Plasma amino acid profiling improves predictive accuracy of adverse events in patients with heart failure

Kouzu, Hidemichi; Katano, Satoshi; Yano, Toshiyuki; Ohori, Katsuhiko; Nagaoka, Ryohei; Inoue, Takuya; Takamura, Yuhei; Ishigo, Tomoyuki; Watanabe, Ayako; Koyama, Masayuki; Nagano, Nobutaka; Fujito, Takefumi; Nishikawa, Ryo; Ohwada, Wataru; Miura, Tetsuji

doi:10.1002/ehf2.13572

Cited by 20 publications

(37 citation statements)

References 44 publications

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“…9 ASM was calculated by DEXA (Horizon A DXA System, HOLOGIC, Waltham, MA, USA) as previously reported. 8,10 ASMI was defined as ASM/height 2 . The cut-off values of ASMI for low muscle mass were <7.00 and <5.40 kg/m 2 for men and women, respectively, according to the criteria of the Asian Working Group for Sarcopenia.…”

Section: Methodsmentioning

confidence: 99%

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Anthropometric parameters‐derived estimation of muscle mass predicts all‐cause mortality in heart failure patients

Katano

Honma²,

Nagaoka

et al. 2022

ESC Heart Failure

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Aims Reduction in appendicular skeletal muscle mass index (ASMI) assessed by dual‐energy X‐ray absorptiometry (DEXA) has been shown to be independently associated with a higher mortality rate in patients with heart failure (HF). However, DEXA is not suitable for measurement of muscle mass in a daily clinical setting and in large population‐based studies. The aim of this study was to determine whether ASMI predicted from anthropometric indicators (predicted ASMI) serves as an alternative to DEXA‐measured ASMI for predicting all‐cause death in HF patients. Methods and Results Data for 539 HF patients who received a DEXA scan and measurements of calf circumferences (CC) and mid‐arm circumferences (MAC) in our hospital were analysed. Predicted ASMI was calculated as we previously reported: predicted ASMI (kg/m2) = [0.214 × weight (kg) + 0.217 × CC (cm) − 0.189 × MAC (cm) + 1.098 (male = 1, female = −1) + 0.576]/height2 (m2). Low ASMI values were defined as <7.00 kg/m2 and <5.40 kg/m2 for men and women, respectively, according to the criteria of the Asian Working Group for Sarcopenia. The median follow‐up period was 1.75 years (interquartile range, 0.96–2.37 years), and 79 patients (15%) died. Kaplan–Meier survival curves showed that patients with low DEXA‐measured ASMI and patients with low predicted ASMI had significantly lower survival rates than those for patients with high ASMI. In multivariate Cox proportional hazard analyses adjusted for age, sex, logarithmic B‐type natriuretic peptide, cystatin C based‐estimated glomerular filtration rate, and gait speed, DEXA‐measured ASMI [hazard ratio (HR), 0.982; 95% confidence interval (CI), 0.967–0.998; P = 0.026] and predicted ASMI (HR, 0.979; 95% CI, 0.962–0.996; P = 0.018) were independent predictors of all‐cause mortality. Inclusion of predicted ASMI into the adjustment model significantly improved continuous net reclassification improvement (0.338; 95% CI, 0.103–0.572; P < 0.01) and integrated discrimination improvement (0.020; 95% CI, 0.004–0.035; P < 0.05) for predicting mortality after discharge. Conclusions Predicted ASMI, as well as DEXA‐measured ASMI, can predict all‐cause death in HF patients, and calculation of predicted ASMI will be useful for detecting high‐risk patients in a daily clinical setting and in large population‐based studies.

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Section: Methodsmentioning

confidence: 99%

“…HF was diagnosed according to the 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic HF 9 . ASM was calculated by DEXA (Horizon A DXA System, HOLOGIC, Waltham, MA, USA) as previously reported 8,10 . ASMI was defined as ASM/height 2 .…”

Section: Methodsmentioning

confidence: 99%

Anthropometric parameters‐derived estimation of muscle mass predicts all‐cause mortality in heart failure patients

Katano

Honma²,

Nagaoka

et al. 2022

ESC Heart Failure

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“…Furthermore, a recently published cohort study observed that high 3-Me-His concentrations and low β-alanine and valine concentrations were independently associated with a significantly higher risk of a composite endpoint of all-cause death and readmission due to worsening HF or lethal arrhythmia in patients with HF. In addition, the inclusion of these amino acid levels in a multivariate model of clinical variables (including N-terminal proBNP levels) significantly improved its predictive capacity, highlighting the role of AAs as potential biomarkers for risk stratification in HF patients [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Metabolomic Profiling of End-Stage Heart Failure Secondary to Chronic Chagas Cardiomyopathy

