Dietary amino acids and the odds of metabolic dysfunction-associated fatty liver disease (MAFLD) among overweight and obese children and adolescents: A principal component analysis approach

Dietary amino acids and the odds of metabolic dysfunction-associated fatty liver disease (MAFLD) among overweight and obese children and adolescents: A principal component analysis approach | 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 Dietary amino acids and the odds of metabolic dysfunction-associated fatty liver disease (MAFLD) among overweight and obese children and adolescents: A principal component analysis approach Ali Nikparast, Parmis Mirzaei, Mohammad hassan Sohouli, Zeinab S Tadayoni, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6687463/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Nov, 2025 Read the published version in Nutrition & Metabolism → Version 1 posted 11 You are reading this latest preprint version Abstract Background Given the limited understanding of how dietary amino acid intake affects metabolic dysfunction-associated fatty liver disease (MAFLD), we examined the potential relationship between dietary amino acid patterns and the odds of MAFLD in children and adolescents with overweight and obesity. Methods This cross-sectional study was conducted on participants aged 6 to 18 years with a WHO body mass index (BMI)-for-age z-score ≥ 1. MAFLD diagnosis followed established consensus definitions. Principal component factor analyses were conducted based on eighteen amino acids. Logistic regression models, adjusted for potential confounders, were used to estimate the odds of MAFLD across amino acid pattern score quartiles. Results A total of 505 (52.9% boys) with mean ± SD age and BMI-for-age-Z-score of 10.0 ± 2.3 and 2.70 ± 1.01, respectively, were enrolled. Three major amino acid patterns were characterized: (1) higher loads by branched chain, lysine, tyrosine, threonine, methionine, histidine, alanine, and aspartic acid; (2) higher loads of proline, serine, glutamic acid, and phenylalanine; (3) higher loads of tryptophan, arginine, glycine, and cysteine. After adjusting for all potential confounders, participants in the highest quartile of the first amino acid pattern tended to be associated with increased odds of MAFLD (OR:2.14; 95%CI:0.97–4.77). There was no significant association for the second and third patterns. Conclusions These novel data suggest that the amino acid composition of an individual’s diet may modify their odds of MAFLD. metabolic dysfunction-associated fatty liver disease amino acid overweight and obese children and adolescents Figures Figure 1 Figure 2 Figure 3 Introduction Metabolic dysfunction-associated fatty liver disease (MAFLD) has emerged as a pressing global health concern, with an alarming rise in its prevalence, particularly among overweight and obese children and adolescents [ 1 ]. MAFLD is characterized by excessive hepatic fat accumulation in the presence of metabolic disturbances, and it is strongly correlated with an elevated risk of cardiometabolic comorbidities, including insulin resistance, dyslipidemia, and type 2 diabetes mellitus (T2DM) [ 2 , 3 ]. Due to its progressive nature, MAFLD can lead to severe liver complications, including fibrosis, cirrhosis, and hepatocellular carcinoma, underscoring its potential to cause significant long-term morbidity [ 2 ]. Despite its growing burden, there is currently no pharmacological treatment specifically approved for MAFLD, emphasizing the critical importance of lifestyle modifications, particularly adopting healthy dietary patterns and increased physical activity, in managing and preventing the disease [ 4 ]. Therefore, early detection and preventive strategies are paramount to curbing the long-term health consequences of MAFLD. Extensive research has examined the relationship between dietary factors, particularly the quality and quantity of carbohydrates and fats, and the risk of hepatic steatosis and its associated metabolic disturbances [ 5 ]. More recent studies have begun to explore the role of dietary protein and amino acids in hepatic fat accumulation, with growing evidence suggesting that these macronutrients may influence not only liver function but also broader metabolic pathways [ 6 – 8 ]. In this framework, the emerging body of literature suggests that certain amino acids, including branched-chain amino acids (BCAAs), sulfur-containing amino acids (SAAs), and aromatic amino acids (AAAs), might be potentially associated with anthropometric indices changes, insulin resistance, T2DM, hepatic steatosis, and nonalcoholic fatty liver diseases (NALFD) in adults [ 9 – 13 ]. Interestingly, we found that increased dietary intake of BCAAs, particularly leucine, may have detrimental effects on the development of NAFLD in overweight and obese children and adolescents [ 14 ]. In this context, it should be noted that amino acids are not consumed independently but rather interact in complex networks that may produce either synergistic or antagonistic effects on health. Such interactions emphasize the need for a holistic approach to dietary analysis rather than focusing on the effects of individual amino acids in isolation. In recent years, factor analysis techniques have gained prominence in nutritional research as an effective tool for identifying dietary patterns that are associated with the risk of chronic diseases [ 15 ]. This method enables researchers to gain a more integrated understanding of how dietary components, including amino acids, interact and contribute to disease outcomes [ 15 , 16 ]. In this context, several studies have investigated the association between dietary amino acid patterns derived from factor analysis and the risk of developing hypertension [ 17 ], dyslipidemia [ 18 ], and dysglycemia [ 19 ] in adults. As a result, it seems that dietary amino acid patterns play an essential role in maintaining and promoting optimal health [ 20 ]. Despite this growing body of work, there remains a lack of research exploring the relationship between dietary amino acid patterns and the odds of MAFLD, particularly in pediatric populations. Given the potential role of amino acid metabolism in liver fat accumulation and metabolic dysfunction, there is a clear need for further investigation into how dietary amino acid intake influences the development of MAFLD in overweight and obese children and adolescents. The present study aims to address this gap by examining the association between dietary amino acid intake patterns and the odds of MAFLD in overweight and obese children and adolescents. Method Study design and population This cross-sectional study was conducted between September 2023 and July 2024 as part of an obesity registry focusing on Iranian children and adolescents [21]. The sample size was determined based on a previous study that examined the association between dietary BCAAs intake and odds of NAFLD [13]. Using G*Power software, the sample size was calculated with the following parameters: an alpha error probability of 0.05, a statistical power of 80% (1-β), a mean (μ) and standard deviation (σ) of 2.35 ± 0.49 for dietary BCAAs intake, and an effect size of 1.68 [13]. Based on these inputs, a minimum of 505 participants was required. Participants were randomly selected from individuals attending outpatient clinics specializing in gastroenterology, hepatology, endocrinology, and nutrition at the Children's Hospital Medical Center in Tehran. Eligible participants were those aged 7 to 18 years who were classified as overweight or obese, defined as having a body mass index-for-age (BMI-for-age) Z-score of 1 or greater, in accordance with World Health Organization (WHO) guidelines [22]. Exclusion criteria included: (1) a history of medical conditions such as renal or liver diseases (e.g., Wilson’s disease, autoimmune liver disease, hemochromatosis, viral hepatitis), thyroid disorders, or malignancies; (2) use of hepatotoxic or steatogenic medications (e.g., valproate, amiodarone), weight-loss drugs, or appetite suppressants; (3) recent dietary modifications within the past year due to illness or weight-loss interventions; and (4) incomplete responses on the food frequency questionnaire (FFQ) (fewer than 35 items) or reports of dietary intake deemed implausible. Under- and over-reporting of dietary intake was identified by comparing reported energy consumption with estimated energy requirements, with deviations beyond ±2 standard deviations leading to exclusion, in accordance with Institute of Medicine guidelines [23]. The study was conducted in compliance with the ethical principles outlined in the Declaration of Helsinki and received approval from the Ethics Committee of the National Nutrition and Food Technology Research Institute (IR.SBMU.NNFTRI.REC.1402.015). Written informed consent was obtained from parents or legal guardians, and assent was secured from the participating children. Anthropometric and Clinical Assessments All anthropometric measurements were conducted by trained pediatric nutritionists using standardized protocols. Body weight was recorded with a calibrated Seca scale (Seca, Hamburg, Germany), with a precision of 100 grams, while height was measured to the nearest 0.5 cm using a stadiometer. BMI was calculated as weight (kg) divided by height squared (m²), and BMI-for-age Z-scores were determined based on internationally recognized growth charts [22]. Waist circumference (WC) was measured at the midpoint between the iliac crest and the lowest rib, with a precision of 0.5 cm. Pubertal status was assessed by a pediatric endocrinologist following the Marshall and Tanner staging criteria, which categorize participants as prepubertal or pubertal based on breast and genital development stages [24, 25]. Physical activity levels were evaluated using the Persian-adapted Modifiable Activity Questionnaire (MAQ), which estimates weekly metabolic equivalent task (MET) hours. This questionnaire has demonstrated high reliability (97%) and moderate validity (49%) among adolescents [26]. Blood pressure was measured manually using a mercury sphygmomanometer with an appropriately sized cuff, following a 15-minute rest period. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were determined using the Korotkoff sound technique, with two readings taken at a one-minute interval, and the average value recorded. Biochemical Assessments Blood samples were collected between 7:00 and 9:00 AM following an overnight fasting period of 12–14 hours. Samples were centrifuged within 30–45 minutes of collection and analyzed on the same day. Fasting blood sugar (FBS) and triglycerides (TG) were measured using enzymatic colorimetric methods. Total cholesterol (TC) and high-density lipoprotein (HDL-C) were determined using cholesterol esterase and phosphotungstic acid methods, respectively. Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald equation [27]. Liver function was assessed by measuring aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels via enzymatic photometry, while gamma-glutamyl transferase (GGT) was quantified using enzymatic colorimetric methods. These analyses were conducted using commercially available kits from Delta Darman Inc. (Tehran, Iran), and processed with a Selectra 2 auto-analyzer (Vital Scientific, Spankeren, The Netherlands). Fasting serum insulin was quantified using the electrochemiluminescence immunoassay (ECLIA) technique, employing Roche Diagnostics kits and a Roche/Hitachi Cobas e-411 analyzer (Roche Diagnostics, GmbH, Mannheim, Germany). Insulin resistance (IR) was estimated using the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), calculated as fasting glucose (mmol/L)×fasting insulin (μU/L))/22.5. The intra-assay and inter-assay coefficients of variation for all biochemical assessments were maintained at ≤5.3%. Dietary Assessment Dietary intake was assessed using a validated and reliable 147-item semi-quantitative FFQ [28, 29]. Trained nutritionists conducted structured interviews with participants and their parents/guardians to determine the frequency and portion sizes of food items consumed over the past year. When children were unable to recall their dietary intake, their mothers provided responses on their behalf. Portion sizes were primarily determined based on U.S. Department of Agriculture (USDA) food serving guidelines, such as one slice of bread, one medium apple, or one cup of dairy. For foods not covered in USDA tables, standard household measures (e.g., one tablespoon of beans, one piece of chicken (leg, breast, or wing), or rice servings classified as large, medium, or small) were used and converted into grams and servings. The nutritional composition of food items was derived primarily from USDA Food Composition Tables (FCTs) due to limitations in the Iranian FCT, which lacks comprehensive data on raw foods and beverages. However, traditional Iranian foods, such as Kashk, were referenced from the Iranian FCT. The dietary intake of amino acids was assessed utilizing the USDA National Nutrient Database for Standard Reference (Release 2019) [30]. This comprehensive database is founded on the chemical analysis of the amino acid composition of over 5,000 food items across a variety of food groups. Each food item listed in the FFQ was assigned values for 18 individual amino acids. The intake of amino acids was calculated by multiplying the frequency of consumption of each food item by its respective amino acid content. Furthermore, we organized the amino acids into six categories based on their chemical structures: branched-chain, aromatic, alkaline, sulfuric, acidic, and alcoholic amino acids. Assessment of MAFLD MAFLD was identified based on the presence of hepatic steatosis, which was assessed following an 8 to 12-hour fasting period. This assessment was conducted using high-resolution B-mode ultrasonography, performed by a trained radiologist. The examination was carried out with a Samsung Medison SonoAce R3 ultrasound machine, utilizing a 7.5–10 MHz linear transducer. In line with the international expert consensus statement, participants were classified as meeting the criteria for MAFLD if they demonstrated hepatic steatosis in conjunction with a BMI for aze Z-score≥1, as established by the World Health Organization growth standards [3]. Statistical analysis Descriptive statistics were used to summarize participants' demographic and clinical characteristics, stratified by MAFLD status. Data normality was assessed using histogram plots and the Kolmogorov–Smirnov test. Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range), while categorical variables were presented as percentages. Group comparisons were performed using independent t-tests, Mann–Whitney U tests, or Chi-square tests, as appropriate. To investigate patterns of amino acid intake, a principal component analysis (PCA) was performed on data from 18 specific amino acids: histidine, arginine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, alanine, aspartic acid, cysteine, glutamic acid, glycine, proline, serine, and tyrosine. The analysis, guided by eigenvalues exceeding 1, the scree plot, and the interpretability of the factors, revealed three distinct patterns. Amino acids with an absolute component loading of 0.3 or above were selected for pattern description; however, it is important to note that all amino acids contributed to the computation of the pattern scores. The Kaiser-Meyer-Olkin (KMO) statistic, which assesses the adequacy of the sample size for factor analysis, registered at 0.76. This result indicates a satisfactory level of appropriateness for conducting the analysis. Bartlett’s test of sphericity was utilized to evaluate the suitability of the correlation matrix for factor analysis, yielding a P value of less than 0.01. Participants’ factor scores were calculated by summing the products of their standardized amino acid intakes and the corresponding factor loadings for each pattern. The amino acid pattern scores were subsequently treated as categorical variables represented in quartiles within the models. Logistic regression models were employed to assess associations between dietary amino acids intake and the odds of MAFLD, adjusting for confounders in three models: (1) unadjusted, (2) adjusted for age and sex, and (3) further adjusted for BMI-for-age Z-score, puberty status, triglycerides, HOMA-IR, physical activity, dietary energy intake, fiber intake, and saturated fat intake. Statistical analyses were conducted using SPSS software (version 26; SPSS Inc., Chicago, IL), with p-values <0.05 considered statistically significant. Results Of the initial 548 overweight and obese children and adolescents enrolled in the study, 31 participants were excluded due to incomplete data related to anthropometric measurements, dietary information, biochemical analyses, or ultrasound results. Additionally, 12 participants were removed for providing implausible dietary reports, characterized by either over-reporting or under-reporting their food intake. As a result, the final analytical sample comprised 505 participants. The demographic characteristics of the participants, including those with and without MAFLD, are summarized in Table 1 . The prevalence of MAFLD in the study population was 38.8%, corresponding to 196 individuals. Among the study population, 52.9% were male, and 23.4% were prepubertal. The mean age and BMI for age Z-score for the study population were 10.0 ± 2.3 years and 2.87±0.98, respectively. As shown in Table 1, individuals with MAFLD had higher values for age, height, weight, WC, BMI, BMI for age Z-score, insulin, HOMA-IR, TG, liver enzymes, and lower values for HDL-C compared to healthy individuals. No significant differences were observed between the two groups in terms of gender, pubertal status, physical activity, exposure to secondhand smoke, FBS, TC, LDL-C, SBP, and DBP. In terms of dietary intake, there were no significant differences between the MAFLD and healthy groups for total energy intake, total carbohydrates, total fats, and total protein (expressed as a percentage of total energy). However, individuals with MAFLD consumed more animal-based proteins and less plant-based proteins compared to the healthy group. Supplementary Table 1 displays the intake of amino acids, including the daily intake in grams, percentage of total protein intake, percentage of total energy intake, and the sources of amino acids (animal or plant-based). Glutamic acid had the highest intake at 22.04 grams per day, while tryptophan had the lowest intake at 1.18 grams per day. Glutamic acid alone accounted for 21% of the total amino acid intake, followed by aspartic acid, leucine, proline, and lysine. The smallest contributions were made by tryptophan, cysteine, methionine, and histidine. The major sources for most amino acids, such as lysine, methionine, isoleucine, valine, leucine, threonine, histidine, alanine, serine, proline, and phenylalanine, were animal-based proteins, while cysteine, arginine, tryptophan, glycine, aspartic acid, and glutamic acid predominantly came from plant-based sources. Compared to those with MAFMD, healthy individuals had higher intakes of aspartic acid, arginine, and proline, while methionine intake was lower in the healthy group. No significant differences were observed for other amino acids between the two groups. Three primary dietary amino acid patterns were identified through factor analysis, utilizing eigenvalues greater than one, as indicated by the scree plot ( Figure 1) . The factor loadings for the amino acids in the extracted patterns are shown in Table 2 . Three patterns were extracted, explaining 78.8% of the total variance in dietary amino acids. The first pattern (eigenvalue = 7.55) had high factor loadings for isoleucine, lysine, tyrosine, leucine, valine, threonine, methionine, histidine, alanine, and aspartic acid. The second pattern (eigenvalue = 4.89) was characterized by higher loadings for proline, serine, glutamic acid, and phenylalanine. The third pattern (eigenvalue = 1.76) was more loadings for tryptophan, arginine, glycine, and cysteine. The correlations between the amino acid intake patterns and food groups and micronutrients are presented in Supplementary Table 2 . The first pattern showed significant positive correlations with food groups from animal sources, including dairy, red meat, fish, poultry, and eggs, as well as with total protein, animal-based protein, total fat, saturated fatty acids, monounsaturated fatty acids, calcium, and zinc. Conversely, it showed negative correlations with plant-based food groups, plant-based protein, sodium, and fiber. The second pattern was positively correlated with animal-based food groups, processed red meat, grains, plant-based protein, sodium, calcium, zinc, magnesium, and fiber, while negatively correlated with red meat, nuts, fruits and vegetables, animal-based protein, total fat, monounsaturated fatty acids, and polyunsaturated fatty acids. The third pattern showed positive correlations with red meat, fish, poultry, eggs, plant-based foods, grains, legumes, nuts, total protein, plant-based protein, sodium, zinc, and magnesium, and negative correlations with animal-based foods, dairy, fruits and vegetables, saturated fatty acids, polyunsaturated fatty acids, and calcium. The demographic characteristics of participants across quartiles of amino acid patterns are presented in Supplementary Table 3 . As the first amino acid pattern score increased, significant reductions in age, WC, and ALT (P < 0.05) were observed. No significant differences were found across quartiles for the second pattern. For the third pattern, triglycerides increased, and HDL-C decreased as the quartile score increased (P < 0.05). The dietary intakes across quartiles of the amino acid patterns are shown in Supplementary Table 4 . With increasing quartiles of the first amino acid pattern, total protein, animal-based protein, total fat, saturated fatty acids, monounsaturated fatty acids, calcium, and zinc intake increased (P < 0.05), while total energy intake, plant-based protein, polyunsaturated fatty acids, total carbohydrates, sodium, and fiber intake decreased (P < 0.05). For the second pattern, animal-based protein, carbohydrates, sodium, calcium, zinc, magnesium, and fiber intake increased (P < 0.05), while plant-based protein, total fat, and polyunsaturated fatty acids decreased (P < 0.05). The third pattern showed increases in total protein, plant-based protein, sodium, zinc, and magnesium (P < 0.05), while animal-based protein, saturated fatty acids, carbohydrates, and calcium intake decreased (P < 0.05). Results from logistic regression analysis for the association between amino acid intake and the odds of developing MAFLD are presented in Supplementary Table 5 and Figure 2 . After adjusting for confounding factors, the odds of MAFLD were 2.17 times higher in the highest quartile of glutamic acid intake compared to the lowest quartile (OR=2.17, 95%CI:1.08-4.36; P-trend=0.05). Moreover, after adjusting for potential confounders, the odds of MAFLD were 71% lower for individuals in the highest quartile of aspartic acid intake (OR=0.29, 95%CI:0.14-0.57; P-trend=0.01) and 61% lower for those in the highest quartile of arginine intake (OR=0.39, 95%CI: 0.20-0.76; P-trend=0.03). The association between dietary amino acid groups and the odds of MAFLD is presented in Supplementary Table 6 and Figure 2 . After adjusting for confounding variables, individuals in the highest SAAs intake had 96% higher odds of developing MAFLD than those in the lowest quartile (OR=1.96, 95%CI:1.01-3.77; P-trend=0.01). Additionally, after adjusting for confounders, individuals in the highest quartile of alkaline amino acid intake had 59% lower odds of MAFLD compared to those in the lowest quartile (OR=0.41, 95%CI:0.19-0.88; P-trend=0.90). The odds of developing MAFLD across quartiles of the amino acid pattern scores are provided in Table 3 . After adjusting for age and gender, individuals in the highest quartile of pattern 1 had a 69% higher risk of developing MAFLD compared to those in the lowest quartile (OR=1.69, 95%CI:1.01-2.85; P-trend=0.04). After further adjustment for confounding variables, individuals in the highest quartile of pattern 1 had 2.14 times higher odds of MAFLD compared to the lowest quartile (OR=2.14, 95%CI:0.97-4.77; P-trend=0.03), though this association was not statistically significant. Additionally, after adjusting for confounders, individuals in the highest quartile of pattern 2 had 51% higher odds of MAFLD compared to those in the lowest quartile (OR=1.51, 95%CI:0.81-2.80), but this was not statistically significant. Conversely, after adjusting for confounders, individuals in the highest quartile of pattern 3 had 38% lower odds of MAFLD compared to those in the lowest quartile (OR=0.62; 95%CI:0.33-1.53), though this association was also not statistically significant. Discussion This study is the first to comprehensively investigate the association between dietary amino acid patterns and the odds of developing MAFLD among overweight and obese children and adolescents. The study's findings provide novel insights into how specific amino acid patterns might influence the odds of MAFLD, underscoring the complex interplay between dietary intake, metabolic pathways, and liver health. We identified three distinct dietary amino acid patterns associated with the likelihood of developing MAFLD. The first dietary amino acid pattern, which exhibited a high loading of BCAAs, methionine, tyrosine, lysine, threonine, histidine, alanine, and aspartic acid, showed a significant association with an increased odds of MAFLD. This pattern was notably correlated with higher intakes of animal-based protein sources, including dairy, red meat, poultry, fish, and eggs. Additionally, it was positively correlated with total fat intake, including saturated fatty acids and monounsaturated fatty acids, as well as with key micronutrients such as calcium and zinc. Conversely, the first dietary amino acid pattern exhibited negative correlations with plant-based foods and nutrients, such as plant proteins, sodium, and dietary fiber. In the final model, after adjusting for all possible confounding variables, increasing adherence to this dietary pattern was associated with a 2.14-fold increase in the odds of developing MAFLD. In addition, with the increase in the quartile