European Journal of Cardiovascular Medicine

Peak expiratory flow rate (PEFR) measurement has gained considerable importance in pediatric respiratory assessment due to its simplicity, cost-effectiveness, and ability to provide valuable information about airway function (1). The widespread availability of handheld peak flow meters has made PEFR measurement accessible for both clinical monitoring and screening purposes, particularly in resource-limited settings where spirometry may not be readily available (2).

PEFR represents the maximum flow rate achieved during a forceful expiration following maximum inspiration and serves as an indicator of large airway function (3). In pediatric practice, PEFR is commonly utilized for monitoring asthma control, assessing response to bronchodilator therapy, and screening for respiratory abnormalities in apparently healthy children (4). The measurement's effort-dependent nature makes it particularly suitable for children who can cooperate with the procedure, typically those above 5 years of age.

The factors influencing PEFR in children are multifaceted and include demographic variables such as age and gender, anthropometric parameters including height, weight, and body composition, environmental exposures, and genetic factors (5). Among these, anthropometric measurements have received considerable attention due to their strong correlation with lung function parameters and their utility in developing predictive equations for different populations (6).

Height has consistently emerged as the strongest predictor of PEFR across various populations, reflecting the close relationship between stature and lung size (7). This relationship is attributed to the parallel growth of the lungs and thoracic cavity with increasing height, resulting in larger lung volumes and enhanced expiratory flow rates. Age contributes to PEFR variation through its association with respiratory muscle development, thoracic expansion, and overall physical maturation (8).

The relationship between body mass index (BMI) and pulmonary function in children presents a complex and sometimes contradictory picture in the literature. Theoretical considerations suggest that BMI could influence PEFR through multiple mechanisms. In normal-weight children, increasing BMI may reflect greater muscle mass and respiratory muscle strength, potentially leading to improved expiratory force generation (9). Conversely, in overweight and obese children, increased adiposity may compromise respiratory function through mechanical effects on chest wall compliance, diaphragmatic excursion, and airway resistance (10).

Studies investigating the BMI-PEFR relationship have yielded variable results. Some research has demonstrated positive correlations between BMI and PEFR in healthy children, particularly in normal weight ranges (11,12). These studies suggest that within physiological BMI ranges, increased body mass may reflect better nutritional status and muscle development, contributing to enhanced respiratory performance. However, other investigations have reported weak or non-significant correlations, questioning the clinical relevance of BMI in predicting PEFR (13,14).

The inconsistency in findings may be attributed to several factors including variations in study populations, age ranges, methodological approaches, and the presence of confounding variables. Additionally, the non-linear relationship between BMI and respiratory function, where the effects differ between underweight, normal weight, overweight, and obese categories, adds complexity to the interpretation of correlation coefficients derived from pooled analyses (15).

Environmental factors have also emerged as important determinants of respiratory function in children. Indoor air pollution, particularly exposure to combustion products from cooking fuels, tobacco smoke, and mosquito repellents, has been associated with reduced lung function parameters. Studies from developing countries have highlighted the negative impact of biomass fuel exposure and indoor air pollutants on children's respiratory health, with demonstrable effects on PEFR measurements.

The clinical significance of understanding BMI-PEFR relationships extends beyond academic interest. In an era of increasing childhood obesity, clarifying whether BMI independently influences respiratory function can inform clinical practice and public health interventions. If BMI significantly impacts PEFR, this relationship could be incorporated into lung function prediction equations and considered in the assessment of respiratory health in pediatric populations.

Furthermore, establishing the relative importance of various anthropometric and environmental factors in determining PEFR can guide healthcare providers in identifying children at risk for respiratory compromise and developing appropriate screening strategies. This is particularly relevant in low- and middle-income countries where childhood malnutrition and obesity may coexist, creating a complex landscape of nutritional status and health outcomes.

Aims

The primary objective of this study was to investigate the correlation between body mass index (BMI) and peak expiratory flow rate (PEFR) in school-going children aged 8-15 years.

