One of the most commonly used ratios is Body Mass Index (BMI) which is a simple ratio of weight over height squared allowing one to be classified as underweight, normal, overweight and obese. Even though BMI does not directly identify body composition, the BMI is strongly related to total body fat in different groups of people, and as such, it is a convenient tool in epidemiology and clinical research relating to obesity-related health outcomes. There is growing evidence that high BMI and related adiposity negatively impact respiratory functioning, and can lead to an increased morbidity of respiratory disease, including asthma, chronic obstructive pulmonary disease (COPD), sleep-disordered breathing. Obesity has a mechanical impact on the respiratory system that includes the restriction of the diaphragmatic excursion and decreased compliance of the chest wall.[1] Adipose tissue buildup, especially in the abdominal and thoracic locations, elevates intra-abdominal pressure, which inhibits the diaphragmatic descent during breathing and causes a restrictive pattern of ventilation with decreased lung volumes, such as forced vital capacity (FVC) and forced expiratory volume within one second (FEV1).[2] A number of cross-sectional studies have shown negative correlations between BMI and spirometric parameters; FVC and FEV1 decreases predictably with increase in BMI, despite the age, sex, and smoking factors being incorporated in the analysis[3,4]. Moreover, high BMI correlates with smaller peak expiratory flow rate (PEFR), which means the poor condition of the large-airway and expiratory muscle strength in overweight and obese patients. [5]Adiposity also affects small-airway patency, as airway resistance in obese subjects and airflow at lower lung volumes tend to be higher. This has been seen in pediatric and adult cohorts meaning that the respiratory compromise of obesity is not age specific.[6] The area of adipose tissue has an effect on the pulmonary impairment. Central (android) obesity that is associated with the presence of visceral fat deposits induces a stronger mechanical restriction of the chest wall when compared to fat deposition in peripheral (gynoid) forms.[7] As a result, patients with central obesity tend to exhibit greater changes in the lung volumes and gas exchange capacity. Common means of estimating body fat percentage include bioelectrical impedance, Deurenberg formula and Siri equation that use this formula to give additional granularity to BMI on its own.[8] Comparative studies of these techniques indicate that the body fat percentage is more strongly correlated with respiratory parameters than BMI thus indicating that direct measurements of adiposity may be more useful in predicting a decrease in pulmonary function.[9] Besides mechanical factors, adiposity causes systemic inflammation and oxidative stress, which can inflame airways and remodel. Obese individuals have been attributed to adipokines with leptin and adiponectin that control immune functions and have been associated with asthma severity and bronchial hyperresponsiveness. High plasma leptin levels are associated with low FEV1/FVC ratio suggesting that obesity metabolic mediators play a role in airflow obstruction beyond their mechanical action. Due to the current increase in the prevalence of obesity worldwide, the interaction between BMI, adiposity, and respiratory activity is essential towards risk categorization and control of respiratory illnesses. Pulmonary function testing (PFTs) could be a worthwhile approach to diagnosing ventilatory dysfunction in overweight and obese patients at an early stage to implement treatment by means of weight loss, respiratory muscle training, and anti-inflammatory medication. In addition, combining accurate body fat measures can increase the prognosis of PFTs in obese groups.[10] This introduction supports the relevance of considering the effect of BMI and body fat distribution on respiratory mechanics and functionality. Through understanding these associations, clinicians and researchers are in a better position to focus on the respiratory health issues that the obesity epidemic presents.

