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ORIGINAL RESEARCH article

Front. Endocrinol., 20 February 2025

Sec. Cardiovascular Endocrinology

Volume 16 - 2025 | https://doi.org/10.3389/fendo.2025.1510910

Circulating branched-chain amino acids and the risk of major adverse cardiovascular events in the UK biobank

  • 1. Department of Endocrinology, Huashan Hospital Affiliated to Fudan University, Shanghai, China

  • 2. Department of Biostatistics, School of Public Health, The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China

  • 3. Department of Endocrinology, Huadong Hospital Affiliated to Fudan University, Shanghai, China

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Abstract

Objective:

To investigate the relationship between circulating branched-chain amino acids (BCAAs) and the risk of major adverse cardiovascular events (MACE) in a national population-based cohort study.

Methods:

UK Biobank, a prospective study involving 22 recruitment centers across the United Kingdom. For this analysis, we included 266,840 participants from the UK Biobank who had available BCAA data and no history of MACE at baseline. Cox regression analysis was conducted to evaluate these associations, adjusting for potential confounders.

Results:

During a 13.80 ± 0.83-year follow-up, 52,598 participants experienced MACE, with the incidence of MACE increasing progressively across quintiles of circulating BCAAs, isoleucine, leucine, and valine. Overall, the fifth quintile exhibited a 7-12% higher MACE risk compared to the second quintile. In males, BCAAs were not associated with MACE risk. However, increased risks were observed for isoleucine (8-12% in higher quintiles), leucine (9% in the first quintile and 6% in the fifth quintile), and valine (8% in the first quintile). In females, higher quintiles of BCAAs, isoleucine, leucine, and valine were associated with increased MACE risk, ranging from 9% to 12%. Among participants under 65y, higher quintiles of BCAAs, isoleucine, and leucine were associated with increased MACE risk, while valine showed no significant association. No association was found in participants aged 65 and older. These analyses were adjusted for multiple potential confounders.

Conclusion:

Generally, higher levels of BCAAs, isoleucine, leucine, and valine were associated with an increased risk of MACE, except in participants older than 65. Additionally, in males, the lowest quintiles of leucine and valine were also associated with an increased risk of MACE.

1 Introduction

Branched-chain Amino Acids (BCAAs) — namely isoleucine, leucine, and valine — are essential amino acids that play a crucial role in metabolic homeostasis through nutritional signaling (1). Elevated levels of BCAAs and their related metabolites are now recognized as metabolic markers for obesity, insulin resistance, and type 2 diabetes in humans (2, 3). Given the close link between metabolic disorders and the pathogenesis of cardiovascular disease (CVD), research suggests that BCAAs may directly contribute to heart failure (HF) (4), vascular disease (5, 6), hypertension (7), and arrhythmias (8).

Currently, the mechanisms underlying the association between BCAAs and cardiovascular disease (CVD) are not fully understood. Known mechanisms include the activation of the serine/threonine protein kinase mTOR by BCAAs (9), mitochondrial dysfunction (10), alterations in cardiac substrate utilization, and platelet activation (11).

Additional studies have found that cardiovascular disease (CVD) can induce changes in tissues that regulate BCAA homeostasis, such as skeletal muscle, liver, and adipose tissue. These changes may contribute to elevated circulating level of BCAAs in individuals with CVD (12). Therefore, further research is needed to elucidate the causal relationship between BCAA dysregulation and CVD.

Currently, there are limited large-scale, prospective clinical studies investigating the causal relationship between BCAAs - including isoleucine, leucine, and valine - and cardiovascular disease (CVD) risk (13). While most studies suggest that high levels of amino acids (AAs) predict increased CVD risk (14), many of these studies have primarily focused on aromatic amino acids and specific populations (such as heart failure patients, women, and the elderly) (13, 1518), and typically involve relatively small sample sizes. Some studies have even suggested that reduced BCAAs levels are associated with an increased risk of major adverse cardiovascular events (MACE), particularly in populations of elderly men over 70 years of age (17). Given the notable differences in BCAA levels between men and women, as well as across age groups, there is a need for large-sample studies that systematically explore how BCAA levels correlate with CVD risk across various demographics.

Our study aimed to investigate the relationship between baseline BCAAs and the risk of major adverse cardiovascular events (MACE) in a large prospective cohort. Additionally, we explored whether gender and age influence this relationship.

2 Materials and methods

2.1 Study design and sample

UK Biobank recruited participants aged between 40 and 70 years from across the UK between 2006 to 2010 (19). At enrollment, comprehensive data were collected on sociodemographic factors (e.g., age, sex), lifestyle behaviors (smoking and drinking status, diet, and physical activity level), medical history, and genetic information through touchscreen questionnaires, physical examinations, and sample analyses. Baseline biochemical assays included amino acids, HbA1c, and blood lipids measurements.

Health-related outcomes were monitored through regular linkages with various national datasets, including primary care records, hospital admissions, and mortality registries (20).

2.2 Standard protocol approvals, registrations, and patient consents

Assessments were conducted at 22 centers across 22 Scotland, England, and Wales as part of the UK Biobank study. The assessment process consisted of five components: written consent, touchscreen questionnaires (including detailed dietary recall), face-to-face interviews with study nurses, physical measurements (such as hand grip strength, spirometry, and bone density scans), and collection of blood, urine, and saliva samples.

The UK Biobank database contains information from 502,359 participants. In this study, participants who had prevalent MACE at the time of recruitment (n=13,546) or had missing values for BCAAs, isoleucine, leucine, and valine (n=222,003) were excluded from the analysis. Ultimately, 266,840 participants were included in the study. The UK Biobank has obtained ethical approval from the NHS National Research Ethics Service (16/NW/0274), and all participants provided informed consent before data collection.

2.3 Outcome measure: MACE

The primary outcome of this study was the incidence of MACE, defined as a composite endpoint including cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, and hospitalization for unstable angina. Cases were identified using hospital inpatient records (primary or secondary hospital diagnosis) or death registry records (underlying or contributory cause of death). Diagnosis were based on the International Cleucinesification of Diseases coding system (ICD-9 and ICD-10).

2.4 BCAAs and covariates

Given the inconsistencies in prior research regarding the correlation between BCAAs levels and the risk of MACE, particularly in males, we hypothesize a potential J-shaped relationship. To more effectively explore this relationship, we have categorized the levels of BCAAs, isoleucine, leucine, and valine into quintiles,Q2 was used as the reference category. Covariates included gender, age, body mass index (BMI) categorized as underweight (<18.5kg/m2), normal (18.5-23.9 kg/m2), overweight (24.0-28.0 kg/m2), and obese (≥28.0 kg/m2) (21). Other covariates included HbA1c, Low-density lipoprotein cholesterol(LDL-c), systolic blood pressure(SBP), smoking and drinking status (never, current, and past), and physical activity level (measured in MET).

2.5 Statistical analysis

Cumulative incidence of MACE was computed according to maternal HDP status. We examined the association of BCAAs level - including isoleucine, leucine, and valine - with MACE using a Cox regression model with follow-up time as the time scale. Adjusted hazard ratios and corresponding 95% confidence intervals were calculated after adjusting for gender, age, body mass index (BMI)(underweight: <18.5, normal: 18.5-23.9, overweight: 24.0-28.0, obese: ≥28.0), HbA1c, LDL-c, SBP, smoking and drinking status(never, current and past), and activity (Metabolic Equivalent of Task, MET).

To evaluate the influence of gender and age on the relationship between BCAAs and MACE, we conducted separate analyses for men and women, as well as for individuals aged 65 and above compared to those below this age threshold.

