Abstract
Objectives
Dietary supplementation with branched-chain amino acids (BCAAs), including leucine, isoleucine, and valine, has shown potential benefits for the metabolic profile. However, emerging population-based studies suggest that BCAAs may mediate pathways related to cardiometabolic risk factors, possibly due to their involvement in the dysregulation of insulin metabolic pathways. This study aimed to investigate the association between BCAAs intake and the odds of nonalcoholic fatty liver disease (NAFLD) in children and adolescents with overweight and obesity.
Methods
This cross-sectional study encompassed individuals aged 6 to 18 years with WHO body mass index (BMI)-for-age z-score ≥ 1. NAFLD diagnosis was done using an ultrasonography scan of the liver and gastroenterologist confirmation. Dietary BCAAs intake was assessed using a validated 147-item food frequency questionnaire. Logistic regression models, adjusted for potential confounders, were used to estimate the odds ratios (OR) and 95% confidence interval (CI) of NAFLD across quartiles of BCAAs intake.
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. After adjusting for potential confounders, participants in the highest quartile of total dietary BCAAs (OR: 1.87;95%CI:1.06–3.28) and leucine (OR: 1.84;95%CI:1.03–3.29) intake had greater odds of developing NAFLD compared with those in the lowest quartile. There was no significant association between dietary valine and isoleucine intake and the odds of NAFLD.
Conclusions
The study findings suggest that increased dietary intake of BCAAs, particularly leucine, may have detrimental effects on the development of NAFLD.
Keywords: Branched-chain amino acid intake, Children, Insulin resistance, Nonalcoholic fatty liver disease, Overweight and obesity
Introduction
The burgeoning global obesity epidemic has reached alarming proportions, particularly regarding its impact on children and adolescents [1]. In 2023, statistics indicated that 1 in 5 children and adolescents were contending with excessive weight, resulting in notable short-term and long-term health risks [2, 3]. The range of health concerns encompasses psychological issues, cardiovascular diseases, diabetes, cancer, musculoskeletal problems, and liver complications [4–6]. Among these health concerns, nonalcoholic fatty liver disease (NAFLD) has become increasingly common in children and adolescents, especially in those who are overweight or obese [7]. NAFLD, known as a low-grade inflammation state, is characterized by an atypical accumulation of fat in the liver cells (exceeding 5% of the cell weight) in individuals who do not consume alcohol or consume less than 20 g per day and have no history of viral hepatitis [8]. In recent years, the prevalence of NAFLD among the general pediatric population has been estimated to range from 3 to 12% [9, 10]. This rate significantly escalates to 40–50% among children and adolescents who are overweight or obese [9, 10]. Furthermore, overweight and obese children and adolescents with NAFLD are at an elevated risk of developing dyslipidemia, type 2 diabetes mellitus (T2DM), and cardiovascular diseases [1, 11]. Notably, according to the literature, there was a significant association between NAFLD in obese adolescents and both hepatic and peripheral insulin resistance, irrespective of obesity [11]. However, the metabolic pathways associated with these phenomena, particularly the development of hepatic insulin resistance, are not yet fully understood.
In recent years, studies have consistently reported increased serum branched-chain amino acids (BCAAs) (including valine, leucine, and isoleucine) and their metabolites concentrations in NAFLD [12–14]. Additionally, dysregulation in serum BCAAs and BCAA-derived acylcarnitines has been associated to the development of insulin resistance and subsequent T2DM [15, 16]. Previous research has indicated that a damaged upregulation of the tricarboxylic acid (TCA) cycle, facilitated by BCAAs, may lead to mitochondrial dysfunction in NAFLD [17]. This implies that changes in the BCAAs metabolism may be a contributing factor to the development of insulin resistance and fatty liver. In this context, it is noteworthy that 80% of the serum BCAAs concentrations are influenced by dietary protein or the BCAAs intake, with the remaining 20% being attributed to the catabolism of the BCAAs metabolites [16, 18]. Research studies involving both animal and human subjects have indicated that supplementing with BCAAs can potentially alleviate liver steatosis in individuals with NAFLD [19–21]. However, findings from recent observational studies have revealed a significant association between dietary BCAAs intake and the development of obesity [22], insulin resistance [23], hypertension [24], and NAFLD [25, 26]. In the framework of a prospective study, greater dietary intake of BCAAs, especially leucine and valine, was significantly associated with a higher risk of developing insulin resistance in adults [23]. Furthermore, Mokhtari and colleagues have demonstrated that adults with the highest dietary intake of BCAAs had 2.82 times higher odds of NAFLD compared to those with the lowest intake [25]. In addition, in the framework of the Fatty Liver in Obesity (FLiO) study, a cross-sectional study showed that dietary BCAAs intake was positively associated with MRI liver fat content after controlling for major confounders [26]. Therefore, as can be deduced from the aforementioned literature that research on the association between dietary BCAAs intake and the odds of NAFLD has predominantly focused on adults. However, there is a notable gap in the existing research as these associations have not been thoroughly investigated in children and adolescents. This highlights the critical need for further studies to comprehensively understand the role of dietary BCAAs intake on the odds of NAFLD in younger age groups. Therefore, we aimed to assess the association between dietary BCAAs intake and the odds of NAFLD in overweight and obese children and adolescents.
