Sedentary behavior and cognitive impairment have a direct impact on patients’ outcomes. An energy metabolic disorder may be involved in the overlap of these comorbid conditions (motoric cognitive risk (MCR)) in patients with chronic obstructive pulmonary disease (COPD). We aimed to explore the linkage between a proapoptotic protein, growth differentiation factor (GDF)-15, and MCR. Physical activity (PA), cognitive function (Japanese version of the Montreal Cognitive Assessment: MOCA-J), and the serum GDF-15 levels were assessed in healthy subjects (n = 14), asthmatics (n = 22), and COPD patients (n = 28). In the entire cohort, serum GDF-15 had negative correlations with exercise (Ex) (ρ = −0.43, p < 0.001) and MoCA-J (ρ = −0.44, p < 0.001), and Ex and MOCA-J showed a positive correlation (ρ = 0.52, p < 0.0001). Compared to healthy subjects and asthmatics, COPD patients showed the highest serum GDF-15 levels and had a significantly higher proportion of subjects with MCR (both sedentary lifestyle (EX < 1.5) and cognitive risk (MoCA-J ≤ 25)). Also, we found that serum GDF-15 has a screening potential (100% sensitivity) greater than aging (67% sensitivity) for detecting MCR in COPD patients. In conclusion, higher serum GDF-15 had interrelationships with a sedentary lifestyle and cognitive risk. This protein was not disease-specific but could be a screening biomarker to detect MCR related to poor health outcomes of COPD patients.

Brain frailty may be related to the pathophysiology of poor clinical outcomes in chronic obstructive pulmonary disease (COPD). This study examines the relationship between hippocampal subfield volumes and frailty and depressive symptoms, and their combined association with quality of life (QOL) in patients with COPD. The study involved 40 patients with COPD. Frailty, depressive symptoms and QOL were assessed using Kihon Checklist (KCL), Hospital Anxiety and Depression Scale (HADS), and World Health Organization Quality of Life Assessment (WHO/QOL-26). Anatomical MRI data were acquired, and volumes of the hippocampal subfields were obtained using FreeSurfer (version 6.0). Statistically, HADS score had significant association with WHO/QOL-26 and KCL scores. KCL scores were significantly associated with volumes of left and right whole hippocampi, presubiculum and subiculum, but HADS score had no significant association with whole hippocampi or hippocampal subfield volumes. Meanwhile, WHO/QOL-26 score was significantly associated with volume of the left CA1. There was a significant association between frailty, depression, and QOL. Hippocampal pathology was related to frailty and, to some extent, with QOL in patients with COPD. Our results suggest the impact of frailty on hippocampal volume and their combined associations with poor QOL in COPD.

The six-minute walking test (6MWT) is an essential test for evaluating exercise tolerance in many respiratory and cardiovascular diseases. Frailty and sarcopenia can cause rapid aging of the cardiovascular system in elderly people. Early detection and evaluation of frailty and sarcopenia are crucial for determining the treatment method. We aimed to develop a wearable measuring system for the 6MWT and propose a method for identifying frailty and quantifying walking muscle strength (WMS). In this study, 60 elderly participants were asked to wear accelerometers behind their left and right ankles during the 6MWT. The gait data were collected by a computer or smartphone. We proposed a method for analyzing walking performance using the stride length (SL) and step cadence (SC) instead of gait speed directly. Four regions (Range I–IV) were divided by cutoff values of SC = 2.0 [step/s] and SL = 0.6 [m/step] for a quick view of the frail state. There were 62.5% of frail individuals distributed in Range III and 72.4% of non-frail individuals in Range I. A concept of a WMS score was proposed for estimating WMS quantitatively. We found that 62.5% of frail individuals were scored as WMS1 and 41.4% of the non-frail elderly as WMS4. The average walking distances corresponding to WMS1–4 were 207 m, 370 m, 432 m, and 462 m, respectively. The WMS score may be a useful tool for quantitatively estimating sarcopenia or frailty due to reduced cardiopulmonary function.