3 Results

3.1 Baseline characteristics

A total of 266840 individuals were included in the final study, 121,067(45.37%) males and 145,773 (54.63%) females. The baseline average age was 56.95 ± 8.08 yrs. During a follow-up period of 13.80 ± 0.83 years, MACE occurred in 52,598(10.47%) participants.

Baseline characteristics showed that BMI, HbA1c, triglyceride, and C-reactive protein (CRP) increased progressively across the quintile of BCAAs, while HDL-C level decreased across the quintiles. Age, SBP, and DBP (diastolic blood pressure) showed consistent trends(Table 1). Similar trends were observed for BMI, blood pressure, HbA1c, triglycerides, CRP, HDL, Age, SBP, and DBP across the quintiles of isoleucine, leucine, and valine.

Table 1

Quintiles of BCAAs
Characteristics Q1 Q2 Q3 Q4 Q5 P value
Age (years) 56.38 ± 8.30 57.12 ± 8.09 57.17 ± 8.04 57.10 ± 8.00 57.00 ± 7.94 <.0001
BMI (kg/m2) 25.54 ± 4.33 26.76 ± 4.53 27.58 ± 4.65 28.28 ± 4.71 28.93 ± 4.86 <.0001
SBP (mmHg) 137.34 ± 20.47 139.47 ± 19.97 140.45 ± 19.58 140.98 ± 19.15 140.68 ± 18.71 <.0001
DBP (mmHg) 80.54 ± 10.62 81.96 ± 10.68 82.71 ± 10.61 83.19 ± 10.63 82.65 ± 10.59 <.0001
HbA1c (%) 34.60 (32.20-37.00) 35.00 (32.60-37.40) 35.20 (32.80-37.80) 35.40 (33.00-38.20) 35.80 (33.20-38.90) <.0001
Total cholesterol (mmol/L) 5.71 ± 1.10 5.75 ± 1.12 5.74 ± 1.13 5.71 ± 1.15 5.66 ± 1.16 <.0001
Triglyceride (mmol/L) 1.11 (0.84-1.52) 1.31 (0.97-1.81) 1.48 (1.08-2.07) 1.70 (1.22-2.38) 2.03 (1.43-2.89) <.0001
HDL-C (mmol/L) 1.63 ± 0.40 1.52 ± 0.38 1.43 ± 0.36 1.36 ± 0.34 1.30 ± 0.33 <.0001
LDL-C (mmol/L) 3.50 ± 0.84 3.59 ± 0.86 3.62 ± 0.86 3.61 ± 0.87 3.56 ± 0.87 <.0001
CRP (mmol/L) 1.06 (0.52-2.30) 1.24 (0.61-2.63) 1.37 (0.68-2.80) 1.46 (0.74-2.97) 1.54 (0.79-3.05) <.0001
Quintiles of Isoleucine
Q1 Q2 Q3 Q4 Q5
Age (years) 56.57 ± 8.27 57.10 ± 8.09 57.12 ± 8.01 56.94 ± 8.04 57.04 ± 7.99 <.0001
BMI (kg/m2) 25.90 ± 4.43 26.87 ± 4.61 27.55 ± 4.69 28.16 ± 4.79 28.59 ± 4.83 <.0001
SBP (mmHg) 137.95 ± 20.30 139.62 ± 20.01 140.47 ± 19.69 140.69 ± 19.18 140.19 ± 18.81 <.0001
DBP (mmHg) 81.00 ± 10.58 82.08 ± 10.69 82.77 ± 10.68 82.99 ± 10.65 82.21 ± 10.61 <.0001
HbA1c (%) 34.70 (32.30-37.10) 35.00 (32.60-37.50) 35.20 (32.80-37.80) 35.40 (32.90-38.20) 35.70 (33.20-38.70) <.0001
Total cholesterol (mmol/L) 5.76 ± 1.11 5.76 ± 1.12 5.73 ± 1.13 5.68 ± 1.14 5.64 ± 1.16 <.0001
Triglyceride (mmol/L) 1.15 (0.86-1.58) 1.33 (0.97-1.85) 1.49 (1.07-2.09) 1.66 (1.18-2.34) 1.97 (1.38-2.79) <.0001
HDL-C (mmol/L) 1.61 ± 0.40 1.51 ± 0.38 1.43 ± 0.36 1.37 ± 0.35 1.32 ± 0.34 <.0001
LDL-C (mmol/L) 3.55 ± 0.85 3.60 ± 0.86 3.60 ± 0.87 3.58 ± 0.87 3.53 ± 0.87 <.0001
CRP (mmol/L) 1.10 (0.54-2.38) 1.25 (0.62-2.65) 1.36 (0.68-2.81) 1.44 (0.73-2.93) 1.50 (0.76-2.99) <.0001
Quintiles of Leucine
Q1 Q2 Q3 Q4 Q5
Age (years) 56.91 ± 8.22 57.10 ± 8.09 57.11 ± 8.04 56.88 ± 8.04 56.77 ± 8.02 <.0001
BMI (kg/m2) 25.85 ± 4.61 26.86 ± 4.67 27.54 ± 4.67 28.16 ± 4.65 28.67 ± 4.71 <.0001
SBP (mmHg) 138.20 ± 20.69 139.48 ± 20.09 140.32 ± 19.58 140.75 ± 19.07 140.16 ± 18.54 <.0001
DBP (mmHg) 80.84 ± 10.72 81.95 ± 10.65 82.68 ± 10.63 83.16 ± 10.64 82.41 ± 10.53 <.0001
HbA1c (%) 34.80 (32.40-37.20) 35.00 (32.60-37.50) 35.20 (32.80-37.80) 35.30 (32.90-38.10) 35.60 (33.10-38.60) <.0001
Total cholesterol (mmol/L) 5.77 ± 1.12 5.76 ± 1.12 5.74 ± 1.12 5.69 ± 1.13 5.61 ± 1.15 <.0001
Triglyceride (mmol/L) 1.14 (0.85-1.60) 1.33 (0.97-1.86) 1.48 (1.07-2.08) 1.65 (1.18-2.32) 1.95 (1.38-2.75) <.0001
HDL-C (mmol/L) 1.63 ± 0.41 1.52 ± 0.38 1.43 ± 0.36 1.36 ± 0.34 1.31 ± 0.33 <.0001
LDL-C (mmol/L) 3.53 ± 0.85 3.59 ± 0.86 3.61 ± 0.86 3.59 ± 0.87 3.54 ± 0.87 0.4907
CRP (mmol/L) 1.13 (0.54-2.49) 1.27 (0.62-2.70) 1.35 (0.68-2.78) 1.42 (0.72-2.89) 1.47 (0.75-2.89) <.0001
Quintiles of Valine
Q1 Q2 Q3 Q4 Q5
Age (years) 56.06 ± 8.35 57.07 ± 8.10 57.21 ± 8.04 57.27 ± 7.95 57.15 ± 7.89 <.0001
BMI (kg/m2) 25.46 ± 4.21 26.71 ± 4.46 27.49 ± 4.58 28.30 ± 4.74 29.13 ± 4.96 <.0001
SBP (mmHg) 136.94 ± 20.37 139.42 ± 19.89 140.33 ± 19.53 141.00 ± 19.23 141.24 ± 18.79 <.0001
DBP (mmHg) 80.44 ± 10.63 81.91 ± 10.65 82.61 ± 10.60 83.08 ± 10.64 83.01 ± 10.57 <.0001
HbA1c (%) 34.50 (32.20-37.00) 35.00 (32.60-37.40) 35.20 (32.80-37.80) 35.50 (33.00-38.20) 36.00 (33.30-39.10) <.0001
Total cholesterol (mmol/L) 5.66 ± 1.08 5.74 ± 1.11 5.74 ± 1.13 5.72 ± 1.15 5.71 ± 1.18 <.0001
Triglyceride (mmol/L) 1.10 (0.84-1.50) 1.31 (0.97-1.79) 1.48 (1.08-2.06) 1.71 (1.22-2.39) 2.08 (1.45-2.98) <.0001
HDL-C (mmol/L) 1.62 ± 0.39 1.52 ± 0.38 1.44 ± 0.36 1.37 ± 0.35 1.30 ± 0.34 <.0001
LDL-C (mmol/L) 3.47 ± 0.83 3.58 ± 0.86 3.61 ± 0.86 3.62 ± 0.88 3.59 ± 0.88 <.0001
CRP(mmol/L) 1.04 (0.51-2.24) 1.23 (0.61-2.56) 1.35 (0.67-2.78) 1.47 (0.75-2.98) 1.59 (0.82-3.18) <.0001

