35:103711. \u003c/li\u003e\n\u003cli\u003eN\u0026eacute;meth B, Ajtay Z, Hejjel L, et al. The issue of plasma asymmetric dimethylarginine reference range - A systematic review and meta-analysis. PLoS One 2017;12:e0177493. \u003c/li\u003e\n\u003cli\u003eShafran I, Probst V, Panzenb\u0026ouml;ck A, et al. Asymmetric Dimethylarginine and NT-proBNP Levels Provide Synergistic Information in Pulmonary Arterial Hypertension. JACC Heart Fail 2024;12:1089-97. \u003c/li\u003e\n\u003cli\u003eMortensen KM, Itenov TS, Stensballe J, et al. Changes in nitric oxide inhibitors and mortality in critically ill patients: a cohort study. Ann Intensive Care 2024;14:133. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of 600 participants with MASLD at baseline\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"779\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.4872%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003eQ4\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.17949%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e43.00(36.00,52.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e43.50(37.00,52.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e43.50(36.00,54.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e51.00(44.00,56.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e76(50.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e79(52.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e105(70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e99(66.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e74(49.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e71(47.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e45(30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e51(34.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003eCurrent Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e14(9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e14(9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e25(16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e20(13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e136(90.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e136(90.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e125(83.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e130(86.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e26.60(24.71,29.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e26.93(25.26,29.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e26.85(24.91,29.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e26.79(25.17,29.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003eSystolic pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e126.00(118.00,138.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e128.00(119.00,136.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e129.00(121.00,139.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e130.00(121.00,140.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003eDiastolic pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e81.00(73.00,88.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e82.00(74.00,89.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e82.00(74.00,89.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e83.00(77.00,92.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003eHeart rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e80.00(72.00,84.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e78.00(70.00,86.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e78.00(70.00,86.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e78.00(72.00,86.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003eDisease history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e43(28.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e45(30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e52(34.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e51(34.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; Diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e12(8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e10(6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e13(8.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e26(17.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; Dyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e9(6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e11(7.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e12(8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e18(12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003eLaboratory tests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; White cells, 10~9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e6.75(5.53,7.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e6.18(5.29,7.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e6.29(5.33,7.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e6.49(5.58,7.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; Platelet counts, 10~9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e267.00(225.00,306.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e245.00(210.50,276.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e244.00(208.00,287.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e239.50(205.00,279.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; ALT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e20.00(15.00,29.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e24.00(17.00,33.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e29.00(20.00,42.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e29.00(21.00,48.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; AST, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e20.40(17.10,24.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e21.30(18.00,25.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e24.20(20.10,29.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e25.75(21.30,33.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; ALP, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e70.00(63.00,84.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e70.50(63.00,87.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e76.00(60.00,87.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e73.00(63.00,80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; GGT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e28.00(23.00,38.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e33.00(20.00,43.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e35.00(27.00,73.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e37.00(27.00,64.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; ALB, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e44.90(43.50,47.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e46.70(44.10,48.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e46.60(44.60,48.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e46.00(44.90,48.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; TBIL, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e13.33(11.28,15.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e13.07(9.57,18.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e13.44(11.62,15.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e14.89(12.42,18.