Iron Deficiency Impacts Diastolic Function, Aerobic Exercise Capacity, and Patient Phenotyping in Heart Failure With Preserved Ejection Fraction: A Subanalysis of the OptimEx-Clin Study

Abstract

Aims: Iron deficiency (ID) is linked to reduced aerobic exercise capacity and poor prognosis in patients with heart failure (HF) with reduced ejection fraction (HFrEF); however, data for HF with preserved ejection fraction (HFpEF) is scarce. We assessed the relationship between iron status and diastolic dysfunction as well as aerobic exercise capacity in HFpEF, and the contribution of iron status to patient phenotyping.

Methods and results: Among 180 patients with HFpEF (66% women; median age, 71 years) recruited for the Optimizing Exercise Training in Prevention and Treatment of Diastolic HF (OptimEx-Clin) trial, baseline iron status, including iron, ferritin, and transferrin saturation, was analyzed (n = 169) in addition to exercise capacity (peak oxygen uptake [peak V̇O2]) and diastolic function (E/e'). ID was present in 60% of patients and was more common in women. In multivariable linear regression models, we found that diastolic function and peak V̇O2 were independently related to iron parameters; however, these relationships were present only in patients with HFpEF and ID [E/e' and iron: β-0.19 (95% confidence interval -0.32, -0.07), p = 0.003; E/e' and transferrin saturation: β-0.16 (-0.28, -0.04), p = 0.011; peak V̇O2 and iron: β 3.76 (1.08, 6.44), p = 0.007; peak V̇O2 and transferrin saturation: β 3.58 (0.99, 6.16), p = 0.007]. Applying machine learning, patients were classified into three phenogroups. One phenogroup was predominantly characterized by the female sex and few HFpEF risk factors but a high prevalence of ID (86%, p < 0.001 vs. other phenogroups). When excluding ID from the phenotyping analysis, results were negatively influenced.

Conclusion: Iron parameters are independently associated with impaired diastolic function and low aerobic capacity in patients with HFpEF and ID. Patient phenotyping in HFpEF is influenced by including ID.

Clinical trial registration: www.ClinicalTrials.gov, identifier NCT02078947.

Keywords: HFpEF; artificial intelligence; diastolic dysfunction; echocardiography; exercise testing; heart failure; iron deficiency; machine learning.

Conflict of interest statement

AG reports travel and accommodation funding by Vifor Pharma. EW reported receiving personal fees from Novartis (honoraria for lectures and advisory board activities), Boehringer Ingelheim (honoraria for advisory board activities), and CVRX (honoraria for lectures) outside the submitted work. AD reported receiving grants from Novartis outside the submitted work. CV reported receiving personal fees from Abbott, Daiichi-Sankyo, Bayer and Edwards Lifesciences (lectures) outside the submitted work. BP reported receiving personal fees from Bayer Healthcare (steering committee, lectures), Merck (steering committee, lectures), Novartis (steering committee, lectures), Servier, AstraZeneca (lectures), Bristol-Myers Squibb (lectures), and Medscape (lectures) outside the submitted work. MH reported receiving grants from Novartis (principal investigator of the Activity Study in HFrEF) and personal fees from Bristol-Myers Squibb, Berlin Chemie-Menarini, Novartis, Daiichi-Sankyo, AstraZeneca, Roche, Abbott (advisory board on exercise and diabetes), Sanofi, Pfizer, Boehringer Ingelheim, and Bayer outside the submitted work. EV was supported by an investigator-initiated grant from Vifor Pharma for this work. No other disclosures were reported. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Gevaert, Mueller, Winzer, Duvinage, Van de Heyning, Pieske-Kraigher, Beckers, Edelmann, Wisløff, Pieske, Adams, Halle and Van Craenenbroeck.

Figures

FIGURE 1

Relationship of iron parameters to E/e′ septal ratio and peak V̇O2 in patients with HFpEF, stratified according to iron status. (A) Transferrin saturation and E/e′ septal ratio, both log scale. (B) Transferrin saturation (log scale) and peak V̇O2. (C) Iron and E/e′ septal ratio, both log scale. (D) Iron (log scale) and peak V̇O2. P-values from linear regression analyses in patients with ID and without ID. ID, iron deficiency; NID, no iron deficiency; peak V̇O2, peak oxygen uptake.

FIGURE 2

Three phenogroups of patients with HFpEF identified through machine learning and their characteristics. Heatmap with columns representing individual patients and rows representing individual characteristics, both grouped in clusters by unsupervised machine learning. Three phenogroups (numbers 1–3) and 9 characteristics clusters (letters A–I) were distinguished. ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; Clin, clinical examination; CVD, cardiovascular disease; Echo, echocardiography; EGFR, estimated glomerular filtration rate; Hist, medical history; KCCQ, Kansas City Cardiomyopathy Questionnaire; LAVi, left atrial volume index; LV, left ventricular; Med, current medication; NT-proBNP, N-terminal pro-B-type natriuretic peptide; NYHA, New York Heart Association; TAPSE, tricuspid annular plane systolic excursion; TR, tricuspid regurgitation; V̇CO2, carbon dioxide production; V̇E, ventilation; V̇O2, oxygen uptake.