Baseline characteristics according to quintiles of BCAAs, isoleucine, leucine and valine.

BMI, Body Mass Index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; HDL-C, High Density Lipoprotein Cholesterol; LDL-C, Low Density Lipoprotein Cholesterin; CRP, C-Reactive Protein.

3.2 Incidence of MACE

In the overall population (Figure 1A), the incidence of MACE increased progressively across the quintiles for each BCAA:

  • For BCAAs: 6.34% (1st quintile), 7.18% (2nd quintile), 8.08% (3rd quintile), 8.79% (4th quintile), and 9.79% (5th quintile).

  • For isoleucine: 6.39% (1st quintile), 7.24% (2nd quintile), 7.99% (3rd quintile), 8.97% (4th quintile), and 9.58% (5th quintile).

  • For leucine: 6.63% (1st quintile), 7.13% (2nd quintile), 8.02% (3rd quintile), 8.65% (4th quintile), and 9.74% (5th quintile).

  • For valine: 6.42% (1st quintile), 7.16% (2nd quintile), 7.98% (3rd quintile), 8.85% (4th quintile), and 9.76% (5th quintile).

Figure 1

Figure 1

(A–E) Incidence of MACE according to the quintiles of BCAAs, isoleucine, leucine and valine. (A) Incidence of MACE according to the quintiles of BCAAs, isoleucine, leucine and valine in overall population. (B) Incidence of MACE according to the quintiles of BCAAs, isoleucine, leucine and valine in males. (C) Incidence of MACE according to the quintiles of BCAAs, isoleucine, leucine and valine in females. (D) Incidence of MACE according to the quintiles of BCAAs, isoleucine, leucine and valine in participants under 65. (E) Incidence of MACE according to the quintiles of BCAAs, isoleucine, leucine and valine in 65 and older.

In males (Figure 1B), for BCAAs, using the second quintile (10.99%) as a reference, the incidence of MACE was higher in the first quintile (11.33%), and then varied slightly in the third (10.64%), fourth (10.85%), and fifth quintiles (12%). For isoleucine, with the second quintile (10.64%) as a reference, the incidence of MACE was higher in the first quintile (10.75%), and continued to increase in the third (10.70%), fourth (11.33%), and fifth quintiles (12.03%). For leucine, taking the second quintile (11.02%) as a reference, the first quintile showed a higher incidence of MACE (12.14%), while the third (10.78%), fourth (10.67%), and fifth quintiles (11.74%) exhibited variations. For valine, using the second quintile (10.7%) as a reference, the incidence of MACE was higher in the first quintile (11.26%) and increased slightly in the third (10.65%), fourth (11%), and fifth quintiles (12.08%).

In females (Figure 1C), for BCAAs, the incidence of MACE increased across the quintiles: 4.8% (1st quintile), 4.9% (2nd quintile), 5.71% (3rd quintile), 6.05% (4th quintile), and 6.37% (5th quintile). For isoleucine, the incidence of MACE rosed gradually across the quintiles: 4.74% (1st quintile), 5.12% (2nd quintile), 5.52% (3rd quintile), 6.04% (4th quintile), and 6.23% (5th quintile). For leucine, the incidence of MACE increased with the quintiles: 5.07% (1st quintile), 5.03% (2nd quintile), 5.52% (3rd quintile), 5.79% (4th quintile), and 6.28% (5th quintile). For valine, the incidence of MACE increased across the quintiles: 4.73% (1st quintile), 4.94% (2nd quintile), 5.54% (3rd quintile), 6.21% (4th quintile), and 6.37% (5th quintile).

In participants under 65 (Figure 1D), the incidence of MACE increased progressively across the quintiles for each BCAA:

  • For BCAAs: 4.99% (1st quintile), 5.66% (2nd quintile), 6.34% (3rd quintile), 7.15% (4th quintile), and 8.08% (5th quintile).

  • For isoleucine: 5.09% (1st quintile), 5.69% (2nd quintile), 6.27% (3rd quintile), 7.30% (4th quintile), and 7.88% (5th quintile).

  • For leucine: 5.17% (1st quintile), 5.57% (2nd quintile), 6.37% (3rd quintile), 6.97% (4th quintile), and 8.10% (5th quintile).

  • For valine: 5.02% (1st quintile), 5.73% (2nd quintile), 6.30% (3rd quintile), 7.13% (4th quintile), and 8.07% (5th quintile).

In all groups, the p-values were <0.05, indicating statistical significance.

In participants aged 65 and older (Figure 1E), the incidence of MACE also increased across the quintiles for each BCAA:

  • For BCAAs: 12.54% (1st quintile), 13.42% (2nd quintile), 15.34% (3rd quintile), 15.86% (4th quintile), and 17.62% (5th quintile).

  • For isoleucine: 12.20% (1st quintile), 13.71% (2nd quintile), 15.32% (3rd quintile), 16.38% (4th quintile), and 17.14% (5th quintile).

  • For leucine: 12.73% (1st quintile), 13.59% (2nd quintile), 15.07% (3rd quintile), 16.16% (4th quintile), and 17.49% (5th quintile).

  • For valine: 13.22% (1st quintile), 13.10% (2nd quintile), 14.88% (3rd quintile), 16.16% (4th quintile), and 17.32% (5th quintile).

In the overall population (Figures 2A–D), the cumulative incidence of MACE was lowest in the first quintile for BCAAs, isoleucine, leucine, and valine, with a gradual increase across the higher quintiles. The log-rank P values for the survival curves in Figures 2A–D were all <0.001.

Figure 2

Figure 2

(A) Cumulative incidence of MACE according to the quintiles of BCAAs. (B) Cumulative incidence of MACE according to the quintiles of isoleucine. (C) Cumulative incidence of MACE according to the quintiles of leucine. (D) Cumulative incidence of MACE according to the quintiles of valine.

3.3 Association between BCAAs and MACE

In the overall population (Table 2A), compared to the second quintile, individuals in the fifth quintile of BCAAs had a 7% increased risk of MACE (95% CI 1.02-1.13) after adjusting for age, gender, BMI, HbA1c, LDL, SBP, smoking and drinking status, and physical activity. For isoleucine, the increased risk of MACE was 8% in the fourth quintile (95% CI 1.03-1.14), and 11% in the fifth quintile (95% CI 1.06-1.16). For leucine, participants in the first quintile had a 5% increased risk of MACE (95% CI 1.00-1.11), while those in the fifth quintile had an 8% increased risk (95% CI 1.03-1.13). For valine, the increased risk was 6% in both the first quintile (95% CI 1.01-1.12) and the fifth quintile (95% CI 1.01-1.11).

