REJUVENATION RESEARCH Volume 17, Number 4, 2014 ª Mary Ann Liebert, Inc. DOI: 10.1089/rej.2014.1549

Relationship Between Respiratory Muscle Strength and Physical Performance in Elderly Hospitalized Patients Renato Giua,1 Claudio Pedone,1,2 Simone Scarlata,1 Irma Carrozzo,1 Francesca Flavia Rossi,1 Vincenzo Valiani,1 and Raffaele Antonelli Incalzi ,3

Abstract

Background: Age-related changes in pulmonary function increase respiratory muscle work. In the face of this increased demand, poor muscle mass, frequently associated with age and multi-morbidity, can reduce endurance and strength of respiratory muscles. Furthermore, poor muscle mass may per se contribute to exercise intolerance and lower physical performance. The aim of the study was to evaluate if respiratory muscle strength has a significant impact on physical performance in an elderly population. Methods: We included 68 patients (28 men and 40 women) aged over 65 years (mean 79 years, standard deviation [SD] 6) in stable condition at discharge from our acute care geriatric ward. We assessed the function of respiratory muscle by measuring maximal inspiratory and expiratory pressures (MIP, MEP) and physical function using the 6-Minute Walk Test (6MWT). Results: The mean age of our sample was 78.2 years (SD 6.1). There was a statistically significant correlation between MIP or MEP and 6MWT distance (MIP, r = 0.43, p < 0.001; MEP, r = 0.41, p = 0.001). The association between respiratory pressures and 6MWT was maintained after adjustment for forced expiratory volume in 1 sec (FEV1), forced vital capacity (FVC), age, sex, fat-free mass index (FFMI), and leg strength. The multiple regression model showed a significant relation between 6-Minute Walk Test distance (6MWD) and both MIP and MEP after correction for sex, age, FEV1, and FVC. Furthermore, MEP can significant predict poorer performance at 6MWD in a multiple logistic regression model. Conclusion: Reduced respiratory muscle strength is independently associated with worse physical performance in elderly patients.

Introduction

T

he aging process causes a reduction of skeletal muscle strength that also affects the respiratory muscles. Starting from the fourth decade, it is reported a decline of about 1% per year of maximal inspiratory and expiratory pressures (MIP, MEP).1 This decline might be attributed to the general ageassociated loss of muscle mass (sarcopenia), but the existence of sarcopenia in respiratory muscles has only recently been demonstrated in animal models.2 However, no data are available in humans. Reduction of MIP and MEP, in turn, may contribute to a reduction in vital capacity (VC), forced vital capacity (FVC), and total lung capacity (TLC).3 A similar proportional reduction in maximum voluntary ventilation has also been observed.4 Moreover, it should be noted that patients with respiratory muscle weakness often show episodic desaturation due to hypopnea or apnea during rapid eye 1 2 3

movement (REM) sleep.5 Older adults frequently show reduced exercise tolerance and lower physical performance. This is related to poor muscle mass, reduction in cardiovascular capacity, and reduction in joint mobility.6 The relationship between aging and reduced physical performance has been studied extensively; however, the relationship between strength of respiratory muscles and lower physical performance has received lesser attention. Only two studies have investigated the relationship between respiratory muscle strength and physical performance,7,8 but they focused on relatively young people (mean age 63 years for the first study, and 50–79 years for the second one) and take into account only chronic obstructive pulmonary disease (COPD) patients and healthy older adults, respectively. Our hypothesis is that lower respiratory muscle strength is related to poorer physical function through its effect on respiratory function, but independently of the presence of

Area di Geriatria, Policlinico Universitario ‘‘Campus Bio-Medico’’, Rome, Italy. Fondazione ‘‘Alberto Sordi’’, Rome, Italy. San Raffaele—Cittadella della Carita`, Taranto, Italy.

