Skip to main content
NCBI home page
Mediators of Inflammation logoLink to Mediators of Inflammation
. 2018 Sep 20;2018:2760272. doi: 10.1155/2018/2760272

Plasma Energy-Balance Metabolites Discriminate Asymptomatic Patients with Peripheral Artery Disease

Anna Hernández-Aguilera 1, Salvador Fernández-Arroyo 2, Noemí Cabre 2, Fedra Luciano-Mateo 2, Gerard Baiges-Gaya 2, Montserrat Fibla 1, Vicente Martín-Paredero 3, Javier A Menendez 4,5, Jordi Camps 1,✉, Jorge Joven 1,2,6
PMCID: PMC6171256  PMID: 30327580

Abstract

Peripheral artery disease (PAD) is a common disease affecting 20–25% of population over 60 years old. Early diagnosis is difficult because symptoms only become evident in advanced stages of the disease. Inflammation, impaired metabolism, and mitochondrial dysfunction predispose to PAD, which is normally associated with other highly prevalent and related conditions, such as diabetes, dyslipidemia, and hypertension. We have measured energy-balance-associated metabolite concentrations in the plasma of PAD patients segregated by the severity of the disease and in plasma of healthy volunteers using a quantitative and targeted metabolomic approach. We found relevant associations between several metabolites (3-hydroxybutirate, aconitate, (iso)citrate, glutamate, and serine) with markers of oxidative stress and inflammation. Metabolomic profiling also revealed that (iso)citrate and glutamate are metabolites with high ability to discriminate between healthy participants and PAD patients without symptoms. Collectively, our data suggest that metabolomics provide significant information on the pathogenesis of PAD and useful biomarkers for the diagnosis and assessment of progression.

1. Introduction

The prevalence of peripheral artery disease (PAD) is now higher than 20% in population over 60 years, and affected patients have a several-fold increased risk of all-cause mortality compared to people without the disease [1]. Age, hypertension, hypercholesterolemia, diabetes, and smoking are recognized risk factors, but the disease progresses silently for decades, with the consequence that if appropriate, effective measures are applied too late, or not implemented at all, will result in atherosclerosis affecting wide portions of the arteries in the lower extremities [2]. Tissue ischemia is a common finding in PAD patients, and an increasing body of evidence supports the notion that inflammation plays an important role in the pathogenesis of this disease, linking oxidative stress, metabolic adaptation, and immunity [3–6]. Consequently, the combined action of abnormal mitochondrial function, increased production of reactive oxygen species, impaired energy metabolism, and the subsequent inflammatory response complicates vascular remodeling, perfusion recovery, and atherosclerosis [7–10].

One of the main challenges clinicians face is early diagnosis of PAD (i.e., during the asymptomatic stages), and many biomarker candidates have been proposed with limited success [11–13]. The emerging metabolomic approaches are providing new clues in understanding atherosclerosis-related cardiovascular disease [14–20], but efforts on PAD have been scarce. Previous studies from our group suggested that serum paraoxonase-1 (PON1) and the chemokine (C-C) motif ligand 2 (CCL2) might be useful biomarkers of PAD [21, 22]: PON1 being a scavenger of excessive reactive oxygen species [23] and CCL2 an inducer of monocyte migration and differentiation into macrophages [24]. As mitochondrial dysfunction and inflammation result in disrupted metabolism [25, 26], we report here that metabolomic profiling of plasma may be useful for identifying patients at increased risk of PAD and that energy-balance metabolites are associated with inflammation and oxidation in these patients. Our results might suggest new biomarkers and therapeutic targets.

2. Materials and Methods

2.1. Participants and Study Design

We performed an observational, cross-sectional study in 201 men with clinically diagnosed PAD attending our vascular surgery department between 2010 and 2015. Patients were classified according to the Fontaine classification in nonsymptomatic stage (grade I), intermittent claudication (grade II), rest pain (grade III), and tissue damage, and necrosis (grade IV) [27]. Diagnostic criteria involved ankle-brachial index (ABI), noninvasive imaging techniques (computerized tomography scan or magnetic resonance imaging), and arteriography when indicated. The exclusion criteria were the presence of acute ischemia, signs of infection, renal failure, liver disease, cancer, or autoimmune disease. Clinical data and laboratory variables were obtained from patients' clinical records. For comparisons, we used biobanked samples (n = 48) from healthy, age-matched men and normal ABI values, whose details have been previously described [28]. Plasma and serum samples from all participants were collected and stored at −80°C until use. Procedures were approved by the Comité d'Ètica d'Investigació Clínica of Hospital Universitari de Sant Joan (EPINOLS/12-03-09/3proj6 and INFLAMET/15-04-30/4proj6), and written informed consent was obtained from all participants.

2.2. Metabolomic Analyses

To detect and quantify metabolites of energy metabolism, we used a previously described method [6]. Briefly, metabolites from plasma (100 μl) were extracted using 400 μl of methanol/water (8 : 2, v/v) and proteins were precipitated for two hours at −20°C. After centrifugation at 14,000 rpm for 10 minutes at 4°C, the supernatant was collected and dried under N2 flow. Metabolites were then derivatized with methoxyamine in pyridine (40 mg/ml) and N-methyl-N-(trimethylsilyl)-trifluoroacetamide and injected into a gas chromatograph coupled with a quadrupole time-of-flight mass spectrometer by an electron impact source. Metabolites were detected and quantified with the use of proper calibration curves.

