Association between amino-acid metabolisms and cardiovascular disease in adults with type 2 diabetes: a population-based cross-sectional study in China and UK Association between amino-acid and CVDs

doi:10.21203/rs.3.rs-3768289/v1

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Association between amino-acid metabolisms and cardiovascular disease in adults with type 2 diabetes: a population-based cross-sectional study in China and UK Association between amino-acid and CVDs

https://doi.org/10.21203/rs.3.rs-3768289/v1

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Objective

We aimed to identify which amino acids are associated with CVDs in adults with type 2 diabetes and compare the results in two different populations.

Method

This study consisted of two study populations, 718 participants with type 2 diabetes aged 18 to 89 were from Dalian metabolomics research (DMR), and 11,002 participants with type 2 diabetes aged 40 to 70 years were from the UK biobank (UKB). Type 2 diabetes and CVDs were defined using self-reported medical history and medical records. Plasma amino-acid metabolites including alanine (Ala), glutamine (Gln), glycine (Gly), histidine (His), leucine (Leu), phenylalanine (Phe), tyrosine (Tyr), and valine (Val) were assessed through liquid chromatography-mass spectrometry or high-throughput nucleic magnetic resonance spectroscopy metabolomic analysis. Stepwise logistic regression was used in data analysis.

Results

Among DMR participants, 74 (10.31%) had CVDs, and 1607 (14.61%) in UKB. In multi-adjusted logistic regression, Ala was positively associated with CVDs both in DMR (β = 0.048, 95% CI: 0.022– 0.074) and UKB (β = 0.009, 95% CI: 0.000– 0.018) participants. Lower Tyr was associated with CVDs in DMR (β=-0.035, 95% CI: -0.061– -0.008), while lower Gly (β=-0.017, 95% CI: -0.026– -0.007) and Val (β= -0.031 95% CI: -0.026– -0.007), and higher Gln (β = 0.010, 95% CI: 0.002– 0.018) and Leu (β = 0.024, 95% CI: 0.007– 0.041) were associated with CVDs in UKB.

Conclusions

High Ala may be an indicator for CVDs in DMR and UKB participants with type 2 diabetes. Tyr, Gly, Val, or Leu is associated with CVDs in type 2 diabetes among DMR/UKB participants.

Amino acid

Cardiovascular disease

Type 2 diabetes mellitus

Metabolism

Amino-acid metabolisms have been linked to cardiovascular diseases (CVDs) and type 2 diabetes mellitus (T2DM), but it is unclear about which are the specific amino acids that related to CVDs in adults with T2DM.
The study compared the difference in amino-acid metabolites-CVDs association among two surveys from China and UK.
High Alanine may be an indicator for CVDs in adults with T2DM.
Metabolites may serve as biomarkers to provide new insights into the occurrence and prevention of CVDs.

Diabetes, which is associated with a high level of blood glucose, an inadequate level of insulin secretion, or resistance to insulin (Kutluturk Karagoz et al., 2020), is becoming a major epidemic in the 21st century (Shaw et al., 2010). The epidemic of diabetes has resulted in a series of complications, such as diabetic retinopathy and diabetic nephropathy, and increased all-cause mortality (Forbes and Cooper, 2013). Certainly, cardiovascular diseases (CVDs) is among the foremost diabetes-related complication and cause of death (Htay et al., 2019).

Over the last decade, it became more and more clear that high-throughput metabolomic techniques and technologies can provide new sight into the risk factors of diabetes (Padberg et al., 2014; Rhee et al., 2011; Wurtz et al., 2012). Growing evidence has demonstrated the possible predictive role of amino-acid metabolisms on CVDs. So far, lower circulating levels of glycine (Gly) (Rom et al., 2022) and hyperhomocysteinemia (Feng et al., 2020) have been reported to be associated with the onset of CVDs. Currently, no previous studies have explored the association between amino acid metabolites and CVDs in type 2 diabetes mellitus (T2DM) population. To date, it remains unclear about which are the specific amino-acid metabolites that related to CVDs in adults with T2DM.

