Abstract
Background
Obesity, especially visceral obesity, is a major health issue globally, leading to metabolic disorders. Visceral fat accumulation is closely linked to insulin resistance and metabolic abnormalities. The role of branched-chain amino acids (BCAAs, e.g. leucine, isoleucine, and valine) in metabolic health, particularly in visceral obesity, remains unclear. Further investigation is needed to elucidate the relationships among body fat composition, BCAAs, and metabolic dysfunction in visceral obesity.
Purpose
This study investigates the correlation between serum BCAAs levels and body fat composition in individuals with visceral obesity, as well as the association between these levels and metabolic indicators. Methods: A total of 105 participants were recruited and categorized into three groups: obesity without type 2 diabetes mellitus (T2DM) (n = 52), obesity with T2DM (n = 32), and healthy controls (n = 21). All participants underwent assessments of anthropometric measurements, biochemical parameters, and serum amino acid profiles. Correlation tests and multiple linear regression models were used to analyze relationships among variables and potential associations.
Results
Serum BCAAs levels were significantly higher in both obese groups than in the healthy, normal-weight control group (p < 0.001). Compared with the healthy normal-weight controls, valine showed the greatest increase (obesity non-T2DM: 36.48 ± 8.33 µg/mL; obesity and T2DM: 43.46 ± 7.68 µg/mL; controls: 30.48 ± 4.19 µg/mL). In Pearson’s analysis, BCAAs were positively associated with visceral fat area (VFA), body mass index (BMI), waist circumference (WC), body fat ratio, and IR-related markers (HOMA-IR, fasting insulin, and HbA1c) (all p < 0.05). Valine demonstrated the strongest correlations with HOMA-IR (r = 0.481, p < 0.001) and WC (r = 0.507, p < 0.001). Strong positive correlations were also observed between BCAAs and fasting glucose, fasting insulin, HbA1c, and lipid parameters. In multivariable models, valine was independently and positively associated with central adiposity, as reflected by WC (standardized β = 0.357, p = 0.001), and with insulin resistance, as assessed by HOMA-IR (standardized β = 0.306, p = 0.003). Leucine and isoleucine were independently associated with WC and fasting insulin levels, while no independent associations were observed for phenylalanine or tyrosine.
Conclusion
Valine is associated with central adiposity and insulin resistance in obesity and was independently associated with WC and HOMA-IR. BCAAs profiling may help identify individuals with increased metabolic risk; however, further prospective studies are required to validate its clinical and predictive value.
Trial Registration
Not applicable
Keywords: Branched-chain amino acids, Type 2 diabetes mellitus, Insulin resistance, Obesity, valine
Introduction
Obesity is a condition characterized by the excessive accumulation of body fat, caused by a long-term imbalance between high energy intake and low energy expenditure [1]. According to the World Health Organization, approximately 43% of adults worldwide are overweight, and 16% are living with obesity [2]. The global prevalence of obesity is increasing and is affecting communities such as Aboriginal, Chinese, and South Asian populations [3]. Obesity is a complex, multifaceted, and persistent chronic condition that is challenging to manage [4]. The global burden of obesity continues to rise, closely linked to the growing prevalence of metabolic disorders such as insulin resistance and type 2 diabetes mellitus (T2DM) [5, 6]. Obesity is closely linked to insulin resistance and T2DM through mechanisms involving adipose tissue dysfunction, inflammation, and impaired insulin signaling [7]. Among various metabolic alterations, circulating metabolites have gained attention as potential indicators and mediators of metabolic dysfunction.
Branched-chain amino acids (BCAAs) are essential amino acids comprising leucine, isoleucine, and valine [8]. BCAAs are primarily obtained from the diet, although they can also be synthesized in small amounts within the body [9]. They are characterized by their branched side-chain structure and a shared catabolic pathway [10, 11]. Early studies demonstrated that circulating BCAAs are elevated in individuals with obesity, and subsequent research has consistently shown strong associations between elevated BCAAs concentrations and insulin resistance and T2DM [12–14]. It is well-known that obese individuals have higher levels of several amino acids, including BCAAs, in their blood than healthy individuals [1]. Significant associations between BCAAs and aromatic amino acids (AAAs, e.g., tyrosine and phenylalanine) and insulin resistance, obesity, as well as future diabetes have been identified and confirmed in American adults and young Finns [15]. In addition, BCAAs, often in combination with AAAs, have been proposed as part of a metabolic signature associated with increased cardiometabolic risk [16].