The secondary objectives were to:

1. Examine the correlation between PEFR and other demographic and anthropometric parameters including age, gender, weight, and height
2. Assess the influence of environmental and familial factors including family history of atopy, exposure to pets, indoor smoking, and mosquito repellents on PEFR
3. Determine the independent contribution of BMI to PEFR variation when controlling for other significant variables

Study Design and Setting

A cross-sectional observational study was conducted among school children aged 8-15 years in secondary schools of Hyderabad, India, between September 2020 and October 2022. The study was carried out in two government schools: Government High School, Adikmet, Vidyanagar, and Nallakunta Government Primary and High School.

Sample Size Calculation

The sample size was calculated based on an expected correlation coefficient of 0.17 between BMI and PEFR, as reported by Andrea Josephine (2017), with an alpha error of 5% (95% confidence interval) and power of 80%. Using the formula for correlation studies, the minimum required sample size was determined to be 208 subjects. The calculation was performed using nMaster software version 2.0 (Biostatistics Resource and Training Centre, Department of Biostatistics, Christian Medical College, Vellore).

Inclusion and Exclusion Criteria

Inclusion Criteria:

1. School children aged between 8 and 15 years
2. Ability to perform peak flow measurement adequately
3. Written informed consent from guardians

Exclusion Criteria:

1. History of febrile illness in the preceding week
2. Symptoms of upper or lower respiratory tract infection in the preceding week
3. Known chronic respiratory disease including bronchial asthma
4. Systemic diseases such as cardiac or renal disorders
5. Obvious deformity of thorax or spine
6. Neuromuscular disorders affecting respiratory function

Data Collection Procedure

After obtaining appropriate institutional approvals and informed consent from guardians, demographic information was collected using a structured questionnaire. The questionnaire included details about age, gender, family history of atopy, environmental exposures including indoor smoking, presence of pets, and use of mosquito repellents.

Anthropometric Measurements

Height was measured to the nearest centimeter using a portable stadiometer with subjects standing erect, barefoot, with heels together and head positioned in the Frankfurt plane. Weight was recorded to the nearest kilogram using a calibrated weighing scale with subjects wearing light clothing and no footwear. Body mass index was calculated using the formula: BMI = weight (kg) / height² (m²).

Children were classified into BMI categories according to WHO BMI-for-age standards: underweight (<5th percentile), healthy weight (5th-85th percentile), overweight (85th-95th percentile), and obese (≥95th percentile).

Peak Expiratory Flow Rate Measurement

PEFR was measured using the Mini Wright's Peak Flow Meter (Ishnee, India) following standard protocols. All measurements were performed with subjects in an upright sitting position after a five-minute rest period. The procedure was explained and demonstrated to each child until familiarity was achieved. Subjects were instructed to form a good seal around the mouthpiece, take a deep inspiration, and exhale as hard and fast as possible into the device. Three trials were performed for each subject, and the highest reading was recorded for analysis.

Statistical Analysis

Data were entered using Microsoft Excel and analyzed using Stata version 12. Descriptive statistics were calculated for all variables. Continuous variables were expressed as mean ± standard deviation or median with interquartile range depending on distribution normality. Categorical variables were summarized using frequencies and proportions.

Correlation between continuous variables was assessed using Pearson's correlation coefficient. Simple linear regression was performed to evaluate individual relationships between independent variables and PEFR. Multiple linear regression analysis was conducted to determine the independent effect of BMI after adjusting for potential confounding factors. Variables with p-values <0.05 in univariate analysis were included in the multiple regression model. Statistical significance was set at p<0.05.

Demographic Characteristics

The study included 208 school children with ages ranging from 8 to 15 years (mean age 9.53 ± 1.34 years). The majority of participants (146, 70.2%) belonged to the pre-adolescent age group of 8-9 years, while 51 (24.5%) were in the early adolescent group (10-13 years) and 11 (5.3%) were in the middle adolescent group (14-15 years).

Gender distribution showed 117 males (56.3%) and 91 females (43.8%). Among males, 76 (36.5%) were pre-adolescent, 31 (14.9%) early adolescent, and 10 (4.8%) middle adolescent. Among females, 70 (33.6%) were pre-adolescent, 20 (9.6%) early adolescent, and 1 (0.5%) middle adolescent.

