Abstract
Background
Being overweight is a growing concern worldwide, characterized by a body mass index (BMI) ranging from 25 to 29.9 kg/m2. This condition can arise from various factors, including poor dietary choices, sedentary lifestyles, genetic predispositions, and environmental influences. The impact of dietary amino acids on metabolic health remains a topic of debate. In this study, we explored the correlation between the ratio of dietary branched-chain amino acids (BCAAs) to aromatic amino acids (AAAs) and metabolic profiles, including C-Peptide levels in overweight individuals.
Methods
A total of 221 overweight participants were enrolled in this study. Dietary intake was assessed using a validated food frequency questionnaire (FFQ). We conducted laboratory tests to measure metabolic variables, lipid profiles, glycemic indicators, and C-Peptide levels.
Results
Participants in the highest tertile of the dietary BCAAs/AAAs ratio exhibited significantly lower blood sugar levels and higher concentrations of high-density lipoprotein (HDL) compared to those in the lowest tertiles, with p-values of 0.033 and 0.003, respectively.
Conclusion
Our findings suggest that a higher dietary ratio of BCAAs to AAAs is linked to improved metabolic health among overweight individuals. However, further longitudinal studies are necessary to clarify the causal relationships between these dietary factors and metabolic outcomes.
Clinical trial number
Not applicable
Keywords: Branched chain amino acids, Aromatic amino acids, C-Peptide, Overweight
Introduction
Overweight is increasingly recognized as a critical public health issue, with a significant portion of the global population classified as overweight, defined by a body mass index (BMI) between 25 and 29.9 kg/m2 [1]. This condition arises when an individual carries more body weight than is considered healthy for their height, often due to a combination of excessive caloric intake, insufficient physical activity, and genetic predisposition. While overweight is often viewed as a less severe condition compared to obesity, which is characterized by a BMI of 30 kg/m2 or greater, it is essential to understand the health risks associated with being overweight [2]. Individuals who are overweight face a heightened risk of various health complications, including cardiovascular diseases, type 2 diabetes, hypertension, and metabolic syndrome [3, 4]. Importantly, being overweight can serve as a precursor to obesity, making early intervention critical in preventing the progression to more severe health issues. Furthermore, the stigma associated with being overweight can lead to psychological effects, including low self-esteem and anxiety, which can impact an individual’s overall quality of life [5, 6]. Differentiating between overweight and obesity is vital for public health strategies and clinical interventions. While both conditions indicate an excess of body weight, the health risks associated with obesity tend to be more pronounced due to the greater accumulation of fat and associated metabolic dysfunction. Therefore, addressing overweight as a distinct condition is crucial in developing effective prevention and management strategies tailored to individuals at risk of progressing to obesity [7].
Among dietary ingredients, amino acids, has been known to be effective in metabolism. Dietary amino acids play a significant role in controlling serum glucose levels through various mechanisms [8]. Amino acids, as the building blocks of proteins, play a crucial role in various physiological processes. Among the twenty standard amino acids, some are classified as branched-chain amino acids (BCAAs) and aromatic amino acids (AAAs) [9]. BCAAs, as essential amino acids, include leucine, isoleucine, and valine and are characterized by their aliphatic side chains with a branch [10]. BCAAs are essential amino acids that are crucial for muscle protein synthesis, energy production, and regulation of blood sugar levels. Conversely, AAAs are involved in neurotransmitter synthesis and various metabolic pathways. The balance between these two groups of amino acids may influence metabolic pathways differently, potentially impacting insulin sensitivity, lipid metabolism, and overall energy homeostasis. [11, 12]. BCAA supplementation before exercise reduces the breakdown of muscle proteins during exercise in humans and attenuate exercise-induced muscle damage and promote recovery from the damage [12, 13]. On the other hand, AAAs, such as phenylalanine, tyrosine, and tryptophan, with an aromatic ring in their structure, are also crucial for various metabolic pathways, including the synthesis of neurotransmitters and hormones [14, 15]. The balance between BCAAs and AAAs in the diet can influence several metabolic processes and, consequently, affect an individual’s physical performance and health. The metabolism of AAAs is intertwined with that of BCAAs, suggesting that an optimal balance between these amino acids is necessary for peak physical condition [16]. In the recent study by Tsunekawa K. et al., among 111 young Japanese adults, serum BCAA to tyrosine ratio was identified as a potent predictor of amino acid imbalance among adults with high skeletal muscle mass [16]. Moreover, several studies have revealed the potential role of plasma BCAAs/AAAs ratio in metabolic health; several studies showed that disturbed circulating BCAAs and AAAs balance might be associated with increased diabetes risk [17, 18].