Therefore, in this study, we aimed to identify which amino acids are associated with CVDs in adults with T2DM and compare the results in two different populations from China and UK.

Study population

Dalian metabolomics research (DMR) is a cross-sectional study based in Liaoning, China. From April 2018 to June 2019, a total of 1024 T2DM participants aged 18 to 89 years were recruited at the Second Hospital of Dalian Medical University, of which, 748 individuals provided blood samples and measured their serum metabolites and biochemical indicators (amino acids, plasma glucose, glycated hemoglobin (HbA1c), lipoproteins, etc.). Participants with missing data on dyslipidemia (n = 7) and HbA1c (n = 23) were excluded. Finally, 718 individuals were left for the analyses.

The Ethics Committee for Clinical Research of the Second Affiliated Hospital of Dalian Medical University approved the ethics of the study and all the participants provided informed written consent.

UK Biobank is a large-scale population-based cohort longitudinal study with 502,412 UK residents aged from 37 to 73 years cc. Among 118,017 participants with data for amino-acid metabolisms, we excluded 107,015 without T2DM at baseline or with missing data on T2DM. Finally, a total of 11,002 participants were included in the current analysis.

The UK Biobank study received ethical approval for the National Health Serviced (NHS) National Research Ethics Services (Ref 11/NW/0382) and all enrolled participants gave their informed and written consent. All data used in this study were obtained from the UK Biobank (http://www.ukbiobank.ac.uk) through application 67048.

T2DM was defined as HbA1c ≥ 6.5%, fasting plasma glucose ≥ 126 mg/dl, random blood glucose ≥ 200 mg/dl, having a history of diabetes, or the use of glucose-lowering medications.

Assessment of amino-acid metabolisms

The DMR utilized liquid chromatography-mass spectrometry for metabolomic analysis. Details of the amino acids measured were published previously (Wang et al., 2016). Briefly, liquid chromatography-mass spectrometry metabolomic analysis was conducted by using an AB Sciex 4000 QTrap system (AB Sciex, Framingham, Massachusetts, USA). Eight-hour fasting blood was taken and stored as dried blood spots. Each dried sample was dissolved in 100 µL fresh mobile phase solution. The mobile phase was 80% acetonitrile aqueous solution. Amino acid quality control standards were provided by Chromsystems (Grafelfing, Germany). The standards were mixed and dissolved in 2 mL pure methanol and stored at 4°C working solution was prepared through 100-fold dilution for metabolite extraction. Analyst V.1.6.0 software (AB Sciex) was used for system control and data collection. ChemoView V.2.0.2 (AB Sciex) was used for data preprocessing.

The UKB utilized high-throughput nucleic magnetic resonance spectroscopy for metabolomic analysis. As part of the UK Biobank Biomarker Project, blood samples were collected from participants at baseline. Details about blood collection, metabolomic biomarker assessment, and quality control have been reported previously (Joshi et al., 2023). Briefly, a high-throughput nucleic magnetic resonance spectroscopy on the Nightingale metabolic biomarker platform was used to undertake metabolomic profiling including amino acids in baseline plasma samples (Würtz et al., 2017). Technical details and epidemiological applications of the metabolic biomarker data have been reviewed (Julkunen et al., 2021). Finally, plasma amino-acid metabolites including alanine (Ala), glutamine (Gln), glycine (Gly), histidine (His), leucine (Leu), phenylalanine (Phe), tyrosine (Tyr), and valine (Val) were engaged in the subsequent analyses.

Assessment of CVDs

CVDs including myocardial infarction, angina, congestive heart failure, and atrial fibrillation was assessed based on self-reported and registered medical history.

