Gut Microbiota-Generated Phenylacetylglutamine and Heart Failure
Abstract
Background:
The gut microbiota-dependent metabolite phenylacetylgutamine (PAGln) is both associated with atherothrombotic heart disease in humans, and mechanistically linked to cardiovascular disease pathogenesis in animal models via modulation of adrenergic receptor signaling.
Methods:
Here we examined both clinical and mechanistic relationships between PAGln and heart failure (HF). First, we examined associations among plasma levels of PAGln and HF, left ventricular ejection fraction, and N-terminal pro-B-type natriuretic peptide in 2 independent clinical cohorts of subjects undergoing coronary angiography in tertiary referral centers (an initial discovery US Cohort, n=3256; and a validation European Cohort, n=829). Then, the impact of PAGln on cardiovascular phenotypes relevant to HF in cultured cardiomyoblasts, and in vivo were also examined.
Results:
Circulating PAGln levels were dose-dependently associated with HF presence and indices of severity (reduced ventricular ejection fraction, elevated N-terminal pro-B-type natriuretic peptide) independent of traditional risk factors and renal function in both cohorts. Beyond these clinical associations, mechanistic studies showed both PAGln and its murine counterpart, phenylacetylglycine, directly fostered HF-relevant phenotypes, including decreased cardiomyocyte sarcomere contraction, and B-type natriuretic peptide gene expression in both cultured cardiomyoblasts and murine atrial tissue.
Conclusions:
The present study reveals the gut microbial metabolite PAGln is clinically and mechanistically linked to HF presence and severity. Modulating the gut microbiome, in general, and PAGln production, in particular, may represent a potential therapeutic target for modulating HF.
Registration:
URL: https://clinicaltrials.gov/; Unique identifier: NCT00590200 and URL: https://drks.de/drks_web/; Unique identifier: DRKS00020915.
What is New?
•
Circulating levels of phenylacetylglutamine (PAGln), a small molecule produced by gut microbes from dietary proteins (phenylalanine specifically), are associated with heart failure (HF), and heart failure associated indices of severity, in clinical cohorts from both the United States and Europe.
•
The association between PAGln levels and heart failure is independent of cardiovascular risk factors, renal function, ejection fraction, and NT-proBNP.
•
PAGln decreases cardiomyocyte contraction in vitro in the presence of sympathetic stimulation.
•
PAGln increases expression of B-type natriuretic peptide, a marker of heart failure severity, in vitro and in vivo.
What are the Clinical Implications?
•
The present studies further establish the growing link between diet, gut microbiome, and HF risk.
•
Plasma levels of PAGln dose-dependently track both with HF risks and multiple indices of heart failure severity, independent of CAD status, and across the spectrum of HF phenotypes, in 2 independent clinical cohorts, regardless of adjustments for multiple comorbidities.
•
Measurement of PAGln levels may provide clinical prognostic value for predicting HF risk independent of traditional risk factors, renal function, and NT-proBNP levels.
•
The association between PAGln and HF was observed among subjects showing normal left ventricular systolic function or normal renal function.
See Editorial by Awoyemi et al
The gut microbiome is known to contribute to a number of human diseases, including cardiovascular disease (CVD), and represents an as yet underappreciated endocrine organ.1–7 The mechanisms by which the gut microbial community contributes to disease processes remain largely unknown. A critical step in addressing this knowledge gap is understanding associations between gut microbiota-generated metabolites and clinical phenotypes, as well as the activity of gut microbiota-derived metabolites, which can function as noncanonical hormones. Moreover, with a better understanding of the molecular participants involved in gut microbiome-linked diseases, we may be able to design effective therapeutic strategies to combat modern lifestyle-related ailments like obesity, diabetes, and CVD.
Using untargeted metabolomics, our laboratory recently identified phenylacetylglutamine (PAGln), a microbiota-derived metaorganismal metabolite associated with CVD and major adverse cardiovascular events (myocardial infarction, stroke, or death).8 Both gain- and loss-of-function studies utilizing a combination of genetic and pharmacological tools showed that PAGln signals within host cells via G-protein coupled receptors, including adrenergic receptors.8 It is well established that sympathetic tone is elevated in heart failure (HF), and sustained activation of the sympathetic nervous system is thought to contribute to the poor prognosis associated with HF.9 Moreover, incorporation of beta blockers into HF treatment regimens is still recommended under the current clinical guidelines.10,11 Given the direct connection among PAGln, CVD, and adrenergic signaling, we sought to examine the clinical association between PAGln and HF, and to investigate whether PAGln may foster or modulate HF relevant phenotypes. Here we show that circulating PAGln levels track with clinical HF risk and disease severity in 2 independent cohorts (US and European). We expand on these clinical associations to show PAGln, as well as its murine counterpart PAGly, directly contributes to physiological manifestations of HF-relevant phenotypes, including B-type natriuretic peptide (BNP) induction, and indirect myocardial suppressant effects (indirect because only observed in presence of a sympathetic agonist). Taken together, our findings support the idea that the gut microbiota-generated metabolite PAGln is a candidate marker of and a contributor to HF-associated phenotypes.
Methods
Data Availability
There are restrictions to the availability of some of the clinical data generated in the present study because we do not have permission in our informed consent from research subjects to share data other than in summary format outside our institution. Where permissible, the datasets generated and/or analyzed during the present studies, or summary data, and all methods used to conduct the research, are available from the corresponding author, upon reasonable request.
Study Approval
All clinical study protocols and informed consent for human subjects complied with the Declaration of Helsinki and received ethical approval by the Cleveland Clinic Institutional Review Board, or by the ethics committee of Charité-Universitätsmedizin Berlin. Written informed consent was obtained from all subjects. All animal model studies were approved by the Institutional Animal Care and Use Committee at the Cleveland Clinic.
