Prevalence and Predictors of Metabolic Syndrome among Patients with Bronchial Asthma: A Cross Sectional Study

Prevalence and Predictors of Metabolic Syndrome among Patients with Bronchial Asthma: A Cross Sectional Study

Abdellah H.K. Ali1 , * Open Modal
Authors Info & Affiliations
The Open Respiratory Medicine Journal 18 Jun 2021 RESEARCH ARTICLE DOI: 10.2174/1874306402115010014



Recent studies have reported the epidemiological link between Metabolic Syndrome (MS) and asthma, but it has rarely been studied in Egypt. The study aimed to investigate the prevalence of MS and its predictors among asthma patients in Egypt.


In total, 320 patients with bronchial asthma were included. The following were assessed: spirometric evaluation, anthropometric indices, blood pressure, fasting blood sugar and serum lipid profile. We analyzed the correlation between metabolic scores and patient characteristics. Predictors of MS were identified using logistic regression analysis.


The prevalence of MS was 57.5% in asthma patients. For asthma patients, low High-Density Lipoprotein (HDL) and abdominal obesity were the commonest metabolic abnormality. Waist circumference, Fasting Blood Sugar (FBS) and triglyceride correlated significantly with asthma (P ‹ 0.05). FBS and DBP were the best predictors of MS.


MS is frequent in asthma patients in Egypt. Obesity and lipid abnormalities were the commonest metabolic abnormality. Screening of these patients for components of metabolic syndrome should be a part of routine workup.


Bronchial asthma and metabolic syndrome are major health problems that have increased rapidly worldwide [1]. Reversible airway obstruction with hyper-responsiveness is the main criteria of bronchial asthma, while essential hypertension, obesity, dyslipidemia, and type-2 diabetes mellitus are the main criteria of MS [2-4].

A quarter of the world’s population is estimated to have MS and increased cardiovascular risk compared with people without it [5]. Diabetes mellitus is 5-fold greater in people with MS [6]. It was found that hyperglycemia and hyper-insulinaemia have clinically relevant negative effects on airway structure, function; subjects exercise capacity and health status [7-9].

To date, the prevalence of metabolic syndrome in asthma subjects has not been studied before in Upper Egypt. Therefore, we aimed to investigate the prevalence and predictors of metabolic syndrome in asthma patients.


2.1. Study Design

A cross-sectional study on 320 asthma patients was performed. The patients admitted to the Department of Pulmonology, Sohag University Hospital were selected. The protocol of the study was reviewed and approved by the Ethical Committee, Sohag university hospital.

2.2. Procedures

Clinical and laboratory findings including history taking, clinical examination, spirometry, blood picture, ESR, liver and kidney functions, lipid profile (HDL-cholesterol, triglycerides), and CRP level were analyzed. Bodyweight, height, BMI (wt in kg/ht in meter2), and waist circumference were measured in all patients [10].

2.3. Evaluation of MS

The definition of Diabetes Federation (IDF) of metabolic syndrome was used to diagnose MS [11]. MS was diagnosed when at least three of the following criteria were present: 1) waist circumference ≥ 94 cm or ≥ 80 cm; 2) systolic blood pressure ≥ 130 mmHg and/or diastolic blood pressure ≥ 85 mmHg or ongoing therapy for hypertension; 3) fasting glucose > 100 mg/dl or ongoing therapy for elevated glucose levels; 4) HDL < 40 mg/dl in men or < 50 mg/dl in women or specific treatment for this lipid abnormality; 5) triglycerides ≥150 mg/ dl or specific treatment for this lipid abnormality.

2.4. Statistical Analysis

Data was collected and analyzed using SPSS v.17.0 (SPSS Inc, Chicago, IL, USA). The ANOVA test was used to assess the differences between the groups; Spearman’s correlation coefficient was used to assess the strength of correlations between variables. To identify independent predictors of MS presence, multivariate logistic regression analysis was conducted. A P value of < 0.05 was considered significant.


3.1. Frequency of MS and its Components

All the biochemical and anthropometric parameters pertaining to the diagnosis of MS are depicted in Table 1. Using IDF definition, MS was diagnosed in 184 (57.5%) patients with asthma.

The percent of each component of the MS was estimated in asthma patients. Abdominal obesity was represented in 40%, low HDL-C levels in 40%, elevated fasting glucose levels in 27.5%, elevated triglyceride levels in 15%, and raised blood pressure in 12.50%. So, in asthma patients, the highest percentage was low HDL and abdominal obesity, then FBS (40%, 40%, and 27.5%) respectively, but TG and hypertension were of the lowest incidence (15%, 12.5%, respectively). The most prevalent component in participants with asthma was waist circumference (40%) and Low HDL (40%) (Fig. 1).

