Edema Index Predicts Cardiorespiratory Fitness in Patients With Heart Failure With Reduced Ejection Fraction and Type 2 Diabetes Mellitus.

Abstract

HomeJournal of the American Heart AssociationVol. 10, No. 8Edema Index Predicts Cardiorespiratory Fitness in Patients With Heart Failure With Reduced Ejection Fraction and Type 2 Diabetes Mellitus Open AccessLetterPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citations ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toOpen AccessLetterPDF/EPUBEdema Index Predicts Cardiorespiratory Fitness in Patients With Heart Failure With Reduced Ejection Fraction and Type 2 Diabetes Mellitus Amr Marawan, MD, Georgia K. Thomas, MD, PhD, Justin M. Canada, MS, PhD, Hayley E. Billingsley, MS, RD, Dave L. Dixon, PharmD, Benjamin W. Van Tassell, PharmD, Dinesh Kadariya, MD, Roshanak Markley, MD, Brando Rotelli, BA, Keyur B. Shah, MD, Le Kang, PhD, Francesco S. Celi, MD, MHSc, Antonio Abbate, MD, PhD and Salvatore Carbone, PhD, MS Amr MarawanAmr Marawan https://orcid.org/0000-0001-8550-6622 Division of Hospital Medicine, , Department of Internal Medicine, School of Medicine, , Virginia Commonwealth University, , Richmond, , VA Division of Cardiology, , Department of Internal Medicine, , VCU Pauley Heart Center, Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author , Georgia K. ThomasGeorgia K. Thomas Division of Hospital Medicine, , Department of Internal Medicine, School of Medicine, , Virginia Commonwealth University, , Richmond, , VA Division of Cardiology, , Department of Internal Medicine, , VCU Pauley Heart Center, Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author , Justin M. CanadaJustin M. Canada Division of Cardiology, , Department of Internal Medicine, , VCU Pauley Heart Center, Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author , Hayley E. BillingsleyHayley E. Billingsley Division of Cardiology, , Department of Internal Medicine, , VCU Pauley Heart Center, Virginia Commonwealth University, , Richmond, , VA Department of Kinesiology and Health Sciences, , College of Humanities and Sciences, , Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author , Dave L. DixonDave L. Dixon https://orcid.org/0000-0001-7560-9521 Department of Pharmacotherapy and Outcomes Science, , School of Pharmacy, , Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author , Benjamin W. Van TassellBenjamin W. Van Tassell Department of Pharmacotherapy and Outcomes Science, , School of Pharmacy, , Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author , Dinesh KadariyaDinesh Kadariya Division of Cardiology, , Department of Internal Medicine, , VCU Pauley Heart Center, Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author , Roshanak MarkleyRoshanak Markley Division of Cardiology, , Department of Internal Medicine, , VCU Pauley Heart Center, Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author , Brando RotelliBrando Rotelli Division of Cardiology, , Department of Internal Medicine, , VCU Pauley Heart Center, Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author , Keyur B. ShahKeyur B. Shah Division of Cardiology, , Department of Internal Medicine, , VCU Pauley Heart Center, Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author , Le KangLe Kang https://orcid.org/0000-0002-4277-902X Department of Biostatistics, , Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author , Francesco S. CeliFrancesco S. Celi https://orcid.org/0000-0001-7218-7052 Division of Endocrinology Diabetes and Metabolism, , Department of Internal Medicine, , Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author , Antonio AbbateAntonio Abbate https://orcid.org/0000-0002-1930-785X Division of Cardiology, , Department of Internal Medicine, , VCU Pauley Heart Center, Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author and Salvatore CarboneSalvatore Carbone * Correspondence to: Salvatore Carbone, PhD, MS, Department of Kinesiology and Health Sciences, College of Humanities and Sciences, Virginia Commonwealth University, 500 Academic Center, Room 113C, 1020 W Grace St, Richmond, VA 23220, PO Box 843021. E‐mail: E-mail Address: [email protected] https://orcid.org/0000-0002-8163-0527 Division of Cardiology, , Department of Internal Medicine, , VCU Pauley Heart Center, Virginia Commonwealth University, , Richmond, , VA Department of Kinesiology and Health Sciences, , College of Humanities and Sciences, , Virginia Commonwealth University, , Richmond, , VA Search for more papers by this author Originally published7 Apr 2021https://doi.org/10.1161/JAHA.120.018631Journal of the American Heart Association. 