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Abstract

Background:

Chronic obstructive pulmonary disease (COPD) is associated with airflow limitation resulting from a combination of small airway disease (SAD) and parenchymal destruction. Although various diagnostic methods for SAD exist, access to these tools can be limited.

Objectives:

This study aimed to explore the correlation between handgrip strength (HGS) and SAD in COPD patients.

Design:

Cross-sectional prospective study.

Methods:

HGS was measured using a hand dynamometer. SAD was evaluated using impulse oscillometry, with results reported as the difference between respiratory resistance at 5 and 20 Hz (R5–R20). SAD was defined as R5–R20 ⩾0.07 kPa/L/s. The receiver operator characteristic (ROC) curves, sensitivity, and specificity values were calculated to determine the optimal cutoff value of HGS for predicting SAD.

Results:

Sixty-four patients (90.6% male) were included. The average age was 72.1 ± 8.3 years, and body mass index was 23.4 ± 4.2 kg/m². FEV₁ was 71.6 ± 21.3%, and HGS was 30.2 ± 8.1 kg. R5–R20 was 0.11 ± 0.08 kPa/L/s. SAD was found in 64.1% of patients. A negative correlation between HGS and R5–R20 was observed (r = −0.332, p = 0.007). The best cutoff value for HGS in detecting SAD was determined to be 28.25 kg, with a sensitivity of 73.9%, specificity of 65.9%, and an area under ROC curve of 0.685 (95% CI 0.550–0.819, p = 0.015).

Conclusion:

Registration:

This study was registered with ClinicalTrials.gov with number NCT06223139.

Plain language summary

Association of handgrip strength with small airway disease in COPD

In chronic obstructive pulmonary disease (COPD), the condition of small airways (those less than 2 mm wide) is closely linked to disease severity. Small airway disease (SAD) leads to reduced lung function, increased retention of excess gas in the lung, and poorer health in COPD patients. While various diagnostic methods exist for SAD, they may not always be accessible. This study investigated handgrip strength as a potential alternative way to assess SAD. Our findings show a significant negative correlation between handgrip strength and SAD in COPD patients. This suggests that handgrip strength could be used alongside existing methods to evaluate small airway dysfunction in these patients.

Keywords

chronic obstructive pulmonary disease handgrip strength impulse oscillometry respiratory resistance small airway disease

Introduction

Chronic obstructive pulmonary disease (COPD) is characterized by progressive airflow limitation and tissue destruction.¹ It is a leading cause of global mortality,² with 90% of cases occurring in low- and middle-income countries.³ Management of COPD follows the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guideline,¹ with treatment strategies based on symptom severity and exacerbation rate.³ Evidence indicates a correlation between pathology in small airways (less than 2 mm diameter) and COPD severity.⁴ Assessing small airway involvement is crucial for optimizing treatment, including the selection of inhaled medications.⁵ Because small airways disease (SAD) is associated with poor spirometry results, increased hyperinflation, and poor health status, it is an important treatment target in COPD management.⁶

Spirometry, a standard diagnostic tool, is commonly used to confirm COPD, classify its severity, and monitor disease progression. However, not all patients can perform spirometry adequately due to the forceful blowing and procedures it requires.^7,8 Small airway assessment involves spirometry parameters such as forced expiratory volume in 1 s (FEV₁)/forced vital capacity (FVC), FEV₁, and forced expiratory flow at 25%–75% of FVC (FEF_25–75). However, these parameters are relatively insensitive to early disease and subtle changes.⁹

Impulse oscillometry (IOS) is an alternative tool which measures airway resistance using sound waves of different frequencies.⁹ Capitalizing on the high sensitivity and specificity for small airways, IOS uses 5 Hz waves to penetrate small airways and 20 Hz waves to reach larger airways. The difference between the resistance measured at 5 Hz (R5) and 20 Hz (R20), referred to as R5–R20, indicates the resistance of the small airways.^5,9 Additionally, IOS measures reactance at 5 Hz (X5), with the difference between inspiratory and expiratory reactance at 5 Hz (∆X5) indicating expiratory flow limitation during tidal breathing.^10,11 The advantage of R5–R20 measurement is that it doesn’t require forced exhalation like spirometry, so it’s easier to use.^9,12 However, a limitation is that initial respiratory system disturbances, such as tongue movement or swallowing, can cause interference.¹²

An alternative method for assessing SAD might be handgrip strength (HGS), which is more accessible than IOS. HGS is used to diagnose sarcopenia¹³ and to assess various chronic conditions.^14,15 Lower HGS is associated with higher mortality and morbidity, and diminished health-related quality of life among COPD patients.¹⁶ Additionally, HGS correlates with peak inspiratory flow rate, which measures the inspiratory effort needed for drug inhalation.¹⁷ The correlation between HGS and SAD remains unexplored. Therefore, this study aimed to investigate the relationship between HGS and SAD in stable COPD patients.