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variations in lung volumes or poor muscle mass. The aim of this study was to evaluate whether reduced MIP or MEP is related to physical performance independently of skeletal muscle mass and strength and spirometric abnormalities. Methods Subjects

We enrolled 68 patients admitted to a Geriatric Unit of a teaching hospital in Rome. Patients were consecutively enrolled during a 1-year period. Inclusion criteria were age over 65 years and agreement to participate in the study. We excluded patients with cognitive impairment (patients unable to complete geriatric or physical evaluation or uncooperative patients), those unable to walk or to complete spirometric exams, patients with active malignancies, or glomerular filtration rate (GFR) < 30 mL/hr. All data were collected after the patients regained stability. Spirometry and maximal respiratory pressure measurement

Spirometry was performed according to American Thoracic Society (ATS)–European Respiratory Society (ERS) guidelines using a water-sealed Stead–Wells spirometer (Biomedin, Padua, Italy). Equations of the European Coal and Steel Community were used as reference equations.9 Spirometries were assessed according to reproducibility and acceptability criteria of the ATS.10 Maximal respiratory pressures were measured according to ATS/ERS guidelines using the previously mentioned spirometer, with a specific pressure sensor and a latex mouthpiece to prevent air losses, according to the ATS/ERS criteria.11 MIP was measured at the residual volume (RV), and MEP was measured at total lung capacity. 6-Minute Walk Test

We used as outcome variable the distance walked in 6 min. The 6-Minute Walk Test (6MWT) has been proved to reliably measure the impact on exercise capacity and endurance in older adults12 with different co-morbidities, including cardiovascular and lung diseases, arthritis, diabetes, cognitive dysfunction, and depression. The test was performed according to ATS guidelines.13 The patient was invited to walk for 6 min at the maximal speed possible with monitoring of oxygen hemoglobin saturation and cardiac frequency. The distance walked (6MWD) was recorded as an absolute value and as a percent of the predicted value.13 Geriatric evaluation

To explore the relationship between physical performance and functional status, personal independence was evaluated by three scales. The Activities of Daily Living (ADL) used the six-item Katz scale14; the Instrumental Activities of Daily Living (IADL) used the eight-item Lawton scale.15 The cognitive evaluation was carried out with a 10-item questionnaire, the Abbreviated Mental Test (AMT), as described by Hodkinson in 1972.16 In this test, a score of 6 or less suggests a cognitive impairment. Prevalence of co-morbidities was estimated with the Cumulative Illness Rating Scale (CIRS), according to Parmelee et al.17

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Bioelectrical impedance analysis

Bioelectrical impedance analysis (BIA) was undertaken to assess body composition. As an index of muscle mass, we considered the fat free mass index (FFMI), calculated as fat free mass expressed in kg/height squared, expressed in meters. BIA was measured using the impedance analyzer BIA 101 (Akern, Florence, Italy). Lower limb strength evaluation

The lower limb strength was evaluated using a hand-held dynamometer (MSC Digital Series, Chatillon, Ametek Inc., USA), with the best of three measurements of the maximum strength produced by flexing the thigh on the abdomen against resistance. The mean value of left and right thighs was considered. Analytic approach

The characteristics of the study sample were reported using descriptive statistics (means and standard deviation [SD] for continuous variables, proportions for categorical variables). The distribution of variables of interest was checked, without finding significant departures from normality. The association with the 6MWD was estimated using the Pearson coefficient of correlation for numeric variables and using the t-test for dichotomous variables. To evaluate whether respiratory pressure is associated with the 6MWD independently of potential confounders, we used linear regressions models including measures of respiratory function, body composition, and muscle strength, as well as all the other variables associated with the outcome in the unadjusted analysis. Nested models were used to evaluate the effect of the inclusion of the variables of interest on the explained variance expressed using the adjusted R2. The base model included only age, sex, and diagnosis of heart failure. The second and third models included MIP and MEP, respectively, whereas the fourth model also included FEV1 and FVC. The models were compared using the F statistic. We also estimated the risk of having a poor physical performance associated with reduced respiratory pressures. For this analysis, the outcome variable was being in the lowest quartile of the 6MWD distribution, and a multivariable logistic model was used to adjust for potential confounders. To provide clinically meaningful estimates, in this analysis MIP and MEP were rescaled so that the odds ratios (ORs) expressed the risk associated with 5% variation of these variables. In all analyses, 6MWD was expressed in meters; a sensitivity analysis using 6MWD expressed as percent of predicted was also performed, without adjustment for age and sex in the multi-variable analysis. All the analyses were performed using SPSS 20.0 for Windows 7 (IBM Inc., 2011, USA). Results

The mean age was 78 years (SD 6); 40 participants were women, and the mean body mass index (BMI) was 27.8 kg/m2 (SD 6.9). Geriatric evaluation showed on average a good functional status; 20.6% of patients had an impairment in at least one of the ADLs and 47.1% in one of he IADLs.

