Abstract
-
Purpose
This study investigated the association of asthma with obesity phenotypes and dyslipidemia among Korean adolescents.
-
Methods
This secondary analysis included 1,723 adolescents aged 10–18 years, using data from the Eighth Korea National Health and Nutrition Examination Survey conducted in 2019–2021. The complex sampling design accounted for stratification, clustering, and sampling weights. Data were analyzed using the t-test, Rao-Scott chi-square test, and binary logistic regression.
-
Results
Factors associated with asthma included body mass index (BMI), waist circumference (WC), high-density lipoprotein cholesterol (HDL-C), age, sex, and smoking-related variables. The adjusted odds of asthma were 1.21-fold higher per 1-unit increase in BMI and 4.50-fold higher per 1-unit increase in WC. Each 1-unit increase in HDL-C was associated with 0.78-fold lower odds of asthma. Compared with adolescents aged 10–12 years, those aged 13–15 and 16–18 years had 0.38-fold and 0.36-fold lower odds of asthma, respectively. Female adolescents had 0.46-fold lower odds than male adolescents. Adolescents who smoked had 4.40-fold higher odds of asthma than those who did not smoke. The odds of asthma were 10.26-fold higher among adolescents who began smoking before the age of 12 years and 10.20-fold higher among those who smoked more than 11 cigarettes per day.
-
Conclusion
Clinical programs for preventing and managing asthma in adolescents should include physical activity promotion and dietary management, as well as abdominal muscle-strengthening exercises in cases of abdominal obesity. Regular monitoring of blood lipid levels, particularly HDL-C levels, should also be included.
-
Key words: Abdominal obesity; Asthma: Adolescent; Obesity; Dyslipidemias
INTRODUCTION
Asthma is the most common chronic inflammatory airway disease among adolescents [
1]. It is characterized by respiratory symptoms such as dyspnea, chest tightness, and cough, accompanied by variable expiratory airflow limitation [
2]. The global prevalence of asthma has been steadily increasing, according to the Centers for Disease Control and Prevention, the prevalence of asthma was estimated to be 8.8% in 2022 [
3], and, the prevalence of asthma among Korean adolescents has increased from approximately 4% in 2007 to about 6% in 2023 [
1]. Despite advances in medical care, insufficient prevention and delayed treatment continue to contribute to asthma-related mortality [
2].
Although obesity has been associated with an increased risk of asthma, it remains a major global health concern and continues to rise at an alarming rate [
4]. In Korea, the prevalence of obesity among adolescents increased from 5.9% in 2006 to 11.7% in 2020 [
5]. Obesity often precedes the onset of asthma [
6] and represents one of its most significant risk factors [
4]. Furthermore, obesity contributes to poor asthma control and more frequent exacerbations, which are often difficult to manage [
6]. It also diminishes treatment effectiveness, particularly for inhaled and systemic corticosteroids, and may be associated with reduced therapeutic responsiveness [
7].
Obesity phenotype refers to the classification of obesity based on patterns of body fat accumulation and distribution, including general obesity measured by body mass index (BMI) [
8] and abdominal obesity (AO) measured by waist circumference (WC) [
9]. One of the most widely used tools for assessing obesity is the BMI. However, BMI is an indicator of relative weight and may not accurately represent body fat distribution [
8,
10]. The health risks associated with obesity are more closely linked to the distribution of body fat than to total body weight [
11]. Alternative measures of fat distribution include AO indices such as WC, which are more effective than BMI in predicting obesity-related diseases [
9].
Obesity, as defined by BMI, is associated with subcutaneous adipose tissue [
2]. In contrast, AO primarily results from visceral fat accumulation, AO-related asthma may result from structural changes in airway diameter and lung volume [
12]. Specifically, studies have demonstrated a correlation between AO and asthma [
13], with every 10 cm increase in WC associated with a 40% higher likelihood of developing asthma [
8]. However, other research has reported no significant association between AO and asthma [
14]. Therefore, this study needs to analyze asthma risk among Korean adolescents by including obesity phenotypes defined not only by BMI but also by AO indicators based on WC.
