Elevated levels of urine isocitrate, hydroxymethylglutarate, and formiminoglutamate are associated with arterial stiffness in Korean adults

Recent evidence suggests that cellular perturbations play an important role in the pathogenesis of cardiovascular diseases. Therefore, we analyzed the association between the levels of urinary metabolites and arterial stiffness. Our cross-sectional study included 330 Korean men and women. The brachial-ankle pulse wave velocity was measured as a marker of arterial stiffness. Urinary metabolites were evaluated using a high-performance liquid chromatograph-mass spectrometer. The brachial-ankle pulse wave velocity was found to be positively correlated with l-lactate, citrate, isocitrate, succinate, malate, hydroxymethylglutarate, α-ketoisovalerate, α-keto-β-methylvalerate, methylmalonate, and formiminoglutamate among men. Whereas, among women, the brachial-ankle pulse wave velocity was positively correlated with cis-aconitate, isocitrate, hydroxymethylglutarate, and formiminoglutamate. In the multivariable regression models adjusted for conventional cardiovascular risk factors, three metabolite concentrations (urine isocitrate, hydroxymethylglutarate, and formiminoglutamate) were independently and positively associated with brachial-ankle pulse wave velocity. Increased urine isocitrate, hydroxymethylglutarate, and formiminoglutamate concentrations were associated with brachial-ankle pulse wave velocity and independent of conventional cardiovascular risk factors. Our findings suggest that metabolic disturbances in cells may be related to arterial stiffness.

, and catecholamine-producing tumors 17 . One study revealed that plasma metabolites appear strongly correlated with PWV in women 18 . Among the urinary metabolites, the tricarboxylic acid cycle (TCA cycle) metabolites, also known as the citric acid cycle metabolites, were considered as byproducts of cellular metabolism important for the biosynthesis of nucleotides, lipids, and proteins [19][20][21] . Several studies revealed that urine metabolites are also associated with mitochondrial dysfunction [22][23][24] and congenital mitochondrial diseases in children 25 .
Considering that cellular perturbations are related to CVD 7,26-28 , as a biomarker of cellular metabolism, urine metabolites may also be associated with CVD. However, the relationship of urine metabolomics analysis with clinical markers of CVD, such as brachial-ankle pulse wave velocity (baPWV), has not been comprehensively evaluated. Therefore, this study aimed to investigate the metabolomic signature according to baPWV to identify the novel metabolites associated with PWV and understand the molecular mechanisms underlying arterial stiffness.

Results
Characteristics of the study participants. The baseline characteristics of the participants are presented in Table 1. The mean age was 57.8 years. Among 330 subjects, 52.7% were men. The mean baPWV was 14.3 ± 3.0 m/s. Precisely, 63.9% of the subjects had significant physical activity and 19.7% had a metabolic syndrome.
The distribution of the levels of key metabolites with baPWV. We selected isocitrate, hydroxymethylglutarate, and formiminoglutamate, which were significantly associated with baPWV. The distribution of the levels of three urine metabolites (isocitrate, hydroxymethylglutarate, and formiminoglutamate) according to baPWV is shown in Fig. 1. Figure 1 shows the positive correlation of isocitrate, hydroxymethylglutarate and formiminoglutamate with baPWV.

Standardized regression coefficients of the levels of key metabolites for baPWV. The relation-
ship between baPWV and the levels of urine metabolites was assessed after adjusting for potential confounders (Fig. 2). In the crude model (model 1), positive relationships of baPWV with the levels of isocitrate, hydroxymethylglutarate, and formiminoglutamate were observed (P = 0.001, P < 0.001, and P < 0.001, respectively). After the adjustment for all the confounders (age, sex, mean blood pressure (BP), heart rate, total cholesterol, highdensity lipoprotein (HDL) cholesterol, alcohol history, physical activity, smoking history, and medication history of hypertension, diabetes, and dyslipidemia), the positive relationship between baPWV and the three metabolites (isocitrate, hydroxymethylglutarate, and formiminoglutamate) levels was significant (P = 0.001, P = 0.029, and P < 0.001, respectively) (model 5). In the subjects excluding those with hypertension, diabetes, or statin use, and alcohol and smoking history (N = 151), the levels of isocitrate, hydroxymethylglutarate, and formiminoglutamate were significantly related to baPWV (P < 0.001, P = 0.005, and P = 0.003, respectively) (model 6).