2021;10:e018631Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: April 7, 2021: Ahead of Print Fluid overload, a cardinal feature of heart failure (HF), is difficult to accurately assess noninvasively. Bioelectrical impedance analysis, an objective, noninvasive, reproducible, and relatively inexpensive method to estimate fluid status,1 allows the measurement of the edema index (EI), a surrogate for extracellular volume status.2 In this cross‐sectional analysis, we investigated whether EI predicts cardiorespiratory fitness (CRF) in patients with HF with reduced ejection fraction (EF) and type 2 diabetes mellitus, and hypothesized that greater EI would predict reduced CRF.The data that support the findings of this study are available from the corresponding author on reasonable request. We prospectively collected data on stable patients with symptomatic HF with reduced EF (New York Heart Association class II–III; left ventricular EF <50%) and type 2 diabetes mellitus. We measured peak oxygen consumption (VO2), a measure of CRF, and exercise time, a measure of functional capacity, during maximal cardiopulmonary exercise testing.3 EI was measured with single‐frequency bioelectrical impedance analysis (RJL System, Inc, Clinton Township, MI) by dividing the percentage of extracellular water by total body water. Subjects underwent venipuncture to measure serum creatinine, CRP (C‐reactive protein), hemoglobin, NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide), and sodium. Health‐related quality of life was assessed using the Minnesota Living With HF Questionnaire.Data are reported as median and interquartile range (IQR). Spearman rank correlation coefficients were estimated to measure the association. Nonparametric Wilcoxon rank‐sum test was used for group comparison. To investigate independent predictors for the response variable, peak VO2, we used penalized quantile regression models with and without adjustments for age, sex, race, body mass index (BMI), biomarkers, and major comorbidities, to model the median peak VO2. Analyses were conducted with SAS 9.4 (SAS Institute, Cary, NC). The Virginia Commonwealth University Institutional Review Board approved the study, and all patients provided written informed consent.Seventy‐two patients (median age, 58 [IQR, 52–62] years; women, n=50 [69%]; Black race, n=34 [47%]; hypertension, n=63 [88%]; hyperlipidemia, n=56 [77%]; median BMI, 33.9 [IQR, 31.2–37.6] kg/m2) were evaluated. Peak VO2 and EI were 15.7 (IQR, 12.8–18.4) mL·kg−1·min−1 (62% [IQR, 55%–69%] predicted) and 46% (44%–48%), respectively. Median respiratory exchange ratio was 1.06 (IQR, 1.03–1.11), 344 (IQR, 113–709) pg/mL for NT‐proBNP, 34% (IQR, 26%–41%) for left ventricular EF, 7.8% (7.2%–8.7%) for glycosylated hemoglobin, 13.3 (IQR, 12.5–14.4) g/dL for hemoglobin, 1.1 (IQR, 0.89–1.3) mg/dL for creatinine, 2.45 (IQR, 1.0–4.7) mg/dL for CRP, and 140 (IQR, 138–142) mEq/L for sodium. Men presented a higher median EI than women (48.2% [IQR, 43.7%–50.4%] versus 44.9% [IQR, 44.1%–46.8%]; P=0.013), and Black people had a significantly greater EI than White people (median, 46.2 [IQR, 44.8–49.7] versus 44.6 [IQR, 43.5–47.0]; P=0.020).EI was positively associated with BMI (ρ=0.388; P=0.001) and negatively associated with age, serum creatinine, and hemoglobin (ρ=−0.239, P=0.040; ρ=−0.296, P=0.011; and ρ=−0.329, P=0.004, respectively). EI was inversely associated with peak VO2 and exercise time (Figure). EI was also inversely associated with the ventilatory anaerobic threshold (ρ=−0.309; P=0.008) and O2 pulse (ρ=−0.308; P=0.008), but not significantly with minute ventilation/carbon dioxide production slope (ρ=−0.091; P=0.440) nor respiratory exchange ratio (ρ=−0.009; P=0.941). Increased BMI, NT‐proBNP, and Minnesota Living With HF Questionnaire score, and lower hemoglobin levels, were also associated with lower peak VO2 (ρ=−0.353, P=0.002; ρ=−0.318, P=0.006; ρ=−0.248, P=0.035; and ρ=0.288, P=0.014, respectively).From univariate quantile regression, each 1% absolute increase in EI was associated with a significant decrease in median peak VO2 (β=−0.613; 95% CI, −0.885 to −0.340; P<0.001), and a significant decrease in median exercise time in seconds (β=−24.0; 95% CI, −35.3 to −12.7; P<0.001). In addition, each 1% absolute increase in EI was associated with significant decreases in 25th percentile peak VO2 (β=−0.455; 95% CI, −0.826 to −0.085; P=0.017) and 25th percentile exercise time (β=−15.4; 95% CI, −26.9 to −3.9; P=0.009). Furthermore, each 1% absolute increase in EI was associated with a significant decrease in 75th percentile peak VO2 (β=−0.506; 95% CI, −0.781 to −0.231; P<0.001) and a significant decrease in 75th percentile exercise time (β=−11.9; 95% CI, −23.7 to −0.2; P=0.046).Using multivariable quantile regression with adaptive least absolute shrinkage and selection operator, EI together with all other collected variables was initially included for modeling the association with peak VO2 and exercise