Metabolite Signatures and Their Mediation Effects on the Relationship Between Mediterranean Diet Adherence and MASLD Progression

Metabolite Signatures and Their Mediation Effects on the Relationship Between Mediterranean Diet Adherence and MASLD Progression | 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 Metabolite Signatures and Their Mediation Effects on the Relationship Between Mediterranean Diet Adherence and MASLD Progression Kai Wang, Shijian Xiang, Qiangsheng He, Chumei Huang, Zhen Yang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6026627/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 Mediterranean Diet (MED) is recommended for managing patients with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD). However, the potential metabolic changes involved in this relationship remain unclear. This study aims to investigate how metabolic biomarkers mediate the association between MED adherence and liver-related events (LRE) and mortality in patients with MASLD. Methods We performed a prospective analysis using UK Biobank data, including 47,429 MASLD participants free of LRE at baseline. MED adherence was assessed as alternate Mediterranean Diet (aMED) score through a validated questionnaire covering 206 foods and 32 beverages. Metabolic biomarkers were measured using high-throughput nucleic magnetic resonance (NMR) spectroscopy. Cox regression and restricted cubic splines assessed the association of aMED, its components, with risk of LRE and mortality. Mediation analysis evaluated the role of metabolites in the relationship between aMED, its components, and MASLD progression. Results Over a median follow-up of 13.3 years, 296 LRE cases and 3,616 deaths occurred. Higher aMED scores (6–9) were associated with lower risks of LRE (HR: 0.553, 95% CI: 0.351–0.874) and mortality (HR: 0.854, 95% CI: 0.762–0.956) compared to the lowest scores (0–3). Linear dose-response relationships were observed for both LRE incidence ( P nonlinear = 0.91) and mortality ( P nonlinear = 0.07). Certain aMED components, including vegetables and legumes, were associated with a reduced risk of LRE, while vegetables, nuts, fish, the MUFA:SFA ratio, and moderate alcohol intake were linked to lower mortality risk. Of 143 metabolites, 46 were significantly associated with aMED. Positive associations included very large HDL particles (n = 3), unsaturated fatty acids (n = 8), albumin, and acetate, while negative associations were found with large VLDL (n = 11), small and middle HDL (n = 13), saturated fatty acids (n = 2), Apo-AI, and creatinine. Five aMED-related lipid metabolites were negatively associated with LRE, while five were positively linked to mortality. Mediation analysis revealed that omega-3 fatty acids, the omega-3 to total fatty acid ratio, and albumin accounted for 7.9%, 11.9%, and 2.6% of the reduction in LRE, and 19.4%, 23.1%, and 4.7% of the mitigation in mortality, respectively. Conclusions Adherence to MED is linked to reduced LRE risk and mortality in MASLD patients. Metabolic biomarkers such as small HDL particles and omega-3 fatty acids may mitigate MASLD progression. alternate Mediterranean Diet Metabolic dysfunction-associated steatotic liver disease Metabolomics Mediation effect Cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Metabolic dysfunction-associated steatotic liver disease (MASLD) has been proposed as an alternative term for nonalcoholic fatty liver disease (NAFLD) to better reflect its strong association with metabolic syndrome (MetS) [ 1 , 2 ]. MASLD is one of the most prevalent chronic liver diseases globally, with the estimated prevalence growing appreciably from 25% in or before 2005 to 37% in 2016 and beyond [ 3 ]. MASLD has emerged as a dominant public health threat [ 4 ] and contributes to a heavy disease burden. Early intervention is essential to prevent MASLD from progressing to severe outcomes such as liver fibrosis, cirrhosis, hepatocellular carcinoma (HCC), and ultimately death [ 5 ]. Given the lack of effective pharmacological treatments for MASLD [ 6 ], adopting healthy lifestyle habits is regarded as the primary clinical recommendation for managing MASLD [ 7 ]. Especially, diet plays a vital role in the reduction of hepatic steatosis in populations with MASLD [ 8 ]. The Mediterranean Diet (MED) is healthy dietary pattern, emphasizing the intake of plant-based foods, healthy fats, and moderate alcohol consumption, which has been linked to various health benefits [ 9 ], including a reduced risk of cardiovascular disease [ 10 ], MetS [ 11 ], and lower mortality [ 12 ]. The association between the MED pattern and a reduced risk of MASLD has been extensively studied [ 13 ]. Existing epidemiological evidence has indicated that MED could mitigate MASLD risk [ 14 ], and a meta-analysis revealed potential protective effects of MED in reducing liver cancer, with a pooled relative risk (RR) of 0.58 (95% CI: 0.46, 0.73) [ 15 ]. Hereby, it has been recommended for the management of MASLD [ 16 ]. Very recently, two cohort studies suggested that MED may alleviate progression of MASLD [ 17 , 18 ]. However, the underlying mechanisms and the role of metabolite signatures in this relationship remain to be fully clarified. Metabolites detected in plasma provide significant potential for health assessment, diagnosis and disease prediction [ 19 ]. Metabolomic signatures are increasingly recognized as critical players in the progression of MASLD [ 20 ]. Prior research has shown that metabolic profile alterations detected are associated with MASLD, particularly extremely large very-low-density lipoprotein (VLDL) triglycerides, which reflected both its presence and potential future risk [ 21 ]. The MED may lower low-density lipoprotein (LDL) levels and increase high-density lipoprotein (HDL) levels, contributing to the alleviation of hepatic steatosis [ 22 ]. However, there is limited evidence linking MED-related metabolic biomarkers with the incidence of LRE and mortality in MASLD patients [ 19 ]. Identifying biomarkers associated with MED and LRE may offer valuable tools for elucidating the metabolic pathways affected by MED [ 23 ]. In this study, we conducted a prospective cohort study using UK biobank data. We aimed to 1) assess the association between adherence to MED and the risk of incident LRE and mortality in patients with MASLD; and 2) investigate the effect of metabolic biomarkers in the relationship between MED, LRE and mortality. 2. Methods 2.1 Study population We derived data from the UK biobank, an ongoing population-based prospective study, and details of the UK biobank have been described elsewhere ( http://www.ukbiobank.ac.uk ). Briefly, the UK Biobank study recruited over 500,000 participants aged 37–73 from 22 assessment centers across England, Wales, and Scotland through March 2006 to December 2010. Information on socio-demographics, habitual diet, lifestyle factors, and medical history was gathered through touch-screen questionnaires, face-to face interviews, and electronic records. Physical measurements and biological specimens were collected through standardized procedures. We included patients with prevalent MASLD at recruitment which defined as presence of fatty liver, accompanied by at least one of five cardiometabolic risk factors, according to multi-society Delphi consensus statement [ 2 ]. Given the restricted sample size of participants undergoing MRI-based hepatic steatosis quantification (n < 50,000), fatty liver index (FLI) was employed as a validated surrogate marker in accordance with international expert consensus recommendations for large-scale epidemiological studies [ 24 ]. FLI was estimated according to triglycerides, Body Mass Index (BMI), abdominal circumference and GGT as prior investigation, and fatty liver was defined by as FLI ≥ 60 [ 25 ]. After the exclusion of individuals who were pregnant, had a history of cancer, non steatotic liver disease, without alcohol information or excessive drinking (female > 20 g/day, male > 30 g/day), a total of 126,217 eligible participants were recruited. We further excluded individuals who had a history of LREs before baseline, without valid baseline dietary data, with implausible total energy intake ( 5000 kcal/d), leaving 47,429 participants for analysis (Figure S1 ). The UK biobank study was approved by North West Multi-Centre Research Ethics Committee, the National Information Governance Board for Health and Social Care in England and Wales and the Community Health Index Advisory Group in Scotland. All participants signed written informed consent forms. 2.2 Assessment of the Mediterranean Diet Participants were invited to complete the Oxford WebQ questionnaire on five different occasions over five years, which has been validated in previous studies and extensively utilized in several epidemiological studies examining dietary patterns associated with chronic diseases [ 26 , 27 ]. Between April 2009 and September 2010, a total of 70,000 participants completed Oxford WebQ in clinic and over 320,000 individuals finished an online 24-hour recall dietary questionnaire in response to email invitations from February 2011 through June 2012. The average measures were calculated based on data from participants who had completed at least one of the questionnaires [ 28 , 29 ]. We adapted the alternate MED (aMED) score [ 30 ], which is a modified version of the traditional MED score to estimate adherence to MED in UK biobank population [ 31 ]. It is constructed based on the consumption of 9 food components (vegetables, legumes, fruits, nuts, whole grains, fish, the ratio of monounsaturated fatty acid (MUFA) to saturated fatty acid (SFA), red/processed meat, and alcohol), and each component was scored as either 0 or 1 point [ 27 ]. Participants who consumed above the median intake for each component were assigned 1 point, otherwise they received 0 points, except for red/processed meat (where intakes below the median were assigned 1 point), and alcohol (where intakes of 5 g/d ≤ alcohol ≤ 15 g/d were assigned 1 point). Finally, the aMED score ranges from 0–9 and we further classified it into three categorical variables (0–3, 4–5, 6–9 score) in line with previous studies [ 32 ]. Details of the components and scoring criteria for aMED score are described in Table S1 of Supplementary Material. 2.3 Assessment of metabolites A venous blood sample was obtained at baseline and stored in a freezer at -80°C. Prior to preparation, frozen samples were thawed gradually at + 4°C overnight, followed by gentle mixing and centrifugation (3 minutes at 3400 xg, + 4°C) to remove any potential precipitate. 249 metabolic biomarkers (168 original measurements and 81 ratios), including lipids, fatty acids, amino acids, ketone bodies and other low-molecular-weight metabolic biomarkers were quantified using high-throughput nuclear magnetic resonance (NMR) spectroscopy between June 2019 and April 2020 in the Nightingale metabolic biomarker platform. Detailed information about NMR platform and experimentation has been described elsewhere ( https://biobank.ctsu.ox.ac.uk/ukb/ukb/docs/nmrm_companion_doc ). In this study, we incorporated 143 metabolites which were directly derived from measures in absolute concentrations (except for fatty acids) and could not be inferred from other biomarkers [ 33 ]. The values of all metabolic biomarkers were first log-transformed and then Z -transformed. For biomarkers with values of zero, these were replaced with the smallest non-zero value within the group divided by square root of 2 [ 33 ] 2.4 Assessment of outcome Incident cases of LRE were identified through cancer and death registries, defined as a composite endpoint that includes complications of cirrhosis (K74.60) and/or HCC (C22.0) and other liver diseases and conditions (Supplementary Material Table S2), based on the International Classification of Diseases (ICD)-10 codes [ 34 ]. Person-years of follow-up were calculated as the interval from the dates of baseline survey until the occurrence of LRE, death, loss to follow-up, or the end of the study period, whichever came first. Dates of death were obtained from death certificates provided by the National Health Service Information Centre for participants in England and Wales, and from the National Health Service Central Register for participants in Scotland [ 35 ]. 