Table 2A

No. of MACE Rate of 1000 Case/Rate of 1000 Crude HR (95% CI) Adjusted HR (95% CI) P value
Quintiles Overall population
BCAAs
Q2 3827 5.512944399 3827/5.51 1.00 (Ref.) 1.00 (Ref.)
Q1 3393 4.866189389 3393/4.87 0.88 (0.84-0.92) 1.04 (0.99-1.10) 0.1322
Q3 4311 6.231970183 4311/6.23 1.13 (1.08-1.18) 1.01 (0.96-1.06) 0.7911
Q4 4687 6.785146554 4687/6.79 1.23 (1.18-1.29) 1.02 (0.97-1.07) 0.3708
Q5 5222 7.573260286 5222/7.57 1.37 (1.32-1.43) 1.07 (1.02-1.13) 0.0035
Isoleucine
Q2 3865 5.566568233 3865/5.57 1.00 (Ref.) 1.00 (Ref.)
Q1 3410 4.884584373 3410/4.88 0.88 (0.84-0.92) 1.03 (0.97-1.08) 0.3294
Q3 4265 6.169475069 4265/6.17 1.11 (1.06-1.16) 1.03 (0.98-1.08) 0.2948
Q4 4788 6.939626693 4788/6.94 1.25 (1.20-1.30) 1.08 (1.03-1.14) 0.001
Q5 5112 7.410744561 5112/7.41 1.33 (1.28-1.39) 1.11 (1.06-1.16) <.0001
Leucine
Q2 3806 5.477228949 3806/5.48 1.00 (Ref.) 1.00 (Ref.)
Q1 3539 5.098394774 3539/5.10 0.93 (0.89-0.97) 1.05 (1.00-1.11) 0.0458
Q3 4280 6.180889455 4280/6.18 1.13 (1.08-1.18) 1.01 (0.96-1.06) 0.7599
Q4 4617 6.680026257 4617/6.68 1.22 (1.17-1.27) 1.00 (0.96-1.06) 0.8505
Q5 5198 7.523889142 5198/7.52 1.37 (1.32-1.43) 1.08 (1.03-1.13) 0.0017
Valine
Q2 3816 5.495114387 3816/5.50 1.00 (Ref.) 1.00 (Ref.)
Q1 3437 4.933773993 3437/4.93 0.90 (0.86-0.94) 1.06 (1.01-1.12) 0.0206
Q3 4258 6.155426855 4258/6.16 1.12 (1.07-1.17) 1.00 (0.96-1.06) 0.8525
Q4 4722 6.831947177 4722/6.83 1.24 (1.19-1.30) 1.04 (0.99-1.09) 0.1355
Q5 5207 7.551508749 5207/7.55 1.37 (1.32-1.43) 1.06 (1.01-1.11) 0.0134

Hazard ratios for associations between MACE and BCAAs in overall population.

Adjusted model:Adjusted for age,gender,BMI,HbA1c,LDL,SBP,smoking and drinking status, physical activity. HR, Hazard ratio.

In males (Table 2B), BCAAs didn’t show any significant association with the risk of MACE. For isoleucine, the risk of mace increased by 8% (95% CI 1.02-1.15) in the fourth quintile and by 12% (95% CI 1.05-1.19) in the fifth quintile. For leucine, the risk of mace increased by 9% (95% CI 1.01-1.18) in the first quintile and 6% (95% CI 1.00-1.12) in the fifth quintile. For valine, the risk of mace increased by 8% (95% CI 1.00-1.16) in the first quintile.

Table 2B

MACE No. of MACE Rate of 1000 Case/Rate of 1000 Crude HR (95% CI) Adjusted HR (95% CI) P value
Quintiles Males
BCAAs
Q2 2194 8.667130207 2194/8.67 1.00 (Ref.) 1.00 (Ref.)
Q1 1432 9.125505822 1432/9.13 1.07 (1.01-1.13) 1.04 (0.96-1.12) 0.3053
Q3 2729 8.359702265 2729/8.36 1.00 (0.95-1.06) 0.95 (0.90-1.02) 0.1476
Q4 3301 8.490216466 3301/8.49 1.02 (0.97-1.08) 0.97 (0.91-1.03) 0.3452
Q5 3888 9.405020018 3888/9.41 1.13 (1.07-1.19) 1.04 (0.98-1.10) 0.2452
Isoleucine
Q2 2186 8.372945994 2186/8.37 1.00 (Ref.) 1.00 (Ref.)
Q1 1573 8.513950953 1573/8.51 1.03 (0.97-1.09) 1.06 (0.99-1.14) 0.1192
Q3 2726 8.409756389 2726/8.41 1.05 (0.99-1.10) 1.03 (0.96-1.09) 0.4453
Q4 3351 8.901891594 3351/8.90 1.07 (1.01-1.13) 1.08 (1.02-1.15) 0.0117
Q5 3708 9.452235805 3708/9.45 1.16 (1.10-1.22) 1.12 (1.05-1.19) 0.0003
Leucine
Q2 2065 8.703454403 2065/8.70 1.00 (Ref.) 1.00 (Ref.)
Q1 1432 9.834921294 1432/9.83 1.10 (1.04-1.16) 1.09 (1.01-1.18) 0.0282
Q3 2736 8.457309338 2736/8.46 0.95 (0.90-1.01) 0.98 (0.92-1.05) 0.5897
Q4 3339 8.352210074 3339/8.35 1.02 (0.96-1.07) 0.98 (0.92-1.04) 0.4873
Q5 3972 9.182550572 3972/9.18 1.08 (1.03-1.14) 1.06 (1.00-1.12) 0.0651
Valine
Q2 2192 8.428108826 2192/8.43 1.00 (Ref.) 1.00 (Ref.)
Q1 1563 9.049615344 1563/9.05 1.04 (0.99-1.10) 1.08 (1.00-1.16) 0.0453
Q3 2717 8.373809497 2717/8.37 1.01 (0.96-1.07) 0.99 (0.93-1.05) 0.7297
Q4 3242 8.595755028 3242/8.60 1.01 (0.96-1.07) 1.00 (0.94-1.06) 0.9707
Q5 3830 9.473554582 3830/9.47 1.14 (1.08-1.20) 1.05 (0.99-1.11) 0.1149

Hazard ratios for associations between MACE and BCAAs in males.

Adjusted model: Adjusted for age,BMI,HbA1c, LDL, SBP, smoking and drinking status, physical activity. HR, Hazard ratio.

In females (Table 2C), compared to the second quintile, the third, fourth, and fifth quintiles of BCAAs had an increased risk of MACE by 9% (95% CI 1.00-1.18), 11% (95% CI 1.02-1.21) and 12% (95% CI 1.03-1.22), respectively. For isoleucine, the fourth and fifth quintiles showed increased risks of 10% (95% CI 1.01-1.19) and 9% (95% CI 1.00-1.18), respectively. The fifth quintile of leucine was associated with an 11% (95% CI 1.02-1.20) increased risk. For valine, the third quintile had an increased risk of MACE by 11% (95% CI 1.02-1.20). All findings were adjusted for several potential confounders.

