Levels of circulatory branched-chain amino acids are associated with dietary regimen. A cross- sectional analysis

doi:10.21203/rs.3.rs-5284683/v1

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Levels of circulatory branched-chain amino acids are associated with dietary regimen. A cross- sectional analysis

https://doi.org/10.21203/rs.3.rs-5284683/v1

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Background & aims:

Higher levels of circulatory branched chain amino acids (BCAA, isoleucine, leucine, and valine) are associated with a higher risk of diabetes and cardiovascular diseases. Diet is the main source of BCAA, but previous studies have shown conflicting results on the influence of diet on circulatory BCAA concentration. We evaluated the association between BCAA concentration and diet in a large population-based sample.

Methods

Data from 2159 participants (58.2% women, mean age 53.4 ± 8.6years) was included. Dietary intake was assessed by questionnaire and included 91 individual foods and 15 different nutrients. BCAA concentrations were measured by stable isotope dilution liquid chromatography-mass spectrometry (LC-MS) assay.

Results

The circulatory BCAA levels were higher in men than in women (250.2 ± 45.2 vs. 207.9 ± 36.1, 136.0 ± 24.4 vs 106.3 ± 17.4, 63.6 ± 13.0 vs 48.1 ± 9.2 µmol/L, p < 0.001). BCAA levels were negatively associated with vegetal protein intake (multivariable-adjusted beta: -0.051 and − 0.063 for Isoleucine and Valine, respectively, p < 0.05) and dairy product (-0.037, -0.063 and − 0.038, respectively, p < 0.05) consumption. The opposite trend was observed for consumption of meat and meat-derived products, but the associations did not reach statistical significance. Of the 15 different nutrients, 13 showed a significant correlation with BCAA. Specifically, total protein, total carbohydrates, polysaccharides, saturated fat (SFA), and total fat all showed significant positive association with BCAA, while total fibre, monosaccharides, and calcium showed significant negative correlations with BCAA.

Conclusions

Levels of circulatory in humans are negatively associated with plant protein and positively associated with polysaccharides, total fat, and animal protein. The associations are gender specific.

Branched chain amino acids

diet

epidemiology

a cross-sectional study.

Branched-chain amino acids (BCAA) are essential dietary amino acids, including isoleucine, leucine, and valine ⁽¹⁾. BCAAs stimulate the secretion of insulin and glucagon, and when taken orally, also stimulate the secretion of glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) ⁽²⁾, levels of circulatory BCAA are positively associated with risk of type 2 diabetes mellitus (T2DM) ⁽³⁾. And the accumulation of BCAA and its metabolites may produce a series of pathological changes ranging from neurological distress to cardiomyopathy⁽⁴⁾, BCAA can inhibit the transport and utilization of pyruvate and fatty acids, but the heart tissue has a sustained high demand for pyruvate and fatty acids as its main fuel sources, long term elevated BCAA may hinder the normal bioenergy homeostasis of the heart, leading to systolic dysfunction and accelerating heart failure under increased mechanical load or aging^(4–6), raised circulating concentrations of BCAA are associated with an increased risk of cardiovascular disease ^{(7, 8)}.

Levels of circulatory BCAA derive from tissue protein turnover and diet, mainly from foods containing protein: red meat, seafood, eggs, and dairy products ⁽⁹⁾. However, previous studies have shown insufficient and conflicting data on the effect of diet on levels of circulatory BCAA in humans ⁽¹⁰⁾. For instance, some studies reported a higher BCAA intake is thought to be associated with levels of circulatory BCAA ^{(11, 12)}, while others did not reveal any association or even a negative association between dietary BCAA intake and levels of circulatory BCAA ^{(10, 13)}. Merz et al. ⁽¹⁰⁾ suggested that fasting levels of circulatory BCAA were associated with the dietary pattern “ high in meat, sausages, sauces, eggs, and ice cream, but low in nuts, cereals, mushrooms, and pulses”, rather than only “total protein intake”. Similarly, Rousseau et al ⁽¹⁴⁾ tested the association between fasting levels of circulatory BCAA and total intake of animal and plant protein, they found that only animal protein intake was associated with levels of circulatory BCAA, further, among single products including processed and organ meats, fish, poultry, eggs, and low- and high-fat dairy products, red meat was the only food type that was significantly and positively associated with levels of circulatory BCAA. Finally, it is unclear whether levels of circulatory BCAA reflect short-term or long-term dietary intake ⁽¹⁵⁾.

The differences among studies regarding the associations between levels of circulatory BCAA and diet could be due to differences in eating habits between countries and regions, as they affect the main source of BCAA. They could also be due to the relatively small sample sizes of some studies: 298 in the study by Merz et al ⁽¹⁰⁾, 199 in the study by Rousseau et al ⁽¹⁴⁾, and only 16 in a study focusing on vegans and omnivores ⁽¹²⁾.

Hence, we assessed the overall and gender-stratified associations between p levels of circulatory BCAA and diet in a large sample, testing a wide array of dietary markers. Our objective was to explore which foods, nutrients and dietary patterns were associated with levels of circulatory BCAA.

Study design and participants

The CoLaus|PsyCoLaus study is a population-based study investigating the epidemiology and genetic determinants of psychiatric and cardiovascular disease in Lausanne, Switzerland ⁽¹⁶⁾. Briefly, a representative sample was collected through a simple, non-stratified random sampling of 19,830 individuals (35% of the source population) aged between 35 and 75. The baseline study was conducted between June 2003 and May 2006; the first follow-up was performed between April 2009 and September 2012.

In each survey, participants attended the survey unit in the morning after an 8-h fast. Participants answered questionnaires, underwent a clinical examination, and had their blood collected. Blood samples were drawn for analyses and additional aliquots were stored at − 80°C for BCAA analysis. For this study, data from the first follow-up was considered.