Materials and methods

Study design and participants

A cross-sectional prospective study was conducted at Thammasat University Hospital, Thailand, from March 2023 to January 2024. COPD patients aged 40 years or older, whose diagnoses were confirmed by spirometry (post-bronchodilator FEV₁/FVC <70%), were included. Exclusion criteria were COPD exacerbation within 3 months, corticosteroid treatment within 6 weeks, inability to perform IOS or HGS assessment, tracheostomy, and requiring mechanical ventilation.

The study collected demographic information, smoking history, comorbidities, GOLD spirometry grade and group, exacerbation history, modified Medical Research Council dyspnea scale grade,¹⁸ and COPD Assessment Test (CAT) score¹⁹ and COPD medication data. Results including FEV₁, FVC, and FEF_25–75 of spirometry performed within 3 months prior to enrollment were also collected.

The GOLD criteria for COPD severity were based on FEV₁ value: grade 1 indicates mild (⩾80% of predicted value), grade 2 indicates moderate (50%–79% of predicted value), and grades 3 and 4 indicate severe and very severe (<50% and <30% of predicted values), respectively.¹ GOLD symptoms and risk were classified into four groups: A, B, C, and D, depending on dyspnea score and history of exacerbation.¹

This study followed the for Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline (Supplemental Material).²⁰

Procedures

SAD was evaluated using IOS performed with the Jaeger MasterScreen-IOS (Carefusion Technologies, San Diego, CA, USA). Patients were instructed to wear a nose clip and sit comfortably during tidal breathing with their neck slightly extended and lips sealed tightly around the mouthpiece while supporting their cheeks with their hands. At least three trials, each lasting 30 s, were conducted, and mean values were calculated. IOS data including R5, R20, R5–R20, X5, resonant frequency (Fres), and area of reactance (AX) were recorded. Results were expressed in kPa/L/s, kPa/L, Hz, or as a percentage of predicted values (%predicted). SAD was defined as R5–R20 being equal to or higher than 0.07 kPa/L/s.^21,22

HGS was measured by Jamar^® hand dynamometer (Asimow Engineering Co., CA, USA) and was reported in kilograms (kg). Patients performed the test while seated, with their dominant hand unsupported, wrist in neutral position, elbow flexed at 90°, and shoulder adducted. They were instructed to squeeze the hand dynamometer as hard as possible for 3–5 s. Three attempts were made with 1-min breaks between each attempt. The maximal value of the three efforts was recorded for final analysis.

Outcomes

The primary outcome was the correlation between HGS and SAD in clinically stable COPD patients. The secondary outcome was the best cutoff value of HGS to predict SAD in COPD patients.

Statistical analysis

The correlation between HGS and SAD in stable COPD patients has not previously been investigated. We hypothesized that HGS would be moderately correlated with R5–R20 (correlation coefficient of 0.4). The estimated sample size for correlation was calculated using 90% power and 5% type I error. Thus, a sample size of 62 participants was needed to demonstrate statistical significance.

Descriptive statistics are presented as number (%) and mean ± standard deviation. Pearson’s correlation was used to determine the correlation between HGS and SAD. To determine the set of variables associated with HGS, we used the linear regression model with R5–R20 as dependent variable. Independent variables: age, sex, height, FEV₁, CAT, and HGS were entered into the regression model. The receiver operator characteristic (ROC) curve was used to determine the best HGS cutoff value to predict SAD. Student’s t-test was used to compare HGS between two COPD stages. Analysis of variance was used to compare HGS across different COPD stages. A two-sides p-value <0.05 was considered statistically significant. Statistical analyses were performed using SPSS version 25.0 software (IBM corp., Armonk, NY, USA).

Results

Participants

Eighty-three COPD patients were screened. Sixty-four patients were included in the study (Figure 1). Ninety percent were men. The average age was 72.1 ± 8.3 years. Most patients were classified as GOLD grade 2 (46.9%) and COPD group B (56.3%). Common comorbidities included hypertension (23.4%) and diabetes (17.2%) (Table 1). CAT score was 16.3 ± 6.4 and HGS was 30.2 ± 8.1 kg (Table 1). Postbronchodilator FEV₁ was 71.6 ± 21.3%, R5–R20 was 0.11 ± 0.08 kPa/L/s, X5 was −0.18 ± 0.11 kPa/L, and AX was 1.86 ± 5.22 kPa/L (Table 2).