Moreover, the balance of BCAAs/AAAs in the diet can significantly influence metabolic processes, including those related to insulin secretion and sensitivity; epidemiological and clinical studies have suggested that an elevated dietary BCAA to AAA ratio is associated with improved metabolic profiles, including lower body fat, enhanced insulin sensitivity, and favorable lipid levels [10, 13, 18]. However, the underlying mechanisms remain complex and multifaceted, involving interactions with genetic, hormonal, and environmental factors.
BCAAs, especially leucine, are known to stimulate insulin secretion; an increased dietary intake of BCAAs could, therefore, enhance insulin production, reflected by elevated C-Peptide levels, as a connecting peptide, and a byproduct of insulin production [19, 20].
C-peptide, a by-product of proinsulin cleavage, is secreted in equimolar amounts with insulin from pancreatic β-cells [21]. Unlike insulin, it is not metabolized by the liver, making it a reliable marker of endogenous insulin secretion [22]. Elevated C-peptide has been linked to insulin resistance, obesity, and type 2 diabetes mellitus (T2DM) [23–25]. Moreover, associations have been reported between C-peptide and body mass index (BMI), lipid profiles, blood pressure, and other metabolic syndrome components [26–28]. Its role extends to cardiovascular health, as high levels are associated with endothelial dysfunction and atherosclerosis [29, 30].
Regarding that the studies conducted on the association between dietary BCAA/AAA ratio and metabolic parameters including lipid profile, glycemic status are scarce, and have generally are performed among patients with diabetes, or cirrhosis. Moreover, these studies have generally measured the plasma ratio of BCAAs/AAAs rather than the dietary BCAAs/AAAs ratio. Also, the relationship between the dietary BCAAs/AAAs ratio and metabolic indices including serum lipids and glycemic markers and C-Peptide has not been evaluated yet. Therefore, in the current cross-sectional study, we aimed to evaluate the association between dietary BCAAs/AAAs ratio with cardiovascular risk factors including lipid profile, glycemic marker, C-Peptide levels and anthropometric features among young overweight adults.
Methods and materials
Study design and population
The current cross-sectional study included young adults aged 18–40 years and was conducted between April 2023 and August 2024 in Abha city, Saudi Arabia. Participants were recruited from community health care centers affiliated with the Ministry of Health, which provide services to a broad range of urban and suburban residents in Abha. These centers were selected to ensure representativeness of the study population. Also, participants were recruited through flyers in out-patient clinics while they have no history of chronic disease like cardiovascular disease, kidney and liver disorders, cancers, or other endocrine disorders. Exclusion criteria included pregnancy, lactation, and use of medications affecting weight. A total of 221 participants were included in the study. The sample size was calculated using a single population proportion formula, assuming a prevalence of 30% for obesity-related metabolic risk factors based on previous studies [31, 32], with a 95% confidence interval, 80% statistical power, and a margin of error of 5%. This calculation yielded a required sample size of 200 participants. To account for possible non-response or incomplete data, we increased the sample size by approximately 10%, leading to a final recruitment target of 221 participants, which was achieved in the present study [33].
Dietary assessment
Dietary intake was assessed using a validated food frequency questionnaire (FFQ) that captured average intake over the past year. FFQ was completed in self-administered manner and was checked by a trained nutritionist. The validity and reliability of FFQ was confirmed before [34]. Any incomplete data were then asked from the participant and be completed. The ratio of BCAAs to AAAs was calculated based on the reported intake of these amino acids using food composition data of United States Department of Agriculture (USDA) National Nutrient Database [35].
Measurement of biochemical factors
Cardiovascular risk factors, including blood pressure, total cholesterol (TC), low density lipoprotein (LDL) cholesterol, high density lipoprotein (HDL) cholesterol, and triglycerides (TG), were measured using standardized procedures. Blood samples were collected after an overnight fast and analyzed using enzymatic methods with an auto-analyzer (Alpha Classic E analyzer). Serum insulin was measured by commercial kits (AccuBind, Insulin, USA, Monobind Inc.). Homeostatic model assessment for insulin resistance (HOMA-IR) was calculated according to the formula: fasting insulin (µIU/L) x fasting glucose (nmol/L)/22.5 [36, 37]. Serum C-Peptide levels were measured using a commercial kit (C-Peptide, Monobind, USA). A trained physician measured the participants’ systolic and diastolic blood pressure using a standard mercury sphygmomanometer (ALPK2, Tokyo, Japan). Measurement was done after participants had rested in a seated position for at least 10 minutes in a quiet environment. Measurements were taken on the right arm, supported at heart level, with an appropriately sized cuff. Each participant provided a 10 ml venous blood sample for analysis.
