BACKGROUND: Chronic obstructive pulmonary disease (COPD) is associated with low muscle mass and function caused by malnutrition and physical inactivity. We aimed to investigate possible associations between serum biomarkers and clinical traits including computed tomography-derived muscle measurements and energy expenditure indices in COPD. METHODS: Total energy expenditure (TEE) was measured by the doubly labeled water method, while physical activity level (PAL) was calculated as TEE/basal metabolic rate. Cross-sections and densities of pectoralis, rectus abdominis, and erector spinae muscles were measured. Serum biomarkers included adiponectin, insulin-like growth factor-1, and high-density lipoprotein (HDL)- and low-density lipoprotein (LDL)-cholesterol (C). RESULTS: HDL-C levels were significantly correlated with all muscle areas, densities, and TEE. Only LDL-C levels were correlated with PAL. CONCLUSIONS: HDL-C level was a potential biomarker for trunk muscle volumes and functions, as well as total energy expenditure in COPD.

Physical inactivity is associated with comorbidities and mortality in chronic obstructive pulmonary disease (COPD) patients. Although non-exercise activity thermogenesis (NEAT) is important for evaluating the physical activity level (PAL) of patients with chronic diseases, it has not yet been assessed in COPD patients. This study included male patients with COPD (n = 28) and high risk for COPD (n = 8). Total energy expenditure (TEE) and basal metabolic rate (BMR) were measured using the doubly labeled water (DLW) method and indirect calorimetry, respectively. PAL was calculated as TEE/BMR, while the NEAT was obtained from a questionnaire. Physical activity was also assessed using an accelerometer. The total NEAT score was correlated with PAL (r = 0.534, P < 0.001), while PAL was correlated more strongly with the non-locomotive NEAT score (r = 0.548, P < 0.001) than the locomotive NEAT score (r = 0.278, P = 0.10). Regarding accelerometer-obtained data, this questionnaire mainly reflected steps/day and the duration of light locomotive and non-locomotive daily activities. The NEAT score is a possible option for evaluating PAL in daily clinical practice. The present results indicated that non-locomotive activity may have a greater impact on PAL than locomotive activity in COPD patients.

Background: Physical inactivity due to cachexia and muscle wasting is well recognized as a sign of poor prognosis in chronic obstructive pulmonary disease (COPD). However, there have been no reports on the relationship between trunk muscle measurements and energy expenditure parameters, such as the total energy expenditure (TEE) and physical activity level (PAL), in COPD. In this study, we investigated the associations of computed tomography (CT)-derived muscle area and density measurements with clinical parameters, including TEE and PAL, in patients with or at risk for COPD, and examined whether these muscle measurements serve as an indicator of TEE and PAL. Methods: The study population consisted of 36 male patients with (n = 28, stage 1–4) and at risk for (n = 8) COPD aged over 50 years. TEE was measured by the doubly labeled water method, and PAL was calculated as the TEE/basal metabolic rate estimated by the indirect method. The cross-sectional areas and densities of the pectoralis muscles, rectus abdominis muscles, and erector spinae muscles were measured. We evaluated the relationship between these muscle measurements and clinical outcomes, including body composition, lung function, muscle strength, TEE, and PAL. Results: All the muscle areas were significantly associated with TEE, severity of emphysema, and body composition indices such as body mass index, fat-free mass, and trunk muscle mass. All trunk muscle densities were correlated with PAL. The product of the rectus abdominis muscle area and density showed the highest association with TEE (r = 0.732) and PAL (r = 0.578). Several trunk muscle measurements showed significant correlations with maximal inspiratory and expiratory pressures, indicating their roles in respiration. Conclusions: CT-derived measurements for trunk muscles are helpful in evaluating physical status and function in patients with or at risk for COPD. Particularly, trunk muscle evaluation may be a useful marker reflecting TEE and PAL.

Objectives This study aimed to compare the estimation error of physical activity level (PAL) estimated using a tri-axial accelerometer between an independent walking group and an assisted walking group with walking aids. Methods Subjects were 6 older adults who could walk independently and 10 older adults requiring walking assistance during gait. Total energy expenditure (TEE) was measured using the doubly labelled water (DLW) method over 2 weeks and PAL was calculated as the measured TEE divided by the basal metabolic rate measured using indirect calorimetry (PAL DLW). The participants wore a tri-axial accelerometer (Active style Pro HJA-750C) on the waist simultaneously as the DLW period, and the estimated PAL was derived from it (PAL ACC). Results The median PAL estimation error in the assisted walking group was -0.30 kcal/day (range: -0.77 to -0.01 kcal/day) and more underestimated than that in the independent walking group (p=0.02). The estimation error of PAL ACC was significantly correlated with PAL DLW (r=-0.80, p<0.01). Conclusions Using the accelerometer, PAL was underestimated for older adults who used walking aids but not for those who walked independently under free-living conditions.

