ORIGINAL ARTICLE
METABOLIC SYNDROME AND RELATED DISORDERS Volume X, Number X, 2015 Mary Ann Liebert, Inc. Pp. 1–8 DOI: 10.1089/met.2014.0132
Association of Branched and Aromatic Amino Acids Levels with Metabolic Syndrome and Impaired Fasting Glucose in Hypertensive Patients Liming Weng, PhD,1 Eoin Quinlivan, PhD,2 Yan Gong, PhD,1 Amber L. Beitelshees, PharmD, MPH,3 Mohamed H. Shahin, MS,1 Stephen T. Turner, MD,4 Arlene B. Chapman, MD,5 John G. Gums, PharmD,6 Julie A. Johnson, PharmD,1,7 Reginald F. Frye, PharmD, PhD,1 Timothy J. Garrett, PhD,2 and Rhonda M. Cooper-DeHoff, PharmD, MS1,7
Abstract
Background: The three branched amino acids (valine, leucine, and isoleucine) and two aromatic amino acids (tyrosine and phenylalanine) have been associated with many adverse metabolic pathways, including diabetes. However, these associations have been identified primarily in otherwise healthy Caucasian populations. We aimed to investigate the association of this five-amino-acid signature with metabolic syndrome and impaired fasting glucose (IFG) in a hypertensive cohort of Caucasian and African Americans. Methods: We analyzed data from the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) studies PEAR and PEAR2 conducted between 2005 and 2014. Subjects were enrolled at the University of Florida (Gainesville, FL), Emory University (Atlanta, GA), and Mayo Clinic (Rochester, MN). A total of 898 patients with essential hypertension were included in this study. Presence of metabolic syndrome and IFG at baseline were determined on the basis of measurements of demographic and biochemical data. Levels of the five amino acids were quantified by liquid chromatography–tandem mass spectroscopy (LC-MS/MS). Results: With a multiple logistic regression model, we found that all five amino acids were significantly associated with metabolic syndrome in both Caucasian and African Americans. IFG and the five amino acids were associated in the Caucasian Americans. Only valine was significantly associated with IFG in African Americans. Conclusion: In both Caucasian and African Americans with uncomplicated hypertension, plasma levels of the five-amino-acid signature are associated with metabolic syndrome. Additionally, in Caucasians we have confirmed the five-amino-acid signature was associated with IFG. are more likely to have hypertension (HTN), and have increased risk for cardiovascular disease (CVD) morbidity and mortality.2,3 DM and HTN are frequent co-morbid conditions and might share common underlying metabolic pathways, including the renin angiotensin aldosterone system, sympathetic nervous system, and insulin resistance.4,5 Early identification of IFG and its risk conditions along with lifestyle and other interventions when possible may reduce the risk for or delay onset of DM or HTN.6 Metabolic
Introduction
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iabetes mellitus (DM) remains a major cause of morbidity and mortality, affecting more than 29 million people in the United States.1 More worrisome is that there are approximately 86 million American adults with undiagnosed elevated glucose levels ( ‡ 100 mg/dL), also known as impaired fasting glucose (IFG) or prediabetes. Those with IFG are more likely to develop DM than those without IFG,
1 Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, College of Pharmacy, Gainesville, Florida. 2 Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, Florida. 3 Department of Medicine and Program in Personalized and Genomic Medicine, University of Maryland, Baltimore, Maryland. 4 Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota. 5 Renal Division, School of Medicine, Emory University, Atlanta, Georgia. 6 Department of Pharmacotherapy and Translational Research, College of Pharmacy and Department of Community Health and Family Medicine, College of Medicine, University of Florida, Gainesville, Florida. 7 Division of Cardiovascular Medicine, Department of Medicine, University of Florida, Gainesville, Florida.
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syndrome, a constellation of adverse metabolic risk factors, including IFG, dyslipidemia, and abdominal obesity, is also associated with increased risk for DM and adverse CV outcomes.7,8 Identifying metabolic biomarkers associated with IFG and metabolic syndrome could potentially provide insight into their underlying mechanisms of pathogenesis and result in potential targets for interventions that could reduce long-term overall risk. Recent advances in metabolomics have allowed for a better understanding of the processes of disease development, providing some insight into the mechanisms of diseases.9 Several independent investigations have documented the correlation between levels of three branched-chain amino acids (BCAA, leucine [Leu], isoleucine [Ile], and valine [Val]) and two aromatic amino acids (AAA, phenylalanine [Phe] and tyrosine [Tyr]), and DM and insulin resistance in adults and adolescents.10–13 However, this five-amino-acid signature has been established primarily within Caucasian observational populations, whereas the presence of risk conditions such as HTN and other race/ethnic groups are largely underrepresented. To our knowledge, the relationship between this five-amino-acid signature and metabolic syndrome has not been established. Therefore, we evaluated the correlation between baseline levels of Leu, Ile, Val, Phe, and Tyr and metabolic syndrome and IFG in individuals with untreated essential HTN enrolled in the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) studies PEAR and PEAR2.14 Our goal was to investigate the association between this five-amino-acid signature and presence of metabolic syndrome or IFG among Caucasian and African American participants with uncomplicated HTN.
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Amino acid quantification Plasma samples collected prior to drug treatment were used for determination of targeted amino acid concentrations (Ile, Leu, Val, Phe, Tyr) by isotopic dilution liquid chromatography–tandem mass spectrometry (LC-MS/MS). Briefly, 25 mL of plasma was mixed with 10 mL of an internal standards solution (containing U-18O-labeled amino acids). The sample was diluted with 100 mL of methanol to precipitate protein, and, after centrifugation, 50 mL of the supernatant was further diluted to 500 mL with 10 mM ammonium acetate in water. Samples (10 mL) were injected onto a Halo PFP column (2.7u, 150 · 2.1 mm; Mac-Mod, Chadds Ford, PA) and isocratically eluted on a ThermoScientific Accela 1000 UHPLC using a mobile phase comprised of 10 mM ammonium acetate in water containing 25% methanol. Tandem mass spectrometry was performed using a ThermoScientific TSQ Quantum Ultra employing heated electrospray ionization (HESI) in positive mode. Amino acids were identified by their co-elution with authentic standards (Ile, Leu, Val, Phe, and Tyr) and by their mass transitions (precursor/product). Differentiation of Leu and Ile was determined by their disparate mass transitions (132/43 and 132/69, respectively); there was no ‘‘cross-talk’’ between the two transitions. Amino acid concentrations were determined by calculating the ratios of endogenous peak areas to those of the internal standards, and comparing these ratios to standard curves prepared using amino acid standards of known concentration. Inter- and intraassay variability (% standard deviation [SD]) for the assay was less than 3%.
Biochemical assays
PEAR studies
Blood samples obtained in the fasting state at baseline and serum were used to measure glucose, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein
Two clinical trials were undertaken to evaluate the pharmacogenomic determinants of the antihypertensive and adverse metabolic responses to beta-blockers (PEAR, atenolol; PEAR2, metoprolol) and thiazide diuretics (PEAR, hydrochlorothiazide; PEAR2, chlorthalidone) in HTN participants without a history of heart disease or DM. Participants in both studies were enrolled from the University of Florida in Gainesville, FL, Mayo Clinic in Rochester, MN, and Emory University in Atlanta, GA. Details regarding enrollment criteria have been previously published, and the studies are registered at clinicaltrials.gov (PEAR, NCT00246519; PEAR2, NCT01203852).14,15 In both studies, male or female patients 17–65 years of age, of any race or ethnicity, with uncomplicated HTN were included. Study participants were excluded if they had complicated HTN, DM (or fasting blood glucose ‡ 126 mg/dL) or documented CVD (including history of angina pectoris, heart failure, cardiac pacemaker, myocardial infarction, stroke, etc.), or if they had very elevated blood pressure [systolic blood pressure (SBP) > 170 mmHg or diastolic blood pressure (DBP) > 110 mmHg], or if they had other complications (pregnancy, renal diseases, sleep apnea, etc). Blood samples were collected at baseline, after a washout period of 4–6 weeks by withdrawing all antihypertensive medications prior to study drug treatment. These baseline samples were used for amino acids, glucose, and other biochemical determinations in this study.