Dyslipidemia is an abnormal blood lipid condition characterized by elevated total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C), or decreased high-density lipoprotein cholesterol (HDL-C) levels [
2,
15]. A significant association has been reported between serum lipid levels and asthma [
16,
17]. In particular, asthmatic adolescents with dyslipidemia have shown a significant correlation with lung function indices [
18]. The prevalence of asthma was higher among adolescents with elevated TC and TG levels [
17,
18], whereas adolescents with asthma exhibited lower HDL-C levels compared with those without asthma [
19]. However, elevated TC, TG, and LDL-C levels, as well as low HDL-C levels, were not significantly associated with asthma in other studies [
20]. Thus, the association between asthma and dyslipidemia remains inconsistent [
17-
20], and research among adolescents is still limited [
17].
In obesity, hypertrophy and dysfunction of adipocytes lead to elevated serum TG levels, decreased HDL, and increased LDL, in other words, dyslipidemia is a common comorbidity in obese patients, and there is a correlation between the 2 factors [
3]. Although obesity and dyslipidemia are independent disease entities, they are closely interrelated and jointly contribute to the development and exacerbation of asthma through systemic inflammation and metabolic dysregulation [
15,
17]. Previous studies have primarily focused on the association between obesity and asthma [
2,
13], dyslipidemia and asthma [
16,
18]. Inflammatory factors associated with asthma include obesity and dyslipidemia [
15], and dyslipidemia increases the risk of asthma in obese individuals [
17]. Nevertheless, research on the combined association of obesity, dyslipidemia, and asthma in Korean adolescents has been limited.
Previous studies have demonstrated that obesity increases the risk of asthma, most frequently assessed using BMI [
3,
6]. However, some individuals may have a normal BMI but still exhibit AO [
10], and the association between AO and asthma has been more consistent [
8,
9]. In addition, asthma has been linked to dyslipidemia [
16], and risk factors for asthma include elevated TC, TG, and LDL-C levels [
17,
18] as well as low HDL-C levels [
19]. Conversely, other studies have found no significant relationship between these lipid parameters and the development of asthma [
20]. So, it is necessary to determine whether specific patterns of dyslipidemia are associated with asthma in Korean adolescents.
Obesity phenotype and dyslipidemia may be differentially associated with asthma. Also, there have been no prior studies that simultaneously examined the relationships between BMI, WC, detailed lipid profile, and asthma in Korean adolescents. Therefore, comprehensive studies are needed to investigate the associations among asthma, obesity phenotype, and dyslipidemia in Korean adolescents.
The objectives of this study were to identify general characteristics, obesity phenotype, and lipid profiles of groups with and without a history of asthma, and to investigate the association between asthma and body weight, AO, and dyslipidemia in Korean adolescents.
This study will provide evidence applicable to nursing practice. In particular, the findings are expected to serve as foundational data for the early identification of health risk factors and the development of lifestyle intervention strategies in school health. Furthermore, this study will contribute to the importance of nurse-led integrated health management and the need for a family-centered approach.
METHODS
Ethical statements: This study was approved by the Institutional Review Board (IRB) of the Hannam University (IRB No. 2024-E-03-01-1019). The requirement for informed consent was waived owing to the retrospective nature of the study.
1. Study Design
This study was an observational, cross-sectional secondary data analysis using data from the Eighth Korea National Health and Nutrition Examination Survey (KNHANES VIII, 2019–2021) to examine the relationships among asthma, obesity phenotypes, and dyslipidemia in Korean adolescents. The reporting of this study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [
21].
Data for adolescents aged 10–18 years were extracted from KNHANES-VIII. The survey employed a 2-stage cluster sampling design: first, sample areas were selected, followed by the selection of households within those areas. Sampling was stratified by administrative divisions such as cities/provinces and neighborhoods (dongs/eup-myeon), as well as housing types. Implicit stratification factors included the proportions of residential areas and the educational levels of household heads.
The KNHANES-VIII dataset comprises health-related questionnaires, physical measurements, and nutrition surveys, all collected by trained personnel. To protect participant confidentiality, the KNHANES-VIII dataset underwent top-coding, bottom-coding, and variable recategorization. These procedures ensured data anonymity and compliance with ethical standards. Following approval from the Korea Centers for Disease Control and Prevention to use the raw data (approval granted on May 10, 2024), this study accessed the data, with the analysis period spanning from August to December 2024.