Assessment of Covariates

Participants underwent comprehensive physical, clinical examinations, and biological sampling at baseline and provided information on demographic characteristics, lifestyle factors, medical conditions, and medication use at baseline through a computerized touchscreen questionnaire. Body mass index (BMI) was calculated as weight(kg)/ height(m²), and categorized into normal weight (< 24.0 kg/m²), overweight (24.0–28.0 kg/m²), and obesity (≥ 28.0 kg/m²). Smoking and alcohol consumption status were categorized as never, former, or current. Blood pressure including systolic blood pressure (SBP) was measured using the Omron 705 IT electronic blood pressure monitor at baseline. Two readings were recorded for each participant and the average of the two measurements (mmHg) was calculated. High-density lipoprotein (HDL) cholesterol was assayed by selective solubilization method (Determiner L-HDL test kit; Kyowa Medex, Tokyo, Japan) in DMR and was assayed using the AU5800 analytical platform (Beckman Coulter) in UKB. Dyslipidemia is defined by the level of HDL cholesterol: HDL < 1.036 mmol/L for men and HDL < 1.295 mmol/L for women. The use of glucose-lowering medications was obtained through self-reporting at baseline. HbA1c was measured by direct infusion mass spectrometry technology equipped with the AB Sciex 4000 QTrap system (AB Sciex, Framingham, MA, USA) in DMR and High Performance Liquid Chromatography analysis on a Bio-Rad VARIANT II Turbo in UKB (Langlois et al., 2018).

Assessment of Lifestyle Index

Responses on each of the three lifestyle factors (including BMI, smoking, and alcohol consumption) were scored as 0 (i.e., obesity, current smoking, and current alcohol consumption), 1 (i.e., overweight, former smoking, and former alcohol consumption), and 2 (i.e., normal weight, abstain from smoking, and abstain from alcohol consumption) based on current evidence and recommendations(Li et al., 2022). Three scores were added to produce a combined lifestyle index for each participant ranging from 0 to 6, which was classified as unfavorable (0–1) and favorable (2–6) lifestyle according to the median of the scores.

Statistical analysis

Continuous data are expressed as mean ± standard deviation (SD) if normally distributed, or median (interquartile range) if not normally distributed. Categorical variable was in numbers (percentage). Characteristics of participants of the two studies by CVDs status (CVDs-free vs. CVDs) were analyzed using chi-square tests for categorical variables and t-test or Wilcoxon rank-sum tests for continuous variables.

Spearman correlation analyses were used to analyze the correlation between the metabolism of each amino acid respectively in DMR and UKB. Stepwise multivariate linear regression models were modeled respectively in the DMR, UKB, and pooled populations to identify CVDs-related amino acid biomarkers. Furthermore, a pooled population including both of the studies was also used to explore the association between amino acid species and CVDs. Age, gender, BMI, smoking, alcohol consumption, SBP, dyslipidemia, and medications were considered potential confounders. Stratified analyses were performed to explore the role of lifestyle index (favorable vs. unfavorable), dyslipidemia (yes vs. no), and HbA1c (≥ 7% vs. <7%) in the association between amino acids and CVDs.

All P-values were two-sided, and we defined statistical significance as P < 0.05. All statistical analyses were performed using Stata SE 16.0 (Stata Corp, College Station, Texas) and R (version 4.1.1).

Characteristics of the study population

Among the DMR participants, a total of 718 participants with T2DM were enrolled, including 74 (10.31%) with CVDs and 644 (89.69%) without CVDs. The mean age of participants in DMR was 59.83±13.07 years, and 48.33% were female. Compared to participants without CVDs, those with CVDs were likely to be older. In addition, the differences in other characteristics were not statistically significant among those with or without CVDs (Table 1).

In the UKB, a total of 11002 participants with T2DM were enrolled, including 1607 (14.61%) with CVDs and 9395 (85.39%) without CVDs. The mean age of participants in UKB was 59.33±7.33 years, and 40.07% were female. Compared to participants without CVDs, those with CVDs were more likely to be old, male, current smoker, consume alcohol, have dyslipidemia, and were less likely to use insulin (Table 1).

Correlation between amino acid metabolisms

In both DMR and UKB, there was a significant correlation between any two amino acid metabolites. However, Gly was found not significantly correlated with His and Leu. In UKB, there is a negative correlation observed between Gly and Leu, Tyr, and Val, whereas in the DMR, Gly is positively correlated with Phe, Tyr, and Val (Table 2).