Method
Study design and population
The present cross-sectional study was conducted between September 2023 and July 2024 in the framework of an obesity registry for Iranian children and adolescents on 548 participants. The sample size was calculated according to the previous study of the dietary BCAAs intake and odds of NALFD, with α err prob = 0.05, Power (1-β err prob) = 0.995, X parm μ = 2.35, X parm σ = 0.49 for dietary BCAAs intake, and effect size = 2.5 [25], the minimum sample size required was 492 individuals. The participants were randomly selected from those referred to the outpatient clinics of Gastroenterology, Hepatology, and Endocrinology at Children’s Hospital Medical Center in Tehran. We included participants according the following criteria: children and adolescents aged 7–18 years old who were overweight or obese, with a body mass index-for-age (BMI-for-age) Z-score of ≥ 1, as per the criteria established by the World Health Organization [27]. We excluded participants based on the following criteria: (1) Individuals with documented medical conditions (as confirmed by physician examination and medical records review), including kidney or liver diseases (Wilson’s disease, autoimmune liver disease, hemochromatosis, viral infections, and alcoholic fatty liver disease), thyroid disorders, and malignancy; (2) those who were using pharmaceutical agents with hepatotoxic or steatogenic potential, such as valproate and amiodarone, or taking weight loss or appetite suppressant pills, or any dietary supplements; (3) individuals who had implemented dietary modifications due to specific illnesses or for weight loss purposes within the past year; and (4) those who completed fewer than 35 items on the food frequency questionnaire, or who had under- or over-reported their dietary intake. In order to identify over- and under-reporting of dietary intakes, we calculated the ratio of energy intake to estimated energy requirement using equations suggested by the Institute of Medicine [28]. Any dietary energy intake that deviated beyond the ± 2 standard deviation range was classified as over- or underreported.
The study protocol was performed in accordance with the Helsinki Declaration and approved by the ethics committee of the National Nutrition and Food Technology Research Institute (IR.SBMU.NNFTRI.REC.1402.015). Following ethical standards, written informed consent was secured from the participant’s parents or legal guardians. Moreover, the consent of all children included was obtained.
Measurements
Qualified nutritionists with significant expertise in pediatrics performed anthropometric measurements using standardized methodologies. Weight was measured using a calibrated scale (Seca, Hamburg, Germany) to the nearest 100 g, with the individual being measured without additional clothing or shoes. Height was measured while standing barefoot using a standard measuring tape, accurate to 0.5 cm. BMI is determined by dividing an individual’s weight in kilograms by the square of their height in meters (kg/m^2). The BMI-for-age z-score was calculated using the latest internationally recognized growth chart standards and references [29, 30]. Waist circumference (WC) was measured to the nearest 0.5 cm using a non-elastic tape positioned midway between the iliac crest and the lowest rib following gentle respiration in a standing position and without exerting pressure on the body surface. A pediatric endocrinologist evaluated pubertal status following the Marshall and Tanner 1970 criteria [31, 32]. Participants were categorized into two groups based on their breast and genital development stages: pre-pubertal (boys at genital stage I, girls at breast stage I) and pubertal (boys at genital stage II or above, girls at breast stage II or above). Physical activity was assessed using the Modifiable Activity Questionnaire (MAQ) to calculate metabolic equivalent task (MET) hours per week MAQ [33]. It is important to note that previous research has demonstrated high reliability (97%) and moderate validity (49%) of the Persian-translated MAQ in adolescents MAQ [33]. Arterial blood pressure was measured manually on the right arm using a mercury sphygmomanometer with an appropriately sized cuff following a 15-minute rest period. The Korotkoff sound technique was employed, with systolic blood pressure (SBP) determined by the onset of the tapping sound and diastolic blood pressure (DBP) determined at the disappearance of the sound. The measurement was taken twice, with at least one minute between measurements, and the average was considered the subject’s blood pressure.
Blood samples were collected between 7:00 and 9:00 AM after a 12–14 h overnight fast. Subsequently, the samples were centrifuged within 30–45 min and analyzed on the same day. Fasting blood sugar (FBS) was measured using the enzymatic colorimetric method with glucose oxidase. Triglyceride (TG) levels were assessed using enzymatic colorimetric analysis with glycerol phosphate oxidase. Total cholesterol (TC) was evaluated using the enzymatic colorimetric method, employing cholesterol esterase and cholesterol oxidase. High-density lipoprotein cholesterol (HDL-C) was measured using the apolipoprotein B-containing lipoproteins with phosphotungstic acid utilizing the enzymatic method. Low-density lipoprotein (LDL-C) was calculated using the Friedewald equation (from the serum TC, TG, and HDL-C concentrations) and expressed in mg/dL [34]. The levels of Aspartate aminotransferase (AST) and Alanine aminotransferase (ALT) were determined using enzymatic photometry, while Gamma-glutamyl transferase (GGT) was assessed using enzymatic calorimetric methods. These analyses were carried out utilizing commercial kits (Delta Darman Inc., Tehran, Iran) and a Selectra 2 auto-analyzer (Vital Scientific, Spankeren, The Netherlands). The quality of biochemical assays was monitored using assayed serum controls in two different concentrations AUDIT, Delta Darman Part, Tehran, Iran). All intra-assay and inter-assay coefficients of variation for all biochemistry analyses were less than 5.3%. Triglyceride-glucose (TyG) index was calculated as Ln [TG (mg/dL) × FBG (mg/dL)/2] [35].