BCAAs metabolism primarily occurs in the mitochondria and is tightly regulated by enzymatic pathways [17]. Two key regulators of this process are the branched-chain α-keto acid dehydrogenase (BCKDH) complex and its associated enzymes: BCKDH kinase and PPM1K phosphatase [18]. Importantly, components of the BCAAs catabolic pathway, including the BCKDH complex, are expressed in adipose tissue. This suggests that adipose tissue contributes to systemic BCAAs metabolism. Dysregulation of BCAAs metabolism may influence circulating BCAAs levels, particularly in conditions of increased visceral adiposity [19, 20]. However, despite extensive evidence linking elevated BCAAs levels to metabolic disorders, it remains unclear whether these alterations are a cause or a consequence of metabolic dysfunction, particularly in the context of visceral obesity. Importantly, central obesity, particularly the accumulation of visceral fat, plays a critical role in the development of metabolic abnormalities. It is also closely associated with an increased risk of metabolic disease, reflecting the impact of dysfunctional adipose tissue [21]. While previous studies have consistently reported elevated circulating BCAAs levels in individuals with obesity [22], the underlying mechanisms and their relationship to body fat distribution are not fully understood.
Visceral adiposity is metabolically active and is more strongly associated with insulin resistance and cardiometabolic risk than general obesity assessed by body mass index (BMI) [23]. Visceral obesity is characterised by the accumulation of triglycerides in abnormal areas of the body, and is associated with the development of comorbid conditions such as dyslipidaemia, hypertension, T2DM, and an increased risk of cardiovascular disease [24].
Therefore, assessment of body fat composition, particularly visceral adiposity, provides a more accurate evaluation of metabolic risk than BMI alone [25]. In addition to body fat distribution, circulating metabolites are recognized as important indicators of metabolic dysfunction. Circulating BCAAs have been identified as key metabolic biomarkers associated with obesity and insulin resistance. This has been demonstrated in metabolomic profiling studies comparing obese and lean individuals, which consistently report elevated circulating BCAAs in obesity [26]. Furthermore, most existing studies have focused on total BCAAs levels, with limited attention given to the individual contribution of specific amino acids. In particular, the role of valine in metabolic dysfunction remains insufficiently characterized compared with leucine and isoleucine. Whether valine is independently associated with insulin resistance and body fat composition beyond overall BCAAs remains unclear. Together, these findings highlight the need for a more detailed understanding of the relationship between BCAAs metabolism and body fat distribution. Given these gaps, the present study aims to investigate the association between serum BCAAs and body fat composition, with a specific focus on visceral fat accumulation, in Chinese adults. We further examine whether individual BCAAs, particularly valine, are independently associated with insulin resistance and metabolic parameters in individuals with obesity. By addressing these questions, this study seeks to clarify the metabolic relevance of BCAAs and better define the specific role of valine in obesity-related metabolic dysfunction.
Methods
Study methodology and participant enrollment
This cross-sectional study included 84 Chinese adults with obesity recruited from the Department of Endocrinology at Northern Jiangsu People’s Hospital between January 2023 and December 2024. The inclusion criteria were as follows: (1) age between 18 and 50 years, (2) BMI ≥ 28 kg/m2, (3) waist circumference (WC) ≥90 cm for men, and ≥85 cm for women, (4) body fat percentage (PBF) >25% for men and >30% for women. The exclusion criteria included the following: (1) secondary obesity, (2) severe cardiovascular, pulmonary, or renal diseases, (3) pregnancy or lactation, (4) use of antidiabetic, antihypertensive, lipid-lowering, or weight-loss medications within the previous 3 months, (5) psychiatric disorders, (6) history of smoking or alcohol abuse. All 84 Participants were classified into two groups based on their diabetes status: the obesity-without-T2DM group (obesity non-T2DM, n = 52) and the obesity-with-newly-diagnosed-T2DM group (obesity and T2DM, n = 32). Newly diagnosed T2DM at baseline was defined in participants without a previous history of diabetes as fasting plasma glucose (FPG) ≥7.0 mmol/L, or 2-hour postprandial blood glucose ≥ 11.1 mmol/L, or glycated hemoglobin (HbA1c) ≥6.5%. Participants with FPG values between 3.9 and 6.1 mmol/L were categorized as having normal glucose tolerance. In addition, 21 age and sex-matched healthy individuals (7 males and 14 females, aged 18–50 years) were enrolled from the hospital’s Health Examination Center as the normal-weight control group. These individuals had a BMI ranging from 18.5 to 23.9 kg/m2, with PBF of 10–20% in males and 15–25% in females.
