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).