2.5 Assessment of variables In accordance with previous studies, we collected a set of covariates including (1) demographic characteristics (age, sex, ethnicity, educational attainment, body mass index [BMI], index of multiple deprivation [IMD]; (2) lifestyle factors (current smoking status, physical activity, sleep duration, total energy intake); (3) chronic comorbidities (hypertension, diabetes); (4) medication use (ACEI, ARBs, Calcium channel blocker, Beta blocker, Statin, Multivitamin, mineral supplements); (5) laboratory data (total cholesterol, LDL, HDL, triglycerides, fasting glucose, hsC-reactive protein). Physical activity levels were assessed using the metabolic equivalent (MET) derived from International Physical Activity Questionnaire-Short Form (IPAQ-SF). Height and weight of participants were measured to calculate BMI, which was defined as weight in kilograms divided by the square of height in meters. Information of comorbidities were collected through the baseline questionnaire, verbal interviews, and electronic health records. Laboratory data were measured using fasting venous blood samples. 2.6 Statistical analysis The baseline characteristics were descripted as means (standard deviation, SD) or medians (interquartile range, IQR) for continuous variables and as numbers (percentages) for categorical variables. Missing covariate data were imputed using the multiple imputation method. Cox proportional hazards regression models were employed to evaluate linkage between aMED and incident LRE and all-cause mortality. We adopted crude model stratified by sex and age at baseline and adjusted for intake of energy. In the multivariable-adjusted model, we additionally adjusted for demographic characteristics, lifestyle factors, chronic comorbidities, medication use, and laboratory data. Schoenfeld tests of proportional hazards assumptions presented no violations, with all P values > 0.05. Restricted cubic spline (RCS) with 3 knots, placed at the 10th, 50th, and 90th percentiles (according to Akaike information criterion [AIC] and Bayesian information criterion [BIC]; Table S3), were used to assess the potential non-linear associations between aMED and outcomes [ 36 ]. We performed a two-stage analysis to investigate effect of metabolic biomarkers in association between aMED and LRE or mortality in MASLD population. In the first stage, we fitted multivariable-adjusted linear regression models to estimate associations between aMED score and metabolic biomarkers, adjusting for above-mentioned covariates. The Benjamini Hochberg method was used and the false discovery rate (FDR) adjusted P value < 0.05 was considered statistically significant [ 37 ]. In the second stage, Cox proportional hazards models were employed to assess the relationships between significant metabolites and LRE or all-cause mortality. Principal component analyses were conducted to capture the most important patterns from a large number of correlated biomarkers, and we found that 12 principal components of 143 metabolites potentially explained over 90% of the variance. If the associations were statistically significant in two-stage analysis, mediating analyses were performed to assess the mediating role of the metabolites in the associations between aMED and LRE or mortality. We also conducted several sensitivity analyses to test robustness of our results. In order to reduce the potential effect of reverse causation, participants who were died or diagnosed with LRE within the first 2 years of follow-up were excluded. E-values were calculated to assess the robustness of the results to potential unmeasured confounding factors. In addition, we employed stratified analysis to estimate effect modifications by individual characteristics (e.g., demographic factors, lifestyle factors, and chronic comorbidities). Statistical significance of interactions between subgroups was examined using the likelihood ratio test. Data analyses were performed using the R version 4.1.3 (R Foundation for Statistical Computing, Vienna, Austria), with “survival” packages for Cox regression models, “rms” package for smoothing nonlinear terms, “mice” package for multiple imputation, and “mediator” for mediation analysis. A two-sided test P < 0.05 or an FDR adjusted P value < 0.05 was defined as statistically significant. 3. Results Table 1 describes the baseline characteristics of UK Biobank participants with MASLD across different aMED scores. The mean (SD) age of 47,429 patients included in this study was 57.3 (7.7) years, and 61.7% of participants were male. Individuals with higher aMED scores were generally more likely to be non-smokers, have attained a college or university degree, have a higher IMD, and engage in physical activities with higher MET. Additionally, individuals with the higher score of aMED tended to have a larger energy intake, longer sleep duration, and greater use of multivitamin and mineral supplements. The correlations between the 9 components scores were generally weak (r < 0.30), except for the correlation between vegetables and fruits intake (r = 0.48; Figure S2) Table 1 Baseline characteristics across aMED score among UK biobank participants. Characteristics aMED score Overall (n = 47,429) 0–3 4–5 6–9 (n = 23,616) (n = 18,389) (n = 5,424) Age, mean (SD), year 57.0 (7.8) 57.5 (7.6) 57.9 (7.5) 57.3 (7.7) Male, n (%) 14,734 (62.4) 11,263 (61.2) 3,244 (59.8) 29,241 (61.7) White, n (%) 22,408 (94.9) 17,375 (94.5) 5,136 (94.7) 44,919 (94.7) BMI, mean (SD), kg/m 2 31.6 (4.6) 31.5 (4.5) 31.3 (4.4) 31.5 (4.5) Physical activity, Median (IQR), MET hours/week 23.1 (38.4) 23.8 (36.7) 24.5 (37.8) 23.4 (37.7) College or university degree, n (%) 7,442 (31.5) 7,172 (39.0) 2,439 (45.0) 17,053 (36.0) Index of multiple deprivation, mean (SD) 17.6 (13.9) 16.5 (13.1) 15.4 (12.2) 16.9 (13.5) Energy intake, mean (SD), kJ 8,531 (2,651) 8,869 (2,454) 9,045 (2,273) 8,721 (2,542) Current smokers, n (%) 2,203 (9.3) 1,178 (6.4) 276 (5.1) 3,657 (7.7) Sleep duration, mean (SD), hour 8.1 (1.1) 8.1 (1.1) 8.1 (1.1) 8.1 (1.1) Hypertension, n (%) 8,321 (35.2) 6,682 (36.3) 1,999 (36.9) 17,002 (35.8) Diabetes, n (%) 1,591 (6.7) 1,204 (6.6) 351 (6.5) 3,146 (6.6) Medications, n (%) ACEI 3,433 (14.5) 2,711 (14.7) 783 (14.4) 6,927 (14.6) ARBs 961 (4.1) 816 (4.4) 240 (4.4) 2,017 (4.3) Calcium channel blocker 984 (6.4) 931 (8.2) 328 (10.1) 2,243 (7.4) Beta blocker 2,089 (8.9) 1,565 (8.5) 469 (8.7) 4,123 (8.7) Statin 5,468 (23.2) 4,318 (23.5) 1,254 (23.1) 11,040 (23.3) Multivitamin 3,001 (12.7) 2,547 (13.9) 798 (14.7) 6,346 (13.4) Mineral supplements 4,808 (20.4) 4,184 (22.8) 1,325 (24.4) 10,317 (21.8) Laboratory data, mean (SD) Total cholesterol, mmol/L 5.7 (1.2) 5.7 (1.2) 5.7 (1.2) 5.7 (1.2) LDL, mmol/L 3.6 (0.9) 3.6 (0.9) 3.6 (0.9) 3.6 (0.9) HDL, mmol/L 1.2 (0.3) 1.2 (0.3) 1.2 (0.3) 1.2 (0.3) Triglycerides, mmol/L 2.4 (1.2) 2.4 (1.2) 2.4 (1.2) 2.4 (1.2) Fasting glucose, mmol/L 5.3 (1.6) 5.3 (1.5) 5.3 (1.4) 5.3 (1.5) hsC-reactive protein, mg/L 3.6 (4.8) 3.4 (4.6) 3.3 (4.5) 3.5 (4.7) Abbreviations: aMED, alternate Mediterranean Diet; SD, standard deviation; IQR, interquartile range; BMI, body mass index; MET hours /week, metabolic equivalent, hours of physical activity per week; ACEI, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; LDL, low-density lipoprotein; HDL, high-density lipoprotein. Table 2 presents the association of aMED score with HR of LRE incidence and mortality. Over a median follow-up of 13.3 years (IQR: 12.7, 14.1 years), 296 cases of LRE, and 3,616 deaths were documented. There was sufficient evidence of inverse associations between the aMED score and reduced risks of LRE incidence and mortality in both crude and multivariable-adjusted models. In multivariable-adjusted model, the estimated HRs for LRE incidence and mortality were 0.796 (95% CI: 0.622, 1.019) and 0.953 (95% CI: 0.889, 1.023) for individuals with aMED scores of 4–5, and 0.553 (95% CI: 0.351, 0.874) and 0.854 (95% CI: 0.762, 0.956) for those with aMED scores of 6–9, respectively, compared with participants with the lowest category of aMED score (0–3). A one-unit increase in the aMED score was associated with an 11.9% decreased risk of LRE incidence (HR: 0.881, 95% CI: 0.814, 0.953) and a 3.0% reduced risk of mortality (HR: 0.970, 95% CI: 0.949, 0.992). Figure 1 demonstrates dose-response associations between aMED score and the risks of LRE incidence and mortality in MASLD patients. Approximately linear dose-response associations were observed for both LRE incidence ( P nonlinear = 0.91) and mortality ( P nonlinear = 0.07). The risk of LRE incidence and mortality progressively declined as the aMED score increased. Table 2 Association of aMED score and liver-related events and mortality in MASLD population. Case/Person-years IR† Model1 HR (95% CI) Mode2 HR (95% CI) LRE aMED score 0–3 (n = 23,616) 168/309,975 54.20 1 1 4–5 (n = 18,389) 107/242,714 44.08 0.792 (0.621, 1.010) 0.796 (0.622, 1.019) 6–9 (n = 5,424) 21/71,834 29.23 0.517 (0.328, 0.814) ** 0.553 (0.351, 0.874) * P trend 0.002 0.004 Per unit increase 0.870 (0.805, 0.940) *** 0.881 (0.814, 0.953) ** Mortality aMED score 0–3 (n = 23,616) 1,863/310,611 599.79 1 1 4–5 (n = 18,389) 1,390/243,075 571.84 0.916 (0.854, 0.982) * 0.953 (0.889, 1.023) 6–9 (n = 5,424) 363/71,940 504.59 0.784 (0.700, 0.878) *** 0.854 (0.762, 0.956) ** P trend < 0.001 0.007 Per unit increase 0.950 (0.929, 0.971) *** 0.970 (0.949, 0.992) ** Abbreviations: HR, hazard ratio; CI, confidence interval; SD, standard deviation; aMED, alternate Mediterranean Diet; MASLD, metabolic dysfunction-associated steatotic liver disease; LRE, liver-related events; IR: incidence rate. †: Per 100,000; * P < 0.05, ** P < 0.01, *** P < 0.001. Model1 is crude Cox regression model adjusted energy and stratified by sex and age. Model2 is multivariable adjusted model additionally adjusted for sociodemographic characteristics (ethnicity, education attainment, index multiple deprivations, and BMI), lifestyle factor (smoking status, physical activity, and daily sleeping time), medications (ACEI, ARB, Calcium channel blocker, Beta blocker, Statin, multivitamins, and mineral use), comorbidities ( hypertension and diabetes) and blood-based measurements (total cholesterol, LDL, HDL, Triglycerides, fasting glucose, and hsC-reactive protein). The main results were robust in sensitivity analyses (Table S4). Compared to our primary findings, the estimated effects remained largely unchanged after excluding LRE incidence and death that occurred within the first 2 years of follow-up. The E-value ranged from 1.29 to 3.28 for LRE incidence and 1.19 to 2.94 for mortality (Table S5). Subgroup analyses stratified by demographic and behavioral characteristics (Fig. 2 ) showed consistent associations of aMED scores with the risks of LRE incidence and mortality, with no significant differences observed between subgroups. Protective and analogous associations were observed for 9 aMED constituents with risks of LRE incidence and mortality (Table S6 & Table S7). Among these, only vegetables (HR: 0.938, 95% CI: 0.899, 0.978) and legumes (HR: 0.715, 95% CI: 0.529, 0.966) exhibited significant inverse associations with LRE risk. Several aMED components, including vegetables, nuts, fish, the MUFA:SFA ratio, and moderate alcohol consumption, were significantly and negatively associated with risk of mortality. Figure 3 illustrates the associations between aMED score and 143 metabolic biomarkers (per SD). After multivariable adjustment in multiple linear models, 46 of the 143 metabolites were significantly associated with aMED score, with FDR-adjusted P values < 0.05. Among those metabolites, very large HDL particles (n = 3), unsaturated fatty acids (n = 8), albumin, and acetate were positively associated with aMED score, with odds ratios (ORs) ranging from 1.006 to 1.075. In contrast, aMED scores were negatively associated with extremely and very large VLDL (n = 11), small and middle HDL (n = 13), saturated fatty acids (n = 2), Apo-AI, and creatinine. The association between the 9 aMED constituents and metabolites are shown in Fig. 4 . Of the 46 aMED associated metabolites, 9 showed significant positive correlations with vegetables (e.g. primarily consist of large LDL, albumin, and unsaturated fatty acids), while 5 (e.g. extremely large VLDL) demonstrated significant negative correlations with vegetables. We further explored the associations between aMED-related metabolites and LRE incidence and mortality. Cox regression models revealed that 7 metabolites candidates were associated with LRE, and 21 metabolites were found to be associated with mortality (Fig. 5 & Figure S3). Specifically, higher levels of phospholipid in small HDL particles and the Omega-3 fatty acids to total fatty acids percentage were associated with reduced risk of LRE. Conversely, significant positive correlations were observed between mortality and serum levels of very large HDL particles, cholesterol, free cholesterol, total lipids, and the ratio of saturated fatty acids to total fatty acids. Figure 6 summarized mediation analysis of metabolites in the associations of aMED score and the risks of LRE and mortality in MASLD patients. Omega-3 fatty acids, the ratio of omega-3 fatty acids to total fatty acids, and albumin significantly mediated the associations between aMED scores and both the risk of LRE and mortality, accounting for 2.6–11.9% and 4.7–23.1% of the total effect, respectively. 