Discussion
In this cross-sectional study of 330 men and women, higher levels of urine isocitrate, hydroxymethylglutarate, and formiminoglutamate were significantly associated with high baPWV after adjusting for conventional cardiovascular (CV) risk factors.
Previous studies have demonstrated that cellular mechanisms, including intracellular hyperglycemia, increasing fatty acid flux and oxidation, and mitochondrial dysfunction, play a role in coronary atherogenesis and cardiomyopathy 7,29-31 .
One of the cellular mechanisms, higher fatty acid flux and oxidation, is related to various heart diseases 30,31 . During cardiac ischemia, elevated rates of fatty acid oxidation result in the inhibition of glucose oxidation by inhibiting the activity of pyruvate dehydrogenase leading to increased production of l-lactate 31 . As in previous reports, higher lactate levels were related to cardiac problems such as heart failure, endocardial damage, and cardiac ischemia 32,33 . In this study, l-lactate levels were positively correlated with baPWV in men, before adjusting for confounders. However, we did not find any significant correlation of l-lactate levels with baPWV after adjusting for confounders. This discrepancy may have arisen from our small sample size and non-homogeneous study population. Further research is needed to investigate the relationship of l-lactate and arterial stiffness in a large population.
Among the cellular perturbations, mitochondrial dysfunction also plays a central role in the development of CVD 7,26,29,30,34 . Mitochondria contribute in oxygen sensing, cellular signaling, cell stress regulation, and energy production. The mitochondrial respiratory chain is the major pathway of energy production. Mitochondria are very sensitive to nutrient and oxygen supply and can adapt to a changing environment. When this adaptation is impaired, it leads to a progressive decline of mitochondrial function, which is associated with abnormalities in the respiratory chain and ATP synthesis, increased oxidative stress, and activation of signaling proteins 35 . Reactive oxygen species (ROS) overproduction then leads to the oxidation of lipids and proteins and promotes atherogenesis by inducing endothelial dysfunction, vessel inflammation, and accumulation of oxidized low-density lipoprotein (LDL) 28 24 . Another previous study indicated that mitochondrial dysfunction is accompanied by the excretion of citric acid cycle intermediates 22 . Elevated levels of one of the citric acid cycle intermediates in urine, isocitrate, may reflect mitochondrial nicotinamide adenine dinucleotide phosphate (NADP) + -isocitrate dehydrogenase inactivity. Mitochondrial NADP+isocitrate dehydrogenase catalyzes the oxidative decarboxylation of isocitrate, producing alpha-ketoglutarate, and regulates cardiomyocyte energy and redox status. Some studies suggest that mitochondrial NADP+-isocitrate  39 , and cell damage after kidney ischemia-reperfusion injury 40 . In our study, we demonstrated that urine isocitrate levels were significantly associated with baPWV and was still significantly correlated with baPWV after adjusting for confounders. Regarding citric acid cycle intermediates, high levels of hydroxymethylglutarate may reflect the inadequate endogenous synthesis of CoQ10 41,42 . Some studies have demonstrated that CoQ10 might play a role in the prevention of heart ailments, inhibition of LDL oxidation, and progression of atherosclerosis 43 . Consistent with previous studies, our results reveal the relationship between baPWV and urine hydroxymethylglutarate; after the adjustment of confounders, urine hydroxylmethylglutarate levels were still significantly associated with baPWV.
Succinate is a metabolic intermediate of the TCA cycle within cells, like hydroxymethylglutarate 44 . In mitochondria, succinate is produced as an intermediate metabolite formed from the conversion of succinyl-CoA www.nature.com/scientificreports/ and is oxidized by succinate dehydrogenase (SDH) to form fumarate. Meanwhile, succinate is also produced from succinic semialdehyde via the γ-aminobutyric acid (GABA) shunt 44 . Generally, succinate is considered an intracellular metabolite, but succinate also has been shown to accumulate in the extracellular tissue environments related to the conditions of stress and inflammation. For instance, in microbial fermentation, succinate is also formed by the reversal of partial TCA cycle reactions [45][46][47] . In this study, urine hydroxymethylglutarate levels were significantly associated with baPWV, but the same was not true for urine succinate. Although hydroxymethylglutarate and succinate are in close connection in the TCA cycle, the mechanisms for succinate release are diverse and unclear, which resulted in the different results. www.nature.com/scientificreports/ Formiminoglutamic acid is an intermediate metabolite in the degradative conversion of histidine to glutamic acid. An increase in urinary formiminoglutamic acid levels may be seen in patients with folic-acid deficiency, vitamin B12 deficiency, and liver disease 48,49 . Some studies showed that folate deficiency is associated with heart disease 50 and CVD 51,52 . Folate supplementation delays the development of atherosclerotic lesion by modulating monocyte chemotactic protein-1 (MCP-1) and vascular endothelial growth factor (VEGF) DNA methylation levels 53 . In our study, urinary formiminoglutamate levels were positively correlated with higher baPWV, independent of confounders.
Our study has several limitations. First, our study was cross-sectional; thus, we could not demonstrate a causative relationship between urine metabolites and baPWV. Second, as noted earlier, we had a small sample, and our population was considerably heterogeneous; the subjects studied had various underlying diseases and a wide difference in age. Third, we lacked sufficient disease history of the subjects which might be related to arterial stiffness. Finally, we did not consider the day by day variability in urine measurement and did not reflect the long-term data. Additional long-term studies about the metabolic status can help overcome this limitation.
In conclusion, our study reveals that higher concentrations of urine isocitrate, hydroxymethylglutarate, and formiminoglutamate were positively correlated with higher baPWV, independent of conventional CV risk factors. Our findings show that the changes in these metabolites were associated with arterial stiffness. Further studies are required to reveal the causal relationship between urine metabolites and baPWV. This may serve as a new useful clinical biomarker for predicting elevated arterial stiffness and may help understand the molecular mechanisms underlying arterial stiffness.