Anthropometric measurements
Participants were instructed to wear minimal clothing, such as lightweight garments or undergarments, to ensure accurate measurements. They removed shoes, socks, jewelry, and any items that could interfere with the process. Height was measured using a stadiometer, with participants standing barefoot, heels together, back against the wall, and head aligned in the Frankfort horizontal plane. Height was recorded to the nearest 0.1 cm. Weight was measured using a calibrated scale, also to the nearest 0.1 kg. Overweight and obesity were defined according to the World Health Organization (WHO) criteria, based on body mass index (BMI). BMI was calculated as weight (kg) divided by height squared (m2). Individuals with a BMI of 25.0–29.9 kg/m2 were classified as overweight, while those with a BMI ≥30 kg/m2 were classified as obese [38].
Waist circumference (WC) was taken at the midpoint between the lower rib cage and iliac crest, recorded to the nearest 0.1 cm. Hip circumference (HC) was measured at the widest part around the buttocks. The waist-to-hip ratio (WHR) was calculated by dividing WC by HC. Body composition was assessed using Bioelectrical Impedance Analysis (BIA) with the InBody 770 device (InBody Co., Seoul, Korea), following the manufacturer’s guidelines. Participants stood barefoot on the InBody scale, ensuring both feet were evenly placed on the electrode plates, and removed shoes, socks, and any heavy clothing or accessories for the measurement [39]. The International Physical Activity Questionnaire (IPAQ), a questionnaire consisting of seven simple items, was used to assess the global level of physical activity (PA). The questionnaire evaluates the frequency and duration of walking, moderate-intensity, and vigorous-intensity activities during the previous week. The IPAQ-SF has been widely used in epidemiological studies and its validity and reliability have been previously confirmed [40].
Statistical analysis
Data were analyzed using SPSS, version 16. Continuous variables were expressed as mean ± standard deviation, and categorical variables as frequencies and percentages. Only dietary intake variables were not normally distributed; therefore, comparisons of these variables across tertiles of dietary BCAA/AAA ratio were performed using the General Linear Model (GLM), adjusting for dietary energy intake. The comparison of cardiovascular risk factors, C-Peptide levels, and anthropometric features between tertiles of BCAA/AAA ratio was assessed using one-way analysis of variance with further adjustments for potential confounders, including age, sex, physical activity, and BMI using GLM. Following the significant ANOVA results, we applied the Tukey’s post hoc test to identify pairwise differences among groups. This test was chosen because it is robust under the assumption of normality and homogeneity of variances, and it effectively controls the family-wise error rate when multiple comparisons are performed.
Also, multinomial logistic regression was conducted to examine potential associations between cardio-metabolic risk factors and C-Peptide levels with dietary BCAA/AAA ratio using three models accordingly: Model I, crude; Model II, adjusted for age and sex; and Model III, adjusted for age, sex, BMI and physical activity. A p-value < 0.05 was considered statistically significant. Participants were categorized into tertiles of dietary BCAA/AAA ratio to enable comparison across different levels of intake. This classification allowed us to better capture the gradient of associations between dietary BCAA/AAA ratio and metabolic outcomes, rather than treating the variable as a continuous measure alone [41].
Results
The characteristics of study participants according to dietary BCAA/AAA tertiles are shown in Table 1. There was no significant difference in mean age, BMI, gender distribution, education, WHR, fat free mass, muscle mass and bone mass of study participants across tertiles of dietary BCAA/AAA, however, fat mass (FM) percentage showed a significant difference, decreasing across the tertiles: 16.19 (6.74) % in the 1st tertile, 15.18 (7.29) % in the 2nd tertile, and 13.07 (5.93) % in the 3rd tertile (p = 0.017).
Table 1.