of adherence to the first amino acid dietary pattern, the P for trend values were significant, which indicated that the higher the adherence to the first amino acid dietary pattern, the higher the chance of developing MAFLD. The significance of these findings becomes apparent when considering the metabolic effects of BCAAs, methionine, and tyrosine, which have been implicated in pathways that promote hepatic fat accumulation, insulin resistance, and dysregulated lipid metabolism [ 31 – 33 ]. Furthermore, the positive correlation with saturated fats and animal proteins suggests that a diet rich in these components may exacerbate the risk of MAFLD through synergistic mechanisms, such as enhanced fatty acid synthesis and impaired insulin signalling [ 34 , 35 ]. Figure 3 further elucidates the potential mechanisms underlying this dietary amino acid pattern’s association with MAFLD. As depicted in Fig. 3 , this pattern is linked to mTORC1 activation, a central regulator of cell growth and metabolism [ 36 ]. mTORC1 plays a crucial role in nutrient sensing, and its activation by BCAAs, particularly leucine, has been shown to disrupt insulin signalling and glucose metabolism [ 31 ]. The result is a cascade of metabolic disturbances, including hypertriglyceridemia and insulin resistance, which contribute to the development of MAFLD [ 37 ]. Moreover, the intake of animal proteins, a hallmark of this pattern, has been associated with promoting inflammatory responses and oxidative stress in the liver, leading to an exacerbation of hepatic fat accumulation and liver injury [ 38 ]. Thus, the combination of BCAAs, methionine, and tyrosine, along with a diet high in animal-based proteins and saturated fats, appears to create a metabolic environment conducive to the development of MAFLD. In this regard, a prospective study within the Tehran Lipid and Glucose Study framework examined the dietary intake of amino acid patterns and the incidence of dysglycemia [ 19 ]. Consistent with the amino acid dietary pattern of our study, one of the dietary patterns extracted in the study by Mirmiran et al. included valine, leucine, isoleucine, tyrosine, methionine, aspartic acid, threonine, alanine, histidine, and serine, which, like our first pattern, was positively correlated with animal sources. The results of this study showed that increased dietary intake of the aforementioned amino acid pattern was associated with a nonsignificant increase in the odds of dysglycemia in adults [ 19 ]. The second amino acid dietary pattern, characterized by higher loads of glutamic acid, proline, serine, and phenylalanine, exhibited mixed associations with MAFLD. While this pattern was positively correlated with food sources rich in animal proteins (such as dairy and processed red meats) and plant-based proteins, as well as with sodium, calcium, zinc, magnesium, and fiber, it was negatively correlated with the intake of fruits, vegetables, and nuts. Interestingly, glutamic acid, a key component of this pattern, strongly correlates with MAFLD odds, supporting previous studies that suggest its role in promoting liver fat accumulation and metabolic dysfunction [ 39 , 40 ]. However, despite these associations, the second pattern did not significantly correlate with an increased likelihood of MAFLD after adjusting for potential confounders, possibly due to the counteracting effects of certain protective dietary components, such as plant-based proteins and fiber. The mixed metabolic effects of this pattern highlight the complexity of nutrient interactions and suggest that the detrimental effects of specific amino acids may be mitigated by the intake of other beneficial nutrients. This is particularly relevant when considering the correlations with processed meats and sodium, which may contribute to inflammation and oxidative stress, further exacerbating liver damage [ 41 ]. In this regard, in the study by Mirmiran et al., increased dietary intake of an extracted amino acid pattern with a higher load of proline and glutamic acid was associated with a significant 24% increase in the incidence of dysglycemia [ 19 ]. Further research involving diverse age groups is essential to conduct a more comprehensive examination of this issue. The third amino acid pattern, enriched with tryptophan, arginine, glycine, and cysteine, was associated with a decreased likelihood of developing MAFLD, although this association did not reach statistical significance in the adjusted models. This pattern was positively correlated with red meat, poultry, fish, and eggs, but it also showed a strong correlation with plant-based foods, including grains, legumes, nuts, and plant proteins. In particular, the intake of arginine, a key amino acid in this pattern, was inversely associated with MAFLD risk, aligning with previous research suggesting that arginine may have hepatoprotective effects [ 42 ]. Arginine has been shown to promote nitric oxide production, which helps regulate vascular tone and insulin sensitivity [ 43 ]. Furthermore, the positive association with plant-based foods and the negative correlation with saturated fatty acids may confer additional metabolic benefits, supporting the hypothesis that diets rich in plant-based proteins, fiber, and antioxidants may help prevent liver fat accumulation and reduce the risk of insulin resistance. Despite these potential protective effects, the association between the third pattern and MAFLD was not statistically significant in our study, possibly due to the relatively small effect size or the complex interplay between dietary components. The proposed mechanisms in Fig. 3 offer additional insight into the potential benefits of the third dietary amino acid pattern. This pattern is associated with several metabolic pathways that promote overall health and may help reduce the risk of MAFLD. Specifically, the amino acids in this pattern are linked to enhanced dopamine synthesis and appetite regulation, which could lead to improved energy balance and reduced fat accumulation [ 44 ]. Furthermore, cysteine and glycine, two amino acids in this pattern, are critical precursors for glutathione synthesis, an important antioxidant that helps protect the liver from oxidative damage [ 45 ]. The protective effects of this pattern may also be attributed to its anti-inflammatory properties, which are mediated by the amino acids that regulate immune function and reduce hepatic inflammation. However, this association's lack of statistical significance suggests that additional factors, such as genetic predisposition, environmental influences, or other dietary components, may be at play. The third extracted amino acid pattern showed a higher load of tryptophan, arginine, glycine, and cysteine. In addition, this pattern was positively correlated with red meat (weak correlation), poultry, fish, eggs, whole foods from plant sources, grains, legumes, nuts, and plant-based proteins, and negatively correlated with whole foods from animal sources, dairy, and saturated fatty acids. In separate analyses of amino acids in the present study, arginine was significantly associated with reduced odds of MAFLD. It is possible that all of the above factors work synergistically to reduce the odds of developing MAFLD, although this association was not significant in our study. Our findings highlight the importance of considering dietary amino acid patterns, rather than isolated amino acids, in understanding the complex relationship between diet and MAFLD. The interactions between various amino acids and other nutrients may have synergistic or antagonistic effects on liver health, and evaluating these interactions using a holistic approach, such as factor analysis, provides a more comprehensive understanding of their role in disease pathogenesis. Furthermore, the findings suggest that dietary interventions aimed at modifying amino acid intake patterns, particularly by reducing BCAAs and increasing plant-based proteins, could be an effective strategy in preventing or managing MAFLD in overweight and obese children and adolescents. Strengths and limitations This study possesses several notable strengths that enhance its scientific rigor and relevance. To the best of our knowledge, it is the first to examine the association between dietary amino acid patterns and the odds of MAFLD in the pediatric population, providing new insights into the complex relationship between diet and liver health. Using validated and reliable questionnaires to assess dietary intake and physical activity ensures the robustness of the data. The presence of mothers during face-to-face interviews likely improved the accuracy of dietary recall and intake quantification among child participants. Additionally, all dietary and anthropometric assessments were conducted by trained pediatric dietitians, minimizing the likelihood of data collection errors and increasing the precision of measurements. However, certain limitations should be acknowledged. The study's cross-sectional design precludes causal inferences, limiting our ability to establish a direct relationship between dietary amino acid intake and the development of MAFLD. While the study utilized a validated FFQ to estimate dietary intake, the potential for measurement errors, such as recall bias or misreporting, remains. Furthermore, despite adjustments for several confounders, the potential for residual confounding due to unmeasured or unidentified factors cannot be entirely excluded, which may influence the interpretation of the results. Additionally, the lack of data on serum amino acid concentrations restricts the ability to directly assess the correlation between dietary intake and amino acid levels in the body. Finally, as the study focused on overweight and obese children and adolescents, the findings may not be directly generalizable to the normal-weight pediatric population. Future research should address these limitations by incorporating longitudinal designs, serum biomarkers, and a broader range of potential confounders to further explore the causal mechanisms behind these associations. Conclusions In conclusion, this study provides novel insights into the role of dietary amino acid patterns in the development of MAFLD. The results suggest that specific amino acid patterns, especially those enriched in BCAAs and sulfur-containing amino acids, may increase the risk of MAFLD, while patterns enriched in plant-based proteins, fiber, and amino acids like arginine may offer protective benefits. Further longitudinal studies are needed to validate these findings and elucidate the underlying mechanisms. The integration of dietary amino acid pattern analysis into clinical practice could provide valuable tools for the early detection and prevention of MAFLD in pediatric populations. Declarations Acknowledgments The authors express their appreciation to the participants of the study for their enthusiastic support and to the staff of the involved hospitals for their valuable help. This study is taken from the Obesity registry program in children at Tehran University of Medical Sciences (IR.TUMS.CHMC. REC.1401.016). We are thankful to Dr. Mohammad Hassan Sohouli and Dr. Afshin Ostovar, head of the obesity registry at Tehran University of Medical Sciences. Author contributions Overall, GA, PR, and MR, supervised the project and approved the final version of the manuscript to be submitted. GA designed the research; PD assessed the non-alcoholic fatty liver disease; PM, ZST, and DB gathered data; AN and MHS analyzed and interpreted the data; AN drafted the initial manuscript; and GA critically revised the manuscript. All authors approved the final version of the manuscript submitted for publication. Funding No financial support was provided in any way for this research. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate National nutrition and Food Technology Research Institute (NNFTRI) ethics committee approved the study protocol (IR.SBMU.NNFTRI.REC.1402.015). All participants provided written informed consent and were informed about the study. All procedures performed in studies involving human participants adhered to the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. References Liu J, Mu C, Li K, Luo H, Liu Y, Li Z: Estimating global prevalence of metabolic dysfunction-associated fatty liver disease in overweight or obese children and adolescents: systematic review and meta-analysis. International journal of public health 2021, 66: 1604371. 