(1) Background: Reduced lung function in early adulthood is associated with future risks to health outcomes that have not been fully explored by gender. We investigated gender-specific relationships between lung function and extrapulmonary variables, assessing their potential as screening markers for respiratory dysfunction in young adults. (2) Methods: The participants were 151 medical students. Clinical data, handgrip strength (HS); body composition parameters such as skeletal muscle mass index (SMI), whole-body phase angle (WBPhA), and bone mineral content (BMC); and pulmonary function variables, vital capacity (VC), forced VC (FVC), and forced expiratory volume in one second (FEV₁), were measured. (3) Results: FEV₁ was significantly correlated with BMI, SMI, WBPhA, BMC, and both left and right HS (p < 0.0001, respectively) across all participants. According to gender, FEV₁ had the strongest positive association with left HS in males (p < 0.0001) and BMC in females (p < 0.0001). The area under the curve for detecting the bottom quartile of FEV₁ was 0.705 (cut-off 41.0 kg, sensitivity 91%) for left HS in males and 0.742 (cut-off 2.11 kg, sensitivity 81%) for BMC in females. (4) Conclusions: Gender-specific relationships between intrapulmonary and extrapulmonary factors such as left HS and BMC could be useful for screening suspected respiratory dysfunction in early adulthood.

Physical inactivity correlates with poor prognosis in chronic obstructive pulmonary disease (COPD) and is suggested to be related to lung hyperinflation. We examined the association between physical activity and the expiratory to inspiratory (E/I) ratio of mean lung density (MLD), the imaging biomarker of resting lung hyperinflation. COPD patients (n = 41) and healthy controls (n = 12) underwent assessment of pulmonary function and physical activity with an accelerometer, as well as computed tomography at full inspiration and expiration. E/I_MLD was calculated by measuring inspiratory and expiratory MLD. Exercise (EX) was defined as metabolic equivalents × duration (hours). COPD patients had higher E/I_MLD (0.975 vs. 0.964) than healthy subjects. When dividing COPD patients into sedentary (EX < 1.5) and non-sedentary (EX ≥ 1.5) groups, E/I_MLD in the sedentary group was statistically higher than that in the non-sedentary group (0.983 vs. 0.972). E/I_MLD > 0.980 was a good predictor of sedentary behavior in COPD (sensitivity, 0.815; specificity, 0.714). Multivariate analysis showed that E/I_MLD was associated with sedentary behavior (odds ratio, 0.39; p = 0.04), independent of age, symptomology, airflow obstruction, and pulmonary diffusion. In conclusion, higher E/I_MLD scores are associated with sedentary behavior and can be a useful imaging biomarker for the early detection of physical inactivity in COPD.

Contracting COPD reduces a patient’s physical activity and restricts everyday activities (physical activity disorder). However, the fundamental cause of physical activity disorder has not been found. In addition, costly and specialized equipment is required to accurately examine the disorder; hence, it is not regularly assessed in normal clinical practice. In this study, we constructed a machine learning model to predict physical activity using test items collected during the normal care of COPD patients. In detail, we first applied three types of data preprocessing methods (zero-padding, multiple imputation by chained equations (MICE), and k-nearest neighbor (kNN)) to complement missing values in the dataset. Then, we constructed several types of neural networks to predict physical activity. Finally, permutation importance was calculated to identify the importance of the test items for prediction. Multifactorial analysis using machine learning, including blood, lung function, walking, and chest imaging tests, was the unique point of this research. From the experimental results, it was found that the missing value processing using MICE contributed to the best prediction accuracy (73.00%) compared to that using zero-padding (68.44%) or kNN (71.52%), and showed better accuracy than XGBoost (66.12%) with a significant difference (p < 0.05). For patients with severe physical activity reduction (total exercise < 1.5), a high sensitivity (89.36%) was obtained. The permutation importance showed that “sex, the number of cigarettes, age, and the whole body phase angle (nutritional status)” were the most important items for this prediction. Furthermore, we found that a smaller number of test items could be used in ordinary clinical practice for the screening of physical activity disorder.