Relationship between asthma and sarcopenia in the elderly: a nationwide study from the KNHANES

Overall and subgroup prevalence of self-reported asthma in US adults: a nationally representative cross-sectional study

BACKGROUND

The purpose of this study is to determine the variations in the prevalence of self-reported asthma among the adult population in the United States of America (USA), analyzing demographic characteristics, physical indicators, living habits, and sarcopenia.

METHODS

10,566 participants from the 2009 to 2018 National Health and Nutrition Examination Survey (NHANES) of the USA who were 20 years of age or older and not pregnant were included in the study.

RESULTS

The prevalence of patients with asthma varies by age, gender, and race. The weighted prevalence is 15.5%, estimated to represent 19.36 million people in the USA (95% CI, 14.5% to 16.6%). The prevalence of self-reported asthma decreases with age, with the highest prevalence among young adults aged 20-25 for both males and females. Females were also more susceptible to asthma compared to males. The increase in asthma prevalence attributed to smoking was most pronounced among African American and Caucasian participants (p < 0.05), while its effect on Mexican American and Asian participants was relatively minor. Notably, the prevalence of asthma was significantly higher in African American and Caucasian participants with sarcopenia compared to those without sarcopenia.

CONCLUSIONS

The prevalence of asthma is associated to varying degrees with factors such as age, gender, smoking, and the presence of sarcopenia. The elevated prevalence of asthma among young people and females warrants attention. Intensifying efforts toward smoking cessation and the scientific management of sarcopenia could be instrumental in reducing the incidence of asthma.

Allergic Phenotypes and Sarcopenia: Evidence from Observational Studies and Mendelian Randomization Analysis

Commonly affected in early-life population, the impact of allergic phenotypes on mid- or late-life health is less discussed. This study is to explore the association of allergic phenotypes including atopic dermatitis (AD), asthma, eosinophils count (EC), and sarcopenia. We conducted observational studies and mendelian randomization (MR) analysis based on UK Biobank (UKB), the China Health and Retirement Longitudinal Study (CHARLS) and data from genome-wide association study (GWAS). Based on the UKB data, AD, asthma and EC were positively correlated with pre-sarcopenia and decreased skeletal muscle mass index and hand grip in fully adjusted model. Asthma and EC were significantly associated with sarcopenia while AD was marginally associated (p = 0.095). Based on the CHARLS cohort, asthma significantly added 109.4% risk for pre-sarcopenia in adjusted model (relative risk = 2.094; p = 0.002), respectively. Both asthma (β = 0.100, p = 0.006) and EC (β = 0.023, p = 0.017) exerted significantly casual effects on pre-sarcopenia. However, as for sarcopenia, merely EC exhibited a significantly casual effect (β = 0.005, p = 0.048). Significant casual effects of AD (β = - 0.027, p = 0.003), asthma (β = - 0.029, p = 0.027) and EC (β = - 0.041, p < 0.001) on decreased appendicular lean mass (ALM) were observed using the inverse-variance weighted method and the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) method. Our results revealed a contributory role of AD, asthma and EC on sarcopenia, especially in terms of decreased ALM, an indicator for sarcopenia diagnosis. The findings of our study will raise the awareness of preventing aging-related disorders or geriatric syndromes among allergic populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00110-4.

Long-term exposure to air pollution and risk of Sarcopenia in adult residents of Taiwan: a nationwide retrospective cohort study

BACKGROUND

Sarcopenia is an age-related, multifactorial syndrome. Previous studies have shown that air pollutants are associated with inflammation and oxidative stress. However, the association between long-term exposure to air pollution and sarcopenia is not completely understood.

METHODS

The Taiwan National Health Research Database (NHIRD) contains medical records of almost all Taiwanese residents. Daily air pollution data collected by the Taiwan Environmental Protection Agency was used to analyze concentrations of sulfur oxide (SO2), carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide (NO2), and particulate matter (PM2.5, PM10). The databases were merged according to the insurants' living area and the location of the air quality monitoring station. We categorized the pollutants into quartiles (Q1, Q2, Q3, and Q4).