FIG. 1. Flow diagram of PEAR and PEAR2 participants enrolled for the analysis. Numbers of patients in PEAR and PEAR2 are indicated, as well as the numbers according to race in each group. CA, Caucasian American; AA, African American; PEAR, Pharmacogenomic Evaluation of Antihypertensive Responses.
Methods and Materials
ASSOCIATION OF AMINO ACIDS WITH METABOLIC SYNDROME AND IFG
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Table 1. Baseline Characteristics of 898 Patients in PEAR and PEAR2 With and Without Metabolic Syndrome in Caucasian and African American Participants Caucasians (n = 543)
Characteristics Women, n (%) Age (years) BMI (kg/m2) Weight (kg) Waist circumference (cm) Smoking n (%) SBP (mmHg) DBP (mmHg) Glucose (mg/dL) Triglycerides (mg/dL) Total cholesterol (mg/dL) High-density lipoprotein (mg/dL) Low-density lipoprotein (mg/dL) Uric acid (mg/dL)
African Americans (n = 355)
No metabolic syndrome n = 279 (51%)
Metabolic syndrome n = 264 (49%)
P value
127 (45.5) 51.1 – 9.4 28.3 – 4.3 82.4 – 15.4 92.9 – 12.1 24 (8.6) 150.5 – 12.3 97.9 – 5.6 89.0 – 7.9 105.3 – 62.3 195.2 – 33.4 55.7 – 12.6 118.6 – 28.8 5.4 – 1.4
106 (40.2) 49.4 – 9.5 32.9 – 5.1 97.7 – 16.2 105.7 – 11.2 42 (15.9) 150.9 – 12.2 98.0 – 5.4 95.9 – 12.7 189.9 – 95.6 198.3 – 35.7 40.1 – 9.5 121.2 – 30.5 6.2 – 1.4
0.2065 0.0406 < 0.0001 < 0.0001 < 0.0001 0.0092 0.672 0.8545 < 0.0001 < 0.0001 0.2922 < 0.0001 0.3127 < 0.0001
Metabolic No metabolic syndrome syndrome n = 229 (65%) n = 126 (35%) 143 (62.5) 48.6 – 9.4 29.9 – 5.6 84.7 – 16.8 93.3 – 12.7 34 (14.9) 151.3 – 12.9 98.9 – 6.9 87.4 – 9.2 74.7 – 27.4 189.1 – 36.9 58.1 – 15.7 116.1 – 31.9 5.3 – 1.4
90 (71.6) 47.7 – 8.4 33.8 – 5.3 95.9 – 16.3 103.2 – 10.2 33 (26.2) 150.6 – 12.7 98.9 – 5.8 96.6 – 13.4 140.2 – 115.0 191.3 – 41.1 43.1 – 11.4 121.2 – 35.5 5.8 – 1.5
P value 0.0881 0.3964 < 0.0001 < 0.0001 < 0.0001 0.009 0.6384 0.9869 < 0.0001 < 0.0001 0.6084 < 0.0001 0.1733 0.0034
Values are presented as mean – standard deviation unless noted otherwise. PEAR, Pharmacogenomic Evaluation of Antihypertensive Responses; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.
cholesterol (HDL-C), triglycerides (TGs), and uric acid with a Hitachi 911 Chemistry Analyzer (Roche Diagnostics, Indianapolis, IN) at the central laboratory at the Mayo Clinic (Rochester, MN), and determined by an automated enzymatic assay using spectrophotometry.16 All samples were tested in duplicate, and the data reported are means of the duplicate samples.
females; (2) HDL-C < 40 mg/dL in males or < 50 mg/dL in females; (3) TGs ‡ 150 mg/dL; (4) fasting glucose ‡ 100 mg/dL; (5) SBP ‡ 130 mmHg or DBP ‡ 85 mmHg.17 Presence of IFG was defined as a baseline fasting glucose ‡ 100 mg/dL whereas a fasting glucose < 100 mg/dL was considered to be normal.18
Definition of metabolic syndrome and IFG
Continuous demographic and baseline characteristics were summarized using mean – SD and compared using the two-sample t-test. Categorical characteristics were summarized as frequencies and percentages (%) and compared using the chi-squared test.
Participants were considered to have metabolic syndrome if any three of the following five characteristics were present: (1) Waist circumference (WC) > 102 cm in males or > 88 cm in
Statistical analysis
Table 2. Baseline Characteristics of 898 Patients in the PEAR and PEAR2 Studies With and Without IFG in Caucasian and African American Participants Caucasians (n = 543) Characteristics Women, n (%) Age (years) BMI (kg/m2) Weight (kg) Waist circumference (cm) Current smoking, n (%) SBP ( mmHg) DBP (mmHg) Glucose (mg/dL) Triglycerides (mg/dL) Total cholesterol (mg/dL) High-density lipoprotein (mg/dL) Low-density lipoprotein (mg/dL) Uric acid (mg/dL) Metabolic syndrome, n (%)
African Americans (n = 355)
No IFG n = 437 (81%)
IFG n = 106 (19%)
P value
No IFG n = 290 (82%)
IFG n = 65 (18%)
P value
197 (45) 49.8 – 9.9 30.1 – 5.2 88.3 – 17.7 98.0 – 13.6 52 (12) 150.4 – 12.4 97.8 – 5.5 88.3 – 6.8 139.9 – 85.6 195.5 – 33.4 49.1 – 14.0 118.8 – 27.7 5.6 – 1.4 172 (39)
36 (34) 52.2 – 7.5 32.3 – 5.2 96.3 – 15.2 103.8 – 11.0 14 (13) 152.0 – 11.7 98.5 – 5.4 109.1 – 9.4 173.3 – 105.2 201.3 – 38.6 44.2 – 11.5 124.7 – 36.5 6.3 – 1.5 92 (87)
0.038 0.007 < 0.0001 < 0.0001 < 0.0001 0.7115 0.219 0.245 < 0.0001 0.003 0.133 0.0002 0.126 0.0001 < 0.0001
195 (67) 47.8 – 9.2 31.0 – 6.0 87.7 – 17.9 96.2 – 13.2 51 (18) 150.8 – 13.1 98.8 – 6.5 86.5 – 7.3 94.2 – 79.7 188.4 – 37.8 53.2 – 16.0 116.7 – 32.8 5.3 – 1.4 75 (26)
38 (59) 50.5 – 8.1 32.3 – 4.6 93.4 – 14.8 99.6 – 10.5 16 (25) 152.2 – 11.9 99.3 – 6.3 109.2 – 9.5 114.6 – 70.1 196.6 – 40.7 50.6 – 16.3 123.1 – 34.9 6.0 – 1.5 51 (79)
0.178 0.030 0.064 0.017 0.025 0.191 0.426 0.623 < 0.0001 0.058 0.122 0.228 0.164 0.0013 < 0.0001
Values are presented as mean – standard deviation unless noted otherwise. PEAR, Pharmacogenomic Evaluation of Antihypertensive Responses; IFG, impaired fasting glucose; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.
< 0.0001 < 0.0001 < 0.0001 < 0.0001 0.001 223.9 – 50.5 121.1 – 30.8 67.6 – 18.5 61.3 – 15.9 52.4 – 11.9 202.8 – 48.6 110.0 – 28.3 60.1 – 17.2 54.0 – 14.6 49.3 – 10.2 Val (mM/L) Leu (mM/L) Ile (mM/L) Tyr (mM/L) Phe (mM/L)
Values are presented as mean – standard deviation. IFG, impaired fasting glucose; Val, valine; Leu, leucine; Ile, isoleucine; Tyr, tyrosine; Phe, phenylalanine.