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Table 1. Global Distribution Data and Correlation to the 6-Minute Walk Test Distance Correlationa (p value)

Mean value/ frequency 78.2 – 6.1 73.9 – 19.5 1.63 – 0.09 41.2% 27.8 – 6.9 20.6% 47.1% 8.8 – 1.6 1.7 – 0.5 88.1 – 24.3 2.3 – 0.7 93.6 – 25.0 74 – 10 58.3 – 34.3 65.6 – 31.8 12.4 – 3.7 19.1 – 3.2 26.5% 25% 33.8% 5.9%

Age Weight Height Sex (men) BMI ADL deficiency(s) IADL deficiency(s) AMT FEV1 (L) FEV1 (%) FVC (L) FVC (%) FEV1/FVC MIP MEP Leg strength FFMI Heart failure COPD Type 2 diabetes melltius Renal failure

0.405 - 0.03 0.266 1.31 - 0.121 - 6.36 - 4.78 0.085 0.493 0.225 0.556 0.295 - 0.134 0.431 0.405 0.379 - 0.79 - 3.219 1.31 - 1.70 - 0.675

(0.001) (0.806) (0.029) (0.196) (0.325) (< 0.001) (< 0.001) (0.489) (< 0.001) (0.65) (< 0.001) (0.014) (0.281) (< 0.001) (0.001) (0.001) (0.524) (0.003) (0.202) (0.097) (0.545)

a Pearson’s coefficient for numeric variables, t-test for dichotomous variables. BMI, body mass index; ADL, Activities of Daily Living; IADL, Instrumental Activities of Daily Living; AMT, Abbreviated Mental Test; FEV1, forced expiratory volume in 1 sec; FVC, forced vital capacity; MIP, maximal expiratory pressure; MEP, maximal expiratory pressure; FFMI, free fat mass index; COPD, chronic obstructive pulmonary disease.

Cognitive function was also on average good, with a mean AMT score of 8.8 (SD 1.6). The mean CIRS score was 2.3 (SD 1.6). In addition, 18 patients (26.5%) were affected by heart failure, 44 (64.7%) by hypertension, 23 (33.8%) by type 2 diabetes mellitus, 7 (10.3%) by coronary disease, and 17 (25%) by COPD. Data on body composition and lower

limb strength showed a mean FFMI of 19.1 kg/m2 (SD 3.2) and a mean lower limb strength of 12.4 kg (SD 3.7). The mean FEV1 % of predicted was of 88% (SD 24) and a mean FVC % of predicted of 94% (SD 25%). The mean 6MWD was 272.7 meters (SD 10.8). Mean respiratory muscle strength measurements (% of predicted) were 58.3% (SD 34.3) for MIP and 65.6% (SD 31.8) for MEP (Table 1). We found a positive correlation between 6MWD and both MIP (r = 0.43, p < 0.001) and MEP (r = 0.41, p = 0.001). The corresponding regression coefficients obtained from unadjusted linear models were 1.12 ( p < 0.001) and 1.13 ( p = 0.001). After adjustment for sex, age, FEV1, FVC, FFMI, leg strength, and diagnosis of heart failure, the coefficients were only partially modified: 0.68 for MIP ( p = 0.01) and 1.00 for MEP ( p < 0.001). The model not including respiratory pressures explained 37% of the total variance. After the inclusion of MIP, the R2 adjusted for number of variables increased to 42.9% (F = 7.17, p = 0.01), while the inclusion of MEP increased the adjusted R2 to 49.5% (F = 15.77, p < 0.001) (Table 2). All the results were confirmed using the 6MWD expressed as % of predicted (Table 2). Compared to a model only including sex, age, and presence of heart failure, the inclusion of MIP increased the adjusted R2 by 12% (from 0.22 to 0.34, F = 12.60 p = 0.001), whereas the corresponding increase including MEP was 10% (from 0.22 to 0.32, F = 10.29, p = 0.002). Finally, the inclusion of spirometric variables along with MIP and MEP increased adjusted R2 to 0.42 (F = 6.58, p < 0.001) (Table 3). For each 5% reduction of MIP, there was a 6.4% increase in the probability of being in the lowest quartile of 6MWD (< 205 mt), with an adjusted OR of 1.06 (95% confidence interval [CI] 0.93–1.22). The corresponding figures for 5% reduction of MEP were 1.24, with a probability of 24% to be in the lowest quartile of 6MWD (95% CI 1.04–1.47) (Table 4). Discussion

