Heart failure (HF) is currently a global public health problem, affecting approximately 26 million people worldwide (Savarese & Lund, 2017). In addition to the severe health outcomes associated with HF, its global burden is significant and expected to grow substantially with the aging population. The economic cost of the disease is primarily driven by frequent hospitalizations and a high mortality rate (Ambrosy et al., 2014; Lloyd-Jones et al., 2002).
Patients with HF experience a marked reduction in overall skeletal muscle strength and exercise capacity, which is detrimental to their prognosis (Bekfani et al., 2020; Fulster et al., 2013; McDonagh et al., 2022; Pina et al., 2003). The prevalence of skeletal muscle wasting ranges from 19% to 52% among patients with HF, and skeletal muscle wasting further reduces the peak oxygen consumption and 6-min walk distance (6MWT; Fulster et al., 2013; Von Haehling et al., 2017). Exercise capacity, which is used for diagnosing, staging, and determining prognosis in patients with HF can be quantified by the 6MWT, treadmill test, cycle stress test, and cardiopulmonary exercise test (Beckers et al., 2012; Corra et al., 2004; Hsich et al., 2009; McElroy et al., 1988). For these patients, the reported protocols for treadmill tests include the ramp, modified Naughton, and modified Bruce protocols. Cycle tests often use a standard ramp protocol.
Recently, handgrip strength (HGS) measurement, a simple clinical tool, is reported to predict exercise capacity in patient populations, such as those with coronary artery disease, or chronic obstructive lung disease (Kim et al., 2020; Kyomoto et al., 2019). However, there is a lack of evidence regarding the relationship of HGS with exercise capacity or prognosis in patients with HF. Therefore, we sought to assess the relationship between HGS and exercise capacity in patients with HF referred for an exercise test. We also evaluated prognostic value of HGS for cardiovascular events in this population.
Methods
Population
This retrospective study included patients with HF and reduced ejection fraction (HFrEF) who participated in a cardiac rehabilitation program at a tertiary cardiovascular center between June 2015 and May 2020. This study was approved by the Institutional Review Board of the respective center (IRB no. 2018GR0295). Due to the retrospective and non-interventional nature of the trial, written informed consent was not required. Patients with a left ventricular ejection fraction (LVEF) of < 40% were included. Patients whose level of physical fitness could not be evaluated for reasons such as hemodynamic instability, comorbidities such as pulmonary or orthopedic diseases, or noncooperation due to neurologic problems, were excluded. The participants in this trial resided in Seoul, South Korea.
Baseline physical fitness was assessed during the first outpatient visit following index admission. Of 212 initially eligible patients, only 173 with baseline physical fitness data, including HGS and maximal oxygen consumption (VO2max) were included in this study (Figure 1). Outcome events were assessed up to the date of the final follow-up visit of each patient. The mean follow-up duration was 1,218 days, and the median duration was 1,125 days (interquartile range, 889–1,488).
The study flowchart.
Citation: Journal of Aging and Physical Activity 2025; 10.1123/japa.2023-0298
Procedure
The HGS was assessed using a hand dynamometer (TKK 5401 Grip-D; Takei, Niigata, Japan). Participants were instructed to grip the dynamometer with maximum force for 3 s, maintaining 90° elbow flexion. Measurements were taken with the subjects seated in a chair without armrests, feet fully resting on the floor, hips positioned as far back in the chair as possible, and hips at approximately 90°. The subjects were verbally encouraged by the researcher. The highest value was recorded after taking three measurements with each hand. Average HGS in both hands was used in this study.
A symptom-limited treadmill test based on a modified Bruce protocol was conducted and typically terminated at a predetermined level (the maximal effort level). Heart rate, blood pressure, and 12-lead electrocardiogram data were recorded at each exercise stage. VO2max was directly measured using a Quark b2 (COSMED) during the exercise test, and expressed as metabolic equivalents (MET). Exercise capacity in METs (where 1 MET is 3.5 ml·kg−1·min−1 of VO2) was estimated (Jette et al., 1990). The following criteria were used to confirm maximal effort: no further increase in heart rate or VO2 with increased exercise intensity, respiratory exchange ratio > 1.10, and rating of perceived exertion >17 on the Borg scale (6–20 scale) or >9 on the category ratio scale (0–10 scale; Borg & Linderholm, 1970; Noble et al., 1983). As a plateau in VO2 with increased exercise intensity is rarely observed in patients with HF, the peak VO2 value recorded was considered VO2max.