0.0002 198.3 – 42.0 220.8 – 49.6 0.0003 0.002 108.4 – 24.0 118.6 – 30.1 0.016 0.0002 61.7 – 15.0 68.3 – 19.9 0.017 < 0.0001 57.2 – 15.9 62.8 – 18.6 0.016 0.004 47.3 – 10.1 51.2 – 12.5 0.025 < 0.0001 208.6 – 48.1 229.5 – 56.7 < 0.0001 112.9 – 27.8 124.6 – 35.6 < 0.0001 61.9 – 16.8 70.6 – 21.4 < 0.0001 55.4 – 14.0 65.6 – 18.5 < 0.0001 49.9 – 10.6 53.9 – 12.4 220.0 – 49.6 118.7 – 28.8 68.1 – 19.2 63.7 – 18.9 51.2 – 12.2
P value P value P value
192.4 – 37.6 105.5 – 21.9 59.9 – 13.3 55.1 – 14.2 46.1 – 9.2
P value IFG n = 65, 18%
African Americans (n = 355)
No IFG n = 290, 82% IFG n = 106, 19% No IFG n = 437, 81% No metabolic Metabolic syndrome syndrome n = 229, 65% n = 126, 35% Amino acid
Plasma levels of the five amino acids according to race and metabolic syndrome or IFG status are summarized in Table 3. Levels of all five amino acids were significantly higher in those with metabolic syndrome or IFG compared with those without metabolic syndrome or IFG, regardless of race. The final multiple logistic regression model for metabolic syndrome association included smoking and uric acid with a C-statistic of 0.661 in Caucasians and 0.624 in African Americans. The final model for IFG included age, weight, and TGs in Caucasians (C-statistic = 0.676) and age, weight, and uric acid in African Americans (C-statistic = 0.669). In Caucasians and African Americans, baseline levels of each of the five amino acids were significantly associated with metabolic syndrome, before and after adjustment (Fig. 2A, B). The adjusted odds ratios associated with metabolic syndrome risk for the five amino acids ranged from 1.007 to 1.030 per mM/L increase in level, with C-statistics = 0.667– 0.699 in Caucasians, whereas the adjusted odds ratios for the five amino acids were 1.015–1.047 with C-statistics = 0.666– 0.700 in African Americans.
No metabolic Metabolic syndrome syndrome n = 279, 51% n = 264, 49%
Association between metabolic syndrome, IFG, and five-amino-acid signature
Caucasian Americans (n = 543)
A total of 898 participants (543 Caucasian Americans and 355 African Americans) enrolled in the PEAR and PEAR2 studies were included in this analysis (Fig. 1). Characteristics at baseline, according to presence of metabolic syndrome and IFG and race, are summarized in Tables 1 and 2, respectively. A total of 49% of Caucasian and 35% of African American participants had metabolic syndrome, whereas 19% of Caucasian and 18% of African American participants had IFG. Overall, in both race groups, those with metabolic syndrome, not surprisingly, had higher glucose, weight, body mass index (BMI), and WC than those without metabolic syndrome. Among those with IFG, similar trends were observed for the anthropometric and other characteristics.
African Americans (n = 355)
Baseline characteristics
Caucasian Americans (n = 543)
Results
Table 3.
Associations between metabolic syndrome and IFG with levels of the five amino acids were evaluated using a multiple logistic regression model. Stepwise logistic regression models were employed to select covariates for the final model for the metabolic syndrome and IFG datasets. Covariates that were different between patients with and without metabolic syndrome and with and without IFG were included in the stepwise analysis and those with P < 0.05 were retained in the final multiple logistic model. For metabolic syndrome analysis, because WC, glucose, HDL-C, TGs, and blood pressure values were incorporated into the definition, they were not included as covariates to prevent confounding in the model. For the IFG model, baseline fasting glucose was not included as a covariate. Levels of the five amino acids were investigated for their associations with the presence of metabolic syndrome or baseline IFG in Caucasian and African American groups separately, both unadjusted and adjusted using the final models. C-statistics were calculated to evaluate the performance of the logistic regression models. All analyses were performed using SAS, version 9.3 (Cary, North Carolina).
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Levels of the 5 Amino Acids According to Race and Baseline Metabolic Syndrome or IFG
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ASSOCIATION OF AMINO ACIDS WITH METABOLIC SYNDROME AND IFG
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FIG. 2. Associations of baseline levels of the five amino acids with metabolic syndrome in Caucasian Americans (A) and African Americans (B) in PEAR and PEAR2. Plotting was based on odds ratio (OR) and 95% confidence interval (CI) of each association, including unadjusted and adjusted. OR was characterized as risk per unit concentration (mM/L) of the amino acid. P values and C-statistics are also shown for each associating relationship. For both Caucasian Americans (n = 543) and African Americans (n = 355), smoking status and uric acid were used for adjustment. Phe, phenylalanine; Tyr, tyrosine; Ile, isoleucine; Leu, leucine; Val, valine.
For IFG, in Caucasians, levels of the five amino acids were significantly associated with IFG, before and after adjustment, with adjusted odds ratios ranging from 1.006 to 1.031 per mM/L increase in level. The corresponding Cstatistics were between 0.686 and 0.722 (Fig. 3A). In Afri-
can Americans, the levels of all five amino acids were significantly associated with baseline IFG prior to adjustment, but only Val remained significantly associated with IFG after adjustment (Fig. 3B), with odds ratio 1.008 and 95% confidence of 1.001–1.014, C-statistic = 0.695.
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FIG. 3. Associations of baseline levels of five amino acids with impaired fasting glucose (IFG) in Caucasian Americans (A) and African Americans (B) in PEAR and PEAR2. Plotting was based on odds ratio (OR) and 95% confidence interval (CI) of each association, including unadjusted and adjusted. OR was characterized as risk per unit concentration (mM/L) of the amino acid. P values and C-statistics are also shown for each associating relationship. For Caucasian Americans (n = 543), age, weight, and triglycerides were used for adjustment. For African Americans (n = 355), age, weight, and uric acid were used for adjustment. Phe, phenylalanine; Tyr, tyrosine; Ile, isoleucine; Leu, leucine; Val, valine.
