INTRODUCTION
Childhood obesity can be considered a global epidemic as it has become common in recent years. According to the World Health Organization, while only 2% of children and adolescents aged 5–19 (31 million young people) were obese in 1990, in the same age range, the prevalence of obesity has increased to 8% (160 million young people) by 2022 [1, 2].
Obesity, defined as enormous fat accumulation in the body, is not merely a cosmetic problem but is also linked to a variety of negative outcomes, such as cardiovascular diseases and metabolic syndrome. One of the less frequently discussed but crucial health effects of childhood obesity is its relationship with asthma [3]. Asthma affects a substantial proportion of the paediatric population and is characterized by variable and recurrent respiratory symptoms [4]. Because asthma and obesity often co-occur, childhood obesity is assumed to be related to asthma. There is a complex interaction between these two entities, involving multiple pathophysiological mechanisms, including systemic inflammation, hormonal imbalances, and mechanical factors related to increased body mass [5]. According to a retrospective cohort study of 507 496 children, obesity directly contributes to 23% to 27% of newly diagnosed instances of asthma in children [6]. In a study, FEV1/FVC was found to be negatively correlated with indicators of fat mass in children with obesity without asthma [7].
Understanding the relationship between body fat composition and asthma is imperative for developing targeted interventions and therapeutic strategies aimed at mitigating the consequences of obesity on asthma morbidity. The rationale for this study stems from the need to elucidate the specific contributions of different fat composition to asthma in the paediatric population.
AIM
In this study we aimed to compare fat mass, fat-free mass, fat distribution rates, body muscle-fat ratio and regional adiposity by measuring the bioelectrical impedance of paediatric patients with and without asthma. Considering the rising prevalence of childhood obesity and asthma, this research aims to provide insights that could inform clinical practices and public health policies.
MATERIAL AND METHODS
STUDY DESIGN AND SUBJECTS
This prospective cross-sectional study was conducted on paediatric patients with obesity at Kayseri City Education and Research Hospital between 1.10.2023 and 1.04.2024. The sample consisted of two subgroups, with and without asthma. Asthma was diagnosed according to the 2023 GINA asthma guidelines by questioning about recurrent respiratory symptoms [8].
The inclusion criteria of the study: patients under the age of 18, being newly diagnosed with asthma, and those with a body mass index (BMI) above 2 standard deviation score (SDS) according to age and gender. The control group included sex-matched children with obesity but without asthma who applied to the Kayseri City Education and Research Hospital Paediatric Endocrinology Clinic. The exclusion criteria of the study were not consenting to participate and having secondary causes of obesity.
ANTHROPOMETRIC EXAMINATIONS
Age, gender, weight, height, body mass index, and standard deviation scores of these auxological data were recorded according to age and gender-specific reference cards. An online calculating application was used to examine these values using data from the Centers for Disease Control and Prevention (CDC) [9]. According to their pubertal stage, the cases were grouped as ‘pre-pubertal’, those who did not yet have pubertal signs, ‘mid-pubertal’ as those between Tanner stage 2 and 4, and ‘fully pubertal’ as those who reached Tanner stage 5 puberty. Pubertal staging was performed using the Marshall-Tanner method [10].
The Tanita MC-780 MA model (Tokyo, Japan) measuring device was used in the analysis of body composition of children over 5 years of age who can stand upright. Reported parameters included fat, fat-free and muscle masses, total body water, and basal metabolic rate. Additionally, right-left arm and leg differences were investigated with segmental analysis. Moreover, 5 kHz, 50 kHz, and 250 kHz resistance values, and phase angles were calculated. Calculated BIA variables included muscle-to-fat ratio (MFR = divide the total weight of muscles by the total weight of fat), and sarcopenic index (SI = divide the total weight of muscles by the square of height). All these non-invasive data can be obtained from the monitor screen in an average of 20 s and can also be obtained as a paper printout.
LABORATORY MEASUREMENTS
Serum glucose, insulin, glycated hemoglobin (HbA1c), lipid profile and liver enzymes, uric acid, cortisol were tested. TSH and free T4 levels were determined by the electrochemiluminescence immunoassay method (ECLIA) using the Cobas 8000 e602 analyzer (Roche Diagnostics GmbH, Mannheim, Germany).