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British Journal of Nutrition 2020, 124: 1-13. Al-Dalaen S, Alzyoud J, Al-Qtaitat A: The effects of L-arginine in modulating liver antioxidant biomarkers within carbon tetrachloride induced hepatotoxicity: experimental study in rats. Biomedical and Pharmacology Journal 2016, 9: 293-298. Piatti P, Monti LD, Valsecchi G, Magni F, Setola E, Marchesi F, Galli-Kienle M, Pozza G, Alberti KGM: Long-term oral L-arginine administration improves peripheral and hepatic insulin sensitivity in type 2 diabetic patients. Diabetes care 2001, 24: 875-880. Miller GD: Appetite regulation: hormones, peptides, and neurotransmitters and their role in obesity. American journal of lifestyle medicine 2019, 13: 586-601. Vairetti M, Di Pasqua LG, Cagna M, Richelmi P, Ferrigno A, Berardo C: Changes in glutathione content in liver diseases: an update. Antioxidants 2021, 10: 364. Tables Table 1. Characteristics of the study participants according to metabolic dysfunction-associated fatty liver disease status. Total sample (N=505) Without MAFLD (N=309) MAFLD (N=196) P-value Demographic data Age (years) 10.0 ± 2.3 9.7 ± 2.1 10.5 ± 2.5 <0.01 Gender (Boys, %) 52.9 52.1 54.1 0.66 Puberty (Prepubertal, %) 23.4 23.2 23.5 0.96 Weight (Kg) 49.6 ± 15.0 46.2 ± 12.3 55.1 ± 17.2 <0.01 Height (cm) 142.2 ± 12.3 140.0 ± 11.4 145.5 ± 12.9 <0.01 Body mass index (Kg/M 2 ) 24.0 ± 3.8 23.1 ± 3.1 24.4 ± 4.4 <0.01 BMI for age z-score 2.87 ± 0.98 2.72 ± 0.80 2.99 ± 0.72 0.02 Waist circumference (cm) 83.5 ± 10.6 80.9 ± 9.4 87.6 ± 10.9 <0.01 Passive smoker (%) 23.8 30.4 23.9 0.13 Physical activity (MET/hour/week) 8.6 (3.0-20.4) 8.9 (2.6-20.4) 7.5 (3.7-20.4) 0.89 Systolic blood pressure (mmHg) 105.0 (97.5-116.0) 105.0 (95.0-115.0) 105.0 (100.0-120.0) 0.26 Diastolic blood pressure (mmHg) 65.0 (60.0-75.0) 65.0 (60.0-75.0) 65.0 (60.0-70.0) 0.84 Biochemical data Fasting serum insulin (mU/mL) 16.4 ± 8.6 15.5 ± 7.9 17.8 ± 9.6 0.01 Fasting blood sugar (mg/dl) 91.1 ± 8.7 90.6 ± 8.9 91.9 ± 8.5 0.11 HOMA-IR 3.73 ± 2.16 3.5 ± 1.9 4.1 ± 2.3 0.01 Triglyceride (mg/dl) 109.5 (81.0-151.5) 110.0 (83.0-152.0) 168.0 (91.0-122.0) 0.01 Cholesterol (mg/dl) 171.0 ± 55.9 172.3 ± 66.8 169.2 ± 31.8 0.54 HDL (mg/dl) 47.1 ± 11.7 49.0 ± 11.6 44.0 ± 11.3 <0.01 LDL-C (mg/dl) 98.0 ± 25.5 97.3 ± 24.4 99.1 ± 27.2 0.43 Alanine aminotransferase (U/L) 16.0 (11.0-22.0) 15.0 (11.0-19.0) 18.0 (13.0-32.0) 0.01 Aspartate amino transferase (U/L) 23.0 (17.0-29.0) 22.0 (16.0-28.0) 25.0 (17.0-32.0) 0.01 Gamma-glutamyl transferase (U/L) 17.0 (15.0-21.0) 16.9 (14.0-19.0) 19.0 (16.0-24.0) 0.01 Dietary intake Energy (Kcal/day) 3046.3 ± 956.3 3069.9 ± 998.5 3009.1 ± 887.0 0.48 Carbohydrate (% of energy) 56.0 ± 6.2 56.1 ± 5.8 56.0 ± 3.7 0.89 Fat (% of energy) 31.1 ± 5.8 31.0 ± 5.5 31.1 ± 3.6 0.76 Total protein (% of energy) 13.4 ± 2.2 13.3 ± 2.1 13.6 ± 2.3 0.10 Animal protein (% of energy) 7.0 ± 2.5 6.7 ± 2.4 7.5 ± 2.6 0.01 Plant protein (% of energy) 6.4 ± 1.4 6.5 ± 1.5 6.2 ± 1.3 0.04 Significant p-values are highlighted in bold. Abbreviations: HOMA-IR, Homeostatic Model Assessment for Insulin Resistance; HDL, high-density lipoprotein; LDL, Low-density lipoprotein. Table 2. Factor loading of amino acids in patterns extracted by factor analysis in participating individuals. Pattern 1 Pattern 2 Pattern 3 Isoleucine 0.925 - - Lysine 0.920 - - Tyrosine 0.886 - - Leucine 0.875 - - Valine 0.870 - - Threonine 0.869 - - Methionine 0.862 - - Histidine 0.604 - - Alanine 0.373 - - Aspartic acid 0.346 - - Proline - 0.917 - Serine - 0.737 - Glutamic acid - 0.735 - Phenylalanine - 0.716 - Tryptophan - - 0.571 Arginine - - 0.540 Glycine - - 0.510 Cysteine - - 0.450 Percentage of explained variance 41.9 27.1 9.8 * Values less than 0.3 were removed for better interpretation of the data. † KMO (Kaiser-Meyer-Olkin) test = 0.76, Bartlett’s test of sphericity = <0.001 Table 3. Multi-variable adjusted odds ratio (95% CI) of metabolic dysfunction-associated fatty liver disease across the amino acid patterns score quartile. Quartile of amino acid pattern score P for Trend * Quartile 1 Quartile 2 Quartile 3 Quartile 4 Pattern 1 Number of cases/total population 45/126 46/126 48/126 57/127 Crude model 1.00 (Ref) 1.03 (0.61-1.73) 1.10 (0.66-1.84) 1.50 (0.90-2.50) 0.11 Model 1 a 1.00 (Ref) 1.06 (0.62-1.79 1.26 (0.74-2.13) 1.69 (1.01-2.85) 0.04 Model 2 b 1.00 (Ref) 0.92 (0.49-1.75) 1.64 (0.81-3.31) 2.14 (0.97-4.77) 0.03 Pattern 2 Number of cases/total population 44/127 42/126 54/126 56/126 Crude model 1.00 (Ref) 0.95 (0.56-1.60) 1.43 (0.86-2.38) 1.50 (0.90-2.50) 0.05 Model 1 1.00 (Ref) 1.04 (0.61-1.77) 1.63 (0.96-2.74) 1.57 (0.93-2.63) 0.03 Model 2 1.00 (Ref) 1.13 (0.61-2.09) 2.01 (1.10-3.65) 1.51 (0.81-2.80) 0.06 Pattern 3 Number of cases/total population 53/126 48/126 48/127 47/126 Crude model 1.00 (Ref) 0.84 (0.51-1.40) 0.82 (0.49-1.36) 0.48 (0.33-1.34) 0.41 Model 1 1.00 (Ref) 0.84 (0.50-1.40) 0.76 (0.45-1.27) 0.76 (0.45-1.28) 0.27 Model 2 1.00 (Ref) 0.71 (0.40-1.26) 0.69 (0.38-1.24) 0.62 (0.33-1.53) 0.13 Obtained by Logistic regression analysis. * P-trend was obtained using a quartile of dietary exposure as an ordinal variable in the model. Significant p-values are highlighted in bold. a Model 1 adjusted for age and sex. B Model 2 additionally adjusted for body mass index z-score, pubertal status, triglycerides, Homeostatic Model Assessment for Insulin Resistance, physical activity, energy intake, percentage of total protein intake, fiber intake (grams per 1000 kcal), and saturated fat (percentage of total energy). Additional Declarations No competing interests reported. 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10:42:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":227712,"visible":true,"origin":"","legend":"\u003cp\u003eThe odds ratio (95% CI) for the association between amino acids intake and the odds of developing MAFLD.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6687463/v1/924f2516a9da315d76ee9431.png"},{"id":83605909,"identity":"aad44dd5-2c31-4f56-a2be-4afb102f16ad","added_by":"auto","created_at":"2025-05-29 10:42:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":706076,"visible":true,"origin":"","legend":"\u003cp\u003eMechanistic pathways of dietary amino acids role in developing metabolic dysfunction-associated fatty liver 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MAFLD is characterized by excessive hepatic fat accumulation in the presence of metabolic disturbances, and it is strongly correlated with an elevated risk of cardiometabolic comorbidities, including insulin resistance, dyslipidemia, and type 2 diabetes mellitus (T2DM) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Due to its progressive nature, MAFLD can lead to severe liver complications, including fibrosis, cirrhosis, and hepatocellular carcinoma, underscoring its potential to cause significant long-term morbidity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite its growing burden, there is currently no pharmacological treatment specifically approved for MAFLD, emphasizing the critical importance of lifestyle modifications, particularly adopting healthy dietary patterns and increased physical activity, in managing and preventing the disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, early detection and preventive strategies are paramount to curbing the long-term health consequences of MAFLD.\u003c/p\u003e \u003cp\u003eExtensive research has examined the relationship between dietary factors, particularly the quality and quantity of carbohydrates and fats, and the risk of hepatic steatosis and its associated metabolic disturbances [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. More recent studies have begun to explore the role of dietary protein and amino acids in hepatic fat accumulation, with growing evidence suggesting that these macronutrients may influence not only liver function but also broader metabolic pathways [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this framework, the emerging body of literature suggests that certain amino acids, including branched-chain amino acids (BCAAs), sulfur-containing amino acids (SAAs), and aromatic amino acids (AAAs), might be potentially associated with anthropometric indices changes, insulin resistance, T2DM, hepatic steatosis, and nonalcoholic fatty liver diseases (NALFD) in adults [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Interestingly, we found that increased dietary intake of BCAAs, particularly leucine, may have detrimental effects on the development of NAFLD in overweight and obese children and adolescents [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this context, it should be noted that amino acids are not consumed independently but rather interact in complex networks that may produce either synergistic or antagonistic effects on health. Such interactions emphasize the need for a holistic approach to dietary analysis rather than focusing on the effects of individual amino acids in isolation. In recent years, factor analysis techniques have gained prominence in nutritional research as an effective tool for identifying dietary patterns that are associated with the risk of chronic diseases [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This method enables researchers to gain a more integrated understanding of how dietary components, including amino acids, interact and contribute to disease outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this context, several studies have investigated the association between dietary amino acid patterns derived from factor analysis and the risk of developing hypertension [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], dyslipidemia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and dysglycemia [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] in adults. As a result, it seems that dietary amino acid patterns play an essential role in maintaining and promoting optimal health [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite this growing body of work, there remains a lack of research exploring the relationship between dietary amino acid patterns and the odds of MAFLD, particularly in pediatric populations. Given the potential role of amino acid metabolism in liver fat accumulation and metabolic dysfunction, there is a clear need for further investigation into how dietary amino acid intake influences the development of MAFLD in overweight and obese children and adolescents. The present study aims to address this gap by examining the association between dietary amino acid intake patterns and the odds of MAFLD in overweight and obese children and adolescents.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003eStudy design and population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study was conducted between September 2023 and July 2024 as part of an obesity registry focusing on Iranian children and adolescents [21]. The sample size was determined based on a previous study that examined the association between dietary BCAAs intake and odds of NAFLD [13]. Using G*Power software, the sample size was calculated with the following parameters: an alpha error probability of 0.05, a statistical power of 80% (1-\u0026beta;), a mean (\u0026mu;) and standard deviation (\u0026sigma;) of 2.35 \u0026plusmn; 0.49 for dietary BCAAs intake, and an effect size of 1.68 [13]. Based on these inputs, a minimum of 505 participants was required.\u003c/p\u003e\n\u003cp\u003eParticipants were randomly selected from individuals attending outpatient clinics specializing in gastroenterology, hepatology, endocrinology, and nutrition at the Children\u0026apos;s Hospital Medical Center in Tehran. Eligible participants were those aged 7 to 18 years who were classified as overweight or obese, defined as having a body mass index-for-age (BMI-for-age) Z-score of 1 or greater, in accordance with World Health Organization (WHO) guidelines [22]. Exclusion criteria included: (1) a history of medical conditions such as renal or liver diseases (e.g., Wilson\u0026rsquo;s disease, autoimmune liver disease, hemochromatosis, viral hepatitis), thyroid disorders, or malignancies; (2) use of hepatotoxic or steatogenic medications (e.g., valproate, amiodarone), weight-loss drugs, or appetite suppressants; (3) recent dietary modifications within the past year due to illness or weight-loss interventions; and (4) incomplete responses on the food frequency questionnaire (FFQ) (fewer than 35 items) or reports of dietary intake deemed implausible. Under- and over-reporting of dietary intake was identified by comparing reported energy consumption with estimated energy requirements, with deviations beyond \u0026plusmn;2 standard deviations leading to exclusion, in accordance with Institute of Medicine guidelines [23].\u003c/p\u003e\n\u003cp\u003eThe study was conducted in compliance with the ethical principles outlined in the Declaration of Helsinki and received approval from the Ethics Committee of the National Nutrition and Food Technology Research Institute (IR.SBMU.NNFTRI.REC.1402.015). Written informed consent was obtained from parents or legal guardians, and assent was secured from the participating children.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometric and Clinical Assessments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll anthropometric measurements were conducted by trained pediatric nutritionists using standardized protocols. Body weight was recorded with a calibrated Seca scale (Seca, Hamburg, Germany), with a precision of 100 grams, while height was measured to the nearest 0.5 cm using a stadiometer. BMI was calculated as weight (kg) divided by height squared (m\u0026sup2;), and BMI-for-age Z-scores were determined based on internationally recognized growth charts [22]. Waist circumference (WC) was measured at the midpoint between the iliac crest and the lowest rib, with a precision of 0.5 cm.