Introduction
Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory condition that is primarily triggered by exposure to harmful particles or gases [1]. The implications of COPD extend beyond the respiratory system, manifesting in various systemic complications, e.g., cardiovascular disease, osteoporosis, depression, lung cancer, muscle wasting, and malnutrition. This malnutrition, affecting between 5% and 50% of patients in severe cases, substantially diminishes exercise capacity, physical activity (PA), and muscle strength [2,3].
Among the multifaceted contributors to malnutrition in COPD, inadequate nutrition plays a critical role [4-6]. Dietary improvements have been shown to significantly enhance muscle strength and overall PA. For example, a controlled, randomized study involving 199 patients with COPD and compromised exercise capacity observed marked enhancements in muscle mass, respiratory, and peripheral muscle strength following nutritional and exercise interventions [7]. Similarly, meta-analyses have confirmed that nutritional supplementation in patients with COPD can lead to improvements in body weight, exercise capacity, and muscle strength, particularly in those who are undernourished [8,9].
Handgrip strength (HGS) is an important measure of muscle strength, and a key tool for diagnosing sarcopenia, especially in COPD, where muscle loss and reduced PA are common. HGS shows strong relationships with muscle strength in both the upper and lower limbs, highlighting its usefulness for diagnosis [10].
Despite the significant impact of malnutrition on muscle strength in patients with COPD, research on the direct and combined effects of dietary intake and PA on muscle strength remains limited. Therefore, this study aims to examine the associations between dietary intake, PA, and HGS as an indicator of muscle strength in individuals with airflow limitation. Additionally, the study seeks to identify specific nutritional components that may be linked to enhanced PA and muscle strength, acknowledging that due to their independent effects on HGS, these relationships may represent correlations, rather than causation.
Materials and Methods
1. Study population
This cross-sectional, observational study utilized data from the 2014 and 2016 Korea National Health and Nutrition Examination Survey (KNHANES VI), a comprehensive program that collects health and nutrition information from the Korean population. The dataset includes demographics, smoking status, lung function, HGS, PA, quality of life, and physician-diagnosed comorbidities, e.g., high blood pressure, diabetes, ischemic heart disease, and stroke. Our study included adults aged ≥40 years who underwent spirometry measurements. We excluded individuals who did not respond to the PA and nutritional intake surveys, and those with HGS measurements and forced expiratory volume per 1 second (FEV1)/forced vital capacity (FVC) ratios exceeding 0.7 (Figure 1).
2. Assessment of spirometry parameters
Spirometry assessments were conducted in a mobile examination center by trained medical staff using dry rolling seal spirometers (Model 2130, Sensor Medics, Yorba Linda, CA, USA). Equipment calibration and quality control procedures were regularly performed, following guidelines from the American Thoracic Society and European Respiratory Society [11]. Post-bronchodilator spirometry was not included in this survey.
Airflow limitation was defined as the FEV1 divided by FVC ratio (FEV1/FVC) ≤0.7, in accordance with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines. Participants were categorized into four groups based on the degree of airflow limitation following the GOLD classification: mild (GOLD stage 1, FEV1 >80% predicted), moderate (GOLD stage 2, 50% predicted≤ FEV1 <80% predicted), severe (GOLD stage 3, 30% predicted≤ FEV1 <50% predicted), and very severe (GOLD stage 4, FEV1 <30% predicted) [12].
3. Assessment of nutritional intake
Daily caloric intake was determined by analyzing the nutritional content of all consumed foods. Nutritional surveys were conducted using a computer-assisted personal interview system. Previous research has established the validity of the 24-hour dietary recall method in large-scale population studies [13]. The assessed nutrients included carbohydrates, lipids, proteins, fiber, beta-carotene, retinol, and vitamins A, B1, B2, B3, and C.
4. Assessment of PA and definition of aerobic PA
PA was assessed using the Korean version of the International Physical Activity Questionnaire, obtained through the Health Interview Survey [14]. Participants reported their PA levels during a typical week. Vigorous PA was defined as activities, e.g., running, mountain climbing, soccer, basketball, that significantly increase the heart rate for at least 10 minutes. Moderate PA included activities, e.g., tennis doubles, volleyball, badminton, or those slightly increasing heart rate for at least 10 minutes. Participants also reported the daily time spent on each activity.