Study Design A descriptive cross-sectional study was conducted at the A descriptive cross sectional study was conducted at Department of Physiology, Shri Gorakshnath Medical College, Gorakhpur, Uttar Pradesh, to investigate the relationship between Body Mass Index and Respiratory Function Tests in healthy adults. Study Population and Setting The study included 100 healthy adults (55 females and 45 males) aged 18-45 years from Gorakhpur and surrounding areas. All participants were recruited from the clinical postgraduate laboratory at the Department of Physiology. Inclusion Criteria • Written informed consent provided • Absence of acute or chronic illness • No endocrine disorders causing obesity • Not on any medications during study period • No exercise activity for past 6 months • Not practicing yoga or relaxation techniques • No psychiatric illness Exclusion Criteria • Physical disabilities • Known cardio-respiratory diseases • Known respiratory allergies • Known musculoskeletal disorders • History of alcohol consumption, smoking, tobacco use, or substance abuse Materials and Equipment Anthropometric Measurements • Stadiometer for height measurement (accuracy: 0.5 cm) • Pedestal-type analogue weighing scale (capacity: 150 kg, accuracy: 100 gm) • Ribbon-type measuring tape "Polychek" make (length: 2 meters, accuracy: 1 mm) Respiratory Function Assessment • BPL ARPEMIS Spirometer (BPL PC-based Spirometer with 31 parameters, version 2009/01) for pulmonary function tests • Nose clips for spirometry procedures Data Collection Protocol Pre-test Preparation Following Institutional Ethical Committee approval, eligible participants were recruited and provided written informed consent. Medical history, dietary habits, drug use, and personal habits were recorded using standardized questionnaires. General physical examination was conducted before testing. Anthropometric Measurements Height: Measured barefoot against a wall using stadiometer, recorded to nearest 0.01 m Weight: Measured in light clothing without shoes on pedestal weighing scale, recorded to nearest 0.1 kg BMI Calculation: Derived using Quetelet's index formula: BMI = Weight (kg) / [Height (m)]² South Asian BMI ranges as prescribed by WHO were applied, with BMI ≥25.0 kg/m² indicating obesity and BMI 23.0-24.9 kg/m² indicating overweight for Asian populations. Respiratory Function Tests Spirometry was performed using computerized spirometer under standardized conditions with ambient temperature and pressure control. Testing was conducted between 9:30 AM and 12:30 PM to avoid circadian variation effects. Pre-test Protocol: • 15-minute rest period before testing • Quiet testing environment with controlled temperature and humidity • Instructions, demonstration, and practice attempts provided • Testing performed in sitting position with nose clip Testing Procedure: Each participant performed three trials following American Thoracic Society recommendations, with at least two reproducible and acceptable maneuvers. Values were considered reproducible when difference between two highest recorded values was ≤5%. Measurement Protocol: Participants took 3-4 quiet breaths, then performed maximal inspiration followed by forceful, complete expiration while standing, followed by rapid complete inspiration. Parameters Recorded Respiratory Parameters: • FVC (Forced Vital Capacity) in liters • FEV₁ (Forced Expiratory Volume in 1st second) in liters • FEV₁/FVC ratio • PEFR (Peak Expiratory Flow Rate) in L/min The maximal values from three trials were used for evaluation and comparison. Statistical Analysis Data analysis was performed using SPSS version 18.0. Descriptive statistics including means and standard deviations were calculated. ANOVA and Independent t-tests were used for group comparisons as appropriate. Pearson's correlation coefficient (r) was applied to assess correlations between BMI and respiratory function parameters. Statistical significance was set at p < 0.05.

