Abstract The metabolic changes that accompany changes in Cardiopulmonary testing (CPET) and heart failure biomarkers (HFbio) are not well known. We undertook metabolomic and lipidomic phenotyping of a cohort of heart failure (HF) patients and utilized Multiple Regression Analysis (MRA) to identify associations to CPET and HFBio test performance (peak oxygen consumption (Peak VO[2]), oxygen uptake efficiency slope (OUES), exercise duration, and minute ventilation-carbon dioxide production slope (VE/VCO[2] slope), as well as the established HF biomarkers of inflammation C-reactive protein (CRP), beta-galactoside-binding protein (galectin-3), and N-terminal prohormone of brain natriuretic peptide (NT-proBNP)). A cohort of 49 patients with a left ventricular ejection fraction < 50%, predominantly males African American, presenting a high frequency of diabetes, hyperlipidemia, and hypertension were used in the study. MRA revealed that metabolic models for VE/VCO[2] and Peak VO[2] were the most fitted models, and the highest predictors’ coefficients were from Acylcarnitine C18:2, palmitic acid, citric acid, asparagine, and 3-hydroxybutiric acid. Metabolic Pathway Analysis (MetPA) used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP). Metabolite Set Enrichment Analysis (MSEA) found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance. The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio. Introduction The prevalence of heart failure (HF) has increased over time in the aging population. In people older than 20, the incidence of HF has increased from 5.7 million Americans between 2009 and 2012 to 6.5 million Americans in 2011. Despite aggressive measures to improve HF management, the five-year mortality rate of HF patients remains approximately 40%—comparable to many forms of cancer[[46]1,[47]2]. Investigations into diagnosis of HF has revealed promising cardiopulmonary tests and biomarkers that allow better disease management following diagnosis[[48]3,[49]4]. Combining patient’s metabolic profiles from HF compromised organs and tissues with HF tests has been demonstrated to provide physicians with an efficient source of clinical information used to both manage and diagnose patients[[50]5]. However, the complex association of these HF tests with changes in the peripheral metabolism of compromised individuals is still under investigation and has failed to reveal the value of circulating metabolites as HF biomarkers[[51]6]. Impaired cardiorespiratory fitness measured during cardiopulmonary exercise testing (CPET) is a hallmark manifestation of heart failure[[52]7] and exercise training reduces all-cause mortality in patients with heart failure and reduced left ventricular ejection fraction (HFrEF)[[53]8]. As such, cardiopulmonary exercise testing (CPET) is an evidence-supported assessment technique routinely used in the functional diagnosis of HF, prognostic evaluation of patients with chronic Heart Failure (CHF), and is also clinically relevant to supplement other clinical data in patients’ screening for heart transplantation[[54]9,[55]10]. Similarly, HF biomarkers (HFBio) such as NT-proBNP, Galectin-3, and C-reactive protein (CRP) have shown promising results as predictors of mortality[[56]9,[57]11]. However, a potential disadvantage of the CPET and HFBio use is that the evaluation of their response alone is insufficient to indicate risk of heart disease nor is it enough to diagnose a heart problem[[58]12,[59]13]. It is now known that cardiac and peripheral metabolic abnormalities may contribute to the pathogenesis of heart failure[[60]14]. Studies of the metabolic profile of HF patients have indicated a rich metabolic modulation that can be identified and used as putative biomarkers[[61]15–[62]17]. However, little is known about the relationship of cardiorespiratory fitness and global metabolic profile in HFrEF. Efforts to correlate clinical and metabolic data are still necessary to fully integrate metabolomics as a translational medicine apparatus[[63]18]. We hypothesized that deep metabolomic and lipidomic phenotyping would reveal novel metabolic and lipid mediators of cardiorespiratory fitness in patients with HFrEF. To test this hypothesis, we employed a Multiple Regression Analysis (MRA) study on the association of both CPET and HFBio with respects to the plasma lipidome and the metabolome of HF patients demonstrating that the metabolic modulation in HF patients depends on the tests’ performance. Although MRA does not