Dietary intake, dietary scores and compliance to dietary recommendations

Dietary intake was assessed using a self-administered, semi-quantitative food frequency questionnaire (FFQ) which also included portion size ⁽¹⁷⁾. This FFQ has been validated in the Geneva population ^{(17, 18)}. Briefly, this FFQ assesses the dietary intake of the previous 4 weeks and consists of 97 different food items that account for more than 90% of the intake of calories, proteins, fat, carbohydrates, alcohol, cholesterol, vitamin D and retinol, and 85% of fibre, carotene and iron. For each item, consumption frequencies ranging from “less than once during the last 4 weeks” to “2 or more times per day” were provided, and the participants indicated the average serving size (smaller, equal or bigger) compared to a reference size. Each participant brought along her/his filled-in FFQ, which was checked for completion by trained interviewers the day of the visit.

For this analysis, we used all 91 items available on the FFQ, together with 16 macronutrients and some micronutrients extracted from it. We also grouped the different food items into categories, i.e., dairy, meat, processed meat, fish, vegetables, and fruits.

Four dietary scores were computed based on the Mediterranean diet. The first Mediterranean dietary score (designated as ‘Mediterranean score 1’) was derived from Trichopoulou et al⁽¹⁹⁾. The score uses consumption frequencies instead of amounts. Briefly, a value of 0 or 1 is assigned to each of seven foods using their gender-specific medians as cut-off. Participants whose consumption frequencies for “healthy” foods (vegetables, fruits, fish, and cereal) were above the median were given the value of 1, while for “unhealthy” foods (meat, dairy products), consumption frequencies below the median were given the value of 1. Two other items were considered: ratio of monounsaturated to saturated fats and moderate alcohol consumption (between 5 and 25 g/day for women and 10 and 50 g/day for men). The score ranges between 0 and 8. The second Mediterranean dietary score (designated as ‘Mediterranean score 2’) adapted to the Swiss population was computed according to Vormund et al ⁽²⁰⁾. It used the same scoring system but considered nine types of “healthy” foods: fruits, vegetables, fish, cereal, salads, poultry, dairy products and wine. The score ranges between 0 and 9. For both scores, higher values represented a healthier diet.

Participants were also dichotomized according to whether they followed the dietary recommendations for fruits, vegetables, meat, fish and dairy products from the Swiss Society of Nutrition ^{(21, 22)}. Compliance was estimated from the FFQ data. The recommendations were ≥ 2 fruit portions/day; ≥3 vegetable portions/day; ≤5 meat portions/week; ≥1 fish portion/week and ≥ 3 dairy products portions/day. In this study, we did not use portion size to compute compliance, but relied on consumption frequencies. This was done as the portion sizes recommended by the Swiss Society of Nutrition do not take into account a subject’s corpulence and caloric needs ⁽²³⁾. As the FFQ queried about fresh and fried fish, two categories of compliance to fish consumption were considered: one included and one excluded fried fish. For each participant, the number of guidelines complied to was computed. Two sums were computed: one used compliance to fish consumption using all types of fish preparation (i.e., including fried fish); the other used compliance to fish consumption using fresh fish only.

Sample preparation and metabolic profiling

Absolute quantification of BCAA was performed by extracting 20 µL of fasting serum samples with 250 µL of ice-cold methanol, adding the internal standard solution of the corresponding BCAA, and diluting it to 300 µL with 0.1% formic acid in water. After the samples were mixed evenly by shaking, they were centrifuged at 4°C and 2700g for 15 minutes. The resulting supernatant was transferred to LC-MS vials prior to injection. A detailed description of the method used has been published⁽²⁴⁾. Briefly, a hydrophilic interaction chromatography (HILIC)-based high-resolution mass spectrometry (HRMS) was developed, taking advantage of HRMS data acquired in full-scan mode. Accuracy was within 90–106% of validated NIST reference plasma concentrations for the panel of measured BCAA. BCAA extraction recoveries were 87–100% on average, depending on the concentration range spiked. The coefficient of variation (CV) was 1–10% and 1–25% for intra- and interday measurements, respectively.

Other covariates

Smoking status was self-reported and categorized as never, former, and current. Marital status was categorized as living alone (i.e., single, divorced or widowed) or living with a partner. Education was categorized into low (compulsory + apprenticeship), medium (high school) or high (university level). Nationality was dichotomized into born in Switzerland or elsewhere. Presence of any type of diet (i.e., for slimming, diabetes, or hypertension) was also collected and categorized as present/absent.

Physical activity was assessed by a questionnaire validated in the population of Geneva⁽²⁵⁾. This self-reported questionnaire assesses the type and duration of 70 kinds of (non)professional activities and sports during the previous week. Sedentary status was defined as spending over 90% of the daily energy in activities below moderate- and high-intensity (defined as requiring at least 4 times the basal metabolic rate⁽²⁶⁾).

Body weight and height were measured with participants barefoot and in light indoor clothes. Body weight was measured in kilograms to the nearest 100 g using a Seca® scale (Hamburg, Germany). Height was measured to the nearest 5 mm using a Seca® (Hamburg, Germany) height gauge. Body mass index (BMI) was computed and categorized into normal (BMI < 25 kg/m²), overweight (25 ≤ BMI < 30 kg/m²) and obese (BMI ≥ 30 kg/m²).

Eligibility and exclusion criteria

Participants with BCAA levels will be considered as eligible. Participants were excluded if they have no dietary markers, if they presented extreme total energy intake values (defined as < 500 or > 3500 kcal/day for women and < 800 or > 4000 kcal/day for men), if they missed any confounder variable or if they reported being on any type of diet (for slimming, antidiabetic, etc.).