Dietary intake assessment
The participants’ dietary intakes were evaluated using a valid and reliable semi-quantitative 147-item food frequency questionnaire (FFQ) [36, 37]. In the present study, trained nutritionists asked children or their parents/legal guardians to indicate how often they consumed each food item during the previous year, whether daily, weekly, or monthly. When children and adolescents had difficulties or could not answer, mothers were asked about the food items in the FFQ. The portion size for each food item on the FFQ was specified using US Department of Agriculture (USDA) serving sizes whenever possible (e.g., one slice of bread, one medium apple, 1 cup of dairy). If USDA serving sizes were unavailable, household measures (e.g., one tablespoon of beans, one leg, breast, or wing of chicken meat, one large, medium, or small plate of rice) were used and then converted to grams and servings. The energy and nutrient contents were sourced from the USDA Food Composition Tables (FCT) due to the incomplete nature of the Iranian FCTs, which have limited data on the nutrient content of raw foods and beverages. However, the Iranian FCTs were utilized for traditional food items, such as Kashk, which were not included in the USDA FCT.
Dietary amino acid intake was calculated utilizing the 2015 USDA National Nutrient Database for Standard Reference (Release 28) (http://www.ars.usda.gov/ba/bhnrc/ndl), which is founded on the chemical analysis of the amino acid composition of over 5000 food items across all food groups. Values for BCAAs, including leucine, isoleucine, and valine, were attributed to each food item in the FFQ. Subsequently, the total dietary BCAAs intake was determined by the multiplication of the frequency of consumption of each food item by its respective leucine, isoleucine, and valine content.
Assessment of NAFLD
Following an 8 to 12-hour fasting period, a skilled radiologist (PD) utilized high-resolution B-mode ultrasonography with a linear 7.5–10 MHz transducer (Samsung Medison SonoAceR3 ultrasound machine) to evaluate hepatic steatosis.
Statistical analysis
The demographic and clinical characteristics of individuals in the total sample and across NAFLD status were analyzed using standard descriptive statistics. The normality of the variable’s distribution was evaluated using histogram charts and the Kolmogorov–Smirnov tests. Participants’ characteristics were represented as mean ± SD or median (25–75 interquartile range) for continuous variables and percentages for categorical variables, stratified by NAFLD status. The characteristics of the participants were compared using various statistical tests appropriate for the nature of the variables. Specifically, an independent sample t-test was utilized for quantitative variables with a normal distribution, a Mann–Whitney test for quantitative variables with a non-normal distribution, and a Chi-square test for qualitative variables with a normal distribution. The association between the dietary BCAAs intake and the odds of NAFLD were evaluated by calculating the multiple-adjusted odds ratios (ORs) using logistic regression analysis. Subjects were categorized according to quartiles of dietary BCAAs, valine, leucine, and isoleucine intake (gram per 2000 kcal intake). The lowest quartile of dietary BCAAs intake served as the reference group. The association between dietary BCAAs intake and the odds of NAFLD was evaluated according to three models: (1) crude model; (2) Model 1, adjusted for age and sex; (3) Model 2, adjusted for model 1 and puberty status, BMI-for-age z-score, PA, SBP, DBP, TC, HDL, LDL, TYG index, saturated fatty acid, trans fatty acid, glycemic load, and total fiber intake (gram per 1000 kcal). In analyzing the trend of ORs across increasing dietary BCAAs categories, we approached the quartile categories as continuous variables. The statistical analyses were conducted utilizing SPSS version 20 (SPSS, Chicago, IL, USA), with a significance level of less than 0.05 for a two-tailed P-value.
Results
Out of a total of 548 overweight and obese children and adolescents, 31 participants were excluded from our study due to incomplete anthropometric and physical activity data, dietary information, as well as biochemical and ultrasound assessments. Furthermore, 12 participants were excluded due to over- or under-reporting. Subsequently, our statistical analysis was based on 505 overweight and obese children and adolescents.
This cross-sectional study included 505 participants (52.9% were boys), with mean ± SD age and BMI-for-age-Z-score of 10.0 ± 2.3 years old and 2.70 ± 1.01, respectively. In the total sample, 76.6% of participants had pubertal status, and 38.8% had NAFLD. Characteristics of the study participants according to NAFLD status are represented in Table 1. As shown in Table 1, no significant differences were found in gender distribution, pubertal status distribution, PA, SBP, and DBP between participants with NAFLD and those without NAFLAD. Participants with NAFLD exhibited significantly higher mean values for age, weight, height, BMI, BMI-for-age z-score, and WC, TG, ALT, AST, GGT, TYG index, and significantly lower HDL levels than those without NAFLD. There were no significant differences in FBS, TC, and LDL levels between participants with and without NAFLD.
Table 1.
