Methods
We conducted a cross-sectional study from January 2015 to October 2016 at a tertiary care institute in north India. All children with asthma attending the Pediatric Chest Clinic at the institute were screened. Those in follow up for at least six months with good adherence and appropriate technique of taking inhaled medications and willing to participate in the study, were enrolled after receiving a written informed consent from either of the parents or the legally authorized representative. Children with diagnosed diabetes mellitus, chronic illness like renal or liver disease and those on insulin, oral hypoglycemic drugs or statins were excluded. Based on the study conducted by Arshi et al 9, we expected prevalence of IR of 40%; to estimate the same with a precision of 10%, the sample size of 92 was calculated.
The primary outcome measure of the study was the prevalence of IR in children with asthma in the age group of 10 to 15 years. The secondary outcome measures were the prevalence of dyslipidemia and MS in the same population, measures of association between metabolic abnormalities (IR and dyslipidemia) and the level of asthma control, and measures of association between metabolic abnormalities and lung function using spirometry.
Diagnosis of asthma was based on assessment by physician, of reversible airflow obstruction. Global Initiative for Asthma (GINA) guidelines, 2016 were used to classify subjects with different levels of asthma control 13. HOMA-IR, calculated as the product of fasting plasma glucose (mmol/L) and fasting serum insulin (microU/mL), divided by 22.5, was used as a marker for IR 14,15. Dyslipidemia was defined as presence of any of these: triglycerides (TG) ≥150 mg/dL, high density lipoprotein cholesterol (HDL-C) <40 mg/dL, TC ≥200 mg/dL or LDL-C ≥130 mg/dL 16,17. MS was defined using the criteria given by Cook et al, as the presence of three criteria out of these five - TG ≥110 mg/dL, HDL-C <40 mg/dL, waist circumference ≥90th centile, fasting glucose ≥110 mg/dL, and blood pressure ≥90th centile 18.
A questionnaire was used to record demographic information and elicit details regarding symptom onset, current disease status, drug history, outdoor activity, and family history. All patients were examined in detail and vital parameters, anthropometric measurements and findings on respiratory system examination were recorded. Standing height and weight were measured using a stadiometer and digital scale respectively. Waist circumference was measured using a stretch-resistant tape, applied horizontally just above the upper lateral border of the right ilium, at the end of a normal expiration. Hip circumference was measured around the widest portion of the buttocks. Indian references were used for assigning centiles and ‘z’ scores to waist circumference, other anthropometric parameters and blood pressure 19-21.
Spirometry was performed using a portable spirometer (Spirolab III from MIR, Italy). The procedure was explained and supervised by an experienced respiratory nursing officer, and was performed in standing position. The best of three efforts was used for interpretation. The absolute and percentage predicted values of following parameters were recorded: Forced Expiratory Volume – 1 second (FEV1), Forced Vital Capacity (FVC), FEV1/FVC ratio, Peak expiratory Flow Rate (PEFR) and Forced Expiratory Flow at 25, 50 and 75% of FVC (FEF25, FEF50, and FEF75). Knudson’s equations with correction for Indian population were used for reference values 22.
An enrolled patient was requested to report for collection of blood samples, after eight hours of fasting, within the next seven days. Approximately 2 ml of blood was collected and transported in a fluoride vial for estimation of glucose, done on a Mindray BS200E Autoanalyzer system (Shenzhen Mindray Bio-Medical Electronics Co, Ltd, Shenzhen, China) on the same day. Another 6 ml of blood was collected in a plain vial for estimation of serum insulin and lipid levels. The sample was allowed to clot at room temperature for 10–20 min, centrifuged at 3000 r.p.m. for 20 min for separation of serum, which was stored at −20°C. An electrochemiluminometric assay on a Cobas e411 Autoanalyzer (Roche Diagnostics, Germany GmbH) was used for serum insulin levels and a Beckman Coulter AU480 Autoanalyzer was used for estimation of lipid levels.
Data analysis was done using SPSS version 26, IBM Corp. Mean with standard deviation (SD) and median with interquartile range (IQR) have been used to present continuous data with normal and non-normal distribution respectively. Categorical data are presented as proportions with 95% confidence interval (CI). Comparison of median values of various metabolic parameters across groups based on asthma symptom control was done using Jonckheere-Terpstra test, and Fisher’s exact test was used for comparing categorical data. An ordinal logistic regression model, with asthma symptom control as the dependent variable and metabolic parameters as independent variables, was used to adjust for BMI when there was a significant difference in metabolic parameters across asthma control groups. A P value of less than 0.05 was taken to be significant for all statistical tests.
Prior approval was taken from the Institute ethics committee (IESC/T-450/23.12.2014).