Study Definition and Outcome
The primary outcome was a composite of readmission for HF and all-cause death. This outcome is the first occurrence of either of these two events. The secondary outcomes were readmission for HF, all-cause death, cardiac death, acute myocardial infarction, stroke, and major adverse cardiovascular events (defined as the composite of total death, acute myocardial infarction, repeat revascularization, stroke, and hospitalization for HF). Readmission for HF was defined as hospitalization due to exacerbated HF symptoms and signs, requiring augmentation of previous medication (Wideqvist et al., 2021). Acute myocardial infarction was defined based on typical symptoms with new significant ST-segment change or elevation of cardiac markers, with at least twice the upper limit of normal levels. Revascularization was defined as a medical procedure involving percutaneous coronary intervention or coronary artery bypass grafting to restore blood flow to the coronary arteries (Curtis et al., 2009). Additionally, exercise capacity, such as the distance on 6MWT, and the MET (VO2max), was assessed. After discharge, patients received optimal medical treatment at the discretion of clinicians according to temporary guidelines.
Statistics
Data are expressed as mean (1 SD) or numbers (percentages), unless otherwise specified. For continuous variables, comparisons between the two groups were conducted using the unpaired t test or Mann–Whitney U rank test. For categorical variables, differences are expressed as counts and percentages and analyzed using χ2 or Fisher’s exact test between groups, as appropriate. Statistical significance was set at p < .05. Pearson’s correlation coefficient was used to assess the relationship between HGS and exercise capacity. Multiple linear and logistic regression analyses were also performed to assess the relationship of HGS with both the distance on 6MWT, and activity levels (MET). Activity levels were categorized into sedentary (≤1.5 METs), light (1.6–2.9 METs), moderate (3.0–5.9 METs), and vigorous activity (≥6.0 METs) based on energy expenditure during specific physical activities (Ainsworth et al., 2011). In the multivariable analysis, backward selection was used to adjust for the following confounders of the association between HGS and exercise capacity or clinical outcomes: sex, age, alcohol consumption, smoking status, body mass index, hypertension, diabetes mellitus, etiology of HF, and LVEF. Specifically, a variable selection procedure was followed in which all variables are initially entered into the model and then sequentially removed if their p value exceeded .10. For further analysis, we calculated area under the curve from the receiver operating characteristic curves to establish the cutoff point of HGS for predicting the primary endpoint. Ideal threshold value was determined as the point where the sum of “sensitivity + specificity – 1” reached its highest level (Youden’s Index). Kaplan–Meier curves were plotted to depict the time to the first clinical outcome event, while the log-rank test was used to assess statistical differences between curves. Cox proportional regression analysis using backward selection of covariates was used to calculate hazard ratios (HRs) between groups. Variance inflation factor was determined for each variable, and high collinearity exceeding 10 was considered. Model performance was assessed using Harrell’s C-index to evaluate the discrimination. The Fine and Gray method was also used to estimate the subdistribution HR for the association of HGS with clinical outcomes, considering all-cause death as a competing risk (Fine & Gray, 1999). All data were processed using SPSS software (version 24.0) and SAS (version 9.4, SAS Institute).
Results
Of the 212 patients with HF in the cardiac rehabilitation cohort, 39 were excluded because of clinical instability, missing baseline physical fitness data, such as HGS or 6MWT, or study refusal (Figure 1). Among 173 patients, the relationship between HGS and exercise capacity was first evaluated using the VO2max (MET) and 6MWT as parameters. The average age of the 173 included patients was 62.0 (13.8) years, and 81.5% were male. Ischemic cardiomyopathy was the most common etiology of HF (56.1%). Dilated cardiomyopathy, valvular cardiomyopathy, and hypertensive heart disease accounted for 11.6%, 7.5%, and 9.8% of HF cases, respectively. The average ejection fraction was 30.3 (7.9)%. The mean values of VO2max, distance on 6MWT and MET were 20.0 (6.0) ml·kg−1·min−1, 374.0 (120.1) m, and 5.7 (1.7), respectively. The baseline characteristics are shown in Table 1.