2.3. Biochemical Analyses

The true physiological substrates for PON1 have not been yet identified. Since PON1 has lactonase and esterase activities [23], we opted to analyze the catalytic activity of PON1 using two different substrates: paraoxon (an ester) and 5-thiobutyl butyrolactone (TBBL; a synthetic lactone). Serum PON1 paraoxonase activity was determined as the rate of hydrolysis of paraoxon at 410 nm and 37°C in a 0.05 mM glycine buffer, pH 10.5 with 1 mM CaCl2. Activities were expressed as U/l (1 U = 1 μmol of paraoxon hydrolyzed per minute). Serum PON1 lactonase activity was measured in an assay containing 1 mM CaCl2, 0.25 mM 5-thiobutyl butyrolactone, and 0.5 mM 5,5′-dithio-bis-2-nitrobenzoic acid (DTNB) in 0.05 mM Tris-HCl buffer, pH = 8.0. The change in absorbance was monitored at 412 nm. Activities were expressed as U/l (1 U = 1 mmol of TBBL hydrolyzed per minute) [29]. Serum PON1 concentrations were determined by an in-house enzyme-linked immunosorbent assay (ELISA) with a rabbit polyclonal antibody generated against the synthetic peptide CRNHQSSYQTRLNALREVQ which is sequence specific for mature PON1 [30]. CCL2 metabolites were measured by ELISA (PeproTech, London, UK). Serum lipid, liver profiles, and glucose concentrations were analyzed by standard tests in a Roche Modular Analytics P800 system. Blood count was analyzed in a Roche Sysmex XT-1800i counter (Roche Diagnostics, Basel, Switzerland).

2.4. Statistical Analysis

Differences between groups were assessed by Student's t-test (parametric) or Mann–Whitney U test (nonparametric). Correlations between variables were assessed by Spearman's rho test. The relative magnitude of observed changes was evaluated using random forest analysis [31]. Receiver operating characteristic (ROC) curves were used to assess the diagnostic accuracy of the measured variables. This analysis represents plots of all the sensitivity/specificity pairs resulting from varying decision thresholds. Sensitivity (or true-positive rate) is the proportion of the sample correctly identified as having a specific disease. Specificity (or true-negative rate) is the proportion of the subjects correctly identified as not having a specific disease. False-positive rate is calculated as 1 − specificity. The area under the curve (AUC) and 95% confidence interval (CI) were calculated. The AUC represents the ability of the test to correctly classify patients according to the investigated alteration. The values of AUC can range between 1 (perfect test) and 0.5 (worthless test) [32]. Statistical analyses were performed with SPSS 22.0 (IBM Corp., Chicago, IL, USA). MetaboAnalyst 3.0 (http://www.metaboanalyst.ca/) was used to generate scores/loading plots and random forest analyses.

3. Results

3.1. Participants' Characteristics

Clinical characteristics and biochemical variables of PAD patients and the control group are shown in Table 1. PAD patients had a mildly higher BMI than those in the control group, and they often had diabetes, hypertension, or dyslipidemia. Serum glucose and triglyceride concentrations were higher in patients, while cholesterol was lower, which is probably the result of medication.

Table 1.

Clinical characteristics, blood count, and biochemical characteristics of the control group and PAD patients.

Control (n = 48) PAD (n = 201) p value
BMI (kg/m2) 24 (22.5–25.3) 25 (22.5–28) 0.021
Diabetes (%) — 64.1 <0.001
Hypertension (%) — 69.2 <0.001
Dyslipidemia — 37.9 <0.001
Red blood cells, ×1012/l 4.9 (4.4–5.2) 4.16 (3.57–4.66) <0.001
Hemoglobin, mmol/l 8.94 (8.32–9.43) 13.30 (11.50–14.90) 0.001
Leukocytes, ×109/l 6.8 (5.4–8.2) 8.17 (6.50–10.22) 0.003
Platelets, ×109/l 233 (205–273) 253 (200–329) ns
Total cholesterol, mmol/l 4.85 (4.40–5.85) 3.90 (3.31–4.94) <0.001
HDL cholesterol, mmol/l 1.34 (1.14–1.61) 0.96 (0.78–1.19) <0.001
LDL cholesterol, mmol/l 2.82 (2.40–3.86) 2.26 (1.77–2.79) <0.001
Triglycerides, mmol/l 0.90 (0.70–1.38) 1.99 (1.40–3.08) <0.001
Glucose, mmol/l 4.70 (4.37–4.92) 5.61 (4.60–6.88) <0.001
ALT, U/l 20 (13.5–24.9) 21.5 (15–34.8) ns
AST, U/l 20 (17.7–24) 21 (16–32) ns
Open in a new tab

BMI: Body mass index; HDL: high-density lipoprotein; LDL: low-density lipoprotein; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ns: not significant. Nonparametric variables are shown as medians, and IQR are in parentheses. Qualitative variables are expressed as percentage of total participants.

3.2. Alterations in Energy-Balance-Associated Metabolites

The plasma concentrations of most energy metabolism intermediates were significantly higher in PAD patients than those in the control group, with the exceptions of fumarate, lactate, and succinate, that were decreased (Figure 1(a)). Glutaminolysis was disrupted in PAD, as shown by glutamate and glutamine increases. Moreover, reactions involving amino acid metabolism seemed to be inhibited in PAD, as serine, valine, isoleucine, and leucine concentrations were increased. TCA cycle was strongly disturbed since (iso)citrate, aconitate, α-ketoglutarate, succinyl-CoA, and malate concentrations were increased, and fumarate and succinate concentrations were decreased in PAD (Figure 1(b)).