Genebank: US Cohort
Plasma samples and associated clinical data were collected as part of studies at a tertiary care referral center. All subjects gave written informed consent, and the Institutional Review Board of the Cleveland Clinic approved all study protocols. Metabolomics studies were performed on sequential samples from a large and well-characterized longitudinal tissue repository with associated clinical database named GeneBank at the Cleveland Clinic: Molecular Determinants of Coronary Artery Disease. GeneBank is registered under ClinicalTrials.gov Identifier: NCT00590200. It is composed of sequential stable subjects without evidence of acute coronary syndrome (cardiac troponin I <0.03 ng/mL) who underwent elective diagnostic coronary angiography (cardiac catheterization or coronary computed tomography) for evaluation of Coronary Artery Disease (CAD). All subjects had extensive clinical and longitudinal outcome data monitored, including adjudicated outcomes over the ensuing 3 to 5 years after enrollment. History of HF was detected by (a) directly asking patient by research personnel, (b) reviewing medical records for confirmation (all patients were seen by cardiologist at Cleveland Clinic before the left heart catheterization), and (c) International Classification of Diseases codes and adjudication by research personnel.12 NT-proBNP levels were measured in all GeneBank samples by the Preventive Research Lab (PRL), a CAP and CLIA reference laboratory. Measurements for NT-ProBNP were completed using the Elecsys proBNP II STAT assay on the Roche Cobas e601 analyzer. For the present studies, we leveraged access to the previously reported PAGln levels obtained using stable isotope dilution liquid chromatography tandem mass spectrometry (LC/MS/MS) from sequential consenting subjects enrolled for whom PAGln, left ventricular ejection fraction, and NT-proBNP data were available (n=3256).8
LipidCardio: European Cohort
Plasma samples (n=833) were also obtained from subjects enrolled in the LipidCardio at the Charité-Universitätsmedizin Berlin: Role of lipoproteins in cardiovascular disease.13 The study was approved by the local research ethics committee (approval number: EA1/135/16) under an approved protocol registered under German Clinical Trial Register (drks.de); Identifier: DRKS00020915. All participants provided written informed consent. Patients aged 18 years and older undergoing cardiac catheterization at a single large academic center (Department of Cardiology, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin), except those with troponin-positive acute coronary syndromes (ACS), were eligible for inclusion. HF was defined as New York Heart Association class ≥2. NT-proBNP was measured in all samples from subjects in the European Cohort using the Elecsys proBNP II STAT assay on the Roche Cobas e601 analyzer.
LC/MS/MS Analysis of PAGln in Human Plasma Samples and PAGly in Mouse Plasma Samples
Stable-isotope-dilution LC/MS/MS was used for quantification of PAGln in human plasma (European Cohort), and PAGly in mouse plasma, as previously described.8 Briefly, ice cold methanol containing internal standard (D5-phenylacetylglutamine) was added to the plasma samples, followed by vortexing and centrifuging (14 000 rpm; 4 °C for 15 min). The clear supernatant was transferred to glass vials with microinserts and submitted to LC/MS/MS analysis. LC/MS/MS analysis was performed on a chromatographic system consisting of 2 Shimadzu LC-30 AD pumps (Nexera X2), a CTO 20AC oven operating at 30 °C, and a SIL-30 AC-MP autosampler in tandem with a triple quadruple mass spectrometer (8050 series, Shimadzu Scientific Instruments, Inc., Columbia, MD). For chromatographic separation, a Kinetex C18 column (50 mm × 2.1 mm; 2.6 μm; Catalog No. 00B-4462-AN, Phenomenex, Torrance, CA) was used. Solvent A (0.1% acetic acid in water) and B (0.1% acetic acid in acetonitrile) were run using the following gradient: 0.0 min (0% B); 0.0 to 2.0 min (0% B); 2.0 to 5.0 min (0%B→20%B); 5.0 to 6.0 min (20%B→60%B); 6.0 to 7.5 min (60%B→70%B); 7.5 to 8.0 min (70%B→100%B); 8.0 to 9.5 min (100%); 9.5 to 10 min (100%B→0%B); 10.0 to 15.0 min (0% B) with a flow rate of 0.4 mL/min and an injection volume of 1 µL. Electrospray ionization in the positive mode was used with multiple reaction monitoring (MRM) for detection of endogenous and stable isotope labeled internal standard. The following transitions were used: m/z 265.2→130.15 for PAGln, m/z 193.8→76.1 for PAGly and m/z 270.1→130.15 for D5-PAGln. D5-PAGln was used as internal standard for both PAGln and PAGly. The following ion source parameters were applied: nebulizing gas flow, 3 l/min; heating gas flow, 10 l/min; interface temperature, 300 °C; desolvation line temperature, 250 °C; heat block temperature, 400 °C; and drying gas flow, 10 l/min. Limit of detection and limit of quantification for PAGln were 0.010 and 0.033 µM, respectively; limit of detection and limit of quantification for PAGly were 0.021 and 0.073 µM, respectively. Quality control samples were run with each batch of samples, and interbatch variations expressed as coefficient of variation were <10% for all analytes monitored. Data were collected and analyzed by LabSolution 5.91 software (Shimadzu).
In Vitro H9c2 Studies
H9c2(2-1) cells (American Type Culture Collection, ATCC; Manassas, VA) were seeded into 10 cm plates at 60% confluency. After seeding (24 hours), cells were then serum starved for 18 hours. Cells were exposed to 100 µM PAGln, 100 µM PAGly, or an equivalent volume of vehicle control for 4 hours. Following exposure, media was removed, cells washed in ice cold PBS, and 0.5 mL RNAlater was added to the plate. Cells were removed by scrapping and stored at −20 ºC for RNA isolation. Prior to isolation, 1 mL of sterile PBS was added to RNAlater samples to allow for the pelleting of cells. Cells were pelleted at 10 000g for 3 minutes. After supernatant was removed, cells were lysed in 250 µL of TRIzol (Ambion, Inc; Austin, TX) by vortexing. Chloroform (50 µL) was added to the TRIzol homogenate and samples incubated at room temperature for 3 minute. Then, tubes were centrifuged at 10 000g for 15 minutes at 4 ºC for phase separation. The aqueous layer was removed, being careful not to disturb the interface, and mixed with 150 µL isopropyl alcohol. Samples were incubated at room temperature for 10 minutes. Tubes were centrifuged at 12 000g for 10 minutes at 4 ºC for pelleting. After the supernatant was removed, RNA pellets were washed twice with 75% ethanol by vortexing and centrifuged at 7500g for 5 minutes at 4 ºC. Supernatant was removed and pellets were allowed to air-dry for 10 minutes before resuspension in DPEC treated water. Nppb (Natriuretic Peptide Precursor B gene) expression was measured using 400 ng of total RNA using the TaqMan RNA-to-Ct 1-step kit (Applied Biosystems, Waltham, MA) on the Applied Biosystems StepOnePlus Real-Time PCR System. Rn00580641_m1 (FAM) was used for detection of Nppb and Rn01775763_g1 (VIC) was used for detection of Gapdh (glyceraldehyde-3-phosphate dehydrogenase).
Animal Husbandry
C57BL6/J male mice (6- to10-weeks-old) were purchased from Laboratory (Bar Harbor, ME) and housed in the Cleveland Clinic Biological Research Unit under strict 14:10 light:dark cycles, with food and water provided ad libitum. Only male mice were used to mitigate the variable of female reproductive hormones on cardiac function.