Table 2C

MACE No. of MACE Rate of 1000 Case/Rate of 1000 Crude HR (95% CI) Adjusted HR (95% CI) P value
Quintiles Females
BCAAs
Q2 1633 3.702577646 1633/3.70 1.00 (Ref.) 1.00 (Ref.)
Q1 1961 3.629214208 1961/3.63 0.99 (0.92-1.06) 1.07 (0.99-1.15) 0.0947
Q3 1582 4.330586064 1582/4.33 1.10 (1.02-1.18) 1.09 (1.00-1.18) 0.0391
Q4 1386 4.589812322 1386/4.59 1.23 (1.15-1.32) 1.11 (1.02-1.21) 0.0121
Q5 1334 4.830969496 1334/4.83 1.29 (1.21-1.39) 1.12 (1.03-1.22) 0.0081
Isoleucine
Q2 1679 3.875408067 1679/3.88 1.00 (Ref.) 1.00 (Ref.)
Q1 1837 3.578391936 1837/3.58 0.93 (0.86-1.00) 1.00 (0.93-1.08) 0.9999
Q3 1539 4.191639143 1539/4.19 1.08 (1.00-1.16) 1.03 (0.95-1.11) 0.4644
Q4 1437 4.583530134 1437/4.58 1.19 (1.11-1.28) 1.10 (1.01-1.19) 0.027
Q5 1404 4.718993192 1404/4.72 1.26 (1.17-1.35) 1.09 (1.00-1.18) 0.0479
Leucine
Q2 1741 3.804509751 1741/3.80 1.00 (Ref.) 1.00 (Ref.)
Q1 2107 3.841130382 2107/3.84 1.01 (0.94-1.09) 1.04 (0.97-1.12) 0.265
Q3 1544 4.184850853 1544/4.18 1.02 (0.95-1.10) 1.04 (0.96-1.13) 0.3002
Q4 1278 4.385866766 1278/4.39 1.12 (1.05-1.21) 1.05 (0.97-1.14) 0.239
Q5 1226 4.746298523 1226/4.75 1.23 (1.14-1.31) 1.11 (1.02-1.20) 0.0182
Valine
Q2 1624 3.738894637 1624/3.74 1.00 (Ref.) 1.00 (Ref.)
Q1 1874 3.576933573 1874/3.58 1.01 (0.94-1.09) 1.06 (0.99-1.15) 0.1059
Q3 1541 4.195671501 1541/4.20 1.16 (1.08-1.25) 1.02 (0.94-1.11) 0.5802
Q4 1480 4.713350545 1480/4.71 1.27 (1.19-1.37) 1.11 (1.02-1.20) 0.011
Q5 1377 4.82738213 1377/4.83 1.38 (1.28-1.48) 1.07 (0.98-1.16) 0.1234

Hazard ratios for associations between MACE and BCAAs in females.

Adjusted model: Adjusted for age,BMI,HbA1c, LDL, SBP, smoking and drinking status, physical activity. HR, Hazard ratio.

In participants younger than 65 (Table 2D), compared to the second quintile, the fifth quintile of BCAAs had an increased risk of MACE by 6% (95% CI 1.00-1.13). For isoleucine, the fourth and fifth quintiles showed increased risks of 8% (95% CI 1.02-1.15) and 12% (95% CI 1.06-1.19), respectively. The fifth quintile of leucine was associated with a 6% (95% CI 1.00-1.13) increased risk. Valine did not show an association with the risk of MACE.

Table 2D

MACE No. of MACE Rate of 1000 Case/Rate of 1000 Crude HR (95% CI) Adjusted HR (95% CI) P value
Quintiles Participants under 60
BCAAs
Q2 2430 4.291746911 2430/4.29 1.00 (Ref.) 1.00 (Ref.)
Q1 2189 3.777087199 2189/3.78 0.87 (0.82-0.92) 1.03 (0.96-1.10) 0.2709
Q3 2729 4.819793268 2729/4.82 1.12 (1.06-1.19) 0.98 (0.92-1.04) 0.5679
Q4 3095 5.437773564 3095/5.44 1.26 (1.20-1.33) 1.02 (0.96-1.08) 0.4441
Q5 3535 6.162704728 3535/6.16 1.43 (1.36-1.51) 1.06 (1.00-1.13) 0.0398
Isoleucine
Q2 2446 4.311673922 2446/4.31 1.00 (Ref.) 1.00 (Ref.)
Q1 2219 3.843043232 2219/3.84 0.89 (0.84-0.94) 1.04 (0.97-1.11) 0.1814
Q3 2707 4.766404339 2707/4.77 1.11 (1.05-1.17) 1.02 (0.96-1.09) 0.5168
Q4 3175 5.559425666 3175/5.56 1.29 (1.22-1.36) 1.08 (1.02-1.15) 0.0079
Q5 3431 6.008799151 3431/6.01 1.39 (1.32-1.47) 1.12 (1.06-1.19) 0.0002
leucine
Q2 2390 4.216573603 2390/4.22 1.00 (Ref.) 1.00 (Ref.)
Q1 2226 3.92082602 2226/3.92 0.92 (0.87-0.98) 1.05 (0.98-1.12) 0.1011
Q3 2756 4.842019686 2756/4.84 1.14 (1.08-1.20) 0.99 (0.93-1.05) 0.9536
Q4 3041 5.304934832 3041/5.30 1.26 (1.19-1.32) 0.97 (0.92-1.03) 0.5421
Q5 3565 6.17034723 3565/6.17 1.46 (1.38-1.53) 1.06 (1.00-1.13) 0.0259
Valine
Q2 2461 4.339959076 2461/4.34 1.00 (Ref.) 1.00 (Ref.)
Q1 2224 3.802831168 2224/3.80 0.87 (0.82-0.92) 1.00 (0.94-1.07) 0.6901
Q3 2700 4.7854608 2700/4.79 1.10 (1.05-1.17) 0.98 (0.92-1.04) 0.4994
Q4 3076 5.420036644 3076/5.42 1.25 (1.19-1.32) 1.02 (0.96-1.08) 0.5107
Q5 3517 6.15808375 3517/6.16 1.42 (1.35-1.50) 1.05 (0.99-1.11) 0.1018

Hazard ratios for associations between MACE and BCAAs in participants under 60.

Adjusted model: Adjusted for gender,BMI,HbA1c, LDL, SBP, smoking and drinking status, physical activity. HR, Hazard ratio.

In participants older than 65 (Table 2E), neither BCAAs nor the individual amino acids—isoleucine, leucine, and valine—showed an association with MACE.

Table 2E

MACE No. of MACE Rate of 1000 Case/Rate of 1000 Crude HR (95% CI) Adjusted HR (95% CI) P value
Quintiles Participants 60 and older
BCAAs
Q2 1397 10.91564915 1397/10.92 1.00 (Ref.) 1.00 (Ref.)
Q1 1204 10.22826325 1204/10.23 0.93 (0.86-1.00) 1.01 (0.92-1.10) 0.8255
Q3 1582 12.60068816 1582/12.60 1.14 (1.06-1.23) 1.03 (0.95-1.12) 0.3273
Q4 1592 13.09137141 1592/13.09 1.20 (1.12-1.29) 1.01 (0.93-1.10) 0.8839
Q5 1687 14.55319995 1687/14.55 1.32 (1.23-1.42) 1.05 (0.97-1.15) 0.1938
Isoleucine
Q2 1419 11.1708903 1419/11.17 1.00 (Ref.) 1.00 (Ref.)
Q1 1191 9.866811717 1191/9.87 0.88 (0.82-0.95) 0.97 (0.89-1.06) 0.5205
Q3 1558 12.62832629 1558/12.63 1.13 (1.05-1.22) 1.03 (0.95-1.12) 0.3751
Q4 1613 13.57190052 1613/13.57 1.22 (1.13-1.31) 1.06 (0.97-1.15) 0.2026
Q5 1681 14.14825049 1681/14.15 1.27 (1.18-1.36) 1.06 (0.98-1.15) 0.1674
Leucine
Q2 1416 11.05679931 1416/11.06 1.00 (Ref.) 1.00 (Ref.)
Q1 1313 10.38744952 1313/10.39 0.95 (0.88-1.03) 1.05 (0.96-1.14) 0.4031
Q3 1524 12.36280451 1524/12.36 1.13 (1.05-1.21) 1.02 (0.94-1.11) 0.8034
Q4 1576 13.36441443 1576/13.36 1.22 (1.14-1.32) 1.01 (0.93-1.10) 0.8145
Q5 1633 14.43818797 1633/14.44 1.32 (1.23-1.42) 1.04 (0.96-1.13) 0.4114
Valine
Q2 1355 10.63754576 1355/10.64 1.00 (Ref.) 1.00 (Ref.)
Q1 1213 10.8497774 1213/10.85 1.00 (0.92-1.08) 1.09 (0.99-1.18) 0.0522
Q3 1558 12.21593578 1558/12.22 1.14 (1.06-1.23) 1.06 (0.97-1.15) 0.2278
Q4 1646 13.31276232 1646/13.31 1.24 (1.15-1.33) 1.07 (0.98-1.16) 0.0956
Q5 1690 14.27221898 1690/14.27 1.33 (1.24-1.43) 1.07 (0.98-1.16) 0.1603