Statistical analyses

Statistical analysis was conducted using Stata version 17.0 (Stata Corp, College Station, TX, USA). Summary statistics are reported as mean ± standard deviation for continuous variables and as number of participants (percentage) for categorical variables. Bivariate between-group comparisons were performed using t-test for continuous variables and chi-square for categorical variables. Normality of the distribution of BCAA was checked via Q-Q plots, and no deviation was found.

As dietary intake values presented with a skewed distribution, bivariate associations between dietary markers and the levels of circulatory BCAA were computed using Spearman correlations for the overall sample and stratified by gender. Multivariable regression analysis assessing the association between dietary markers and levels of circulatory BCAA were conducted using linear regression adjusting on age (continuous), smoking status (never, former, current), educational level (low, medium, high), marital status (yes, no), born in Switzerland (yes, no), presence of a diet (yes, no), BMI categories (normal, overweight) and sedentary status (yes, no). Results were presented as standardized beta coefficients, as their interpretation is similar to the correlation coefficients.

Bivariate analysis of the associations between dietary recommendations and levels of circulatory BCAA were assessed by comparing levels of circulatory BCAA between compliant and non-compliant participants using student’s t-test and the results were expressed as mean ± standard deviation, Multivariable analysis was conducted using analysis of variance, adjusting for the same variables as above, and results were expressed as multivariable-adjusted mean ± standard error. As this was an exploratory study, and similar to other studies^{(27, 28)}, we decided not to correct for multiple testing, and we defined statistical significance as a two-sided test with p < 0.05.

Selection of participants

Of the initial 5064 participants, 2577 (50.9%) were considered as eligible for BCAA analysis. Of them, 2159 (83.8%) were included in this study. The reasons for exclusion are indicated in supplementary Fig. 1 and the characteristics of the included and the excluded participants are indicated in supplementary table 1. Included participants were more frequently women (1,257 [58.2] vs. 1,450 [49.9]%, p < 0.001), younger (53.4 ± 8.6 vs. 61.0 ± 10.7 years, p < 0.001), with a higher educational level (600 [27.8] vs. 479 [16.5] %, p < 0.001), less frequently former smokers (748 [34.7] vs. 1,135 [39.9]%, p < 0.001), on a diet (476 [22.1] vs. 1,098 [37.8]%, p < 0.001) or sedentary (1,082 [50.1] vs. 1,323 [65.4] %, p < 0.001), and none had obesity.

Table 1 presents the characteristics of the sample according to gender. Women were older (54.3 ± 8.8 vs. 52.2 ± 8.2 years, p < 0.001), less frequently living in couple (643 [51.2] vs. 604 [67.0] %, p < 0.001), less frequently of higher education (300 [23.9] vs. 300 [33.3] %, p < 0.001), more frequently on a diet (316 [25.1] vs. 160 [17.7] %, p < 0.001) or sedentary (704 [56.0] vs. 378 [41.9] %, p < 0.001), and less frequently with overweight (363 [28.9)] vs. 450 [49.9] %, p < 0.001). BCAA levels were higher in men than in women: valine (250.2 ± 45.2 vs. 207.9 ± 36.1 µmol/L, p < 0.001); leucine (136.0 ± 24.4 vs. 106.3 ± 17.4 µmol/L, p < 0.001); isoleucine (63.6 ± 13.0 vs. 48.1 ± 9.2 µmol/L, p < 0.001).

Table 1

characteristics of the sample, overall and by gender
	Women	Men	P-value
Sample size	1257	902
Age (years)	54.3 ± 8.8	52.2 ± 8.2	< 0.001
Born in Switzerland (%)	814 (64.8)	557 (61.8)	0.153
Living in couple (%)	643 (51.2)	604 (67.0)	< 0.001
Educational level (%)			< 0.001
High	300 (23.9)	300 (33.3)
Medium	376 (29.9)	236 (26.2)
Low	581 (46.2)	365 (40.5)
Smoking categories (%)			0.833
Never	541 (43.0)	377 (41.8)
Former	430 (34.2)	318 (35.3)
Current	286 (22.8)	207 (23.0)
On a diet § (%)	316 (25.1)	160 (17.7)	< 0.001
Sedentary (%)	704 (56.0)	378 (41.9)	< 0.001
Body mass index (kg/m²)	23.3 ± 3.0	24.9 ± 2.5	< 0.001
BMI categories (%)			< 0.001
Normal	894 (71.1)	452 (50.1)
Overweight	363 (28.9)	450 (49.9)
BCAA (µmol/L)
Valine	207.9 ± 36.1	250.2 ± 45.2	< 0.001
Leucine	106.3 ± 17.4	136.0 ± 24.4	< 0.001
Isoleucine	48.1 ± 9.2	63.6 ± 13.0	< 0.001
§, low calorie, low salt, for diabetes or dyslipidaemia. BMI, body mass index; BCAA, branched-chain amino acids. Results are expressed as number of participants (column percentage) for categorical variables and as average ± standard deviation for continuous variables. Between-group comparisons performed using chi-square for categorical variables and student’s t-test for continuous variables.

Associations between individual food items, food categories, and BCAA

The bivariate associations between intake of 91individual food items and levels of circulatory BCAA are shown in supplementary table 2 for all the individual values of the Spearman correlations. Dairy products (yogurt and cheese), bread and cereals, fruits and vegetables were negatively associated with BCAA levels, while meat, sugary products (honey and jam) and alcoholic beverages (wine, beer, and spirits) were positively associated with BCAA, the strongest association being observed for vegetables, meat, and alcoholic beverages. Overall, the associations between foods and BCAA levels tended to be stronger in men than in women (Supplementary table 2).

After Multivariable analysis, except for the negative correlation between fruit, vegetable, water intake and levels of circulatory BCAA, most associations were no longer statistically significant, which was not present before (Supplementary table 3).