Baseline Clinical Characteristics
| Variables | Total (173) |
|---|---|
| Age, years, mean (SD) | 62.0 (13.8) |
| Male, n (%) | 141 (81.5) |
| BMI, kg/m2, mean (SD) | 24.4 (3.8) |
| Waist-to-hip ratio, mean (SD) | 0.90 (0.08) |
| Basal metabolic rate, ml/min, mean (SD) | 1,422.4 (189.0) |
| Systolic blood pressure, mm Hg, mean (SD) | 119.6 (17.4) |
| Diastolic blood pressure, mm Hg, mean (SD) | 75.3 (14.4) |
| Heart rate, mean (SD) | 84.7 (17.2) |
| Smoking, n (%) | 45 (26.0) |
| Alcohol, n (%) | 60 (34.7) |
| Comorbidity | |
| Hypertension, n (%) | 81 (46.8) |
| Diabetes mellitus, n (%) | 55 (31.8) |
| Hyperlipidemia, n (%) | 128 (74.0) |
| Atrial fibrillation, n (%) | 38 (22.0) |
| End-stage renal disease, n (%) | 4 (2.3) |
| Cerebrovascular disease, n (%) | 12 (6.9) |
| Pulmonary disease, n (%) | 9 (5.2) |
| Previous revascularization, n (%) | 55 (31.8) |
| HF etiology | |
| Ischemic cardiomyopathy, n (%) | 97 (56.1) |
| Dilated cardiomyopathy, n (%) | 20 (11.6) |
| Valvular heart disease, n (%) | 13 (7.5) |
| Hypertensive heart disease, n (%) | 17 (9.8) |
| Other causes, n (%) | 26 (15.0) |
| Echocardiography findings | |
| LVEF, %, mean (SD) | 30.3 (7.9) |
| LVDd, mm, mean (SD) | 58.5 (7.9) |
| E/e’, mean (SD) | 17.2 (9.5) |
| Laboratory findings | |
| Fasting glucose, mg/dl, mean (SD) | 123.1 (58.8) |
| Hemoglobin A1c, %, mean (SD) | 6.2 (1.0) |
| Hemoglobin, g/dl, mean (SD) | 13.2 (2.1) |
| Creatinine, mg/dl, median (25th–75th percentile) | 0.89 (0.74–1.02) |
| Total cholesterol, mg/dl, mean (SD) | 151.2 (39.9) |
| NT-proBNP, pg/ml, median (25th–75th percentile) | 307.8 (95.7–1,786.0) |
| Medication | |
| Aspirin, n (%) | 99 (57.2) |
| Clopidogrel, n (%) | 103 (59.5) |
| Renin-angiotensin-system inhibitor, n (%) | 161 (93.1) |
| Spironolactone, n (%) | 103 (59.5) |
| Calcium channel blocker, n (%) | 30 (17.3) |
| Beta blocker, n (%) | 152 (87.9) |
| ARNI, n (%) | 59 (34.1) |
| SGLT2 inhibitors, n (%) | 43 (24.9) |
| NOAC, n (%) | 47 (27.2) |
| Statin, n (%) | 125 (72.3) |
| Physical fitness | |
| Peak | 20.0 (6.0) |
| Actual MET, mean (SD) | 5.7 (1.7) |
| Distance on 6MWT, m, mean (SD) | 374.0 (120.1) |
Note. Values are mean (SD), median (interquartile range), or number (%). ARNI = angiotensin receptor neprilysin inhibitor; BMI = body mass index; E/e' = Mitral Doppler E wave to mitral annulus velocity ratio; HF = heart failure; LVDd = left ventricular diastolic dimension; MET = metabolic equivalents; NOAC = non-vitamin K antagonist oral anticoagulants; NT-proBNP = N-terminal prohormone of brain natriuretic peptide; SGLT2 inhibitors = sodium glucose cotransporter 2 inhibitors; 6MWT = 6-min walk test; LVEF = left ventricular ejection fraction.