Of the total 22,559 participants, 1,723 adolescents aged 10–18 years were included after excluding those younger than 10 or older than 18 years, as well as non-respondents. Among these participants, 112 individuals (6.1%) were classified in the asthma group, and 1,611 (93.9%) were categorized in the non-asthma group.
3. Measurements
In this study, the general characteristics of the participants included age groups (10–12 years, 13–15 years, and 16–18 years), sex, smoking status (non-smoker or smoker), age at smoking onset (≤12 years, 13–15 years, and 16–18 years), and daily cigarette consumption categorized as ≤5, 6–10, or ≥11 cigarettes per day.
1) Asthma-related characteristics
Asthma-related variables in KNHANES-VIII included self-reported physician diagnosis of asthma (“Have you ever been diagnosed with asthma by a doctor?”; yes or no), age at asthma onset (categorized as ≤9 years, 10–12 years, and 13–18 years), and asthma medication usage, which was classified as regular use, irregular use, or no use.
2) Measurements of general obesity and abdominal obesity
In this study, general obesity was assessed using BMI, calculated by dividing body weight in kilograms by height in meters squared (kg/m
2) [
22]. Anthropometric measurements were conducted in the morning after an overnight fast, in accordance with the KNHANES-VIII protocol. For height measurement, participants stood barefoot on a flat surface in an upright position with their heels together and feet slightly apart. While maintaining straight knees and relaxed arms at their sides, they faced forward with their line of sight kept level. After taking a deep breath and holding it, height was recorded in centimeters to the nearest 0.1 cm. Before weight measurement, the scale was calibrated and reset to zero. Participants, dressed in light clothing, stood still with arms relaxed and eyes facing forward. Body weight was measured in kilograms and recorded to one decimal place.
AO was determined using WC. According to the KNHANES-VIII protocol, WC was measured while participants stood upright and maintained normal, relaxed breathing. A non-stretchable measuring tape was placed horizontally at the midpoint between the lower margin of the last palpable rib and the upper border of the iliac crest, and the measurement was recorded to the nearest 0.1 cm [
22].
3) Lipid profile measurements
In KNHANES-VIII, blood samples were collected in the early morning following an overnight fast. Experienced medical personnel performed venipuncture using a sterile vacuum needle inserted into the participant’s antecubital vein. Blood was drawn into specialized vacuum tubes designed for component separation. Serum samples were stored under controlled low-temperature conditions and preserved according to standardized handling protocols until analysis. Uniform procedures were consistently applied to all participants to ensure measurement reliability and accuracy.
Serum lipid profiles, including TC, TG, LDL-C, and HDL-C, were analyzed using an enzymatic colorimetric assay. All analyses were conducted with the Hitachi Labospect analyzer (Hitachi) [
22].
This study used data from KNHANES-VIII, applying a complex sampling design that accounted for stratification, clustering, and sampling weights. Descriptive statistics of participants’ characteristics were presented as unweighted frequencies, weighted percentages, estimated means, and standard errors. Group differences in general characteristics, BMI, WC, and lipid profile parameters between the asthma and non-asthma groups were analyzed using the t-test and Rao-Scott chi-square test. Binary logistic regression analyses were performed to identify factors associated with asthma among adolescents. Adjusted odds ratios (AORs) and 95% confidence intervals (CIs) were calculated for each variable. All statistical analyses were performed using IBM SPSS ver. 27.0 (IBM Corp.).
RESULTS
1. General Characteristics of Participants
Among the study population, 112 participants (6.1%) were classified in the asthma history group, whereas 1,611 participants (93.9%) were categorized in the non-asthma history group. The distribution of asthma onset age was as follows: 4.1% at ≤9 years, 1.1% at 10–12 years, and 0.8% at 13–18 years. Regarding asthma medication use, 0.6% of participants reported regular use, 1.5% reported irregular use, and 4.0% reported no use of medication.