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; TyG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e8.62(8.29,8.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e8.71(8.38,9.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e8.86(8.47,9.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e8.95(8.58,9.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; TC, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e5.20(4.34,5.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e5.31(4.57,6.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e5.16(4.65,5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e5.47(4.46,6.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; Triglyceride, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e1.32(0.98,1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e1.43(1.03,1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e1.55(1.18,2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e1.64(1.17,2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; HDL, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e1.24(1.11,1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e1.29(1.11,1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e1.22(1.07,1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e1.24(1.13,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; LDL, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e3.20(2.68,3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e3.27(2.70,3.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e3.27(2.92,3.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e3.43(2.70,3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; CRP, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e0.85(0.31,2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e1.06(0.41,2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e0.90(0.47,1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e0.98(0.41,2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; Glucose, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e5.31(4.96,5.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e5.24(4.91,5.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e5.40(4.97,5.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e5.48(5.09,6.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; HbA1c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e5.50(5.30,5.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e5.50(5.20,5.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e5.60(5.30,5.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e5.60(5.40,6.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; Creatinine, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e68.00(58.20,80.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e70.40(58.10,83.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e75.10(65.75,81.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e72.80(63.90,80.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; FIB-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e0.70(0.51,1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e0.82(0.62,0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e0.76(0.61,1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e1.00(0.77,1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.4872%;\"\u003e\n \u003cp\u003e\u0026nbsp; APRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e0.24(0.18,0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e0.27(0.21,0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e0.30(0.23,0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3333%;\"\u003e\n \u003cp\u003e0.33(0.25,0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.17949%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eAll participants were classified into 4 quartiles (Q1, ADMA level \u0026lt;13.90 ng/ml; Q2, 13.90 \u0026le; ADMA level\u0026lt; 23.30 ng/ml; Q3, 23.30 \u0026le; ADMA level \u0026lt; 40.95 ng/ml; Q4, ADMA level\u0026ge;40.95 ng/ml).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviation: MASLD, metabolic dysfunction-associated steatotic liver disease; BMI, body mass index; ALT, alanine aminotransferase; AST, aspartate transaminase; ALP, alkaline phosphatase; GGT, \u0026gamma;-Glutamyl transferase; ALB, albumin; TBIL, total bilirubin; TC, total cholesterol; HDL, high density lipoprotein cholesterol; LDL, low density lipoprotein cholesterol; CRP, C-reactive protein; HbA1c, hemoglobin A1C; FIB-4, Fibrosis 4 score; APRI, aspartate aminotransferase-to-platelet ratio index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. The association between baseline ADMA and subclinical carotid atherosclerosis in participants with MASLD\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"688\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eOutcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003eEvents/participants, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 27.3913%;\"\u003e\n \u003cp\u003eUnivariable model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28.0435%;\"\u003e\n \u003cp\u003eMultivariable model\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 37.337%;\"\u003e\n \u003cp\u003eSubclinical carotid atherosclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e1.039 (1.029-1.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e1.035 (1.022-1.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e52/150 (34.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e52/150 (34.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e1.000 (0.622-1.609)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e0.989 (0.580-1.687)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e76/150 (50.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e1.936 (1.217-3.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e1.853 (1.112-3.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e119/150 (79.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e7.234 (4.306-12.155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e3.810 (2.055-7.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 37.337%;\"\u003e\n \u003cp\u003eCarotid plaque\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e1.022 (1.015-1.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e1.017 (1.007-1.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e25/150 (16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e21/150 (14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e0.814 (0.433-1.529)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e0.739 (0.351-1.552)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e36/150 (24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e1.579 (0.893-2.792)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e1.528 (0.804-2.902)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e69/150 (46.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e4.259 (2.492-7.281)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e2.576 (1.361-4.874)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4674%;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8696%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9348%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.587%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4565%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eAdjusted for age, gender, smoke, disease history of hypertension and diabetes, body mass index, platelet counts, ALT, AST, TC, Triglyceride, HDL, LDL, and glucose.