Consequently, participants were categorized into two groups based on their PA levels: the active aerobic PA, and non-active aerobic PA groups. Participants in the active aerobic PA group met at least one of the following criteria: engaging in at least 2 hours 30 minutes of moderate-intensity PA per week, engaging in at least 1 hour 15 minutes of high-intensity PA per week, or a combination of moderate and high-intensity activities (with 1 minute of high-intensity activity considered equivalent to 2 minutes of moderate-intensity activity).
5. Assessment of HGS and definition of low HGS
HGS was assessed using a digital grip strength dynamometer (TKK 5401, Takei Scientific Instruments Co. Ltd., Tokyo, Japan). Each participant underwent three measurements for each hand. Trained medical staff instructed the participants to sit and hold the dynamometer with the second finger nodes of their working hand at a 90° angle to the handle. They were then asked to squeeze the dynamometer as tightly as possible. Subsequently, the participants slowly stood up, and the HGS was recorded as they exhaled. An 1-minute rest period was provided between each HGS measurement. The highest HGS value from the six measurements obtained was used for further analysis. Based on the HGS measurements, the participants were categorized into two groups: the normal HGS and low HGS groups. The classification was determined using a predefined cut-off value of 28.6 kg for men, and 16.4 kg for women [15].
6. Definition of smoking status
According to the National Health Interview Survey, a “current smoker” is defined as an adult who has smoked more than 100 cigarettes in their lifetime, and is currently smoking. An adult who has smoked at least 100 cigarettes in their lifetime, but has ceased smoking at the time of the survey, is referred to as a “former smoker.” A “never-smoker” is defined as an adult who has never smoked, or has smoked fewer than 100 cigarettes in their lifetime [16].
7. Ethics
The Institutional Review Board (IRB approval number: 2013-07CON-03-4C, 2013-12EXP-03-5C, and 2018-01-03-PA) approved the KNHANES protocol of the Korean Centers for Disease Control and Prevention. All participants provided informed consent to participate in this study.
8. Statistical analysis
The data are presented as the mean±standard deviation for continuous variables, and as numbers (percentages) for categorical variables. Categorical variables were compared using the chi-square test, and continuous variables were compared using the Student’s t-test. Pearson’s correlation coefficients were calculated to assess the association between HGS and variables of total food intake, water, calories, protein, lipid, and carbohydrate intake. Forest plots were used to visually represent the results of logistic regression analyses, showing the associations between nutritional intake and PA using odds ratios (ORs) and 95% confidence intervals (CIs). Univariate and multivariate logistic regression analyses examined factors associated with aerobic PA and HGS, presenting ORs with 95% CIs and p-values. The factors included age, sex, body mass index (BMI), alcohol ingestion, smoking status, and various nutritional and clinical parameters. Statistical significance was defined as p<0.05 for all comparisons. All statistical analyses were performed using IBM SPSS Statistics for Windows, version 25.0 (IBM Corporation, Armonk, NY, USA).
Results
1. Baseline characteristics of the participants
Overall, 622 individuals with airflow limitation completed all questionnaires (Figure 1). Table 1 summarizes the baseline characteristics of these participants. The mean age of the participants was 66.8±9.19 years, with men constituting 73.3% of the study population. The mean FEV1/FVC ratio was 62.9±6.73, and 5.1% of the participants were classified as GOLD stage III/IV. Approximately 48.1% of the participants engaged in active aerobic PA per week. The mean HGS of the dominant hand was 31.4±8.94 kg, and 113 (18.2%) participants had low HGS based on the defined criterion (<28.6 kg in men, and <16.4 kg in women).