Characteristics of study participants according to dietary BCAA/AAA tertiles
| Variable | N | Mean (SD) | P-value* | |
|---|---|---|---|---|
| Age (year) | 1 tertile (1.85-1.97) | 72 | 22.56 (3.58) | 0.513 |
| 2 tertile (1.97-2) | 75 | 23.25 (3.45) | ||
| 3 tertile (2-2.08) | 74 | 22.90 (3.71) | ||
| BMI (kg/m2) | 1 tertile (1.85-1.97) | 72 | 27.90 (3.75) | 0.268 |
| 2 tertile (1.97-2) | 75 | 26.31 (3.62) | ||
| 3 tertile (2-2.08) | 74 | 27.96 (3.04) | ||
| Gender [%male] | 1 tertile (1.85-1.97) | 72 | 34 (47.2) | 0.119 |
| 2 tertile (1.97-2) | 75 | 34 (45.9) | ||
| 3 tertile (2-2.08) | 74 | 45 (60.0) | ||
| Education [% < 12y] | 1 tertile (1.85-1.97) | 72 | 28 (38.9) | 0.731 |
| 2 tertile (1.97-2) | 75 | 37 (50.0) | ||
| 3 tertile (2-2.08) | 74 | 30 (40.0) | ||
| PA [Met.min/week] | 1 tertile (1.85-1.97) | 72 | 449.86 (29.95) | 0.749 |
| 2 tertile (1.97-2) | 75 | 478.00 (39.30) | ||
| 3 tertile (2-2.08) | 74 | 491.21 (45.86) | ||
| WHR | 1 tertile (1.85-1.97) | 72 | 0.79 (0.07) | 0.793 |
| 2 tertile (1.97-2) | 75 | 0.79 (0.06) | ||
| 3 tertile (2-2.08) | 74 | 0.80 (0.07) | ||
| FM (%) | 1 tertile (1.85-1.97) | 72 | 16.19 (6.74) | 0.017 |
| 2 tertile (1.97-2) | 75 | 15.18 (7.29) | ||
| 3 tertile (2-2.08) | 74 | 13.07 (5.93) | ||
| FFM (%) | 1 tertile (1.85-1.97) | 72 | 53.29 (11.92) | 0.292 |
| 2 tertile (1.97-2) | 75 | 53.20 (10.67) | ||
| 3 tertile (2-2.08) | 74 | 55.76 (11.14) | ||
| Muscle Mass (kg) | 1 tertile (1.85-1.97) | 72 | 50.61 (11.36) | 0.287 |
| 2 tertile (1.97-2) | 75 | 50.49 (10.14) | ||
| 3 tertile (2-2.08) | 74 | 52.97 (10.62) | ||
| Bone Mass (kg) | 1 tertile (1.85-1.97) | 72 | 2.67 (0.56) | 0.294 |
| 2 tertile (1.97-2) | 75 | 2.67 (0.50) | ||
| 3 tertile (2-2.08) | 74 | 2.79 (0.51) | ||
BCAA/AAA, branched chain amino acid to aromatic amino acid ratio; SD, standard deviation; BMI, Body mass index; PA, physical activity; WHR, waist-to-hip ratio; FM, Fat Mass; FFM, Fat Free Mass. *P values derived from one way ANOVA
Table 2 summarizes the dietary intake of study participants according to BCAA/AAA tertiles. Dietary protein intake exhibited a significant difference, increasing across tertiles: 79.04 (38.95) g/d in the 1st tertile, 81.87 (32.68) g/d in the 2nd tertile, and 99.32 (56.83) g/d in the 3rd tertile (p = 0.011). There was no statistically significant difference in terms of dietary carbohydrate, fat, fiber, grains/cereals, beans/legumes, vegetables and fruits among the tertiles of dietary BCAA/AAA (p > 0.05). However, dietary intake of meat, fish, and poultry (MFP) showed a significant increase across tertiles: 83.04 (54.44) g/d in the 1st tertile, 104.87 (86.83) g/d in the 2nd tertile, and 159.14 (161.73) g/d in the 3rd tertile (p < 0.001). Similarly, dairy intake significantly increased across tertiles: 259.30 (191.60) g/d in the 1st tertile, 353.57 (193.18) g/d in the 2nd tertile, and 520.51 (423.01) g/d in the 3rd tertile (p < 0.001).
Table 2.