4. Discussion This prospective cohort study, using data from over 47,000 UK Biobank participants, investigated the relationship between MED adherence and the risk of liver-related events (LRE) and mortality in MASLD patients, along with its effects on metabolites. We found that a higher aMED score was associated with lower risks of LRE and mortality in a linear dose-response manner. The vegetable and legume components were linked to reduced LRE incidence, while most aMED components were associated with lower mortality. Out of 143 metabolites, 46 were significantly associated with aMED, including very large HDL and linoleic acid: total fatty acids, which were linked to LRE incidence, and metabolites like very large HDL, cholesterol, and free cholesterol, which were associated with mortality. Omega-3 fatty acids, the omega-3 to total fatty acid ratio, and albumin partially mediated the associations between MED adherence and both LRE incidence and mortality, providing insights into the underlying mechanisms. Existing evidence suggested that adherence to the MED may offer numerous health benefit for patients with MASLD, including a reduction in hepatic steatosis [ 17 , 38 ]. For example, a randomized controlled trial involving 56 subjects with MASLD found a significant reduction in hepatic steatosis after 12 weeks of the MED adherence ( P < 0.01), with mean (SD) relative reductions of 32.4% (± 25.5%) [ 39 ]. Another study reported that adherence to MED significantly reduced body weight, proton density fat fraction of the liver, total cholesterol, gamma-glutamyl transferase, and triglyceride concentrations in patients with MASLD [ 40 ]. Besides, a prospective observational cohort study performed in 655 consecutive MASLD outpatients demonstrated that better adherence to the MED showed lower platelet activation and liver collagen deposition [ 41 ]. Consistent with prior studis, our results indicated the potential benefits of the MED in progression of MASLD, specifically in ameliorating the risk of LRE and mortality. This finding is also supported by an analogous study, which reported a protective effect of MED on chronic liver disease and severe liver disease [ 18 ]. In the present study, several components of the aMED, particularly vegetables and legumes, were associated with a reduced risk of MASLD progression. Identical to our findings, previous studies have suggested that higher consumption of vegetables might lower liver fat content [ 42 ], reduce the risk of developing MASLD [ 43 ], and decrease the risk of end-stage liver disease and all-cause mortality in patients with MASLD [ 17 ]. These findings support the idea that a diet rich in whole vegetables and legumes may serve as both a preventive strategy and therapeutic approach for the management of MASLD [ 16 ]. The benefits of vegetables and legumes may not only stem from their ability to reduce total caloric intake or from the wide range of micronutrients they provide [ 44 ], but also through mechanisms involving the gut-brain-liver axis [ 45 ], alleviating endoplasmic reticulum stress, inflammation, and lipid accumulation [ 46 ]. Regarding other components of aMED, prior randomized controlled clinical trials have identified that increased consumption of nuts and reduced intake of red meat benefit patients with MASLD [ 47 ]. In our study, we also found that food groups (such as nuts, fish, and the MUFA:SFA ratio) were negatively associated with mortality in patients with MASLD, with HR ranging from 0.82 to 0.96. While potential protective effects of aMED components were observed, more sophisticated studies are still required for further validation. Previous studies have indicated that adherence to MED was associated with specific metabolites [ 48 ], which can characterize both adherence to MED and metabolic responses to it [ 49 ]. Consistent with prior metabolomic study [ 23 ], our study found that adherence to the MED was positively correlated with unsaturated fatty acids and HDL, while being negatively associated with saturated fatty acids and VLDL. A quasi-experimental study from Sweden further found that a 6-day MED intervention notably enhanced other beneficial metabolites such as caffeine and beta-carotene [ 48 ]. Another randomized clinical trial from Spain found that acylcarnitines and steroids were significantly associated with the aMED [ 50 ]. As for components of aMED, positive associations were observed between fish/nuts and unsaturated fatty acid such as omega-3/omega-6 fatty acids, which were in line with findings from Spanish and American cohort studies [ 26 ]. However, a cohort study from Fenland observed that of among the AMDS components, fish consumption mainly contributed to variations in phospholipids [ 51 ]. These inconsistences highlight the need to explore the relationship between food components and other sensitive or specific metabolites beyond lipids, to comprehensively characterize dietary biomarkers [ 52 ]. Metabolites, as intermediates or end products of metabolism, may play critical roles in liver cell physiology and may serve as key signals for the progression of MASLD [ 53 , 54 ]. In line with previous studies [ 55 ], our analyses showing that 7 aMED-related lipid metabolites were significantly related to LRE, and 21 were associated with mortality. Of these metabolites, small HDL particles, omega-3 fatty acids, and albumin inversely related to LRE, while the ratio of saturated fatty acids to total fatty acids showed positive correlation with mortality. These metabolic signatures not only hold promise as potential biomarkers for predicting the progression of MASLD, but also suggest shared metabolic pathways [ 56 ]. We further found 3 serum aMED-related lipid metabolites or index, (i.e. omega-3 fatty acids, the ratio of omega-3 fatty acids to total fatty acids, and albumin), partially mediated the association between aMED and the incidence of LRE and mortality. A double-blinded, placebo-controlled, randomized controlled trial also demonstrated a reduction in liver fat following omega-3 fatty acid intervention over a 12 months [ 57 ]. Previous studies also reported protective effect of small-sized HDL particles [ 58 ]. Other metabolites, which might have a mediating role in the progression of MASLD, warrant further investigation. The potential mechanisms of omega-3 fatty acids in reducing the progression of MASLD are likely related to their anti-inflammatory and antifibrotic effects, as well as their ability to reduce liver injury by suppressing de novo lipogenesis and enhancing mitochondrial, peroxisomal, and microsomal fatty acid oxidation [ 59 ]. Further research focusing on metabolites is needed to elucidate their role as potential biomarkers for the early detection of MASLD progression. A key strength of our study is the use of the UK Biobank, a nationwide, prospective cohort with a well-validated follow-up of 13.3 years. This allowed for detailed assessment of aMED-related metabolites and their mediating role in MASLD progression, offering mechanistic insights into aMED's potential preventive effects. The comprehensive data also enabled adjustment for multiple confounders, improving the reliability of our findings. Additionally, our analysis of both linear and nonlinear associations between aMED and outcomes provides a unique contribution to this study. However, there are several limitations should be acknowledged. Firstly, our observational design limits the ability to draw causal inferences between MED adherence, the risk of LRE or mortality, and the potential metabolites involved. Secondly, given that dietary assessment relied on self-reports, recall bias was unavoidablely introduced to some extent. However, the Oxford WebQ Online 24-Hour Dietary Questionnaire employed in the study had undergone extensive validation against biomarkers [ 60 ]. Thirdly, the generalizability and reproducibility of our findings may be constrained by the fact that participants were predominantly middle-aged, white, European individuals. Further physiological studies conducted in diverse and independent populations are warranted to validate the findings of our study. Fourthly, although we accounted for a number of covariates, some unmeasured factors may still influence the association between aMED and the progression of MASLD. The estimated E-value indicated that unmeasured confounders were unlikely to significantly bias the observed associations (Table S5). Fithly, we did not fully assess the impact of dietary changes over time. However, dietary patterns tend to remain stable in individuals [ 61 ]. Sixthly, the assessment of metabolites was cross-sectional and conducted simultaneously with dietary data, rather than over the intervention period, which prevents us from directly concluding that changes in metabolites were caused by adherence to MED. Further longtitude studies are wrranted to explore the effects of aMED on the associated metabolites. 5. Conclusions In conclusion, our study provides evidence that better adherence to the MED and its components were associated with a reduced risk of LRE and mortality in patients with MASLD. The observed reductions in risk may be partly explained by several aMED-related metabolic biomarkers, such as albumin and omega-3 fatty acids. However, the potential biological mechanisms underlying aMED-related metabolites remain unexplained. Future interventional and experimental studies are essential to further identify additional mediating biomarkers and to uncover the pathways and underlying mechanisms involved in these associations. Abbreviations AMED alternate Mediterranean Diet MASLD metabolic dysfunction-associated steatotic liver disease LRE liver-related events NMR nucleic magnetic resonance IQR interquartile range HR hazard ratio CI confidence interval FDR false discovery rate HDL high-density lipoprotein OR odds ratio HCC hepatocellular carcinoma MetS metabolic syndrome VLDL very-low-density lipoprotein LDL low-density lipoprotein MUFA monounsaturated fatty acid SFA saturated fatty acid BMI body mass index IMD index of multiple deprivation MET metabolic equivalent IPAQ-SF International Physical Activity Questionnaire-Short Form SD standard deviation RCS Restricted cubic spline AIC Akaike information criterion BIC Bayesian information criterion. Declarations Ethics approval and consent to participate The UK Biobank was approved by the National Research Ethics Committee (REC ID: 16/NW/0274). Electronic written informed consent was obtained from all participants. Consent to publish: Not applicable. Availability of data and materials: The data that support the findings of this study are available from UK Biobank project site, subject to registration and application process. Further details can be found at https://www.ukbiobank.ac.uk. The code used in this study is available upon reasonable request to the corresponding author. Funding This work was supported by the Shenzhen Medical Research Fund (No. A2403069, C2401002), the Funding of Shenzhen Clinical Research Center for Gastroenterology (Gastrointestinal Surgery, No.LCYSSQ20220823091203008), the Natural Science Foundation of China (No. 82103913, 82473707), the Research Supporting Start-up Fund for Associate researcher, of SAHSYSU (No. ZSQYRSSFAR0004), the Startup Fund for the 100 Top Talents Program, SYSU (No. 392012), and Shenzhen Key Laboratory of Chinese Medicine Active substance screening and Translational Research (No. ZDSYS20220606100801003). Author contribution Kai Wang : Writing–original draft, Writing–review & editing, Formal analysis; Shijian Xiang : Writing – review & editing, Validation, Visualization; Qiangsheng He : Data curation, Writing – review & editing, Methodology; Chumei Huang : Writing–review & editing; Zhen Yang : Writing–review & editing; Renjie Li : Writing–review & editing; Anran Liu : Writing–review & editing; Ruisheng Cai : Writing–review & editing; Ningning Mi : Writing–review & editing; Zixin Liang : Writing–review & editing; Zuofeng Xu : Writing–review& editing, Supervision, Funding acquisition; Jinqiu Yuan : Resources, Writing–review& editing, Supervision, Funding acquisition; Bin Xia : Data curation, Writing–review& editing, Supervision, Funding acquisition. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors wish to acknowledge the contributions of UK biobank for assistance in providing data. Data Availability Statement: This research got access to the UK Biobank (https://www.ukbiobank.ac.uk) under application number 51671. Further information is available from the corresponding author upon request. References Chan WK, Chuah KH, Rajaram RB, Lim LL, Ratnasingam J, Vethakkan SR. Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): A State-of-the-Art Review. J Obes Metab Syndr. 2023;32:197–213. Rinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023;78:1966–86. Riazi K, Azhari H, Charette JH, Underwood FE, King JA, Afshar EE, et al. The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2022;7:851–61. Miao L, Targher G, Byrne CD, Cao YY, Zheng MH. 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Greenwood DC, Hardie LJ, Frost GS, Alwan NA, Bradbury KE, Carter M, et al. Validation of the Oxford WebQ Online 24-Hour Dietary Questionnaire Using Biomarkers. Am J Epidemiol. 2019;188:1858–67. Wang DD, Leung CW, Li Y, Ding EL, Chiuve SE, Hu FB, et al. Trends in dietary quality among adults in the United States, 1999 through 2010. JAMA Intern Med. 2014;174:1587–95. Additional Declarations No competing interests reported. Supplementary Files SupplimentaryMaterial.pdf Additional file 1.docx: Table S1. Components and scoring criteria of the aMED score. Table S2. Criteria for liver-related events. Table S3. Akaike information criterion (AIC) and Bayesian information criterion (BIC) values of multivariate-adjusted model with different knots. Table S4. Sensitivity analyses of association between aMED and LRE incidence and mortality. Table S5. E-value for point estimates and the upper bound of the 95% confidence intervals of the hazard ratios. Table S6. Association of component of aMED score and liver-related events and mortality in MASLD population. Table S7.Association of component of aMED score and mortality in MASLD population. Figure S1. Flowchart of participants inclusion and exclusion. Figure S2. The spearman’s rank correlations between 9 components of aMED. Figure S3. Associations of metabolites (per SD) with risk of mortality in patients with MASLD. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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02:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6026627/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6026627/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76575819,"identity":"565248c9-edf1-4a74-90eb-bfd0b1dc949b","added_by":"auto","created_at":"2025-02-18 14:09:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63005,"visible":true,"origin":"","legend":"\u003cp\u003eConcentration-response curves between aMED score and liver-related events and mortality in MASLD population.\u003c/p\u003e\n\u003cp\u003eAbbreviations: HR, hazard ratio; CI, confidence interval; LRE, liver-related events, MASLD, metabolic dysfunction-associated steatotic liver disease; aMED, alternate Mediterranean Diet.\u003c/p\u003e","description":"","filename":"Figure131.png","url":"https://assets-eu.researchsquare.com/files/rs-6026627/v1/528487fa0eecbc6c112344a2.png"},{"id":76577773,"identity":"458fe711-51e9-4ba4-9ccb-beb777d3b46b","added_by":"auto","created_at":"2025-02-18 14:25:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59815,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of HR with 95% CI for LRE incidence and mortality associated with a unit rise in aMED score.\u003c/p\u003e\n\u003cp\u003eAbbreviations: HR, hazard ratio; CI, confidence interval; MET, metabolic equivalent; LRE, liver-related events; aMED, alternate Mediterranean Diet.\u003c/p\u003e","description":"","filename":"Figure132.png","url":"https://assets-eu.researchsquare.com/files/rs-6026627/v1/dc589721e1942f69a4cfb2ec.png"},{"id":76575825,"identity":"f8cbbbed-ffe2-40bb-9b40-a81cc81d41ea","added_by":"auto","created_at":"2025-02-18 14:09:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":530420,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of aMED with 143 metabolites (per SD) in patients with MASLD. HRs (per unit) were stratified by sex and age and adjusted for sociodemographic characteristics (ethnicity, education attainment, index multiple deprivations, and BMI), lifestyle factor (smoking status, physical activity, and daily sleeping time), medications (ACEI, ARB, Calcium channel blocker, Beta blocker, Statin multivitamins, and mineral use), comorbidities (hypertension and diabetes) and blood-based measurements (total cholesterol, LDL, HDL, Triglycerides, fasting glucose, and hsC-reactive protein). *\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001 (FDR-adjusted \u003cem\u003eP\u003c/em\u003e values).\u003c/p\u003e\n\u003cp\u003eAbbreviations: SD, standard deviation;MASLD, metabolic dysfunction-associated steatotic liver disease; HRs, hazard ratios; BMI, body mass index; Apo-A1, apolipoprotein A1; Apo-B, apolipoprotein B; Apo-LP, apolipoprotein; DHA, docosahexaenoic acid; FA, fatty acids; FDR, false discovery rate; HDL, high-density lipoproteins; HDL-D, high-density lipoprotein particle diameter; IDL, intermediate-density lipoproteins; L, large; LA, linoleic acid; LDL, low density lipoproteins; LDL-D, low-density lipoprotein particle diameter; LP, lipoprotein; M, medium; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; S, small; SFA, saturated fatty acids; VLDL, very-low-density lipoproteins; VLDL-D, very-low-density lipoprotein particle diameter; XL, very large; XS, very small; XXL, extremely large.\u003c/p\u003e","description":"","filename":"Figure133.png","url":"https://assets-eu.researchsquare.com/files/rs-6026627/v1/1ba03cde21bfddc560eb005e.png"},{"id":76575830,"identity":"a59a90ff-37e2-49a1-99b7-92cac317cb0b","added_by":"auto","created_at":"2025-02-18 14:09:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":158737,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between aMED-related metabolites and 9 components of aMED. *\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001.\u003c/p\u003e\n\u003cp\u003eAbbreviations: aMED, alternate Mediterranean Diet; LDL, low density lipoproteins; VLDL, very-low-densitylipoproteins; HDL, high-density lipoproteins; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; LP, lipoprotein.\u003c/p\u003e","description":"","filename":"Figure134.png","url":"https://assets-eu.researchsquare.com/files/rs-6026627/v1/5818c281e90da188e991fc7a.png"},{"id":76576472,"identity":"088d1079-a566-4f6b-bc95-7b171e392c04","added_by":"auto","created_at":"2025-02-18 14:17:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":14354,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of metabolites (per SD) with risk of LRE in patients with MASLD.\u003c/p\u003e\n\u003cp\u003eAbbreviations: SD, standard deviation; LRE, liver-related events; MASLD, metabolic dysfunction-associated steatotic liver disease; HDL, high-density lipoproteins; XL, very large; S, small; FAw3, omega-3 fatty acids;FA, fatty acids; LA, linoleic acid; HR, hazard ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure135.png","url":"https://assets-eu.researchsquare.com/files/rs-6026627/v1/1513e91f5073ec45e48dcb4a.png"},{"id":76575821,"identity":"a197077c-d694-4b21-8f53-f6d840628056","added_by":"auto","created_at":"2025-02-18 14:09:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":46563,"visible":true,"origin":"","legend":"\u003cp\u003eMediating effects of biomarkers on the association of aMED with risk of LRE (A–C) and mortality (D–F) among MASLD population.\u003c/p\u003e\n\u003cp\u003eAbbreviations: aMED, alternate Mediterranean Diet; MASLD, metabolic dysfunction-associated steatotic liver disease; LRE, liver-related events; IE, inderect effect; DE, derect effect; FAw-3, omega-3 fatty acids; FA, fatty acids.\u003c/p\u003e","description":"","filename":"Figure136.png","url":"https://assets-eu.researchsquare.com/files/rs-6026627/v1/4621fa0b1a1519d391d66c31.png"},{"id":76585701,"identity":"eb7860b4-08d7-4fe4-849d-94d6b1c50f06","added_by":"auto","created_at":"2025-02-18 15:46:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1935100,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6026627/v1/eb9b0b17-528d-400a-90c0-9300ab9cd60f.pdf"},{"id":76575823,"identity":"c66980b8-2e6e-4529-b06d-842874c3844b","added_by":"auto","created_at":"2025-02-18 14:09:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1025132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1.docx: Table S1. \u003c/strong\u003eComponents and scoring criteria of the aMED score. \u003cstrong\u003eTable S2. \u003c/strong\u003eCriteria for liver-related events. \u003cstrong\u003eTable S3. \u003c/strong\u003eAkaike information criterion (AIC) and Bayesian information criterion (BIC) values of multivariate-adjusted model with different knots. \u003cstrong\u003eTable S4. \u003c/strong\u003eSensitivity analyses of association between aMED and LRE incidence and mortality. \u003cstrong\u003eTable S5. \u003c/strong\u003eE-value for point estimates and the upper bound of the 95% confidence intervals of the hazard ratios. \u003cstrong\u003eTable S6.\u003c/strong\u003e Association of component of aMED score and liver-related events and mortality in MASLD population. \u003cstrong\u003eTable S7.\u003c/strong\u003eAssociation of component of aMED score and mortality in MASLD population. \u003cstrong\u003eFigure S1.\u003c/strong\u003e Flowchart of participants inclusion and exclusion. \u003cstrong\u003eFigure S2\u003c/strong\u003e. The spearman’s rank correlations between 9 components of aMED. \u003cstrong\u003eFigure S3\u003c/strong\u003e. Associations of metabolites (per SD) with risk of mortality in patients with MASLD.\u003c/p\u003e","description":"","filename":"SupplimentaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6026627/v1/bcf15187584e4a9cdf36bb76.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolite Signatures and Their Mediation Effects on the Relationship Between Mediterranean Diet Adherence and MASLD Progression","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD) has been proposed as an alternative term for nonalcoholic fatty liver disease (NAFLD) to better reflect its strong association with metabolic syndrome (MetS) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MASLD is one of the most prevalent chronic liver diseases globally, with the estimated prevalence growing appreciably from 25% in or before 2005 to 37% in 2016 and beyond [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. MASLD has emerged as a dominant public health threat [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and contributes to a heavy disease burden. Early intervention is essential to prevent MASLD from progressing to severe outcomes such as liver fibrosis, cirrhosis, hepatocellular carcinoma (HCC), and ultimately death [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given the lack of effective pharmacological treatments for MASLD [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], adopting healthy lifestyle habits is regarded as the primary clinical recommendation for managing MASLD [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Especially, diet plays a vital role in the reduction of hepatic steatosis in populations with MASLD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Mediterranean Diet (MED) is healthy dietary pattern, emphasizing the intake of plant-based foods, healthy fats, and moderate alcohol consumption, which has been linked to various health benefits [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], including a reduced risk of cardiovascular disease [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], MetS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and lower mortality [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The association between the MED pattern and a reduced risk of MASLD has been extensively studied [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Existing epidemiological evidence has indicated that MED could mitigate MASLD risk [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and a meta-analysis revealed potential protective effects of MED in reducing liver cancer, with a pooled relative risk (RR) of 0.58 (95% CI: 0.46, 0.73) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Hereby, it has been recommended for the management of MASLD [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Very recently, two cohort studies suggested that MED may alleviate progression of MASLD [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the underlying mechanisms and the role of metabolite signatures in this relationship remain to be fully clarified.