Table 1.
The demographic and metabolic characteristics of the study groups.
Clinical Parameters Asthma (320)
Age 45.60 ± 11.11
Sex -
Females 224 (70%)
Males 96 (30%)
BMI (kg/m2) 23.54 ± 3.18
Waist circumference 97.98 ± 15.55
SBP 133 ± 18.76
DBP 81 ± 10.57
FBS (mg/dl) 151.05 ± 65.36
Triglyceride (mg/dl) 121.41 ± 106.59
HDL-Chol (mg/dl) 49.0 ± 15.8
CRP 6.3 ± 2.1
FEV1 44.85 ± 17.89
FEV1/Fvc 65.15 ± 13.02
Met syndrome 184 (57.5%)
Data are presented as mean ± SD or as frequency and percentage; BMI: Body Mass Index; CRP: C reactive protein; FBS: Fasting blood sugar; SBP: systolic blood pressure; DBP: diastolic blood pressure; LDL: Low Density Lipoprotein; HDL: High Density Lipoprotein; FEV-1: Forced Expiratory Volume in the first second; Mets: Metabolic syndrome.

Asthma patients who did not meet the criteria for MS (n-136), 24 (7.5%) patients had no features of metabolic syndrome while 56 (17.5%) had at least one parameter of metabolic syndrome and 56 (17.5%) had 2 parameters of metabolic syndrome (Fig. 2).

Fig. (1). Distribution of the components of metabolic syndrome among the study population FBS:Fasting blood sugar; TG: Triglycerides; HDL: High Density Lipoprotein.
Fig. (2). Number of MS componants MS; metabolic syndrome.
Table 2.
Criteria of metabolic syndrome of the study population.
- Asthma P value
Clinical Parameters MS No MS
Age 46.45 44.75 0.63
Females 37.50% 32.50% 0.313
Males 20% 10% 0.053
BMI 23.99 ± 3.72 21.55 ± 4.33 0.065
SBP 138.5 ± 19.40 127.5 ± 16.8 0.06
DBP 87.5 ± 4.729 74.5 ± 10.87 0.001
FEV1 41.95 ± 20.84 47.75 ± 14.3 0.31
FEV1/FVC 63.9 ± 15.9 64.4 ± 9.56 0.72
FBS 191.80 ± 70.39 110.3 ± 17.81 0.001
Triglyceride (mg/dl) 155.4 ± 140.1 87.4 ± 35.37 0.042
HDL-Chol (mg/dl) 58.5 ± 9.5 51.7 ± 7.5 0.47
CRP 7.2 ± 1.54* 5.6 ± 2.69 0.03
MS: Metabolic syndrome; BMI: Body Mass Index; CRP: C reactive protein; FBS: Fasting blood sugar; SBP: systolic blood pressure; DBP: diastolic blood pressure; LDL: Low Density Lipoprotein; HDL: High Density Lipoprotein; FEV-1: Forced Expiratory Volume in the first second.

3.2. Characteristics of Patients with and without MS

Comparing asthma group who did not have MS and asthma group with MS, there were significant differences between the 2 groups regarding waist circumference (P value= 0.0158), CRP (P value =0.025), DBP (P value =0.0157), triglycerides (P value = 0.0264), and fasting blood glucose (P value <0.0001) (Table 2).

3.3. Correlation between MS and Clinical Parameters

The correlations between clinical parameters and MS observed in subjects affected by MS are shown in Table 3. In a patient of asthma, FEV1(r = 0.33, P < 0.03), SBP(r = 0.32, P < 0.04), DBP (r = 0.62, P < 0.001) and FBS (r = 0.68, P < 0.001) were positively associated with MS.

3.4. Predictors of Metabolic Syndrome

In order to predict the presence of a MS in patients with asthma, logistic regression analysis was done to identify predictors of MS. The independent variables which predict the presence of MS are presented in Tables 3 and 4. The best predictors of MS in asthma were FBS (P=0.002, OR=0.63, 95% CI: 2.69-5.01), and DBP (P=0.017, OR=0.78, 95% CI: 3.40-5.33) (Table 4).


Ours is the first study in Upper Egypt to evaluate the prevalence of MS in patients with bronchial asthma and to examine the correlations between MS with comorbidities and asthma characteristics.

The main findings of the current study were (57.5%) patients with asthma had MS. The prevalence of metabolic syndrome varies worldwide; its prevalence is not uniform. It has been reported to be 18–46% in different local studies [12]. 0ur finding was higher than the results obtained from a Nigerian report, where 27% of the participants were found to be victims of MS [13]. Uzunlulu et al., studied the prevalence of metabolic syndrome in asthma patients which was 36.7%, being lower than our study [14]. Diverse criteria used to diagnose it and differences between the populations in different studies (physical activity, diet, lifestyle, age, smoking etc.) may partly explain the differences in the prevalence of metabolic syndrome.

