Participant characteristics

This study is a post hoc analysis of data from prospectively enrolled outpatients with stable systolic HF and left ventricular ejection fraction (LVEF) below 50% and New York Heart Association (NYHA) class I–IV. The data were collected as part of the cross‑sectional component of the project “Predicting Adverse Clinical Outcomes in Patients with Implanted Defibrillating Devices” (grant number TEAM/2009–4/4), awarded by the Foundation for Polish Science for research conducted between October 2010 and February 2014.^14,15 The study received approval from the Bioethics Committee of the Poznan University of Medical Sciences (363/10) and adhered to the principles outlined in the Declaration of Helsinki.¹⁶ Study participation was voluntary. All participants provided informed written consent.

Clinical evaluation

From an initial cohort of 457 patients, all women were excluded, leaving 389 men. From this group, 57 individuals were further excluded due to incomplete functional or echocardiographic data. The specific reasons for exclusion were: inability to safely perform the 6MWT (primarily due to prior stroke or gait impairment; n = 30), temporary technical failure of the hand dynamometer (n = 15), and poor echocardiographic image quality that precluded reliable LVEF assessment (n = 12). The final study population comprised 332 men who had complete datasets, including confirmed LVEF below 50% and results from both the 6MWT and MHG assessments.

During a standard cardiac examination, brachial systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) were measured in a seated position using either an Omron M3 (Omron, Kyoto, Japan) or a Colin BPM 7000 (Colin, Sue, Japan) oscillometric sphygmomanometer. Heart rate (HR) was recorded simultaneously.

Biochemical measurements

Venous blood samples were taken for routine biochemical and hematological tests, including concentrations of creatinine, C‑reactive protein (CRP), N‑terminal pro–B‑type natriuretic peptide (NT‑proBNP), hemoglobin, estimated glomerular filtration rate (eGFR) by the Modification of Diet in Renal Disease formula, ionized calcium, and white blood cell count. The methods for analyzing the samples were the same as those used in previous papers related to the project.^14,15

Transthoracic echocardiography

Standard echocardiography was performed using either an Acuson CV70 (Siemens, Erlangen, Germany) or a MyLab 30 CV (Esaote, Florence, Italy) device, in accordance with current guidelines.¹⁷ Left ventricular end‑diastolic dimension, left ventricular end‑systolic dimension, and left atrial (LA) dimension, LVEF value, aortic root diameter, and proximal right ventricular outflow were measured, together with the thickness of the interventricular septum and left ventricular posterior wall, mitral E and A velocities, mitral E wave deceleration time, and the ratios of E to e' and E to A. The measurement method was the same as previously reported.^14,15

Maximal handgrip strength measurement

MHG was measured 3 times on each hand in a seated position using a Jamar hydraulic hand dynamometer (Sammons Preston Rolyan, Bolingbrook, Illinois, United States),¹⁸ with the highest value used for analysis.

Six‑minute walk test

For the 6MWT, the patients walked along a 32‑meter straight, level corridor,¹⁹ and the final distance was recorded with an accuracy of 1 meter. After completing the 6MWT, the patients reported fatigue using the rate of perceived exertion on the standard Borg visual scale. Arterial blood oxygen saturation was measured continuously during the test.

Patient categorization

The patients were stratified into 3 categories based on the whole‑group median scores of MHG (40 kg) and 6MWT (480 m; Table 1). Category 1 included the patients with higher endurance and muscle strength, that is, with the 6MWT and MHG results above their respective medians (>480 m and >40 kg); category 2 included the individuals with either median or below‑median endurance (indicated by the 6MWT) or median or below‑median strength (indicated by the MHG); and category 3 grouped those with the lowest endurance and muscle strength, with the 6MWT and MHG results at or below their respective medians (<⁠480 m and <⁠40 kg).