Figure 1.

Figure 1

Open in a new tab

3.3. Relationships between Energy-Balance-Associated Metabolites, PON1 and CCL2

We observed significant inverse correlations between metabolites and PON1-related variables and direct correlations between metabolites and CCL2. The strongest correlations were observed between serum PON1 concentration and PON1 lactonase activity with 3-hydroxybutirate, aconitate, (iso)citrate, glutamate, and serine (Table 2).

Table 2.

Spearman's correlation coefficients for PON1 concentration, PON1 paraoxonase activity, PON1 lactonase activity, CCL2 concentration, and related metabolites.

PON1 concentration Paraoxonase activity Lactonase activity CCL2 concentration
Spearman's rho p value Spearman's rho p value Spearman's rho p value Spearman's rho p value
3-hydroxybutirate −0.640 <0.001 −0.309 <0.001 −0.496 <0.001 0.241 0.009
α-Ketoglutarate −0.021 ns −0.183 0.029 −0.029 ns −0.045 ns
Aconitate −0.641 <0.001 −0.394 <0.001 −0.552 <0.001 0.345 <0.001
Alanine 0.153 ns 0.253 0.002 0.275 0.001 −0.155 ns
Aspartate −0.241 0.004 −0.187 0.026 −0.349 <0.001 0.184 0.046
(Iso)citrate −0.592 <0.001 −0.166 0.048 −0.381 <0.001 0.303 0.001
Fumarate 0.295 <0.001 −0.053 ns 0.166 0.048 −0.014 ns
Glucose 0.011 ns 0.109 ns 0.053 ns −0.098 ns
Glutamate −0.590 <0.001 −0.425 <0.001 −0.589 <0.001 .0171 ns
Glutamine −0.263 0.002 −0.059 ns −0.198 0.018 0.187 0.043
Isoleucine −0.342 <0.001 −0.101 ns −0.299 <0.001 0.167 ns
Lactate 0.277 0.001 0.086 ns 0.175 0.036 −0.104 ns
Leucine −0.229 0.006 0.008 ns −0.136 ns 0.079 ns
Malate −0.356 <0.001 −0.240 0.004 −0.279 0.001 0.225 0.014
Pyruvate 0.356 <0.001 −0.034 ns 0.158 ns −0.163 ns
Serine −0.595 <0.001 −0.294 <0.001 −0.534 <0.001 0.295 0.001
Succinate −0.355 <0.001 −0.194 0.021 −0.342 <0.001 0.110 ns
Succinyl-CoA −0.223 0.008 −0.101 ns −0.154 ns 0.025 ns
Valine −0.035 ns 0.155 ns 0.076 ns −0.009 ns
Open in a new tab

PON1: Paraoxonase 1; CCL2: chemokine (C-C) motif ligand 2; ns: not significant.

3.4. Metabolites Are Linked to PAD Comorbidities, Age, and BMI

Univariate analyses confirmed that many metabolites were associated with diabetes, hypertension, or dyslipidemia (Tables 3–5), while multivariate analyses (principal component analyses) revealed that the combination of these metabolites was not able to separate groups regarding hypertension (Figure 2(a)) and dyslipidemia (Figure 2(b)). Random forest analysis showed that glucose and isoleucine were associated with diabetes in PAD patients and glucose (as expected) had the highest discriminant capacity (Figure 2(c)). Hyperlipidemic and normolipemic patients showed significant differences in alanine, aspartate, glucose, isoleucine, lactate, leucine, succinyl-CoA, and valine concentrations and, among them, isoleucine had the highest discriminant capacity (Figure 2(d)). Fumarate, glucose, isoleucine, lactate, malate, serine, and pyruvate were associated with hypertension in PAD patients, and serine was the metabolite with the best discriminant capacity (Figure 2(e)). Aconitate, fumarate, and malate were associated with age, and aconitate, alanine, aspartate, glucose, isoleucine, leucine, and valine were correlated with BMI (Table 6 and Figure 3). All these metabolites were excluded for further analysis as candidates for PAD biomarker in order to avoid the influence of confounding factors.

Table 3.

Metabolite concentration in patients segregated according to the presence of diabetes.

Metabolite Diabetes
No Yes
3-hydroxybutirate 0.27 (0.13–0.39) 0.27 (0.14–0.40)
Aconitate 3.75 (2.38–6.50) 4.60 (2.67–6.28)
α-Ketoglutarate 4.24 (2.56–7.22) 4.13 (2.76–6.50)
Alanine 210.6 (147.5–266.3) 189.6 (139.0–271.4)
Aspartate 172.5 (133.3–215.8) 179.7 (143.0–225.1)
(Iso)citrate 721.7 (584.42–867.3) 665.6 (538.0–880.5)
Fumarate 0.26 (0.19–0.41) 0.24 (0.18–0.37)
Glucose 4546.0 (4104.0–5115.5) 4959.3 (4040.8–5804.9)a
Glutamate 1457.2 (743.0–2912.4) 1416.9 (671.7–2684.2)
Glutamine 5073.4 (3054.2–7754.5) 4742.5 (1842.3–8881.2)
Isoleucine 57.14 (47.71–63.99) 63.68 (52.83–75.20)a
Lactate 367.2 (283.9–423.6) 341.0 (297.1–441.6)
Leucine 85.07 (70.62–94.86) 86.68 (69.33–109.65)
Malate 2.38 (1.85–4.31) 2.45 (1.88–3.56)
Pyruvate 12.47 (3.64–22.87) 12.83 (3.55–21.89)
Serine 147.5 (101.7–167.4) 137.6 (109.4–169.5)
Succinate 9.57 (8.35–15.11) 9.70 (8.47–15.23)
Succinyl-CoA 10.14 (7.74–15.12) 10.46 (7.37–17.62)
Valine 102.0 (83.0–129.4) 104.1 (80.7–138.9)
Open in a new tab