Figure 1.

Flowchart of COPD patient recruitment to the study.

Table 1.

Baseline characteristics of patients with COPD.

Characteristics	Data (n = 64)
Age, years	72.1 ± 8.3
Male sex	58 (90.6)
Height, m	1.64 ± 0.09
BMI, kg/m²	23.3 ± 4.2
Smoking, pack-years	22.2 ± 13.4
Dominant right hand	59 (92.2)
Comorbidity
Diabetes	11 (17.2)
Hypertension	15 (23.4)
Lung cancer	1 (1.6)
Bronchiectasis	1 (1.6)
COPD grade
1	22 (34.4)
2	30 (46.9)
3	11 (17.2)
4	1 (1.6)
COPD group
A	5 (7.8)
B	36 (56.3)
C	18 (28.1)
D	5 (7.8)
AECOPD in the last 1 year	15 (23.4)
AECOPD with hospitalization the in last 1 year	6 (9.4)
Functional performance
mMRC, points	2.05 ± 0.78
CAT, points	16.3 ± 6.4
6MWD, m	372.5 ± 93.9
HGS, kg	30.2 ± 8.1
Medication
SABA	10 (15.6)
SABA/SAMA	23 (35.9)
ICS/LABA	20 (31.3)
LAMA	19 (29.7)
LABA/LAMA	18 (28.1)
ICS/LABA/LAMA	19 (29.7)
PDE-4 inhibitor	14 (21.9)
Macrolide	4 (6.3)

Data presented as mean ± SD or n (%).

6MWD, 6-min walking distance; AECOPD, acute exacerbation of COPD; BMI, body mass index; CAT, COPD Assessment Test; COPD, chronic obstructive pulmonary disease; HGS, handgrip strength; ICS, inhaled corticosteroid; LABA, long-acting beta2-agonist; LAMA, long-acting muscarinic antagonist; mMRC, modified Medical Research Council; PDE-4, phosphodiesterase-4; SABA, short-acting beta2-agonist; SAMA, short-acting muscarinic antagonist.

Table 2.

Data on spirometry and impulse oscillometry.

Pulmonary functions	Data (n = 64)
Spirometry data
Post-bronchodilator FVC, L	2.76 ± 0.90
Post-bronchodilator FVC, %predicted	93.7 ± 20.6
FVC change after bronchodilator, %	5.2 ± 8.6
Post-bronchodilator FEV₁, L	1.60 ± 0.62
Post-bronchodilator FEV₁, %predicted	71.6 ± 21.3
FEV₁ change after bronchodilator, %	7.8 ± 8.2
Post-bronchodilator FEV₁/FVC, %	58.2 ± 15.6
FEF_25–75, L/s	1.26 ± 4.07
FEF_25–75, %predicted	33.9 ± 19.3
FEF_25–75 after bronchodilator, %	14.9 ± 17.9
Impulse oscillometry data
R5, kPa/L/s	0.39 ± 0.14
R5, %predicted	239.6 ± 953.3
R20, kPa/L/s	0.27 ± 0.07
R20, %predicted	96.3 ± 26.7
R5–R20, kPa/L/s	0.11 ± 0.08
X5, kPa/L	−0.18 ± 0.11
X5, %predicted	619.9 ± 502.6
Fres, Hz	18.26 ± 5.91
AX, kPa/L	1.86 ± 5.22

Data presented as mean ± SD or n (%).

AX, area of reactance; FEF_25–75, forced expiratory flow at 25%–75% of FVC; FEV₁, forced expiratory volume in 1 s; Fres, resonant frequency; FVC, forced vital capacity; R5–R20, resistance at 5 Hz minus 20 Hz; X5, reactance at 5 Hz.

Association between HGS and SAD

SAD was found in 64.1% of patients. A negative correlation between HGS and R5–R20 was observed (r = −0.332, p = 0.007; Figure 2). The equation for predicting SAD is R5–R20 (kPa/L/s) = 0.21 – 0.00317 x HGS (Figure 2). Linear regression analysis showed the correlation between R5–R20 and HGS adjusted by age, sex, height, FEV₁, and CAT (Table 3).

Figure 2.

The correlation between R5–R20 and hand grip strength (HGS). The equation for predicting SAD is R5–R20 (kPa/L/s) = 0.21 – 0.00317 x HGS.