Characteristics of the study participants according to non-alcoholic fatty liver status
| Total sample (N = 505) | Without NAFLD (N = 309) | NAFLD (N = 196) | P-value* | |
|---|---|---|---|---|
| Age (years) | 10.0 ± 2.3 | 9.7 ± 2.1 | 10.5 ± 2.5 | < 0.001 |
| Gender (Boys, %) | 267 (52.9) | 106 (54.1) | 161 (52.1) | 0.66 |
| Puberty (%) | 387 (76.6) | 150 (76.5) | 237 (76.7) | 0.96 |
| Weight (Kg) | 49.6 ± 15.0 | 46.2 ± 12.3 | 55.1 ± 17.2 | < 0.001 |
| Height (cm) | 142.2 ± 12.3 | 140.0 ± 11.4 | 145.5 ± 12.9 | < 0.001 |
| Body mass index (Kg/M2) | 24.0 ± 3.8 | 23.1 ± 3.1 | 25.5 ± 4.4 | < 0.001 |
| BMI for age z-score | 2.70 ± 1.01 | 2.54 ± 0.80 | 2.97 ± 1.23 | < 0.001 |
| Waist circumference (cm) | 83.5 ± 10.6 | 80.9 ± 9.5 | 87.6 ± 10.9 | < 0.001 |
| 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) | 106.3 ± 16.4 | 105.4 ± 16.6 | 107.8 ± 15.6 | 0.14 |
| Diastolic blood pressure (mmHg) | 67.2 ± 12.9 | 66.7 ± 11.8 | 68.2 ± 14.83 | 0.25 |
| Biochemical data | ||||
| Fasting blood sugar (mg/dL) | 91.1 ± 8.8 | 90.7 ± 8.9 | 91.9 ± 8.5 | 0.11 |
| Triglyceride (mg/dL) | 124.2 ± 61.7 | 116.4 ± 55.7 | 136.5 ± 67.0 | < 0.001 |
| Cholesterol (mg/dL) | 171.1 ± 55.9 | 172.3 ± 66.9 | 169.2 ± 31.8 | 0.54 |
| HDL (mg/dl) | 47.1 ± 11.8 | 49.0 ± 11.6 | 44.0 ± 11.3 | < 0.001 |
| LDL-C (mg/dL) | 98.0 ± 25.6 | 97.3 ± 24.5 | 99.1 ± 27.2 | 0.44 |
| Alanine amino transferase (U/L) | 20.0 ± 16.9 | 16.2 ± 10.0 | 25.9 ± 23.0 | < 0.001 |
| Aspartate amino transferase (U/L) | 24.3 ± 12.6 | 22.9 ± 11.6 | 26.3 ± 13.8 | < 0.001 |
| Gamma-glutamyl transferase (U/L) | 18.8 ± 7.9 | 17.0 ± 4.6 | 21.6 ± 10.9 | < 0.001 |
| Triglyceride-glucose index | 8.53 ± 0.48 | 8.46 ± 0.48 | 8.63 ± 0.47 | < 0.001 |
| 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 ± 6.7 | 0.89 |
| Protein (% of energy) | 13.4 ± 2.2 | 13.3 ± 2.1 | 13.6 ± 2.3 | 0.20 |
| Fat (% of energy) | 33.1 ± 5.8 | 33.0 ± 5.6 | 33.2 ± 6.2 | 0.77 |
| Saturated fat (% of energy) | 10.6 ± 2.6 | 10.5 ± 2.44 | 10.7 ± 2.8 | 0.40 |
| Polyunsaturated fat (% of energy) | 6.9 ± 1.9 | 6.9 ± 1.8 | 6.8 ± 2.1 | 0.80 |
| Fiber (g/1000 kcal) | 17.2 ± 6.0 | 16.9 ± 5.6 | 17.5 ± 6.5 | 0.28 |
| Glycemic load | 250.5 ± 83.6 | 251.7 ± 85.0 | 248.7 ± 81.4 | 0.68 |
| Branched-chain amino acid (% of energy) | 2.4 ± 0.45 | 2.39 ± 0.42 | 2.45 ± 0.49 | 0.12 |
| Branched-chain amino acid (g/day) | 18.3 ± 6.4 | 18.3 ± 6.6 | 18.2 ± 6.0 | 0.96 |
| Valine (% of energy) | 0.74 ± 0.14 | 0.73 ± 0.13 | 0.75 ± 0.15 | 0.14 |
| Valine (g/day) | 5.6 ± 1.9 | 5.6 ± 2.0 | 5.6 ± 1.8 | 0.96 |
| Leucine (% of energy) | 1.06 ± 0.19 | 1.05 ± 0.18 | 1.08 ± 0.21 | 0.15 |
| Leucine (g/day) | 8.0 ± 2.8 | 8.0 ± 2.9 | 8.0 ± 2.6 | 0.94 |
| Isoleucine (% of energy) | 0.61 ± 0.11 | 0.60 ± 0.10 | 0.62 ± 0.12 | 0.12 |
| Isoleucine (g/day) | 4.6 ± 1.6 | 0.10 ± 0.04 | 0.09 ± 0.03 | 0.98 |
Significant p-values are highlighted in bold
* p-values were determined using the independent-samples t-test and the chi-square test for continuous and categorical variables
Abbreviations: BMI: body mass index; HDL: high-density lipoprotein; LDL: Low-density lipoprotein
After assessing nutrient intake and food groups, the mean ± SD for BCAAs intake was 18.3 ± 6.4 (g/day). The highest contribution of dietary BCAAs intake pertained to leucine (43.9%), followed by valine (30.7%) and isoleucine (25.4%). The mean dietary intakes of BCAAs, leucine, valine, and isoleucine, based on mg/kg body weight, were 393.5, 172.8, 120.7, and 99.9, respectively. There were no significant differences between individuals with and without NAFLD regarding their dietary macro- and micronutrient intake.