Anthropometric and body composition measurements
Body measurements, including body weight (BW), height, WC, and hip circumference (HC), were obtained using standard protocols with participants wearing light clothing and without shoes. BMI was calculated as follows: BMI = weight (kg) / height2 (m2). Obesity classification followed the World Health Organization criteria.
Body composition was assessed using bioelectrical impedance analysis (BIA) with the InBody770 multi-frequency body composition analyzer (Biospace Co., Seoul, South Korea). The device uses segmental analysis at frequencies of 5, 50, 250, and 500 kHz to estimate body fat mass (BF), PBF, fat-free mass (FFM), skeletal muscle mass (SMM), and visceral fat area (VFA), as well as basal metabolic rate (BMR). Participants stood barefoot on the platform electrodes and held the hand electrodes with their palms and thumbs during the measurement process. After resting in a seated position for at least five minutes, participants’ blood pressure was measured twice, and the mean of the two readings was used for analysis.
Biochemical analyses
Blood samples were collected from all participants after an overnight fasting period of at least 8 hours. FPG, fasting insulin (FINS), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured using an automated biochemical analyzer.
Insulin resistance was assessed using the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), calculated as HOMA-IR = [FPG (mmol/L) × FINS (mU/L)]/22.5. The Insulin Sensitivity Index (ISI) was calculated using the standard formula: ISI = 22.5 / [FPG (mmol/L) × FINS (mU/L)]. We expressed glucose in mmol/L and insulin in pmol/L. Insulin concentrations were converted to µU/mL using the conversion factor reported by Matthews et al. (1985): 1 pmol/L = 0.1667 µU/mL [27].
Serum BCAAs, including valine, leucine, and isoleucine, were quantified using liquid chromatography–tandem mass spectrometry (LC–MS/MS) following a targeted metabolomics approach as described by Newgard et al. (2009) [26]. Quality control samples were included in each batch. The intra- and inter-assay coefficients of variation never exceeded 10%.
Data and Statistical analysis
Statistical analysis was performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism version 9.0. The Shapiro–Wilk test was used to assess the normality of the data distribution. Continuous variables were presented as the mean ± standard deviation (SD) if normally distributed or as the median with the interquartile range (IQR) if not normally distributed. Categorical variables were expressed as frequencies and percentages (n %). One-way analysis of variance (ANOVA) with Bonferroni post hoc tests was used to compare groups for normally distributed variables. The Kruskal–Wallis test was applied to non-normally distributed data. A Pearson correlation analysis was performed to evaluate the relationship between circulating BCAAs and metabolic markers. Multiple linear regression models were constructed to determine whether BCAAs were independently associated with metabolic indicators after adjustment for relevant covariates. Before performing regression analyses, model assumptions were assessed by examining residual distribution, linearity, and homoscedasticity. Multicollinearity was evaluated using tolerance and variance inflation factor values. No major violations were observed that materially affected the interpretation of the final models. A p-value below 0.05 was considered statistically significant.
Results
Participant characteristics and metabolic profile
A total of 105 participants were included in the analysis: 52 obese individuals without T2DM, 32 obese individuals with T2DM, and 21 normal-weight controls. The demographic and metabolic characteristics of the study population are summarised in Table 1. The age and sex distributions were comparable across the three groups.
Table 1.