2. Comparison of baseline characteristics according to aerobic PA and HGS
Based on aerobic PA, the active aerobic PA group (n=299) was characterized by a slightly lower mean age (65.9±9.15 years vs. 67.7±9.16 years, p=0.13) and a higher proportion of males (77.6% vs. 69.3%), compared to the non-active aerobic PA group (n=323) (Figure 2). Additionally, the active aerobic PA group exhibited better lung function, as indicated by significantly higher FEV1 and FVC values (2.3±0.60 vs. 2.1±0.63, p=0.001; 3.65±0.82 vs. 3.39±0.92, p<0.001). Laboratory findings revealed that the active aerobic PA group had lower glycated hemoglobin levels (5.92±0.66 vs. 6.03±0, p=0.004) and higher hemoglobin/hematocrit levels (14.5±1.32 g/dL vs. 14.1±1.49 g/dL, p=0.008; 43.5±3.61 g/dL vs. 42.9±4.15 g/dL, p=0.044, respectively). Moreover, the active aerobic PA group reported a higher EuroQol Five-Dimension (EQ-5D) index, indicating a better quality of life, compared to the non-active aerobic PA group (0.94±0.09 vs. 0.91±0.13, p=0.001). Regarding HGS, participants in the active aerobic PA group demonstrated significantly higher HGS values for both hands, HGS of the dominant hand, and HGS/BMI, while the prevalence of low HGS was significantly lower in the active aerobic PA group.
Participants in the low HGS group (n=113) were characterized by a significantly higher mean age (72.8±6.73 years vs. 65.5±9.24 years, p<0.001) and lower BMI (23.2±2.99 kg/m2 vs. 24±2.98 kg/m2, p=0.015), compared to those in the normal HGS group (n=509) (Figure 2). Alcohol consumption was also less common in the low HGS group (37.8% vs. 57.4%, p<0.001). Lung function parameters, including FEV1, FVC, and FEV1/FVC, were significantly lower in the low HGS group (1.94±0.50 vs. 2.27±0.63, p<0.001; 3.15±0.71 vs. 3.60±0.9, p<0.001; 61.6±6.97 vs. 63.1±6.65, p=0.03, respectively). Moreover, participants in the low HGS group had a lower EQ-5D index, and a lower prevalence of active aerobic PA. In addition, participants in the low HGS group demonstrated a significantly higher rate of activity limitation, and a lower rate of vigorous and moderate activity for leisure or aerobic PA (Table 1).
3. Comparison of nutritional component intake according to aerobic PA and HGS
Among the 622 participants, those engaged in aerobic PA (n=299) and those with higher HGS (n=509) had significantly higher total food intake (1,501.9±749.93 g vs. 1,576.9±732.83 g, p=0.016), water (1,051.0±649.72 g vs. 1,112.7±634.11 g, p=0.022), calories (1,953.1±753.84 kcal vs. 2,026.8±769.09 kcal, p=0.019), protein (64.7±31.67 g vs. 68.6±31.43 g, p=0.003), and lipid (34.6±27.01 g vs. 37.7±27.85 g, p=0.005), but not carbohydrates (320.1±124.25 g vs. 324.9±124.46 g, p=0.496), compared to those in the inactive PA (n=323) and low HGS (n=113) groups (Table 2).
The forest plot showed the ORs and their 95% CIs for nutritional intake in relation to aerobic PA and low HGS (Figure 3). For active aerobic PA, increases in the intake of food, water, calories, protein, and lipids are associated with a slightly higher likelihood of engaging in active aerobic PA, with ORs of (95% CI, 1.03 to 1.09). Moreover, for low HGS, higher intakes of food, water, calories, protein, and lipids are associated with a lower likelihood of having low HGS, with ORs <1 (95% CI, 0.82 to 0.96). Carbohydrate intake did not show a clear association with active aerobic PA or low HGS.
4. Correlation between HGS and nutritional component intake
Protein and caloric intake had strong positive correlations with HGS (correlation coefficients=0.344 and 0.346, respectively; p<0.001). These correlations were stronger compared to those with carbohydrate, lipid, and water intake, which had lower correlation coefficients of 0.242, 0.256, and 0.147, respectively (Table 3).
5. Factors associated with active aerobic PA and HGS
Multivariate logistic regression analyses did not show significant associations between total food intake, nutritional components, and active aerobic PA or HGS among the participants. However, increasing age and lower BMI were significantly associated with low HGS (OR, 0.898; 95% CI, 0.865 to 0.931; p<0.001 and OR, 1.093; 95% CI, 1.104 to 1.179; p=0.02, respectively) (Tables 4, 5).
Discussion
This study explored the association between nutritional intake, PA, and muscle strength, focusing on HGS in individuals with airflow limitation. Our findings indicate a potential link where increased nutritional intake correlates with enhanced PA and greater HGS, emphasizing the importance of dietary factors in managing and improving physical activities in this population.