Table 2 represents the characteristics of the study participants according to quartiles of dietary BCAAs intake. Across the quartile of dietary BCAAs intake, participants in the fourth quartile of dietary BCAAs intake had higher values of WC and PA levels than those in the first quartile (P-value for trend < 0.05). However, the other demographic, anthropometric and biochemical variables showed no significant trends across quartiles of dietary BCAAs intake. Participants in the highest quartile of dietary BCAAs intake had higher dietary intakes of total protein, BCAAs, valine, leucine, isoleucine, saturated fat and lower dietary intakes of carbohydrate, total fat, polyunsaturated fat, fiber (g/1000 kcal), and glycemic load score compared to those in the first quartile (P-value for trend < 0.05).
Table 2.
Characteristics of the study participants according to quartiles of dietary branched-chain amino acid intake
| Quartiles | |||||
|---|---|---|---|---|---|
| Q1 (n = 123) | Q2 (n = 124) | Q3 (n = 130) | Q4 (n = 128) | P for trend* | |
| Age (years) | 9.9 ± 2.2 | 10.2 ± 2.4 | 10.0 ± 2.2 | 9.8 ± 2.4 | 0.52 |
| Gender (Boys, %) | 54 (20.2) | 67 (25.1) | 77 (28.8) | 69 (25.8) | 0.10 |
| Puberty (%) | 89 (23.0) | 101 (26.1) | 101 (26.1) | 96 (24.8) | 0.37 |
| Weight (Kg) | 50.3 ± 15.4 | 48.6 ± 12.2 | 49.8 ± 15.8 | 49.7 ± 16.2 | 0.80 |
| Height (cm) | 141.1 ± 12.3 | 142.2 ± 11.6 | 142.9 ± 12.9 | 142.0 ± 12.3 | 0.95 |
| Body mass index (Kg/M2) | 24.6 ± 4.2 | 23.7 ± 3.1 | 23.8 ± 3.6 | 24.1 ± 4.3 | 0.26 |
| BMI for age z-score | 2.68 ± 1.03 | 2.75 ± 1.10 | 2.79 ± 1.13 | 2.91 ± 1.20 | 0.01 |
| Waist circumference (cm) | 80.1 ± 9.7 | 81.7 ± 10.0 | 84.2 ± 11.2 | 86.2 ± 10.6 | 0.01 |
| Physical activity (MET/hour/week) | 3.7 (1.9–15.0) | 8.9 (2.0–21.0) | 10.0 (3.7–20.4) | 9.4 (3.7–22.4) | 0.03 |
| Systolic blood pressure (mmHg) | 106.6 ± 17.9 | 105.5 ± 12.5 | 105.9 ± 19.6 | 107.0 ± 15.0 | 0.92 |
| Diastolic blood pressure (mmHg) | 67.4 ± 11.9 | 67.9 ± 15.1 | 67.1 ± 14.2 | 66.3 ± 10.2 | 0.84 |
| Biochemical data | |||||
| Fasting blood sugar (mg/dL) | 89.1 ± 8.0 | 90.0 ± 9.3 | 91.8 ± 9.0 | 93.5 ± 8.9 | 0.86 |
| Triglyceride (mg/dL) | 129.5 ± 616 | 122.2 ± 59.1 | 120.7 ± 58.4 | 124.7 ± 65.3 | 0.68 |
| Triglyceride-glucose index | 8.66 ± 0.45 | 8.61 ± 0.49 | 8.62 ± 0.49 | 8.67 ± 0.48 | 0.27 |
| Cholesterol (mg/dL) | 169.7 ± 31.0 | 176.2 ± 99.4 | 164.9 ± 25.4 | 173.8 ± 34.3 | 0.38 |
| HDL (mg/dL) | 45.9 ± 11.4 | 47.8 ± 12.3 | 47.5 ± 10.7 | 47.1 ± 12.5 | 0.63 |
| LDL-C (mg/dL) | 99.6 ± 25.2 | 95.4 ± 25.3 | 95.6 ± 21.9 | 101.5 ± 29.2 | 0.15 |
| Alanine amino transferase (U/L) | 22.3 ± 10.8 | 19.9 ± 15.3 | 17.2 ± 11.2 | 20.7 ± 13.0 | 0.11 |
| Aspartate amino transferase (U/L) | 23.4 ± 13.0 | 25.3 ± 14.4 | 23.9 ± 9.6 | 24.5 ± 13.1 | 0.70 |
| Gamma-glutamyl transferase (U/L) | 19.4 ± 8.1 | 18.8 ± 5.8 | 17.4 ± 4.9 | 19.6 ± 11.4 | 0.10 |