The bivariate associations between food categories and BCAA are shown in supplementary table 4. Positive associations were found for meat and processed meat, and negative associations with vegetables and fruits, and similar albeit non-significant associations were found when the analysis was split be gender. The results of the multivariable associations between food categories and BCAA are summarized in Table 2. Negative associations were found for dairy, vegetables, and fruits, but no longer for meat or processed meat. Dairy was negatively associated with leucine, isoleucine and valine, vegetables were negatively associated with isoleucine and valine; fruits were negatively associated with isoleucine and valine as well.

Table 2

multivariable analysis of the associations between blood levels of branched chain amino acids and the main food groups, CoLaus|PsyColaus study, Lausanne, Switzerland, 2009–2012, overall and stratified by gender.
	Leucine		Isoleucine		Valine
Food groups, gr/day	Beta	P-value	Beta	P-value	Beta	P-value
ALL
Dairy	-0.037	0.029	-0.063	< 0.001	-0.041	0.028
Meat	0.026	0.140	0.018	0.303	0.032	0.096
Processed meat	0.022	0.200	0.022	0.213	0.028	0.143
Fish	0.000	0.978	-0.008	0.625	0.013	0.501
Vegetables	-0.029	0.088	-0.051	0.003	-0.038	0.043
Fruits	-0.031	0.084	-0.039	0.030	-0.041	0.034
WOMEN
Dairy	-0.074	0.019	-0.065	0.019	-0.082	0.010
Meat	0.032	0.312	0.019	0.501	0.031	0.340
Processed meat	0.026	0.415	0.012	0.675	0.042	0.189
Fish	0.032	0.321	-0.028	0.320	0.040	0.207
Vegetables	-0.032	0.323	-0.064	0.022	-0.035	0.277
Fruits	-0.022	0.496	-0.076	0.007	-0.023	0.484
MEN
Dairy	-0.023	0.419	-0.095	0.003	-0.020	0.479
Meat	0.026	0.348	0.022	0.499	0.045	0.107
Processed meat	0.024	0.384	0.033	0.307	0.012	0.665
Fish	-0.038	0.177	0.003	0.934	-0.017	0.555
Vegetables	-0.039	0.163	-0.063	0.050	-0.047	0.091
Fruits	-0.053	0.063	-0.015	0.643	-0.064	0.024
Results are expressed as standardized beta coefficients. Analysis conducted using linear regression adjusting for gender (ALL only), age (continuous), smoking (never, former, current), educational level (high, medium, low), marital status (in couple, alone), born in Switzerland (yes, no), on a diet (yes, no), BMI categories (normal, overweight), and sedentary status (yes, no). Statistically significant coefficients (p < 0.05) are indicated in red.

Associations between nutrients and BCAA

The bivariate associations between 15 nutrients and levels of circulatory BCAA are shown in supplementary table 5. Positive associations were found between BCAA and most nutrients, excepting total fibre (negative association), monosaccharides and calcium (no association).

After multivariable analysis, the results of the associations between nutrients and levels of circulatory BCAA are summarized in Table 3. Negative associations were found between all three BCAA and vegetal protein, total carbohydrates, monosaccharides and polysaccharides, and total fibre, and similar results were obtained when the analysis was split according to gender.

Table 3

multivariable analysis of the associations between blood levels of branched chain amino acids and nutrients, CoLaus|PsyColaus study, Lausanne, Switzerland, 2009–2012, overall and stratified by gender.
	Leucine		Isoleucine		Valine
Nutrients	Beta	P-value	Beta	P-value	Beta	P-value
ALL
Total protein, gr/day	-0.015	0.405	-0.041	0.023	-0.007	0.717
Vegetal protein, gr/day	-0.053	0.003	-0.068	< 0.001	-0.041	0.001
Animal protein, gr/day	0.004	0.841	-0.021	0.238	0.017	0.384
Total carbohydrates, gr/day	-0.063	< 0.001	-0.041	< 0.001	-0.041	< 0.001
Monosaccharides, gr/day	-0.057	0.001	-0.072	< 0.001	-0.041	< 0.001
Polysaccharides, gr/day	-0.045	0.012	-0.056	0.002	-0.051	0.010
Total fat, gr/day	-0.019	0.274	-0.030	0.087	-0.024	0.211
Saturated fat (SFA), gr/day	-0.027	0.135	-0.042	0.020	-0.031	0.109
Monounsaturated fat (MUFA), gr/day	-0.010	0.563	-0.018	0.293	-0.012	0.535
Polyunsaturated fat (PUFA), gr/day	-0.011	0.545	-0.011	0.539	-0.025	0.200
Total fibre, gr/day	-0.042	0.016	-0.060	0.001	-0.055	0.004
Cholesterol, mg/day	-0.004	0.822	-0.018	0.300	0.004	0.839
Alcohol, mL/day	0.027	0.132	0.027	0.136	-0.014	0.492
Calcium, mg/day	-0.031	0.068	-0.058	0.001	-0.024	0.199
Iron, mg/day	-0.017	0.319	-0.040	0.020	-0.032	0.093
WOMEN
Total protein, gr/day	-0.019	0.549	-0.055	0.084	-0.012	0.700
Vegetal protein, gr/day	-0.071	0.025	-0.097	0.002	-0.065	0.042
Animal protein, gr/day	0.004	0.898	-0.030	0.355	0.010	0.757
Total carbohydrates, gr/day	-0.085	0.008	-0.104	0.001	-0.088	0.006
Monosaccharides, gr/day	-0.084	0.008	-0.086	0.007	-0.096	0.003
Polysaccharides, gr/day	-0.060	0.059	-0.090	0.005	-0.053	0.095
Total fat, gr/day	-0.042	0.188	-0.059	0.066	-0.048	0.129
Saturated fat (SFA), gr/day	-0.044	0.164	-0.069	0.031	-0.051	0.110
Monounsaturated fat (MUFA), gr/day	-0.035	0.266	-0.045	0.158	-0.037	0.242
Polyunsaturated fat (PUFA), gr/day	-0.029	0.357	-0.037	0.253	-0.050	0.118
Total fibre, gr/day	-0.057	0.079	-0.071	0.030	-0.045	0.163
Cholesterol, mg/day	-0.004	0.902	-0.031	0.333	-0.001	0.976
Alcohol, mL/day	0.026	0.434	0.026	0.436	-0.014	0.663
Calcium, mg/day	-0.058	0.069	-0.090	0.005	-0.049	0.127
Iron, mg/day	-0.015	0.630	-0.043	0.175	-0.021	0.507
MEN
Total protein, gr/day	-0.021	0.459	-0.041	0.139	-0.005	0.858
Vegetal protein, gr/day	-0.046	0.099	-0.055	0.051	-0.067	0.018
Animal protein, gr/day	-0.005	0.853	-0.026	0.351	0.022	0.437
Total carbohydrates, gr/day	-0.058	0.040	-0.072	0.010	-0.083	0.003
Monosaccharides, gr/day	-0.056	0.045	-0.088	0.002	-0.083	0.003
Polysaccharides, gr/day	-0.035	0.216	-0.026	0.357	-0.048	0.089
Total fat, gr/day	0.002	0.947	-0.002	0.930	-0.001	0.975
Saturated fat (SFA), gr/day	-0.012	0.675	-0.016	0.555	-0.014	0.630
Monounsaturated fat (MUFA), gr/day	0.014	0.613	0.007	0.789	0.012	0.660
Polyunsaturated fat (PUFA), gr/day	0.011	0.687	0.023	0.419	0.003	0.912
Total fibre, gr/day	-0.049	0.081	-0.079	0.005	-0.077	0.006
Cholesterol, mg/day	-0.008	0.781	-0.013	0.643	0.009	0.736
Alcohol, mL/day	0.050	0.075	0.047	0.091	-0.003	0.904
Calcium, mg/day	-0.025	0.372	-0.056	0.043	-0.014	0.621
Iron, mg/day	-0.030	0.290	-0.057	0.042	-0.050	0.077
Results are expressed as standardized beta coefficients. Analysis conducted using linear regression adjusting for gender (ALL only), age (continuous), smoking (never, former, current), educational level (high, medium, low), marital status (in couple, alone), born in Switzerland (yes, no), on a diet (yes, no), BMI categories (normal, overweight), and sedentary status (yes, no). Statistically significant coefficients (p < 0.05) are indicated in red.