The mean age of all participants was 14.17 years, with males comprising 53.2% and females 46.8% of the total sample. Smokers accounted for 6.6% of participants, with ages of onset ranging from 16–18 years (3.0%), 13–15 years (2.7%) and ≤12 years (0.9%). Among smokers, the amount of smoking cigarettes per day was 6–10 (2.4%), ≤5 (2.2%), and ≥11 (2.0%). The participants’ mean height was 162.02 cm, mean weight was 57.21 kg, mean BMI was 21.54 kg/m
2, and the mean WC was 72.46 cm. The mean lipid profile values were as follows: TC 154.45 mg/dL, TG 72.66 mg/dL, LDL-C 87.97 mg/dL, and HDL-C 59.06 mg/dL (
Table 1).
The average age of participants in the asthma group was significantly higher than that of the non-asthma group (t=42.22, p<.001). When classified by age group, those aged 16–18 years accounted for 46.9% of the asthma group, followed by those aged 13–15 years (37.6%) and 10–12 years (15.5%). In contrast, the non-asthma group included 35.9% aged 16–18 years, 31.1% aged 13–15 years, and 32.9% aged 10–12 years. The distribution showed a statistically significant difference between groups (χ2=14.03, p=.047). With respect to sex, males constituted 74.7% of the asthma group and 51.8% of the non-asthma group, while females accounted for 25.3% and 48.2%, respectively. This difference was statistically significant (χ2= 20.76, p=.003).
Smoking-related characteristics also significantly differed between groups. The asthma group had a higher proportion of smokers (29.3% vs. 5.1%), an earlier smoking onset (≤12 years: 10.2% vs. 0.3%), and a greater daily cigarette consumption (≥11 cigarettes/day: 11.6% vs. 1.5%) compared with the non-asthma group (p<.001 for all).
The asthma group showed significantly higher levels of BMI (t=2.25,
p=.025) and WC (t=2.71,
p=.007) compared with the non-asthma group. Regarding lipid profiles, the asthma group had significantly lower HDL-C levels than the non-asthma group (t=5.38,
p<.001) (
Table 2).
To identify factors associated with asthma in adolescents, binary logistic regression analysis was performed. Variables included in the model were those that showed statistically significant differences between asthma and non-asthma groups (
Table 2). In addition, previous studies have reported that elevated serum TC, TG, and LDL-C levels are risk factors for asthma [
17,
18]. These lipid parameters may promote systemic inflammation, exacerbate airway inflammation, and consequently worsen asthma symptoms [
19]. Accordingly, these variables were also included in the analysis. The final model incorporated BMI, WC, lipid profile components, age, sex, smoking status, age at smoking initiation, and daily smoking amount.
In the multicollinearity diagnostics performed in this study, BMI and WC exhibited identical values for the variance inflation factor (VIF) 5.034 and tolerance 0.210. Given that no universally accepted VIF cut-off has been established, these values suggest that multicollinearity is unlikely to be a major concern in the present study [
23].
In addition, multicollinearity among smoking-related variables was assessed. The results showed acceptable VIF and tolerance values for smoking status (VIF=1.003, tolerance=0.997), smoking onset age (VIF=1.970, tolerance=0.508), and smoking amount (VIF=1.966, tolerance=0.509), indicating that multicollinearity among these variables was unlikely to be substantial. Model explanatory power was confirmed by Cox & Snell R2=.402 and Nagelkerke R2=.750, demonstrating that the selected variables adequately explained variance in asthma risk and confirming the model’s robustness.
The AORs indicated that each one-unit increase in BMI and WC was associated with 1.21-fold (95% CI, 1.09–1.34; p<.001) and 4.50-fold (95% CI, 1.94–10.43; p<.001) higher odds of asthma, respectively, while a one-unit increase in HDL-C was related to 0.78-fold (95% CI, 0.65–0.94; p=.011) lower odds of asthma. Compared with adolescents aged 10–12 years, those aged 13–15 and 16–18 years had 0.38-fold (95% CI, 0.18–0.79; p=.012) and 0.36-fold (95% CI, 0.16–0.80; p=.020) lower risks, respectively. Females had a 0.46-fold (95% CI, 0.10–0.92; p=.004) lower risk compared to males.