| Dietary Intake | |||||
| Energy (Kcal/day) | 3127.8 ± 1044.9 | 3174.0 ± 967.1 | 3057.1 ± 952.6 | 3132.1 ± 827.1 | 0.22 |
| Carbohydrate (% of energy) | 56.3 ± 6.6 | 56.2 ± 5.6 | 55.1 ± 5.4 | 54.6 ± 6.7 | 0.04 |
| Protein (% of energy) | 11.8 ± 0.9 | 12.6 ± 0.6 | 14.0 ± 0.6 | 16.1 ± 1.6 | < 0.01 |
| Fat (% of energy) | 34.1 ± 6.7 | 33.5 ± 5.5 | 32.4 ± 5.3 | 31.6 ± 5.5 | 0.03 |
| Saturated fat (% of energy) | 9.9 ± 2.5 | 10.6 ± 2.5 | 10.6 ± 2.2 | 11.0 ± 2.8 | 0.01 |
| Polyunsaturated fat (% of energy) | 7.4 ± 2.3 | 7.1 ± 2.0 | 6.5 ± 1.6 | 6.3 ± 1.6 | < 0.01 |
| Fiber (g/1000 kcal) | 18.7 ± 6.5 | 17.6 ± 6.0 | 16.6 ± 5.5 | 15.7 ± 5.5 | 0.01 |
| Glycemic load | 269.4 ± 98.7 | 267.0 ± 80.4 | 247 ± 80.6 | 219.4 ± 62.4 | < 0.01 |
| Branched chain amino acid (% of energy) | 1.88 ± 0.17 | 2.23 ± 0.07 | 2.51 ± 0.09 | 3.00 ± 0.32 | < 0.01 |
| Branched chain amino acid (g/day) | 14.7 ± 5.0 | 17.7 ± 5.4 | 19.2 ± 6.1 | 21.4 ± 6.9 | < 0.01 |
| Valine (% of energy) | 0.57 ± 0.05 | 0.68 ± 0.02 | 0.77 ± 0.03 | 0.92 ± 0.09 | < 0.01 |
| Valine (g/day) | 4.5 ± 1.5 | 5.4 ± 1.6 | 5.9 ± 1.8 | 6.5 ± 2.0 | < 0.01 |
| Leucine (% of energy) | 0.82 ± 0.08 | 0.98 ± 0.03 | 1.10 ± 0.04 | 1.31 ± 0.14 | < 0.01 |
| Leucine (g/day) | 6.4 ± 2.2 | 7.8 ± 2.4 | 8.4 ± 2.7 | 9.4 ± 3.0 | < 0.01 |
| Isoleucine (% of energy) | 0.47 ± 0.04 | 0.56 ± 0.02 | 0.63 ± 0.02 | 0.76 ± 0.08 | < 0.01 |
| Isoleucine (g/day) | 3.7 ± 1.3 | 4.5 ± 1.4 | 4.9 ± 1.5 | 5.4 ± 1.8 | < 0.01 |
*Chi-square and linear regression were used to test the trend of qualitative and quantitative variables across quartiles of BCAAs (as median value in each quartile), respectively
Significant p-values are highlighted in bold
Abbreviations: BMI: body mass index; HDL: high-density lipoprotein; LDL: Low-density lipoprotein
Table 3 demonstrates the ORs and 95% CI of the highest compared to lowest total BCAAs intake as well as across categories of valine, leucine, and isoleucine intake for odds of NAFLD. There was no significant association between the highest total BCAAs intake compared to the lowest intake and the odds of NAFLD neither in crude nor in adjusted model 1. In the fully adjusted model, those with the highest total dietary BCAAs intake compared to those with the lowest intake had significantly higher odds of NAFLD (OR: 1.87; 95% CI: 1.06–3.25). In terms of amino acids, participants who were in the highest quartile of leucine intake compared to those in the lowest quartile had significantly higher odds of NALFD in the crude model (OR: 1.69; 95%CI:1.01–2.84), in the age and sex adjusted model (OR: 1.86; 95%CI:1.09–3.16), and in the fully adjusted model (OR: 1.84; 95%CI:1.03–3.29). However, there was no significant associated between dietary valine and isoleucine intake and the odds of NAFLD neither in crude nor in adjusted models.
Table 3.