Associations between amino acids and CVDs in two studies

In DMR population, Ala and Tyr were retained in the stepwise multivariate linear regression model, with the β (95% confidence interval, CI) was 0.048 (0.022, 0.074) for Ala and -0.035 (-0.061, -0.008) for tyrosine. In the UKB, Ala (β=0.009, 95% CI: 0.000–0.018), Gln (β=0.010, 95% CI: 0.002–0.018), Gly (β=0.017, 95% CI: -0.026– -0.07), Leu (β=0.024, 95% CI: 0.007–0.041), and Val (β=0.031, 95% CI: -0.048– -0.013) were retained in the stepwise multivariate linear regression model (Table 3).

The association between amino acids and CVDs in the pooled population

The correlation between the eight amino acids in the two populations is shown in the supplementary material. Higher level of Ala (β=0.013, 95% CI: 0.005– 0.022), Gln (β=0.009, 95% CI: 0.001– 0.016), and Leu (β=0.018, 95% CI: 0.003– 0.033) were associated with increased risk of CVDs in the pooled population, while higher level of Gly (β=-0.017, 95% CI: -0.026– -0.008) and Val (β=-0.023, 95% CI: -0.038– -0.007) were associated with decreased risk of CVDs in the pooled population (Table S1).

After stratified by lifestyle, Ala (β=0.015, 95% CI: 0.001– 0.029), Gln (β=0.018, 95% CI:0.004– 0.032), and Gly (β=-0.029, 95% CI: -0.045– -0.013) were independently associated with CVDs among those with unfavorable lifestyle. Ala (β=0.016, 95% CI: 0.006– 0.026), Gly (β=-0.011, 95% CI: -0.021– -0.001), Leu (β=0.027, 95% CI: 0.010– 0.044), and Val (β=-0.033, 95% CI: -0.051– -0.015) were independently associated with CVDs among those with favorable lifestyle (Table 4).

After stratified by dyslipidemia, Ala (β=0.009, 95% CI: -0.000– 0.019), Gln (β=0.011, 95% CI:0.002– 0.021), and Gly (β=-0.020, 95% CI: -0.031– -0.009) were independently associated with CVDs in the non-dyslipidemia group. Ala (β=0.016, 95% CI: 0.003– 0.029), Leu (β=0.020, 95% CI: -0.003– 0.043), Phe (β=0.013, 95% CI: -0.002– 0.027), and Val (β=-0.034, 95% CI: -0.057– -0.010) were independently associated with CVDs in the dyslipidemia group (Table 4).

After stratified by Hb1ac, Ala (β=0.014, 95% CI:0.004– 0.024), Gln (β=0.015, 95% CI: 0.006– 0.024), Gly (β=-0.014, 95% CI: -0.025– -0.003), Leu (β=0.017, 95% CI: -0.002– 0.035), and Val (β=-0.025, 95% CI: -0.044– -0.006) were independently associated with CVDs among individuals with Hb1AC <7%. Gly (β=-0.020, 95% CI: -0.034– -0.005) was independently associated with CVDs among individuals with Hb1AC ≥7% (Table 4).

In the current study, we explored the relationship between amino acids and CVDs among individuals with T2DM using two different populations. We found that Ala may be a specific metabolic biomarker for CVDs in people with T2DM. The association of amino acids with CVDs may vary across different populations and across different states of risk factors (including lifestyle index, dyslipidemia, and Hb1AC).

Ala, which is a non-essential amino acid, was found to have a positive effect on the risk of CVDs both in DMR and UKB populations. As the common risk factor in the two populations, Ala is contained in many enzymes. Evidence showed that both asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA) play a crucial role in the development of CVDs. Ala is an integral part of the Alanine-glyoxylate aminotransferase 2 (AGXT2), which is reported to be related to ADMA and SDMA metabolism (Hu et al., 2017). Besides, it is also an important part of the process of gluconeogenesis for it is one of the components of the Ala aminotransferase (ALT), which is found primarily in the liver. Recent studies showed that the elevation of ALT indicated a variety of metabolic abnormalities (Vozarova et al., 2002), with a higher ALT as a risk factor for T2DM (Doi et al., 2007; Ohlson et al., 1988). A cohort study in Korea showed that elevated ALT levels are independently associated with increased CVDs- or diabetes-related mortality in Koreans (Yun et al., 2009). Fatty liver might be a mechanism for high ALT-CVDs association (Marchesini et al., 2001), and fatty liver is associated with insulin resistance (MA et al., 1995 Dec; T et al., 1995 Dec), which is a well-known risk factor for diabetes.