Associations between dietary scores and BCAA

The bivariate relationship between four dietary scores and levels of circulatory BCAA is shown in supplementary table 6. Both AHEIs were negatively associated with all BCAA, while no association was found for the Mediterranean diet scores. When the analysis was split by gender, similar associations were observed, although they did not reach statistical significance.

After multivariable analysis, the results are summarized in Table 4. Again, significant negative associations were found between both versions of AHEI and isoleucine and valine in the whole sample, while no associations were found for the Mediterranean diet scores or for Leucine. When the analysis was split by gender, similar associations were observed, although they did not reach statistical significance.

Table 4

multivariable analysis of the associations between blood levels of branched chain amino acids and dietary scores, CoLaus|PsyColaus study, Lausanne, Switzerland, 2009–2012, overall and stratified by gender.
	Leucine		Isoleucine		Valine
	Beta	P-value	Beta	P-value	Beta	P-value
ALL
Mediterranean diet score (Trichopoulou)	-0.011	0.516	-0.016	0.362	-0.020	0.299
Mediterranean diet score (Vormund)	0.001	0.974	-0.019	0.283	-0.008	0.657
Alternate healthy eating index (Version 1)	-0.025	0.153	-0.042	0.017	-0.040	0.040
Alternate healthy eating index (Version 2)	-0.026	0.146	-0.043	0.016	-0.040	0.041
WOMEN
Mediterranean diet score (Trichopoulou)	-0.032	0.325	-0.035	0.281	-0.032	0.322
Mediterranean diet score (Vormund)	-0.006	0.862	-0.031	0.334	-0.008	0.797
Alternate healthy eating index (Version 1)	-0.038	0.253	-0.059	0.074	-0.038	0.250
Alternate healthy eating index (Version 2)	-0.038	0.251	-0.058	0.078	-0.038	0.255
MEN
Mediterranean diet score (Trichopoulou)	0.007	0.801	-0.001	0.982	-0.007	0.803
Mediterranean diet score (Vormund)	0.005	0.855	-0.016	0.569	-0.008	0.791
Alternate healthy eating index (Version 1)	-0.020	0.476	-0.043	0.134	-0.044	0.125
Alternate healthy eating index (Version 2)	-0.022	0.433	-0.045	0.117	-0.045	0.117
Results are expressed as standardized beta coefficients. Analysis conducted using linear regression adjusting for gender (ALL only), age (continuous), smoking (never, former, current), educational level (high, medium, low), marital status (in couple, alone), born in Switzerland (yes, no), on a diet (yes, no), BMI categories (normal, overweight), and sedentary status (yes, no). Statistically significant coefficients (p < 0.05) are indicated in red.

Associations between compliance to dietary recommendations and BCAA

The bivariate associations between compliance to dietary recommendations and BCAA are summarized in supplementary table 7. Compliance to the dietary recommendations led to lower BCAA levels, namely for isoleucine, and this association was also present in men but not in women.

After multivariable analysis, the results are summarized in Table 5. Participants who complied to dairy and to at least three recommendations (At least 3 guidelines with all types of fish; at least 3 guidelines excluding fried fish) had lower BCAA levels, and similar albeit non-significant results were obtained when the analysis was split by gender.