Table 1. Baseline Characteristics of Study Population

BMI Distribution

According to WHO BMI-for-age standards, 20 children (9.6%) were classified as underweight, 129 (62.0%) as healthy weight, 41 (19.7%) as overweight, and 18 (8.7%) as obese. Gender-specific analysis revealed that among males, 11 (9.4%) were underweight, 74 (63.2%) healthy weight, 20 (17.1%) overweight, and 12 (10.3%) obese. Among females, 9 (9.9%) were underweight, 55 (60.5%) healthy weight, 21 (23.1%) overweight, and 6 (6.6%) obese.

Environmental Exposures

Environmental assessment revealed that 12 children (5.8%) were exposed to indoor smoking, 13 (6.3%) had pets at home, and 118 (56.7%) used mosquito repellents. Family history of atopy was present in 4 fathers (1.9%), 5 mothers (2.4%), and 5 siblings (2.4%).

Gender Differences in PEFR

Table 2. Gender-wise PEFR Distribution

Analysis revealed that males had significantly higher PEFR values compared to females (223.42 ± 63.87 vs 185.49 ± 33.11 L/min, p<0.001).

Simple Linear Regression Analysis

Table 3. Simple Linear Regression Analysis with PEFR as Dependent Variable

Simple linear regression analysis demonstrated that female gender, height, and BMI had significant associations with PEFR (p<0.05). The beta coefficient for BMI was 2.65, indicating an expected increase in PEFR of 2.65 L/min for each unit increase in BMI.

Correlation Analysis

Table 4. Correlation Matrix Between Anthropometric Variables and PEFR

The correlation analysis revealed that PEFR had the strongest correlation with height (r=0.63), followed by age (r=0.54), weight (r=0.47), and BMI (r=0.20). The correlation between BMI and PEFR was weak but statistically significant.

BMI Category-specific Analysis

Table 5. Correlation Between BMI and PEFR by BMI Categories

BMI showed significant correlations with PEFR only in the healthy weight and overweight categories. No significant correlation was observed in the underweight and obese categories, suggesting that the BMI-PEFR relationship is non-linear and varies across different BMI ranges.

Multiple Regression Analysis

A multiple linear regression model was constructed with PEFR as the dependent variable, including age, gender, BMI, family history of atopy, pets at home, mosquito repellent use, and indoor smoking exposure as independent variables. The model explained 41.9% of the variance in PEFR (R² = 0.419).

In the multiple regression model, female gender (β = -28.98, p<0.001), age (β = 19.62, p<0.001), mosquito repellent exposure (β = -13.45, p = 0.036), and BMI (β = 0.97, p<0.001) remained significant predictors of PEFR. However, when height was included instead of age in alternative models, BMI's significance was reduced, suggesting that the BMI-PEFR relationship is mediated by other anthropometric factors.

This cross-sectional study of 208 school children aged 8-15 years provides important insights into the relationship between BMI and PEFR in a pediatric population. The principal finding is that while BMI demonstrates a weak positive correlation with PEFR in simple regression analysis (r=0.20, p<0.001), this relationship becomes less significant when controlling for other anthropometric and demographic variables, particularly height, age, and gender.

The observed weak positive correlation between BMI and PEFR in our study is consistent with several previous investigations. Taksande et al. reported similar correlation coefficients for BMI with PEFR (r=0.19 in males and r=0.24 in females) in their study of 1,078 rural Indian school children (11). Similarly, Manjareeka et al. found a correlation coefficient of 0.30 between BMI and PEFR in 868 tribal children from Odisha (12). These findings support the existence of a measurable, albeit weak, relationship between BMI and respiratory function in pediatric populations.

However, our findings also align with studies that have questioned the clinical significance of the BMI-PEFR relationship. Pistelli et al., in their comprehensive study of 2,176 Italian children, reported that when BMI was included in multiple regression models alongside other anthropometric variables, its contribution to PEFR variation became minimal (r=0.109) (13). This suggests that the apparent correlation between BMI and PEFR may be largely mediated by other factors, particularly those related to body size and maturation.