Discussion We have shown, for the first time, the associations of the plasma levels of Leu, Ile, Val, Phe, and Tyr with metabolic syndrome and IFG in a population of Caucasian and African Americans with essential HTN. Our findings confirm those previously published in observational populations of Caucasian adults and adolescents that used diabetes and insulin resistance as phenotypes and extends them to African Americans as well as those with HTN, a very common chronic condition.19 Previously, we have shown a correla-
tion between this same five-amino-acid signature and incident IFG after treatment with the beta blocker atenolol, which is known to be associated with adverse metabolic effects.20 Whereas IFG represents one point along the dysmetabolic continuum that usually ends in DM, metabolic syndrome represents a broader adverse metabolic state that encompasses abdominal obesity, dyslipidemia, and IFG. Heretofore, the only evidence of BCAA involvement in metabolic syndrome was indicated by an observation that leucine supplementation moderately improved metabolic syndrome in mice fed a high-fat diet.21 However, the effect
ASSOCIATION OF AMINO ACIDS WITH METABOLIC SYNDROME AND IFG
of dietary supplementation with leucine is controversial. In terms of adiposity, while some research indicates that leucine supplementation decreases fat tissue in mice,22 others indicate there is no effect on lipid metabolism.23 In contrast, Guo et al. demonstrated that a leucine-deficient diet dramatically reduces fat mass as well as lipid metabolism.24 Metabolic syndrome and IFG are both physiologic states that increase risk for CVD and diabetes.6 In a populationbased study of CVD and diabetes among non-Hispanic whites and Mexican Americans, individuals that had normal weight but were metabolically unhealthy displayed diabetes risk with an odds ratio of 2.5 [1.1–5.6] and CVD risk with an odds ratio of 2.9 [1.3–6.4], across gender and race.25 Metabolic syndrome differs from IFG, due to the inclusion of additional conditions, including WC, blood pressure, HDLC, and TGs. Because there is a high correlation between metabolic syndrome and insulin resistance, many investigators believe that insulin resistance mediates the metabolic risk factors in metabolic syndrome.8,26 It is not surprising that, in a population > 50 years old, 69% of them with IFG had metabolic syndrome, whereas about 50% of metabolic syndrome co-occurred with IFG.27 In our hypertensive cohort, we found that 72% of IFG patients had metabolic syndrome and 42% of patients with metabolic syndrome displayed IFG disorder. Metabolic syndrome and IFG were also highly correlated (P < 0.0001; data not shown). Circulating levels of BCAAs are directly regulated by BCAA catabolic enzymes. BCAA catabolic enzymes were markedly reduced in fat tissues of obese persons with metabolic syndrome compared with weight-matched healthy obese subjects.28 In addition to diabetes, the three BCAAs have been found to be related to CVD, stroke, and liver function,29–31 and these conditions are correlated with diabetes.32–34 These observations underscore the importance of these amino acids in metabolic pathways, and their value in monitoring adverse metabolic changes warrants further investigation. The concept that the metabolic state could reflect the physiologic status of human health has been gaining awareness in the biomedical community. Recently, metabolomic studies have dramatically enhanced the understanding of disease mechanisms and drug discoveries. Therefore, identifying a metabolite signature that can be used as biomarkers for disease development or for predicting drug responses may be a useful tool.35,36 Our work confirms and extends the findings of previous publications and strongly suggests the associations of the five amino acids with metabolic syndrome in a hypertensive population across races. Amino acids have long been recognized as nutritional building blocks for protein synthesis. Recently, accumulating evidence has suggested that some amino acids could actually regulate many metabolic processes, such as glucose and lipid metabolism, particularly the BCAAs.36 However, to date, their exact roles in those metabolic pathways remain largely unknown. In terms of glucose metabolism, it is not clear whether the increased amino acid levels are causal or just a subsequent reaction to glucose change and insulin resistance. Although more investigation is warranted, several papers have indicated the existence of amino acid sensors in humans that sense the changes of amino acid levels and trigger corresponding metabolic responses, such as the serine/threonineprotein kinase general control nonderepressible 2 (GCN2), activating transcription factor 4 (ATF4), mechanistic target of rapamycin (mTOR), and 5¢-adenosine monophosphate-acti-
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vated protein kinase (AMPK).37–40 Tremblay et al. demonstrated that BCAAs are able to impair glucose transport via the mTOR kinase pathway in skeletal muscle cells.41 A limitation of our study is the relatively small African American sample size. Even though there was a clear trend of the associations of the five amino acids with baseline IFG in African Americans, only valine remained statistically significant after adjustment. However, when metabolic syndrome was examined, all five amino acids were strongly associated, suggesting the relationship is similar in Caucasian and African Americans. Our findings need to be confirmed in additional African American populations. Overall, our work in a hypertensive population demonstrates that plasma levels of Val, Leu, Ile, Tyr, and Phe are associated with baseline metabolic syndrome in Caucasian and African Americans and are associated with IFG in Caucasian Americans. Our findings extend those previously observed and highlight the importance of this amino acid signature as an additional biomarker to identify those with phenotypes that increase risk for diabetes. Further investigation into the mechanistic underpinnings of the relationship between the amino acid signature and adverse metabolic characteristics is warranted.
Acknowledgments This work was funded by the Pharmacometabolomics Research Network (RC2 GM092729) and the National Institutes of Health (NIH) Pharmacogenomics Research Network (U01 GM074492). Additional funding includes: K23 HL086558 (RMC-D), K23 HL091120 (ALB), and grants from the NIH National Center for Research Resources to the University of Florida (UL1 TR000064), Emory University (UL1 TR000454), and Mayo Clinic (UL1 TR000135).
Author Disclosure Statement No competing financial interests exist.
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9. Vinayavekhin N, Homan EA, Saghatelian A. Exploring disease through metabolomics. ACS Chem Biol 2010;5:91–103. 10. Wang TJ, Larson MG, Vasan RS, et al. Metabolite profiles and the risk of developing diabetes. Nat Med 2011;17:448–453. 11. Walford GA, Davis J, Warner AS, et al. Branched chain and aromatic amino acids change acutely following two medical therapies for type 2 diabetes mellitus. Metabolism 2013;62:1772–1778. 12. Wurtz P, Soininen P, Kangas AJ, et al. Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care 2013;36:648–655. 13. McCormack SE, Shaham O, McCarthy MA, et al. Circulating branched-chain amino acid concentrations are associated with obesity and future insulin resistance in children and adolescents. Pediatr Obes 2013;8:52–61. 14. Johnson JA, Boerwinkle E, Zineh I, et al. Pharmacogenomics of antihypertensive drugs: Rationale and design of the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) study. Am Heart J 2009;157:442–449. 15. Hamadeh IS, Langaee TY, Dwivedi R, et al. Impact of CYP2D6 polymorphisms on clinical efficacy and tolerability of metoprolol tartrate. Clin Pharmacol Ther 2014;96:175–181. 16. Cooper-DeHoff RM, Wen S, Beitelshees AL, et al. Impact of abdominal obesity on incidence of adverse metabolic effects associated with antihypertensive medications. Hypertension 2010;55:61–68. 17. Grundy SM, Brewer HB Jr, Cleeman JI, et al. Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 2004; 109:433–438. 18. Genuth S, Alberti KG, Bennett P, et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003;26: 3160–3167. 19. Lewanczuk R. Hypertension as a chronic disease: What can be done at a regional level? Can J Cardiol 2008;24:483–484. 20. Cooper-Dehoff RM, Hou W, Weng L, et al. Is diabetes mellitus-linked amino acid signature associated with betablocker-induced impaired fasting glucose? Circ Cardiovasc Genet 2014;7:199–205. 21. Freudenberg A, Petzke KJ, Klaus S. Comparison of highprotein diets and leucine supplementation in the prevention of metabolic syndrome and related disorders in mice. J Nutr Biochem 2012;23:1524–1530. 22. Zhang Y, Guo K, LeBlanc RE, et al. Increasing dietary leucine intake reduces diet-induced obesity and improves glucose and cholesterol metabolism in mice via multimechanisms. Diabetes 2007;56:1647–1654. 23. Nairizi A, She P, Vary TC, et al. Leucine supplementation of drinking water does not alter susceptibility to diet-induced obesity in mice. J Nutr 2009;139:715–719. 24. Guo F, Cavener DR. The GCN2 eIF2alpha kinase regulates fatty-acid homeostasis in the liver during deprivation of an essential amino acid. Cell Metab 2007;5:103–114. 25. Aung K, Lorenzo C, Hinojosa MA, et al. Risk of developing diabetes and cardiovascular disease in metabolically unhealthy normal-weight and metabolically healthy obese individuals. J Clin Endocrinol Metab 2014;99:462–468. 26. Hanley AJ, Wagenknecht LE, D’Agostino RB Jr, et al. Identification of subjects with insulin resistance and betacell dysfunction using alternative definitions of the metabolic syndrome. Diabetes 2003;52:2740–2747. 27. Alexander CM, Landsman PB, Grundy SM. Metabolic syndrome and hyperglycemia: Congruence and divergence. Am J Cardiol 2006;98:982–985.