a p < 0.01.

Table 4.

Metabolite concentrations in patients segregated according to the presence of hypertension.

Metabolite Hypertension
No Yes
3-hydroxybutirate 0.21 (0.14–0.38) 0.35 (0.14–0.42)
Aconitate 4.59 (2.54–6.65) 4.13 (2.60–6.60)
α-Ketoglutarate 4.71 (2.81–6.67) 3.70 (2.51–7.07)
Alanine 213.5 (152.7–273.7) 184.3 (137.3–255.1)
Aspartate 181.6 (141.4–239.5) 173.1 (137.3–200.4)
(Iso)citrate 721.8 (566.6–934.0) 654.2 (476.5–815.9)
Fumarate 0.27 (0.19–0.40) 0.22 (0.17–0.30)a
Glucose 4955.9 (4459.1–5679.9) 4418.1 (4134.8–5191.7)b
Glutamate 1335.8 (725.0–2628.5) 1786.6 (691.1–2891.7)
Glutamine 5083.3 (2842.0–9072.2) 4742.5 (1700.9–7003.8)
Isoleucine 63.52 (52.24–75.19) 57.88 (51.23–63.22)a
Lactate 373.5 (308.3–452.5) 332.3 (279.5–405.9)a
Leucine 90.17 (71.33–109.35) 84.54 (69.77–93.84)
Malate 2.79 (1.96–4.09) 2.23 (1.75–3.08)a
Pyruvate 13.86 (3.85–24.11) 8.78 (2.72–18.15)a
Serine 135.3 (106.1–162.1) 161.0 (103.6–172.0)a
Succinate 9.15 (8.42–15.04) 11.71 (8.49–15.47)
Succinyl-CoA 11.21 (7.72–16.31) 9.94 (7.52–14.25)
Valine 107.3 (86.6–136.8) 96.6 (78.8–134.8)
Open in a new tab

a p < 0.05; bp < 0.01.

Table 5.

Metabolite concentrations in patients segregated according to the presence of dyslipidemia.

Metabolite Dyslipidemia
No Yes
3-hydroxybutirate 0.31 (0.16–0.40) 0.19 (0.13–0.419)
Aconitate 4.64 (2.91–6.61) 4.22 (2.36–6.16)
α-Ketoglutarate 3.99 (2.44–6.55) 5.05 (3.05-6.90)
Alanine 180.5 (125.1–247.5) 233.6 (171.4–302.8)b
Aspartate 168.6 (134.1–205.4) 191.7 (148.8–253.2)a
(Iso)citrate 678.6 (545.4–864.5) 712.3 (566.1–959.1)
Fumarate 0.23 (0.18–0.36) 0.27 (0.19–0.40)
Glucose 4663.9 (4172.3–5417.5) 4925.3 (4503.6–5684.9)a
Glutamate 1556.2 (679.8–2785.7) 1342.5 (763.7–2372.1)
Glutamine 4742.5 (2007.9–7169.0) 5073.4 (2219.3–10427.7)
Isoleucine 57.90 (49.81–68.46) 65.02 (55.35–77.95)b
Lactate 334.7 (274.2–435.6) 393.3 (323.2–457.6)b
Leucine 83.96 (66.70–98.71) 92.19 (76.10–109.92)a
Malate 2.37 (1.83–3.66) 2.87 (2.12–3.80)
Pyruvate 9.50 (3.08–21.00) 13.58 (6.00–25.78)
Serine 150.0 (104.9–170.3) 131.9 (106.0–18.8)
Succinate 10.82 (8.44–15.23) 9.11 (8.46–15.15)
Succinyl-CoA 9.79 (6.71–14.00) 13.36 (8.90–18.47)b
Valine 99.3 (78.6–125.5) 113.3 (89.6–139.4)b
Open in a new tab

a p < 0.05; bp < 0.01.

Figure 2.

Figure 2

Open in a new tab

Table 6.

Spearman's correlation coefficients for age, body mass index, and related metabolites.

Age BMI
Spearman's rho p value Spearman's rho p value
3-Hydroxybutirate 0.057 ns 0.022 ns
Aconitate 0.205 0.003 −0.236 0.010
α-Ketoglutarate 0.054 ns −0.031 ns
Alanine −0.081 ns 0.230 0.013
Aspartate 0.083 ns 0.256 0.005
(Iso)citrate 0.113 ns 0.147 ns
Fumarate 0.220 0.002 −0.085 ns
Glucose −0.031 ns 0.202 0.029
Glutamate 0.063 ns −0.032 ns
Glutamine 0.007 ns 0.157 ns
Isoleucine 0.124 ns 0.194 0.036
Lactate −0.023 ns 0.034 ns
Leucine −0.005 ns 0.220 0.017
Malate 0.248 <0.001 −0.079 ns
Pyruvate −0.009 ns 0.103 ns
Serine 0.062 ns −0.112 ns
Succinate 0.011 ns −0.145 ns
Succinyl-CoA 0.038 ns 0.134 ns
Valine −0.122 ns 0.241 0.009
Open in a new tab

BMI: Body mass index; ns: not significant.