The comparison of dietary consumption of food groups among participants according to dietary BCAA/AAA tertiles
| Variable | N | Mean (SD) | P-value * | |
|---|---|---|---|---|
| Energy (kcal/d) | 1 tertile (1.85-1.97) | 72 | 2453.95 (1130.90) | 0.872 |
| 2 tertile (1.97-2) | 75 | 2403.14 (866.91) | ||
| 3 tertile (2-2.08) | 74 | 2487.99 (983.57) | ||
| Protein (g/d) | 1 tertile (1.85-1.97) | 72 | 79.04 (38.95) | 0.011 |
| 2 tertile (1.97-2) | 75 | 81.87 (32.68) | ||
| 3 tertile (2-2.08) | 74 | 99.32 (56.83) | ||
| Carbohydrate (g/d) | 1 tertile (1.85-1.97) | 72 | 356.20 (145.57) | 0.808 |
| 2 tertile (1.97-2) | 75 | 353.80 (129.79) | ||
| 3 tertile (2-2.08) | 74 | 342.45 (133.28) | ||
| Fat (g/d) | 1 tertile (1.85-1.97) | 72 | 87.24 (63.08) | 0.632 |
| 2 tertile (1.97-2) | 75 | 79.87 (36.64) | ||
| 3 tertile (2-2.08) | 74 | 84.78 (36.35) | ||
| Fiber (g/d) | 1 tertile (1.85-1.97) | 72 | 17.00 (11.50) | 0.413 |
| 2 tertile (1.97-2) | 75 | 15.86 (6.61) | ||
| 3 tertile (2-2.08) | 74 | 15.15 (6.28) | ||
| Grains/ cereals (g/d) | 1 tertile (1.85-1.97) | 72 | 493.61 (218.00) | 0.730 |
| 2 tertile (1.97-2) | 75 | 519.43 (251.62) | ||
| 3 tertile (2-2.08) | 74 | 493.38 (218.42) | ||
| Beans/ legumes (g/d) | 1 tertile (1.85-1.97) | 72 | 39.63 (64.76) | 0.731 |
| 2 tertile (1.97-2) | 75 | 33.60 (30.39) | ||
| 3 tertile (2-2.08) | 74 | 34.85 (44.92) | ||
| MFP (g/d) | 1 tertile (1.85-1.97) | 72 | 83.04 (54.44) | <0.001 |
| 2 tertile (1.97-2) | 75 | 104.87 (86.83) | ||
| 3 tertile (2-2.08) | 74 | 159.14 (161.73) | ||
| Dairy (g/d) | 1 tertile (1.85-1.97) | 72 | 259.30 (191.60) | <0.001 |
| 2 tertile (1.97-2) | 75 | 353.57 (193.18) | ||
| 3 tertile (2-2.08) | 74 | 520.51 (423.01) | ||
| Vegetables (g/d) | 1 tertile (1.85-1.97) | 72 | 253.85 (176.97) | 0.485 |
| 2 tertile (1.97-2) | 75 | 258.51 (164.81) | ||
| 3 tertile (2-2.08) | 74 | 288.33 (219.15) | ||
| Fruits (g/d) | 1 tertile (1.85-1.97) | 72 | 377.22 (356.02) | 0.124 |
| 2 tertile (1.97-2) | 75 | 366.97 (241.50) | ||
| 3 tertile (2-2.08) | 74 | 294.56 (172.15) | ||
BCAA/AAA, branched chain amino acid to aromatic amino acid ratio; SD, standard deviation;, meat, fish, poultry; *Energy-adjusted P-values are derived from general linear model
Table 3 presents the comparison of clinical biomarkers among study participants categorized by dietary BCAA/AAA tertiles. FBS levels showed a trend towards significance, with means of 97.06 (9.60) mg/dl, 95.21 (9.80) mg/dl, and 93.73 (9.72) mg/dl for the 1st, 2nd, and 3rd tertiles, respectively (p = 0.120). However, after adjustment for age, gender, and BMI, the difference became significant (p = 0.033). Similarly, HDL- cholesterol levels exhibited significant differences, increasing across tertiles of BCAA/AAA (p = 0.003). No significant difference was observed for other biochemical biomarkers. Table 4 represents odds ratios for biochemical risk factors across tertiles of dietary BCAA/AAA. Participants in the highest dietary BCAA/AAA tertile had greater odds of higher HDL across all models compared with the first tertile (p < 0.05).
Table 3.