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28. Lackey DE, Lynch CJ, Olson KC, et al. Regulation of adipose branched-chain amino acid catabolism enzyme expression and cross-adipose amino acid flux in human obesity. Am J Physiol Endocrinol Metab 2013;304:E1175– E1187. 29. Kimberly WT, Wang Y, Pham L, et al. Metabolite profiling identifies a branched chain amino acid signature in acute cardioembolic stroke. Stroke 2013;44:1389–1395. 30. Magnusson M, Lewis GD, Ericson U, et al. A diabetespredictive amino acid score and future cardiovascular disease. Eur Heart J 2013;34:1982–1989. 31. Tajiri K, Shimizu Y. Branched-chain amino acids in liver diseases. World J Gastroenterol 2013;19:7620–7629. 32. Garcia-Compean D, Jaquez-Quintana JO, Gonzalez-Gonzalez JA, et al. Liver cirrhosis and diabetes: Risk factors, pathophysiology, clinical implications and management. World J Gastroenterol 2009;15:280–288. 33. Khoury JC, Kleindorfer D, Alwell K, et al. Diabetes mellitus: A risk factor for ischemic stroke in a large biracial population. Stroke 2013;44:1500–1504. 34. Yakubovich N, Gerstein HC. Serious cardiovascular outcomes in diabetes: The role of hypoglycemia. Circulation 2011;123:342–348. 35. Kaddurah-Daouk R, Kristal BS, Weinshilboum RM. Metabolomics: A global biochemical approach to drug response and disease. Annu Rev Pharmacol Toxicol 2008;48: 653–683. 36. Newgard CB, An J, Bain JR, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 2009;9:311–326. 37. Wek SA, Zhu S, Wek RC. The histidyl-tRNA synthetaserelated sequence in the eIF-2 alpha protein kinase GCN2 interacts with tRNA and is required for activation in response to starvation for different amino acids. Mol Cell Biol 1995;15:4497–4506. 38. Carraro V, Maurin AC, Lambert-Langlais S, et al. Amino acid availability controls TRB3 transcription in liver through the GCN2/eIF2alpha/ATF4 pathway. PLoS One 2010;5:e15716. 39. Lynch CJ, Role of leucine in the regulation of mTOR by amino acids: Revelations from structure-activity studies. J Nutr 2001;131:861S–865S. 40. Leclerc I, Rutter GA. AMP-activated protein kinase: A new beta-cell glucose sensor?: Regulation by amino acids and calcium ions. Diabetes 2004;53(Suppl 3):S67–S74. 41. Tremblay F, Marette A. Amino acid and insulin signaling via the mTOR/p70 S6 kinase pathway. A negative feedback mechanism leading to insulin resistance in skeletal muscle cells. J Biol Chem 2001;276:38052–38060.
Address correspondence to: Rhonda M. Cooper-DeHoff, PharmD, MS Associate Professor Department of Pharmacotherapy and Translational Research and Division of Cardiovascular Medicine Colleges of Pharmacy and Medicine Associate Director, Center for Pharmacogenomics University of Florida PO Box 100486 Gainesville, FL 32610-0486 E-mail: [email protected]
METABOLIC SYNDROME AND RELATED DISORDERS Volume X, Number X, 2015 Mary Ann Liebert, Inc. Pp. 1–8 DOI: 10.1089/met.2014.0132
Association of Branched and Aromatic Amino Acids Levels with Metabolic Syndrome and Impaired Fasting Glucose in Hypertensive Patients Liming Weng, PhD,1 Eoin Quinlivan, PhD,2 Yan Gong, PhD,1 Amber L. Beitelshees, PharmD, MPH,3 Mohamed H. Shahin, MS,1 Stephen T. Turner, MD,4 Arlene B. Chapman, MD,5 John G. Gums, PharmD,6 Julie A. Johnson, PharmD,1,7 Reginald F. Frye, PharmD, PhD,1 Timothy J. Garrett, PhD,2 and Rhonda M. Cooper-DeHoff, PharmD, MS1,7
Abstract
Background: The three branched amino acids (valine, leucine, and isoleucine) and two aromatic amino acids (tyrosine and phenylalanine) have been associated with many adverse metabolic pathways, including diabetes. However, these associations have been identified primarily in otherwise healthy Caucasian populations. We aimed to investigate the association of this five-amino-acid signature with metabolic syndrome and impaired fasting glucose (IFG) in a hypertensive cohort of Caucasian and African Americans. Methods: We analyzed data from the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) studies PEAR and PEAR2 conducted between 2005 and 2014. Subjects were enrolled at the University of Florida (Gainesville, FL), Emory University (Atlanta, GA), and Mayo Clinic (Rochester, MN). A total of 898 patients with essential hypertension were included in this study. Presence of metabolic syndrome and IFG at baseline were determined on the basis of measurements of demographic and biochemical data. Levels of the five amino acids were quantified by liquid chromatography–tandem mass spectroscopy (LC-MS/MS). Results: With a multiple logistic regression model, we found that all five amino acids were significantly associated with metabolic syndrome in both Caucasian and African Americans. IFG and the five amino acids were associated in the Caucasian Americans. Only valine was significantly associated with IFG in African Americans. Conclusion: In both Caucasian and African Americans with uncomplicated hypertension, plasma levels of the five-amino-acid signature are associated with metabolic syndrome. Additionally, in Caucasians we have confirmed the five-amino-acid signature was associated with IFG. are more likely to have hypertension (HTN), and have increased risk for cardiovascular disease (CVD) morbidity and mortality.2,3 DM and HTN are frequent co-morbid conditions and might share common underlying metabolic pathways, including the renin angiotensin aldosterone system, sympathetic nervous system, and insulin resistance.4,5 Early identification of IFG and its risk conditions along with lifestyle and other interventions when possible may reduce the risk for or delay onset of DM or HTN.6 Metabolic
Introduction
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iabetes mellitus (DM) remains a major cause of morbidity and mortality, affecting more than 29 million people in the United States.1 More worrisome is that there are approximately 86 million American adults with undiagnosed elevated glucose levels ( ‡ 100 mg/dL), also known as impaired fasting glucose (IFG) or prediabetes. Those with IFG are more likely to develop DM than those without IFG,
1 Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, College of Pharmacy, Gainesville, Florida. 2 Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, Florida. 3 Department of Medicine and Program in Personalized and Genomic Medicine, University of Maryland, Baltimore, Maryland. 4 Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota. 5 Renal Division, School of Medicine, Emory University, Atlanta, Georgia. 6 Department of Pharmacotherapy and Translational Research, College of Pharmacy and Department of Community Health and Family Medicine, College of Medicine, University of Florida, Gainesville, Florida. 7 Division of Cardiovascular Medicine, Department of Medicine, University of Florida, Gainesville, Florida.
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syndrome, a constellation of adverse metabolic risk factors, including IFG, dyslipidemia, and abdominal obesity, is also associated with increased risk for DM and adverse CV outcomes.7,8 Identifying metabolic biomarkers associated with IFG and metabolic syndrome could potentially provide insight into their underlying mechanisms of pathogenesis and result in potential targets for interventions that could reduce long-term overall risk. Recent advances in metabolomics have allowed for a better understanding of the processes of disease development, providing some insight into the mechanisms of diseases.9 Several independent investigations have documented the correlation between levels of three branched-chain amino acids (BCAA, leucine [Leu], isoleucine [Ile], and valine [Val]) and two aromatic amino acids (AAA, phenylalanine [Phe] and tyrosine [Tyr]), and DM and insulin resistance in adults and adolescents.10–13 However, this five-amino-acid signature has been established primarily within Caucasian observational populations, whereas the presence of risk conditions such as HTN and other race/ethnic groups are largely underrepresented. To our knowledge, the relationship between this five-amino-acid signature and metabolic syndrome has not been established. Therefore, we evaluated the correlation between baseline levels of Leu, Ile, Val, Phe, and Tyr and metabolic syndrome and IFG in individuals with untreated essential HTN enrolled in the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) studies PEAR and PEAR2.14 Our goal was to investigate the association between this five-amino-acid signature and presence of metabolic syndrome or IFG among Caucasian and African American participants with uncomplicated HTN.