Table 3.

Multiple logistic regression analysis for R5–R20 and handgrip strength adjusted by age, sex, height, FEV₁, and CAT score.

Variables	Regression coefficients	95% CI of coefficients	p-value
Intercept	0.502	0.022 to 0.983	0.041
HGS	−0.002	−0.004 to 0.000	0.093
Age	−0.001	−0.003 to 0.001	0.399
Female sex	0.010	−0.049 to 0.070	0.732
Height	−0.001	−0.004 to 0.001	0.267
FEV₁	−0.002	−0.002 to −0.001	<0.001
CAT	0.002	−0.001 to 0.006	0.108

CAT, Chronic obstructive pulmonary disease Assessment Test; FEV₁, forced expiratory volume in 1 s; HGS, handgrip strength; R5–R20, resistance at 5 Hz minus 20 Hz.

HGS cutoff value for detecting SAD

The best HGS cutoff value for detecting SAD was determined to be 28.25 kg, with 73.9% sensitivity, 65.9% specificity, and an area under the ROC curve of 0.685 (95% CI 0.550–0.819, p = 0.015). In contrast, the optimal cutoff values for FEV₁ and FEF_25–75 were 74.35% and 31.25%, respectively, each demonstrating a similar sensitivity of 73.9% (Figure 3; Table 4). HGS exhibited a lower area under the ROC curve compared to FEV₁ and FEF_25–75. However, HGS demonstrated higher specificity than FEF_25–75 (Figure 3; Table 4).

Figure 3.

The receiver operating characteristic plot of HGS, FEV₁, and FEF_25–75 for detecting small airway disease.

Table 4.

Cutoff values of handgrip strength and pulmonary functions for detecting small airway disease in patients with chronic obstructive pulmonary disease.

Variable	Cutoff value	AUC	95% CI	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)	p-value
HGS, kg	28.25	0.685	0.550–0.819	73.9	65.9	79.5	58.6	0.015
FEV₁, %	74.35	0.751	0.629–0.872	73.9	68.3	80.6	59.4	0.001
FEF_25–75, %	31.25	0.715	0.582–0.848	73.9	61.0	77.2	56.7	0.005

AUC, area under the ROC curve; CI, confidence interval; FEF_25–75, forced expiratory flow at 25%–75% of forced vital capacity; FEV₁, forced expiratory volume in 1 s; HGS, handgrip strength; NPV, negative predictive value; PPV, positive predictive value.

In addition, HGS in COPD grades 1, 2, and combined 3 and 4 were 29.19 ± 9.38, 32.18 ± 7.52, and 27.24 ± 6.16, respectively (p = 0.157; Table 5). HGS in COPD groups A, B, and combined C and D were 36.90 ± 5.41, 30.63 ± 9.17, and 28.14 ± 5.87, respectively (p = 0.081; Table 5). There was a significant difference in HGS between COPD groups A and combined C and D (p = 0.005; Table 5).

Table 5.

Comparison of handgrip strength in across stages of COPD.

COPD classification	Number of patients (n = 64)	Handgrip strength (kg)	p-value
Grade
1	22 (34.4)	29.19 ± 9.38	0.157
2	30 (46.9)	32.18 ± 7.52
3 and 4	12 (18.7)	27.24 ± 6.16
Group
A	5 (7.8)	36.90 ± 5.41*	0.081
B	36 (56.3)	30.63 ± 9.17
C and D	23 (35.9)	28.14 ± 5.87*

Data presented as mean ± SD or n (%).

COPD, chronic obstructive pulmonary disease.

p = 0.005 for the comparison between group A and combined C and D.

Discussion

This study is the first to identify a correlation between HGS and SAD assessed by IOS. Additionally, it determined an HGS cutoff value for detecting SAD in patients with clinically stable COPD. The study also derived an equation to predict SAD using the HGS factor.

In our study, the prevalence of SAD was 64.1%, which is lower than the 74% reported by Crisafulli et al.²² They used an R5–R20 cutoff value of 0.07 kPa/L/s, and the prevalence of SAD increased with higher GOLD grades and greater impact on health.²²

The prevalence of SAD varies depending on clinical factors and pulmonary function cutoff values. Li et al. defined SAD as at least two of the three spirometry parameters (FEF_25%–75%, FEF_50%, and FEF_75%) being less than 65% predicted. ROC analyses revealed that the cutoff values of IOS parameters to predict SAD were R5 greater than 0.30 kPa/L/s, R5–R20 greater than 0.015 kPa/L/s, AX greater than 0.30 kPa/L, and Fres greater than 11.23 Hz.²³ In our COPD study, the optimal cutoff values for detecting SAD were 74.35% for FEV₁ and 31.25% for FEF_25–75, each demonstrating a similar sensitivity of 73.9%.