Odds ratio (95% CI) of non-alcoholic fatty liver disease across quartile of branched-chain amino acid intakes
| Quartiles | P-trend* | ||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | ||
| Total BCAAs | |||||
| Median intake | 9.72 | 11.16 | 12.57 | 14.44 | |
| Case/total | 47/123 | 43/124 | 44/130 | 62/128 | |
| Crude | 1 (ref) | 0.86 (0.51–1.44) | 0.83 (0.49–1.39) | 1.52 (0.92–2.51) | 0.12 |
| Model 1a | 1 (ref) | 0.81 (0.48–1.38) | 0.82 (0.48–1.38) | 1.57 (0.94–2.62) | 0.09 |
| Model 2b | 1 (ref) | 1.07 (0.61–1.88) | 0.97 (0.55–1.69) | 1.87 (1.06–3.28) | 0.05 |
| Valine | |||||
| Median intake | 2.93 | 3.42 | 3.84 | 4.46 | |
| Case/total | 46/114 | 45/130 | 48/136 | 57/125 | |
| Crude | 1 (ref) | 0.78 (0.46–1.32) | 0.81 (0.48–1.35) | 1.24 (0.74–2.07) | 0.38 |
| Model 1a | 1 (ref) | 0.78 (0.46–1.33) | 0.79 (0.46–1.34) | 1.31 (0.77–2.22) | 0.29 |
| Model 2b | 1 (ref) | 0.87 (50-1.53) | 0.74 (0.42–1.30) | 1.40 (0.79–2.50) | 0.36 |
| Leucine | |||||
| Median intake | 4.25 | 4.91 | 5.46 | 6.33 | |
| Case/total | 40/11 | 44/123 | 49/142 | 63/129 | |
| Crude | 1 (ref) | 0.98 (0.58–1.69) | 0.93 (0.55–1.57) | 1.69 (1.01–2.84) | 0.05 |
| Model 1a | 1 (ref) | 0.99 (0.58–1.72) | 0.92 (0.54–1.57) | 1.86 (1.09–3.16) | 0.03 |
| Model 2b | 1 (ref) | 1.09 (0.61–1.93) | 0.88 (0.50–1.55) | 1.84 (1.03–3.29) | 0.07 |
| Isoleucine | |||||
| Median intake | 2.45 | 2.81 | 3.16 | 3.65 | |
| Case/total | 44/112 | 47/132 | 48/133 | 57/128 | |
| Crude | 1 (ref) | 0.85 (0.51–1.44) | 0.87 (0.52–1.46) | 1.24 (0.74–2.08) | 0.38 |
| Model 1a | 1 (ref) | 0.83 (0.49–1.42) | 0.85 (0.50–1.45) | 1.31 90.77–2.25) | 0.28 |
| Model 2b | 1 (ref) | 0.96 (0.55–1.69) | 0.94 (0.54–1.64) | 1.46 (0.82–2.60) | 0.22 |
Obtained by Logistic regression analysis
* Ptrend was obtained by the use of quartile of dietary branched-chain amino acid intake as an ordinal variable in the model
Significant p-values are highlighted in bold
Abbreviations: BCAAs: branched-chain amino acids
a Model 1 adjusted for age and sex
b Model 2 additionally adjusted for puberty status, body mass index z-score, physical activity, systolic blood pressure, diastolic blood pressure, Cholesterol, high-density lipoprotein, low-density lipoprotein, Triglyceride-glucose index, saturated fatty acid, trans fatty acid, glycemic load, and total fiber intake (per 1000 kcal)
Discussion
This is the first observational study to evaluate the association between dietary BCAAs intake and the odds of NAFLD among overweight and obese children and adolescents. Our findings showed that increased dietary intake of BCAAs, particularly leucine, may have detrimental effects on the development of NAFLD, suggesting a potential role in the pathogenesis of the disease.
BCAAs, as essential amino acids, play a diverse role in body metabolism [38, 39]. They make up 35% of the essential amino acids in muscle protein, 40% of the required amino acids, and approximately 50% of the essential amino acids in the food supply for mammals [39]. These amino acids are crucial building blocks for proteins that maintain energy balance and act as essential nutrient signals with direct and indirect effects [38, 39]. In recent years, several studies have consistently indicated that increased serum BCAAs levels are associated with an elevated risk of obesity, insulin resistance, T2DM, and NAFLD [12–14, 39]. In this framework, the majority of serum BCAA levels are determined by dietary protein or BCAA intake, which makes up about 80% of the total levels. The remaining 20% is contributed by the breakdown products of BCAAs [16, 18]. A recent meta-analysis has also revealed that the administration of oral BCAAs significantly increased the circulating leucine profile following supplementation [40]. In 2019, a meta-analysis pooling seven observational studies on the association between dietary BCAAs intake and the risk of obesity/T2DM showed that individuals with the highest dietary BCAAs intake exhibited a 38% lower likelihood of being obese, yet a 32% higher likelihood of developing T2DM when compared to those with the lowest dietary BCAAs intake with high degree of heterogeneity [40]. In the framework of the Tehran Lipid and Glucose Study, Asghari and colleagues demonstrated that participants in the highest tertile for total BCAAs, leucine, and valine intake after controlling for demographic characteristics, glycemic load, dietary energy intake, saturated fat, and dietary fiber had a greater risk of incident insulin resistance than subjects in the lowest tertile [23]. However, no significant association was found between greater dietary BCAAs, leucine, isoleucine, and valine intake and the risk of hyperinsulinemia, β-cell dysfunction, or insulin insensitivity [23]. The observed inconclusive results could be associated with variations in study characteristics, such as study design, sample size, population demographics, ethnicities, genetic factors, dietary patterns, and nutritional culture. Interestingly, a prospective nested case-control study conducted within a Chinese adult cohort unveiled a noteworthy association between dietary BCAAs intake and the amplification of genetic predisposition to elevated fasting glucose levels and heightened risk of T2DM [41]. The study revealed a positive association between increased dietary BCAAs intake and heightened T2DM risk among individuals exhibiting a high genetic predisposition [41]. Conversely, individuals with a low genetic predisposition demonstrated an inverse relationship [41]. It is imperative to conduct further research in order to integrate these findings into widespread public health initiatives effectively.