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Amino acid quantification Plasma samples collected prior to drug treatment were used for determination of targeted amino acid concentrations (Ile, Leu, Val, Phe, Tyr) by isotopic dilution liquid chromatography–tandem mass spectrometry (LC-MS/MS). Briefly, 25 mL of plasma was mixed with 10 mL of an internal standards solution (containing U-18O-labeled amino acids). The sample was diluted with 100 mL of methanol to precipitate protein, and, after centrifugation, 50 mL of the supernatant was further diluted to 500 mL with 10 mM ammonium acetate in water. Samples (10 mL) were injected onto a Halo PFP column (2.7u, 150 · 2.1 mm; Mac-Mod, Chadds Ford, PA) and isocratically eluted on a ThermoScientific Accela 1000 UHPLC using a mobile phase comprised of 10 mM ammonium acetate in water containing 25% methanol. Tandem mass spectrometry was performed using a ThermoScientific TSQ Quantum Ultra employing heated electrospray ionization (HESI) in positive mode. Amino acids were identified by their co-elution with authentic standards (Ile, Leu, Val, Phe, and Tyr) and by their mass transitions (precursor/product). Differentiation of Leu and Ile was determined by their disparate mass transitions (132/43 and 132/69, respectively); there was no ‘‘cross-talk’’ between the two transitions. Amino acid concentrations were determined by calculating the ratios of endogenous peak areas to those of the internal standards, and comparing these ratios to standard curves prepared using amino acid standards of known concentration. Inter- and intraassay variability (% standard deviation [SD]) for the assay was less than 3%.
Biochemical assays
PEAR studies
Blood samples obtained in the fasting state at baseline and serum were used to measure glucose, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein
Two clinical trials were undertaken to evaluate the pharmacogenomic determinants of the antihypertensive and adverse metabolic responses to beta-blockers (PEAR, atenolol; PEAR2, metoprolol) and thiazide diuretics (PEAR, hydrochlorothiazide; PEAR2, chlorthalidone) in HTN participants without a history of heart disease or DM. Participants in both studies were enrolled from the University of Florida in Gainesville, FL, Mayo Clinic in Rochester, MN, and Emory University in Atlanta, GA. Details regarding enrollment criteria have been previously published, and the studies are registered at clinicaltrials.gov (PEAR, NCT00246519; PEAR2, NCT01203852).14,15 In both studies, male or female patients 17–65 years of age, of any race or ethnicity, with uncomplicated HTN were included. Study participants were excluded if they had complicated HTN, DM (or fasting blood glucose ‡ 126 mg/dL) or documented CVD (including history of angina pectoris, heart failure, cardiac pacemaker, myocardial infarction, stroke, etc.), or if they had very elevated blood pressure [systolic blood pressure (SBP) > 170 mmHg or diastolic blood pressure (DBP) > 110 mmHg], or if they had other complications (pregnancy, renal diseases, sleep apnea, etc). Blood samples were collected at baseline, after a washout period of 4–6 weeks by withdrawing all antihypertensive medications prior to study drug treatment. These baseline samples were used for amino acids, glucose, and other biochemical determinations in this study.
FIG. 1. Flow diagram of PEAR and PEAR2 participants enrolled for the analysis. Numbers of patients in PEAR and PEAR2 are indicated, as well as the numbers according to race in each group. CA, Caucasian American; AA, African American; PEAR, Pharmacogenomic Evaluation of Antihypertensive Responses.
Methods and Materials
ASSOCIATION OF AMINO ACIDS WITH METABOLIC SYNDROME AND IFG
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Table 1. Baseline Characteristics of 898 Patients in PEAR and PEAR2 With and Without Metabolic Syndrome in Caucasian and African American Participants Caucasians (n = 543)
Characteristics Women, n (%) Age (years) BMI (kg/m2) Weight (kg) Waist circumference (cm) Smoking n (%) SBP (mmHg) DBP (mmHg) Glucose (mg/dL) Triglycerides (mg/dL) Total cholesterol (mg/dL) High-density lipoprotein (mg/dL) Low-density lipoprotein (mg/dL) Uric acid (mg/dL)
African Americans (n = 355)
No metabolic syndrome n = 279 (51%)
Metabolic syndrome n = 264 (49%)
P value
127 (45.5) 51.1 – 9.4 28.3 – 4.3 82.4 – 15.4 92.9 – 12.1 24 (8.6) 150.5 – 12.3 97.9 – 5.6 89.0 – 7.9 105.3 – 62.3 195.2 – 33.4 55.7 – 12.6 118.6 – 28.8 5.4 – 1.4
106 (40.2) 49.4 – 9.5 32.9 – 5.1 97.7 – 16.2 105.7 – 11.2 42 (15.9) 150.9 – 12.2 98.0 – 5.4 95.9 – 12.7 189.9 – 95.6 198.3 – 35.7 40.1 – 9.5 121.2 – 30.5 6.2 – 1.4
0.2065 0.0406 < 0.0001 < 0.0001 < 0.0001 0.0092 0.672 0.8545 < 0.0001 < 0.0001 0.2922 < 0.0001 0.3127 < 0.0001
Metabolic No metabolic syndrome syndrome n = 229 (65%) n = 126 (35%) 143 (62.5) 48.6 – 9.4 29.9 – 5.6 84.7 – 16.8 93.3 – 12.7 34 (14.9) 151.3 – 12.9 98.9 – 6.9 87.4 – 9.2 74.7 – 27.4 189.1 – 36.9 58.1 – 15.7 116.1 – 31.9 5.3 – 1.4
90 (71.6) 47.7 – 8.4 33.8 – 5.3 95.9 – 16.3 103.2 – 10.2 33 (26.2) 150.6 – 12.7 98.9 – 5.8 96.6 – 13.4 140.2 – 115.0 191.3 – 41.1 43.1 – 11.4 121.2 – 35.5 5.8 – 1.5
P value 0.0881 0.3964 < 0.0001 < 0.0001 < 0.0001 0.009 0.6384 0.9869 < 0.0001 < 0.0001 0.6084 < 0.0001 0.1733 0.0034
Values are presented as mean – standard deviation unless noted otherwise. PEAR, Pharmacogenomic Evaluation of Antihypertensive Responses; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.
cholesterol (HDL-C), triglycerides (TGs), and uric acid with a Hitachi 911 Chemistry Analyzer (Roche Diagnostics, Indianapolis, IN) at the central laboratory at the Mayo Clinic (Rochester, MN), and determined by an automated enzymatic assay using spectrophotometry.16 All samples were tested in duplicate, and the data reported are means of the duplicate samples.
females; (2) HDL-C < 40 mg/dL in males or < 50 mg/dL in females; (3) TGs ‡ 150 mg/dL; (4) fasting glucose ‡ 100 mg/dL; (5) SBP ‡ 130 mmHg or DBP ‡ 85 mmHg.17 Presence of IFG was defined as a baseline fasting glucose ‡ 100 mg/dL whereas a fasting glucose < 100 mg/dL was considered to be normal.18
Definition of metabolic syndrome and IFG
Continuous demographic and baseline characteristics were summarized using mean – SD and compared using the two-sample t-test. Categorical characteristics were summarized as frequencies and percentages (%) and compared using the chi-squared test.