We found a positive and significant correlation between physical performance expressed by the distance walked in

Table 2. Linear Regression Models of 6MWD in a Simple Model and in Model with MIP or MEP

2

R corrected Fa

Age Sex FEV1 % FVC % Leg strength FFMI Heart failure MIP MEP a

Model A

Model B

Model C

0.370 6.63

0.429 7.17

0.495 15.77

Bb

p value

Bb

p value

Bb

p value

- 5.78 41.76 0.21 0.89 6.76 - 5.27 - 19.03 — —

(< 0.001) (0.05) (0.75) (0.19) (0.01) (0.1) (0.38) — —

- 5.23 38.84 0.2 0.7 5.45 - 5.1 - 21.36 0.68 —

(< 0.001) (0.06) (0.76) (0.28) (0.03) (0.10) (0.30) (0.01) —

- 4.96 41.40 0.38 0.85 7.36 - 4.16 - 9.90 — 0.99

(< 0.001) (0.03) (0.53) (0.16) (0.002) (0.15) (0.61) — (< 0.001)

Analysis of variance (ANOVA). Regression coefficient. MIP, maximal expiratory pressure; MIP, maximal inspiratory pressure; FEV1, forced expiratory volume in 1 sec; FVC, forced vital capacity; FFMI, free fat mass index. b

RESPIRATORY STRENGTH AND PHYSICAL PERFORMANCE

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Table 3. Linear Regression Between 6-Minute Walking Test Distance (Meters) and Sex, Age, Presence of Heart Failure (for Each Model), MIP (For Model B and D), MEP (for Models C and D), and FEV1 and FVC (For Model D)

2

R corrected Fa

Age Sex Heart failure MIP MEP FVC % FEV1 %

Model A

Model B

Model C

Model D

0.22 7.22

0.34 12.60

0.32 10.29

0.42 6.58

Bb

p value

Bb

p value

Bb

p value

Bb

p value

- 5.3 23.9 - 51.5 — — — —

0,001 0.22 0.02 — — — —

- 4.5 21.5 - 48.4 0.93 — — —

0,004 0.24 0.02 0.001 — — —

- 4.7 25.8 - 43 — 0.92 — —

0,003 0.16 0.04 — 0.002 — —

- 4.5 45.9 - 24 0.45 0.74 1.1 0.01

0,002 0.016 0.24 0.13 0.02 0.98 0.086

a

Analysis of variance (ANOVA). Regression coefficient. MIP, maximal inspiratory pressure; MEP, maximal expiratory pressure; FEV1, forced expiratory volume in 1 sec; FVC, forced vital capacity. b

6 min and the function of respiratory muscles expressed by maximum respiratory pressures. This association was independent of the skeletal muscle mass and leg strength. In addition, it is interesting to note that both MIP and MEP show a significant regression coefficient in the regression model including sex, age, and presence of heart failure (Table 3). To our knowledge, there are only a few studies investigating the association between maximum respiratory pressures and physical performance in older adults. Dourado et al. found a significant correlation between MIP and 6MWD (r = 0.53 p < 0.01),7 with an adjusted regression coefficient of 1.806 ( p = 0.002). This study included 38 COPD patients without cardiovascular disease (mean age 62.8 – 8.8 years).7 Our study expands these findings on a larger and less selected sample. In a group of healthy, community-living older adults, Watsford et al. found significant correlations between walking speed and MEP in males (r = 0.35, p < 0.05) and between walking speed and MIP during a 2-min stage inspiring against resistance, both in males (r = 0.40, p < 0.05) and females (r = 0.35, p < 0.05).8 This study, however, was performed on healthy subjects who were much younger (mean age 50 years)

than ours. Furthermore, our results are adjusted for indexes of respiratory function. Finally, it should be noted that we used the 6MWD, which is a test for submaximal performance,18 as an index of physical performance, whereas in the study mentioned above the average walking speed over 1 mile was used. Our results are in line with others showing that respiratory muscle strength is related to mobility decline (even after correction for lower extremity strength)19 and mortality in the elderly.20 Furthermore, lower respiratory muscle strength is related to an increased incidence of loss of the ability to ambulate even after correction for potential confounders such as body composition or physical activity.21 This evidence suggests that the negative prognostic implications of reduced respiratory muscle strength is not be confounded by pulmonary function and/or poor muscle mass. Our data, indeed, indicate that MIP and MEP are directly associated with physical performance independently of FEV1, FVC, FFMI, and leg strength. In addition, models also taking into account MIP and MEP can explain more variation in physical performance compared to the model only including clinical variables. Compared to MIP, MEP was more strongly associated with the risk of being in the lower quartile of physical