The comparison of clinical biomarkers among participants according to dietary BCAA/AAA tertiles
| Variable | N | Mean | P-value* | P-value ** | |
|---|---|---|---|---|---|
| FBS (mg/dl) | 1 tertile (1.85-1.97) | 72 | 77.06 (9.60) | 0.120 | 0.033 |
| 2 tertile (1.97-2) | 75 | 75.21 (9.80) | |||
| 3 tertile (2-2.08) | 74 | 73.73 (9.72) | |||
| HOMA-IR | 1 tertile (1.85-1.97) | 72 | 1.78 (1.85) | 0.404 | 0.405 |
| 2 tertile (1.97-2) | 75 | 1.96 (2.15) | |||
| 3 tertile (2-2.08) | 74 | 1.56 (1.04) | |||
| QUICKI | 1 tertile (1.85-1.97) | 72 | 0.36 (0.04) | 0.337 | 0.467 |
| 2 tertile (1.97-2) | 75 | 0.36 (0.04) | |||
| 3 tertile (2-2.08) | 74 | 0.37 (0.07) | |||
| Insulin (mIU/l) | 1 tertile (1.85-1.97) | 72 | 9.42 (9.83) | 0.505 | 0.426 |
| 2 tertile (1.97-2) | 75 | 10.58 (10.96) | |||
| 3 tertile (2-2.08) | 74 | 8.73 (5.83) | |||
| Cholesterol (mg/dl) | 1 tertile (1.85-1.97) | 72 | 141.85 (66.48) | 0.212 | 0.134 |
| 2 tertile (1.97-2) | 75 | 163.46 (89.08) | |||
| 3 tertile (2-2.08) | 74 | 156.19 (67.57) | |||
| HDL (mg/dl) † | 1 tertile (1.85-1.97) | 72 | 43.87 (11.81) | 0.005 | 0.003 |
| 2 tertile (1.97-2) | 75 | 47.06 (10.70) | |||
| 3 tertile (2-2.08) | 74 | 49.93 (10.50) | |||
| TG (mg/dl) | 1 tertile (1.85-1.97) | 72 | 98.05 (52.12) | 0.298 | 0.716 |
| 2 tertile (1.97-2) | 75 | 108.37(63.29) | |||
| 3 tertile (2-2.08) | 74 | 113.96 (70.39) | |||
| LDL (mg/dl) | 1 tertile (1.85-1.97) | 72 | 78.36 (61.41) | 0.366 | 0.265 |
| 2 tertile (1.97-2) | 75 | 94.72 (87.28) | |||
| 3 tertile (2-2.08) | 74 | 83.46 (62.61) | |||
| C-Peptide (ng/ml) | 1 tertile (1.85-1.97) | 72 | 1.85 (1.77) | 0.716 | 0.644 |
| 2 tertile (1.97-2) | 75 | 2.11 (2.04) | |||
| 3 tertile (2-2.08) | 74 | 1.98 (1.74) | |||
BCAA/AAA, branched chain amino acid to aromatic amino acid ratio; SD, standard deviation; FBS, fasting blood sugar; HOMAI-IR, homeostatic model assessment for insulin resistance; QUICKI, quantitative insulin sensitivity check index; HDL, high density lipoprotein cholesterol; TG, triglyceride; LDL, low density lipoprotein cholesterol; *P-values derived from one-way ANOVA; ** P-values derived from general linear models (GLM) after adjustment for age, gender, PA and BMI. †According to Tukey’s post hoc comparisons, the significant difference was between first tertile with other two tertiles
Table 4.
Biochemical variables of study participants by tertiles of dietary BCAA/AAA
| Variable | Dietary BCAA/AAA tertiles (N=221) | |||||
|---|---|---|---|---|---|---|
| 1st (N=72) |
2nd (N=75) |
3rd (N=74) |
||||
| OR (CI) | P-value | OR (CI) | P-value | |||
| FBS (mg/dl) | Model I |
1 REF |
0.950 (0.894-1.028) | 0.237 | 0.976 (0.900-1.058) | 0.554 |
| Model II | 0.965 (0.899-1.036) | 0.322 | 0.989 (0.908-1.071) | 0.736 | ||
| Model III | 0.969 (0.902-1.041) | 0.385 | 0.991 (0.911-1.079) | 0.840 | ||
| HOMA-IR | Model I |
1 REF |
2.50 (0.265-23.67) | 0.423 | 0.361 (0.013-9.69) | 0.544 |
| Model II | 1.983 (0.208-18.94) | 0.552 | 0.257(0.009-7.409) | 0.428 | ||
| Model III | 2.067 (0.208-20.54) | 0.535 | 0.239 (0.008-7.42) | 0.414 | ||
| QUICKI | Model I |
1 REF |
0.964 (0.786-1.025) | 0.964 | 0.812 (0.758-1.078) | 0.562 |
| Model II | 0.922 (0.785-1.014) | 0.834 | 0.925 (0.880-1.099) | 0.740 | ||
| Model III | 0.752 (0.625-1.047) | 0.814 | 0.898 (0.784-1.012) | 0.751 | ||
| Insulin (mIU/l) | Model I |
1 REF |
0.852 (0.555-1.307) | 0.852 | 1.203 (0.655-2.207) | 0.552 |
| Model II | 0.891 (0.579-1.373) | 0.602 | 1.287 (0.691-2.36) | 0.427 | ||