imply causality for the HF performance, this analysis intends to reveal the metabolic changes underlying the complex HF pathology to maximize the relevance of the HF tests and its potential for HF outcome prognosis. Materials and methods Patients This research was approved by the Virginia Commonwealth University (VCU) Institutional Review Board (VCU IRB number HM15339). Written informed consent was obtained from all participants. A post-hoc analysis was performed on patients who were enrolled in the REDHART study[[64]19], which included patients with a left ventricular ejection fraction < 50% and high-sensitivity C-reactive protein (hsCRP) ≥2 mg/L and who were recently discharged after a hospitalization for HF. The present analysis includes plasma samples collected from 49 patients at baseline prior to randomization, since we were interested in the baseline analysis only without the cofounding randomization for drugs and place groups of the original study. Because the study was a post-hoc analysis, there were only enough plasma samples from 49 patients out of 52 in the REDHART study. Also, there were not health matched control group in the REDHART study, the reason for the absence of comparison with health subject in our analysis. The design and results of REDHART have been reported previously[[65]19]. All patients underwent maximal aerobic exercise testing using a metabolic cart and a treadmill according to a conservative ramping treadmill protocol. Patients exercised to exhaustion (peak respiratory exchange ratio ≥1.00 and preferably ≥1.10) and those who were unable to complete the cardiopulmonary exercise test were excluded. The core CPET lab at University of Illinois at Chicago conducted all analyses of cardiopulmonary exercise test variables (peak oxygen consumption, oxygen uptake efficiency slope, exercise duration, minute ventilation-carbon dioxide production [VE/VCO[2]] as described previously[[66]19]. Lipidomic and metabolomic data acquisition Lipid and metabolite extraction for LC-MS/MS analyses Blood plasma lipids extraction was carried out using a biphasic solvent system of cold methanol, methyl tert-butyl ether (MTBE), and water with some modifications[[67]20]. In detail, 225 μL of cold methanol containing a mixture of odd chain and deuterated lipid internal standards [lysoPE(17:1), lysoPC(17:0), PC(12:0/13:0), PE(17:0/17:0), PG(17:0/17:0), sphingosine (d17:1), d7 cholesterol, SM(17:0), C17 ceramide, d3 palmitic acid, MG(17:0/0:0/0:0), DG(18:1/2:0/0:0), DG(12:0/12:0/0:0), and d5 TG(17:0/17:1/17:0)] was added to a 20 μL blood plasma aliquot in a 1.5 mL polypropylene tube, and then vortexed. Next, 750 μL of cold MTBE was added, followed by vortexing and shaking with an orbital mixer. Phase separation was induced by adding 188 μL of MS-grade water. Upon vortexing (20s) the sample was centrifuged at 12,300 rpm for 2 min. The upper organic phase was collected in two 300 μL aliquots and evaporated with a vapor trap. Dried extracts were resuspended using 110 μL of a methanol/toluene (9:1, v/v) mixture containing CUDA (50 ng/ml; internal standard for quality control of injection) with support of vortexing (10 s), and centrifuged at 800 rpm for 5 min, followed by transferring 100 uL of the supernatant into autosampler vial with an insert. An aliquot of 125 μL of the lower polar layer was evaporated to dryness in a SpeedVac, resuspended in acetonitrile, and used for metabolite analysis via HILIC LC-MS/MS method. Metabolomics: GC-MS metabolite extraction 30μl of plasma sample was added to a 1.0 mL of pre-chilled (-20°C) extraction solution composed of acetonitrile, isopropanol and water (3: 3: 2, v/v/v). Sample were vortexed and shaken for 5 min at 4°C using the Orbital Mixing Chilling/Heating Plate. Next, the mixture was centrifuged for 2min at 14,000 rcf. 450μL of the supernatant was dried with cold trap concentrator. The dried aliquot was then reconstituted with 450 μL acetonitrile:water (50:50) solution and centrifuged for 2 min at 14,000 rcf. The supernatant was transferred to a polypropylene tube and subjected to drying in a cold trap. The process of derivatization began with addition of 10 μL of 40 mg/mL Methoxyamine hydrochloride solution to each dried sample and standard. Samples were shaken at maximum speed at 30 °C for 1.5 hours. Then, 91 μL of MSTFA + FAME mixture was added to each sample and standard and capped immediately. After shaking at maximum speed at 37 °C, the content was transferred to glass vials with inserts