Similar Results were also found in another amino acid mentioned in this study. Gly was found to have a negative effect on the incidence of CVDs and T2DM in some studies (Ding et al., 2015; Wittemans et al., 2019), and the possible mechanism might be Gly could mitigate CVDs through antioxidant effects mediated by induction of glutathione biosynthesis (O et al., 2022 Jun).

In a 12-year follow-up study in Finnish Men researchers found that Gln was significantly associated with decreased risk of stroke and CVDs events (Jauhiainen et al., 2021). Another research also showed that Gln can be regarded as a kind of Conditionally essential nutrient (CENs), which has demonstrated favorable clinical effects in CVDs (Kendler, 2006). However, a positive association was found between Gln and CVDs in our study, the reason may be that our research was based on the T2DM population.

Leu is a kind of branched amino acid (BCAA). which has been shown to influence lipid and energy metabolism in vivo and in vitro by accelerating fatty acid oxidation as well as cytokine synthesis and/or secretion, favoring a reduction in adiposity. It has been suggested that increasing energy expenditure and removing toxic lipids by increasing the prevalence and activity of LEUs may be a promising treatment strategy for obesity and its accompanying diseases, such as diabetes, and cardiovascular diseases while in normal metabolism (Zhang et al., 2020). However, in impaired metabolism, higher BCAAs are related to higher risks of T2DM and CVDs (Tobias et al., 2018). A hospital-based cross-sectional study also showed that higher BCAAs intake, in particular Leu and Val, might be associated with a lower risk of CVDs in T2DM patients, which is similar to our results (Zheng et al., 2022).

The strength of this study is the combination of two studies with different populations and relatively large sample sizes. Besides, the connection between epidemiology and metabolomics also helps to better understand the association between amino acids and CVDs. However, there are some limitations noticed in our study. First, the association between amino acids and CVDs was examined based on cross-sectional data, thus the temporality of the observed association is unclear. Second, the detection of amino acids in the DMR and the UKB database could not completely overlap, which restricted the possibility of analyzing a wider variety of amino acid-CVDs associations. Third, potential confusion caused by unmeasured factors (such as an unhealthy diet) could not be completely ruled out.

In conclusion, high Ala may be an indicator of CVDs among adults with T2DM in both of the two independent cross-sectional studies. As the nature of the cross-sectional study, prospective cohort studies are needed to replicate our findings. Our study had important public health implications, for CVDs is a sort of common and serious complication in T2DM, although intensive management of hyperglycemia, hypertension, and abnormal lipids can reduce the risk of CVDs in T2DM, the residual risk of CVDs remains substantially high (Huang et al., 2017). Our study suggests that metabolites may serve as biomarkers to provide new insights into the occurrence and prevention of diseases.

Article Highlight

Amino-acid metabolisms have been linked to cardiovascular diseases (CVDs) and type 2 diabetes mellitus (T2DM), but it is unclear about which are the specific amino acids that related to CVDs in adults with T2DM.

The study compared the difference in amino-acid metabolites-CVDs association among two surveys from China and UK.

High Alanine may be an indicator for CVDs in adults with T2DM.

Metabolites may serve as biomarkers to provide new insights into the occurrence and prevention of CVDs.

Fundings:

This work is supported by the National Key Research and Development Program of China (2021YFA1301202), National Natural Science Foundation of China (82273676), Liaoning Province Scientific and Technological Project (2021JH2/10300039).

Competing Interest:

Authors have no competing interest to declare.