RESULTS

Our study population consisted of 286,044 patients, among whom 54.9% were female and 45.1% were male. Compared to Q1 levels of pollutants, Q4 levels of SO2 (adjusted hazard ratio [aHR] = 8.43; 95% confidence interval [CI] = 7.84, 9.07); CO (aHR = 3.03; 95%CI = 2.83, 3.25); NO (aHR = 3.47; 95%CI = 3.23, 3.73); NO2 (aHR = 3.72; 95%CI = 3.48, 3.98); PM2.5 (aHR = 21.9; 95% CI = 19.7, 24.5) and PM10 (aHR = 15.6; 95%CI = 14.1, 17.4) increased risk of sarcopenia.

CONCLUSIONS

Our findings indicated a significantly increased risk of sarcopenia in both male and female residents exposed to high levels of air pollutants.

Machine Learning for Sarcopenia Prediction in the Elderly Using Socioeconomic, Infrastructure, and Quality-of-Life Data

Since the WHO's 2021 aging redefinition emphasizes "healthy aging" by focusing on the elderly's ability to perform daily activities, sarcopenia, which is defined as the loss of skeletal muscle mass, is now becoming a critical health concern, especially in South Korea with a rapidly aging population. Therefore, we develop a prediction model for sarcopenia by using machine learning (ML) techniques based on the Korea National Health and Nutrition Examination Survey (KNHANES) data 2008-2011, in which we focus on the role of socioeconomic status (SES), social infrastructure, and quality of life (QoL) in the prevalence of sarcopenia. We successfully identify sarcopenia with approximately 80% accuracy by using random forest (RF) and LightGBM (LGB), CatBoost (CAT), and a deep neural network (DNN). For prediction reliability, we achieve area under curve (AUC) values of 0.831, 0.868, and 0.773 for both genders, males, and females, respectively. Especially when using only male data, all the models consistently exhibit better performance overall. Furthermore, using the SHapley Additive exPlanations (SHAP) analysis, we find several common key features, which mainly contribute to model building. These include SES features, such as monthly household income, housing type, marriage status, and social infrastructure accessibility. Furthermore, the causal relationships of household income, per capita neighborhood sports facility area, and life satisfaction are analyzed to establish an effective prediction model for sarcopenia management in an aging population.

Lifestyles and the risk of an asthma attack in adult asthma patients: a cross-sectional study using NHANES database

BACKGROUND

The influence of physical activity, diet and sleep on asthma has been well documented by recent studies respectively. However, few studies focus on the relationship between asthma attack and the overall lifestyle, which comprises interrelated lifestyle factors. This study aims to investigate the influence of lifestyles on the ratio of asthma attack. Data were extracted from the NHANES database (2017 to May 2020).

METHODS

A total of 834 asthmatic patients were enrolled and divided into non-asthma attack (N.=460) and asthma attack (N.=374) groups. The risk factors for asthma attacks were preliminarily identified by univariate logistic analysis, then multivariate logistic analysis was employed to select independent risk factors other than lifestyles and further determine the association between lifestyles and asthma attacks.

RESULTS

After multivariate logistic analysis, engagement of vigorous activity (Model 1 P=0.010, Model 2 P=0.016, Model 3 P=0.012), engagement of moderate activity (Model 1 P=0.006, Model 2 P=0.008, Model 3 P=0.003) and sleep disorder (Model1 P=0.001, Model 2 P<0.001, Model 3 P=0.008) were determined as independent risk factors of lifestyles for an asthma attack in the past year.

CONCLUSIONS

This research documented that, for asthmatic patients, engagement of vigorous activity, engagement of moderate activity, and sleep disorder will make an asthma attack more likely to happen.

Sarcopenia Prediction for Elderly People Using Machine Learning: A Case Study on Physical Activity

Sarcopenia is a well-known age-related disease that can lead to musculoskeletal disorders and chronic metabolic syndromes, such as sarcopenic obesity. Numerous studies have researched the relationship between sarcopenia and various risk factors, leading to the development of predictive models based on these factors. In this study, we explored the impact of physical activity (PA) in daily life and obesity on sarcopenia prediction. PA is easier to measure using personal devices, such as smartphones and watches, or lifelogs, than using other factors that require medical equipment and examination. To demonstrate the feasibility of sarcopenia prediction using PA, we trained various machine learning models, including gradient boosting machine (GBM), xgboost (XGB), lightgbm (LGB), catboost (CAT), logistic regression, support vector classifier, k-nearest neighbors, random forest (RF), multi-layer perceptron, and deep neural network (DNN), using data samples from the Korea National Health and Nutrition Examination Survey. Among the models, the DNN achieved the most precise accuracy on average, 81%, with PA features across all data combinations, and the accuracy increased up to 90% with the addition of obesity information, such as total fat mass and fat percentage. Considering the difficulty of measuring the obesity feature, when adding waist circumference to the PA features, the DNN recorded the highest accuracy of 84%. This model accuracy could be improved by using separate training sets according to gender. As a result of measurement with various metrics for accurate evaluation of models, GBM, XGB, LGB, CAT, RF, and DNN demonstrated significant predictive performance using only PA features including waist circumference, with AUC values at least around 0.85 and often approaching or exceeding 0.9. We also found the key features for a highly performing model such as the quantified PA value and metabolic equivalent score in addition to a simple obesity measure such as body mass index (BMI) and waist circumference using SHAP analysis.