| Model III | 0.887 (0.571-1.378) | 0.594 | 1.309 (0.693-2.472) | 0.406 | ||
| Cholesterol (mg/dl) | Model I |
1 REF |
1.002 (0.998-1.007) | 0.329 | 0.999 (0.993-1.005) | 0.735 |
| Model II | 1.003 (0.997-1.008) | 0.331 | 1.00 (0.994-1.006) | 0.907 | ||
| Model III | 1.003 (0.997-1.008) | 0.211 | 1.00 (0.994-1.006) | 0.919 | ||
| HDL (mg/dl) | Model I |
1 REF |
1.020 (0.986-1.056) | 0.240 | 1.048 (1.02-1.086) | 0.008 |
| Model II | 1.025 (0.989-1.061) | 0.175 | 1.052 (1.015-1.091) | 0.006 | ||
| Model III | 1.023 (0.987-1.059) | 0.494 | 1.051 (1.014-1.090) | 0.007 | ||
| TG (mg/dl) | Model I |
1 REF |
1.002 (0.996-1.008) | 0.585 | 1.003 (0.997-1.010) | 0.280 |
| Model II | 1.000 (0.944-1.007) | 0.945 | 1.001 (0.994-1.007) | 0.843 | ||
| Model III | 1.002 (0.995-1.010) | 0.494 | 1.003 (0.996-1.010) | 0.425 | ||
| LDL (mg/dl) | Model I |
1 REF |
1.002 (0.997-1.009) | 0.140 | 0.999 (0.958-1.002) | 0.658 |
| Model II | 1.001 (0.999-1.008) | 0.258 | 0.989 (0.974-1.006) | 0.874 | ||
| Model III | 1.031 (0.989-1.076) | 0.569 | 0.998 (0.987-1.008) | 0.258 | ||
| C-Peptide (ng/ml) | Model I |
1 REF |
1.079 (0.896-1.300) | 0.422 | 0.999 (0.817-1.221) | 0.992 |
| Model II | 1.093 (0.905-1.321) | 0.365 | 1.015 (0.827-1.246) | 0.885 | ||
| Model III | 1.015 (0.912-1.339) | 0.309 | 1.025 (0.833-1.261) | 0.817 | ||
BCAA/AAA, branched chain amino acid to aromatic amino acid ratio; SD, standard deviation; FBS, fasting blood sugar; HOMAI-IR, homeostatic model assessment for insulin resistance; QUICKI, quantitative insulin sensitivity check index; HDL, high density lipoprotein cholesterol; TG, triglyceride; LDL, low density lipoprotein cholesterol; OR, odds ratio; CI, confidence interval. The multivariate multinomial logistic regression was used for estimation of ORs and confidence interval (CI). Model I: crude, Model II: adjusted for age and sex, Model III: adjusted for age, BMI, sex, physical activity. The bolded values show the statistically significance
Discussion
In the present study we investigated the relationship between dietary branched chain amino acid to aromatic amino acid ratio (BCAA/AAA) and various clinical biomarkers among young overweight adults. Our findings reveal several noteworthy observations that contribute to the understanding of dietary influences on metabolic health. Firstly, we observed significant differences in protein intake (e.g. both total dietary protein intake and meat/fish/poultry intake) across BCAA/AAA tertiles, with higher dietary protein and MFP consumption in the highest tertile of BCAA/AAA. The observed increase in protein intake was accompanied by a significant decrease in fat mass percentage, suggesting a potential role of dietary BCAAs in modulating body composition. Also, higher dietary BCAA/AAA ratio was associated with reduced serum glucose and increased HDL concentrations. Disturbance in plasma BCAA/AAA ratio is known to be associated with metabolic syndrome [42]; BCAA catabolism is associated with subcutaneous white adipose tissue expansion and promotes adipogenesis [43]. In some other studies increased plasma BCAA/AAA ratio was associated with increased risk of obesity-related metabolic parameters including metabolic syndrome [44], future heart events [45] and insulin resistance [46]. However, in the current study, for the first time, we evaluated the role of dietary but not plasma BCAA/AAA ratio metabolic health of overweight individuals.