Díaz

Burgess

Burchmore

et al. 2022

IJMS

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Chronic Chagas cardiomyopathy (CCC) is the most frequent and severe clinical form of chronic Chagas disease, representing one of the leading causes of morbidity and mortality in Latin America, and a growing global public health problem. There is currently no approved treatment for CCC; however, omics technologies have enabled significant progress to be made in the search for new therapeutic targets. The metabolic alterations associated with pathogenic mechanisms of CCC and their relationship to cellular and immunopathogenic processes in cardiac tissue remain largely unknown. This exploratory study aimed to evaluate the potential underlying pathogenic mechanisms in the failing myocardium of patients with end-stage heart failure (ESHF) secondary to CCC by applying an untargeted metabolomic profiling approach. Cardiac tissue samples from the left ventricle of patients with ESHF of CCC etiology (n = 7) and healthy donors (n = 7) were analyzed using liquid chromatography-mass spectrometry. Metabolite profiles showed altered branched-chain amino acid and acylcarnitine levels, decreased fatty acid uptake and oxidation, increased activity of the pentose phosphate pathway, dysregulation of the TCA cycle, and alterations in critical cellular antioxidant systems. These findings suggest processes of energy deficit, alterations in substrate availability, and enhanced production of reactive oxygen species in the affected myocardium. This profile potentially contributes to the development and maintenance of a chronic inflammatory state that leads to progression and severity of CCC. Further studies involving larger sample sizes and comparisons with heart failure patients without CCC are needed to validate these results, opening an avenue to investigate new therapeutic approaches for the treatment and prevention of progression of this unique and severe cardiomyopathy.

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“…After stabilization of HF, blood samples for measurements of amino acid concentrations were collected in a fasting state. Plasma amino acid concentrations were measured using ultraperformance liquid chromatography (LSI Medience Corporation, Tokyo, Japan) as previously described [ 20 , 27 , 28 ]. Blood samples were collected in sodium heparin tubes (NIPRO, Osaka, Japan) and transported to the laboratory at room temperature.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, plasma amino acid profiling is a useful approach for elucidating the pathogenesis of disease and response to treatment, as is omics [ 15 – 19 ]. Indeed, the results of our recent study showed that specific patterns of changes in circulating amino acid concentrations improved the predictive ability for adverse events [ 20 ], indicating the utility of plasma amino acid profiling in risk stratification of HF patients. Intriguingly, urinary amino acid concentrations have been shown to be modulated by genetic and pharmacological inhibition of SGLT2 [ 21 , 22 ], possibly leading to changes in plasma amino acid profiles.…”

Section: Introductionmentioning

confidence: 99%

Elevated circulating level of β-aminoisobutyric acid (BAIBA) in heart failure patients with type 2 diabetes receiving sodium-glucose cotransporter 2 inhibitors

Katano

Yano

Kouzu

et al. 2022

Cardiovasc Diabetol

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Aims The mechanism by which a sodium-glucose cotransporter inhibitor (SGLT2i) induces favorable effects on diabetes and cardiovascular diseases including heart failure (HF) remains poorly understood. Metabolomics including amino acid profiling enables detection of alterations in whole body metabolism. The aim of this study was to determine whether plasma amino acid profiles are modulated by SGLT2i use in HF patients with type 2 diabetes mellitus (T2DM). Methods We retrospectively examined 81 HF patients with T2DM (68 ± 11 years old; 78% male). Plasma amino acid concentrations in a fasting state after stabilization of HF were determined using ultraperformance liquid chromatography. To minimize potential selection bias in the retrospective analyses, the differences in baseline characteristics between patients receiving an SGLT2i and patients not receiving an SGLT2i were controlled by using an inverse probability of treatment weighting (IPTW)-adjusted analysis. Results Of amino acids measurable in the present assay, plasma β-aminoisobutyric acid (BAIBA), an exercise-induced myokine-like molecule also known as 3-aminoisobutyric acid or 3-amino-2-methyproponic acid, was detected in 77% of all patients and the proportion of patients in whom plasma BAIBA was detected was significantly higher in patients receiving an SGLT2i than in patients not receiving an SGLT2i (93% vs. 67%, p = 0.01). Analyses in patients in whom plasma BAIBA was detected showed that plasma BAIBA concentration was significantly higher in patients receiving an SGLT2i than in patients not receiving an SGLT2i (6.76 ± 4.72 vs. 4.56 ± 2.93 nmol/ml, p = 0.03). In multivariate logistic regression analyses that were adjusted for age and sex, SGLT2i use was independently associated with BAIBA detection. The independent association between BAIBA and SGLT2i use remained after inclusion of body mass index, HF with reduced ejection fraction, ischemic etiology, renal function, NT-proBNP, albumin, hemoglobin, and HbA1c into the Cox proportional hazards model. When the differences in baseline characteristics between patients receiving an SGLT2i and patients not receiving an SGLT2i were controlled by using an IPTW-adjusted analysis, least squares mean of plasma BAIBA concentration was significantly higher in patients receiving an SGLT2i than in patients not receiving an SGLT2i. Conclusion SGLT2i use is closely associated with increased circulating BAIBA concentration in HF patients with T2DM.

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Plasma amino acid profiling improves predictive accuracy of adverse events in patients with heart failure

Cited by 20 publications

References 44 publications

Anthropometric parameters‐derived estimation of muscle mass predicts all‐cause mortality in heart failure patients

Anthropometric parameters‐derived estimation of muscle mass predicts all‐cause mortality in heart failure patients

Metabolomic Profiling of End-Stage Heart Failure Secondary to Chronic Chagas Cardiomyopathy

Elevated circulating level of β-aminoisobutyric acid (BAIBA) in heart failure patients with type 2 diabetes receiving sodium-glucose cotransporter 2 inhibitors

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