Baseline demographic and clinical characteristics of participants in the three study groups
| Variable | Obesity non-T2DM (n = 52) | Obesity with T2DM (n = 32) | Control (n = 21) | Pa | Pb | Pc |
|---|---|---|---|---|---|---|
| Age (years) | 27.17 ± 5.30 | 29.05 ± 5.96 | 25.95 ± 4.30 | 1.000 | 0.186 | 0.542 |
| Sex (F:M) | 44:08:00 | 22:10 | 14:07 | 0.413 | 1.000 | 0.725 |
| BW (kg) | 106.27 ± 27.36 | 108.24 ± 25.95 | 54.82 ± 7.13 | <0.001 | <0.001 | 0.457 |
| BMI (kg/m2) | 38.43 ± 7.83 | 38.64 ± 7.98 | 20.18 ± 1.32 | <0.001 | <0.001 | 0.461 |
| BF (kg) | 49.07 ± 17.97 | 52.99 ± 14.72 | 12.10 ± 2.13 | <0.001 | <0.001 | 0.733 |
| PBF (%) | 44.61 ± 5.69 | 45.38 ± 5.38 | 23.45 ± 2.94 | <0.001 | <0.001 | 0.936 |
| FFM (kg) | 58.77 ± 13.24 | 61.06 ± 11.80 | 40.44 ± 3.92 | <0.001 | <0.001 | 0.866 |
| SMM (kg) | 55.48 ± 12.56 | 57.71 ± 11.21 | 38.00 ± 3.69 | <0.001 | <0.001 | 0.855 |
| VFA (cm2) | 206.13 ± 47.73 | 221.85 ± 30.13 | 59.33 ± 10.90 | <0.001 | <0.001 | 0.283 |
| BMR (kcal/day) | 1625.40 ± 261.76 | 1756.85 ± 324.25 | 1261.57 ± 111.92 | <0.001 | <0.001 | 0.315 |
| WC (cm) | 118.17 ± 14.09 | 123.83 ± 12.94 | 81.71 ± 4.93 | <0.001 | <0.001 | 0.314 |
| HC (cm) | 117.57 ± 19.34 | 123.55 ± 17.00 | 94.43 ± 3.69 | <0.001 | <0.001 | 0.511 |
| FPG (mmol/L) | 5.26 ± 0.61 | 8.35 ± 2.62 | 4.86 ± 0.51 | 0.021 | <0.001 | <0.001 |
| FINS (pmol/L) | 201.38 ± 110.04 | 246.79 ± 163.25 | 21.22 ± 12.00 | <0.001 | <0.001 | 0.602 |
| HbA1c (%) | 5.48 ± 0.40 | 7.73 ± 1.65 | 5.05 ± 0.36 | <0.001 | <0.001 | <0.001 |
| HOMA-IR | 6.91 ± 4.19 | 12.90 ± 7.90 | 0.65 ± 0.34 | <0.001 | <0.001 | <0.001 |
| ISI | 0.30 ± 0.59 | 0.14 ± 0.13 | 2.00 ± 0.98 | <0.001 | <0.001 | 0.219 |
| TG (mmol/L) | 1.46 ± 0.95 | 2.36 ± 2.59 | 0.70 ± 0.24 | <0.001 | 0.03 | 0.376 |
| TC (mmol/L) | 4.74 ± 0.99 | 4.78 ± 1.07 | 4.01 ± 0.60 | 0.011 | 0.029 | 1.000 |
| HDL-C (mmol/L) | 1.01 ± 0.26 | 0.94 ± 0.21 | 1.54 ± 0.27 | <0.001 | <0.001 | 0.838 |
| LDL-C (mmol/L) | 3.34 ± 0.82 | 3.25 ± 0.83 | 2.18 ± 0.45 | <0.001 | <0.001 | 1.000 |
Note: Data are means±SD or n (%) and one-way ANOVA
aP: obesity non-T2DM group vs.Control group; bP: Obesity and T2DM group vs.Control group; cP: obesity non-T2DM group VS Obesity and T2DM group
BW, body weight; BMI, body mass index; BF, body fat; PBF, body fat percentage; FFM, fat free mass; SMM, skeletal muscle mass; VFA, visceral Fat area; BMR, basal metabolic rate; WC, waist circumference; HC, hip circumference; FPG, fasting plasma glucose; FINS, fasting insulin; HOMA-IR, homeostasis model assessment of insulin resistance index; ISI, Insulin sensitivity index; TG, taotal triglycerides; TC, total cholesterol; HDL-c, high density lipoprotein cholesterol; LDL-c, low density lipoprotein cholesterol
Both obese groups exhibited significantly higher body weight and BMI than the control group (both p < 0.001). Similarly, all measures of body composition, including BF, PBF, VFA, and WC, were markedly higher in the obese groups than in the control group (all p < 0.001). However, no significant differences were observed between the two obese groups.
In terms of metabolic parameters, both obese groups exhibited significantly higher levels of TG, TC, and LDL-C, as well as lower levels of HDL-C, compared to the control group. Glucose metabolism parameters, including FPG, FINS, HbA1c, and HOMA-IR, were also significantly higher in the obese groups. Notably, the obesity and T2DM group exhibited the highest FPG and HbA1c levels.
Serum amino acid concentrations
There were significant differences in serum amino acid concentrations among the study groups. As shown in Table 2 and Fig. 1, levels of BCAAs, including valine, leucine, and isoleucine, were significantly higher in both obese groups compared with the control group. Notably, the Obesity and T2DM group had the highest BCAAs concentrations, with valine and isoleucine showing significant differences between the two obese groups.AAAs exhibited relatively modest intergroup differences, with significantly elevated levels of phenylalanine and tyrosine observed in the Obesity and T2DM group compared with the control group.