Smoking was a major risk factor: smokers had 4.40-folds (95% CI, 1.69–11.46;
p<.001) higher odds of asthma than non-smokers. The risk was 10.26-fold (95% CI, 6.08–47.36;
p=.043) higher among those who began smoking at ≤12 years, 9.13-fold (95% CI, 3.04–27.39;
p<.001) higher at 13–15 years, and 4.71-fold (95% CI, 2.21–18.23;
p=.030) higher at 16–18 years. Smoking 6–10 cigarettes daily increased asthma risk by 6.07-fold (95% CI, 2.18–16.86;
p=.001), while smoking ≥11 cigarettes daily increased it by 10.20-fold (95% CI, 4.08–25.54;
p<.001) compared with non-smokers (
Table 3).
DISCUSSION
Our study explores the relationship between asthma, obesity phenotype, and dyslipidemia among Korean adolescents. Unlike previous research, our investigation takes a comprehensive approach by examining the interconnections between BMI, WC, lipid profiles, and asthma in this specific population.
The significance of this study lies in its potential to contribute to the development of preventive strategies for asthma in adolescents, particularly by considering the combined influence of obesity and lipid imbalances.
In this study, BMI and WC were significantly higher in the asthma group than in the non-asthma group, and, multiple logistic regression analysis showed that asthma risk increased by 1.21-fold with higher BMI and 4.50-fold with higher WC. Among adolescents classified as obese based on BMI, asthma prevalence was significantly higher than among their normal-weight peers [
2,
3]. Obesity increases asthma risk by approximately 1.28-fold [
6] and is also linked to greater disease severity [
3]. The systemic, non-atopic inflammation observed in these patients is linked to metabolic abnormalities that may impair lung function [
19]. Additionally, obese individuals with asthma exhibit reduced responsiveness to inhaled corticosteroids [
2,
24].
AO, as reflected by WC, is more strongly associated with asthma risk than overall obesity measured by BMI [
8]. This underscores the importance of fat distribution, particularly central adiposity, in the pathogenesis of asthma beyond total body weight [
9]. Therefore, the researchers believe that, as a new perspective for preventing adolescent asthma, managing obesity-particularly preventing AO caused by visceral fat accumulation-could play a crucial role in asthma prevention.
BMI and WC serve as indicators of general obesity and AO, respectively [
3,
6,
8,
13]. In the current study, both indicators showed significant associations with asthma. In a study examining the association between central obesity and asthma in adolescents, the risk increased 3.55-fold for each unit rise in WC [
8]. The association between AO and asthma remained significant even after adjusting for BMI, suggesting that AO may independently influence asthma risk [
13]. AO induces low-grade systemic inflammation due to visceral fat accumulation, which alters airway inflammation and immune regulation, thereby increasing asthma susceptibility [
8,
9]. BMI has limitations in accurately reflecting body fat distribution and may misclassify individuals with high muscle mass as obese, whereas WC directly indicates abdominal visceral fat volume [
8], and AO can worsen asthma symptoms by limiting diaphragmatic movement, and decreasing lung volumes [
25]. The researchers interpreted the stronger association between WC and asthma observed in this study as suggesting that fat distribution, rather than total body mass, is more closely related to asthma.
In this study, low HDL-C was identified as a significant factor associated with adolescent asthma. HDL-C is an anti-inflammatory lipoprotein that protects against airway inflammation and hyperresponsiveness [
19], and HDL-C levels are inversely correlated with C-reactive protein, a biomarker of systemic inflammation [
26]. Mechanistically, decreased HDL-C can impair reverse cholesterol transport and increase oxidative stress, thereby activating respiratory inflammatory pathways and elevating asthma susceptibility [
19], which supports the findings of our study. However, several studies have reported that TC, TG, and LDL-C are risk factors for asthma [
17-
19], findings that differ from those of this study. These discrepancies suggest that the relationship between lipid levels and asthma may be influenced by complex, multifactorial mechanisms rather than a single pathway [
18]. Future studies should therefore adjust for potential confounding variables such as age, sex, and genetic factors, and employ longitudinal designs to better clarify the causal relationship between lipid metabolism and asthma. The researchers interpreted low HDL-C among lipid profiles as an important factor in assessing asthma risk in adolescents. Therefore, improving HDL-C levels during adolescence may help prevent and manage asthma.