\u003c/p\u003e \u003cp\u003eMetabolites detected in plasma provide significant potential for health assessment, diagnosis and disease prediction [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Metabolomic signatures are increasingly recognized as critical players in the progression of MASLD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Prior research has shown that metabolic profile alterations detected are associated with MASLD, particularly extremely large very-low-density lipoprotein (VLDL) triglycerides, which reflected both its presence and potential future risk [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The MED may lower low-density lipoprotein (LDL) levels and increase high-density lipoprotein (HDL) levels, contributing to the alleviation of hepatic steatosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, there is limited evidence linking MED-related metabolic biomarkers with the incidence of LRE and mortality in MASLD patients [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Identifying biomarkers associated with MED and LRE may offer valuable tools for elucidating the metabolic pathways affected by MED [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we conducted a prospective cohort study using UK biobank data. We aimed to 1) assess the association between adherence to MED and the risk of incident LRE and mortality in patients with MASLD; and 2) investigate the effect of metabolic biomarkers in the relationship between MED, LRE and mortality.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eWe derived data from the UK biobank, an ongoing population-based prospective study, and details of the UK biobank have been described elsewhere (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ukbiobank.ac.uk\u003c/span\u003e\u003cspan address=\"http://www.ukbiobank.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Briefly, the UK Biobank study recruited over 500,000 participants aged 37\u0026ndash;73 from 22 assessment centers across England, Wales, and Scotland through March 2006 to December 2010. Information on socio-demographics, habitual diet, lifestyle factors, and medical history was gathered through touch-screen questionnaires, face-to face interviews, and electronic records. Physical measurements and biological specimens were collected through standardized procedures.\u003c/p\u003e \u003cp\u003eWe included patients with prevalent MASLD at recruitment which defined as presence of fatty liver, accompanied by at least one of five cardiometabolic risk factors, according to multi-society Delphi consensus statement [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Given the restricted sample size of participants undergoing MRI-based hepatic steatosis quantification (n\u0026thinsp;\u0026lt;\u0026thinsp;50,000), fatty liver index (FLI) was employed as a validated surrogate marker in accordance with international expert consensus recommendations for large-scale epidemiological studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. FLI was estimated according to triglycerides, Body Mass Index (BMI), abdominal circumference and GGT as prior investigation, and fatty liver was defined by as FLI\u0026thinsp;\u0026ge;\u0026thinsp;60 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. After the exclusion of individuals who were pregnant, had a history of cancer, non steatotic liver disease, without alcohol information or excessive drinking (female\u0026thinsp;\u0026gt;\u0026thinsp;20 g/day, male\u0026thinsp;\u0026gt;\u0026thinsp;30 g/day), a total of 126,217 eligible participants were recruited. We further excluded individuals who had a history of LREs before baseline, without valid baseline dietary data, with implausible total energy intake (\u0026lt;\u0026thinsp;500 kcal/d or \u0026gt;\u0026thinsp;5000 kcal/d), leaving 47,429 participants for analysis (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The UK biobank study was approved by North West Multi-Centre Research Ethics Committee, the National Information Governance Board for Health and Social Care in England and Wales and the Community Health Index Advisory Group in Scotland. All participants signed written informed consent forms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessment of the Mediterranean Diet\u003c/h2\u003e \u003cp\u003eParticipants were invited to complete the Oxford WebQ questionnaire on five different occasions over five years, which has been validated in previous studies and extensively utilized in several epidemiological studies examining dietary patterns associated with chronic diseases [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Between April 2009 and September 2010, a total of 70,000 participants completed Oxford WebQ in clinic and over 320,000 individuals finished an online 24-hour recall dietary questionnaire in response to email invitations from February 2011 through June 2012. The average measures were calculated based on data from participants who had completed at least one of the questionnaires [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We adapted the alternate MED (aMED) score [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which is a modified version of the traditional MED score to estimate adherence to MED in UK biobank population [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It is constructed based on the consumption of 9 food components (vegetables, legumes, fruits, nuts, whole grains, fish, the ratio of monounsaturated fatty acid (MUFA) to saturated fatty acid (SFA), red/processed meat, and alcohol), and each component was scored as either 0 or 1 point [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Participants who consumed above the median intake for each component were assigned 1 point, otherwise they received 0 points, except for red/processed meat (where intakes below the median were assigned 1 point), and alcohol (where intakes of 5 g/d\u0026thinsp;\u0026le;\u0026thinsp;alcohol\u0026thinsp;\u0026le;\u0026thinsp;15 g/d were assigned 1 point). Finally, the aMED score ranges from 0\u0026ndash;9 and we further classified it into three categorical variables (0\u0026ndash;3, 4\u0026ndash;5, 6\u0026ndash;9 score) in line with previous studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Details of the components and scoring criteria for aMED score are described in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e of Supplementary Material.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Assessment of metabolites\u003c/h2\u003e \u003cp\u003eA venous blood sample was obtained at baseline and stored in a freezer at -80\u0026deg;C. Prior to preparation, frozen samples were thawed gradually at +\u0026thinsp;4\u0026deg;C overnight, followed by gentle mixing and centrifugation (3 minutes at 3400 xg, +\u0026thinsp;4\u0026deg;C) to remove any potential precipitate. 249 metabolic biomarkers (168 original measurements and 81 ratios), including lipids, fatty acids, amino acids, ketone bodies and other low-molecular-weight metabolic biomarkers were quantified using high-throughput nuclear magnetic resonance (NMR) spectroscopy between June 2019 and April 2020 in the Nightingale metabolic biomarker platform. Detailed information about NMR platform and experimentation has been described elsewhere (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ctsu.ox.ac.uk/ukb/ukb/docs/nmrm_companion_doc\u003c/span\u003e\u003cspan address=\"https://biobank.ctsu.ox.ac.uk/ukb/ukb/docs/nmrm_companion_doc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In this study, we incorporated 143 metabolites which were directly derived from measures in absolute concentrations (except for fatty acids) and could not be inferred from other biomarkers [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The values of all metabolic biomarkers were first log-transformed and then \u003cem\u003eZ\u003c/em\u003e-transformed. For biomarkers with values of zero, these were replaced with the smallest non-zero value within the group divided by square root of 2 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Assessment of outcome\u003c/h2\u003e \u003cp\u003eIncident cases of LRE were identified through cancer and death registries, defined as a composite endpoint that includes complications of cirrhosis (K74.60) and/or HCC (C22.0) and other liver diseases and conditions (Supplementary Material Table S2), based on the International Classification of Diseases (ICD)-10 codes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Person-years of follow-up were calculated as the interval from the dates of baseline survey until the occurrence of LRE, death, loss to follow-up, or the end of the study period, whichever came first. Dates of death were obtained from death certificates provided by the National Health Service Information Centre for participants in England and Wales, and from the National Health Service Central Register for participants in Scotland [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Assessment of variables\u003c/h2\u003e \u003cp\u003eIn accordance with previous studies, we collected a set of covariates including (1) demographic characteristics (age, sex, ethnicity, educational attainment, body mass index [BMI], index of multiple deprivation [IMD]; (2) lifestyle factors (current smoking status, physical activity, sleep duration, total energy intake); (3) chronic comorbidities (hypertension, diabetes); (4) medication use (ACEI, ARBs, Calcium channel blocker, Beta blocker, Statin, Multivitamin, mineral supplements); (5) laboratory data (total cholesterol, LDL, HDL, triglycerides, fasting glucose, hsC-reactive protein). Physical activity levels were assessed using the metabolic equivalent (MET) derived from International Physical Activity Questionnaire-Short Form (IPAQ-SF). Height and weight of participants were measured to calculate BMI, which was defined as weight in kilograms divided by the square of height in meters. Information of comorbidities were collected through the baseline questionnaire, verbal interviews, and electronic health records. Laboratory data were measured using fasting venous blood samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe baseline characteristics were descripted as means (standard deviation, SD) or medians (interquartile range, IQR) for continuous variables and as numbers (percentages) for categorical variables. Missing covariate data were imputed using the multiple imputation method. Cox proportional hazards regression models were employed to evaluate linkage between aMED and incident LRE and all-cause mortality. We adopted crude model stratified by sex and age at baseline and adjusted for intake of energy. In the multivariable-adjusted model, we additionally adjusted for demographic characteristics, lifestyle factors, chronic comorbidities, medication use, and laboratory data. Schoenfeld tests of proportional hazards assumptions presented no violations, with all \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026gt;\u0026thinsp;0.05. Restricted cubic spline (RCS) with 3 knots, placed at the 10th, 50th, and 90th percentiles (according to Akaike information criterion [AIC] and Bayesian information criterion [BIC]; Table S3), were used to assess the potential non-linear associations between aMED and outcomes [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe performed a two-stage analysis to investigate effect of metabolic biomarkers in association between aMED and LRE or mortality in MASLD population. In the first stage, we fitted multivariable-adjusted linear regression models to estimate associations between aMED score and metabolic biomarkers, adjusting for above-mentioned covariates. The Benjamini Hochberg method was used and the false discovery rate (FDR) adjusted \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In the second stage, Cox proportional hazards models were employed to assess the relationships between significant metabolites and LRE or all-cause mortality. Principal component analyses were conducted to capture the most important patterns from a large number of correlated biomarkers, and we found that 12 principal components of 143 metabolites potentially explained over 90% of the variance. If the associations were statistically significant in two-stage analysis, mediating analyses were performed to assess the mediating role of the metabolites in the associations between aMED and LRE or mortality.