The mean HGS in both hands was significantly associated with distance on 6MWT (r = .564, p < .001) and MET (r = .419, p < .001; Figure 2). Similar findings were observed when comparing exercise capacity between dominant (Supplementary Figure S1 [available online] and non-dominant hands (Supplementary Figure S2 [available online]). In the multivariable analysis, only age (p = .036) and HGS (p < .001) were significant predictors of distance on 6MWT among confounders including sex, age, alcohol consumption, smoking status, body mass index, hypertension, diabetes mellitus, etiology of HF, and LVEF (Supplementary Table S1 [available online]. Similarly, HGS was associated with attainable physical activity (MET) levels (Supplementary Table S2 [available online]. The area under the curve obtained from receiver operating characteristic curves for HGS as a predictor of achieving sedentary (≤1.5 METs), light (1.6–2.9 METs), moderate (3.0–5.9 METs), and vigorous activity (≥6.0 METs) was 0.741 (95% confidence interval [CI] [0.631, 0.851]; p < .001); 0.667 (95% CI [0.547, 0.786]; p = .008); 0.572 (95% CI [0.486, 0.657]; p = .109); and 0.727 (95% CI [0.649, 0.804]; p < .001), respectively (Supplementary Figure S3 [available online]). HGS is more closely related with either sedentary or vigorous activity rather than light or moderate activity.
Correlations between exercise capacity (distance walked on 6MWT [A] and cardiorespiratory fitness [MET, B]) and handgrip strength. 6MWT = 6-min walk test; MET = metabolic equivalents.
Citation: Journal of Aging and Physical Activity 2025; 10.1123/japa.2023-0298
During a mean follow-up of 3.3 years, the primary outcome (composite of readmission for HF or all-cause death) was observed in 36 (20.8%) patients. Overall, 11 deaths (10.9%) occurred. Acute myocardial infarction occurred in eight patients (4.6%), stroke in seven (4%), revascularization in 17 (9.8%), and hospitalization for HF in 34 (19.7%).
In the univariate Cox regression analysis for predicting primary outcome using various exercise parameters, HGS was found to be comparable to age, actual MET, and distance on 6MWT as a prognostic index (HR, 0.94; 95% CI [0.91, 0.97]; p < .001; Table 2). Through receiver operating characteristic curve analysis predicting the primary outcome, the optimal cutoff value for HGS was determined to be 24.9 kg (Figure 3). This value demonstrated a sensitivity of 63.5% and 1-specificity of 25% in predicting survival time. The area under the curve was 0.702 (95% CI [0.610, 0.794]; p < .001), indicating its significance as a predictor.
Univariable Analysis for Parameters of Exercise Capacity in Prediction of Primary Outcome
| Variable | B ± SE | p | Hazard ratio [95% CI] |
|---|---|---|---|
| Age (per 1 year increase) | 0.030 ± 0.014 | .029 | 1.03 [1.00, 1.06] |
| Peak | –0.070 ± 0.032 | .030 | 0.93 [0.88, 0.99] |
| Actual MET (per 1 unit increase) | –0.215 ± 0.114 | .058 | 0.81 [0.65, 1.01] |
| Distance on 6MWT (per 1 m increase) | –0.004 ± 0.001 | .001 | 1.00 [0.99, 1.00] |
| HGS (per 1 kg increase) | –0.062 ± 0.017 | <.001 | 0.94 [0.91, 0.97] |
Note. Values are mean ± SD. Hazard ratios, 95% CIs, and p values were calculated using the Cox regression analysis. MET = metabolic equivalents; 6MWT = 6-min walk test; HGS = handgrip strength; CI = confidence interval.
Receiver operating characteristic curve for handgrip strength to predict future events of the primary outcome (area under the curve 0.702; 95% confidence interval [0.610, 0.794]; p < .001). Handgrip strength ≥24.9 had sensitivity of 63.5% and 1-specificity of 25% for patient survival on the primary endpoint.