As per the available literature, only two prior observational studies have evaluated the association between dietary BCAAs intake and the odds of NALFD in adults [25, 26]. In 2020, a cross-sectional study showed a positive association between greater dietary BCAAs intake and liver fat content, liver iron concentration, ferritin levels, and glucose metabolism markers (glycated hemoglobin, triglyceride, and glucose index) after controlling for demographic and dietary energy, carbohydrate and protein, fruits and vegetables intake [26]. Furthermore, in the framework of a case-control study conducted in 2022, it was observed that individuals with the highest dietary intake of BCAAs exhibited 2.82 times higher odds of NAFLD in comparison to those with the lowest dietary intake of BCAAs [25]. This association was found to be significant even after adjusting for variables such as age, sex, BMI, PA, smoking, socioeconomic status, as well as dietary energy, fat, and fiber intake [25]. The findings from studies investigating the impact of BCAAs supplementation on liver health significantly diverged when compared to those of observational studies, failing to establish a definitive consensus. A study by Zhang et al. demonstrated that BCAAs supplementation can mitigate weight gain induced by heightened adipose lipolysis in mice subjected to a high-fat diet [42]. However, it was also observed that supplementation with BCAAs may lead to liver injury due to increased adipose lipolysis, resulting in conditions such as hyperlipidemia, insulin resistance, and hepatic lipotoxicity [42]. In this manner, BCAAs activate AMPKα2, thereby inducing lipolysis in the adipocyte and increasing plasma free fatty acids (FFA), leading to hepatic FFA accumulation [42]. In the liver, BCAA activates mTORC, inhibiting FFA to TG conversion and autophagy, exacerbating FFA lipotoxicity [42]. We hypothesized that excessive dietary BCAAs intake could directly contribute to fat accumulation in the liver, possibly through oxidative stress or inflammatory processes. Inflammation and oxidative stress are significant factors in the development of NAFLD, including liver fat accumulation and hepatic inflammation, known as a non-alcoholic steatohepatitis (NASH), and insulin resistance [15, 43].
On the contrary, Beppu and colleagues showed that BCAA supplementation enhanced functional liver regeneration in individuals undergoing portal vein embolization followed by hepatectomy [44]. In addition, Honda et al. proposed that the supplementation of BCAAs could potentially mitigate hepatic steatosis and liver injury linked to NASH [19]. In animal model studies, Takegoshi documented the positive effects of BCAA supplementation, encompassing the induction of an antifibrotic impact, prevention of apoptosis in hepatocytes, and reduction in the incidence of hepatocellular carcinoma in a NASH mouse model [45]. Furthermore, the supplementation of BCAAs demonstrated improvement in liver fibrosis and inhibition of tumor growth in a rat model of hepatocellular carcinoma with liver cirrhosis [46]. The literature indicates that the majority these studies have demonstrated positive effects of BCAA, although there have been some inconsistencies. It is important to emphasize that the impact of dietary BCAA consumption or supplementation appears to vary depending on the stage of NAFLD. Specifically, circulating amino acid levels are notably elevated in the early stages of NAFLD/NASH, but decrease rapidly in cirrhosis [47]. Currently, it remains uncertain whether the elevated serum BCAAs levels in individuals with NAFLD stem from increased liver protein breakdown, impaired muscle function, obesity, and/or presence of insulin resistance, or impaired tissue metabolism [48]. Consequently, the consumption of BCAAs may have detrimental effects in the early stages of NAFLD, while proving beneficial in cases of cirrhosis. However, further research is essential to gain a comprehensive understanding of the impact of BCAAs on overall liver health.
Strengths and limitations
Our investigation has revealed several strengths that significantly enhance the overall quality of our work. To the best of our knowledge, this study represents the first attempt to evaluate the association between dietary BCAA intake and the likelihood of developing NAFLD in the pediatric population. We utilized validated and reliable questionnaires to evaluate the nutritional data and physical activity levels of the participants. Furthermore, the presence of the participants’ mothers during face-to-face interviews facilitated the children’s recall and quantification of their dietary intake. Additionally, all dietary and anthropometric assessments were conducted by proficient dietitians specialized in the pediatric field, thus mitigating potential errors in data collection. However, there are certain limitations that warrant consideration. One limitation of the study pertains to its cross-sectional design, which precludes drawing conclusions about causality. Furthermore, despite the use of a validated FFQ for estimating nutritional intakes, the potential for measurement error cannot be entirely disregarded. Additionally, even after adjusting for confounding variables, it is imperative to acknowledge the potential for residual confounding resulting from unidentified or unmeasured factors. Moreover, we did not have available data on the genetic backgrounds of the participants and their serum BCAA levels, which are essential for conducting further investigations.
Conclusion
Our study demonstrated that excessive dietary intake of BCAAs, particularly leucine were significantly associated with higher odds of NAFLD in overweight and obese children and adolesccents. A healthful dietary pattern characterized by a well-balanced composition of BCAAs is being suggested as a potential strategy for NAFLD management. Additional research is necessary to understand the molecular interactions and mechanisms associated with NAFLD, insulin resistance, and dietary BCAAs intake.
Acknowledgements
The authors would like to acknowledge all participants, their parents, and the staff of the Research Institute for Endocrine Sciences for their time and valuable help.
Author contributions
Overall, GA, PR, and AH supervised the project and approved the final version of the manuscript to be submitted. GA and MR designed the research; PD assessed the non-alcoholic fatty liver disease; MT evaluated biochemical analysis; 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
This study was supported by the Shahid Beheshti University of Medical Sciences (SBMU).
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The protocol of this study was approved by the ethics committee of the Shahid Beheshti University of Medical Sciences. Written informed consents were obtained from participants’ parents or legal guardians.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Pejman Rouhani, Email: Rohanipejmanmd@gmail.com.
Golaleh Asghari, Email: g_asghari@hotmail.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.