Author Contribution

Wei-Li Xu, Qiang Zhang and Zhong-Ze Fang designed this study. Jing-Yang Su, Shu-Qi Wang, Jiao Wang, Yang Li, Wan-Ying Wang and Wei Zhang participated in the data management of this study. Jing-Yang Su and Peng-Zhe Xie prepared the table 1-4 and supplemental material. Jing-Yang Su wrote the main manuscript text. All authors reviewed the manuscript.

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Table 1. Characteristics of the study population.

Characteristics	Dalian metabolomics research study (N=718)			UK Biobank (N=11,002)
Characteristics	CVDs-free (n=644)	CVDs (n=74)	P	CVDs-free (n=9,395)	CVDs (n=1,607)	P
Age	59.09±13.25	66.33±9.19	<0.001	58.87±7.44	62.02±6.00	<0.001
Female	309 (47.98%)	38 (51.35%)	0.583	3,976 (42.32%)	433 (26.94%)	<0.001
BMI, kg/m²
Normal weight	159 (24.68%)	17 (22.97%)	0.385	5,128 (54.59%)	949 (59.05%)	<0.001
Overweight	270 (41.93%)	37 (50.00%)		3,266 (34.76%)	538 (33.48%)
Obesity	215 (33.39%)	20 (27.03%)		1000 (10.65%)	120 (7.47%)
Smoking			0.982			<0.001
Never	474 (73.95%)	54 (72.97%)		4.370 (47.00%)	580 (36.53%)
Former	52 (8.11%)	6 (8.11%)		3,679 (39.57%)	792 (49.87%)
Current	115 (17.94%)	14 (18.92%)		1,248 (13.43%)	216 (13.60%)
Alcohol consumption			0.841			<0.001
Never	561 (87.52%)	63 (85.14%)		761 (8.14%)	151 (9.45%)
Former	21 (3.28%)	3 (4.05%)		630 (6.74%)	146 (9.14%)
Current	59 (9.20%)	8 (10.81%)		7,961 (85.12%)	1,301 (81.41%)
SBP, mm Hg	147.82±21.06	151.27±20.35	0.181	142.51±17.86	138.87±18.43	0.108
Dyslipidemia	313 (48.60%)	40 (54.05%)	0.374	3,586 (43.81%)	735 (52.88%)	<0.001
HbA1c, %	8.50 (7.10,10.20)	8.25 (7.12,10.00)	0.637	6.27 (5.76,7.05)	6.39 (5.90,7.23)	0.087
Medications			0.128			<0.001
Insulin	367 (56.99%)	49 (66.22%)		979 (10.42%)	266 (16.55%)
Non-insulin	277 (43.01%)	25 (33.78%)		8,416 (89.58%)	1,341 (83.45%)

Values are mean ± SD, n (%), or median (interquartile range). BMI, body mass index; SBP, systolic blood pressure; HbA1c, glycated hemoglobin.

Table 2. Correlation coefficients between amino acid metabolites.

Amino acids	Alanine	Glutamine	Glycine	Histidine	Leucine	Phenylalanine	Tyrosine	Valine
Alanine	r_{a =1.00} r_{b =1.00}
Glutamine	r_{a =0.34} r_{b =0.07}	r_{a =1.00} r_{b =1.00}
Glycine	r_{a =0.36} r_{b =0.06}	r_{a =0.16} r_{b =0.32}	r_{a =1.00} r_{b =1.00}
Histidine	r_{a =0.32} r_{b =0.15}	r_{a =0.50} r_{b =0.36}	r_{a =0.04} r_{b =0.13*}	r_{a =1.00} r_{b =1.00}
Leucine	r_{a =0.33} r_{b =0.28}	r_{a =0.13} r_{b =0.16}	r_{a =0.08} r_{b =-0.09 *}	r_{a =0.29} r_{b =0.31}	r_{a =1.00} r_{b =1.00}
Phenylalanine	r_{a =0.55} r_{b =0.18}	r_{a =0.29} r_{b =0.16}	r_{a =0.41} r_{b =0.02}	r_{a =0.20} r_{b =0.28}	r_{a =0.37} r_{b =0.48}	r_{a =1.00} r_{b =1.00}
Tyrosine	r_{a =0.59} r_{b =0.18}	r_{a =0.23} r_{b =0.11}	r_{a =0.40} r_{b =-0.07}	r_{a =0.20} r_{b =0.22}	r_{a =0.41} r_{b =0.45}	r_{a =0.71} r_{b =0.51}	r_{a =1.00} r_{b =1.00}
Valine	r_{a =0.50} r_{b =0.36}	r_{a =0.19} r_{b =0.08}	r_{a =0.23} r_{b = -0.14}	r_{a =0.22} r_{b =0.26}	r_{a =0.60} r_{b =0.86}	r_{a =0.63} r_{b =0.43}	r_{a =0.60} r_{b =0.44}	r_{a =1.00} r_{b =1.00}