Hazard ratios for associations between MACE and BCAAs in 60 and older.

Adjusted model: Adjusted for gender,BMI,HbA1c, LDL, SBP, smoking and drinking status, physical activity. HR, Hazard ratio.

4 Discussion

In this cohort study using the UK Biobank, individuals in the highest quintiles of BCAAs, isoleucine, leucine, and valine had an increased risk of future MACE,except for those aged over 65. This elevated risk was independent of age, gender, BMI, HbA1c, LDL, systolic blood pressure (SBP), smoking and drinking status, and activity level. Additionally, the lowest quintiles of isoleucine and valine also showed elevated MACE risk compared to the second quintiles. In males, higher quintiles of isoleucine, the first and fifth quintiles of leucine, and the first quintile of valine were associated with a higher future risk of MACE. In females, increased MACE risk was observed in the third, fourth, and fifth quintiles of BCAAs; the fourth and fifth quintiles of isoleucine; the fifth quintile of leucine; and the fourth quintile of valine. For individuals under 65, the highest quintiles of BCAAs, isoleucine, and leucine were also associated with a higher risk of MACE. However, in those aged 65 and older, no significant association was found between BCAAs and MACE.

Previous studies have also suggested that high levels of BCAAs are associated with a higher risk of CVD. A nested case-control study conducted by Olle Melander et al. involving 253 pairs of subjects used a baseline AA-score to assess future CVD risk (14). However, this study focused primarily on the DM-AA score and did not address specific amino acid levels. Additionally, the amino acid score only included isoleucine and aromatic amino acids. Another study using data from American and European cohorts indicated that certain amino acids and their gut metabolites might signal the risk of major adverse cardiovascular events (MACE), but it only involved aromatic amino acids (15). A study of 138 heart failure patients found that leucine, valine, and their derivatives predicted mortality risk better than NT-proBNP, though the impact of heart failure itself on amino acid levels remains unclear (13). A study of 700 European Americans (16) suggested a positive correlation between BCAA levels and coronary artery disease (CAD) risk. However, as a cross-sectional study, it could not establish causality. Conversely, a study of 2,346 African Americans found that high leucine levels were associated with a reduced risk of coronary heart disease. This discrepancy may be due to unique socioeconomic factors affecting African Americans, which could potentially influence a range of diseases, including cancer, anxiety, and cardiovascular conditions. Additionally, reduced BCAA levels could be associated with frailty, MACE, and mortality (17).

Our research also conducted subgroup analyses across different gender and age groups. The findings suggests that in males, high levels of isoleucine and leucine, as well as low levels of leucine and valine, are positively correlated with an increased risk of MACE. The subgroup analysis in females reveals that high levels of BCAAs, isoleucine, and leucine are positively correlated with the risk of MACE. Additionally, the fourth quintile of valine, rather than the fifth, was associated with an increased risk of MACE. However, a study involving 27,041 women suggests that the predictive ability of BCAAs for CVD is similar to that of LDL, with isoleucine, leucine, and valine all showing positive association with CVD (18). Additionally, a Finnish study found that isoleucine and leucine are positively associated with cardiovascular events in women, whereas valine did not exhibit a significant correlation (22). Consequently, the association between valine levels and MACE risk in females remains contentious.

The differences in results between genders may be due to the following reasons. Our study suggests that BCAAs are positively correlated with the risk of MACE in the general population. In males, BCAAs levels are negatively correlated with age(the β=-0.04558,p<0.001), meaning that BCAAs levels are lower in elderly males. This may be related to muscle catabolism, which significantly affects circulating BCAAs levels. Muscle atrophy in elderly males and the decrease in BCAAs levels may indicate a more frail state. Therefore, in males, BCAAs are “J”-shaped correlated with the risk of MACE. In contrast, In females included in our study, BCAAs level are positively correlated with age(the β=0.06863,p<0.001). We believed that females inherently have lower muscle mass, and the effect of age-related muscle atrophy on circulating BCAA levels is relatively small. This may explain the differing correlation between BCAAs and MACE risk across genders. Females inherently have lower muscle mass, and the effect of age-related muscle atrophy on circulating BCAA levels is relatively small.

The inconsistent results regarding parity between the US study and ours may be attributed to differences in study period, sample size, and population sociodemographic characteristics, particularly age distribution.

In the subgroup analysis by age, high levels of BCAAs, isoleucine, and leucine were positively correlated with MACE in individuals under 65 years old, but no such correlation was observed in those over 65. Limited research on BCAA levels and aging exists, but one study found that decreased BCAA levels in elderly men over 70 were associated with frailty, MACE, and mortality (23). This discrepancy might be due to a decline in BCAA levels with age (2426) or frailty (27).

We conducted an analysis in men over 65y and found a J-shaped or J-like correlation between the incidence of MACE and the quintile levels of total BCAAs, isoleucine, leucine, and valine. This could be attributed to muscle catabolism, which has a substantial impact on circulating BCAA levels. The muscle atrophy observed in elderly men, along with the decline in BCAA levels, might signal a more frail state. As a result, in men, BCAAs exhibit a “J”-shaped correlation with the risk of MACE.

Compared to previous studies with smaller sample sizes, our study with a larger sample size more comprehensively demonstrates the correlation between BCAA levels and MACE in men. This does not conflict with previous studies but rather serves as a complement.

Research suggests that in cardiovascular diseases, BCAA metabolism is disrupted due to downregulation of Krüppel-like factor 15 (KLF15), mediated by TAK1 and p38 MAPK signaling (28, 29). This disruption leads to decreased expression of BCAA metabolic enzymes such as BCAT2 (30, 31), BCKDH (10, 12), and PPM1K (32), causing BCAAs and BCKAs to accumulate in the heart. Cardiac injury further disrupts BCAA metabolism in peripheral tissues, increasing circulating BCAA and BCKA levels and their delivery to the heart. Leucine activates mTOR in the heart, inhibiting autophagy through ULK1, promoting insulin resistance via S6K-mediated phosphorylation of insulin receptor substrate 1 (33), and stimulating protein synthesis by phosphorylating 4E-BP1 (34). BCKAs also increase 4E-BP1 phosphorylation and activate the MEK-ERK MAPK pathway, but impair mitochondrial complex I (3, 35), causing oxidative stress. In ischemia-reperfusion injury, Ppm1k deletion leads to BCAA and BCKA accumulation (36), worsening the injury by reducing glucose transport and oxidation (37). In obesity and insulin resistance, BCAA accumulation decreases fatty acid oxidation and increases triglyceride storage. Elevated BCKA levels in obesity and type 2 diabetes impair AKT (38) and PDH in the heart, affecting fuel selection. It’s unclear whether these metabolic changes cause cardiac dysfunction or if cardiac alterations impact BCAA metabolism in other tissues. BCAAs also hinder vascular relaxation through mTOR-dependent ROS generation (39, 40) and promote thrombosis by stimulating tropomodulin 3 propionylation. The valine-derived metabolite 3-HIB (41) increases lipid transport via FATP3 and FATP4. The impact of BCAA-lipid interactions on atherosclerosis is still uncertain.