Statistical analysis

Continuous data, assessed for normality using histograms, QQ plots, and the D’Agostino–Pearson test, were non‑normally distributed and are presented as medians and interquartile ranges (IQRs).²⁰ Group comparisons were performed using the Kruskal–Wallis test (nonparametric analysis of variance) and Dunn–Bonferroni post hoc test for pairwise comparisons. Categorical data were summarized as frequencies and percentages and analyzed using the Pearson χ² test, with the Fisher exact test applied where appropriate. The Cochran–Armitage test was utilized to assess for linear trends in categorical variables across the 3 ordinal patient categories. Given the exploratory and descriptive nature of the clinical phenotyping, P values for comparisons of baseline characteristics and comorbidities are reported as nominal, unadjusted values without correction for multiple testing.

For continuous variables that showed significant differences in the Kruskal–Wallis test, we conducted an analysis of covariance (ANCOVA). To maintain model parsimony and ensure a sufficient event‑per‑variable ratio across all subsequent regression models—particularly for outcomes with low event rates (eg, digoxin use; n = 36)—the number of adjustment covariates was constrained. We selected age, diabetes status, and active smoking for adjustment, as these covariates demonstrated potential confounding relationships with the CPR categories based on their unadjusted univariate associations (assessed via post hoc nonparametric analysis of variance for age and χ² tests for diabetes and smoking). Results are presented as estimated marginal means and SE across the 3 patient categories, with post hoc pairwise comparisons. Additionally, since resting HR differed across categories, we compared the proportions of patients with HR above 75 bpm. Because nonparametric tests were utilized in the univariable analyses, a sensitivity analysis using a rank ANCOVA (Conover–Iman approach applied to rank‑transformed variables) was additionally performed to verify the robustness of the parametric multivariable models against distributional deviations. Due to the severe right‑skewness in the distribution of NT‑proBNP levels and the nonlinear nature of its relationships, a base‑10 logarithmic transformation (log₁₀ NT‑proBNP) was applied prior to ANCOVA to meet parametric test assumptions.

We performed univariate logistic regression, consistently adjusted for the same 3 covariates (age, diabetes, and smoking) to ensure comparability with the ANCOVA models, to examine the association between HF functional categories (1, 2, and 3) treated as an ordinal independent variable, and various binary clinical outcomes, such as NYHA class III–IV, chronic atrial fibrillation (AF), and resting HR above 75 bpm. These clinical outcomes were selected based on binary variables with significant results in the Fisher exact test. Odds ratios (ORs) reflect the change in odds of each outcome per 1‑category increase in HF functional severity. A 2‑sided P value below 0.05 was considered significant. All analyses were performed using PQStat v.1.8.4.142 (PQStat Software, Poznań, Poland).

Potential therapeutic interventions to improve muscle strength and exercise capacity in systolic heart failure

CPR can be improved in patients with advanced systolic HF. Cardiac rehabilitation and training with repeated endurance and resistance exercises are recommended for HF patients.²⁷ Hormone therapy (testosterone or growth hormone) may increase muscle mass and strength,²⁸ and dietary supplementation (creatine, branched‑chain amino acids) shows promise.^29-31 Additionally, trimetazidine, a metabolic modulator, has been shown to improve skeletal muscle performance in HF.³² The claim that improving the condition of patients with HF can improve their clinical outcome to the level of patients without sarcopenia is speculative and requires further research.

By complementing traditional risk scores and clinical assessments, our simple classification might enhance phenotype‑driven decision‑making and help tailor therapeutic strategies, particularly in outpatient settings where rapid, functional evaluation is essential.

Practical implications and future directions

Despite their limitations, our findings offer immediate, practical implications for clinical assessment. The proposed CPR categorization, derived from 2 rapid bedside tests (<⁠10 min total), provides a simple framework for phenotyping men with HF with reduced EF (HFrEF) during a routine visit. Identifying a patient in category 3 could prompt a more comprehensive evaluation for frailty, occult sarcopenia (even with normal BMI), and treatable contributors, such as anemia or elevated HR, and facilitate the initiation of proper adjunctive therapy to improve muscle mass, strength, and endurance. Conversely, a category 1 profile might support the safety of more intensive exercise prescriptions. Future prospective studies are essential to validate these cutoffs, establish their prognostic value for clinical events, and test whether interventions targeted at improving CPR category can modify patient outcomes.