In Vivo Cardiac Gene Expression
Animals were IP injected with pH-neutralized PAGln (50 mg/kg; n=16), PAGly (50 mg/kg; n=16) or isotonic vehicle control (n=14). PAGln and PAGly were dissolved in normal saline and neutralized with NaOH; the vehicle control solution consisted of an equivalent amount of NaOH/HCl added to the normal saline. Fifteen minutes after injection, animals were euthanized by Ketamine/Xylazine overdose (300 mg/kg + 30 mg/kg); blood was collected by cardiac puncture of the right ventricle. Right after blood collection, hearts were excised from the animal and stored in RNAlater. At a later time, the entire left atria was isolated from each heart. Atria were lysed in 250 µL of TRIzol by bead beating with a single 5.0-mm zirconium oxide bead. Chloroform (50 µL) was added to the TRIzol homogenate and samples were incubated at room temperature for 3 minutes. Tubes were centrifuged at 10 000g for 15 minutes at 4 ºC for phase separation. The aqueous layer was removed, being careful not to disturb the interface, and mixed with 150 µL isopropyl alcohol. Samples were incubated at room temperature for 10 minutes. Tubes were centrifuged at 12 000g for 10 minutes at 4 ºC for pelleting. After the supernatant was removed, RNA pellets were washed twice with 75% ethanol by vortexing and centrifuged at 10 000g for 8 minutes at 4 ºC. Supernatant was removed, and pellets were allowed to air-dry for 10 minutes before resuspension in DPEC treated water. Nppb expression was measured using 1.5-µL total RNA using the TaqMan RNA-to-Ct 1-step kit (Applied Biosystems; Waltham, MA) on the Applied Biosystems StepOnePlus Real-Time PCR System. Mm00435304_g1 (FAM) was used for detection of Nppb and Mm99999915_g1 (VIC) was used for detection of Gapdh. The investigators were not blinded for these studies.
Mouse Cardiomyocytes Contractility Studies
Adult wild-type mouse cardiomyocytes were isolated, and contractility studies were performed utilizing the IonOptix System (Myopace, Milton, MA) as previously described.14,15 Briefly, myocytes were isolated and plated on glass chamber slides, which were placed on a Leica microscopic stage connected to a field stimulator specifically designed for driving isolated myocytes (MyoPace, IonOptix). Cardiomyocytes were stimulated at 3 Hz and imaged with a variable field-rate camera (MyoCam, IonOptix) using edge-detection and sarcomere length technology. Myocytes were treated with 10 µM of epinephrine and cardiomyocytes contractility, transients were recorded in the presence or absence of 100 µM PAGln or PAGly, and pre-incubated for 15 minutes, before addition of epinephrine. Single sarcomere contraction cycles were recorded with time as contractility transients. Sarcomere length (µm) and sarcomere shortening (µm) were measured considering 5 different scanning windows.
Statistics
Human Studies
The Wilcoxon rank sum test or Student t test for continuous variables and χ2 test for categorical variables were used to examine the difference between the groups. Odds ratio for binary HF and corresponding 95% confidence intervals according to PAGIn quartiles in the US Cohort and the European Cohort were calculated using both univariable (unadjusted) and multivariable (adjusted) logistic regression models. Logistic regression model was adjusted for traditional cardiac risk factors in a multivariable model, including age, sex, smoking status, systolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, high sensitive C-reactive protein, diabetes, and obesity (in the US Cohort). Additional adjustment for estimated glomerular filtration rate (mL/min per 1.73 m2) is calculated on the basis of the Chronic Kidney Disease Epidemiology Collaboration 2021 CKD-EPI Creatinine equation.16 The classification of heart failure (HF), according to left ventricle ejection fraction (LVEF; HF with preserved [HFpEF; LFEF ≥50%], mildly reduced [HFmrEF; 40%<LVEF<50%] and reduced [HFrEF; LVEF ≤40%] ejection fraction), was defined according to the 2022 AHA/ACC/HFSA guideline for the management of HF.17 The presence of CAD was defined by luminal stenosis of at least 50% in any major coronary artery, any history of myocardial infarction, history of percutaneous coronary intervention, and/or known coronary artery bypass graft in the US cohort. In the European Cohort, CAD was defined by a luminal reduction of a major epicardial vessel >50%.
Association between PAGln and clinical measurement (including LVEF or NT-proBNP levels) was statistically assessed by Kruskal-Wallis test and Spearman correlation after normality testing.
Murine and Cell Culture Studies
Murine and cell culture gene expression were assessed by 1-way ANOVA test followed by Tukey multiple comparison post hoc for comparing vehicle control versus PAGly and vehicle control versus PAGln. In the ionotropy experiments, Kruskal-Wallis test was used for multiple comparisons and nonparametric Mann-Whitney test for pairwise comparisons between vehicle control with Epi, PAGly, or PAGln treatments.
All analyses were performed with RStudio-R version 4.1.2. (2021-11-01; Vienna, Austria), Stata 17.0 (Stata Corp, College Station, TX), or GraphPad Prism 9. A P value of <0.05 was considered statistically significant.
Results
PAGln Is Elevated in Patients With HF and Independently Associated With HF Risk
We previously reported that plasma levels of PAGln are associated with CVD and incident major adverse cardiovascular events (myocardial infarction, stroke, or death).8 In this same study, we showed, through a variety of mechanistic investigations, that PAGln signaled via adrenergic receptors. Because altered sympathetic tone is a key contributor to the development of HF,18,19 we sought to assess if systemic PAGln levels show a clinical association with HF risk and severity. For initial analyses, we leveraged access to previously reported PAGln data (US Cohort; see Methods section for details), in order to examine the association between PAGln levels, HF, LVEF, and NT-proBNP. Subject (n=3256) clinical characteristics, demographics, and laboratory values are provided in Table 1, which shows a middle-aged cohort with high prevalence of CVD and related risk factors. Subjects with the adjudicated diagnosis of HF (characterized in Table S1) had higher systemic levels of PAGln compared with nonHF subjects (P<0.0001; Figure 1A). Further, individuals with HF without CAD had higher levels of PAGln than control subjects (no-HF and no-CAD; Figure S1A), and individuals with CAD and HF had higher levels of PAGln than subjects with CAD alone (Figure S1A). Moreover, an increased risk for HF was observed among those with higher circulating levels of PAGln (eg, fourth versus first quartile), even following adjustments for traditional CVD risk factors and indices of kidney function (Figure 1B).