The strength of correlation between height and PEFR in our study (r=0.63) underscores the fundamental importance of stature in determining lung function. This finding is consistent with extensive literature demonstrating height as the strongest predictor of pulmonary function parameters across different populations (14). The physiological basis for this relationship lies in the parallel growth of the thoracic cavity and lungs with increasing height, resulting in proportional increases in lung volumes and expiratory flow rates.

Our observation that the BMI-PEFR correlation varies across different BMI categories provides important insights into the non-linear nature of this relationship. The absence of significant correlation in underweight (r=0.42, p=0.62) and obese children (r=-0.205, p=0.85), while maintaining significance in healthy weight (r=0.20, p<0.001) and overweight categories (r=0.56, p<0.001), suggests that the relationship between body composition and respiratory function differs across the BMI spectrum.

This pattern may be explained by the dual nature of BMI as a measure of both fat mass and lean body mass. In healthy weight children, increasing BMI likely reflects greater muscle mass and overall nutritional status, contributing to enhanced respiratory muscle strength and improved expiratory performance. Conversely, in obese children, the negative effects of excess adiposity on chest wall compliance and diaphragmatic function may outweigh any potential benefits of increased muscle mass (15).

The significant negative correlation between mosquito repellent exposure and PEFR (β = -13.45, p = 0.036) represents a novel finding with important public health implications. Mosquito coils and liquidators release fine particulate matter, aromatic hydrocarbons, and toxic gases including formaldehyde vapors, with one mosquito coil producing emissions equivalent to 75-137 cigarettes (16). Our finding supports previous research by Azizi et al., who demonstrated increased respiratory symptoms in children exposed to mosquito coil smoke (17).

The gender difference observed in our study, with males having significantly higher PEFR values than females (223.42 vs 185.49 L/min, p<0.001), is consistent with established patterns in pediatric pulmonary function literature. This difference becomes more pronounced during adolescence due to gender-specific changes in thoracic dimensions, respiratory muscle development, and hormonal influences on lung growth (18).

Our study's findings have several clinical and research implications. The weak independent contribution of BMI to PEFR variation suggests that routine BMI measurement alone is insufficient for predicting respiratory function in children. Height, age, and gender emerge as more reliable predictors and should be prioritized in clinical assessments and the development of predictive equations for pediatric PEFR values.

The identification of mosquito repellent exposure as a significant negative predictor of PEFR highlights the importance of assessing environmental exposures in children's respiratory health evaluations. This finding is particularly relevant in developing countries where mosquito control measures are widely used and may contribute to indoor air pollution.

From a methodological perspective, our study's strength lies in its adequate sample size, standardized measurement procedures, and comprehensive assessment of potential confounding variables. The use of WHO BMI-for-age percentiles for classification and the inclusion of environmental factors in the analysis enhance the robustness of our findings.

However, several limitations should be acknowledged. The cross-sectional design precludes establishment of causal relationships between BMI and PEFR. The study population was limited to a specific geographic region and age range, potentially limiting generalizability to other populations. Additionally, we did not differentiate between lean body mass and fat mass components of BMI, which could provide more precise insights into body composition effects on respiratory function.

Future research should consider longitudinal designs to examine how BMI-PEFR relationships evolve with growth and development. Investigation of body composition using more sophisticated methods such as bioelectrical impedance or dual-energy X-ray absorptiometry could provide clearer insights into the mechanisms underlying the BMI-respiratory function relationship.

This study demonstrates that BMI has a weak positive correlation with PEFR in school-going children aged 8-15 years, but this relationship is not clinically significant when other anthropometric and demographic factors are considered. Height emerges as the strongest predictor of PEFR, followed by age and gender. The BMI-PEFR relationship varies across different BMI categories, being significant only in healthy weight and overweight children.

The apparent correlation between BMI and PEFR appears to be largely mediated by other factors related to growth, development, and body size rather than representing an independent physiological relationship. Healthcare providers should prioritize height, age, and gender when assessing respiratory function in children, while maintaining awareness that BMI extremes (underweight and obesity) may have different implications for respiratory health.

The significant negative association between mosquito repellent exposure and PEFR represents an important environmental health finding that warrants further investigation and public health consideration, particularly in regions where such products are widely used for vector control.

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