Skin prick testing (SPT) was performed using commercial inhalant allergen extracts containing pollen, mite, cat and dog epithelium, mould and cockroach. The test was applied on the volar side of the forearm by gently pricking the skin surface with drops of the commercial allergen extract with a separate lancet. The induration diameter was measured 15 min after the application of the allergen extracts. The results were compared to two controls: histamine and saline, which served as positive and negative, respectively. Wheal diameter of 3 mm and above were evaluated in favour of atopy [11]. SPT was applied to all patients with asthma except those who were admitted to the clinic during an acute attack and had a history of using antihistamines and antitussives in recent days.
STATISTICAL ANALYSIS
The data were examined using IBM Corporation (Armonk, New York, USA) SPSS version 24.0 software. The mean and standard deviation (SD) values were calculated for numerical data and percentage (%) was calculated for categorical data. The normality of the variables distribution was analyzed with the Shapiro-Wilk test. Furthermore, variables with skewness and kurtosis values between +2 and -2 were considered to be normally distributed. The categorical variables were evaluated using the χ2 test. The Student’s T test was used to compare two groups whose variables were normally distributed; if not, the Mann-Whitney U test was utilized. A p-value of less than 0.05 was defined as statistical significance. Power analysis was performed with post-hoc G-Power; when designed with 2 groups (obese asthma vs. obese control) in the Student’s T test, with 60 patients for each group, the critical t value was calculated as 1.98, the effect size as 0.52, α as 0.05 and the power as 0.81.
RESULTS
A total of 120 children with obesity, 60 children with or without asthma (30 female/30 male), were included in the study. SPT was applied in 49 patients with asthma, 26 of whom did not have atopy and 23 did.
When examined in terms of puberty, 32 children with asthma were pre-pubertal, 22 were mid-pubertal, and 6 were pubertal Tanner stage 5. In the control group, 25 children were pre-pubertal, 24 were mid-pubertal, and 11 were at pubertal Tanner stage 5. No statistical difference was detected between the groups in terms of pubertal stages (p = 0.299). Comparison of the distribution of pubertal stages by gender was similar between groups (Table 1).
Table 1
Comparison of pubertal stages between groups
Comparison of demographic and anthropometric data, and laboratory measurements of the groups is shown in Table 2. The mean weight of children with asthma was 59.1 ±17.6 kg, while it was 66.6 ±16.8 kg for the control group (p = 0.019). The mean height was higher among the control group (143.3 ±13.9 vs. 148.5 ±12.2, p = 0.031). The BMI also indicated a significant difference, children with asthma having a lower BMI compared to controls (28.1 ±3.9 kg/m2 vs. 29.7 ±4.3 kg/m2, p = 0.032). Parameters such as age, weight standard deviation score (SDS), height SDS, BMI SDS did not show statistically significant differences between the groups. The pulmonary function parameters are shown in Table 3.
Table 2
Comparison of clinical and biochemical differences between groups
[i] Data are presented as (mean ± SD). Student’s T test was used. *The Mann-Whitney U test was used. BMI – body mass index, SDS – standard deviation score, FPG – fasting plasma glucose, HbA1c – glycosylated haemoglobin A1c, LDL-C – low-density lipoprotein cholesterol, HDL-C – high-density lipoprotein cholesterol, Total-C – total cholesterol.
Table 3
The pulmonary function parameters of the patient group
[i] FEF25 – forced expiratory flow at 25% of forced vital capacity, FEF50 – forced expiratory flow at 50% of forced vital capacity, FEF75 – forced expiratory flow at 75% of forced vital capacity, FEV1 – forced expiratory volume in the 1st second, FVC – forced vital capacity, PEFR – peak expiratory flow rate, SD – standard deviation. *Percentages of predicted values for FEV1, FVC, FEF25, FEF50, FEF75, and PEFR are given.