Table 2.
Serum amino acid levels among the three study groups
| Obesity non-T2DM | Obesity and T2DM | Control | aP | bP | CP | |
|---|---|---|---|---|---|---|
| Val (ug/ml) | 36.48±8.33 | 43.46±7.68 | 30.48±4.19 | 0.008 | ＜0.001 | 0.002 |
| Leu (ug/ml) | 23.38±7.12 | 27.08±7.07 | 19.54±3.53 | 0.012 | 0.001 | 0.161 |
| ILe (ug/ml) | 12.59±4.27 | 15.63±4.54 | 10.12±1.78 | 0.003 | <0.001 | 0.045 |
| Phe (ug/ml) | 16.40±5.65 | 18.79±6.08 | 14.17±2.83 | 0.093 | 0.014 | 0.369 |
| Tyr (ug/ml) | 13.82±4.23 | 15.24±3.94 | 12.07±1.92 | 0.063 | 0.009 | 0.472 |
Note: Data are means±SD and one-way ANOVA; Val, valine; Leu, leucine; ILe, isoleucine; Phe, phenylalanine; Tyr, tyrosine
Val, valine; Leu, leucine; ILe, isoleucine; Phe, phenylalanine; Tyr, tyrosine
aP: obesity non-T2DM group vs.Control group ; bP: Obesity and T2DM group vs.Control group ; cP: obesity non-T2DM group VS Obesity and T2DM group
Fig. 1.
Comparison of serum BCAAs levels across three comparison groups. * indicates p < 0.05 vs control, # indicates p < 0.05 vs obesity non-T2DM
Correlation between amino acids and metabolic indices
Pearson’s correlation analysis revealed that BCAAs, including valine, leucine, and isoleucine, were positively correlated with FINS and HOMA-IR, and negatively correlated with HDL-C. Valine showed the strongest associations among these, with significant positive correlations with FPG and HbA1c, indicating a close relationship with glucose metabolism.
Among the BCAAs, valine showed positive correlations with body fat indicators, including BF (r = 0.450), PBF (r = 0.400), VFA (r = 0.454), and WC (r = 0.507), all significant at p < 0.001. These results suggest that higher valine levels are significantly associated with fat accumulation and overall fat distribution. Leucine and isoleucine also demonstrated positive correlations with BF, PBF, VFA, and WC, although these associations were somewhat weaker. Together, these findings suggest that elevated BCAAs levels, particularly valine, are linked to increased body fat and visceral fat. Detailed correlation coefficients are presented in Table 3. In addition, a heatmap illustrating the relationships between amino acid levels and metabolic variables is shown in Fig. 2
Table 3.
Pearson correlation between serum BCAAs levels and metabolic parameters
| Age | BW | BMI | BF | PBF | FFM | SMM | VFA | BMR | WC | HC | FPG | FINS | HbA1c | HOMA-IR | ISI | TG | TC | HDL-C | LDL-C | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Val | R | -0.002 | 0.475 | 0.496 | 0.45 | 0.4 | 0.441 | 0.445 | 0.454 | 0.437 | 0.507 | 0.433 | 0.302 | 0.485 | 0.457 | 0.481 | -0.429 | 0.215 | 0.218 | -0.332 | 0.293 |
| P | 0.98 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | 0.004 | <0.001 | <0.001 | <0.001 | <0.001 | 0.043 | 0.04 | 0.001 | 0.005 | |
| Leu | R | −0.046 | 0.383 | 0.4 | 0.354 | 0.304 | 0.372 | 0.375 | 0.345 | 0.358 | 0.408 | 0.345 | 0.106 | 0.388 | 0.306 | 0.332 | −0.231 | 0.198 | 0.152 | −0.267 | 0.206 |
| P | 0.645 | <0.001 | <0.001 | 0.001 | 0.004 | <0.001 | <0.001 | 0.001 | 0.001 | <0.001 | 0.001 | 0.324 | <0.001 | 0.004 | 0.001 | 0.03 | 0.063 | 0.156 | 0.011 | 0.053 | |
| Ile | R | 0.007 | 0.397 | 0.409 | 0.37 | 0.328 | 0.374 | 0.378 | 0.385 | 0.39 | 0.421 | 0.356 | 0.155 | 0.455 | 0.309 | 0.414 | −0.227 | 0.198 | 0.118 | −0.34 | 0.192 |
| P | 0.943 | <0.001 | <0.001 | <0.001 | 0.002 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | 0.001 | 0.147 | <0.001 | 0.003 | <0.001 | 0.009 | 0.062 | 0.272 | 0.001 | 0.071 | |
| Phe | R | −0.121 | 0.23 | 0.318 | 0.258 | 0.28 | 0.21 | 0.215 | 0.272 | 0.149 | 0.309 | 0.216 | 0.08 | 0.309 | 0.219 | 0.275 | −0.15 | 0.08 | 0.155 | −0.085 | 0.17 |