\u003c/p\u003e \u003cp\u003eWe also conducted several sensitivity analyses to test robustness of our results. In order to reduce the potential effect of reverse causation, participants who were died or diagnosed with LRE within the first 2 years of follow-up were excluded. E-values were calculated to assess the robustness of the results to potential unmeasured confounding factors. In addition, we employed stratified analysis to estimate effect modifications by individual characteristics (e.g., demographic factors, lifestyle factors, and chronic comorbidities). Statistical significance of interactions between subgroups was examined using the likelihood ratio test.\u003c/p\u003e \u003cp\u003eData analyses were performed using the R version 4.1.3 (R Foundation for Statistical Computing, Vienna, Austria), with \u0026ldquo;survival\u0026rdquo; packages for Cox regression models, \u0026ldquo;rms\u0026rdquo; package for smoothing nonlinear terms, \u0026ldquo;mice\u0026rdquo; package for multiple imputation, and \u0026ldquo;mediator\u0026rdquo; for mediation analysis. A two-sided test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or an FDR adjusted \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was defined as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the baseline characteristics of UK Biobank participants with MASLD across different aMED scores. The mean (SD) age of 47,429 patients included in this study was 57.3 (7.7) years, and 61.7% of participants were male. Individuals with higher aMED scores were generally more likely to be non-smokers, have attained a college or university degree, have a higher IMD, and engage in physical activities with higher MET. Additionally, individuals with the higher score of aMED tended to have a larger energy intake, longer sleep duration, and greater use of multivitamin and mineral supplements. The correlations between the 9 components scores were generally weak (r\u0026thinsp;\u0026lt;\u0026thinsp;0.30), except for the correlation between vegetables and fruits intake (r\u0026thinsp;=\u0026thinsp;0.48; Figure S2)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics across aMED score among UK biobank participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eaMED score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;47,429)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026ndash;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u0026ndash;9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23,616)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;18,389)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5,424)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, mean (SD), year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.0 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.5 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.9 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.3 (7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,734 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,263 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,244 (59.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29,241 (61.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22,408 (94.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,375 (94.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,136 (94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44,919 (94.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, mean (SD), kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.6 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.5 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.3 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.5 (4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity, Median (IQR), MET hours/week\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.1 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8 (36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.5 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.4 (37.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCollege or university degree, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,442 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,172 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,439 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17,053 (36.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndex of multiple deprivation, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.6 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.5 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.4 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.9 (13.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnergy intake, mean (SD), kJ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,531 (2,651)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,869 (2,454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,045 (2,273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,721 (2,542)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smokers, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,203 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,178 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e276 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,657 (7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep duration, mean (SD), hour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,321 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,682 (36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,999 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17,002 (35.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,591 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,204 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e351 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,146 (6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,433 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,711 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e783 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,927 (14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARBs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e961 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e816 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,017 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium channel blocker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e984 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e931 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e328 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,243 (7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta blocker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,089 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,565 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e469 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,123 (8.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,468 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,318 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,254 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,040 (23.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivitamin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,001 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,547 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e798 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,346 (13.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMineral supplements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,808 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,184 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,325 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,317 (21.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory data, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6 (0.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.3 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsC-reactive protein, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5 (4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: aMED, alternate Mediterranean Diet; SD, standard deviation; IQR, interquartile range; BMI, body mass index; MET hours /week, metabolic equivalent, hours of physical activity per week; ACEI, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; LDL, low-density lipoprotein; HDL, high-density lipoprotein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the association of aMED score with HR of LRE incidence and mortality. Over a median follow-up of 13.3 years (IQR: 12.7, 14.1 years), 296 cases of LRE, and 3,616 deaths were documented. There was sufficient evidence of inverse associations between the aMED score and reduced risks of LRE incidence and mortality in both crude and multivariable-adjusted models. In multivariable-adjusted model, the estimated HRs for LRE incidence and mortality were 0.796 (95% CI: 0.622, 1.019) and 0.953 (95% CI: 0.889, 1.023) for individuals with aMED scores of 4\u0026ndash;5, and 0.553 (95% CI: 0.351, 0.874) and 0.854 (95% CI: 0.762, 0.956) for those with aMED scores of 6\u0026ndash;9, respectively, compared with participants with the lowest category of aMED score (0\u0026ndash;3). A one-unit increase in the aMED score was associated with an 11.9% decreased risk of LRE incidence (HR: 0.881, 95% CI: 0.814, 0.953) and a 3.0% reduced risk of mortality (HR: 0.970, 95% CI: 0.949, 0.992). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrates dose-response associations between aMED score and the risks of LRE incidence and mortality in MASLD patients. Approximately linear dose-response associations were observed for both LRE incidence (\u003cem\u003eP\u003c/em\u003e\u003csub\u003enonlinear\u003c/sub\u003e = 0.91) and mortality (\u003cem\u003eP\u003c/em\u003e\u003csub\u003enonlinear\u003c/sub\u003e = 0.07). The risk of LRE incidence and mortality progressively declined as the aMED score increased.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of aMED score and liver-related events and mortality in MASLD population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase/Person-years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIR\u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel1 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMode2 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaMED score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;3 (n\u0026thinsp;=\u0026thinsp;23,616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168/309,975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;5 (n\u0026thinsp;=\u0026thinsp;18,389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107/242,714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.792 (0.621, 1.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.796 (0.622, 1.019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;9 (n\u0026thinsp;=\u0026thinsp;5,424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21/71,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.517 (0.328, 0.814) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.553 (0.351, 0.874) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer unit increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.870 (0.805, 0.940) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.881 (0.814, 0.953) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaMED score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;3 (n\u0026thinsp;=\u0026thinsp;23,616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,863/310,611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e599.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;5 (n\u0026thinsp;=\u0026thinsp;18,389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,390/243,075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e571.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.916 (0.854, 0.982) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.953 (0.889, 1.023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;9 (n\u0026thinsp;=\u0026thinsp;5,424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e363/71,940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e504.