According to the present study, age and sex were significant factors influencing asthma, with asthma prevalence decreasing with age and males exhibiting a higher risk than females. Previous studies have similarly reported that the risk of developing asthma decreases with age [
9], and that adolescents aged 16 years or older have a lower risk compared to those aged 12–13 years [
27], supporting the current findings. Earlier studies have reported that younger adolescents may be more sensitive to environmental and physiological risk factors, leading to a higher susceptibility to asthma [
9,
27]. Based on the findings of our study, the researcher interprets that the increased sensitivity in younger adolescents may contribute to a higher risk of developing asthma. Research has also demonstrated a higher prevalence of asthma in males than in females among adolescents [
18,
27], with one study reporting that the risk of asthma was 1.32 times higher in males than in females [
9]. Generally, asthma is more common and tends to be more severe in prepubescent males than in females of the same age. Before the age of 14, the prevalence of asthma in males is approximately twice that observed in females [
2]. This sex-based difference is believed to be influenced by fluctuations in sex hormones, which may affect asthma onset and severity [
28].
In this study, smoking status, age at smoking initiation, and amount of smoking were identified as risk factors for asthma. The risk increased with earlier smoking initiation and higher cumulative exposure. Consistent with previous research, asthma was significantly associated with a higher prevalence of smoking among adolescents [
29]. In individuals with asthma, smoking accelerates lung function decline through mechanisms involving airway inflammation, oxidative stress, and airway remodeling. These pathophysiological changes heighten airway hyperresponsiveness and mucus hypersecretion, thereby worsening asthma symptoms [
2]. Furthermore, smoking reduces the efficacy of both inhaled and systemic corticosteroids by impairing glucocorticoid receptor function and disrupting anti-inflammatory pathways, leading to poorer asthma control [
29]. The researcher interprets that early smoking initiation and daily cigarette consumption contribute significantly to the increased risk of asthma. Therefore, the researcher believes that an early smoking prevention education program, particularly targeting adolescents and elementary school students, is crucial. Such educational interventions could effectively raise awareness of risk factors prior to smoking initiation and may play a key role in the prevention of asthma.
This study focused on identifying factors related to asthma in adolescents to support preventive strategies The findings of this study have important implications for nursing practice. The observed associations between BMI, WC, and asthma risk highlight the need for comprehensive obesity assessment, including the evaluation of AO, in school health nursing. Furthermore, the relationship between lipid profiles, such as HDL-C, and asthma underscores the importance of integrated health management through lifestyle modification. In particular, a family-centered nursing approach that takes into account familial influence may play a critical role in improving adolescents’ health behaviors and preventing asthma.
A major strength of this study lies in its use of a reliable, nationally representative dataset. Additionally, it examined the relationship between asthma and obesity using both BMI and AO indicators. Another strength is the inclusion of adolescents within a narrow age range and with minimal comorbidities that could affect dyslipidemia status. These features enhance the credibility of the interpretation that obesity type and dyslipidemia may be risk factors for asthma.
Limitations of this study include the fact that the question used to assess asthma diagnosis (“Have you ever been diagnosed with asthma by a doctor?”) reflects lifetime prevalence. It was unable to distinguish between active asthma, which reflects current symptoms, and treatment status. Asthma diagnosed in childhood may remit over time during adolescence, which may lead to discrepancies between lifetime asthma prevalence and actual current asthma status. In addition, lifetime asthma prevalence cannot clearly determine the temporal relationship between asthma diagnosis and associated factors, limiting the causal interpretation of the study results. Also, our study is limited in its ability to clearly establish the temporal sequence between metabolic/obesity-related markers and the onset of asthma during adolescence. Future studies should employ longitudinal designs with repeated measurements over time to better elucidate the causal pathways between metabolic factors and asthma development in adolescent populations. Furthermore, because this study analyzed secondary data, it had limitations in considering potential confounding variables such as physical activity, allergic diseases, and secondhand smoke exposure. Therefore, the current study focused on the association between adolescent asthma and obesity phenotypes and dyslipidemia indicators. Finally, potential measurement variability in WC and lipid levels may have introduced random error. WC measurements can vary depending on measurement site, and respiratory phase, while lipid levels may fluctuate due to fasting status, or laboratory procedures. Repeated measurements, standardized protocols, or using averaged values across multiple assessments could help minimize such variability in future research. The use of representative data from KNHANES VIII enhances the generalizability of the present findings to Korean adolescents. However, as the study population was restricted to Korean adolescents, caution is warranted when applying these findings to populations with different ethnic, environmental, and socioeconomic backgrounds.