Citation: Journal of Aging and Physical Activity 2025; 10.1123/japa.2023-0298
The participants were categorized into two groups based on a cutoff HGS value of 24.9 kg. Participants with HGS < 24.9 kg were placed in the low HGS group, and those with HGS ≥ 24.9 kg were placed in the normal HGS group. Participants with lower HGS were more likely to be older adults and female, and less likely to be current smokers or alcohol drinkers. These participants also tended to have lower body mass index, basal metabolic rate, left ventricular diastolic dimension, and hemoglobin level. However, they exhibited a higher E/e ratio on echocardiography. The etiology of HF did not differ significantly between groups (Supplementary Table S3 [available online]). The value of VO2max was lower in the lower HGS group compared with the normal HGS group (17.0 [5.5] ml·kg−1·min−1 vs. 21.9 [5.6] ml·kg−1·min−1, respectively; p < .001). The mean distance on 6MWT in the lower HGS group was 306.97 m (117.14 m), 118.66 m (16.22 m) less than that in the normal HGS group (Supplementary Figure S4 [available online]). Additionally, the average actual MET in the lower HGS group of 4.86 (1.56) was 1.40 (0.27) less than that in the normal HGS group.
Participants with lower HGS had an HR of 4.40 (95% CI [2.07, 9.36]; p < .001) for the composite of readmission for HF or all-cause death (Table 3). This was largely attributed to the increased risk of readmission for HF (HR, 4.76; 95% CI [2.15, 10.53]; p < .001). Participants with lower HGS had no significant increase in risk of all-cause death, cardiac death, acute myocardial infarction, revascularization, or stroke compared with the normal HGS group (Supplementary Figure S5 [available online]). However, the risk of major adverse cardiovascular events (composite of all-cause death, readmission for HF, acute myocardial infarction, revascularization, or stroke) increased significantly in participants with lower HGS (HR 2.36; 95% CI [1.35, 4.13]; p = .003) compared with the normal HGS group. This relationship between lower HGS and the primary outcome was similar even after adjusting for confounding factors (HR, 6.33; 95% CI [2.80, 14.28]; p < .001). After adjusting for age in a competing risk model, patients with lower HGS had a higher risk of first readmission for HF (Sub-HR 4.14; 95% CI [1.99, 8.61]; p < .001; Supplementary Table S4 [available online]). The time-to-event curve and log-rank test of the primary outcome between the two groups are shown in Figure 4.
Clinical Outcomes
| End points | Lower HGS (n = 77) | Normal HGS (n = 96) | HR [95% CI] lower HGS vs. normal HGS | |||
|---|---|---|---|---|---|---|
| Crude | p | Adjusteda | p | |||
| Primary outcome | ||||||
| Readmission for HF or all-cause death | 27 (35.1) | 9 (9.4) | 4.40 [2.07, 9.36] | <.001 | 6.44 [2.86, 14.50] | <.001 |
| Secondary outcomes | ||||||
| Readmission for HF | 26 (33.8) | 8 (8.3) | 4.76 [2.15, 10.53] | <.001 | 6.75 [2.87, 15.88] | <.001 |
| All-cause death | 7 (9.1) | 4 (4.2) | 2.39 [0.70, 8.15] | .166 | 4.24 [0.85, 21.10] | .078 |
| Cardiac death | 3 (3.9) | 2 (2.2) | 1.84 [0.31, 11.01] | .505 | 5.60 [0.19, 165.76] | .319 |
| Myocardial infarction | 5 (6.5) | 3 (3.1) | 2.36 [0.56, 9.87] | .241 | 3.08 [0.51, 18.71] | .222 |
| Revascularization | 7 (9.1) | 10 (10.4) | 0.96 [0.37, 2.53] | .766 | 1.61 [0.53, 4.88] | .397 |
| Stroke | 4 (5.2) | 3 (3.1) | 1.90 [0.43, 8.51] | .400 | 1.75 [0.22, 13.73] | .592 |
| Ischemic stroke | 2 (2.6) | 2 (2.1) | ||||
| Hemorrhagic stroke | 2 (2.6) | 1 (1.0) | ||||
| MACE | 32 (41.6) | 20 (20.8) | 2.36 [1.35, 4.13] | .003 | 2.74 [1.49, 5.04] | .001 |
Note. HRs, 95% CIs, and p values were calculated using Cox regression analysis. CI = confidence interval; BMI = body mass index; HF = heart failure; HGS = handgrip strength; MACE = major adverse cardiovascular events, including all-cause death, readmission for HF, recurrent myocardial infarction, revascularization, or stroke.