r_a represents correlation coefficients between amino acid metabolites among Liaoning; r_b represents correlation coefficients between amino acid metabolites among UK Biobank. ^*P<0.05

Table 3. Stepwise regression for amino acids in Dalian metabolomics research study and UK Biobank populations.

Amino acids	Dalian metabolomics research study		UK Biobank
Amino acids	β (95%CI)	P	β (95%CI)	P
Alanine	0.048 (0.022, 0.074)	<0.001	0.009 (0.000, 0.018)	0.044
Glutamine	-	-	0.010 (0.002, 0.018)	0.018
Glycine	-	-	-0.017 (-0.026, -0.007)	<0.001
Histidine	-	-	-	-
Leucine	-	-	0.024 (0.007, 0.041)	0.005
Phenylalanine	-	-	-	-
Tyrosine	-0.035 (-0.061, -0.008)	<0.010	-	-
Valine	-	-	-0.031 (-0.048, -0.013)	0.001

The variables in the stepwise regression model included alanine, glutamine, glycine, histidine, leucine, phenylalanine, tyrosine, valine, age, sex, smoking, alcohol consumption, body mass index, systolic blood pressure, glycated hemoglobin (<7%, ≥7%), and medications.

“-” indicates that the amino acid was not included in the model.

Table 4. Stratified analysis of amino acids in the pooled population.

Amino acids	Lifestyle		Dyslipidemia		HbA1c
	Unfavorable (n=3,761)	Favorable (n=7,825)	Non-dyslipidemia (n=5,620)	Dyslipidemia (n=4,674)	﹤7% (n=7,807)	≥7% (n=3,387)
	β (95%CI)	β (95%CI)	β (95%CI)	β (95%CI)	β (95%CI)	β (95%CI)
Alanine	0.015 (0.001, 0.029)	0.016 (0.006, 0.026)	0.009 (-0.000, 0.019)^a	0.016 (0.003, 0.029)	0.014 (0.004, 0.024)	-
Glutamine	0.018 (0.004, 0.032)	-	0.011 (0.002, 0.021)	-	0.015 (0.006, 0.024)	-
Glycine	-0.029 (-0.045, -0.013)	-0.011 (-0.021, -0.001)	-0.020 (-0.031, -0.009)	-	-0.014 (-0.025, -0.003)	-0.020 (-0.034, -0.005)
Histidine	-	-	-	-	-	-
Leucine	-	0.027 (0.010, 0.044)	-	0.020 (-0.003, 0.043)^b	0.017 (-0.002, 0.035)^d	-
Phenylalanine	-	-	-	0.013 (-0.002, 0.027)^c	-	-
Tyrosine	-	-	-	-	-	-
Valine	-	-0.033 (-0.051, -0.015)	-	-0.034 (-0.057, -0.010)	-0.025 (-0.044, -0.006)	-

“-” indicates that the amino acid was not included in the model.

^aP=0.061; ^bP=0.081; ^cP=0.084; ^dP=0.074.

No competing interests reported.

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Association between amino-acid metabolisms and cardiovascular disease in adults with type 2 diabetes: a population-based cross-sectional study in China and UK Association between amino-acid and CVDs

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