\u003c/p\u003e\n\u003cp\u003ePubertal status was assessed by a pediatric endocrinologist following the Marshall and Tanner staging criteria, which categorize participants as prepubertal or pubertal based on breast and genital development stages [24, 25]. Physical activity levels were evaluated using the Persian-adapted Modifiable Activity Questionnaire (MAQ), which estimates weekly metabolic equivalent task (MET) hours. This questionnaire has demonstrated high reliability (97%) and moderate validity (49%) among adolescents [26].\u003c/p\u003e\n\u003cp\u003eBlood pressure was measured manually using a mercury sphygmomanometer with an appropriately sized cuff, following a 15-minute rest period. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were determined using the Korotkoff sound technique, with two readings taken at a one-minute interval, and the average value recorded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiochemical Assessments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood samples were collected between 7:00 and 9:00 AM following an overnight fasting period of 12\u0026ndash;14 hours. Samples were centrifuged within 30\u0026ndash;45 minutes of collection and analyzed on the same day. Fasting blood sugar (FBS) and triglycerides (TG) were measured using enzymatic colorimetric methods. Total cholesterol (TC) and high-density lipoprotein (HDL-C) were determined using cholesterol esterase and phosphotungstic acid methods, respectively. Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald equation [27].\u003c/p\u003e\n\u003cp\u003eLiver function was assessed by measuring aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels via enzymatic photometry, while gamma-glutamyl transferase (GGT) was quantified using enzymatic colorimetric methods. These analyses were conducted using commercially available kits from Delta Darman Inc. (Tehran, Iran), and processed with a Selectra 2 auto-analyzer (Vital Scientific, Spankeren, The Netherlands).\u003c/p\u003e\n\u003cp\u003eFasting serum insulin was quantified using the electrochemiluminescence immunoassay (ECLIA) technique, employing Roche Diagnostics kits and a Roche/Hitachi Cobas e-411 analyzer (Roche Diagnostics, GmbH, Mannheim, Germany). Insulin resistance (IR) was estimated using the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), calculated as fasting\u0026nbsp;glucose\u0026nbsp;(mmol/L)\u0026times;fasting\u0026nbsp;insulin\u0026nbsp;(\u0026mu;U/L))/22.5. The intra-assay and inter-assay coefficients of variation for all biochemical assessments were maintained at \u0026le;5.3%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDietary Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDietary intake was assessed using a validated and reliable 147-item semi-quantitative FFQ [28, 29]. Trained nutritionists conducted structured interviews with participants and their parents/guardians to determine the frequency and portion sizes of food items consumed over the past year. When children were unable to recall their dietary intake, their mothers provided responses on their behalf. Portion sizes were primarily determined based on U.S. Department of Agriculture (USDA) food serving guidelines, such as one slice of bread, one medium apple, or one cup of dairy. For foods not covered in USDA tables, standard household measures (e.g., one tablespoon of beans, one piece of chicken (leg, breast, or wing), or rice servings classified as large, medium, or small) were used and converted into grams and servings. The nutritional composition of food items was derived primarily from USDA Food Composition Tables (FCTs) due to limitations in the Iranian FCT, which lacks comprehensive data on raw foods and beverages. However, traditional Iranian foods, such as Kashk, were referenced from the Iranian FCT.\u003c/p\u003e\n\u003cp\u003eThe dietary intake of amino acids was assessed utilizing the USDA National Nutrient Database for Standard Reference (Release 2019) [30]. This comprehensive database is founded on the chemical analysis of the amino acid composition of over 5,000 food items across a variety of food groups. Each food item listed in the FFQ was assigned values for 18 individual amino acids. The intake of amino acids was calculated by multiplying the frequency of consumption of each food item by its respective amino acid content. Furthermore, we organized the amino acids into six categories based on their chemical structures: branched-chain, aromatic, alkaline, sulfuric, acidic, and alcoholic amino acids.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of MAFLD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMAFLD was identified based on the presence of hepatic steatosis, which was assessed following an 8 to 12-hour fasting period. This assessment was conducted using high-resolution B-mode ultrasonography, performed by a trained radiologist. The examination was carried out with a Samsung Medison SonoAce R3 ultrasound machine, utilizing a 7.5\u0026ndash;10 MHz linear transducer. In line with the international expert consensus statement, participants were classified as meeting the criteria for MAFLD if they demonstrated hepatic steatosis in conjunction with a BMI for aze Z-score\u0026ge;1, as established by the World Health Organization growth standards [3].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to summarize participants\u0026apos; demographic and clinical characteristics, stratified by MAFLD status. Data normality was assessed using histogram plots and the Kolmogorov\u0026ndash;Smirnov test. Continuous variables were expressed as mean \u0026plusmn; standard deviation (SD) or median (interquartile range), while categorical variables were presented as percentages. Group comparisons were performed using independent t-tests, Mann\u0026ndash;Whitney U tests, or Chi-square tests, as appropriate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo investigate patterns of amino acid intake, a principal component analysis (PCA) was performed on data from 18 specific amino acids: histidine, arginine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, alanine, aspartic acid, cysteine, glutamic acid, glycine, proline, serine, and tyrosine. The analysis, guided by eigenvalues exceeding 1, the scree plot, and the interpretability of the factors, revealed three distinct patterns. Amino acids with an absolute component loading of 0.3 or above were selected for pattern description; however, it is important to note that all amino acids contributed to the computation of the pattern scores.\u003c/p\u003e\n\u003cp\u003eThe Kaiser-Meyer-Olkin (KMO) statistic, which assesses the adequacy of the sample size for factor analysis, registered at 0.76. This result indicates a satisfactory level of appropriateness for conducting the analysis. Bartlett\u0026rsquo;s test of sphericity was utilized to evaluate the suitability of the correlation matrix for factor analysis, yielding a P value of less than 0.01. Participants\u0026rsquo; factor scores were calculated by summing the products of their standardized amino acid intakes and the corresponding factor loadings for each pattern. The amino acid pattern scores were subsequently treated as categorical variables represented in quartiles within the models.\u003c/p\u003e\n\u003cp\u003eLogistic regression models were employed to assess associations between dietary amino acids intake and the odds of MAFLD, adjusting for confounders in three models: (1) unadjusted, (2) adjusted for age and sex, and (3) further adjusted for BMI-for-age Z-score, puberty status, triglycerides, HOMA-IR, physical activity, dietary energy intake, fiber intake, and saturated fat intake. Statistical analyses were conducted using SPSS software (version 26; SPSS Inc., Chicago, IL), with p-values \u0026lt;0.05 considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOf the initial 548 overweight and obese children and adolescents enrolled in the study, 31 participants were excluded due to incomplete data related to anthropometric measurements, dietary information, biochemical analyses, or ultrasound results. Additionally, 12 participants were removed for providing implausible dietary reports, characterized by either over-reporting or under-reporting their food intake. As a result, the final analytical sample comprised 505 participants.\u003c/p\u003e\n\u003cp\u003eThe demographic characteristics of the participants, including those with and without MAFLD, are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e. The prevalence of MAFLD in the study population was 38.8%, corresponding to 196 individuals. Among the study population, 52.9% were male, and 23.4% were prepubertal. The mean age and BMI for age Z-score for the study population were 10.0 \u0026plusmn; 2.3 years and 2.87\u0026plusmn;0.98, respectively. As shown in Table 1, individuals with MAFLD had higher values for age, height, weight, WC, BMI, BMI for age Z-score, insulin, HOMA-IR, TG, liver enzymes, and lower values for HDL-C compared to healthy individuals. No significant differences were observed between the two groups in terms of gender, pubertal status, physical activity, exposure to secondhand smoke, FBS, TC, LDL-C, SBP, and DBP. In terms of dietary intake, there were no significant differences between the MAFLD and healthy groups for total energy intake, total carbohydrates, total fats, and total protein (expressed as a percentage of total energy). However, individuals with MAFLD consumed more animal-based proteins and less plant-based proteins compared to the healthy group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e displays the intake of amino acids, including the daily intake in grams, percentage of total protein intake, percentage of total energy intake, and the sources of amino acids (animal or plant-based). Glutamic acid had the highest intake at 22.04 grams per day, while tryptophan had the lowest intake at 1.18 grams per day. Glutamic acid alone accounted for 21% of the total amino acid intake, followed by aspartic acid, leucine, proline, and lysine. The smallest contributions were made by tryptophan, cysteine, methionine, and histidine. The major sources for most amino acids, such as lysine, methionine, isoleucine, valine, leucine, threonine, histidine, alanine, serine, proline, and phenylalanine, were animal-based proteins, while cysteine, arginine, tryptophan, glycine, aspartic acid, and glutamic acid predominantly came from plant-based sources. Compared to those with MAFMD, healthy individuals had higher intakes of aspartic acid, arginine, and proline, while methionine intake was lower in the healthy group. No significant differences were observed for other amino acids between the two groups.\u003c/p\u003e\n\u003cp\u003eThree primary dietary amino acid patterns were identified through factor analysis, utilizing eigenvalues greater than one, as indicated by the scree plot (\u003cstrong\u003eFigure 1)\u003c/strong\u003e. The factor loadings for the amino acids in the extracted patterns are shown in \u003cstrong\u003eTable 2\u003c/strong\u003e. Three patterns were extracted, explaining 78.8% of the total variance in dietary amino acids. The first pattern (eigenvalue = 7.55) had high factor loadings for isoleucine, lysine, tyrosine, leucine, valine, threonine, methionine, histidine, alanine, and aspartic acid. The second pattern (eigenvalue = 4.89) was characterized by higher loadings for proline, serine, glutamic acid, and phenylalanine. The third pattern (eigenvalue = 1.76) was more loadings for tryptophan, arginine, glycine, and cysteine.\u003c/p\u003e\n\u003cp\u003eThe correlations between the amino acid intake patterns and food groups and micronutrients are presented in \u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e. The first pattern showed significant positive correlations with food groups from animal sources, including dairy, red meat, fish, poultry, and eggs, as well as with total protein, animal-based protein, total fat, saturated fatty acids, monounsaturated fatty acids, calcium, and zinc. Conversely, it showed negative correlations with plant-based food groups, plant-based protein, sodium, and fiber. The second pattern was positively correlated with animal-based food groups, processed red meat, grains, plant-based protein, sodium, calcium, zinc, magnesium, and fiber, while negatively correlated with red meat, nuts, fruits and vegetables, animal-based protein, total fat, monounsaturated fatty acids, and polyunsaturated fatty acids. The third pattern showed positive correlations with red meat, fish, poultry, eggs, plant-based foods, grains, legumes, nuts, total protein, plant-based protein, sodium, zinc, and magnesium, and negative correlations with animal-based foods, dairy, fruits and vegetables, saturated fatty acids, polyunsaturated fatty acids, and calcium.