aMultivariable Cox proportional hazard regression model was adjusted for age, sex, BMI, smoking, alcohol consumption, hypertension, diabetes mellitus, HF etiology, and left ventricular ejection fraction.
Cumulative Kaplan–Meier event analysis of readmission for heart failure or all-cause death. The upper curve (dashed red line) shows the cumulative risk for handgrip strength < 24.9 kg, while the lower curve (solid blue line) shows risk for handgrip strength ≥ 24.9 kg.
Citation: Journal of Aging and Physical Activity 2025; 10.1123/japa.2023-0298
Univariate analysis showed that only age (HR 1.03; 95% CI [1.00, 1.06]; p = .029) and lower HGS (HR 4.40; 95% CI [2.07, 9.36]; p < .001) were predictors of primary outcome (Table 4). However, during multivariable analysis, male (HR 3.22; 95% CI [1.36, 8.15]; p = .009), alcohol consumption (HR 0.41; 95% CI [0.18, 0.94]; p = .035), LVEF (HR 0.93; 95% CI [0.89, 0.98]; p = .002) and lower HGS (HR 6.44; 95% CI [2.86, 14.50]; p < .001) emerged as independent predictors of primary outcome. The Harrell’s C-index of the multivariable model was 0.776 for the primary outcome. Meanwhile, the C-indexes of the models for readmission for HF and all-cause death, 0.780 and 0.670, respectively.
Independent Predictors for Primary Outcome
| Univariable analysis | Multivariable analysis | |||
|---|---|---|---|---|
| HR [95% CI] | p | HR [95% CI] | p | |
| Sex (male) | 0.88 [0.40, 1.93] | .876 | 3.22 [1.36, 8.15] | .009 |
| Age (per 1 year increase) | 1.03 [1.00, 1.06] | .029 | ||
| Alcohol | 0.53 [0.24, 1.17] | .116 | 0.41 [0.18, 0.94] | .035 |
| Smoking | 1.29 [0.59, 2.83] | .528 | ||
| BMI (per 1 unit increase in kg/m2) | 0.96 [0.88, 1.05] | .374 | ||
| Hypertension | 1.01 [0.52, 1.93] | .967 | ||
| Diabetes mellitus | 0.80 [0.41, 1.58] | .525 | ||
| HF etiology | ||||
| Ischemic cardiomyopathy | 1.07 [0.55, 2.07] | .842 | ||
| Dilated cardiomyopathy | 1.01 [0.36, 2.87] | .981 | ||
| Valvular heart disease | 0.46 [0.18, 1.19] | .109 | ||
| Hypertensive heart disease | 1.66 [0.64, 4.27] | .295 | ||
| Other causes | 0.31 [0.07, 1.28] | .106 | ||
| LVEF (per 1% increase) | 0.96 [0.93, 1.00] | .066 | 0.93 [0.89, 0.98] | .002 |
| Lower HGS | 4.40 [2.07, 9.36] | <.001 | 6.44 [2.86, 14.50] | <.001 |
Note. Backward selection multivariable analysis used all covariates listed in univariable analysis. HGS = handgrip strength; HF = heart failure; CI = confidence interval; LVEF = left ventricular ejection fraction; BMI = body mass index; HGS = handgrip strength.