Methods
Study population. The participants enrolled in this cross-sectional study included adults who visited the Chaum Life Center, CHA University, Seoul, Republic of Korea. Among all adults who attended the periodic medical check-up from November 2016 to December 2018, 927 participants agreed to participate in the study and had undergone urine metabolomic analyses. Subjects were required not to take supplements, including vitamin and herbs, for at least 1 week. We excluded 591 subjects who had not undergone measurements of baPWV. Subjects with renal disease, malignant disease, thyroid disease, collagen diseases, or infections, and acute disease, abnormal liver function, and a history of stroke, angina, or myocardial infarction were also excluded. Finally, 174 men and 156 women were enrolled in our study. This study was approved by the institutional review board of CHA Bundang Medical Center. The participants provided informed consent prior to enrollment. All the procedures were carried out in accordance with the relevant guidelines.
Medical history, metabolic syndrome and lifestyle habits. The medical history, medication, and lifestyle habits of the subjects were collected. The presence of metabolic syndrome (MetS) was defined by the National Cholesterol Education Program Adult Treatment Panel III criteria 54 . The cutoff values for central obesity were applied in accordance with a well-validated previous Korean study 55 . The MetS was defined by the presence of three or more of the following components: (1) waist circumference ≥ 90 cm for man and ≥ 85 cm for women, (2) systolic BP ≥ 130 mmHg or diastolic BP ≥ 85 mmHg or antihypertensive medication use, (3) HDL cholestrol < 1.04 mmol/L for men and 1.29 mmol/L for women, (4) triglyceride > 1.69 mmol/L, and (5) elevated fasting blood glucose ≥ 6.11 mmol/L or taking hypoglycemic agents.
Patients were categorized as non-smokers or current smokers based on their smoking habits. Significant alcohol consumption was defined as > 21 standard drinks/week in men and > 14 standard drinks/week in women over a 2-year period 56 . Significant physical activity was defined as ≥ 3 exercise/week. Anthropometric measurements. Height and weight were measured in centimeters and kilograms, respectively, using standardized protocols while the subjects were dressed in light clothing and had their shoes off. Body mass index (BMI) was calculated from height and weight. BP was measured after resting for 10 min in a sitting position using an automatic sphygmomanometer (TM-2655P, A&D Company, Tokyo, Japan) with an appropriate cuff size. The mean BP calculated using a formula in which the diastolic BP is doubled and added to the systolic BP and the composite sum was divided by 3 57 .
Biochemical measurements. Blood samples were collected and subsequently analyzed at a central certified laboratory at the CHA Gangnam Medical Center. Blood samples were collected from the antecubital vein early in the morning after an 8-h overnight fast. Fasting plasma glucose, high-density lipoprotein (HDL) cholesterol, and triglyceride levels were measured using a chemistry autoanalyzer (Hitachi 7600-110, Tokyo, Japan). Glomerular filtration rate (GFR) determined using the Modification of Diet in Renal Disease method 58 .