While the study did not find significant differences in dietary intake among groups with varying levels of HGS, this outcome underscores the complexity of the factors influencing muscle strength, and the possible overshadowing impact of other variables, e.g., age, disease severity, and overall PA levels. These findings suggest that while diet is a crucial element of health, its direct impact on muscle strength may be interdependent with other lifestyle or physiological factors not fully isolated in this study.
In this study, protein intake emerged as a significant factor associated with PA in individuals with airflow limitation, including patients with COPD. Notably, higher protein intake has been reported to have a positive effect on PA, even in the general population. For example, inadequate protein intake in community-dwelling older adults aged 70 to 79 years was associated with an increased risk of PA limitation over a 6-year period [17]. Conversely, a higher protein intake demonstrated a dose-dependent protective effect on walking speed [18]. These findings further support the potential role of protein intake in promoting PA in patients with COPD [19]. Furthermore, protein intake has been found to be associated with muscle strength in the general population. Cross-sectional studies conducted in the United States revealed a positive association between total protein intake and parameters, e.g., leg lean muscle mass and quadriceps strength [20]. However, the relationship between protein intake and muscle strength in older adults remains inconclusive, with some studies reporting no association [21,22]. However, in the context of COPD, muscle protein breakdown contributes to muscle wasting. Nutritional support can play a vital role in counteracting this process and compensating for protein loss, leading to improved nutritional status and functional capacity in patients with COPD [8,23]. For example, oral dietary supplements containing high-quality protein enriched with leucine, administered as an adjunct to exercise training over 4 months, have shown improvements in quadriceps strength and cycle endurance compared to placebo, in patients with moderate to severe COPD [24]. These findings, along with the results of our study, suggest that protein intake may have a beneficial impact on PA levels and muscle strength in patients with COPD.
Sarcopenia is the loss of muscle mass and strength that occurs with aging. In patients with COPD, there is an increased risk of developing sarcopenia [25]. The reduced PA due to the respiratory symptoms of COPD can accelerate muscle loss. Additionally, COPD can lead to a state of chronic inflammation and nutritional deficiencies, both of which can contribute to muscle wasting. In 50 patients with COPD (65±7 years; FEV1 51%±14% predicted), strong correlations were found between HGS and muscle strength, particularly in the quadriceps, suggesting HGS as a reliable, simple measure to assess muscle strength in patients with COPD [10]. Our study demonstrated that increasing age and lower BMI are significantly associated with lower HGS. A Southeast Asian study investigating sarcopenia in 121 patients with COPD (mostly men) found that 24% of these patients had sarcopenia. The important factors associated with sarcopenia included age ≥75 years, more severe COPD, a higher Modified Medical Research Council Dyspnea Scale score, and obesity. The study concluded that sarcopenia is a significant issue in patients with COPD, with age, disease severity, and BMI as major associated factors [26]. Proper nutrition is essential for older and underweight individuals, as it is pivotal to maintaining muscle strength and overall functionality. This is particularly crucial for individuals with conditions such as COPD, where adequate nutrition can help preserve muscle mass and support better health outcomes.
This study had some limitations. First, the study design is cross-sectional, which limits the ability to establish causal relationships between nutritional intake, PA, and HGS. Longitudinal or interventional studies would be required to determine the directionality and causality of these associations. Second, the study population was comprised of individuals with airflow limitations from the Korean National Health and Nutrition Examination Survey, which may limit the generalizability of the findings to patients with COPD. Third, the study included only a small proportion of individuals with severe or moderate airflow limitation (defined as FEV1 ≤50%), and over 30% of the population comprised non-smokers with airflow limitation. Fourth, the study has the potential lack of novelty in the findings, as our results predominantly confirm well-established associations between low HGS and factors, e.g., older age and lower BMI. However, the confirmation of these relationships in our specific population contributes to these well-known associations, and underscores the importance of these factors in clinical assessments and interventions.
In conclusion, our study provides evidence supporting the relationship between nutritional intake, particularly protein intake, PA, and muscle strength in individuals with airflow limitation. These findings emphasize the importance of nutritional support in enhancing PA levels and muscle strength in this population. Future research should focus on identifying specific nutritional components and mechanisms that have the greatest impact on these parameters, enabling the development of targeted interventions to optimize functional outcomes in patients with COPD.
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