Table 5

multivariable analysis of blood levels in µmol/L of branched chain amino acids according to compliance to Swiss dietary guidelines, CoLaus|PsyColaus study, Lausanne, Switzerland, 2009–2012, overall and stratified by gender.
	Leucine			Isoleucine			Valine
Gender / guideline	Non complier	Complier	P-value	Non complier	Complier	P-value	Non complier	Complier	P-value
ALL
Fruits ≥ 2/day	119.1 ± 0.6	118.1 ± 0.7	0.281	54.9 ± 0.3	54.3 ± 0.4	0.201	226.5 ± 1.1	224.1 ± 1.3	0.174
Vegetables ≥ 3/day	118.8 ± 0.4	117.2 ± 1.6	0.337	54.8 ± 0.2	53.1 ± 0.9	0.071	225.9 ± 0.9	220.8 ± 3.2	0.129
Meat ≤ 5/week	120.0 ± 0.7	117.9 ± 0.6	0.020	54.9 ± 0.4	54.5 ± 0.3	0.430	227.6 ± 1.4	224.2 ± 1.1	0.056
Fish all ≥ 1/week	118.8 ± 0.7	118.7 ± 0.5	0.917	55.0 ± 0.4	54.5 ± 0.3	0.318	225.0 ± 1.5	225.8 ± 1.0	0.651
Fish not fried ≥ 1/week	118.5 ± 0.6	119.0 ± 0.7	0.592	54.7 ± 0.3	54.5 ± 0.4	0.574	224.9 ± 1.1	226.4 ± 1.3	0.378
Dairy ≥ 3/day	119.0 ± 0.4	115.7 ± 1.5	0.031	54.9 ± 0.2	52.0 ± 0.8	< 0.001	226.1 ± 0.9	219.1 ± 2.9	0.019
At least 3 guidelines ^a	119.2 ± 0.5	117.0 ± 0.9	0.029	55.0 ± 0.3	53.5 ± 0.5	0.005	226.8 ± 1.0	221.5 ± 1.8	0.010
At least 3 guidelines ^b	119.0 ± 0.5	117.2 ± 1.0	0.122	54.9 ± 0.3	53.4 ± 0.6	0.014	226.3 ± 0.9	221.9 ± 2.0	0.048
WOMEN
Fruits ≥ 2/day	106.6 ± 0.7	105.8 ± 0.7	0.429	48.5 ± 0.4	47.7 ± 0.4	0.123	209.3 ± 1.4	206.3 ± 1.5	0.148
Vegetables ≥ 3/day	106.2 ± 0.5	105.9 ± 1.6	0.865	48.2 ± 0.3	47.2 ± 0.8	0.276	208.3 ± 1.1	204.1 ± 3.4	0.239
Meat ≤ 5/week	106.8 ± 0.9	105.9 ± 0.6	0.400	48.2 ± 0.5	48.1 ± 0.3	0.829	208.7 ± 1.8	207.6 ± 1.2	0.602
Fish all ≥ 1/week	106.3 ± 0.8	106.2 ± 0.6	0.878	48.4 ± 0.4	47.9 ± 0.3	0.421	208.2 ± 1.7	207.8 ± 1.2	0.840
Fish not fried ≥ 1/week	106.1 ± 0.7	106.3 ± 0.7	0.826	48.2 ± 0.3	48.0 ± 0.4	0.714	207.7 ± 1.4	208.2 ± 1.5	0.778
Dairy ≥ 3/day	106.2 ± 0.5	106.2 ± 1.6	0.975	48.2 ± 0.3	46.6 ± 0.9	0.061	207.9 ± 1.1	208.2 ± 3.4	0.925
At least 3 guidelines ^a	106.4 ± 0.6	105.7 ± 0.9	0.493	48.4 ± 0.3	47.4 ± 0.5	0.072	208.7 ± 1.2	206.0 ± 1.9	0.226
At least 3 guidelines ^b	106.3 ± 0.5	105.8 ± 1.0	0.693	48.3 ± 0.3	47.3 ± 0.5	0.097	208.4 ± 1.1	206.3 ± 2.2	0.407
MEN
Fruits ≥ 2/day	136.0 ± 0.9	135.1 ± 1.4	0.585	63.6 ± 0.5	63.5 ± 0.7	0.900	249.9 ± 1.7	249.0 ± 2.5	0.775
Vegetables ≥ 3/day	136.0 ± 0.8	130.4 ± 3.9	0.159	63.7 ± 0.4	60.0 ± 2.1	0.074	249.9 ± 1.5	242.8 ± 7.2	0.332
Meat ≤ 5/week	137.4 ± 1.1	134.1 ± 1.1	0.033	63.9 ± 0.6	63.3 ± 0.6	0.470	252.6 ± 2.0	246.7 ± 2.0	0.044
Fish all ≥ 1/week	135.8 ± 1.3	135.7 ± 0.9	0.955	63.9 ± 0.7	63.4 ± 0.5	0.554	248.1 ± 2.5	250.3 ± 1.7	0.470
Fish not fried ≥ 1/week	135.5 ± 1.0	136.1 ± 1.3	0.721	63.7 ± 0.5	63.3 ± 0.7	0.613	248.8 ± 1.8	251.0 ± 2.4	0.450
Dairy ≥ 3/day	136.4 ± 0.8	128.2 ± 2.7	0.004	64.0 ± 0.4	59.2 ± 1.4	0.002	251.1 ± 1.5	232.5 ± 5.0	< 0.001
At least 3 guidelines ^a	136.6 ± 0.8	131.6 ± 1.9	0.018	64.0 ± 0.4	61.6 ± 1.0	0.038	251.3 ± 1.6	241.3 ± 3.5	0.011
At least 3 guidelines ^b	136.3 ± 0.8	131.7 ± 2.3	0.067	63.9 ± 0.4	61.4 ± 1.2	0.068	250.7 ± 1.5	240.8 ± 4.3	0.031
^a, using all types of fish; ^b, excluding fried fish. Results are expressed as multivariable adjusted mean ± standard error. Between-group comparisons performed using analysis of variance adjusting for gender (ALL only), age (continuous), smoking (never, former, current), educational level (high, medium, low), marital status (in couple, alone), born in Switzerland (yes, no), on a diet (yes, no), BMI categories (normal, overweight), and sedentary status (yes, no). Statistically significant (p < 0.05) are indicated in red.