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.784 (0.700, 0.878) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.854 (0.762, 0.956) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer unit increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.950 (0.929, 0.971) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.970 (0.949, 0.992) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: HR, hazard ratio; CI, confidence interval; SD, standard deviation; aMED, alternate Mediterranean Diet; MASLD, metabolic dysfunction-associated steatotic liver disease; LRE, liver-related events; IR: incidence rate.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026dagger;: Per 100,000; *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel1 is crude Cox regression model adjusted energy and stratified by sex and age.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel2 is multivariable adjusted model additionally adjusted for sociodemographic characteristics (ethnicity, education attainment, index multiple deprivations, and BMI), lifestyle factor (smoking status, physical activity, and daily sleeping time), medications (ACEI, ARB, Calcium channel blocker, Beta blocker, Statin, multivitamins, and mineral use), comorbidities ( hypertension and diabetes) and blood-based measurements (total cholesterol, LDL, HDL, Triglycerides, fasting glucose, and hsC-reactive protein).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe main results were robust in sensitivity analyses (Table S4). Compared to our primary findings, the estimated effects remained largely unchanged after excluding LRE incidence and death that occurred within the first 2 years of follow-up. The E-value ranged from 1.29 to 3.28 for LRE incidence and 1.19 to 2.94 for mortality (Table S5). Subgroup analyses stratified by demographic and behavioral characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed consistent associations of aMED scores with the risks of LRE incidence and mortality, with no significant differences observed between subgroups. Protective and analogous associations were observed for 9 aMED constituents with risks of LRE incidence and mortality (Table S6 \u0026amp; Table S7). Among these, only vegetables (HR: 0.938, 95% CI: 0.899, 0.978) and legumes (HR: 0.715, 95% CI: 0.529, 0.966) exhibited significant inverse associations with LRE risk. Several aMED components, including vegetables, nuts, fish, the MUFA:SFA ratio, and moderate alcohol consumption, were significantly and negatively associated with risk of mortality.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the associations between aMED score and 143 metabolic biomarkers (per SD). After multivariable adjustment in multiple linear models, 46 of the 143 metabolites were significantly associated with aMED score, with FDR-adjusted \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Among those metabolites, very large HDL particles (n\u0026thinsp;=\u0026thinsp;3), unsaturated fatty acids (n\u0026thinsp;=\u0026thinsp;8), albumin, and acetate were positively associated with aMED score, with odds ratios (ORs) ranging from 1.006 to 1.075. In contrast, aMED scores were negatively associated with extremely and very large VLDL (n\u0026thinsp;=\u0026thinsp;11), small and middle HDL (n\u0026thinsp;=\u0026thinsp;13), saturated fatty acids (n\u0026thinsp;=\u0026thinsp;2), Apo-AI, and creatinine. The association between the 9 aMED constituents and metabolites are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Of the 46 aMED associated metabolites, 9 showed significant positive correlations with vegetables (e.g. primarily consist of large LDL, albumin, and unsaturated fatty acids), while 5 (e.g. extremely large VLDL) demonstrated significant negative correlations with vegetables.\u003c/p\u003e\u003cp\u003eWe further explored the associations between aMED-related metabolites and LRE incidence and mortality. Cox regression models revealed that 7 metabolites candidates were associated with LRE, and 21 metabolites were found to be associated with mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026amp; Figure S3). Specifically, higher levels of phospholipid in small HDL particles and the Omega-3 fatty acids to total fatty acids percentage were associated with reduced risk of LRE. Conversely, significant positive correlations were observed between mortality and serum levels of very large HDL particles, cholesterol, free cholesterol, total lipids, and the ratio of saturated fatty acids to total fatty acids. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e summarized mediation analysis of metabolites in the associations of aMED score and the risks of LRE and mortality in MASLD patients. Omega-3 fatty acids, the ratio of omega-3 fatty acids to total fatty acids, and albumin significantly mediated the associations between aMED scores and both the risk of LRE and mortality, accounting for 2.6\u0026ndash;11.9% and 4.7\u0026ndash;23.1% of the total effect, respectively.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis prospective cohort study, using data from over 47,000 UK Biobank participants, investigated the relationship between MED adherence and the risk of liver-related events (LRE) and mortality in MASLD patients, along with its effects on metabolites. We found that a higher aMED score was associated with lower risks of LRE and mortality in a linear dose-response manner. The vegetable and legume components were linked to reduced LRE incidence, while most aMED components were associated with lower mortality. Out of 143 metabolites, 46 were significantly associated with aMED, including very large HDL and linoleic acid: total fatty acids, which were linked to LRE incidence, and metabolites like very large HDL, cholesterol, and free cholesterol, which were associated with mortality. Omega-3 fatty acids, the omega-3 to total fatty acid ratio, and albumin partially mediated the associations between MED adherence and both LRE incidence and mortality, providing insights into the underlying mechanisms.\u003c/p\u003e \u003cp\u003eExisting evidence suggested that adherence to the MED may offer numerous health benefit for patients with MASLD, including a reduction in hepatic steatosis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. For example, a randomized controlled trial involving 56 subjects with MASLD found a significant reduction in hepatic steatosis after 12 weeks of the MED adherence (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with mean (SD) relative reductions of 32.4% (\u0026plusmn;\u0026thinsp;25.5%) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Another study reported that adherence to MED significantly reduced body weight, proton density fat fraction of the liver, total cholesterol, gamma-glutamyl transferase, and triglyceride concentrations in patients with MASLD [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Besides, a prospective observational cohort study performed in 655 consecutive MASLD outpatients demonstrated that better adherence to the MED showed lower platelet activation and liver collagen deposition [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Consistent with prior studis, our results indicated the potential benefits of the MED in progression of MASLD, specifically in ameliorating the risk of LRE and mortality. This finding is also supported by an analogous study, which reported a protective effect of MED on chronic liver disease and severe liver disease [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, several components of the aMED, particularly vegetables and legumes, were associated with a reduced risk of MASLD progression. Identical to our findings, previous studies have suggested that higher consumption of vegetables might lower liver fat content [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], reduce the risk of developing MASLD [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and decrease the risk of end-stage liver disease and all-cause mortality in patients with MASLD [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These findings support the idea that a diet rich in whole vegetables and legumes may serve as both a preventive strategy and therapeutic approach for the management of MASLD [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The benefits of vegetables and legumes may not only stem from their ability to reduce total caloric intake or from the wide range of micronutrients they provide [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], but also through mechanisms involving the gut-brain-liver axis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], alleviating endoplasmic reticulum stress, inflammation, and lipid accumulation [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Regarding other components of aMED, prior randomized controlled clinical trials have identified that increased consumption of nuts and reduced intake of red meat benefit patients with MASLD [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In our study, we also found that food groups (such as nuts, fish, and the MUFA:SFA ratio) were negatively associated with mortality in patients with MASLD, with HR ranging from 0.82 to 0.96. While potential protective effects of aMED components were observed, more sophisticated studies are still required for further validation.\u003c/p\u003e \u003cp\u003ePrevious studies have indicated that adherence to MED was associated with specific metabolites [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], which can characterize both adherence to MED and metabolic responses to it [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Consistent with prior metabolomic study [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], our study found that adherence to the MED was positively correlated with unsaturated fatty acids and HDL, while being negatively associated with saturated fatty acids and VLDL. A quasi-experimental study from Sweden further found that a 6-day MED intervention notably enhanced other beneficial metabolites such as caffeine and beta-carotene [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Another randomized clinical trial from Spain found that acylcarnitines and steroids were significantly associated with the aMED [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. As for components of aMED, positive associations were observed between fish/nuts and unsaturated fatty acid such as omega-3/omega-6 fatty acids, which were in line with findings from Spanish and American cohort studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, a cohort study from Fenland observed that of among the AMDS components, fish consumption mainly contributed to variations in phospholipids [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. These inconsistences highlight the need to explore the relationship between food components and other sensitive or specific metabolites beyond lipids, to comprehensively characterize dietary biomarkers [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetabolites, as intermediates or end products of metabolism, may play critical roles in liver cell physiology and may serve as key signals for the progression of MASLD [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In line with previous studies [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], our analyses showing that 7 aMED-related lipid metabolites were significantly related to LRE, and 21 were associated with mortality. Of these metabolites, small HDL particles, omega-3 fatty acids, and albumin inversely related to LRE, while the ratio of saturated fatty acids to total fatty acids showed positive correlation with mortality. These metabolic signatures not only hold promise as potential biomarkers for predicting the progression of MASLD, but also suggest shared metabolic pathways [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. We further found 3 serum aMED-related lipid metabolites or index, (i.e. omega-3 fatty acids, the ratio of omega-3 fatty acids to total fatty acids, and albumin), partially mediated the association between aMED and the incidence of LRE and mortality. A double-blinded, placebo-controlled, randomized controlled trial also demonstrated a reduction in liver fat following omega-3 fatty acid intervention over a 12 months [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Previous studies also reported protective effect of small-sized HDL particles [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Other metabolites, which might have a mediating role in the progression of MASLD, warrant further investigation. The potential mechanisms of omega-3 fatty acids in reducing the progression of MASLD are likely related to their anti-inflammatory and antifibrotic effects, as well as their ability to reduce liver injury by suppressing de novo lipogenesis and enhancing mitochondrial, peroxisomal, and microsomal fatty acid oxidation [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Further research focusing on metabolites is needed to elucidate their role as potential biomarkers for the early detection of MASLD progression.