Figure 3.

Figure 3

Open in a new tab

3.5. Metabolic Biomarkers of PAD

Metabolites included in further analysis were (iso)citrate, glutamate, succinate, 3-hydroxybutirate, α-ketoglutarate, and glutamine. From them, (iso)citrate and glutamate were those showing the greatest differences between patients and controls and between the different patient groups according to the Fontaine scale (Figure 4). To evaluate the capacity of these metabolites to discriminate between groups, we performed random forest analyses and ROC curves confirming that (iso)citrate and glutamate were the most powerful metabolites to separate the healthy individuals from PAD patients (Figures 5(a) and 5(b)), and the healthy individuals from asymptomatic or nearly asymptomatic patients (grades I and II), with AUC > 0.95 (Figures 5(c) and 5(d)). They were also useful to discriminate between the different subgroups of patients (Figure 5(e)).

Figure 4.

Figure 4

Open in a new tab

∗ p < 0.05 and ∗∗p < 0.001 for the control samples.

Figure 5.

Figure 5

Open in a new tab

4. Discussion

Energy-balance-associated metabolites are related to oxidative stress and inflammation. We found in PAD patients significant alterations in energy metabolism, particularly evident through the citrate-aconitate-(iso)citrate conversions. The mitochondrial enzymes involved in these reactions are (iso)citrate dehydrogenase (IDH2) and aconitase 2 (ACO2). Both enzymes are crucial for normal mitochondrial function [33]. In mice, decreased IDH2 expression contributes to atherosclerosis progression by increasing oxidative stress [34]. An oxidative environment inactivates aconitase, which in turn undergoes age-dependent oxidative modification. Whether IDH2 and ACO2 may be the cause or consequence of mitochondrial dysfunction in PAD requires further studies [8].

Mitochondrial dysfunction, oxidative stress, and inflammation are closely related [24, 25]. The present study shows the existence of significant inverse correlations between various metabolites and PON1-related variables and direct correlations with CCL2. These correlations are stronger for PON1 concentration and lactonase activity. This may be due to the observation that PON1 is located (among other cellular organelles) in the membranes of the mitochondria, protecting them from oxidative stress [25]. Therefore, alterations in the TCA cycle may directly affect PON1. The correlations between metabolites and paraoxonase activity are weaker than those found for lactonase activity, but this is probably due to the differential impact of genetic polymorphisms [23]. Metabolites showing the strongest and more consistent correlations with PON1 and CCl2 were 3-hydroxybutirate, aconitate, (iso)citrate, glutamate, and serine. 3-Hydroxybutirate is a ketone body, and studies on the effects of ketones on oxidative stress and inflammation are contradictory. For example, it has been reported that these compounds inhibit mitochondrial production of reactive oxygen species in rat and mice neurons [35–37] while they activate NF-κB, upregulate NADPH oxidase, elevate oxidative stress, and induce the expression of proinflammatory cytokines in endothelial cells and hepatocytes [38, 39]. Our results are in agreement with this latter possibility. It has been suggested that these contradictory findings reflect tissue-specific differences because the source of reactive oxygen species in neurons differ from that in nonneuronal cells [35]. Aconitate is the precursor of itaconate, and this metabolite regulates metabolic remodeling and mitochondrial respiration in inflammatory macrophages [40]. There is little information on the associations of (iso)citrate, glutamate, and serine with oxidative stress and inflammation, but studies suggest that glutamate is pro-oxidant and proinflammatory [41] while (iso)citrate and serine may elicit the opposite effect [42, 43].

Energy-balance-associated metabolites might be considered as PAD biomarkers. Early diagnosis is important in these patients because preventive treatment has potential benefits in the progression of PAD. Current biomarkers are indeed risk factors [11, 13], and dyslipidemia, hypertension, or diabetes plays independent roles in atherogenesis. In our population, approximately 80% of patients had one or more of these complications. Their effect on metabolite concentrations is unknown but probably multifactorial. For instance, branched-chain amino acids (BCAA) were influenced by hypertension, diabetes, and dyslipidemia confirming previous associations [44–47]. We therefore discarded metabolites influenced by confounding variables, and we found six candidates with statistically significant differences in concentration between the control group and PAD patients: 3-hydroxybutyrate, α-ketoglutarate, glutamate, glutamine, (iso)citrate, and succinate. These candidates were useful to distinguish between PAD patients and the control group and also to discriminate between different clinical stages. The conversion of (iso)citrate to α-ketoglutarate is mediated by IDH2, and increased concentrations of this metabolite have been associated with a worse cardiovascular prognosis [48]. Interestingly, glutamate, another metabolite with a good discriminant capacity, is the substrate for many enzymes located in the mitochondria [49] and plays an important role in the mechanical function of the ischemic myocardium [50]. Understanding glutamate overproduction in the blood of patients with atherosclerosis requires further research. Our more interesting finding indicates that (iso)citrate and glutamate may discriminate healthy participants from PAD patients in the asymptomatic or early symptomatic stages (Fontaine grades I and II). Simple measurements may then provide clinical tools to assess patients at risk but without symptoms. Larger studies using sensitive metabolomic techniques are warranted to confirm these findings and to identify specific metabolic pathways associated with increased risk of PAD.