CONCLUSION
This study identified high body weight, AO, low HDL-C, age, sex, and smoking as significant risk factors for asthma. In particular, obesity-related indicators and serum lipid levels played crucial roles in asthma development, with an increased risk observed among males and smokers. Comprehensive approaches addressing obesity are essential in adolescent asthma prevention and management clinical practice programs, incorporating multifaceted strategies such as promoting physical activity and dietary control for weight regulation. For AO, interventions should focus on reducing WC through abdominal muscle strengthening exercises and lifestyle modifications that improve fat distribution. In addition, regular monitoring and appropriate management of serum lipid levels especially HDL-C are necessary. Early intervention and smoking cessation education targeting high-risk groups with early smoking initiation and heavy smoking habits are critical components of adolescent asthma prevention.
A comprehensive and personalized prevention and management strategy can help reduce asthma incidence and prevent symptom exacerbation. Considering that effective weight management is crucial for asthma prevention in both males and females, integrated interventions involving exercise, physical activity, behavioral therapy, smoking cessation, and dietary improvement through health education and counseling are recommended.
ARTICLE INFORMATION
Table 1.General characteristics of subjects (N=1,723)
|
Characteristic |
Value |
|
Asthma diagnosis |
|
|
No |
1,611 (93.9) |
|
Yes |
112 (6.1) |
|
Asthma onset age (yr) |
|
|
Non-asthma |
1,611 (93.9) |
|
≤9 |
84 (4.1) |
|
10–12 |
17 (1.1) |
|
13–18 |
11 (0.8) |
|
Asthma drug administration |
|
|
Non-asthma |
1,611 (93.9) |
|
Regular drug administration |
12 (0.6) |
|
Irregular drug administration |
20 (1.5) |
|
No drug administration |
80 (4.0) |
|
Age (yr) |
14.17±0.07 |
|
10–12 |
639 (31.9) |
|
13–15 |
560 (31.6) |
|
16–18 |
524 (36.6) |
|
Sex |
|
|
Male |
946 (53.2) |
|
Female |
777 (46.8) |
|
Smoking status |
|
|
Non-smoker |
1,628 (93.4) |
|
Smoker |
95 (6.6) |
|
Smoking onset age (yr) |
|
|
Non-smoker |
1,628 (93.4) |
|
≤12 |
15 (0.9) |
|
13–15 |
41 (2.7) |
|
16–18 |
39 (3.0) |
|
Amount of smoking (cigarettes/day) |
|
|
Non-smoker |
1,628 (93.4) |
|
≤5 |
30 (2.2) |
|
6–10 |
39 (2.4) |
|
≥11 |
26 (2.0) |
|
Height (cm) |
162.02±0.35 |
|
Weight (kg) |
57.21±0.53 |
|
Body mass index (kg/m²) |
21.54±0.15 |
|
Waist circumference (cm) |
72.46±0.38 |
|
Total cholesterol (mg/dL) |
154.45±0.82 |
|
Triglyceride (mg/dL) |
72.66±0.89 |
|
Low-density lipoprotein cholesterol (mg/dL) |