Discussion
The main findings of the present study are as follows: (a) HGS had a significant relationship with exercise capacity in patients with HF referred for exercise testing; (b) HGS was a strong predictor of attainable distance levels during the 6MWT and various MET levels; and (c) in this population, lower HGS was significantly associated with increased risk of composite of readmission for HF or all-cause death, but was primarily driven by readmission for HF. HGS correlated linearly with both distance on 6MWT and MET in individuals with HF. In addition, it was the only factor that consistently predicted all exercise capacity categories, and was among the most robust predictors in each category. Moreover, its prognostic value for primary outcomes was comparable to that of other exercise parameters, such as age, actual MET, and distance on 6MWT. The risk for composite of readmission for HF or all-cause death in multivariable analysis was increased sixfold in participants with lower HGS (<24.9 kg) compared with those with normal HGS (≥24.9 kg). Lower HGS (<24.9 kg) was one of the strongest predictors of primary outcome even after adjusting for confounding factors. Model performance was good, reflecting the high prognostic value of HGS. This was particularly evident in predicting readmission for HF, along with LVEF.
Although HGS was strongly associated with readmission for HF, the absence of a strong association between HGS and all-cause or cardiovascular death was unexpected. Notably, despite our study showing no statistically significant difference in mortality, divergence was observed in the Kaplan–Meier curve for cumulative risks, indicating a possible trend toward significance. The lack of statistical significance might be due to the small sample size or relatively short follow-up. An observational study of 148 Japanese patients with HF demonstrated that HGS was associated with mortality during a mean follow-up period of 3.6 years (Izawa et al., 2009). However, all-cause death was reported in 15 patients (10%) at almost 1.5 times the death rate in our study (6.4%). Although inclusion criteria required an LVEF of <45%, more than half of patients did not receive optimal medical therapy for HF.
Previous studies have highlighted muscle wasting in patients with HF. The Studies Investigating Comorbidities Aggravating Heart Failure (SICA-HF) showed muscle atrophy as a frequent comorbidity with 19.5% prevalence among patients with HF; in addition, it is associated with lower exercise capacity. Other cross-sectional studies have also reported a relationship between quadriceps strength and exercise capacity in this disease subset (Cicoira et al., 2001; Hülsmann et al., 2004; Izawa et al., 2012). Considering the accuracy of HGS in reflecting an individual’s muscle strength status (Bohannon, 2019; Lauretani et al., 2003; Lee, 2021), it is reasonable to assume that it can predict exercise capacity in patients with HF. To our knowledge, the relationship between HGS and exercise capacity parameters, such as MET, or distance on 6MWT has not yet been investigated in patients with HF.
Growing evidence suggests that HGS also has a prognostic value in diverse populations. The Prospective Urban–Rural Epidemiology (PURE) study reported an association between HGS and mortality and cardiovascular events in the general population (Leong et al., 2015). Furthermore, similar associations have been reported in populations with diabetes mellitus, chronic kidney disease, cardiac disease, and cancer (Hamasaki et al., 2017; Kim et al., 2019; Kotoh et al., 2020; Lopez-Jaramillo et al., 2014; Pavasini et al., 2019). However, studies on the prognostic value of HGS, particularly in patients with HFrEF, are limited. One strength of our study is its specific focus on patients with HFrEF. In contrast to previous studies that included both HFrEF and HF with preserved ejection fraction (Izawa et al., 2009; Laukkanen et al., 2020; McNallan et al., 2013; Onoue et al., 2016), our inclusion criteria were strictly defined as a LVEF of <40%. Thus, we aimed to provide insights into HGS, exercise capacity, and prognosis of the population of interest.