5. Conclusion

Our metabolomic approach supports a relevant association of plasma concentrations of energy-balance-associated metabolites with oxidative stress and inflammation and reveals (iso)citrate and glutamate as candidate biomarkers for discriminating PAD patients without symptomatic disease.

Acknowledgments

This work was supported by grants from the Plan Nacional de I+D+I, Spain, Instituto de Salud Carlos III (Grant no. PI15/00285, co-founded by the European Regional Development Fund (EU FEDER)) to Jorge Joven and the Ministerio de Ciencia e Innovación (Grant no. SAF2012–38914) to Javier A. Menendez. The authors also acknowledge the support by the Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR) (Grant nos. 2014 SGR1227 and 2014 SGR229) and the Fundació La Marató de TV3.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors' Contributions

Anna Hernández-Aguilera and Salvador Fernández-Arroyo contributed equally to this work.

References

  • 1.Fowkes F. G. R., Rudan D., Rudan I., et al. Comparison of global estimates of prevalence and risk factors for peripheral artery disease in 2000 and 2010: a systematic review and analysis. Lancet. 2013;382(9901):1329–1340. doi: 10.1016/S0140-6736(13)61249-0. [DOI] [PubMed] [Google Scholar]
  • 2.Criqui M. H. Peripheral arterial disease - epidemiological aspects. Vascular Medicine. 2017;6(Supplement 1):3–7. doi: 10.1177/1358836X0100600i102. [DOI] [PubMed] [Google Scholar]
  • 3.Wellen K. E., Hotamisligil G. S. Inflammation, stress, and diabetes. The Journal of Clinical Investigation. 2005;115(5):1111–1119. doi: 10.1172/JCI25102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Hotamisligil G. S. Inflammation and metabolic disorders. Nature. 2006;444(7121):860–867. doi: 10.1038/nature05485. [DOI] [PubMed] [Google Scholar]
  • 5.Bernal-Mizrachi C., Gates A. C., Weng S., et al. Vascular respiratory uncoupling increases blood pressure and atherosclerosis. Nature. 2005;435(7041):502–506. doi: 10.1038/nature03527. [DOI] [PubMed] [Google Scholar]
  • 6.Riera-Borrull M., Rodríguez-Gallego E., Hernández-Aguilera A., et al. Exploring the process of energy generation in pathophysiology by targeted metabolomics: performance of a simple and quantitative method. Journal of the American Society for Mass Spectrometry. 2016;27(1):168–177. doi: 10.1007/s13361-015-1262-3. [DOI] [PubMed] [Google Scholar]
  • 7.Bénit P., Letouzé E., Rak M., et al. Unsuspected task for an old team: succinate, fumarate and other Krebs cycle acids in metabolic remodeling. Biochimica et Biophysica Acta (BBA) - Bioenergetics. 2014;1837(8):1330–1337. doi: 10.1016/j.bbabio.2014.03.013. [DOI] [PubMed] [Google Scholar]
  • 8.Calderón-Santiago M., Priego-Capote F., Galache-Osuna J. G., Luque de Castro M. D. Method based on GC-MS to study the influence of tricarboxylic acid cycle metabolites on cardiovascular risk factors. Journal of Pharmaceutical and Biomedical Analysis. 2013;74:178–185. doi: 10.1016/j.jpba.2012.10.029. [DOI] [PubMed] [Google Scholar]
  • 9.Makris K. I., Nella A. A., Zhu Z., et al. Mitochondriopathy of peripheral arterial disease. Vascular. 2007;15(6):336–343. doi: 10.2310/6670.2007.00054. [DOI] [PubMed] [Google Scholar]
  • 10.Kemp G. J. Mitochondrial dysfunction in chronic ischemia and peripheral vascular disease. Mitochondrion. 2004;4(5-6):629–640. doi: 10.1016/j.mito.2004.07.017. [DOI] [PubMed] [Google Scholar]
  • 11.Hazarika S., Annex B. H. Biomarkers and genetics in peripheral artery disease. Clinical Chemistry. 2016;63(1):236–244. doi: 10.1373/clinchem.2016.263798. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Kim E. S. H., Wattanakit K., Gornik H. L. Using the ankle-brachial index to diagnose peripheral artery disease and assess cardiovascular risk. Cleveland Clinic Journal of Medicine. 2012;79(9):651–661. doi: 10.3949/ccjm.79a.11154. [DOI] [PubMed] [Google Scholar]
  • 13.Upadhyay R. K. Emerging risk biomarkers in cardiovascular diseases and disorders. Journal of Lipids. 2015;2015:50. doi: 10.1155/2015/971453.971453 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Ussher J. R., Elmariah S., Gerszten R. E., Dyck J. R. B. The emerging role of metabolomics in the diagnosis and prognosis of cardiovascular disease. Journal of the American College of Cardiology. 2016;68(25):2850–2870. doi: 10.1016/j.jacc.2016.09.972. [DOI] [PubMed] [Google Scholar]
  • 15.Laaksonen R. Identifying new risk markers and potential targets for coronary artery disease: the value of the lipidome and metabolome. Cardiovascular Drugs and Therapy. 2016;30(1):19–32. doi: 10.1007/s10557-016-6651-8. [DOI] [PubMed] [Google Scholar]