Participants were considered to have metabolic syndrome if any three of the following five characteristics were present: (1) Waist circumference (WC) > 102 cm in males or > 88 cm in
Statistical analysis
Table 2. Baseline Characteristics of 898 Patients in the PEAR and PEAR2 Studies With and Without IFG in Caucasian and African American Participants Caucasians (n = 543) Characteristics Women, n (%) Age (years) BMI (kg/m2) Weight (kg) Waist circumference (cm) Current smoking, n (%) SBP ( mmHg) DBP (mmHg) Glucose (mg/dL) Triglycerides (mg/dL) Total cholesterol (mg/dL) High-density lipoprotein (mg/dL) Low-density lipoprotein (mg/dL) Uric acid (mg/dL) Metabolic syndrome, n (%)
African Americans (n = 355)
No IFG n = 437 (81%)
IFG n = 106 (19%)
P value
No IFG n = 290 (82%)
IFG n = 65 (18%)
P value
197 (45) 49.8 – 9.9 30.1 – 5.2 88.3 – 17.7 98.0 – 13.6 52 (12) 150.4 – 12.4 97.8 – 5.5 88.3 – 6.8 139.9 – 85.6 195.5 – 33.4 49.1 – 14.0 118.8 – 27.7 5.6 – 1.4 172 (39)
36 (34) 52.2 – 7.5 32.3 – 5.2 96.3 – 15.2 103.8 – 11.0 14 (13) 152.0 – 11.7 98.5 – 5.4 109.1 – 9.4 173.3 – 105.2 201.3 – 38.6 44.2 – 11.5 124.7 – 36.5 6.3 – 1.5 92 (87)
0.038 0.007 < 0.0001 < 0.0001 < 0.0001 0.7115 0.219 0.245 < 0.0001 0.003 0.133 0.0002 0.126 0.0001 < 0.0001
195 (67) 47.8 – 9.2 31.0 – 6.0 87.7 – 17.9 96.2 – 13.2 51 (18) 150.8 – 13.1 98.8 – 6.5 86.5 – 7.3 94.2 – 79.7 188.4 – 37.8 53.2 – 16.0 116.7 – 32.8 5.3 – 1.4 75 (26)
38 (59) 50.5 – 8.1 32.3 – 4.6 93.4 – 14.8 99.6 – 10.5 16 (25) 152.2 – 11.9 99.3 – 6.3 109.2 – 9.5 114.6 – 70.1 196.6 – 40.7 50.6 – 16.3 123.1 – 34.9 6.0 – 1.5 51 (79)
0.178 0.030 0.064 0.017 0.025 0.191 0.426 0.623 < 0.0001 0.058 0.122 0.228 0.164 0.0013 < 0.0001
Values are presented as mean – standard deviation unless noted otherwise. PEAR, Pharmacogenomic Evaluation of Antihypertensive Responses; IFG, impaired fasting glucose; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.
< 0.0001 < 0.0001 < 0.0001 < 0.0001 0.001 223.9 – 50.5 121.1 – 30.8 67.6 – 18.5 61.3 – 15.9 52.4 – 11.9 202.8 – 48.6 110.0 – 28.3 60.1 – 17.2 54.0 – 14.6 49.3 – 10.2 Val (mM/L) Leu (mM/L) Ile (mM/L) Tyr (mM/L) Phe (mM/L)
Values are presented as mean – standard deviation. IFG, impaired fasting glucose; Val, valine; Leu, leucine; Ile, isoleucine; Tyr, tyrosine; Phe, phenylalanine.
0.0002 198.3 – 42.0 220.8 – 49.6 0.0003 0.002 108.4 – 24.0 118.6 – 30.1 0.016 0.0002 61.7 – 15.0 68.3 – 19.9 0.017 < 0.0001 57.2 – 15.9 62.8 – 18.6 0.016 0.004 47.3 – 10.1 51.2 – 12.5 0.025 < 0.0001 208.6 – 48.1 229.5 – 56.7 < 0.0001 112.9 – 27.8 124.6 – 35.6 < 0.0001 61.9 – 16.8 70.6 – 21.4 < 0.0001 55.4 – 14.0 65.6 – 18.5 < 0.0001 49.9 – 10.6 53.9 – 12.4 220.0 – 49.6 118.7 – 28.8 68.1 – 19.2 63.7 – 18.9 51.2 – 12.2
P value P value P value
192.4 – 37.6 105.5 – 21.9 59.9 – 13.3 55.1 – 14.2 46.1 – 9.2
P value IFG n = 65, 18%
African Americans (n = 355)
No IFG n = 290, 82% IFG n = 106, 19% No IFG n = 437, 81% No metabolic Metabolic syndrome syndrome n = 229, 65% n = 126, 35% Amino acid
Plasma levels of the five amino acids according to race and metabolic syndrome or IFG status are summarized in Table 3. Levels of all five amino acids were significantly higher in those with metabolic syndrome or IFG compared with those without metabolic syndrome or IFG, regardless of race. The final multiple logistic regression model for metabolic syndrome association included smoking and uric acid with a C-statistic of 0.661 in Caucasians and 0.624 in African Americans. The final model for IFG included age, weight, and TGs in Caucasians (C-statistic = 0.676) and age, weight, and uric acid in African Americans (C-statistic = 0.669). In Caucasians and African Americans, baseline levels of each of the five amino acids were significantly associated with metabolic syndrome, before and after adjustment (Fig. 2A, B). The adjusted odds ratios associated with metabolic syndrome risk for the five amino acids ranged from 1.007 to 1.030 per mM/L increase in level, with C-statistics = 0.667– 0.699 in Caucasians, whereas the adjusted odds ratios for the five amino acids were 1.015–1.047 with C-statistics = 0.666– 0.700 in African Americans.
No metabolic Metabolic syndrome syndrome n = 279, 51% n = 264, 49%
Association between metabolic syndrome, IFG, and five-amino-acid signature
Caucasian Americans (n = 543)
A total of 898 participants (543 Caucasian Americans and 355 African Americans) enrolled in the PEAR and PEAR2 studies were included in this analysis (Fig. 1). Characteristics at baseline, according to presence of metabolic syndrome and IFG and race, are summarized in Tables 1 and 2, respectively. A total of 49% of Caucasian and 35% of African American participants had metabolic syndrome, whereas 19% of Caucasian and 18% of African American participants had IFG. Overall, in both race groups, those with metabolic syndrome, not surprisingly, had higher glucose, weight, body mass index (BMI), and WC than those without metabolic syndrome. Among those with IFG, similar trends were observed for the anthropometric and other characteristics.
African Americans (n = 355)
Baseline characteristics
Caucasian Americans (n = 543)
Results
Table 3.
Associations between metabolic syndrome and IFG with levels of the five amino acids were evaluated using a multiple logistic regression model. Stepwise logistic regression models were employed to select covariates for the final model for the metabolic syndrome and IFG datasets. Covariates that were different between patients with and without metabolic syndrome and with and without IFG were included in the stepwise analysis and those with P < 0.05 were retained in the final multiple logistic model. For metabolic syndrome analysis, because WC, glucose, HDL-C, TGs, and blood pressure values were incorporated into the definition, they were not included as covariates to prevent confounding in the model. For the IFG model, baseline fasting glucose was not included as a covariate. Levels of the five amino acids were investigated for their associations with the presence of metabolic syndrome or baseline IFG in Caucasian and African American groups separately, both unadjusted and adjusted using the final models. C-statistics were calculated to evaluate the performance of the logistic regression models. All analyses were performed using SAS, version 9.3 (Cary, North Carolina).