Our investigation has several strengths. Firstly, it is one of the few large-scale prospective studies to systematically analyze the correlation between branched-chain amino acid levels and the risk of MACE across diverse populations. Secondly, it accounts for lifestyle factors such as glucose and lipid metabolism markers, smoking, alcohol consumption, and physical activity.

However, there are limitations to our study. Firstly, our results are limited to white European participants to avoid genetic heterogeneity, which may restrict the generalizability sof our findings to other ethnic groups. Secondly, the study did not include data on the metabolic products of amino acids and their potential impact on MACE. Future research could address this gap by testing serum samples for these metabolic products and analyzing their correlation with MACE. Lastly, the diagnosis of MACE was based on past medical history, which may have led to the omission of patients with asymptomatic cardiovascular disease.

5 Conclusion

Our study consistently observed a positive association between BCAAs, as well as the individual amino acids, isoleucine, leucine, and valine, and the risk of MACE in the overall population. Additionally, a higher risk of MACE was found in females with the lowest quintile of valine and in individuals younger than 65 with the lowest quintile of isoleucine.

Statements

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The UK Biobank data were accessed under application number 96511.

Ethics statement

The studies involving humans were approved by NHS National Research Ethics Service (16/NW/0274). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

WS: Formal analysis, Funding acquisition, Investigation, Writing – original draft. RL: Data curation, Validation, Writing – review & editing. YL: Resources, Supervision, Writing – review & editing. YEY: Data curation, Methodology, Supervision, Writing – review & editing. BL: Methodology, Supervision, Writing – review & editing. YFY: Data curation, Methodology, Resources, Supervision, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (No. 82300954), China International Medical Exchange Foundation(No. KY2024-056) and Huashan Hospital Internal Startup Fund (No.2021QD023).

Acknowledgments

The authors would like to thank the staff at the UK Biobank for their efforts in designing, collecting, and organizing the UK Biobank data, as well as for creating the public database.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

  • 1

    Platell C Kong SE McCauley R Hall JC . Branched-chain amino acids. J Gastroenterol Hepatology. (2000) 15:706–17. doi: 10.1046/j.1440-1746.2000.02205.x

  • 2

    White PJ Newgard CB . Branched-chain amino acids in disease. Sci (New York N.Y.). (2019) 363:582–83. doi: 10.1126/science.aav0558

  • 3

    White PJ McGarrah RW Herman MA Bain JR Shah SH Newgard CB . Insulin action, type 2 diabetes, and branched-chain amino acids: A two-way street. Mol Metab. (2021) 52:101261. doi: 10.1016/j.molmet.2021.101261

  • 4

    Hunter WG Kelly JP McGarrah RW 3rd Khouri MG Craig D Haynes C et al . Metabolomic profiling identifies novel circulating biomarkers of mitochondrial dysfunction differentially elevated in heart failure with preserved versus reduced ejection fraction: evidence for shared metabolic impairments in clinical heart failure. J Am Heart Assoc. (2016) 5. doi: 10.1161/JAHA.115.003190

  • 5

    Shah SH Bain JR Muehlbauer MJ Stevens RD Crosslin DR Haynes C et al . Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events. Circulation. Cardiovasc Genet. (2010) 3:207–14. doi: 10.1161/CIRCGENETICS.109.852814

  • 6

    Bhattacharya S Granger CB Craig D Haynes C Bain J Stevens RD et al . Validation of the association between a branched chain amino acid metabolite profile and extremes of coronary artery disease in patients referred for cardiac catheterization. Atherosclerosis. (2014) 232:191–6. doi: 10.1016/j.atherosclerosis.2013.10.036

  • 7

    Flores-Guerrero JL Groothof D Connelly MA Otvos JD Bakker SJL Dullaart RPF . Concentration of branched-chain amino acids is a strong risk marker for incident hypertension. Hypertension. (2019) 74:1428–35. doi: 10.1161/HYPERTENSIONAHA.119.13735

  • 8

    Portero V Nicol T Podliesna S Marchal GA Baartscheer A Casini S et al . Chronically elevated branched chain amino acid levels are pro-arrhythmic. Cardiovasc Res. (2022) 118:1742–57. doi: 10.1093/cvr/cvab207

  • 9

    Wolfson RL Chantranupong L Saxton RA Shen K Scaria SM Cantor JR et al . Sestrin2 is a leucine sensor for the mTORC1 pathway. Sci (New York N.Y.). (2016) 351:43–8. doi: 10.1126/science.aab2674

  • 10

    Walejko JM Christopher BA Crown SB Zhang GF Pickar-Oliver A Yoneshiro T et al . Branched-chain α-ketoacids are preferentially reaminated and activate protein synthesis in the heart. Nat Commun. (2021) 12:1680. doi: 10.1038/s41467-021-21962-2

  • 11

    Xu Y Jiang H Li L Chen F Liu Y Zhou M et al . Branched-chain amino acid catabolism promotes thrombosis risk by enhancing tropomodulin-3 propionylation in platelets. Circulation. (2020) 142:4964. doi: 10.1161/CIRCULATIONAHA.119.043581

  • 12

    Neinast MD Jang C Hui S Murashige DS Chu Q Morscher RJ et al . Quantitative analysis of the whole-body metabolic fate of branched-chain amino acids. Cell Metab. (2019) 29:41729.e4. doi: 10.1016/j.cmet.2018.10.013

  • 13

    Lanfear DE Gibbs JJ Li J She R Petucci C Culver JA et al . Targeted metabolomic profiling of plasma and survival in heart failure patients. JACC Heart Fail. (2017) 5:823–32. doi: 10.1016/j.jchf.2017.07.009

  • 14

    Magnusson M Lewis GD Ericson U Orho-Melander M Hedblad B Engstrom G et al . A diabetes-predictive amino acid score and future cardiovascular disease. Eur Heart J. (2013) 34:1982–9. doi: 10.1093/eurheartj/ehs424

  • 15

    Nemet I Li XS Haghikia A Li L Wilcox J Romano KA et al . Atlas of gut microbe-derived products from aromatic amino acids and risk of cardiovascular morbidity and mortality. Eur Heart J. (2023) 44:3085–96. doi: 10.1093/eurheartj/ehad333

  • 16

    Chevli PA Freedman BI Hsu FC Xu J Rudock ME Ma L et al . Plasma metabolomic profiling in subclinical atherosclerosis: the Diabetes Heart Study. Cardiovasc Diabetol. (2021) 20:231. doi: 10.1186/s12933-021-01419-y

  • 17

    Cruz DE Tahir UA Hu J Ngo D Chen ZZ Robbins JM et al . Metabolomic analysis of coronary heart disease in an African American cohort from the jackson heart study. JAMA Cardiol. (2022) 7:184–94. doi: 10.1001/jamacardio.2021.4925

  • 18

    Tobias DK Lawler PR Harada PH Demler OV Ridker PM Manson JE et al . Circulating branched-chain amino acids and incident cardiovascular disease in a prospective cohort of US women. Circ Genom Precis Med. (2018) 11:e002157. doi: 10.1161/CIRCGEN.118.002157

  • 19

    Bycroft C Freeman C Petkova D Band G Elliott LT Sharp K et al . The UK Biobank resource with deep phenotyping and genomic data. Nature. (2018) 562:203–09. doi: 10.1038/s41586-018-0579-z

  • 20

    Sudlow C Gallacher J Allen N Beral V Burton P Danesh J et al . UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PloS Med. (2015) 12:e1001779. doi: 10.1371/journal.pmed.1001779

  • 21

    Zhou BF . Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults–study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ sciences: BES. (2002) 15:8396.