  • 16.Lewis G. D., Asnani A., Gerszten R. E. Application of metabolomics to cardiovascular biomarker and pathway discovery. Journal of the American College of Cardiology. 2008;52(2):117–123. doi: 10.1016/j.jacc.2008.03.043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.German J. B., Hammock B. D., Watkins S. M. Metabolomics: building on a century of biochemistry to guide human health. Metabolomics. 2005;1(1):3–9. doi: 10.1007/s11306-005-1102-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Matthews H., Hanison J., Nirmalan N. “Omics”-informed drug and biomarker discovery: opportunities, challenges and future perspectives. Proteomes. 2016;4(3) doi: 10.3390/proteomes4030028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Ward-Caviness C. K., Xu T., Aspelund T., et al. Improvement of myocardial infarction risk prediction via inflammation-associated metabolite biomarkers. Heart. 2017;103(16):1278–1285. doi: 10.1136/heartjnl-2016-310789. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Ali S. E., Farag M. A., Holvoet P., Hanafi R. S., Gad M. Z. A comparative metabolomics approach reveals early biomarkers for metabolic response to acute myocardial infarction. Scientific Reports. 2016;6(1, article 36359) doi: 10.1038/srep36359. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Rull A., Garcia R., Fernandez-Sender L., et al. The role of combined assessment of defense against oxidative stress and inflammation in the evaluation of peripheral arterial disease. Current Molecular Medicine. 2011;11(6):453–464. doi: 10.2174/156652411796268713. [DOI] [PubMed] [Google Scholar]
  • 22.Hernández-Aguilera A., Sepúlveda J., Rodríguez-Gallego E., et al. Immunohistochemical analysis of paraoxonases and chemokines in arteries of patients with peripheral artery disease. International Journal of Molecular Sciences. 2015;16(12):11323–11338. doi: 10.3390/ijms160511323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Camps J., Marsillach J., Joven J. The paraoxonases: role in human diseases and methodological difficulties in measurement. Critical Reviews in Clinical Laboratory Sciences. 2009;46(2):83–106. doi: 10.1080/10408360802610878. [DOI] [PubMed] [Google Scholar]
  • 24.Camps J., Rodríguez-Gallego E., García-Heredia A., et al. Paraoxonases and chemokine (C-C motif) ligand-2 in noncommunicable diseases. Advances in Clinical Chemistry. 2014;63:247–308. doi: 10.1016/B978-0-12-800094-6.00007-8. [DOI] [PubMed] [Google Scholar]
  • 25.Camps J., García-Heredia A., Hernández-Aguilera A., Joven J. Paraoxonases, mitochondrial dysfunction and non-communicable diseases. Chemico-Biological Interactions. 2016;259(Part B):382–387. doi: 10.1016/j.cbi.2016.04.005. [DOI] [PubMed] [Google Scholar]
  • 26.Hernández-Aguilera A., Rull A., Rodríguez-Gallego E., et al. Mitochondrial dysfunction: a basic mechanism in inflammation-related non-communicable diseases and therapeutic opportunities. Mediators of Inflammation. 2013;2013:13. doi: 10.1155/2013/135698.135698 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Novo S. Classification, epidemiology, risk factors, and natural history of peripheral arterial disease. Diabetes, Obesity & Metabolism. 2002;4(Supplement 2):S1–S6. doi: 10.1046/j.1463-1326.2002.0040s20s1.x. [DOI] [PubMed] [Google Scholar]
  • 28.Joven J., Espinel E., Rull A., et al. Serum fatty acid synthase concentration is increased in patients with hepatitis viral infection and may assist in the prediction of liver steatosis. Journal of Clinical Virology. 2011;51(3):199–201. doi: 10.1016/j.jcv.2011.04.003. [DOI] [PubMed] [Google Scholar]
  • 29.Marsillach J., Aragonès G., Beltrán R., et al. The measurement of the lactonase activity of paraoxonase-1 in the clinical evaluation of patients with chronic liver impairment. Clinical Biochemistry. 2009;42(1-2):91–98. doi: 10.1016/j.clinbiochem.2008.09.120. [DOI] [PubMed] [Google Scholar]
  • 30.Martinelli N., García-Heredia A., Roca H., et al. Paraoxonase-1 status in patients with hereditary hemochromatosis. Journal of Lipid Research. 2013;54(5):1484–1492. doi: 10.1194/jlr.P028977. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Grootveld M. Introduction to the applications of chemometric techniques in ‘omics’ research: common pitfalls, misconceptions and ‘rights and wrongs’. In: Grootveld M., editor. Metabolic Profiling: Disease and Xenobiotics. Cambridge: Royal Society of Chemistry; 2014. [DOI] [Google Scholar]
  • 32.Zweig M. H., Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry. 1993;39(4):561–577. [PubMed] [Google Scholar]
  • 33.Ku H. J., Ahn Y., Lee J. H., Park K. M., Park J.-W. IDH2 deficiency promotes mitochondrial dysfunction and cardiac hypertrophy in mice. Free Radical Biology & Medicine. 2015;80:84–92. doi: 10.1016/j.freeradbiomed.2014.12.018. [DOI] [PubMed] [Google Scholar]