In terms of biochemical markers, the triglyceride levels were significantly lower in children with asthma (112 ±66.4 mg/dl vs. 123 ±48.2 mg/dl, p = 0.037). Other components of lipid profiles (HDL-C, LDL-C and Total-C) were similar between the groups. Moreover, the thyroid-stimulating hormone (TSH) levels were significantly lower in the patient group than in controls (2.08 ±0.9 mU/l vs. 2.85 ±1.4 mU/l, p = 0.001). The free thyroxine (Free-T4) was significantly higher in the children with asthma compared to controls (13.3 ±1.7 ng/l vs. 12.6 ±1.7 ng/l, p = 0.032). Insulin levels approached statistical significance but did not meet the threshold (18.3 ±9.4 mU/l in patients vs. 22.3 ±11.6 mU/l in controls, p = 0.064)
Bioelectrical impedance analysis revealed significant differences in body composition parameters. Fat mass (FM), fat-free mass (FFM), muscle mass (MM) and bone mass (BM) were significantly lower in the group of patients affected by asthma compared to controls (p = 0.021, p = 0.037, p = 0.036, p = 0.041, respectively). Additionally, total body water (TBW) was lower in patients compared to controls (26.4 ±7.6 kg vs. 29.4 ±7.6 kg, p = 0.032). Segmental body composition analysis indicated that children with asthma had significantly lower FM, FFM and MM in various segments of the body, such as the right leg and left arm. However, the percentages of these parameters were statistically similar. Comparison of the groups in terms of bioelectrical impedance analysis outputs is shown in Table 4.
Table 4
Bioelectrical impedance analysis comparisons between groups
There was no statistical difference between the groups in the comparison of bioelectrical impedance analysis according to gender. Comparison of bioelectrical impedance analysis outputs of the groups according to gender is shown in Table 5.
Table 5
Bioelectrical impedance analysis comparisons between groups according to gender
The mean age of the children with atopic asthma was significantly higher than in the non-atopic group (11.4 ±1.8 vs. 9 ±2.3, p < 0.001). Gender distribution was significantly different between groups. The majority of children with non-atopic asthma were girls (17.4% vs. 73%, p < 0.001). There was no statistical difference between the groups in the comparison of total FM, FFM and MM percentages according to asthma phenotypes. Basal metabolic rate was significantly higher in the atopic group (1677 ±214 vs. 1387 ±246, p < 0.001). FFM and MM were significantly higher in the atopic group in almost all compartments. Comparison of clinical and bioelectrical impedance analysis according to the presence of atopy in children with asthma is shown in Table 6.
Table 6
Comparisons of clinical and bioelectrical impedance analysis according to the presence of atopy in children with asthma
[i] Data are presented as (mean ± SD). Student’s T test was used. *χ2 test was used. ϕFisher’s exact test was used. BMI – body mass index, SDS – standard deviation score, FM – fat mass, FFM – fat-free mass, MM – muscle mass, TBW – total body water, BM – bone mass, BMR – basal metabolic rate, PhA – phase angle, MFR – muscle-to-fat ratio, SI – sarcopenic index.
DISCUSSION
In order to fill a major gap in the literature regarding the intricate relationship between these two conditions, the current study examined the variations in body composition and fat distribution between children with and without asthma. The findings of our study, which unexpectedly showed no significant differences in BMI-SDS, fat mass percentage, and distribution between children with and without asthma, contribute to the ongoing discussion about this intricate relationship.
Obesity and asthma have a reciprocal association, with both conditions influencing each other in various ways. More than 200,000 people participated in a study by Larsson and Burgess, which found causality between higher BMI and increased risk of asthma [12]. Additionally, a longer-term analysis of asthma history in early life revealed an increased risk of obesity later in life. According to this study, children initially diagnosed with asthma had a 51% increased risk of acquiring obesity during lifetime [13]. This could be attributable to asthma medications, atherogenic effects due to increased inflammation, decreased physical activity or mood changes [5, 14, 15].