Characteristics | All participants (n=3256) | Participants without HF (n=2544) | Participants with HF (n=712) | P value |
---|---|---|---|---|
Age, mean ± SD, y | 62.8±10.8 | 61.9±10.8 | 66.1±10.5 | <0.001 |
Male sex, n (%) | 2091 (64.2) | 1670 (65.6) | 421 (59.1) | <0.01 |
Current smoking, n (%) | 435 (13.4) | 354 (13.9) | 81 (11.4) | 0.092 |
Systolic blood pressure, mm Hg | 132.0 (119.0–146.0) | 133.0 (120.0–147.0) | 130.0 (116.0–145.0) | <0.001 |
Diastolic blood pressure, mm Hg | 74.0 (67.0–82.0) | 76.0 (68.0–83.0) | 71.0 (63.0–80.0) | <0.001 |
BMI, kg/m2 | 28.7 (25.6–32.5) | 28.7 (25.7–32.4) | 28.1 (25.1–32.9) | 0.20 |
Diabetes, n (%) | 1031 (31.7) | 744 (29.2) | 287 (40.3) | <0.001 |
CAD, n (%) | 2494 (76.7) | 1909 (75.1) | 585 (82.2) | <0.001 |
HDL, mg/dL | 34.1 (28.2–41.1) | 34.6 (28.7–41.4) | 31.7 (26.0–39.7) | <0.001 |
LDL, mg/dL | 96.0 (78.0–118.0) | 98.0 (80.0–119.0) | 91.0 (72.8–112.0) | <0.001 |
TG, mg/dL | 119.0 (86.0–171.0) | 119.0 (85.0–174.0) | 118.5 (87.0–165.0) | 0.49 |
hs-CRP, mg/L | 2.47 (1.08–6.03) | 2.22 (0.97–5.36) | 3.90 (1.57–9.01) | <0.001 |
LVEF, % | 55.0 (45.0–60.0) | 55.0 (50.0–65.0) | 40.0 (25.0–55.0) | <0.001 |
NT-proBNP, pg/mL | 239.3 (88.3–742.2) | 175.2 (71.8–453.5) | 895.0 (348.8–2040.2) | <0.001 |
eGFR, mL/min per 1.73 m² | 91.2 (76.4–100.2) | 93.1 (80.6–101.3) | 79.8 (61.5–94.3) | <0.001 |
The cohort is composed of sequential stable subjects without evidence of acute coronary syndrome (cardiac troponin I <0.03 ng/mL) who underwent elective diagnostic coronary angiography (cardiac catheterization or coronary computed tomography) for evaluation of coronary artery disease (CAD).
Continuous data are presented as median (interquartile range or 25th percentile to 75th percentile), and categorical variables are presented as %.
The Wilcoxon rank sum test or Welch 2-sample t test for continuous variables and the χ2 test for categorical variables were used to determine significant difference between groups.
Estimated glomerular filtration rate (eGFR; mL/min per 1.73 m2) is calculated on the basis of the Chronic Kidney Disease Epidemiology Collaboration 2021 CKD-EPI Creatinine equation.16 BMI indicates body mass index; eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; hs-CRP, high-sensitivity C-reactive protein; LDL, low-density lipoprotein; LVEF, left ventricular ejection fraction; NT-proBNP, N-terminal pro-B-type natriuretic peptide; and TG, triglyceride.

We next sought to validate the observed association between circulating PAGln levels and HF risk using an independent cohort. For these analyses, we measured serum PAGln levels from subjects enrolled in a similar registry of sequential cardiology patients undergoing coronary angiography at a tertiary referral center in Europe (European Cohort; for details, see Methods above). Subject clinical characteristics, demographics, and laboratory values are provided in Table 2, which shows an elderly cohort with high CVD risk factor prevalence. As observed in the US Cohort, subjects with HF in the European Cohort (characterized in Table S1) had significantly higher serum levels of PAGln compared with those without HF (Figure 1C). Further, individuals with HF without coronary artery disease (CAD) had higher levels of PAGln than control subjects (no-HF and no-CAD; Figure S1B), and individuals with CAD and HF had higher levels of PAGln than subjects with CAD alone (Figure S1B). Moreover, circulating PAGln levels were again associated with increased risk of HF (fourth versus first quartile), even after multivariable logistic regression adjustments for both traditional CVD risk factors and measures of kidney function (Figure 1D). The association between elevated levels of PAGln and HF risk observed within both the US and European Cohorts also appears to hold true when individuals (combined cohort analysis) are categorized by LVEF status (ie, HFpEF, LVEF ≥50; HF with mildly reduced ejection fraction [HFmEF], 40<LVEF<50; or HF with reduced ejection fraction [HFrEF], LVEF ≤40; Figure 2).
Characteristics | All participants (n=829) | Participants without HF (n=276) | Participants with HF (n=553) | P value |
---|---|---|---|---|
Age, mean ± SD, y | 72.8±10.9 | 66.2±10.5 | 76.1±9.5 | <0.001 |
Male sex, n (%) | 582 (70.2) | 170 (61.6) | 412 (74.3) | <0.001 |
Smoking, n (%) | 140 (16.9) | 71 (25.7) | 69 (12.5) | <0.001 |
Hypertension, n (%) | 667 (80.4) | 207 (75.0) | 460 (83.2) | <0.01 |
Diabetes, n (%) | 231 (27.9) | 57 (20.7) | 174 (31.5) | <0.01 |
CAD, n (%) | 573 (69.1) | 113 (40.9) | 460 (83.6) | <0.001 |
HDL, mg/dL | 48.0 (39.0–60.0) | 51.0 (40.0–63.0) | 47.0 (39.0–58.0) | 0.012 |
LDL, mg/dL | 92.0 (69.0–122.0) | 104.0 (77.0–134.0) | 87.0 (66.0–114.0) | <0.001 |
TG, mg/dL | 118.0 (89.0–167.0) | 120.0 (91.0–166.0) | 117.0 (89.0–167.0) | 0.77 |
hs-CRP, mg/L | 1.9 (0.8–5.1) | 1.6 (0.8–3.75) | 2.1 (0.8–5.6) | 0.059 |
LVEF, % | 60.0 (52.0–66.0) | 65.0 (62.0–70.3) | 56.0 (48.0–63.0) | <0.001 |
NT-proBNP, pg/mL | 313.0 (108.0–1024.0) | 126.0 (57.0–358.5) | 501.0 (173.0–1421.0) | <0.001 |
eGFR, mL/min per 1.73 m² | 74.0 (60.3–90.7) | 84.7 (67.0–95.8) | 69.0 (56.8–85.6) | <0.001 |
The cohort is composed of sequential stable subjects without evidence of acute coronary syndrome (cardiac troponin I <0.03 ng/mL) who underwent elective diagnostic coronary angiography (cardiac catheterization or coronary computed tomography) for evaluation of coronary artery disease (CAD).
Continuous data are presented as median (interquartile range or 25th percentile to 75th percentile), and categorical variables are presented as %.
The Wilcoxon rank sum test or Welch 2-sample t test for continuous variables and the χ2 test for categorical variables were used to determine significant difference between groups.
eGFR (mL/min per 1.73 m2) is calculated on the basis of the Chronic Kidney Disease Epidemiology Collaboration 2021 CKD-EPI Creatinine equation.
16 eGFR indicates estimated glomerular filtration rate; HDL, high-density lipoprotein; hs-CRP, high-sensitivity C-reactive protein; LDL, low-density lipoprotein; LVEF, left ventricular ejection fraction; NT-proBNP, N-terminal pro-B-type natriuretic peptide; and TG, triglyceride.