The intricate interactions among various factors contributing to HGS and cardiovascular outcomes present challenges in identifying the underlying mechanisms. Earlier studies have suggested that exercise capacity, including distance on 6MWT and VO2 max, is associated with readmission for HF or mortality (Alahdab et al., 2009; O’Neill et al., 2005; Shah et al., 2001; Tabata et al., 2014). In addition to muscle mass and overall strength, HGS can predict incidence of chronic illness, nutritional status, physical activity, quality of life, and independence in daily life (Kerr et al., 2006; Parahiba et al., 2021; Taekema et al., 2010). Thus, individuals with higher HGS are more likely to participate in resistance training (Ruiz et al., 2008) and leisure-time physical activity (Dodds et al., 2013), and exhibit better cardiorespiratory fitness (Kandola et al., 2020). Taken together, these factors could protect against cardiovascular events. Frailty is measured by the extent of functional capacity decline across numerous organ systems, and is often linked to muscle fatigue, and increased vulnerability to symptomatic HF (Dent et al., 2016; Talha et al., 2023). Decrease in relative HGS also signifies frailty, sarcopenia, and age-related muscle mass loss (Sousa-Santos & Amaral, 2017); these factors play a major role in reduction of muscle strength, leading to readmission for HF (Lee et al., 2021). Excessive activation of the sympathetic nervous system and upregulation of cytokines can also lead to muscle wasting and reduced muscle mass (Lavine & Sierra, 2017), which are associated with a decrease in VO2max or distance on 6MWT.
This study underscores the potential utility of HGS as a risk assessment tool in clinical settings. HGS is a relatively simple, inexpensive, and practical method for stratifying the risk of cardiac events. Additionally, HGS may offer an alternative method for evaluating exercise capacity, particularly in patients with cardiac disorders. This is particularly relevant in clinical decision-making for these patients, as it presents an opportunity to integrate physical performance assessment into the decision-making process. The role of muscular fitness in cardiovascular health could not be overemphasized. Muscle-strengthening activities play a key role in maintaining and improving muscle strength and represent a promising intervention pathway. Therefore, resistance training as an intervention method may offer clinical benefits regarding cardiovascular health, including improvements in muscle mass, strength, endurance and overall structural integrity (Williams et al., 2007). Therefore, future research should consider the impact of muscle-strengthening activities, such as resistance training on enhancing cardiovascular health, and mitigating risk of HF readmission. Altogether, HGS could be both an important risk assessment tool and a critical target for potential interventions designed to improve patient outcomes.
This study has several limitations. First, HGS varies between sexes, and male and female patients differ in cardiac condition, physiological outcomes, and psychosocial and physical activity (Izawa et al., 2008). However, our relatively small sample size meant that the results of sex-specific analyses regarding cardiovascular events were statistically insignificant. Of 173 participants, 141 (81.5%) were male. Further studies with larger sample sizes are warranted to reliably investigate potential sex differences in the influence of HGS on exercise capacity and prognosis. Second, due to the observational nature of this study, we cannot draw firm conclusions about the causal relationship between HGS and cardiac events. Although we adjusted for potential confounders, residual confounders may still affect the association between HGS and study outcomes. Third, owing to its relatively small sample size and short follow-up, this study was underpowered to establish significant differences in hard outcomes, such as mortality or acute myocardial infarction. Although mortality was higher in patients with lower HGS, the incidence was too low to achieve statistical significance. Additionally, the competing risk analysis using the Fine and Gray method was adjusted only for age due to limitations in the sample size and the small number of all-cause death events (as a competing risk), which prevented proper fitting of a more comprehensive multivariable model. Fourth, we observed only moderate correlation between HGS and exercise capacity, with correlation coefficients of .564 for the 6MWT and .419 for the METs. This indicates that a higher HGS does not guarantee a higher exercise capacity. Nonetheless, our findings suggest that HGS measurement can be predictive of whether an individual is likely to attain a specific level of exercise capacity. Finally, as this was a single-center study of patients with HF conducted at a tertiary medical center in Korea, results may not apply to other populations. Specifically, the cutoff value of 24.9 kg of HGS requires external validation in different population subsets with HFrEF to confirm its predictive value. Nevertheless, the significant findings of our study encourage the pursuit of larger multicenter studies with extended follow-up periods to further validate and reinforce our results.
Conclusions
In the present study, HGS was positively associated with exercise capacity and was an important prognostic index in patients with HFrEF referred for cardiac rehabilitation. Lower HGS was associated with a higher risk of readmission for HF, and all-cause death. This highlights the importance of assessing and monitoring HGS as a means of evaluating exercise capacity, overall health, and the potential risk of adverse outcomes in patients with HF.
Acknowledgments
The authors thank all patients and practitioners who took part in the research.
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