\u003c/p\u003e \u003cp\u003eA key strength of our study is the use of the UK Biobank, a nationwide, prospective cohort with a well-validated follow-up of 13.3 years. This allowed for detailed assessment of aMED-related metabolites and their mediating role in MASLD progression, offering mechanistic insights into aMED's potential preventive effects. The comprehensive data also enabled adjustment for multiple confounders, improving the reliability of our findings. Additionally, our analysis of both linear and nonlinear associations between aMED and outcomes provides a unique contribution to this study. However, there are several limitations should be acknowledged. Firstly, our observational design limits the ability to draw causal inferences between MED adherence, the risk of LRE or mortality, and the potential metabolites involved. Secondly, given that dietary assessment relied on self-reports, recall bias was unavoidablely introduced to some extent. However, the Oxford WebQ Online 24-Hour Dietary Questionnaire employed in the study had undergone extensive validation against biomarkers [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Thirdly, the generalizability and reproducibility of our findings may be constrained by the fact that participants were predominantly middle-aged, white, European individuals. Further physiological studies conducted in diverse and independent populations are warranted to validate the findings of our study. Fourthly, although we accounted for a number of covariates, some unmeasured factors may still influence the association between aMED and the progression of MASLD. The estimated E-value indicated that unmeasured confounders were unlikely to significantly bias the observed associations (Table S5). Fithly, we did not fully assess the impact of dietary changes over time. However, dietary patterns tend to remain stable in individuals [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Sixthly, the assessment of metabolites was cross-sectional and conducted simultaneously with dietary data, rather than over the intervention period, which prevents us from directly concluding that changes in metabolites were caused by adherence to MED. Further longtitude studies are wrranted to explore the effects of aMED on the associated metabolites.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, our study provides evidence that better adherence to the MED and its components were associated with a reduced risk of LRE and mortality in patients with MASLD. The observed reductions in risk may be partly explained by several aMED-related metabolic biomarkers, such as albumin and omega-3 fatty acids. However, the potential biological mechanisms underlying aMED-related metabolites remain unexplained. Future interventional and experimental studies are essential to further identify additional mediating biomarkers and to uncover the pathways and underlying mechanisms involved in these associations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealternate Mediterranean Diet\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMASLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetabolic dysfunction-associated steatotic liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLRE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eliver-related events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003enucleic magnetic resonance\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehepatocellular carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMetS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetabolic syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003every-low-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMUFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emonounsaturated fatty acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esaturated fatty acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eindex of multiple deprivation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetabolic equivalent\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIPAQ-SF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Physical Activity Questionnaire-Short Form\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRestricted cubic spline\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian information criterion.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UK Biobank was approved by the National Research Ethics Committee (REC ID: 16/NW/0274). Electronic written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from UK Biobank project site, subject to registration and application process. Further details can be found at https://www.ukbiobank.ac.uk. The code used in this study is available upon reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the\u0026nbsp;Shenzhen Medical Research Fund (No. A2403069, C2401002), the Funding of Shenzhen Clinical Research Center for Gastroenterology (Gastrointestinal Surgery, No.LCYSSQ20220823091203008), the Natural Science Foundation of China (No. 82103913, 82473707), the Research Supporting Start-up Fund for Associate researcher, of SAHSYSU (No. ZSQYRSSFAR0004), the Startup Fund for the 100 Top Talents Program, SYSU (No. 392012), and Shenzhen Key Laboratory of Chinese Medicine Active substance screening and Translational Research (No. ZDSYS20220606100801003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKai Wang\u003c/strong\u003e: Writing\u0026ndash;original draft, Writing\u0026ndash;review \u0026amp; editing, Formal analysis;\u0026nbsp;\u003cstrong\u003eShijian Xiang\u003c/strong\u003e: Writing \u0026ndash; review \u0026amp; editing, Validation, Visualization;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eQiangsheng He\u003c/strong\u003e: Data curation, Writing \u0026ndash; review \u0026amp; editing, Methodology; \u003cstrong\u003eChumei Huang\u003c/strong\u003e: Writing\u0026ndash;review \u0026amp; editing;\u003cstrong\u003e\u0026nbsp;Zhen Yang\u003c/strong\u003e: Writing\u0026ndash;review \u0026amp; editing; \u003cstrong\u003eRenjie Li\u003c/strong\u003e: Writing\u0026ndash;review \u0026amp; editing;\u003cstrong\u003e\u0026nbsp;Anran Liu\u003c/strong\u003e: Writing\u0026ndash;review \u0026amp; editing; \u003cstrong\u003eRuisheng Cai\u003c/strong\u003e: Writing\u0026ndash;review \u0026amp; editing; \u003cstrong\u003eNingning Mi\u003c/strong\u003e: Writing\u0026ndash;review \u0026amp; editing; \u003cstrong\u003eZixin Liang\u003c/strong\u003e: Writing\u0026ndash;review \u0026amp; editing; \u003cstrong\u003eZuofeng Xu\u003c/strong\u003e: Writing\u0026ndash;review\u0026amp; editing, Supervision, Funding acquisition; \u003cstrong\u003eJinqiu Yuan\u003c/strong\u003e: Resources, Writing\u0026ndash;review\u0026amp; editing, Supervision, Funding acquisition;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBin Xia\u003c/strong\u003e: Data curation, Writing\u0026ndash;review\u0026amp; editing, Supervision, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge the contributions of UK biobank for assistance in providing data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research got access to the UK Biobank (https://www.ukbiobank.ac.uk) under application number 51671. Further information is available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChan WK, Chuah KH, Rajaram RB, Lim LL, Ratnasingam J, Vethakkan SR. Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): A State-of-the-Art Review. J Obes Metab Syndr. 2023;32:197\u0026ndash;213.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023;78:1966\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiazi K, Azhari H, Charette JH, Underwood FE, King JA, Afshar EE, et al. The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. 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JAMA Intern Med. 2014;174:1587\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"alternate Mediterranean Diet, Metabolic dysfunction-associated steatotic liver disease, Metabolomics, Mediation effect, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-6026627/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6026627/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMediterranean Diet (MED) is recommended for managing patients with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD). However, the potential metabolic changes involved in this relationship remain unclear. This study aims to investigate how metabolic biomarkers mediate the association between MED adherence and liver-related events (LRE) and mortality in patients with MASLD.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003e We performed a prospective analysis using UK Biobank data, including 47,429 MASLD participants free of LRE at baseline. MED adherence was assessed as alternate Mediterranean Diet (aMED) score through a validated questionnaire covering 206 foods and 32 beverages. Metabolic biomarkers were measured using high-throughput nucleic magnetic resonance (NMR) spectroscopy. Cox regression and restricted cubic splines assessed the association of aMED, its components, with risk of LRE and mortality. Mediation analysis evaluated the role of metabolites in the relationship between aMED, its components, and MASLD progression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOver a median follow-up of 13.3 years, 296 LRE cases and 3,616 deaths occurred. Higher aMED scores (6\u0026ndash;9) were associated with lower risks of LRE (HR: 0.553, 95% CI: 0.351\u0026ndash;0.874) and mortality (HR: 0.854, 95% CI: 0.762\u0026ndash;0.956) compared to the lowest scores (0\u0026ndash;3). Linear dose-response relationships were observed for both LRE incidence (\u003cem\u003eP\u003c/em\u003e\u003csub\u003enonlinear\u003c/sub\u003e = 0.91) and mortality (\u003cem\u003eP\u003c/em\u003e\u003csub\u003enonlinear\u003c/sub\u003e = 0.07). Certain aMED components, including vegetables and legumes, were associated with a reduced risk of LRE, while vegetables, nuts, fish, the MUFA:SFA ratio, and moderate alcohol intake were linked to lower mortality risk. Of 143 metabolites, 46 were significantly associated with aMED. Positive associations included very large HDL particles (n\u0026thinsp;=\u0026thinsp;3), unsaturated fatty acids (n\u0026thinsp;=\u0026thinsp;8), albumin, and acetate, while negative associations were found with large VLDL (n\u0026thinsp;=\u0026thinsp;11), small and middle HDL (n\u0026thinsp;=\u0026thinsp;13), saturated fatty acids (n\u0026thinsp;=\u0026thinsp;2), Apo-AI, and creatinine. Five aMED-related lipid metabolites were negatively associated with LRE, while five were positively linked to mortality. Mediation analysis revealed that omega-3 fatty acids, the omega-3 to total fatty acid ratio, and albumin accounted for 7.9%, 11.9%, and 2.6% of the reduction in LRE, and 19.4%, 23.1%, and 4.7% of the mitigation in mortality, respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAdherence to MED is linked to reduced LRE risk and mortality in MASLD patients. Metabolic biomarkers such as small HDL particles and omega-3 fatty acids may mitigate MASLD progression.\u003c/p\u003e","manuscriptTitle":"Metabolite Signatures and Their Mediation Effects on the Relationship Between Mediterranean Diet Adherence and MASLD Progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-18 14:09:46","doi":"10.21203/rs.3.rs-6026627/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":"ff9ef8db-ab05-4773-80b3-10e206b45251","owner":[],"postedDate":"February 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-18T15:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-18 14:09:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6026627","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6026627","identity":"rs-6026627","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}