Increased PAGln Levels Are Dose-Dependently Associated With Reduced Left Ventricular Systolic Function and Subclinical Indices of Myocardial Strain
In both the US and European Cohorts, increasing plasma levels of PAGln were dose-dependently associated with declining LVEF (Figure 3A and 3B; ρ=−0.144 [P<0.0001] and ρ=−0.185 [P<0.0001]). Similarly, increasing levels of PAGln in both the US and European Cohorts showed a dose-dependent association with increasing serum levels of N-terminal prohormone of BNP (NT-proBNP), a cardiac hormone generated in response to subclinical myocardial strain20 (Figure 3C and 3D; ρ=0.293 [P<0.0001] and ρ=0.324 [P<0.0001]). Of interest, we found that the association of PAGln with NT-proBNP remained significant in both the US and European Cohorts, when looking at the subsets of subjects with preserved left ventricular systolic function (eg, LVEF ≥50; P<0.0001 [Kruskal-Wallis test] for both cohorts; Figure S2A and S2B), as well as in the subsets with normal renal function (eg, estimated glomerular filtration rate ≥90 mL/min per 1.73 m2; P<0.0001 and P=0.001 [Kruskal-Wallis test] for US and EU Cohorts, respectively; Figure S3). Moreover, an increased risk for HF was observed among those with higher circulating levels of PAGln (eg, fourth versus first quartile), even following adjustments for traditional CVD risk factors, indices of kidney function and LVEF and NT-proBNP (Figure S4).

PAGln and PAGly Rapidly Induce Myocardial BNP Gene Expression
Given the striking association observed between PAGln and NT-proBNP levels in the 2 clinical cohorts, we sought to determine if PAGln, or its rodent counterpart phenylacetylglycine (PAGly),8 could directly induce BNP (Nppb) gene expression. To initially test this hypothesis in vitro, we used the immortalized rat cardiomyoblast cell line, H9c2. After 4 hours of exposure to pathophysiologically relevant levels of either PAGln or PAGly (Methods), Nppb (BNP) expression (normalized to Gapdh) was observed to be significantly increased (3.71-fold for PAGln [P=0.03], 3.66-fold for PAGly [P=0.03]) compared with vehicle-treated cells (Figure 4A). Advancing next to in vivo studies, we observed that when systemic levels of either PAGln or PAGly were acutely elevated after intraperitoneal injection (versus vehicle control), Nppb expression (normalized to Gapdh) in the left atria was significantly increased compared with vehicle-treated animals (1.66- and 1.46-fold increase, P=0.01 and P=0.02, 15 min after injection of PAGln or PAGly, respectively; Figure 4B). Intraperitoneal injection of PAGly also lead to increased Nppb expression within left ventricle (1.5-fold, P=0.001; data not shown).

PAGln Promotes a Negative Inotropic Effect in the Presence of Sympathetic Stimulation
Having observed a clinical association between PAGln and reduced LVEF, we next examined the effect of PAGln or PAGly on cardiomyocyte function. When examining freshly isolated primary murine cardiomyocytes, PAGln or PAGly treatment alone did not alter the baseline shortening of cardiomyocyte sarcomere length in response to repetitive field stimulation compared with vehicle treatment (Figure 5A through 5D, black vehicle versus blue line, PAGln or PAGly). Epinephrine (Epi) treatment alone resulted in a significant increase in inotropic response of the primary adult murine cardiomyocytes (Figure 5A through 5D, green line, Epi). However, when cardiomyocytes were incubated with both Epi and either PAGln or PAGly, the positive inotropic effect of Epi was significantly blunted, as indicated by the reduction in Epi-induced increase in contraction (sarcomere shortening; Figure 5A through 5D, red line, Epi+PAGln or Epi+PAGly). These results are consistent with PAGln (and PAGly) fostering an overall myocardial depressant effect during sympathetic stimulation.

Discussion
Over the past decade growing attention has been paid to a potential mechanistic link between the gut microbiome and HF development. While most studies thus far have focused either on alterations in gut microbial inhabitants in HF versus nonHF subjects,21–23 or enhanced gut leakiness that accompanies bowel wall edema, fostering systemic inflammation and potentially adverse remodeling,24 there is accumulating evidence that generation of bioactive metabolites by the intestinal microbiota can directly impact vascular and myocardial phenotypes and function.25–27 The present study adds to this latter body of evidence and shows clinically that systemic levels of PAGln, a gut microbiota-dependent metabolite, dose-dependently track both with HF risks and multiple indices of HF severity, in 2 independent clinical cohorts, regardless of adjustments for multiple comorbidities (Figure 6). The association between PAGln level and HF risk held true independent of CAD status, and across the spectrum of HF phenotypes. In fact, the associations with CVD, LVEF, and NT-proBNP were also observed among the subset of subjects showing normal left ventricular systolic function and the subset with normal renal function, suggesting that association between PAGln and these phenotypes occurs chronologically before clinically overt HF development or comorbidities like renal dysfunction develop.

Our studies further show that PAGln (and PAGly) can likewise foster HF-relevant biological activities that may contribute to the strong underlying clinical associations observed. Indeed, the strong associations between PAGln and NT-proBNP levels may arise because of the direct ability of PAGln to elicit transcription (enhanced Nppb RNA was observed in cardiomyoblasts in culture, and within myocardial tissues in vivo following acute infusion). Moreover, acute exposure to PAGln altered sarcomere function. Although exposure to PAGln alone had a minimal effect, it significantly suppressed the effect of the known agonist epinephrine, demonstrating an indirect negative ionotropic effect. In all cardiac muscles, the relationship between rate and force generation depends on the abundance of proteins involved in calcium cycling between the cytosol, myofilaments, and sarcoplasmic reticulum.28 Previous reports have demonstrated that changes in circulating BNP (and/or NT-proBNP) can precede clinically overt changes in left ventricle function and serve as a strong clinical prognostic indicator associated with mortality in patients without (and with) HF.29–32 While chronic sympathetic drive is thought to, in part, underlie the pathophysiology of HF,9 epinephrine-elicited effects are commonly blunted in HF myocardial tissues, as indicated by reduced contractility and chronotropic responses.33 Interestingly, PAGln did not acutely alter the baseline sarcomere function in isolated murine cardiomyocytes, but it altered sarcomere function by attenuating epinephrine-induced sarcomere shortening. The mechanisms of how PAGln fosters this effect are unclear, but deserve further attention in future studies.