| P | 0.218 | 0.03 | 0.002 | 0.015 | 0.008 | 0.048 | 0.043 | 0.01 | 0.162 | 0.003 | 0.042 | 0.454 | 0.003 | 0.039 | 0.009 | 0.159 | 0.45 | 0.146 | 0.431 | 0.111 | |
| Tyr | R | −0.136 | 0.387 | 0.409 | 0.377 | 0.297 | 0.387 | 0.391 | 0.315 | 0.327 | 0.401 | 0.339 | 0.029 | 0.448 | 0.153 | 0.36 | −0.245 | 0.022 | 0.09 | −0.247 | 0.186 |
| P | 0.167 | <0.001 | <0.001 | <0.001 | 0.005 | <0.001 | <0.001 | 0.003 | 0.002 | <0.001 | 0.001 | 0.785 | <0.001 | 0.152 | 0.001 | 0.02 | 0.837 | 0.399 | 0.02 | 0.081 | |
Note: Pearson r correlation, Significance (two-tailed) P＜0.05, statistically significant values are indicated in bold
Fig. 2.
Heatmap of Pearson correlation coefficients between serum amino acid levels and metabolic parameters. Color intensity represents the strength and direction of correlations, with blue indicating positive correlations and red indicating negative correlations. Statistical significance was determined separately at p < 0.05
Multiple linear stepwise regression analysis
A multiple linear stepwise regression analysis was performed to identify the independent associations between circulating BCAAs and metabolic outcomes. In these models, Val, Leu, Ile, Phe, and Tyr were each entered as a dependent variable. Independent variables included BW, BMI, BF, PBF, FFM, SMM, VFA, BMR, WC, HC, FPG, FINS, HOMA-IR, ISI, TG, TC, HDL-c, LDL-c. All models were adjusted for age and sex. Stepwise selection was applied with entry and removal criteria set at p < 0.05 and p > 0.10, respectively. The results showed that valine was independently and positively associated with WC (β = 0.357, p = 0.001) and HOMA-IR (β = 0.306, p = 0.003), with a model R2 of 0.328 (adjusted R2 = 0.312). Leu was independently positively associated with WC (β = 0.283, p = 0.013) and FINS (β = 0.242, p = 0.034), with a model R2 of 0.210 (adjusted R2 = 0.191). Ile was also independently positively associated with WC (β = 0.254, p = 0.022) and FINS (β = 0.324, p = 0.004), with a model R2 of 0.254 (adjusted R2 = 0.237). No independent associations were observed between phenylalanine, tyrosine, and the studied metabolic parameters. These findings demonstrate that valine is independently associated with both WC and IR, as assessed by HOMA-IR, whereas Leu and Ile are independently associated with WC and fasting insulin levels. Detailed regression results are shown in Table 4, and independent associations from the multivariable models are illustrated in Fig. 3.
Table 4.
Multivariate stepwise linear regression analysis
| DV | IV | B | SE | β | t | P | Tol | VIF | 95% CI | R2 | Adjusted R2 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Val | Constant | 17.262 | 4.397 | - | 3.926 | 0.000 | - | - | 8.521–26.003 | 0.328 | 0.312 |
| WC | 0.150 | 0.042 | 0.357 | 3.519 | 0.001 | 0.760 | 1.315 | 0.065–0.234 | |||
| HOMA-IR | 0.413 | 0.137 | 0.306 | 3.020 | 0.003 | 0.760 | 1.315 | 0.141–0.685 | |||
| Leu | Constant | 10.719 | 3.836 | - | 2.795 | 0.006 | - | - | 3.094–18.343 | 0.210 | 0.191 |
| WC | 0.095 | 0.038 | 0.283 | 2.530 | 0.013 | 0.733 | 1.364 | 0.020–0.170 | |||
| FINS | 0.012 | 0.006 | 0.242 | 2.159 | 0.034 | 0.733 | 1.364 | 0.001–0.023 | |||
| Ile | Constant | 5.090 | 2.328 | - | 2.186 | 0.032 | - | - | 0.462–9.718 | 0.254 | 0.237 |
| WC | 0.053 | 0.023 | 0.254 | 2.333 | 0.022 | 0.733 | 1.364 | 0.008–0.099 | |||
| FINS | 0.010 | 0.003 | 0.324 | 2.976 | 0.004 | 0.733 | 1.364 | 0.003–0.017 |
Note: “–” indicates not applicable. Models were adjusted for age and sex. DV, dependent variable; IV, independent variable; SE, standard error; B, unstandardized regression coefficient; β, standardized regression coefficient; Tol, tolerance; VIF, variance inflation factor; CI, confidence interval
Fig. 3.