87.97±0.91 |
|
High-density lipoprotein cholesterol (mg/dL) |
59.06±0.45 |
Table 2.General characteristics, obesity phenotypes, and lipid profile-related characteristics of non-asthma groups and asthma-diagnosis groups (N=1,723)
|
Characteristic |
Non-asthma group |
Asthma group |
t/χ2 (p) |
|
Total |
1,611 (93.9) |
112 (6.1) |
|
|
Mean age (yr) |
14.10±0.07 |
15.10±0.35 |
42.22 (<.001) |
|
Age (yr) |
|
|
14.03 (.047) |
|
10–12 |
611 (32.9) |
28 (15.5) |
|
|
13–15 |
516 (31.1) |
44 (37.6) |
|
|
16–18 |
484 (35.9) |
40 (46.9) |
|
|
Sex |
|
|
20.76 (.003) |
|
Male |
866 (51.8) |
80 (74.7) |
|
|
Female |
745 (48.2) |
32 (25.3) |
|
|
Smoking status |
|
|
94.26 (<.001) |
|
Non-smoker |
1,545 (94.9) |
83 (70.7) |
|
|
Smoker |
66 (5.1) |
29 (29.3) |
|
|
Smoking onset age (yr) |
|
|
155.61 (<.001) |
|
Non-smoker |
1,545 (94.9) |
83 (70.7) |
|
|
≤12 |
6 (0.3) |
9 (10.2) |
|
|
13–15 |
28 (2.2) |
13 (9.9) |
|
|
16–18 |
32 (2.6) |
7 (9.3) |
|
|
Amount of smoking (cigarettes/day) |
|
|
105.29 (<.001) |
|
Non-smoker |
1,545 (94.9) |
83 (70.7) |
|
|
≤5 |
26 (2.1) |
4 (6.5) |
|
|
6–10 |
24 (1.5) |
15 (11.2) |
|
|
≥11 |
16 (1.5) |
10 (11.6) |
|
|
Body mass index (kg/m²) |
21.36±0.15 |
23.30±0.84 |
2.25 (.025) |
|
Waist circumference (cm) |
72.12±0.39 |
75.81±2.04 |
2.71 (.007) |
|
Total cholesterol (mg/dL) |
154.38±5.25 |
163.91±0.87 |
1.79 (.073) |
|
Triglyceride (mg/dL) |
72.50±9.79 |
79.29±2.96 |
0.44 (.506) |
|
LDL-C (mg/dL) |
87.12±16.53 |
96.60±3.81 |
0.50 (.615) |
|
HDL-C (mg/dL) |
59.21±0.47 |
54.63±1.20 |
5.38 (<.001) |
Table 3.Factors affecting adolescents asthma (N=1,723)
|
Variable |
AOR (95% CI) |
p
|
|
Body mass index (kg/m2) |
1.21 (1.09–1.34) |
<.001 |
|
Waist circumference (cm) |
4.50 (1.94–10.43) |
<.001 |
|
Total cholesterol (mg/dL) |
1.01 (0.99–1.02) |
.490 |
|
Triglyceride (mg/dL) |
1.07 (0.98–1.15) |
.152 |
|
Low-density lipoprotein cholesterol (mg/dL) |
1.13 (0.96–1.32) |
.116 |
|
High-density lipoprotein cholesterol (mg/dL) |
0.78 (0.65–0.94) |
.011 |
|
Age (ref: 10–12 yr) |
|
|
|
13–15 |
0.38 (0.18–0.79) |
.012 |
|
16–18 |
0.36 (0.16–0.80) |
.020 |
|
Sex (ref: male) |
|
|
|
Female |
0.46 (0.10–0.92) |
.004 |
|
Smoking status (ref: non-smoker) |
|
|
|
Smoker |
4.40 (1.69–11.46) |
<.001 |
|
Smoking onset age (ref: non-smoker) |
|
|
|
≤12 |
10.26 (6.08–47.36) |
.043 |
|
13–15 |
9.13 (3.04–27.39) |
<.001 |
|
16–18 |
4.71 (2.21–18.23) |
.030 |
|
Amount of smoking (cigarettes/day) (ref: non-smoker) |
|
|
|
≤5 |
3.73 (0.70–19.89) |
.458 |
|
6–10 |
6.07 (2.18–16.86) |
.001 |
|
≥11 |
10.20 (4.08–25.54) |
<.001 |
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