An improved understanding of how PAGln interacts with adrenergic receptors, thereby altering sympathetic signaling, at biochemical and molecular levels, should prove informative in future investigations. Similarly, although PAGln is known to signal through adrenergic receptors, and prior animal model studies showed that the adverse atherothrombotic effects of PAGln in a murine model could be attenuated with carvedilol,8 a widely used beta blocker in clinical practice, the role of the multiple distinct adrenergic receptors in transmitting the phenotypes observed with PAGln in cell culture, and in vivo, remains to be explored. Indeed, there are 9 distinct adrenergic receptors with differential expression on multiple cells present in myocardial and vascular tissues. Thus far, every adrenergic receptor examined (α2A, 2B, B2) has shown interaction and signaling via PAGln.8 Further studies dissecting out the contribution of distinct adrenergic receptors to different PAGln effects similarly deserves attention.
This study has several limitations. Although our clinical findings were replicated in 2 geographically distinct cohorts and show remarkable consistency in associations and findings, these studies still require further validation. The clinical cohorts examined were enrolled at tertiary referral centers among sequential subjects undergoing coronary angiography and are notable for their high cardiovascular risk factor burden. As angiographic cohorts, they also likely show under-representation of subjects with nonischemic cardiomyopathies. In fact, another limitation of the present studies is that neither cohort had HFpEF versus HF with reduced ejection fraction (HFrEF) phenotypic characterization at time of initial enrollment, nor did they have ability to address the relationship between PAGln and adverse myocardial remodeling, fibrosis, or echocardiographic myocardial strain parameters. It is also worth noting that samples measured in this study represent only fasting levels at a single time point. The role of post prandial, or serial (cumulative exposure) levels of PAGln to HF phenotypes and risks remains to be seen. In addition, dietary records were not available for participants in either cohort. As a product of microbial fermentation of dietary protein (Phe), it is tempting to speculate that a diet low in protein, in general, or protein Phe, in particular (eg, such as used for treatment of subjects with phenylketonuria34–36), might lower circulating levels of PAGln. However, virtually nothing is known about dietary patterns and their impact on systemic PAGln levels. Further, there are likely multiple microbial contributors within the gut microbial community that have capacity to produce phenylacetic acid, the precursor of PAGln. The metabolic transformation catalyzed by the protein encoded by the gut microbial porA gene has previously been shown to have the capacity to convert the Phe-derived metabolite phenylypyruvic acid into phenylacetic acid.8,37 However, whether other genes/enzymes and to what capacity are involved in phenylacetic acid generation in vivo is currently unknown. In general, interactions among gut microbes, diet, and host genotype (nutrgenomics) to guide dietary interventions in the context of cardiometabolic diseases remains an understudied area.38
Although the present studies show a strong association between PAGln levels and both HF risk and numerous HF-associated phenotypes, the overall impact of PAGln on HF development is not clear. Indeed, although the negative ionotropic effect fostered by PAGln observed may underlie its association with reduced LV systolic function and reduced EF, one could argue that PAGln could have an overall beneficial effect, like beta blocker therapy in HF subjects. Similarly, it is not clear if PAGln-induced BNP generation via expression of Nppb (Natriuretic Peptide Precursor B gene) is an adaptive or maladaptive process in HF. Furthermore, the observation that PAGln levels track with NT-proBNP levels in subjects with preserved left ventricular systolic ejection fraction raises the intriguing possibility of a potential contribution of PAGln, and thus gut microbiota, in HFpEF. Finally, though PAGln is a gut-microbe-dependent metabolite, its generation also relies on host conjugation of phenylacetic acid within the liver. So it is feasible that impaired hepatic phenylacetic acid conjugation among heart failure patients may contribute to observed differences in circulating PAGln levels.
The present study raises the exciting possibility that the gut microbiome may be a participant in—and thus an attractive target for—novel therapeutics for the management of HF. Prior studies looking at an alternative gut microbial metabolite, trimethylamine-N-oxide (TMAO), showed that chronic exposure (at high pathophysiological levels) contributes to myocardial fibrosis, reduced LV function, and adverse remodeling. Importantly, these effects were also reversed by drugs specifically designed to target the gut microbial pathways that generate TMAO.39 In an analogous fashion, the present study, coupled with prior studies linking PAGln to atherothrombotic adverse phenotypes both clinically and mechanistically,8 suggest that subjects with elevated PAGln levels may respond to alternative interventions – such as those targeting microbial production of PAGln (via dietary changes or small molecule nonlethal drugs) or use of broad spectrum beta blockers. Further studies to explore these and other potential interventions that can modulate PAGln levels directly, or PAGln elicited effects, are warranted.
This study has several limitations. Although our clinical findings were replicated in 2 geographically distinct cohorts and show remarkable consistency in associations and findings, these studies still require further validation. The clinical cohorts examined were enrolled at tertiary referral centers among sequential subjects undergoing coronary angiography and are notable for their high cardiovascular risk factor burden. As angiographic cohorts, they also likely show underrepresentation of subjects with nonischemic cardiomyopathies. It is worth noting that we observed no significant effects on the overall association of PAGln with HF risk when including metrics of obesity (BMI) within the overall model. Moreover, in separate analyses, we have looked at the relationship between PAGln levels and obesity within the US Cohort, where BMI data are available, and noted that there was an inverse association (ρ=–0.04, P=0.028), whereby subjects with higher PAGln levels tend to have a lower BMI. We therefore see no evidence that the presence of obesity affects PAGln generation, nor that the relationship between PAGln and HF risk is linked to obesity.
Article Information
Source of Funding
This work is supported by grants from the National Institutes of Health (NIH) and the Office of Dietary Supplements (P01 HL147823, R01HL103866, R01HL126827) and the Foundation Leducq (17CVD01). Dr Romano was supported in part by training grant T32HL134622 from the National Heart, Lung, and Blood Institute of the NIH. Dr Witkowski was supported by an award from the Deutsche Forschungsgemeinschaft (WI 5229/1-1). Mass spectrometry studies were performed on instrumentation housed in a facility supported in part through a Shimadzu Center of Excellence award.
Supplemental Material
Table S1
Figures S1–S4
Footnote
Nonstandard Abbreviations and Acronyms
- BNP
- B-type natriuretic peptide
- CAD
- coronary artery disease
- CI
- confidence intervals
- CVD
- cardiovascular disease
- Epi
- epinephrine
- HF
- heart failure
- HFmrEF
- heart failure with mid-range ejection fraction
- HFpEF
- heart failure with preserved ejection fraction
- HFrEF
- heart failure with reduced ejection fraction
- LC/MS/MS
- liquid chromatography tandem mass spectrometry
- LVEF
- left ventricular ejection fraction
- MRM
- multiple reaction monitoring
- NT-proBNP
- N-terminal pro-B-type natriuretic peptide
- PAGln
- phenylacetylgutamine
- PAGly
- phenylacetylglycine
- TMAO
- trimethylamine N-oxide
Supplemental Material
File (circhf_circhf-2022-009972-t_supp1.pdf)
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References
1.
Backhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI. Host-bacterial mutualism in the human intestine. Science. 2005;307:1915–1920. doi: 10.1126/science.1104816
2.
Blaser MJ. The microbiome revolution. J Clin Invest. 2014;124:4162–4165. doi: 10.1172/JCI78366
3.
Brown JM, Hazen SL. The gut microbial endocrine organ: bacterially derived signals driving cardiometabolic diseases. Annu Rev Med. 2015;66:343–359. doi: 10.1146/annurev-med-060513-093205
4.
Fenneman AC, Rampanelli E, Yin YS, Ames J, Blaser MJ, Fliers E, Nieuwdorp M. Gut microbiota and metabolites in the pathogenesis of endocrine disease. Biochem Soc Trans. 2020;48:915–931. doi: 10.1042/BST20190686
5.
Scott AJ, Alexander JL, Merrifield CA, Cunningham D, Jobin C, Brown R, Alverdy J, O’Keefe SJ, Gaskins HR, Teare J, et al. International cancer microbiome consortium consensus statement on the role of the human microbiome in carcinogenesis. Gut. 2019;68:1624–1632. doi: 10.1136/gutjnl-2019-318556
6.
Tang WHW, Backhed F, Landmesser U, Hazen SL. Intestinal microbiota in cardiovascular health and disease: JACC state-of-the-art review. J Am Coll Cardiol. 2019;73:2089–2105. doi: 10.1016/j.jacc.2019.03.024
7.
Witkowski M, Weeks TL, Hazen SL. Gut microbiota and cardiovascular disease. Circ Res. 2020;127:553–570. doi: 10.1161/CIRCRESAHA.120.316242
8.
Nemet I, Saha PP, Gupta N, Zhu W, Romano KA, Skye SM, Cajka T, Mohan ML, Li L, Wu Y, et al. A cardiovascular disease-linked gut microbial metabolite acts via adrenergic receptors. Cell. 2020;180:862–877.e22. doi: 10.1016/j.cell.2020.02.016
9.
Cohn JN, Levine TB, Olivari MT, Garberg V, Lura D, Francis GS, Simon AB, Rector T. Plasma norepinephrine as a guide to prognosis in patients with chronic congestive heart failure. N Engl J Med. 1984;311:819–823. doi: 10.1056/NEJM198409273111303
10.
Bozkurt B, Hershberger RE, Butler J, Grady KL, Heidenreich PA, Isler ML, Kirklin JK, Weintraub WS. 2021 ACC/AHA key data elements and definitions for heart failure: a report of the American College of Cardiology/American Heart Association task force on clinical data standards (writing committee to develop clinical data standards for heart failure). Circ Cardiovasc Qual Outcomes. 2021;14:e000102. doi: 10.1161/HCQ.0000000000000102
11.
Yancy CW, Jessup M, Bozkurt B, Butler J, CaseyColvin DEMM, Drazner MH, Filippatos GS, Fonarow GC, Givertz MM, Hollenberg SM, et al. 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines and the heart failure Society of America. J Am Coll Cardiol. 2017;70:776–803. doi: 10.1016/j.jacc.2017.04.025.
12.
Hammadah M, Brennan ML, Wu Y, Hazen SL, Tang WH. Usefulness of relative hypochromia in risk stratification for nonanemic patients with chronic heart failure. Am J Cardiol. 2016;117:1299–1304. doi: 10.1016/j.amjcard.2016.01.023
13.
König M, Joshi S, Leistner DM, Landmesser U, Sinning D, Steinhagen-Thiessen E, Demuth I. Cohort profile: role of lipoproteins in cardiovascular disease—the LipidCardio study. BMJ Open. 2019;9:e030097. doi: 10.1136/bmjopen-2019-030097
14.
Li D, Wu J, Bai Y, Zhao X, Liu L. Isolation and culture of adult mouse cardiomyocytes for cell signaling and in vitro cardiac hypertrophy. J Vis Exp. 2014;87:51357. doi: 10.3791/51357
15.
Vasudevan NT, Mohan ML, Gupta MK, Hussain AK, Naga Prasad SV. Inhibition of protein phosphatase 2A activity by PI3Kgamma regulates beta-adrenergic receptor function. Mol Cell. 2011;41:636–648. doi: 10.1016/j.molcel.2011.02.025
16.
Inker LA, Eneanya ND, Coresh J, Tighiouart H, Wang D, Sang Y, Crews DC, Doria A, Estrella MM, Froissart M, et al. New creatinine- and cystatin c-based equations to estimate gfr without race. N Engl J Med. 2021;385:1737–1749. doi: 10.1056/NEJMoa2102953
17.
Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM, Deswal A, Drazner MH, Dunlay SM, Evers LR, et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2022;145:e895–e1032. doi: 10.1161/CIR.0000000000001063
18.
Tang WH, Francis GS. Neurohormonal upregulation in heart failure. Heart Fail Clin. 2005;1:1–9. doi: 10.1016/j.hfc.2004.11.002
19.
Grassi G, D’Arrigo G, Pisano A, Bolignano D, Mallamaci F, Dell’Oro R, Quarti-Trevano F, Seravalle G, Mancia G, Zoccali C. Sympathetic neural overdrive in congestive heart failure and its correlates: systematic reviews and meta-analysis. J Hypertens. 2019;37:1746–1756. doi: 10.1097/HJH.0000000000002093
20.
Heil B, Tang WH. Biomarkers: their potential in the diagnosis and treatment of heart failure. Cleve Clin J Med. 2015;82:S28–S35. doi: 10.3949/ccjm.82.s2.05
21.
Beale AL, O’Donnell JA, Nakai ME, Nanayakkara S, Vizi D, Carter K, Dean E, Ribeiro RV, Yiallourou S, Carrington MJ, et al. The gut microbiome of heart failure with preserved ejection fraction. J Am Heart Assoc. 2021;10:e020654. doi: 10.1161/JAHA.120.020654
22.
Gutierrez-Calabres E, Ortega-Hernandez A, Modrego J, Gomez-Gordo R, Caro-Vadillo A, Rodriguez-Bobada C, Gonzalez P, Gomez-Garre D. Gut microbiota profile identifies transition from compensated cardiac hypertrophy to heart failure in hypertensive rats. Hypertension. 2020;76:1545–1554. doi: 10.1161/HYPERTENSIONAHA.120.15123
23.
Mayerhofer CCK, Kummen M, Holm K, Broch K, Awoyemi A, Vestad B, Storm-Larsen C, Seljeflot I, Ueland T, Bohov P, et al. Low fibre intake is associated with gut microbiota alterations in chronic heart failure. ESC Heart Fail. 2020;7:456–466. doi: 10.1002/ehf2.12596
24.