Graphical illustration of the independent associations between circulating BCAAs and metabolic parameters identified in the multivariable stepwise regression analyses
Discussion
This cross-sectional study systematically evaluated the association between BCAAs, body fat composition, and metabolic parameters in obese individuals with or without T2DM. A total of 105 participants were included in the study, comprising obese individuals and normal-weight controls. The results showed that circulating BCAAs were significantly higher in obese individuals, especially those with T2DM, than in the control group. Among the individual amino acids, valine exhibited the strongest correlation with insulin resistance (HOMA-IR) (r = 0.481, p < 0.001), alongside consistent correlations with adiposity indices and glycemic parameters. Furthermore, multivariable regression analysis confirmed that valine was independently associated with WC (β = 0.357, p = 0.001) and HOMA-IR (β = 0.306, p = 0.003), suggesting that valine is independently related to both central adiposity and insulin resistance. In addition to valine, leucine and isoleucine were also independently associated with WC and FINS, suggesting that multiple BCAAs may contribute to central adiposity-related metabolic disturbances and insulin resistance. These findings suggest that alterations in BCAAs metabolism, particularly valine metabolism, are closely associated with insulin resistance and central adiposity in individuals with obesity. These findings are consistent with previous studies reporting elevated circulating BCAAs levels in obesity and their close relationship with insulin resistance and metabolic dysfunction [26]. The independent association between valine and insulin resistance, even after adjusting for measures of adiposity, suggests that valine’s role is not solely mediated by fat accumulation. Although VFA was found to correlate with both BCAAs levels and insulin resistance, it did not remain an independent predictor in multivariable models. Visceral adipose tissue is metabolically active and drains directly into the portal circulation, meaning it has a disproportionate impact on hepatic metabolism and systemic insulin sensitivity.
Excessive fat accumulation in adipose tissue is mainly caused by physical inactivity, diet, and genetic predisposition [28]. Numerous studies have supported the hypothesis that the accumulation of intra-abdominal fat plays a specific role in linking visceral obesity with metabolic dysfunction [3]. Several studies have examined the differences in the regulation of BCAAs and AAAs metabolism in abdominal fat, particularly in visceral adipose tissue. These studies found that the expression of catabolic enzymes for BCAAs and certain AAAs is predominantly altered in visceral adipose tissue [29]. The accumulation of visceral fat serves as both a potential risk factor and an early diagnostic marker for obesity-related diseases [28]. Although many studies have examined changes in plasma metabolites in obesity, few have focused on biomarkers specific to visceral adipose tissue or sought to identify amino acids linked to visceral fat [30]. Previous studies have reported associations between circulating BCAAs and visceral fat accumulation [31]; however, most investigations have focused on total BCAAs concentrations rather than the individual contribution of specific amino acids [32]. The current study extends existing evidence by demonstrating that valine, in particular, exhibits a stronger and independent relationship with insulin resistance. This finding highlights the importance of analysing individual amino acid profiles rather than aggregated BCAAs measures to better understand metabolic heterogeneity in obesity.
The underlying mechanisms linking elevated BCAAs levels to metabolic dysfunction are multifactorial. Impaired BCAAs catabolism, particularly reduced activity of the branched-chain α-keto acid dehydrogenase complex, has been reported in obesity. This impairment leads to the accumulation of circulating BCAAs and their intermediate metabolites. These metabolites can interfere with insulin signalling pathways and contribute to metabolic inflexibility [33]. In addition, elevated BCAAs have been shown to activate the mTOR pathway, contributing to insulin resistance through feedback inhibition of insulin signalling. As adipose tissue, particularly visceral fat, plays a central role in BCAAs metabolism, dysfunction in this compartment may amplify these metabolic effects [34].