\u003c/p\u003e\n\u003cp\u003eThe demographic characteristics of participants across quartiles of amino acid patterns are presented in \u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e. As the first amino acid pattern score increased, significant reductions in age, WC, and ALT (P \u0026lt; 0.05) were observed. No significant differences were found across quartiles for the second pattern. For the third pattern, triglycerides increased, and HDL-C decreased as the quartile score increased (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eThe dietary intakes across quartiles of the amino acid patterns are shown in \u003cstrong\u003eSupplementary Table 4\u003c/strong\u003e. With increasing quartiles of the first amino acid pattern, total protein, animal-based protein, total fat, saturated fatty acids, monounsaturated fatty acids, calcium, and zinc intake increased (P \u0026lt; 0.05), while total energy intake, plant-based protein, polyunsaturated fatty acids, total carbohydrates, sodium, and fiber intake decreased (P \u0026lt; 0.05). For the second pattern, animal-based protein, carbohydrates, sodium, calcium, zinc, magnesium, and fiber intake increased (P \u0026lt; 0.05), while plant-based protein, total fat, and polyunsaturated fatty acids decreased (P \u0026lt; 0.05). The third pattern showed increases in total protein, plant-based protein, sodium, zinc, and magnesium (P \u0026lt; 0.05), while animal-based protein, saturated fatty acids, carbohydrates, and calcium intake decreased (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eResults from logistic regression analysis for the association between amino acid intake and the odds of developing MAFLD are presented in \u003cstrong\u003eSupplementary Table 5 and Figure 2\u003c/strong\u003e. After adjusting for confounding factors, the odds of MAFLD were 2.17 times higher in the highest quartile of glutamic acid intake compared to the lowest quartile (OR=2.17, 95%CI:1.08-4.36; P-trend=0.05). Moreover, after adjusting for potential confounders, the odds of MAFLD were 71% lower for individuals in the highest quartile of aspartic acid intake (OR=0.29, 95%CI:0.14-0.57; P-trend=0.01) and 61% lower for those in the highest quartile of arginine intake (OR=0.39, 95%CI: 0.20-0.76; P-trend=0.03).\u003c/p\u003e\n\u003cp\u003eThe association between dietary amino acid groups and the odds of MAFLD is presented in \u003cstrong\u003eSupplementary Table 6 and Figure 2\u003c/strong\u003e. After adjusting for confounding variables, individuals in the highest SAAs intake had 96% higher odds of developing MAFLD than those in the lowest quartile (OR=1.96, 95%CI:1.01-3.77; P-trend=0.01). Additionally, after adjusting for confounders, individuals in the highest quartile of alkaline amino acid intake had 59% lower odds of MAFLD compared to those in the lowest quartile (OR=0.41, 95%CI:0.19-0.88; P-trend=0.90).\u003c/p\u003e\n\u003cp\u003eThe odds of developing MAFLD across quartiles of the amino acid pattern scores are provided in \u003cstrong\u003eTable 3\u003c/strong\u003e. After adjusting for age and gender, individuals in the highest quartile of pattern 1 had a 69% higher risk of developing MAFLD compared to those in the lowest quartile (OR=1.69, 95%CI:1.01-2.85; P-trend=0.04). After further adjustment for confounding variables, individuals in the highest quartile of pattern 1 had 2.14 times higher odds of MAFLD compared to the lowest quartile (OR=2.14, 95%CI:0.97-4.77; P-trend=0.03), though this association was not statistically significant. Additionally, after adjusting for confounders, individuals in the highest quartile of pattern 2 had 51% higher odds of MAFLD compared to those in the lowest quartile (OR=1.51, 95%CI:0.81-2.80), but this was not statistically significant. Conversely, after adjusting for confounders, individuals in the highest quartile of pattern 3 had 38% lower odds of MAFLD compared to those in the lowest quartile (OR=0.62; 95%CI:0.33-1.53), though this association was also not statistically significant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to comprehensively investigate the association between dietary amino acid patterns and the odds of developing MAFLD among overweight and obese children and adolescents. The study\u0026apos;s findings provide novel insights into how specific amino acid patterns might influence the odds of MAFLD, underscoring the complex interplay between dietary intake, metabolic pathways, and liver health. We identified three distinct dietary amino acid patterns associated with the likelihood of developing MAFLD.\u003c/p\u003e\n\u003cp\u003eThe first dietary amino acid pattern, which exhibited a high loading of BCAAs, methionine, tyrosine, lysine, threonine, histidine, alanine, and aspartic acid, showed a significant association with an increased odds of MAFLD. This pattern was notably correlated with higher intakes of animal-based protein sources, including dairy, red meat, poultry, fish, and eggs. Additionally, it was positively correlated with total fat intake, including saturated fatty acids and monounsaturated fatty acids, as well as with key micronutrients such as calcium and zinc. Conversely, the first dietary amino acid pattern exhibited negative correlations with plant-based foods and nutrients, such as plant proteins, sodium, and dietary fiber. In the final model, after adjusting for all possible confounding variables, increasing adherence to this dietary pattern was associated with a 2.14-fold increase in the odds of developing MAFLD. In addition, with the increase in the quartile of adherence to the first amino acid dietary pattern, the P for trend values were significant, which indicated that the higher the adherence to the first amino acid dietary pattern, the higher the chance of developing MAFLD. The significance of these findings becomes apparent when considering the metabolic effects of BCAAs, methionine, and tyrosine, which have been implicated in pathways that promote hepatic fat accumulation, insulin resistance, and dysregulated lipid metabolism [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, the positive correlation with saturated fats and animal proteins suggests that a diet rich in these components may exacerbate the risk of MAFLD through synergistic mechanisms, such as enhanced fatty acid synthesis and impaired insulin signalling [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e further elucidates the potential mechanisms underlying this dietary amino acid pattern\u0026rsquo;s association with MAFLD. As depicted in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, this pattern is linked to mTORC1 activation, a central regulator of cell growth and metabolism [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. mTORC1 plays a crucial role in nutrient sensing, and its activation by BCAAs, particularly leucine, has been shown to disrupt insulin signalling and glucose metabolism [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The result is a cascade of metabolic disturbances, including hypertriglyceridemia and insulin resistance, which contribute to the development of MAFLD [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Moreover, the intake of animal proteins, a hallmark of this pattern, has been associated with promoting inflammatory responses and oxidative stress in the liver, leading to an exacerbation of hepatic fat accumulation and liver injury [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. Thus, the combination of BCAAs, methionine, and tyrosine, along with a diet high in animal-based proteins and saturated fats, appears to create a metabolic environment conducive to the development of MAFLD. In this regard, a prospective study within the Tehran Lipid and Glucose Study framework examined the dietary intake of amino acid patterns and the incidence of dysglycemia [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consistent with the amino acid dietary pattern of our study, one of the dietary patterns extracted in the study by Mirmiran et al. included valine, leucine, isoleucine, tyrosine, methionine, aspartic acid, threonine, alanine, histidine, and serine, which, like our first pattern, was positively correlated with animal sources. The results of this study showed that increased dietary intake of the aforementioned amino acid pattern was associated with a nonsignificant increase in the odds of dysglycemia in adults [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe second amino acid dietary pattern, characterized by higher loads of glutamic acid, proline, serine, and phenylalanine, exhibited mixed associations with MAFLD. While this pattern was positively correlated with food sources rich in animal proteins (such as dairy and processed red meats) and plant-based proteins, as well as with sodium, calcium, zinc, magnesium, and fiber, it was negatively correlated with the intake of fruits, vegetables, and nuts. Interestingly, glutamic acid, a key component of this pattern, strongly correlates with MAFLD odds, supporting previous studies that suggest its role in promoting liver fat accumulation and metabolic dysfunction [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, despite these associations, the second pattern did not significantly correlate with an increased likelihood of MAFLD after adjusting for potential confounders, possibly due to the counteracting effects of certain protective dietary components, such as plant-based proteins and fiber. The mixed metabolic effects of this pattern highlight the complexity of nutrient interactions and suggest that the detrimental effects of specific amino acids may be mitigated by the intake of other beneficial nutrients. This is particularly relevant when considering the correlations with processed meats and sodium, which may contribute to inflammation and oxidative stress, further exacerbating liver damage [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. In this regard, in the study by Mirmiran et al., increased dietary intake of an extracted amino acid pattern with a higher load of proline and glutamic acid was associated with a significant 24% increase in the incidence of dysglycemia [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Further research involving diverse age groups is essential to conduct a more comprehensive examination of this issue.\u003c/p\u003e\n\u003cp\u003eThe third amino acid pattern, enriched with tryptophan, arginine, glycine, and cysteine, was associated with a decreased likelihood of developing MAFLD, although this association did not reach statistical significance in the adjusted models. This pattern was positively correlated with red meat, poultry, fish, and eggs, but it also showed a strong correlation with plant-based foods, including grains, legumes, nuts, and plant proteins. In particular, the intake of arginine, a key amino acid in this pattern, was inversely associated with MAFLD risk, aligning with previous research suggesting that arginine may have hepatoprotective effects [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. Arginine has been shown to promote nitric oxide production, which helps regulate vascular tone and insulin sensitivity [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. Furthermore, the positive association with plant-based foods and the negative correlation with saturated fatty acids may confer additional metabolic benefits, supporting the hypothesis that diets rich in plant-based proteins, fiber, and antioxidants may help prevent liver fat accumulation and reduce the risk of insulin resistance. Despite these potential protective effects, the association between the third pattern and MAFLD was not statistically significant in our study, possibly due to the relatively small effect size or the complex interplay between dietary components.\u003c/p\u003e\n\u003cp\u003eThe proposed mechanisms in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e offer additional insight into the potential benefits of the third dietary amino acid pattern. This pattern is associated with several metabolic pathways that promote overall health and may help reduce the risk of MAFLD. Specifically, the amino acids in this pattern are linked to enhanced dopamine synthesis and appetite regulation, which could lead to improved energy balance and reduced fat accumulation [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, cysteine and glycine, two amino acids in this pattern, are critical precursors for glutathione synthesis, an important antioxidant that helps protect the liver from oxidative damage [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. The protective effects of this pattern may also be attributed to its anti-inflammatory properties, which are mediated by the amino acids that regulate immune function and reduce hepatic inflammation. However, this association\u0026apos;s lack of statistical significance suggests that additional factors, such as genetic predisposition, environmental influences, or other dietary components, may be at play.\u003c/p\u003e\n\u003cp\u003eThe third extracted amino acid pattern showed a higher load of tryptophan, arginine, glycine, and cysteine. In addition, this pattern was positively correlated with red meat (weak correlation), poultry, fish, eggs, whole foods from plant sources, grains, legumes, nuts, and plant-based proteins, and negatively correlated with whole foods from animal sources, dairy, and saturated fatty acids. In separate analyses of amino acids in the present study, arginine was significantly associated with reduced odds of MAFLD. It is possible that all of the above factors work synergistically to reduce the odds of developing MAFLD, although this association was not significant in our study.