time, separately. The most parsimonious quantile regression models were obtained on the basis of the selected nonzero regression coefficients, for 25th, 50th, and 75th percentiles of the response variables. EI remained negatively associated with 25th percentile peak VO2 (β=−0.431; 95% CI, −0.790 to −0.072; P=0.019), with median peak VO2 (β=−0.364; 95% CI, −0.589 to −0.138; P=0.002), and with 75the percentile peak VO2 (β=−0.430; 95% CI, −0.810 to −0.052; P=0.027). Age was significantly associated with median peak VO2 (β=−0.161; 95% CI, −0.260 to −0.062; P=0.002), as well as BMI with median peak VO2 (β=−0.231; 95% CI, −0.369 to −0.093; P=0.001) and Minnesota Living With HF Questionnaire with median peak VO2 (β=−0.032; 95% CI, −0.058 to −0.006; P=0.018).Similarly, EI remained negatively associated with 25th percentile exercise time (β=−20.7; 95% CI, −29.3 to −12.2; P<0.001), median exercise time (β=−14.1; 95% CI, −22.5 to −5.5; P=0.002), and 75th percentile exercise time (β=−13.9; 95% CI, −29.9 to 1.9; P=0.083). Also, age was significantly associated with median exercise time (β=−4.6; 95% CI, −7.9 to −1.2; P=0.009), NT‐proBNP was associated with median exercise time (β=−0.063; 95% CI, −0.101 to −0.025; P=0.003), and Minnesota Living With HF Questionnaire was associated with median exercise time (β=−0.998; 95% CI, −1.869 to −0.128; P=0.025).In this study, we showed for the first time that bioelectrical impedance analysis–measured EI, which reflects increased extracellular volume, serves as an independent predictor of CRF in patients with HF with reduced EF and type 2 diabetes mellitus. Greater EI was also associated with worse functional capacity (ie, exercise time).In patients with acute decompensated HF, EI was previously found to be a predictor of HF readmissions and all‐cause mortality.4 Our study, however, included long‐term stable patients, therefore complementing the prior study on the potential utility of EI. Measuring EI in this population could therefore provide an early opportunity for optimization of medical therapy, perhaps resulting in increased CRF.In conclusion, although limited by the cross‐sectional nature of the study and relatively small sample size, we have shown that an increased EI was associated with worse CRF and functional capacity in patients with HF with reduced EF and type 2 diabetes mellitus.Sources of FundingNone.DisclosuresDr Carbone is supported by a Career Development Award 19CDA34660318 from the American Heart Association. The remaining authors have no disclosures to report.Download figureDownload PowerPointFigure. 1 Edema index, cardiorespiratory fitness, and functional capacity.Edema index was inversely associated with peak oxygen consumption (VO2) (A) and exercise time (B), measured during maximal cardiopulmonary exercise testing in patients with type 2 diabetes mellitus and heart failure with reduced ejection fraction.Footnotes* Correspondence to: Salvatore Carbone, PhD, MS, Department of Kinesiology and Health Sciences, College of Humanities and Sciences, Virginia Commonwealth University, 500 Academic Center, Room 113C, 1020 W Grace St, Richmond, VA 23220, PO Box 843021. E‐mail: [email protected]eduFor Sources of Funding and Disclosures, see page 3.References1 Earthman CP. Body composition tools for assessment of adult malnutrition at the bedside: a tutorial on research considerations and clinical applications. JPEN J Parenter Enteral Nutr. 2015; 39:787–822. DOI: 10.1177/0148607115595227.CrossrefMedlineGoogle Scholar2 Pellicori P, Kaur K, Clark AL. Fluid management in patients with chronic heart failure. Card Fail Rev. 2015; 1:90–95. DOI: 10.15420/cfr.2015.1.2.90CrossrefMedlineGoogle Scholar3 Carbone S, Billingsley HE, Canada JM, Bressi E, Rotelli B, Kadariya D, Dixon DL, Markley R, Trankle CR, Cooke R, et al. The effects of canagliflozin compared to sitagliptin on cardiorespiratory fitness in type 2 diabetes mellitus and heart failure with reduced ejection fraction: the CANA‐HF study. Diabetes Metab Res Rev. 2020; 36:e3335. DOI: 10.1002/dmrr.3335.CrossrefGoogle Scholar4 Liu MH, Wang CH, Huang YY, Tung TH, Lee CM, Yang NI, Liu PC, Cherng WJ. Edema index established by a segmental multifrequency bioelectrical impedance analysis provides prognostic value in acute heart failure. J Cardiovasc Med (Hagerstown). 2012; 13:299–306. DOI: 10.2459/JCM.0b013e328351677f.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetails April 20, 2021Vol 10, Issue 8Article InformationMetrics Copyright © 2021 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley BlackwellThis is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.https://doi.org/10.1161/JAHA.120.018631PMID: 33825487 Manuscript receivedAugust 29, 2020Manuscript acceptedFebruary 8, 2021Originally publishedApril 7, 2021 PDF download SubjectsHeart Failure