Table 1: Distribution of Subjects in BMI Categories Category BMI Range Males (n = 45) Females (n = 55) Total (n = 100) Category A BMI < 18.5 10 (40%) 15 (60%) 25 (25%) Category B BMI 18.5 – 22.9 18 (42.8%) 24 (57.2%) 42 (42%) Category C BMI ≥ 23.0 17 (51.5%) 16 (48.5%) 33 (33%) In the study, subjects were categorized into three BMI categories. In Category A, which represents a BMI of less than 18.5, there were 25 subjects in total (25% of the sample), with 10 males (40%) and 15 females (60%). In Category B, which corresponds to a BMI range of 18.5 – 22.9, there were 42 subjects (42% of the sample), consisting of 18 males (42.8%) and 24 females (57.2%). Finally, Category C, for subjects with a BMI of 23.0 or greater, included 33 subjects (33% of the sample), with 17 males (51.5%) and 16 females (48.5%). Table 2: BMI and Body Fat Percentage (Bio-Impedance) for Males and Females Category BMI (Mean ± SD) Body Fat (Bio-Impedance) p-value Males Category A 17.75 ± 0.89 19.05 ± 2.50 < 0.001 Category B 21.08 ± 1.09 20.69 ± 1.43 < 0.001 Category C 26.79 ± 2.34 27.18 ± 1.96 < 0.001 Females Category A 16.97 ± 0.90 17.37 ± 2.21 < 0.001 Category B 20.99 ± 1.41 21.66 ± 2.11 < 0.001 Category C 26.28 ± 3.00 27.30 ± 3.37 < 0.001 Table 2 presents the mean BMI and body fat percentage (measured by bio-impedance) for both males and females across different BMI categories. For males, in Category A (BMI < 18.5), the average BMI was 17.75 ± 0.89 with a body fat percentage of 19.05 ± 2.50, showing a statistically significant difference (p < 0.001). In Category B (BMI 18.5 – 22.9), the average BMI was 21.08 ± 1.09, with a body fat percentage of 20.69 ± 1.43, also showing a significant result (p < 0.001). Finally, in Category C (BMI ≥ 23.0), the average BMI was 26.79 ± 2.34, and the body fat percentage was 27.18 ± 1.96, again with a p-value of < 0.001. For females, in Category A (BMI < 18.5), the mean BMI was 16.97 ± 0.90, and the body fat percentage was 17.37 ± 2.21 (p < 0.001). In Category B (BMI 18.5 – 22.9), the mean BMI was 20.99 ± 1.41, and the body fat percentage was 21.66 ± 2.11, with a significant result (p < 0.001). In Category C (BMI ≥ 23.0), the mean BMI was 26.28 ± 3.00, and the body fat percentage was 27.30 ± 3.37, with a p-value of < 0.001. Table 3: Pulmonary Function Tests (PEFR) and Correlation with Body Fat in Males and Females Category PEFR (Mean ± SD) r-value (Bio-Impedance) r-value (Deurenberg Formula) r-value (Siri's Formula) p-value Males Category A 371.31 ± 13.50 -0.4265 -0.5171 -0.3361 < 0.001 Category B 376.64 ± 8.77 -0.5171 -0.5171 -0.3361 < 0.001 Category C 360.52 ± 13.82 -0.3361 -0.5171 -0.3361 < 0.001 Females Category A 307.92 ± 17.42 +0.2920 +0.3089 +0.3344 < 0.001 Category B 329.11 ± 12.03 +0.3089 +0.3089 +0.3344 < 0.001 Category C 324.54 ± 28.98 +0.3344 +0.3089 +0.3344 < 0.001 Table 3 illustrates the mean Pulmonary Expiratory Flow Rate (PEFR) and its correlation with body fat measured through three different formulas (Bio-Impedance, Deurenberg, and Siri's Formula) for both males and females across different BMI categories. For males, in Category A (BMI < 18.5), the average PEFR was 371.31 ± 13.50, with a significant negative correlation with body fat (r-value: -0.4265, -0.5171, and -0.3361 for the three formulas, respectively), all showing a p-value of < 0.001. In Category B (BMI 18.5 – 22.9), the average PEFR was 376.64 ± 8.77, with similarly negative correlations (r-values: -0.5171 for both Bio-Impedance and Deurenberg formulas, and -0.3361 for Siri's Formula), with a p-value of < 0.001. In Category C (BMI ≥ 23.0), the PEFR was 360.52 ± 13.82, and the correlation with body fat remained negative across the three formulas (r-values: -0.3361, -0.5171, and -0.3361, respectively), with a p-value of < 0.001. For females, in Category A (BMI < 18.5), the average PEFR was 307.92 ± 17.42, with positive correlations between PEFR and body fat (r-values: +0.2920, +0.3089, and +0.3344 for Bio-Impedance, Deurenberg, and Siri's formulas, respectively), all with a p-value of < 0.001. In Category B (BMI 18.5 – 22.9), the PEFR was 329.11 ± 12.03, with similarly positive correlations (r-values: +0.3089 for both Bio-Impedance and Deurenberg formulas, and +0.3344 for Siri's Formula), with a p-value of < 0.001. In Category C (BMI ≥ 23.0), the PEFR was 324.54 ± 28.98, and the correlation with body fat remained positive across all formulas (r-values: +0.3344, +0.3089, and +0.3344, respectively), with a p-value of < 0.001. Table 4: Correlation Coefficients (r-value) for Body Fat and Heart Rate (HR) in Males and Females Method r-value (Males) r-value (Females) Closest Correlation Bio-Impedance +0.7004 +0.8689 Bio-Impedance (Males), Deurenberg Formula (Females) Deurenberg Formula +0.6373 +0.8767 Deurenberg Formula Siri’s Formula +0.6323 +0.8317 Bio-Impedance (Males), Deurenberg Formula (Females) Table 4 displays the correlation coefficients (r-values) for body fat and heart rate (HR) in both males and females, with a focus on the method used for measurement. For males, the Bio-Impedance method showed the highest correlation with a value of +0.7004, indicating a strong positive relationship between body fat and heart rate. The Deurenberg formula also demonstrated a strong positive correlation of +0.6373. Siri’s formula, though still