Our findings, which unexpectedly showed no significant differences in BMI-SDS, fat mass percentage, and distribution between children with and without asthma, contrast with numerous prior studies that reported strong correlations between BMI and asthma. This discrepancy highlights the need for further investigations into potential confounding factors and underlying mechanisms. It seems the notion that the increase in obesity could account for the increase in asthma was a straightforward one with plausibility. However, there are also a few studies that did not find a link between the 2 entities. According to a large cross-sectional study of 11,199 children aged 4 to 11 conducted in Canada, comparing highest and lowest body mass index categories, the odds ratio for asthma was 1.02 [16]. In another study from Scotland and England, the odds ratio for trends in asthma was calculated for body mass index and was discovered to be 1.04 [17]. The absence of significant differences in body composition between the groups suggests that asthma and obesity may have an association rather than a causal relationship. In addition to obesity, there may be other co-factors to asthma in children with overweight. Potential confounders, such as genetic predispositions, environmental exposures, or differential responses to corticosteroid treatments, warrant further exploration.
The concerning increase in paediatric obesity and overweight makes accurate methods for assessing these indicators even more crucial [18]. Although BMI is the most widely applied metric to assess the weight of children due to its ease of calculation, unfortunately BMI does not provide information about the amount and distribution of fat mass [19]. In recent studies, bioelectrical impedance analysis in children has been found to be superior to classical anthropometric measurements in determining body composition such as fat mass, lean mass, fat distribution rates and body muscle-fat ratio [20]. As a result of our study, although weight and masses of the body’s components were higher in the control group, there was no statistically significant difference in the percentage of any of these parameters between the groups. Body fat distribution was similar in the patients with asthma and controls. A study showed that truncal (central) adiposity is associated with asthma only in non-atopic children [21]. In our study, the number of atopic and non-atopic asthmatic patients was 23 and 26, respectively. This balanced distribution may explain the similar fat distribution in body compartments in the group of patients affected by asthma. There was no difference between the fat mass percentage and body fat distribution of the control group and the group of asthma patients when compared separately according to gender. There are various studies with conflicting results on this subject. An earlier study that measured BMI and body composition by using bioelectrical impedance analysis revealed no correlation between either adiposity or asthma in men [22]. Another study found that the development of asthma in girls was predicted by higher BMI and adiposity, and there was a higher correlation between body fat and asthma than BMI [23].
The association between asthma and obesity is not always parallel. For example, Papoutsakis et al. found that the effect of body structure/composition on asthma in children varies depending on asthma phenotype and gender [24]. While asthma is more common in males during prepubertal ages, it shifts towards the female gender after puberty. As a result, asthma is more common in women in adulthood [25, 26]. Han et al. conducted a cross-sectional study involving 7,615 adults and discovered an inverse association between estradiol and testosterone, with the prevalence of asthma being higher in obese women and non-obese males [25]. Additionally, during pubertal years, changes occur in body proportions and fat distribution. Due to hormonal changes during puberty, muscle mass develops in men, while fat mass increases in women [27]. To prevent this effect from affecting our results, the distribution of adolescence periods of children with and without asthma included in our study was similar.
The role of atopy in the association between asthma and obesity is uncertain. While there are studies showing a tendency to have atopy with increasing BMI in girls [28], there are also studies showing that there is no relationship between increasing fat percentage and atopy [23, 29]. Many studies in the literature have shown that obesity has a stronger effect on asthma in non-atopic children [30–32]. In our study, atopic and non-atopic distribution was similar in patients with obesity and affected by asthma. As a result of our comparison according to asthma phenotypes, although there were differences in age and gender between the groups, the percentages of the masses of the body’s components were not different. The higher mean age and male gender in the atopic group may explain the higher basal metabolic rate, segmental lean and muscle masses.
CONCLUSIONS
According to the results of our study, since bioelectrical impedance analysis does not show any differences between the patients with or without asthma affected by obesity and between asthma phenotypes, there is no additional benefit in its use in routine practice. To the best of our knowledge, this study is the first attempt to compare fat mass percentage, distribution, and sarcopenic index between children with and without asthma by bioelectrical impedance analysis. One of the strengths of our study was that measurement could be performed almost simultaneously with the diagnosis of asthma. The limitations of our study, including a relatively small sample size and lack of detailed data on eating habits and physical activity, should be addressed in future research. Additionally, incorporating respiratory function tests could provide a more comprehensive understanding of the asthma-obesity relationship. Nevertheless, this pioneering study provides an important foundation for future research and paves the way for more comprehensive investigations.