Measurements of baPWV.
BaPWV was measured using a non-invasive vascular screening device (VP-1000 plus, Omron Healthcare, CA, USA). Subjects were examined in the supine position for at least 5 min, with elbows and ankles fastened to the blood pressure cuff. Electrocardiographic electrodes sheet and telepathy were attached on both wrists and placed at the second intercostal space at the left margin of the sternum. These values were measured after individuals had rested for at least 5 min. We adopted the average value of the left and right baPWV.
Measurements of urine metabolites. After 59,60 . The creatinine concentrations of the urine samples were determined before the analysis, and urinary concentrations of metabolites were normalized with urine creatinine to minimize the variability in urine concentrations. The urine samples (300 μL) of the subjects were transferred into the autosampler vial. The samples were prepared according to the standardized protocol and injected into the HPLC-tandem mass spectrometry (HPLC-MS/MS) system. The HPLC-MS/MS analyses were conducted with AB Sciex Triple Quad 4500 MD (Framingham, MA, USA) with an electrospray ionization interface. The quantification was performed using an Organic Acids Urine LC-MS/MS analysis kit (ZIVAK, Kocaeli, Turkey). Injection volumes were 20.0 μL, and the flow rate throughout the analysis was 0.25 mL/min. The selected reaction monitoring transitions and the related optimized declustering potential, collision energy, and collision cell exit potential for the different analytes were determined according to the manufacturer's manual. For all metabolites, a standard curve of known concentrations consisting of three concentrations was built. Quantification of metabolites in samples was based on these standard curves and carried out automatically with the MS controller MultiQuant MD 3.0.2.
Statistical analysis. SPSS version 25.0 (IBM, Armonk, NY, USA) was used to perform all data analyses.
All continuous variables are reported as means ± standard deviations (SDs) or median (interquartile range). The categorical variables are expressed as number (percentage). We converse normalized the metabolite data, as the metabolite concentrations were not normally distributed. To identify the metabolites that associate with PWV, Spearman correlation analysis of the metabolite levels with baPWV was performed.
The metabolites that were significantly related to baPWV in both men and women were considered as key metabolites. We drew scatter plots to show the association between key metabolites and baPWV. Fit lines were also drawn in the plots.
To avoid the effects of the confounding factors, associations of metabolites with urine metabolites were tested using covariate-adjusted multivariable regression to examine the relationship between urine metabolites and baPWV with the adjustment of confounding factors. Model 1 means a crude model. First, demographic factors, age, and sex were included in model 2. Then, mean BP and heart rate were additionally included in Model 3 as strongly influencing factors for PWV. Total cholesterol, HDL cholesterol levels, and BMI, which are conventional CV risk factors, were additionally included in model 4. As factors that may potentially affect PWV, medication history of hypertension, diabetes mellitus, and dyslipidemia, smoking and alcohol history, and physical activity were additionally included in model 5. To minimize the effects of the medications for hypertension, diabetes mellitus, and dyslipidemia and smoking and alcohol history, the regression models were analyzed again in the subjects without a medication history of hypertension, diabetes mellitus, or statin use, and alcohol and smoking history (model 6). For all analyses, a P-value < 0.05 was considered statistically significant.