positive, showed the lowest correlation at +0.6323. In females, the Bio-Impedance method had a very strong correlation of +0.8689, which was the highest in the study. The Deurenberg formula also displayed a similarly high correlation at +0.8767, suggesting a very close relationship between body fat and heart rate. Siri's formula provided a slightly lower correlation of +0.8317. The closest correlations for males were found with Bio-Impedance, and for females, the Deurenberg formula exhibited the strongest relationship with body fat and heart rate. Table 5: Intra Categorical Analysis of BMI and Pulmonary Function Tests (FEV1/FVC) in Males and Females Test Parameter r-value (Males) r-value (Females) Closest Correlation BMI +0.9447 +0.9541 Deurenberg Formula FEV1 -0.3163 -0.1293 Deurenberg Formula (Males), Bio-Impedance (Females) FVC -0.2445 -0.0218 Deurenberg Formula FEV1/FVC -0.1698 -0.1434 Deurenberg Formula Table 5 presents the intra-categorical analysis of BMI and pulmonary function tests (FEV1/FVC) for both males and females, along with their correlation coefficients (r-values). For males, BMI showed a very strong positive correlation with pulmonary function tests, with an r-value of +0.9447, indicating a close relationship. For females, BMI had a similarly high correlation with pulmonary function, with an r-value of +0.9541. Regarding the specific pulmonary function tests, FEV1 (Forced Expiratory Volume in 1 second) showed a moderate negative correlation with BMI for males (r-value: -0.3163) and a weaker negative correlation for females (r-value: -0.1293). The FVC (Forced Vital Capacity) also showed a weak negative correlation for both males (r-value: -0.2445) and females (r-value: -0.0218). The FEV1/FVC ratio showed the weakest negative correlations for both genders, with r-values of -0.1698 for males and -0.1434 for females.

The distribution of BMI categories with females who are more underweight and males more overweight/obese indicate sex disparity in body composition, which agrees with past population findings.[11,12]. Close positive relationships between the BMI and the body fat percentage particularly using Bio-Impedance and Deurenberg equations validate the use of BMI as a valid variable to indicate body fat in this group.[13] Medical parameters including heart rate variability (HRV) and blood pressure were most correlated with the method of Deurenberg, which indicated that this approach is useful in predicting the body fat in relation to cardiovascular outcomes.[14] The results of body fat corresponded to gender differences with Bio-Impedance correlations related more with systolic blood pressure in males, replicating the results of separate adiposity effects by sex.[13-18]. Autonomic function tests: HRV metrics (SDNN, RMSSD) were found to decline significantly with increasing BMI, and this result suggests a compromise in autonomic control in accordance with findings of other researchers that obesity or increased body fat leads to diminished HRV. The positive positive correlation between systolic blood pressure and higher BMI categories is consistent with established cardiovascular risk relationships.[18-20]. FEV1 and FVC parameters of pulmonary functions were weakly negatively correlated with BMI and body fat percentage. [21]This finding of impaired lung functioning among overweight/obese males is consistent with previous research that has documented a slight loss of lung volumes and flow rates in incremental adiposity especially in men.[22] The lesser correlations among females and by alternative methods of body fat estimation are also in accordance with inconsistent results in the literature of BMI/pulmonary function relationships.[23-25]. By and large, the paper supports the existing correlations between BMI, body fat percentage, cardiovascular autonomic dysfunction, and slight but significant pulmonary function impairment, and the importance of the Deurenberg formula to cardiovascular risk assessment using body fat as the predictor.

This study underscores the importance of BMI and body fat percentage in assessing health risks, particularly cardiovascular and pulmonary health. The findings highlight the variability of these relationships across gender and measurement methods, indicating that body fat estimation techniques, such as the Deurenberg formula, may offer more accurate insights, especially in cardiovascular assessments. Further studies with larger sample sizes could help refine these observations and offer more nuanced insights into the interplay between BMI, body fat, and health outcomes.

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