and cap immediately. Lipids: LC-MS/MS conditions Untargeted lipid analysis was obtained with Sciex TripleTOF 6600 coupled to Agilent 1290 LC. Lipids were separated on an Acquity UPLC CSH C18 column (100 × 2.1 mm; 1.7 μm) (Waters, Milford, MA, USA). The column was maintained at 65 °C and the flow-rate of 0.6 mL/min. The mobile phases consisted of (A) 60:40 (v/v) acetonitrile:water with 10 mM ammonium acetate and (B) 90:10 (v/v) isopropanol:acetonitrile with 10 mM ammonium acetate. The separation was conducted following a stepwise gradient: 0–2 min 15–30% (B), 2–2.5 min 48% (B), 2.5–11 min 82% (B), 11–11.5 min 99% (B), 11.5–12 min 99% (B), 12–12.1 15% (B), 12–14 min 15% (B). Negative and positive electrospray ionization (ESI) modes were applied with nitrogen serving as the desolvation gas and the collision gas. The parameters for detection of lipids were as follows: Curtain Gas: 35; CAD: High; Ion Spray Voltage: 4500 V; Source Temperature: 350°C; Gas 1: 60; Gas 2: 60; Declustering Potential: +/- 80V, and collision energies +/- 10. Metabolites HILIC: LC-MS/MS conditions Detection of water soluble plasma metabolites was carried out on Sciex TripleTOF 6600 coupled to Agilent 1290 LC. Analytes were separated on an Acquity UPLC BEH Amide Column, 130Å, 1.7 μm, 2.1 mm X 150 mm (Waters, Milford, MA, USA). The column was maintained at 45 °C and the flow-rate of 0.4 mL/min. The mobile phases consisted of (A) water with 10 mM ammonium formate, 0.125% formic acid, and (B) acetonitrile:water 90:10 (v/v) with 10 mM ammonium formate, 0.125% formic acid. The analytes separation was conducted following a stepwise gradient: 0–2 min 100% (B), 2–7.7 min 70% (B), 7.7–9.5 min 40% (B), 9.5–10.25 min 30% (B), 10.25–12.75 min 100% (B), 12.75–16.75 100% (B). A sample volume of 1 μL and 3 μL were injected for positive and negative mode, respectively. Negative and positive electrospray ionization (ESI) modes were applied with nitrogen serving as the desolvation gas and the collision gas. The parameters for detection of lipids were as follows: Curtain Gas: 35; CAD: High; Ion Spray Voltage: 4500 V; Source Temperature: 300°C; Gas 1: 60; Gas 2: 60; Declustering Potential: +/- 80V, and collision energies +/- 10. Metabolites: GC-MS conditions A Leco Pegasus IV time of flight mass spectrometer coupled with Agilent 6890 GC equipped with a Gerstel automatic liner exchange system (ALEX) that included a multipurpose sample (MPS2) dual rail, and a Gerstel CIS cold injection system (Gerstel, Muehlheim, Germany) was used to complement HILIC metabolite analysis. The transfer line was maintained at 280 °C. Chromatography separation was achieved on a 30 m long, 0.25 mm i.d. Rtx-5Sil MS column (0.25 μm 95% dimethyl 5% diphenyl polysiloxane film) with the addition of a 10 m integrated guard column (Restek, Bellefonte PA) with helium (99.999%; Airgas, Radnor, PA, U.S.A.) at a constant flow of 1 mL/min. The oven temperature was held constant at 50°C for 1 min and then ramped at 20°C/min to 330°C at which it is held constant for 5 min. The GC temperature program was set as follows: 50°C to 275°C final temperature at a rate of 12 °C/s and hold for 3 minutes. The injection volume was 1 μL in splitless mode at 250 °C. Electron impact ionization at 70V was employed with an ion source temperature of 250°C. The scan mass ranged from 85 to 500 Da with acquisition rate of 17 spectra/second. Statistical analysis The statistical workflow used to find predictors of the CPET and HFBio tests, and reveal the metabolic pathways associated with HF, is depicted in [68]Fig 1. Lipidomic and metabolomic data were presented as peak heights normalized by mTIC, a form of sample normalization[[69]21]. Using mTIC allows for the merger of the two databases. Outliers were eliminated using the Interquartile range (IQR) from a box-and-whisker plot with Turkey’s method detecting outliers as any value for the determined test outside of 1.5 times the IQR[[70]22]. Metabolic data were filtered to exclude detected prescribed drugs, and from the lipidomic data only phospholipids, with detected fatty acids composition, were used in the analysis. The final 316 lipids and 167 metabolites were used in the analysis. Fig 1. Statistical analysis workflow outlining the steps taken to find predictors of HF test performance using metabolomic and lipidomic data. [71]Fig 1 [72]Open in a new tab Stepwise MRA was validated with bootstrap 95% confidence interval of the main regression estimates. Search of published threshold references