HGS is primarily used for diagnosing sarcopenia.¹³ It is also used to assess various chronic conditions.^14,15 COPD-related sarcopenia is associated with an increase in oxidative stress-related factors and a reduction in respiratory muscle strength.²⁴ A Korean study by Kim et al. found that lower HGS was significantly associated with airflow limitation in the general population.²⁵ Similarly, a study by Strandkvist et al. revealed that patients with GOLD grades 3–4 had lower HGS compared to those without COPD, and HGS was associated with FEV₁ % of predicted value.²⁶ Lower HGS was also correlated with reduced FVC and FEV₁ in a study by Shah et al.²⁷ Additionally, a study by Martinez et al. demonstrated that HGS was associated with body composition, airway thickness, body mass index, emphysema, and exacerbation rate.²⁸ A study by Guedes-Aguiar et al. showed that HGS was used to assess the outcomes of pulmonary rehabilitation using whole-body vibration exercise, which was generated in systemic vibratory therapy, in COPD patients.²⁹ The 6-min walk test (6MWT) is used to assess functional walking capacity and endurance. A meta-analysis by Ferte et al. revealed a significant improvement in functional performance measured by 6MWT and an improvement in quadriceps muscle strength in COPD patients following a resistance training program.³⁰ Moreover, the 6MWT was used to evaluate functional performance in COPD patients after acupuncture treatment.³¹

HGS can also be used to assess critically ill patients.^32,33 A study conducted in Thailand by Saiphoklang et al. found that HGS correlated with the rapid shallow breathing index, a weaning parameter, in mechanically ventilated patients.³⁴

In concordance with these findings, our results revealed a negative correlation between HGS and R5–R20, indicating that COPD patients with lower HGS are more likely to have SAD. Our study determined an optimal HGS cutoff value of 28.25 kg for detecting SAD, as indicated by the area under the ROC curve, which demonstrated high sensitivity and specificity. Therefore, HGS may serve as an alternative test in evaluating SAD, complementing other diagnostic tools such as spirometry and IOS. Moreover, HGS might be utilized to assess functional capacity and measure outcomes following a rehabilitation program.

This study has limitations. First, the sample size was small, with only 10% of participants being women, only 7.8% in COPD group D, and only 1.6% grade 4. This limited sample may not be generalizable to clinical practice. Second, this study was conducted at a single research center in Thailand, which may limit its applicability to other ethnicities or countries. Third, the participants were clinically stable COPD patients without acute exacerbations, home oxygen therapy, or mechanical ventilation, so the HGS cutoff value may not apply to more severe cases. Finally, data on muscle mass and function (e.g. walking speed) were not collected for sarcopenia patients. Future studies should investigate the correlation between HGS and SAD in various patient settings, including patients using home oxygen therapy or mechanical ventilation, as well as patients with other pulmonary diseases.

Conclusion

SAD is common in COPD patients, and HGS is significantly negatively correlated with SAD. This tool might serve as an alternative or adjunctive assessment for small airway dysfunction in COPD patients. Furthermore, HGS might be utilized to assess functional capacity and measure outcomes following a rehabilitation program.

Supplemental Material

sj-docx-1-tar-10.1177_17534666241281675 – Supplemental material for Association between handgrip strength and small airway disease in patients with stable chronic obstructive pulmonary disease

Supplemental material, sj-docx-1-tar-10.1177_17534666241281675 for Association between handgrip strength and small airway disease in patients with stable chronic obstructive pulmonary disease by Thanapon Keawon and Narongkorn Saiphoklang in Therapeutic Advances in Respiratory Disease

Footnotes

The authors thank Michael Jan Everts,Faculty of Medicine,Thammasat University,for proofreading this manuscript. This work was supported by Thammasat University Research Unit in Allergy and Respiratory Medicine,Thailand.

Declarations

ORCID iD

Narongkorn Saiphoklang

Supplemental material

Supplemental material for this article is available online.

References

Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease (2023 report), file:///C:/Users/user/Downloads/GOLD-2023-ver-1.3-17Feb2023_WMV.pdf (2023, accessed 1 April 2024).

GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396: 1204–1222.

World Health Organization. Chronic obstructive pulmonary disease (COPD), Key facts, https://www.who.int/en/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd) (2023, accessed 21 March 2024).

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Supplementary Material

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