The association of asthma duration with body mass index and Weight-Adjusted-Waist index in a nationwide study of the U.S. adults

BACKGROUNDS

The association between obesity and asthma has been of interest, but whether the duration of asthma has an effect on obesity is still limitedly studied.

AIM

The purpose of this study was to investigate the association between asthma duration and obesity-related indexes, where obesity-related indexes include Body mass index (BMI) and Weight-adjusted-waist index (WWI).

METHODS

Data from National Health and Nutrition Examination Survey (NHANES) 2009-2018 were obtained to conduct this cross-sectional study. Duration of asthma was used as the independent variable and obesity-related indexes as the response variables. Multiple linear regression was used to assess the association between the independent variable and the response variables, and subsequently smoothed curve fitting and threshold effect analysis were performed to clarify whether there was a nonlinear correlation between the independent variable and the response variables. Finally, subgroup analysis was conducted to find sensitive populations.

RESULTS

A total of 9170 participants were included in the analysis. Asthma duration was statistically different between the two groups when all participants were grouped by median WWI (Q1 < 11.65, Q2 ≥ 11.65) (P < 0.001), but not by median BMI (Q1 < 31.8, Q2 ≥ 31.8) (P = 0.130). There was a positive association between asthma duration and WWI [β = 0.016, 95% CI (0.016, 0.017)], but a negative one with BMI [β = - 0.098, 95% CI (- 0.112, - 0.085)], and the correlations between the independent and response variables became more pronounced with increasing asthma duration (P for trend < 0.01). In addition, there were nonlinear relationships between asthma duration with BMI and WWI (log likelihood ratio < 0.001), with the best valid inflection points for asthma duration being 2 years (with WWI as the response variable) and 3 years (with BMI as the response variable), respectively. In the subgroup analysis, the positive association between asthma duration and WWI was more pronounced in the participants who were male, aged less than 40 years, and had asthma onset before 12 years of age. In contrast, when BMI was used as the response variable, the negative association between it and asthma duration was more pronounced among participants of female, aged 60 years or older, and with asthma onset less than 12 years of age.

CONCLUSIONS

In US adults, asthma duration might cause changes in obesity-related indexes. Longer asthma duration might cause weight loss, but might increase the risk of abdominal obesity.

Bronchial Asthma and Sarcopenia: An Upcoming Potential Interaction

BACKGROUND

Sarcopenia seems to be an emerging health issue worldwide, concerning the progressive loss of skeletal muscle mass, accompanied by adverse outcomes. Asthma is a chronic inflammatory respiratory condition that is widespread in the world, affecting approximately 8% of adults. Although data are scarce, we aim to shed light on the potential association between low muscle mass and asthma and point out any probable negative feedback on each other.

METHODS

We searched within the PubMed, Scopus, MEDLINE, and Google Scholar databases.

STUDY SELECTIONS

Three studies were included in our analysis. Only original studies written in English were included, while the references of the research articles were thoroughly examined for more relevant studies. Moreover, animal model studies were excluded.

RESULTS

2% to 17% of asthmatics had sarcopenia according to the existent literature. Sarcopenic asthmatic patients seem to have reduced lung function, while their mortality risk may be increased. Furthermore, patients with asthma- chronic obstructive pulmonary disease (COPD) overlap syndrome phenotype and sarcopenia might have a higher risk of osteopenia and osteoporosis progression, leading consequently to an increased risk of fractures and disability.

CONCLUSIONS

Emerging data support that pulmonologists should be aware of the sarcopenia concept and be prepared to evaluate the existence of low muscle mass in their asthmatic patients.