The primary aim of this study was to provide an exploratory, cross‑sectional description of a novel functional construct of CPR. Future prospective studies are now essential to determine the independent prognostic value of the CPR categories for hard clinical end points, such as mortality, and to test whether interventions aimed at improving physical reserve can modify the patient’s category and subsequent clinical trajectory.

Limitations

This study has several limitations. First, it included only men, which limits the generalizability of the findings by excluding women with systolic HF. Given known sex‑based differences in muscle mass, exercise capacity, and HF pathophysiology, future studies should validate this functional classification in women. Second, the cross‑sectional and observational design precludes causal inference; while associations between functional impairment and clinical severity are evident, the directionality of these relationships cannot be established. However, it is likely that the optimal cutoff values for MHG and 6MWT would differ in women with systolic HF.

Third, the use of cohort‑specific median values to define CPR categories limits the immediate external validity of the proposed cutoffs. These thresholds are population‑specific and may not apply universally across cohorts with different demographic or clinical profiles. However, as CPR is a novel construct lacking established thresholds, this data‑driven approach was necessary to enable its initial operationalization and stratification, providing a basis for future validation against clinical outcomes in external populations.

Fourth, although the 6MWT and MHG are practical and widely used tools, they may not fully capture the multidimensional nature of physical function, including balance, coordination, or lower‑limb strength. Incorporating additional performance‑based or imaging assessments could provide a more comprehensive evaluation of musculoskeletal health in HF.

Fifth, this was a post hoc, exploratory analysis of a retrospectively assembled cohort. The hypothesis regarding the CPR categorization was generated after data collection, necessitating cautious interpretation and prospective validation. Furthermore, the exclusion of patients unable to complete functional tests may have introduced a selection bias toward a less frail population, potentially limiting the generalizability of the findings to the most severely impaired HF patients. This post hoc analysis reflects a treatment era (2010–2014) when standard therapy for HFrEF primarily included β-blockers, ACEIs or ARBs, and mineralocorticoid receptor antagonists.³³ Since then, guideline‑directed medical therapy has evolved to include angiotensin receptor‑neprilysin inhibitors and sodium‑glucose cotransporter 2 inhibitors, which have demonstrated additional benefits in HF management.³³ As such, generalizability of our findings to contemporary HF populations may be limited. Nevertheless, our results offer a valuable baseline for understanding functional phenotypes in HF and may help quantify the incremental benefits of newer therapies in future longitudinal studies. The characteristics of HF patients have also changed; contemporary cohorts include a greater proportion of women and patients with preserved LVEF.³⁴

Conclusions

In men with systolic HF, combined deficits in muscle strength and endurance define a distinct clinical phenotype that, in this cross‑sectional analysis, was associated with markers of more advanced cardiac dysfunction, greater comorbidity burden, and more intensive treatment patterns. These functional impairments were independently associated with markers of HF severity, regardless of age, diabetes, or smoking status.

Our proposed, exploratory categorization, based on internally derived cutoffs in MHG and 6MWT, offers a pragmatic method for phenotypic stratification that captures clinically meaningful variation in current HF severity.

As a proof‑of‑concept, incorporating the MHG and 6MWT into routine HF assessments could help clinicians identify patients with pronounced functional impairment who might benefit from a more focused evaluation for frailty and sarcopenia. Given that both tests are rapid, inexpensive, and feasible in most outpatient settings, this approach merits further evaluation in prospective studies.

Future research should prospectively validate these findings, establish outcome‑driven cutoffs, and determine whether this functional categorization provides independent prognostic value for clinical events. Furthermore, interventional studies are needed to evaluate whether therapies aimed at improving muscle function and endurance can modify the CPR category and, ultimately, clinical outcomes.