\u003c/p\u003e\n\u003cp\u003eOur findings highlight the importance of considering dietary amino acid patterns, rather than isolated amino acids, in understanding the complex relationship between diet and MAFLD. The interactions between various amino acids and other nutrients may have synergistic or antagonistic effects on liver health, and evaluating these interactions using a holistic approach, such as factor analysis, provides a more comprehensive understanding of their role in disease pathogenesis. Furthermore, the findings suggest that dietary interventions aimed at modifying amino acid intake patterns, particularly by reducing BCAAs and increasing plant-based proteins, could be an effective strategy in preventing or managing MAFLD in overweight and obese children and adolescents.\u003c/p\u003e\n\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\n\u003cp\u003eThis study possesses several notable strengths that enhance its scientific rigor and relevance. To the best of our knowledge, it is the first to examine the association between dietary amino acid patterns and the odds of MAFLD in the pediatric population, providing new insights into the complex relationship between diet and liver health. Using validated and reliable questionnaires to assess dietary intake and physical activity ensures the robustness of the data. The presence of mothers during face-to-face interviews likely improved the accuracy of dietary recall and intake quantification among child participants. Additionally, all dietary and anthropometric assessments were conducted by trained pediatric dietitians, minimizing the likelihood of data collection errors and increasing the precision of measurements. However, certain limitations should be acknowledged. The study\u0026apos;s cross-sectional design precludes causal inferences, limiting our ability to establish a direct relationship between dietary amino acid intake and the development of MAFLD. While the study utilized a validated FFQ to estimate dietary intake, the potential for measurement errors, such as recall bias or misreporting, remains. Furthermore, despite adjustments for several confounders, the potential for residual confounding due to unmeasured or unidentified factors cannot be entirely excluded, which may influence the interpretation of the results. Additionally, the lack of data on serum amino acid concentrations restricts the ability to directly assess the correlation between dietary intake and amino acid levels in the body. Finally, as the study focused on overweight and obese children and adolescents, the findings may not be directly generalizable to the normal-weight pediatric population. Future research should address these limitations by incorporating longitudinal designs, serum biomarkers, and a broader range of potential confounders to further explore the causal mechanisms behind these associations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study provides novel insights into the role of dietary amino acid patterns in the development of MAFLD. The results suggest that specific amino acid patterns, especially those enriched in BCAAs and sulfur-containing amino acids, may increase the risk of MAFLD, while patterns enriched in plant-based proteins, fiber, and amino acids like arginine may offer protective benefits. Further longitudinal studies are needed to validate these findings and elucidate the underlying mechanisms. The integration of dietary amino acid pattern analysis into clinical practice could provide valuable tools for the early detection and prevention of MAFLD in pediatric populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their appreciation to the participants of the study for their enthusiastic support and to the staff of the involved hospitals for their valuable help. This study is taken from the Obesity registry program in children at Tehran University of Medical Sciences (IR.TUMS.CHMC. REC.1401.016). We are thankful to Dr. Mohammad Hassan Sohouli and Dr. Afshin Ostovar, head of the obesity registry at Tehran University of Medical Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, GA, PR, and MR, supervised the project and approved the final version of the manuscript to be submitted. GA designed the research; PD assessed the non-alcoholic fatty liver disease; PM, ZST, and DB gathered data; AN and MHS analyzed and interpreted the data; AN drafted the initial manuscript; and GA critically revised the manuscript. All authors approved the final version of the manuscript submitted for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo financial support was provided in any way for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational nutrition and Food Technology Research Institute (NNFTRI) ethics committee approved the study protocol (IR.SBMU.NNFTRI.REC.1402.015). All participants provided written informed consent and were informed about the study. All procedures performed in studies involving human participants adhered to the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu J, Mu C, Li K, Luo H, Liu Y, Li Z: \u003cstrong\u003eEstimating global prevalence of metabolic dysfunction-associated fatty liver disease in overweight or obese children and adolescents: systematic review and meta-analysis.\u003c/strong\u003e \u003cem\u003eInternational journal of public health \u003c/em\u003e2021, \u003cstrong\u003e66:\u003c/strong\u003e1604371.\u003c/li\u003e\n\u003cli\u003ePipitone RM, Ciccioli C, Infantino G, La Mantia C, Parisi S, Tulone A, Pennisi G, Grimaudo S, Petta S: \u003cstrong\u003eMAFLD: a multisystem disease.\u003c/strong\u003e \u003cem\u003eTherapeutic advances in endocrinology and metabolism \u003c/em\u003e2023, \u003cstrong\u003e14:\u003c/strong\u003e20420188221145549.\u003c/li\u003e\n\u003cli\u003eEslam M, Alkhouri N, Vajro P, Baumann U, Weiss R, Socha P, Marcus C, Lee WS, Kelly D, Porta G: \u003cstrong\u003eDefining paediatric metabolic (dysfunction)-associated fatty liver disease: an international expert consensus statement.\u003c/strong\u003e \u003cem\u003eThe Lancet Gastroenterology \u0026amp; 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review and meta-analysis.\u003c/strong\u003e \u003cem\u003eBritish Journal of Nutrition \u003c/em\u003e2020, \u003cstrong\u003e124:\u003c/strong\u003e1-13.\u003c/li\u003e\n\u003cli\u003eAl-Dalaen S, Alzyoud J, Al-Qtaitat A: \u003cstrong\u003eThe effects of L-arginine in modulating liver antioxidant biomarkers within carbon tetrachloride induced hepatotoxicity: experimental study in rats.\u003c/strong\u003e \u003cem\u003eBiomedical and Pharmacology Journal \u003c/em\u003e2016, \u003cstrong\u003e9:\u003c/strong\u003e293-298.\u003c/li\u003e\n\u003cli\u003ePiatti P, Monti LD, Valsecchi G, Magni F, Setola E, Marchesi F, Galli-Kienle M, Pozza G, Alberti KGM: \u003cstrong\u003eLong-term oral L-arginine administration improves peripheral and hepatic insulin sensitivity in type 2 diabetic patients.\u003c/strong\u003e \u003cem\u003eDiabetes care \u003c/em\u003e2001, \u003cstrong\u003e24:\u003c/strong\u003e875-880.\u003c/li\u003e\n\u003cli\u003eMiller GD: \u003cstrong\u003eAppetite regulation: hormones, peptides, and neurotransmitters and their role in obesity.\u003c/strong\u003e \u003cem\u003eAmerican journal of lifestyle medicine \u003c/em\u003e2019, \u003cstrong\u003e13:\u003c/strong\u003e586-601.\u003c/li\u003e\n\u003cli\u003eVairetti M, Di Pasqua LG, Cagna M, Richelmi P, Ferrigno A, Berardo C: \u003cstrong\u003eChanges in glutathione content in liver diseases: an update.\u003c/strong\u003e \u003cem\u003eAntioxidants \u003c/em\u003e2021, \u003cstrong\u003e10:\u003c/strong\u003e364.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"774\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 774px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Characteristics of the study participants according to metabolic dysfunction-associated fatty liver disease status.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eTotal sample (N=505)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eWithout MAFLD (N=309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMAFLD (N=196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e10.0 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e9.7 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e10.5 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eGender (Boys, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e52.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e52.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e54.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003ePuberty (Prepubertal, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eWeight (Kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e49.6 \u0026plusmn; 15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e46.2 \u0026plusmn; 12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e55.1 \u0026plusmn; 17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e142.2 \u0026plusmn; 12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e140.0 \u0026plusmn; 11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e145.5 \u0026plusmn; 12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eBody mass index (Kg/M\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e24.0 \u0026plusmn; 3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e23.1 \u0026plusmn; 3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e24.4 \u0026plusmn; 4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eBMI for age z-score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e2.87 \u0026plusmn; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e2.72 \u0026plusmn; 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e2.99 \u0026plusmn; 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eWaist circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e83.5 \u0026plusmn; 10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e80.9 \u0026plusmn; 9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e87.6 \u0026plusmn; 10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003ePassive smoker (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e23.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003ePhysical activity (MET/hour/week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e8.6 (3.0-20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e8.9 (2.6-20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e7.5 (3.7-20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e105.0 (97.5-116.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e105.0 (95.0-115.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e105.0 (100.0-120.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e65.0 (60.0-75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e65.0 (60.0-75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e65.0 (60.0-70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiochemical data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eFasting serum insulin (mU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e16.4 \u0026plusmn; 8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e15.5 \u0026plusmn; 7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e17.8 \u0026plusmn; 9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eFasting blood sugar (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e91.1 \u0026plusmn; 8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e90.6 \u0026plusmn; 8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e91.9 \u0026plusmn; 8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e3.73 \u0026plusmn; 2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e4.1 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eTriglyceride (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e109.5 (81.0-151.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e110.0 (83.0-152.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e168.0 (91.0-122.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eCholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e171.0 \u0026plusmn; 55.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e172.3 \u0026plusmn; 66.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e169.2 \u0026plusmn; 31.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eHDL (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e47.1 \u0026plusmn; 11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e49.0 \u0026plusmn; 11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e44.0 \u0026plusmn; 11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eLDL-C (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e98.0 \u0026plusmn; 25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e97.3 \u0026plusmn; 24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e99.1 \u0026plusmn; 27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eAlanine aminotransferase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e16.0 (11.0-22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e15.0 (11.0-19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e18.0 (13.0-32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eAspartate amino transferase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e23.0 (17.0-29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e22.0 (16.0-28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e25.0 (17.0-32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eGamma-glutamyl transferase (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e17.0 (15.0-21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e16.9 (14.0-19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e19.0 (16.0-24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDietary intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eEnergy (Kcal/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e3046.3 \u0026plusmn; 956.