INTRODUCTION: Evaluation of total energy expenditure (TEE) and physical activity level (PAL) is important for treatment of patients with type 2 diabetes mellitus (T2DM). However, the validity of accelerometers (ACC) and physical activity questionnaires (PAQ) for estimating TEE and PAL remains unknown in elderly populations with T2DM. We evaluated the accuracy of TEE and PAL results estimated by an ACC (TEEACC and PALACC) and a PAQ (TEEPAQ and PALPAQ) in elderly patients with T2DM. METHODS: Fifty-one elderly patients with T2DM (aged 61-79 years) participated in this study. TEEACC was calculated with PALACC using a triaxial ACC (Active style Pro HJA-750c) over 2 weeks and predicted basal metabolic rate (BMR) by Ganpule's equation. TEEPAQ was estimated using predicted BMR and the PALPAQ from the -Japan Public Health Center Study-Long questionnaire. We compared the results to TEEDLW measured with the doubly labeled water (DLW) method and PALDLW calculated with BMR using indirect calorimetry. RESULTS: TEEDLW was 2,165 ± 365 kcal/day, and TEEACC was 2,014 ± 339 kcal/day; TEEACC was strongly correlated with TEEDLW (r = 0.87, p < 0.01) but significantly underestimated (-150 ± 183 kcal/day, p < 0.05). There was no significant difference in TEEPAQ and TEEDLW (-49 ± 284 kcal/day), while the range of difference seemed to be larger than TEEACC. PALDLW, PALACC, and PALPAQ were calculated to be 1.71 ± 0.17, 1.69 ± 0.16, and 1.78 ± 0.24, respectively. -PALACC was strongly correlated with PALDLW (r = 0.71, p < 0.01), and there was no significant difference between the 2 values. PALPAQ was moderately correlated with PALDLW (r = 0.43, p < 0.01) but significantly overestimated. Predicted BMR was significantly lower than the BMR -measured by indirect calorimetry (1,193 ± 186 vs. 1,262 ± 155 kcal/day, p < 0.01). CONCLUSIONS: The present ACC and questionnaire showed acceptable correlation of TEE and PAL compared with DLW method in elderly patients with T2DM. Systematic errors in estimating TEE may be improved by the better equation for predicting BMR.

Objective: Assessment of total energy expenditure (TEE) is essential for appropriate recommendations regarding dietary intake and physical activity in patients with and without diabetes mellitus (DM). However, few reports have focused on TEE in patients with DM, particularly in Asian countries. Therefore, we evaluated TEE in Japanese patients with DM using the doubly labeled water (DLW) method and physical activity level (PAL). Research design and methods: In this cross-sectional observational study, we evaluated 52 patients with type 2 DM and 15 patients without DM. Free-living TEE was measured over 12-16 days by the DLW method, and PAL was calculated as TEE divided by the basal metabolic rate (BMR) as assessed by indirect calorimetry. The equivalence margin was defined as 5 kcal/kg/day. Results: The numbers of patients with DM treated with insulin, oral antidiabetic drugs, and diet were 18 (34.6%), 20 (38.5%), and 14 (26.9%), respectively. The mean±SD level of glycated hemoglobin was 6.9%±0.8% and 5.5%±0.3% in the DM and non-DM group, respectively (p<0.001). The mean body mass index was 23.3±3.0 and 22.7±2.1 kg/m2 in the DM and non-DM group, respectively. The mean TEE per kilogram body weight adjusted for sex and age was 36.5 kcal/kg/day and 37.5 kcal/kg/day in the DM and non-DM group, respectively, with no significant difference (mean difference, -1.0 kcal/kg/day; 95% CI -4.2 to 2.3 kcal/kg/day). The BMR tended to be higher in the DM than in the non-DM group (mean difference, 33 kcal/day; 95% CI, -15 to 80 kcal/day). The mean PAL adjusted for sex and age was 1.71 and 1.81 in the DM and non-DM group, respectively, without a significant difference (mean difference, -0.10; 95% CI -0.21 to 0.01). Conclusion: TEE was comparable between Japanese patients with and without DM. Trial registration number: UMIN000023051.

Malnutrition is a severe problem among elderly residents living in long-term care facilities. A simple and accurate estimation for total daily energy expenditure (TEE) is required in order to provide them with an adequate amount of food. The purpose of this study was to validate a physical activity assessment tool for estimating TEE among elderly residents. The data of 58 subjects aged 69-99 y were analyzed in this study. The one-day physical activity recall was filled out by facility staff for a typical day. TEE was measured by the doubly labeled water (DLW) method (TEEDLW), and predicted by one-day physical activity recall using the basal metabolic rate (pTEEBMR) and body weight (pTEEBW). The TEEDLW, pTEEBMR and pTEEBW were 1,129±196 kcal/d, 1,186±251 kcal/d and 1,326±236 kcal/d, respectively. In a regression model, body weight, movement means and sex explained 39.0% of the variance in the difference between pTEEBMR and TEEDLW (p<0.05). Percentage of fat mass, body weight, and Barthel Index except mobility explained 47.3% of the variance in the difference between pTEEBW and TEEDLW (p<0.05). The current results suggested that pTEEBW is not recommended to estimate TEE because the error depends on body size, and pTEEBMR was significantly correlated with TEEDLW but also included systematic errors in elderly residents. In order to improve the accuracy of the present assessment to estimate TEE, it is necessary to establish a new equation of basal metabolic rate for elderly residents and consider sex and movement means to estimate TEE.