To our knowledge, this is one of the largest studies assessing the associations between levels of circulatory BCAA and dietary intake in a general population. Our results indicate that the circulatory levels of BCAA are positively associated with meat products and negatively associated with fruits and vegetables. Those associations might vary according to gender.

Associations between individual food items, food categories, and BCAA

Levels of circulatory BCAAs are highly dependent on protein source ⁽⁹⁾, but whether levels of circulatory BCAA individually reflect protein intake have been insufficiently studied. In this study, we found that dairy products (yogurt and cheese), bread and cereals, fruits and vegetables were negatively associated with BCAA levels, while meat, sugary products (honey and jam) and alcoholic beverages (wine, beer and spirits) showed a positive but inconsistent association with levels of circulatory BCAA. Merz et al ⁽¹⁰⁾ evaluated 298 participants and used reduced rank regression to derive six dietary patterns that were positively associated with levels of circulatory BCAA. The pattern explaining the largest variation in BCAA (32.5%) was composed of food groups such as meat, sausage, sauce, chicken eggs, and ice cream, while the food pattern showing a negative association with BCAA was composed of nuts, grains, mushrooms, and legumes. This result is in accordance with our findings. In addition, we also found associations between different beverage consumption and levels of circulatory BCAA, such as tea showing a negative association, and wine showing a positive association.

The relatively lower association between meat and processed meat with levels of circulatory BCAA found in our study could be due to the selection criteria, as only apparently healthy participants were eligible for BCAA assessment. Indeed, Rousseau et al. ⁽¹⁴⁾ reported that the positive association between animal protein or red meat intake and levels of circulatory BCAA was found in overweight people or individuals with metabolic syndrome, but not in healthy individuals with normal weight. They believe that gender seems to be a key factor in this association. In healthy individuals, BCAA may be more influenced by gender (and other metabolic factors) than by protein source intake. Similar results have also been reported by Schmidt et al, who conducted a cross-sectional analysis on the relationship between plasma amino acid concentrations in 392 participants with different dietary habits, targeting the male population. The results also showed that there was no significant difference in circulatory BCAA concentrations among meat eaters, fish, vegetarians, and vegans, and even the circulatory BCAA concentrations of fish eaters and vegetarians were slightly higher than those of meat eaters⁽²⁹⁾.

We also conducted separate research and analysis based on gender differences. Compared with female participants, the levels of circulatory BCAA in male participants was more closely related to diet, possibly due to higher amounts or types of food consumed. Other possible explanations include the higher BMI among males, the higher prevalence of diets among females. Among male participants, meat and derived products all showed a significant positive correlation with BCAA, but these were not found in female participants. The negative correlation between kiwi and BCAA in female participants was not found in male participants. These performances may be related to the different dietary preferences of men and women. After multivariable analysis, the trend associated with circulatory BCAA concentrations disappeared in male participants, but in female participants, Fruit/aroma yogurt, Berries (strawberries, blueberries) still showed a significant negative association with the levels of circulatory BCAA. Therefore, in the female population, those who have a dietary habit of eating more yogurt and fruits may have lower levels of circulatory BCAA.

Associations between nutrients and BCAA

Strong negative associations were found between all three BCAA and vegetal protein, total carbohydrates, mono and polysaccharides, and total fibre, while no association was found with total and animal protein intake. Our findings are somewhat different from those of Rousseau et al. ⁽¹⁴⁾ and Hamaya et al⁽³⁰⁾. who reported positive associations between levels of circulatory BCAA and animal protein but not with plant protein. in fact, compared to plant protein, animal protein contains more BCAA. This difference may be related to the fact that we did not include participants with obesity or to the different sources of plant protein, such as mung beans contain a lot of dietary fibre. In addition, different animal- or plant-based diets consistently change the composition of the gut microbiota, which is also a source of levels of circulatory BCAA, those changes could also influence the associations between nutrients and levels of circulatory BCAA ^(31–33).

Associations between dietary scores and BCAA

Our results showed a significant negative association between AHAI scores and levels of circulatory BCAA, while no association was found with Mediterranean diet scores. This is different from the findings of Canela et al. ⁽³⁴⁾, who conducted a dietary intervention on 251 type 2 diabetes patients and 694 controls and found that the Mediterranean diet rich in extra virgin olive oil significantly reduced the plasma BCAA levels. Possible explanations for our results include the analysis of a free-living, non-intervention sample, or that we only counted the participants' diet in the past four weeks instead of a whole year as in the study by Canela et al. Our results also do not replicate those of Hamaya et al ⁽³⁰⁾, who analysed the association between the 2010 Alternative Healthy Diet Index (non-alcoholic) and the Alternative Mediterranean Diet Score with BCAA levels in women. The authors concluded that levels of circulatory BCAA were associated with BMI, but were not significantly related to diet, physical activity, and other lifestyle factors. Still, the authors noted an inverse correlation between the fruits and the nuts/legumes scores of the AHEI and total BCAA levels, a finding partly in agreement with ours. The authors also reported an inverse association between AHEI and individual isoleucine levels, a finding also in agreements with ours.

Associations between compliance to dietary recommendations and BCAA

Compliance to at least three recommendations was associated with lower levels of circulatory BCAA, mainly isoleucine and valine. Conversely, no if little effect was found when compliance to a single recommendation was studied. Our results suggest that compliance to several dietary recommendations, a marker of a healthy diet, is associated with lower levels of circulatory BCAA. We failed to find any similar study in the literature to which we could compare our results. Overall, our results indicate that adopting a healthy diet, i.e., complying to several dietary recommendations, is associated with lower BCAA levels.