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e3069.9 \u0026plusmn; 998.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e3009.1 \u0026plusmn; 887.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eCarbohydrate (% of energy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e56.0 \u0026plusmn; 6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e56.1 \u0026plusmn; 5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e56.0 \u0026plusmn; 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eFat (% of energy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e31.1 \u0026plusmn; 5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e31.0 \u0026plusmn; 5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e31.1 \u0026plusmn; 3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eTotal protein (% of energy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e13.4 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e13.3 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e13.6 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eAnimal protein (% of energy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e7.0 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e6.7 \u0026plusmn; 2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e7.5 \u0026plusmn; 2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003ePlant protein (% of energy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e6.4 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e6.5 \u0026plusmn; 1.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e6.2 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 774px;\"\u003e\n \u003cp\u003eSignificant p-values are highlighted in bold.\u003c/p\u003e\n \u003cp\u003eAbbreviations: HOMA-IR, Homeostatic Model Assessment for Insulin Resistance; HDL, high-density lipoprotein; LDL, Low-density lipoprotein.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"735\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 735px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eFactor loading of amino acids in patterns extracted by factor analysis in participating individuals.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003ePattern 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003ePattern 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003ePattern 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Isoleucine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Lysine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Tyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Leucine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Valine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Threonine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Methionine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Histidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Alanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Aspartic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Proline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Serine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Glutamic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Phenylalanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Tryptophan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Arginine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Glycine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Cysteine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 244px;\"\u003e\n \u003cp\u003e\u0026nbsp;Percentage of explained variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e41.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 735px;\"\u003e\n \u003cp\u003e* Values less than 0.3 were removed for better interpretation of the data.\u003c/p\u003e\n \u003cp\u003e\u0026dagger; KMO (Kaiser-Meyer-Olkin) test = 0.76, Bartlett\u0026rsquo;s test of sphericity = \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"777\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 777px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Multi-variable adjusted odds ratio (95% CI) of metabolic dysfunction-associated fatty liver disease across the amino acid patterns score quartile.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 467px;\"\u003e\n \u003cp\u003eQuartile of amino acid pattern score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eP for Trend\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eQuartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePattern 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 537px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eNumber of cases/total population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e45/126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e46/126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e48/126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e57/127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eCrude model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.03 (0.61-1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.10 (0.66-1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.50 (0.90-2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.06 (0.62-1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.26 (0.74-2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.69 (1.01-2.85)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.92 (0.49-1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.64 (0.81-3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e2.14 (0.97-4.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePattern 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 537px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eNumber of cases/total population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e44/127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e42/126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e54/126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e56/126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eCrude model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.95 (0.56-1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.43 (0.86-2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.50 (0.90-2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.04 (0.61-1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.63 (0.96-2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.57 (0.93-2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1.13 (0.61-2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.01 (1.10-3.65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.51 (0.81-2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePattern 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 537px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eNumber of cases/total population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e53/126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e48/126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e48/127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e47/126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eCrude model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.84 (0.51-1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.82 (0.49-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.48 (0.33-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.84 (0.50-1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.76 (0.45-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.76 (0.45-1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.00 (Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.71 (0.40-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e0.69 (0.38-1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.62 (0.33-1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 777px;\"\u003e\n \u003cp\u003eObtained by Logistic regression analysis.\u003c/p\u003e\n \u003cp\u003e* P-trend was obtained using a quartile of dietary exposure as an ordinal variable in the model.\u003c/p\u003e\n \u003cp\u003eSignificant p-values are highlighted in bold.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eModel 1 adjusted for age and sex.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eB\u003c/sup\u003eModel 2 additionally adjusted for body mass index z-score, pubertal status, triglycerides, Homeostatic Model Assessment for Insulin Resistance, physical activity, energy intake, percentage of total protein intake, fiber intake (grams per 1000 kcal), and saturated fat (percentage of total energy).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"metabolic dysfunction-associated fatty liver disease, amino acid, overweight and obese, children and adolescents","lastPublishedDoi":"10.21203/rs.3.rs-6687463/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6687463/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGiven the limited understanding of how dietary amino acid intake affects metabolic dysfunction-associated fatty liver disease (MAFLD), we examined the potential relationship between dietary amino acid patterns and the odds of MAFLD in children and adolescents with overweight and obesity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted on participants aged 6 to 18 years with a WHO body mass index (BMI)-for-age z-score\u0026thinsp;\u0026ge;\u0026thinsp;1. MAFLD diagnosis followed established consensus definitions. Principal component factor analyses were conducted based on eighteen amino acids. Logistic regression models, adjusted for potential confounders, were used to estimate the odds of MAFLD across amino acid pattern score quartiles.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 505 (52.9% boys) with mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD age and BMI-for-age-Z-score of 10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3 and 2.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01, respectively, were enrolled. Three major amino acid patterns were characterized: (1) higher loads by branched chain, lysine, tyrosine, threonine, methionine, histidine, alanine, and aspartic acid; (2) higher loads of proline, serine, glutamic acid, and phenylalanine; (3) higher loads of tryptophan, arginine, glycine, and cysteine. After adjusting for all potential confounders, participants in the highest quartile of the first amino acid pattern tended to be associated with increased odds of MAFLD (OR:2.14; 95%CI:0.97\u0026ndash;4.77). There was no significant association for the second and third patterns.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese novel data suggest that the amino acid composition of an individual\u0026rsquo;s diet may modify their odds of MAFLD.\u003c/p\u003e","manuscriptTitle":"Dietary amino acids and the odds of metabolic dysfunction-associated fatty liver disease (MAFLD) among overweight and obese children and adolescents: A principal component analysis approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-29 10:42:34","doi":"10.21203/rs.3.rs-6687463/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-05T09:40:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-03T14:48:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-02T15:02:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-27T14:30:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24259712385766519966768175031268052561","date":"2025-06-12T07:17:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84245538419323616754451861322364869568","date":"2025-06-06T22:57:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29026528053787437054430464963020221219","date":"2025-06-06T20:23:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-27T09:08:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-21T10:52:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-21T10:51:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrition \u0026 Metabolism","date":"2025-05-17T13:52:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ec5e382-9986-48fc-b769-93e080c54069","owner":[],"postedDate":"May 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T15:59:58+00:00","versionOfRecord":{"articleIdentity":"rs-6687463","link":"https://doi.org/10.1186/s12986-025-01023-x","journal":{"identity":"nutrition-and-metabolism","isVorOnly":false,"title":"Nutrition \u0026 Metabolism"},"publishedOn":"2025-11-03 15:57:17","publishedOnDateReadable":"November 3rd, 2025"},"versionCreatedAt":"2025-05-29 10:42:34","video":"","vorDoi":"10.1186/s12986-025-01023-x","vorDoiUrl":"https://doi.org/10.1186/s12986-025-01023-x","workflowStages":[]},"version":"v1","identity":"rs-6687463","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6687463","identity":"rs-6687463","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}