  • 22

    Wurtz P Havulinna AS Soininen P Tynkkynen T Prieto-Merino D Tillin T et al . Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation. (2015) 131:774–85. doi: 10.1161/CIRCULATIONAHA.114.013116

  • 23

    Le Couteur DG Ribeiro R Senior A Hsu B Hirani V Blyth FM et al . Branched chain amino acids, cardiometabolic risk factors and outcomes in older men: the concord health and ageing in men project. J Gerontol A Biol Sci Med Sci. (2020) 75:1805–10. doi: 10.1093/gerona/glz192

  • 24

    Chaleckis R Murakami I Takada J Kondoh H Yanagida M . Individual variability in human blood metabolites identifies age-related differences. Proc Natl Acad Sci United States America. (2016) 113:4252–9. doi: 10.1073/pnas.1603023113

  • 25

    Kouchiwa T Wada K Uchiyama M Kasezawa N Niisato M Murakami H et al . Age-related changes in serum amino acids concentrations in healthy individuals. Clin Chem Lab Med. (2012) 50:861–70. doi: 10.1515/cclm-2011-0846

  • 26

    Pitkänen HT Oja SS Kemppainen K Seppä JM Mero AA . Serum amino acid concentrations in aging men and women. Amino Acids. (2003) 24:413–21. doi: 10.1007/s00726-002-0338-0

  • 27

    Adachi Y Ono N Imaizumi A Muramatsu T Andou T Shimodaira Y et al . Plasma amino acid profile in severely frail elderly patients in Japan. Int J Gerontology. (2018) 12:290–93. doi: 10.1016/j.ijge.2018.03.003

  • 28

    Fan L Hsieh PN Sweet DR Jain MK . Krüppel-like factor 15: Regulator of BCAA metabolism and circadian protein rhythmicity. Pharmacol Res. (2018) 130:123–26. doi: 10.1016/j.phrs.2017.12.018

  • 29

    Jeyaraj D Haldar SM Wan X McCauley MD Ripperger JA Hu K et al . Circadian rhythms govern cardiac repolarization and arrhythmogenesis. Nature. (2012) 483:96–9. doi: 10.1038/nature10852

  • 30

    Uddin GM Karwi QG Pherwani S Gopal K Wagg CS Biswas D et al . Deletion of BCATm increases insulin-stimulated glucose oxidation in the heart. Metabolism: Clin Experimental. (2021) 124:154871. doi: 10.1016/j.metabol.2021.154871

  • 31

    Raffel S Falcone M Kneisel N Hansson J Wang W Lutz C et al . BCAT1 restricts αKG levels in AML stem cells leading to IDHmut-like DNA hypermethylation. Nature. (2017) 551:384–88. doi: 10.1038/nature24294

  • 32

    Sun H Olson KC Gao C Prosdocimo DA Zhou M Wang Z et al . Catabolic defect of branched-chain amino acids promotes heart failure. Circulation. (2016) 133:2038–49. doi: 10.1161/CIRCULATIONAHA.115.020226

  • 33

    Schachter D Sang JC . Aortic leucine-to-glutamate pathway: metabolic route and regulation of contractile responses. Am J Physiol Heart Circulatory Physiol. (2002) 282:H1135–48. doi: 10.1152/ajpheart.00457.2001

  • 34

    Zhenyukh O González-Amor M Rodrigues-Diez RR Esteban V Ruiz-Ortega M Salaices M et al . Branched-chain amino acids promote endothelial dysfunction through increased reactive oxygen species generation and inflammation. J Cell Mol Med. (2018) 22:4948–62. doi: 10.1111/jcmm.2018.22.issue-10

  • 35

    Nishi K Yoshii A Abell L Zhou B Frausto R Ritterhoff J et al . Branched-chain keto acids inhibit mitochondrial pyruvate carrier and suppress gluconeogenesis in hepatocytes. Cell Rep. (2023) 42:112641. doi: 10.1016/j.celrep.2023.112641

  • 36

    Lian K Guo X Wang Q Liu Y Wang RT Gao C et al . PP2Cm overexpression alleviates MI/R injury mediated by a BCAA catabolism defect and oxidative stress in diabetic mice. Eur J Pharmacol. (2020) 866:172796. doi: 10.1016/j.ejphar.2019.172796

  • 37

    Li T Zhang Z Kolwicz SC Jr. Abell L Roe ND Kim M et al . Defective branched-chain amino acid catabolism disrupts glucose metabolism and sensitizes the heart to ischemia-reperfusion injury. Cell Metab. (2017) 25:374–85. doi: 10.1016/j.cmet.2016.11.005

  • 38

    Uddin GM Zhang L Shah S Fukushima A Wagg CS Gopal K et al . Impaired branched chain amino acid oxidation contributes to cardiac insulin resistance in heart failure. Cardiovasc Diabetol. (2019) 18:86. doi: 10.1186/s12933-019-0892-3

  • 39

    McGinnis GR Tang Y Brewer RA Brahma MK Stanley HL Shanmugam G et al . Genetic disruption of the cardiomyocyte circadian clock differentially influences insulin-mediated processes in the heart. J Mol Cell Cardiol. (2017) 110:8095. doi: 10.1016/j.yjmcc.2017.07.005

  • 40

    Latimer MN Sonkar R Mia S Frayne IR Carter KJ Johnson CA et al . Branched chain amino acids selectively promote cardiac growth at the end of the awake period. J Mol Cell Cardiol. (2021) 157:3144. doi: 10.1016/j.yjmcc.2021.04.005

  • 41

    Jang C Oh SF Wada S Rowe GC Liu L Chan MC et al . A branched-chain amino acid metabolite drives vascular fatty acid transport and causes insulin resistance. Nat Med. (2016) 22:421–6. doi: 10.1038/nm.4057

Summary

Keywords

branched-chain amino acids (BCAAs), major adverse cardiovascular events (MACE), UK Biobank, isoleucine, leucine, valine

Citation

Sun W, Lin R, Li Y, Yao Y, Lu B and Yu Y (2025) Circulating branched-chain amino acids and the risk of major adverse cardiovascular events in the UK biobank. Front. Endocrinol. 16:1510910. doi: 10.3389/fendo.2025.1510910

Received

14 October 2024

Accepted

21 January 2025

Published

20 February 2025

Volume

16 - 2025

Edited by

Gaetano Santulli, Albert Einstein College of Medicine, United States

Reviewed by

Yucun Niu, Harbin Medical University, China

Qian Zhu, Guangdong Provincial People’s Hospital, China

Chian Ju Jong, The University of Iowa, United States

Updates

Copyright

*Correspondence: Wanwan Sun, ; Bin Lu,

†These authors have contributed equally to this work

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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