Higher HDL cholesterol levels were observed in participants with higher dietary BCAA/AAA ratios, which may indicate a protective effect on cardiovascular health. This finding is consistent with emerging evidence linking BCAAs to lipid metabolism and cardiovascular risk disease (CVD) reduction; in the study by Zheng L et al. [47], among 419 patients with type 2 diabetes who have been diagnosed with CVD, increase in total dietary BCAA, dietary leucine and dietary valine intake were associated with 54%, 64% and 54% reduction in CVD risk, respectively. In other study, among subjects with hypertriglyceridemia, dietary BCCA, particularly leucine, was associated with improvements in dyslipidemia and atherosclerosis-related factors [48]. This is probably because of the role of dietary leucine in prevention of atherosclerosis by down regulation of triglyceride-rich VLDL and inhibition of triglyceride biosynthesis in macrophages [49]. On the other hand, some of the studies reported controversial results; for example increased plasma concentrations of BCAA was positively associated with MetS component [50], impaired fasting glucose [51] and BMI [52]. Although it is reported that 80% of dietary BCAA enters into circulation [53], however, increased plasma BCAA is a probably a consequence of disease and not from its dietary sources [51]. Limiting diet derived amino acids, promotes protein oxidation as a consequence of impaired protein synthesis therefore [54], adequate BCAA consumption is necessary for optimum protein and glucose metabolism [55]. This controversy, is probably because of difference in dietary source or amount of intake of BCAA and AAA, or because of non-linear association between dietary sources of these amino acids with disease risk indicating that the risk of diseases may increase as certain thresholds of some nutrients [56, 57]. For example, most of these adverse effects are reported at higher intake of BCAA like for example its supplementation and ingestion of higher amount to its usual dietary intake [57, 58].
In our study, increased protein intake, meat, fish poultry and dairy products was observed in highest tertiles of dietary BCAAs/AAAs. Dietary proteins are the most important dietary sources of BCAA, particularly those in meat, fish, dairy products and eggs [48]. The summary of these effects are presented in Fig. 1.
Fig. 1.
Underlying mechanisms of BCAA/AAA action in the body
In our study, participants were categorized into tertiles of dietary BCAA/AAA ratio. The lowest tertile reflects a dietary pattern with relatively lower intake of BCAAs, while the highest tertile represents greater consumption of BCAA-rich foods, particularly animal-based protein sources. Interestingly, individuals in the highest tertile of dietary BCAA/AAA ratio had lower fasting blood glucose levels and higher HDL cholesterol concentrations, suggesting a potentially favorable metabolic profile. This may indicate that, in our population, higher dietary BCAA/AAA ratio could reflect a more balanced protein intake or overall healthier dietary habits, which might contribute to improved glycemic control and lipid metabolism.
It is important to note the limitations of our study, including its cross-sectional design, which limits causal inference. Additionally, dietary intake was assessed using self-reported methods, which may introduce reporting biases. Also, we did not measure circulating biomarkers of amino acids, which restricts the ability to directly link dietary intake with biochemical status. Although the use of a validated FFQ allowed us to estimate amino acid intake with reasonable accuracy. Future studies are recommended to incorporate both dietary assessment and biomarker measurements to provide a more comprehensive evaluation of the BCAA/AAA ratio and its health implications and to further elucidate the potential mechanisms underlying the observed associations and to establish causality between dietary BCAAs and metabolic health outcomes.
In conclusion, our findings suggest that dietary BCAA/AAA ratios may be associated with FBS and HDL cholesterol levels among overweight individuals, highlighting a potential role of dietary amino acids in modulating cardiovascular health. Further research is needed to explore these relationships in greater detail and to determine the implications for dietary recommendations and interventions aimed at improving metabolic health.
Acknowledgements
The authors extend their appreciation to University Higher Education Fund for funding this research work under Research Support Program for Central labs at King Khalid University through the project number CL/PRI/B/2.
Author contributions
SAG, MYM, MS and MK were involved in data collection and subjects’ recruitment. SAG and MYM supervised the project and involved in first study’s hypothesis generation. AJK and MS and EFO were involved in hypothesis generation and statistics. SAG was also involved in data collection, data analysis and supervision of the project. LB and SG were involved in statistical approaches and drafting the paper. ZA and AN were also involved in data collection and revision of the paper. All of the authors contributed in writing the draft of manuscript and agreed to its submission.
Funding
None.
Data availability
The datasets of the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Written informed consent was obtained from all of the participants of the study. All methods in the current research were performed in accordance with the declaration of Helsinki’s guidelines and regulations. The protocol of the current study has been approved by the ethics committee of King Khalid University through the project number CL/PRI/B/2.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Saade Abdalkareem Jasim, Email: abdalkareemjasimsaade@gmail.com.
Enwa Felix Oghenemaro, Email: felixoghenemaroenwa@gmail.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 of the current study are available from the corresponding author on reasonable request.