\u003c/p\u003e\n\u003cp\u003eAbbreviation: ADMA, asymmetric dimethylarginine; MASLD, metabolic dysfunction-associated steatotic liver disease; OR, odd ratio; CI, confidence interval; IQR, interquartile range.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. The association of baseline ADMA, changes in ADMA and the risk of SCA progression\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"697\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003eLevel, IQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28.1077%;\"\u003e\n \u003cp\u003eUnivariable model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 28.9493%;\"\u003e\n \u003cp\u003eMultivariable model\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003eRR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003eRR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003eBase ADMA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e23.30 (13.90, 40.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e0.997 (0.986-1.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003e0.995 (0.983-1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003e\u0026Delta;ADMA\u003csub\u003e1y-0y\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e10.26 (0.07, 40.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e1.052 (1.040-1.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003e1.056 (1.042-1.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003e\u0026Delta;% ADMA\u003csub\u003e1y-0y\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e48.46 (0.32, 123.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e1.013 (1.010-1.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003e1.014 (1.011-1.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003eMASLDs without SCA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003eBase ADMA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e18.51 (12.51, 26.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e1.008 (0.992-1.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003e1.009 (0.988-1.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003e\u0026Delta;ADMA\u003csub\u003e1y-0y\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e11.07 (2.91, 31.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e1.095 (1.068-1.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003e1.104 (1.068-1.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003e\u0026Delta;% ADMA\u003csub\u003e1y-0y\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e64.23 (11.53, 163.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e1.012 (1.008-1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003e1.013 (1.008-1.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003eMASLDs with SCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003eBase ADMA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e32.90 (19.45, 51.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e1.003 (0.988-1.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003e0.998 (0.981-1.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003e\u0026Delta;ADMA\u003csub\u003e1y-0y\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e7.82 (-2.75, 34.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e1.033 (1.020-1.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003e1.040 (1.022-1.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.1972%;\"\u003e\n \u003cp\u003e\u0026Delta;% ADMA\u003csub\u003e1y-0y\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e38.12 (-11.61, 88.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5141%;\"\u003e\n \u003cp\u003e1.013 (1.008-1.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.762%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.8507%;\"\u003e\n \u003cp\u003e1.018 (1.010-1.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.0986%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eAdjusted for age, gender, smoke, disease history of hypertension and diabetes, body mass index, platelet counts, ALT, AST, TC, Triglyceride, HDL, LDL, and glucose.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ADMA, asymmetric dimethylarginine; IQR, interquartile range; RR, relative risk; CI, confidence interval; MASLD, metabolic dysfunction-associated steatotic liver disease; SCA, subclinical carotid atherosclerosis.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"metabolic dysfunction-associated steatotic liver disease, asymmetric dimethylarginine, carotid intima-media thickness, carotid plaque, cohort","lastPublishedDoi":"10.21203/rs.3.rs-7078667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7078667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Asymmetric dimethylarginine (ADMA) is a promising marker for assessing cardiovascular disease risk; however, it is unclear whether ADMA can predict subclinical carotid atherosclerosis (SCA) in individuals with metabolic dysfunction-associated steatotic liver disease (MASLD). This study aimed to investigate the association between baseline ADMA levels, changes in ADMA, and the risk of SCA in MASLD adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A total of 600 MASLD adults were prospectively recruited. Serum ADMA concentrations were measured at baseline and one year of follow-up. The SCA was defined as the presence of carotid intima-media thickness and carotid plaque. Logistic regression and propensity score analysis was conducted to evaluate the association between baseline ADMA levels, changes in ADMA, and SCA risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The risk of SCA increased significantly with higher ADMA levels. Compared with participants in the lowest quartile of baseline ADMA levels, the multivariable-adjusted odds ratios were 0.989 (95% confidence interval, CI: 0.580-1.687), 1.853 (1.112-3.088) and 3.810 (2.055-7.063) for those in Quartile 2, Quartile 3 and Quartile 4, respectively. At 1-year follow-up, SCA progression occurred in 65 (12.45%) participants. Both absolute and relative changes in ADMA levels from baseline to one year were significantly associated with SCA progression, with adjusted relative risks being 1.056 (95% CI: 1.042-1.069) and 1.014 (95% CI: 1.011-1.018), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: ADMA levels associated with SCA in MASLD adults, while dynamic changes in ADMA could help predict the progression of SCA in these individuals.\u003c/p\u003e","manuscriptTitle":"Association between asymmetric dimethylarginine, its change and subclinical carotid atherosclerosis in metabolic dysfunction-associated steatotic liver disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 11:08:19","doi":"10.21203/rs.3.rs-7078667/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9fe4e481-9865-4d5f-8c80-7934d763edb0","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T11:08:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 11:08:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7078667","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7078667","identity":"rs-7078667","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}