Table 4. Logistic Regression Model of 5% Percentiles of MIP or 5% Percentiles of MEP and 6MWD Panel A

Age Sex Heart failure FEV1 % FVC % FFMI Leg strength 5% MIP

Panel B

Odds ratio

p value

95% CI

0.87 1.35 0.44 0.99 1.03 0.91 1.22 1.06

0.03 0.72 0.27 0.69 0.36 0.43 0.09 0.36

0.76–0.99 0.26–6.90 0.10–1.90 0.94–1.04 0.97–1.09 0.73–1.14 0.97–1.53 0.93–1.22

Age Sex HF FEV1 % FVC % FFMI Leg strength 5% MEP

Odds ratio

p value

95% CI

0.83 1.30 0.53 1.00 1.03 0.90 1.43 1.24

0.02 0.78 0.44 0.98 0.27 0.37 0.02 0.01

0.72–0.97 0.21–7.91 0.11–2.62 0.95–1.06 0.98–1.09 0.71–1.14 1.06–1.93 1.04–1.47

MIP, maximal inspiratory pressure; MEP, maximal expiratory pressure; 6MWD, 6-Minute Walk Test distance; CI, confidence interval; FEV1, forced expiratory volume in 1 sec; FVC, forced vital capacity; FFMI, free fat mass index.

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function. This difference could be probably due to a methodological reason. Measurement of MIP is more demanding, and the maneuver may be performed sub-optimally by elderly patients, producing a biased estimate. In addition, measurement of MIP could be unreliable, although this bias can be limited if all of the recommendations for accuracy suggested by ATS/ERS statement are followed.11 In interpreting the relationship between respiratory muscles and physical performance, it should be noted that these muscles are not only involved in respiration; therefore, their relationship with physical function might be independent of their role in determining respiratory performance. This is particularly true for muscles such as the abdominal muscles, but non-respiratory functions have been established also for the diaphragm,22,23 which plays a role in stabilizing the spine and it is also activated in advance of limb muscles during destabilizing postural disturbance.24,25 Our study has some limitations. First, the small sample size may have resulted in limited power for detecting a significant association of some of the variables taken into account with the outcome. Second, we recruited our subjects in an acute care ward, and although they were in stable condition when the assessment was made, we cannot exclude that a ‘‘carry-over’’ effect of the acute illness may have influenced the physical performance. Third, our patients were relatively un-selected and as such affected by multiple chronic conditions. Many co-morbidities may influence physical performance, but there are only a few that can also impair respiratory muscle strength, the most obvious being COPD and heart failure. We accounted for the confounding effect of these conditions by adjusting our analysis for indexes of respiratory function and the presence of heart failure. Fourth, COPD affects diaphragm strength in patients with a high residual volume/total lung capacity ratio. Furthermore, dynamic hyperinflation could per se affect 6MWD, and basal FEV and FEV1 are not representative of exercise FVC reduction secondary to air trapping. Finally, the body composition was evaluated using BIA, which is an easy, relatively low-cost, and widely available technique, but its results may be affected by multiple confounders.26 In conclusion, our data suggest that respiratory muscle strength in an elderly population is associated with physical performance. MIP and MEP could be useful for both discriminatory and prognostic purposes in elderly populations. Acknowledgments

This is an original work; it has never been published nor is under consideration for publication elsewhere. All the authors have substantially contributed to data collection and manuscript drafting and revision. Author Disclosure Statement

No competing financial interests exist. References

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Address correspondence to: Renato Giua Area di Geriatria–Universita` Campus Bio-Medico Via Alvaro del Portillo 200 00128 Rome Italy E-mail: [email protected] Received: January 19, 2014 Accepted: April 21, 2014