From a clinical perspective, the present findings suggest that BCAAs profiles, particularly valine, may help identify individuals with increased metabolic risk. Compared with traditional anthropometric measures such as BMI or WC, metabolite-based indicators may provide earlier insight into metabolic disturbances. However, the clinical utility of BCAAs profiling remains to be established, particularly regarding cost-effectiveness, standardisation, and predictive value across diverse populations. Because this study is cross-sectional, these findings should be considered exploratory, and further validation in prospective cohorts is required before any clinical application.
Several limitations should be considered when interpreting these findings. Firstly, the cross-sectional design prevents any causal inferences regarding the relationship between BCAAs and insulin resistance. Therefore, the observed associations should be interpreted as exploratory and hypothesis-generating rather than predictive or causal. Secondly, the relatively modest sample size may limit the generalisability. Additionally, dietary intake, physical activity, and protein intake were not comprehensively assessed and may represent sources of residual confounding. Finally, direct measurements of BCAAs catabolic enzyme activity were not performed, limiting mechanistic interpretation. Future studies should employ longitudinal designs to clarify the temporal relationship between alterations in BCAAs and insulin resistance. Interventional studies targeting BCAAs metabolism may further elucidate causality. In addition, integrative metabolomic approaches combined with tissue-specific analyses could provide deeper insights into the role of amino acid metabolism in visceral obesity and metabolic disease.
Conclusion
Elevated BCAAs, particularly valine, are associated with central adiposity and insulin resistance in obesity. These findings suggest that BCAAs profiling may help identify individuals with increased metabolic risk. Valine, which was independently associated with HOMA-IR, may represent a potential marker of insulin resistance; however, further prospective studies are required to validate its predictive value and explore the role of BCAAs metabolism in metabolic disturbances.
Acknowledgements
We would like to thank the Yangzhou City Social Development Research Project (No: YZ2023128) for funding this study. Special thanks to the Ethical Committee of Northern Jiangsu People’s Hospital for their approval, and to all the participants for their consent to participate. We also appreciate the contributions of our co-authors, Fatima Gul, Asad Ullah, and Yue Li, for conceptualization, study design, data collection, and manuscript drafting, and of Dunmin She and Ying Li for their guidance and final approval of the manuscript.
Abbreviations
- BCAAs
Branched-Chain Amino Acids
- T2DM
Type 2 Diabetes Mellitus
- IR
Insulin Resistance
- VFA
Visceral Fat Area
- BMI
Body Mass Index
- HOMA-IR
Homeostasis Model Assessment of Insulin Resistance
- HbA1c
Hemoglobin A1c
- BCKDH
Branched-Chain α-Keto Acid Dehydrogenase
- BCKD
Branched-Chain Keto Acid Dehydrogenase
- PPM1K
Protein Phosphatase 1K
- FPG
Fasting Plasma Glucose
- FINS
Fasting Insulin
- TG
Total Triglycerides
- TC
Total Cholesterol
- HDL-C
High-Density Lipoprotein Cholesterol
- LDL-C
Low-Density Lipoprotein Cholesterol
- ISI
Insulin Sensitivity Index
- LC-MS/MS
Liquid Chromatography-Mass Spectrometry/Mass Spectrometry
- SPSS
Statistical Package for the Social Sciences
- ANOVA
Analysis of Variance
- SD
Standard Deviation
- CV
Coefficient of Variation
- ICPC
International Classification of Primary Care
- BIA
Bioelectrical Impedance Analysis
- BMR
Basal Metabolic Rate
- WC
Waist Circumference
- HC
Hip Circumference
Author contributions
Fatima Gul1, Asad Ullah1, Yue Li1: Conceptualization of the research idea and hypothesis. Design of the study methodology. Data collection and data analysis. Drafting the initial manuscript and revising it critically for intellectual content. Zhimin Qian, Ling Cao Literature review and identification of key sources. Dunmin She and Ying Li: Supervision of the overall research project. Funding acquisition and project management. Final approval of the manuscript for submission.
Funding
This research was funded by the Yangzhou City Social Development, Research Project (YZ2023128).
Data availability
The data generated and analyzed in this study are available from the corresponding author upon request.
Declarations
Ethics approval and consent to participate
This work was conducted with the approval of the Ethical Committee of Northern Jiangsu People’s Hospital Affiliated to Yangzhou University (2021KY025-2). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki and its later amendments. Informed consent was obtained from each patient after a full explanation of the purpose and nature of all procedures to be used.
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.
Fatima Gul, Asad Ullah, and Yue Li contributed equally and share first authorship.
Contributor Information
Dunmin She, Email: sdm1979@126.com.
Ying Li, Email: liying1631230@163.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 data generated and analyzed in this study are available from the corresponding author upon request.