Combined with the previous findings, our results suggest that adopting a healthy diet rich in fruits, vegetables, and in dairy products is associated with lower levels of circulatory BCAA, which could be protective towards the development of cardiometabolic diseases.

Strengths and limitations

This study has several strengths. First, it included only healthy, community-dwelling participants, thus allowing to establishment of the associations between levels of circulatory BCAA and dietary intake in a group devoid of major cardiovascular risk factors. Second, it is the most comprehensive regarding food items, and one of the biggest regarding sample size (N = 2159), being outnumbered only by the studies of Floegel et al (N = 2380) ⁽³⁵⁾ and Hamaya et al (N = 18,897) ⁽³⁰⁾, although the last study only included women (Supplementary table 12).

This study also has some limitations. First, it was conducted in a specific region, and we know that diet changes according to region in Switzerland⁽³⁶⁾. Hence, the results might differ depending on the geographical region, although our results agree with those of studies conducted in other countries. Second, participants were selected and apparently healthy (i.e., not diabetic, not obese). Still, our findings agree with other studies that included people with diabetes or obesity⁽¹⁴⁾. We did not correct for multiple testing, which would reduce the number of significant associations. Still, this was also the approach conducted by other studies⁽²⁸⁾. Dietary intake was based on self-reported data, which might not fully correspond to actual dietary intake. Still, it would be impractical and very time-consuming to assess dietary intake in a large study using other methods such as 24h recalls. Finally, the differences in levels of circulatory BCAA observed were small, usually less than 5 µmol/L, and might not be clinically relevant. Still, they suggest that dietary patterns might influence levels of circulatory BCAA.

In a large sample of apparently healthy, community-dwelling participants, levels of circulatory BCAA are negatively associated with dairy, vegetables and fruits, vegetable protein, total carbohydrates (including monosaccharides and polysaccharides) and fibre. The associations appear to differ by gender.

AHEI, alternate healthy eating index

BCAA, branched-chain amino acids.

BMI, body mass index

FFQ, food frequency questionnaire

HILIC, hydrophilic interaction chromatography

HRMS, high-resolution mass spectrometry

LC-MS, liquid chromatography-mass spectrometry

MUFA, monounsaturated fatty acids

PUFA, polyunsaturated fatty acids

SFA, saturated fatty acids

T2DM, type 2 diabetes mellitus

Ethics approval and consent to participate

The institutional Ethics Committee of the University of Lausanne, which afterwards became the Ethics Commission of Canton Vaud (www.cer-vd.ch) approved the baseline CoLaus study (reference 16/03). The approval was renewed for the first (reference 33/09) follow-up. The approval for the entire CoLaus|PsyCoLaus study was confirmed in 2021 (reference PB_2018-00038). The study was performed in agreement with the Helsinki declaration and its former amendments, and in accordance with the applicable Swiss legislation. All participants gave their signed informed consent before entering the study.

Consent for publication

Not applicable.

Availability of data and materials

The data of CoLaus|PsyCoLaus study used in this article cannot be fully shared as they contain potentially sensitive personal information on participants. According to the Ethics Committee for Research of the Canton of Vaud, sharing these data would be a violation of the Swiss legislation with respect to privacy protection. However, coded individual-level data that do not allow researchers to identify participants are available upon request to researchers who meet the criteria for data sharing of the CoLaus|PsyCoLaus Datacenter (CHUV, Lausanne, Switzerland). Any researcher affiliated to a public or private research institution who complies with the CoLaus|PsyCoLaus standards can submit a research application to [email protected] or [email protected]. Proposals requiring baseline data only, will be evaluated by the baseline (local) Scientific Committee (SC) of the CoLaus and PsyCoLaus studies. Proposals requiring follow-up data will be evaluated by the follow-up (multicentric) SC of the CoLaus|PsyCoLaus cohort study. Detailed instructions for gaining access to the CoLaus|PsyCoLaus data used in this study are available at www.colaus-psycolaus.ch/professionals/how-to-collaborate/.

Competing interests

The authors declare that they have no competing interests Funding.

Funding

The CoLaus study was supported by research grants from GlaxoSmithKline, the Faculty of Biology and Medicine of Lausanne, the Swiss National Science Foundation (grants 33CSCO-122661, 33CS30-139468, 33CS30-148401, 33CS30_177535 and 3247730_204523) and the Swiss Personalized Health Network (grant 2018DRI01). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Authors' contributions

Keyuan Liu: investigation, formal analysis, visualisation, writing – original draft. Rebecca Borreggine: sample preparation, analysis, and data processing. Hector Gallart-Ayala: data quality assessment, validation, review. Julijana Ivanisevic: validation, investigation, resources, writing – review & editing. Pedro Marques-Vidal: conceptualization, methodology, formal analysis, resources, data curation, writing – review & editing, supervision. Pedro Marques-Vidal had full access to the data and is the guarantor of the study.

Acknowledgements

Not applicable.

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Levels of circulatory branched-chain amino acids are associated with dietary regimen. A cross- sectional analysis

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Abstract

Background & aims:

Methods

Results

Conclusions

Background

Methods

Study design and participants

Dietary intake, dietary scores and compliance to dietary recommendations

Sample preparation and metabolic profiling

Other covariates

Eligibility and exclusion criteria

Statistical analyses

Results

Selection of participants

Associations between individual food items, food categories, and BCAA

Associations between nutrients and BCAA

Associations between dietary scores and BCAA

Associations between compliance to dietary recommendations and BCAA

Discussion

Associations between individual food items, food categories, and BCAA

Associations between nutrients and BCAA

Associations between dietary scores and BCAA

Associations between compliance to dietary recommendations and BCAA

Strengths and limitations

Conclusion

Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

Acknowledgements

References

Additional Declarations

Supplementary Files

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