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Levels of the 5 Amino Acids According to Race and Baseline Metabolic Syndrome or IFG
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ASSOCIATION OF AMINO ACIDS WITH METABOLIC SYNDROME AND IFG
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FIG. 2. Associations of baseline levels of the five amino acids with metabolic syndrome in Caucasian Americans (A) and African Americans (B) in PEAR and PEAR2. Plotting was based on odds ratio (OR) and 95% confidence interval (CI) of each association, including unadjusted and adjusted. OR was characterized as risk per unit concentration (mM/L) of the amino acid. P values and C-statistics are also shown for each associating relationship. For both Caucasian Americans (n = 543) and African Americans (n = 355), smoking status and uric acid were used for adjustment. Phe, phenylalanine; Tyr, tyrosine; Ile, isoleucine; Leu, leucine; Val, valine.
For IFG, in Caucasians, levels of the five amino acids were significantly associated with IFG, before and after adjustment, with adjusted odds ratios ranging from 1.006 to 1.031 per mM/L increase in level. The corresponding Cstatistics were between 0.686 and 0.722 (Fig. 3A). In Afri-
can Americans, the levels of all five amino acids were significantly associated with baseline IFG prior to adjustment, but only Val remained significantly associated with IFG after adjustment (Fig. 3B), with odds ratio 1.008 and 95% confidence of 1.001–1.014, C-statistic = 0.695.
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FIG. 3. Associations of baseline levels of five amino acids with impaired fasting glucose (IFG) in Caucasian Americans (A) and African Americans (B) in PEAR and PEAR2. Plotting was based on odds ratio (OR) and 95% confidence interval (CI) of each association, including unadjusted and adjusted. OR was characterized as risk per unit concentration (mM/L) of the amino acid. P values and C-statistics are also shown for each associating relationship. For Caucasian Americans (n = 543), age, weight, and triglycerides were used for adjustment. For African Americans (n = 355), age, weight, and uric acid were used for adjustment. Phe, phenylalanine; Tyr, tyrosine; Ile, isoleucine; Leu, leucine; Val, valine.
Discussion We have shown, for the first time, the associations of the plasma levels of Leu, Ile, Val, Phe, and Tyr with metabolic syndrome and IFG in a population of Caucasian and African Americans with essential HTN. Our findings confirm those previously published in observational populations of Caucasian adults and adolescents that used diabetes and insulin resistance as phenotypes and extends them to African Americans as well as those with HTN, a very common chronic condition.19 Previously, we have shown a correla-
tion between this same five-amino-acid signature and incident IFG after treatment with the beta blocker atenolol, which is known to be associated with adverse metabolic effects.20 Whereas IFG represents one point along the dysmetabolic continuum that usually ends in DM, metabolic syndrome represents a broader adverse metabolic state that encompasses abdominal obesity, dyslipidemia, and IFG. Heretofore, the only evidence of BCAA involvement in metabolic syndrome was indicated by an observation that leucine supplementation moderately improved metabolic syndrome in mice fed a high-fat diet.21 However, the effect
ASSOCIATION OF AMINO ACIDS WITH METABOLIC SYNDROME AND IFG
of dietary supplementation with leucine is controversial. In terms of adiposity, while some research indicates that leucine supplementation decreases fat tissue in mice,22 others indicate there is no effect on lipid metabolism.23 In contrast, Guo et al. demonstrated that a leucine-deficient diet dramatically reduces fat mass as well as lipid metabolism.24 Metabolic syndrome and IFG are both physiologic states that increase risk for CVD and diabetes.6 In a populationbased study of CVD and diabetes among non-Hispanic whites and Mexican Americans, individuals that had normal weight but were metabolically unhealthy displayed diabetes risk with an odds ratio of 2.5 [1.1–5.6] and CVD risk with an odds ratio of 2.9 [1.3–6.4], across gender and race.25 Metabolic syndrome differs from IFG, due to the inclusion of additional conditions, including WC, blood pressure, HDLC, and TGs. Because there is a high correlation between metabolic syndrome and insulin resistance, many investigators believe that insulin resistance mediates the metabolic risk factors in metabolic syndrome.8,26 It is not surprising that, in a population > 50 years old, 69% of them with IFG had metabolic syndrome, whereas about 50% of metabolic syndrome co-occurred with IFG.27 In our hypertensive cohort, we found that 72% of IFG patients had metabolic syndrome and 42% of patients with metabolic syndrome displayed IFG disorder. Metabolic syndrome and IFG were also highly correlated (P < 0.0001; data not shown). Circulating levels of BCAAs are directly regulated by BCAA catabolic enzymes. BCAA catabolic enzymes were markedly reduced in fat tissues of obese persons with metabolic syndrome compared with weight-matched healthy obese subjects.28 In addition to diabetes, the three BCAAs have been found to be related to CVD, stroke, and liver function,29–31 and these conditions are correlated with diabetes.32–34 These observations underscore the importance of these amino acids in metabolic pathways, and their value in monitoring adverse metabolic changes warrants further investigation. The concept that the metabolic state could reflect the physiologic status of human health has been gaining awareness in the biomedical community. Recently, metabolomic studies have dramatically enhanced the understanding of disease mechanisms and drug discoveries. Therefore, identifying a metabolite signature that can be used as biomarkers for disease development or for predicting drug responses may be a useful tool.35,36 Our work confirms and extends the findings of previous publications and strongly suggests the associations of the five amino acids with metabolic syndrome in a hypertensive population across races. Amino acids have long been recognized as nutritional building blocks for protein synthesis. Recently, accumulating evidence has suggested that some amino acids could actually regulate many metabolic processes, such as glucose and lipid metabolism, particularly the BCAAs.36 However, to date, their exact roles in those metabolic pathways remain largely unknown. In terms of glucose metabolism, it is not clear whether the increased amino acid levels are causal or just a subsequent reaction to glucose change and insulin resistance. Although more investigation is warranted, several papers have indicated the existence of amino acid sensors in humans that sense the changes of amino acid levels and trigger corresponding metabolic responses, such as the serine/threonineprotein kinase general control nonderepressible 2 (GCN2), activating transcription factor 4 (ATF4), mechanistic target of rapamycin (mTOR), and 5¢-adenosine monophosphate-acti-
7
vated protein kinase (AMPK).37–40 Tremblay et al. demonstrated that BCAAs are able to impair glucose transport via the mTOR kinase pathway in skeletal muscle cells.41 A limitation of our study is the relatively small African American sample size. Even though there was a clear trend of the associations of the five amino acids with baseline IFG in African Americans, only valine remained statistically significant after adjustment. However, when metabolic syndrome was examined, all five amino acids were strongly associated, suggesting the relationship is similar in Caucasian and African Americans. Our findings need to be confirmed in additional African American populations. Overall, our work in a hypertensive population demonstrates that plasma levels of Val, Leu, Ile, Tyr, and Phe are associated with baseline metabolic syndrome in Caucasian and African Americans and are associated with IFG in Caucasian Americans. Our findings extend those previously observed and highlight the importance of this amino acid signature as an additional biomarker to identify those with phenotypes that increase risk for diabetes. Further investigation into the mechanistic underpinnings of the relationship between the amino acid signature and adverse metabolic characteristics is warranted.
Acknowledgments This work was funded by the Pharmacometabolomics Research Network (RC2 GM092729) and the National Institutes of Health (NIH) Pharmacogenomics Research Network (U01 GM074492). Additional funding includes: K23 HL086558 (RMC-D), K23 HL091120 (ALB), and grants from the NIH National Center for Research Resources to the University of Florida (UL1 TR000064), Emory University (UL1 TR000454), and Mayo Clinic (UL1 TR000135).
Author Disclosure Statement No competing financial interests exist.
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Address correspondence to: Rhonda M. Cooper-DeHoff, PharmD, MS Associate Professor Department of Pharmacotherapy and Translational Research and Division of Cardiovascular Medicine Colleges of Pharmacy and Medicine Associate Director, Center for Pharmacogenomics University of Florida PO Box 100486 Gainesville, FL 32610-0486 E-mail: [email protected]