  • 34.Fu X., Huang X., Li P., Chen W., Xia M. 7-Ketocholesterol inhibits isocitrate dehydrogenase 2 expression and impairs endothelial function via microRNA-144. Free Radical Biology & Medicine. 2014;71:1–15. doi: 10.1016/j.freeradbiomed.2014.03.010. [DOI] [PubMed] [Google Scholar]
  • 35.Maalouf M., Sullivan P. G., Davis L., Kim D. Y., Rho J. M. Ketones inhibit mitochondrial production of reactive oxygen species production following glutamate excitotoxicity by increasing NADH oxidation. Neuroscience. 2007;145(1):256–264. doi: 10.1016/j.neuroscience.2006.11.065. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Yin J., Han P., Tang Z., Liu Q., Shi J. Sirtuin 3 mediates neuroprotection of ketones against ischemic stroke. Journal of Cerebral Blood Flow and Metabolism. 2015;35(11):1783–1789. doi: 10.1038/jcbfm.2015.123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Marosi K., Kim S. W., Moehl K., et al. 3-Hydroxybutyrate regulates energy metabolism and induces BDNF expression in cerebral cortical neurons. Journal of Neurochemistry. 2016;139(5):769–781. doi: 10.1111/jnc.13868. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Kanikarla-Marie P., Jain S. K. Hyperketonemia (acetoacetate) upregulates NADPH oxidase 4 and elevates oxidative stress, ICAM-1, and monocyte adhesivity in endothelial cells. Cellular Physiology and Biochemistry. 2015;35(1):364–373. doi: 10.1159/000369702. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Shi X., Li X., Li D., et al. β-Hydroxybutyrate activates the NF-κB signaling pathway to promote the expression of pro-inflammatory factors in calf hepatocytes. Cellular Physiology and Biochemistry. 2014;33(4):920–932. doi: 10.1159/000358664. [DOI] [PubMed] [Google Scholar]
  • 40.Lampropoulou V., Sergushichev A., Bambouskova M., et al. Itaconate links inhibition of succinate dehydrogenase with macrophage metabolic remodeling and regulation of inflammation. Cell Metabolism. 2016;24(1):158–166. doi: 10.1016/j.cmet.2016.06.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Parfenova H., Basuroy S., Bhattacharya S., et al. Glutamate induces oxidative stress and apoptosis in cerebral vascular endothelial cells: contributions of HO-1 and HO-2 to cytoprotection. American Journal of Physiology. Cell Physiology. 2006;290(5):C1399–C1410. doi: 10.1152/ajpcell.00386.2005. [DOI] [PubMed] [Google Scholar]
  • 42.Paim B. A., Velho J. A., Castilho R. F., Oliveira H. C. F., Vercesi A. E. Oxidative stress in hypercholesterolemic LDL (low-density lipoprotein) receptor knockout mice is associated with low content of mitochondrial NADP-linked substrates and is partially reversed by citrate replacement. Free Radical Biology & Medicine. 2008;44(3):444–451. doi: 10.1016/j.freeradbiomed.2007.10.005. [DOI] [PubMed] [Google Scholar]
  • 43.Maralani M. N., Movahedian A., Javanmard ShH Antioxidant and cytoprotective effects of L-serine on human endothelial cells. Research in Pharmaceutical Sciences. 2012;7(4):209–215. [PMC free article] [PubMed] [Google Scholar]
  • 44.Wang T. J., Larson M. G., Vasan R. S., et al. Metabolite profiles and the risk of developing diabetes. Nature Medicine. 2011;17(4):448–453. doi: 10.1038/nm.2307. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Ho J. E., Larson M. G., Ghorbani A., et al. Metabolomic profiles of body mass index in the Framingham heart study reveal distinct cardiometabolic phenotypes. PLoS One. 2016;11(2, article e0148361) doi: 10.1371/journal.pone.0148361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Yang P., Hu W., Fu Z., et al. The positive association of branched-chain amino acids and metabolic dyslipidemia in Chinese Han population. Lipids in Health and Disease. 2016;15(1):p. 120. doi: 10.1186/s12944-016-0291-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Fujii T., Yura S., Tatsumi K., et al. Branched-chain amino acid supplemented diet during maternal food restriction prevents developmental hypertension in adult rat offspring. Journal of Developmental Origins of Health and Disease. 2011;2(3):176–183. doi: 10.1017/S204017441100002X. [DOI] [PubMed] [Google Scholar]
  • 48.Cheng S., Larson M. G., McCabe E. L., et al. Distinct metabolomic signatures are associated with longevity in humans. Nature Communications. 2015;6(1):p. 6791. doi: 10.1038/ncomms7791. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Danbolt N. C. Glutamate uptake. Progress in Neurobiology. 2001;65(1):1–105. doi: 10.1016/S0301-0082(00)00067-8. [DOI] [PubMed] [Google Scholar]
  • 50.Dinkelborg L. M., Kinne R. K., Grieshaber M. K. Transport and metabolism of L-glutamate during oxygenation, anoxia, and reoxygenation of rat cardiac myocytes. The American Journal of Physiology. 1996;270, 5, Part 2:H1825–H1832. doi: 10.1152/ajpheart.1996.270.5.H1825. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Articles from Mediators of Inflammation are provided here courtesy of Wiley

RESOURCES