Sandek A, Bauditz J, Swidsinski A, Buhner S, Weber-Eibel J, von Haehling S, Schroedl W, Karhausen T, Doehner W, Rauchhaus M, et al. Altered intestinal function in patients with chronic heart failure. J Am Coll Cardiol. 2007;50:1561–1569. doi: 10.1016/j.jacc.2007.07.016
25.
Li XS, Obeid S, Klingenberg R, Gencer B, Mach F, Raber L, Windecker S, Rodondi N, Nanchen D, Muller O, et al. Gut microbiota-dependent trimethylamine N-oxide in acute coronary syndromes: a prognostic marker for incident cardiovascular events beyond traditional risk factors. Eur Heart J. 2017;38:814–824. doi: 10.1093/eurheartj/ehw582
26.
Organ CL, Otsuka H, Bhushan S, Wang Z, Bradley J, Trivedi R, Polhemus DJ, Tang WH, Wu Y, Hazen SL, et al. Choline diet and its gut microbe-derived metabolite, trimethylamine N-oxide, exacerbate pressure overload-induced heart failure. Circ Heart Fail. 2016;9:e002314. doi: 10.1161/CIRCHEARTFAILURE.115.002314
27.
Troseid M, Ueland T, Hov JR, Svardal A, Gregersen I, Dahl CP, Aakhus S, Gude E, Bjorndal B, Halvorsen B, et al. Microbiota-dependent metabolite trimethylamine-N-oxide is associated with disease severity and survival of patients with chronic heart failure. J Intern Med. 2015;277:717–726. doi: 10.1111/joim.12328
28.
Endoh M. Force-frequency relationship in intact mammalian ventricular myocardium: physiological and pathophysiological relevance. Eur J Pharmacol. 2004;500:73–86. doi: 10.1016/j.ejphar.2004.07.013
29.
York MK, Gupta DK, Reynolds CF, Farber-Eger E, Wells QS, Bachmann KN, Xu M, HarrellWang FETJ. B-type natriuretic peptide levels and mortality in patients with and without heart failure. J Am Coll Cardiol. 2018;71:2079–2088. doi: 10.1016/j.jacc.2018.02.071.
30.
Tang WH, Francis GS, Morrow DA, Newby LK, Cannon CP, Jesse RL, Storrow AB, Christenson RH, Apple FS, Ravkilde J, et al. National academy of clinical biochemistry laboratory medicine practice guidelines: clinical utilization of cardiac biomarker testing in heart failure. Circulation. 2007;116:e99–109. doi: 10.1161/CIRCULATIONAHA.107.185267
31.
Grodin JL, Liebo MJ, Butler J, Metra M, Felker GM, Hernandez AF, Voors AA, McMurray JJ, Armstrong PW, O’Connor C, et al. prognostic implications of changes in amino-terminal pro-b-type natriuretic peptide in acute decompensated heart failure: insights from ASCEND-HF. J Card Fail. 2019;25:703–711. doi: 10.1016/j.cardfail.2019.04.002
32.
Tang WH, Francis GS, Morrow DA, Newby LK, Cannon CP, Jesse RL, Storrow AB, Christenson RH, Committee N. National academy of clinical biochemistry laboratory medicine practice guidelines: clinical utilization of cardiac biomarker testing in heart failure. Clin Biochem. 2008;41:210–221. doi: 10.1016/j.clinbiochem.2007.07.002
33.
Lymperopoulos A, Rengo G, Koch WJ. Adrenergic nervous system in heart failure: pathophysiology and therapy. Circ Res. 2013;113:739–753. doi: 10.1161/CIRCRESAHA.113.300308
34.
Firman S, Witard OC, O’Keeffe M, Ramachandran R. Dietary protein and protein substitute requirements in adults with phenylketonuria: a review of the clinical guidelines. Clin Nutr. 2021;40:702–709. doi: 10.1016/j.clnu.2020.11.003
35.
MacDonald A, van Wegberg AMJ, Ahring K, Beblo S, Belanger-Quintana A, Burlina A, Campistol J, Coskun T, Feillet F, Gizewska M, et al. PKU dietary handbook to accompany PKU guidelines. Orphanet J Rare Dis. 2020;15:171. doi: 10.1186/s13023-020-01391-y
36.
van Wegberg AMJ, MacDonald A, Ahring K, Belanger-Quintana A, Blau N, Bosch AM, Burlina A, Campistol J, Feillet F, Gizewska M, et al. The complete European guidelines on phenylketonuria: diagnosis and treatment. Orphanet J Rare Dis. 2017;12:162. doi: 10.1186/s13023-017-0685-2
37.
Dodd D, Spitzer MH, Van Treuren W, Merrill BD, Hryckowian AJ, Higginbottom SK, Le A, Cowan TM, Nolan GP, Fischbach MA, et al. A gut bacterial pathway metabolizes aromatic amino acids into nine circulating metabolites. Nature. 2017;551:648–652. doi: 10.1038/nature24661
38.
Ferguson JF, Allayee H, Gerszten RE, Ideraabdullah F, Kris-Etherton PM, Ordovas JM, Rimm EB, Wang TJ, Bennett BJ; American Heart Association Council on Functional G. Nutrigenomics, the microbiome, and gene-environment interactions: new directions in cardiovascular disease research, prevention, and treatment: a scientific statement from the American Heart Association. Circ Cardiovasc Genet. 2016;9:291–313. doi: 10.1161/HCG.0000000000000030
39.
Roberts AB, Gu X, Buffa JA, Hurd AG, Wang Z, Zhu W, Gupta N, Skye SM, Cody DB, Levison BS, et al. Development of a gut microbe-targeted nonlethal therapeutic to inhibit thrombosis potential. Nat Med. 2018;24:1407–1417. doi: 10.1038/s41591-018-0128-1
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Received: 20 July 2022
Accepted: 20 September 2022
Published online: 16 December 2022
Published in print: January 2023
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Disclosures
Disclosures Dr Hazen reports being named as co-inventor on pending and issued patents held by the Cleveland Clinic relating to cardiovascular diagnostics and therapeutics, and being a paid consultant formerly for Procter & Gamble and currently with Zehna Therapeutics. He also reports having received research funds from Procter & Gamble, Zehna Therapeutics, and Roche Diagnostics and being eligible to receive royalty payments for inventions or discoveries related to cardiovascular diagnostics or therapeutics from Cleveland HeartLab, a wholly owned subsidiary of Quest Diagnostics, Procter & Gamble, and Zehna Therapeutics. J.A. Buffa reports having received royalty payments from Proctor & Gamble. Dr Tang reports being a consultant for Sequana Medical A.G., Owkin Inc, Relypsa Inc, and PreCardiac Inc and having received honorarium from Springer Nature for authorship/editorship and American Board of Internal Medicine for exam writing committee participation, all unrelated to the subject and contents of this article. The other authors have reported that they have no relationships relevant to the contents of this article to disclose.
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