In our study, the most prevalent component in participants with asthma was waist circumference (40%) and low HDL (40%). A previous study reported that type 2 diabetes mellitus, dyslipidaemia and hypertension, tend to occur more in asthma patients. The prevalence of obesity in asthma patients and the use of steroid therapy by asthmatics may be the cause. Adeyeye et al., found that the prevalence of obesity in asthma patients was 49.4% which is higher than our own study [13].

Table 3.
Correlation between clinical parameters and MS.
Test parameter Asthma with MS
r P
Age 0.05 0.74
Sex 0.1 0.5
BMI 0.26 0.1
SBP 0.32 0.04
DBP 0.62 0.001
FEV1 0.33 0.03
FEV1/FVC 0.24 0.13
FBS (mg/dl) 0.68 0.001
Total chol (mg/dl) 0.24 0.13
riglyceride (mg/dl) 0.18 0.26
HDL-Chol (mg/dl) 0.08 0.59
CRP 0.01 0.93
MS: Metabolic syndrome; BMI: Body Mass Index; CRP: C reactive protein; FBS: Fasting blood sugar; SBP: systolic blood pressure; DBP: diastolic blood pressure; LDL: Low Density Lipoprotein; HDL: High Density Lipoprotein; FEV-1: Forced Expiratory Volume in the first second; FVC: Forced vital capacity.
Table 4.
Outcomes of logistic regression on asthma.
  OR CI P-value
FBS 0.63 2.69-5.01 0.002
DBP 0.78 3.40-5.33 0.017
DM: Diabetes mellitus; DBP: diastolic blood pressure; FBS; fasting blood sugar; OR; Odds ratio; CI; confidence interval.

Obesity and asthma are both characterized by inflammation. Obesity leads to a proinflammatory milieu characterized by increased levels of TNF-α, plasminogen activation inhibitor-1, angio-tensinogen, plasminogen activation inhibitor-1, IL-6, leptin, and decreased adiponectin levels which enhance endothelial functions. The TNF-α is also included in the initiation of allergic airway responses in asthma. Meta-analysis has shown the association between obesity and the development of asthma [15]. Asthma is a complex syndrome that encompasses multiple phenotypes. The relationship with obesity has been addressed in the past; however, the underlying mechanism of such a relationship seems to be more complex, and not explained by the body weight alone. The metabolic syndrome carries a condition of systemic inflammation that could potentially explain the influence on asthma onset and severity. This is a rather unexplored area that could potentially open new scenarios in the diagnostic algorithm and in the strategic approach, with a more comprehensive assessment of the disease.

We found a statistically significant difference between patients with MS and patients without MS regarding metabolic parameters but no statistical significance regarding functional parameters. So, the incidence of MS is not related to the severity of the disease. In our study, we did not find any significant association between the presence of MS in asthmatic patients and pulmonary function tests. In a similar study by Adeyeye etal., it was reported that the pulmonary function test was not affected by the presence of MS. This because that our asthma patients were already diagnosed and were on treatment for asthma [13]. In contrast, Leone found an independent relationship between lung function and MS in both sexes [16].

An interesting observation was that patients with MS in asthma had significantly higher CRP compared with those without MS. Park et al., showed that the MS -asthma association was substantially mediated by insulin resistance and systemic inflammation. MS and each MS component were significantly associated with insulin resistance and systemic inflammation [17]. This observation indicates that the presence of MS in asthma patients is associated with more intensive systemic inflammation. In recent studies, it has been reported that a decline in pulmonary function has been linked to increased inflammatory markers [18].

Another objective of the study was to explore the association of individual components of MS with asthma patients. The association with individual components was further explored using the logistic regression models. In a patient of asthma, the data showed a significant positive association with FEV1, SBP, DBP and FBS. These findings suggest that MS-related factors could contribute to the relationship between MS, and asthma. MS components such as abdominal obesity, hyperglycemia [19], dyslipidemia [20] and hypertension [21] accelerate lung function decline. Several studies showed that persons with impaired lung function are at greater risk of developing hypertension, and cardiovascular disease, insulin resistance and diabetes mellitus, within 10 years [22-23]

The last objective of the study was to explore the determinants of association between the individual components of MS and asthma. Among the components of MS; FBS, DBP is the best predictor of MS.


The prevalence of metabolic syndrome is high in patients with BA. It shows the importance of the development of comprehensive strategies aimed at the prevention and treatment of co-morbidities. Correction of the risk factors of MS may have a significant role in the prevention of complications and improvement of quality of life of patients with BA.


The study protocol was approved by the Institutional Ethics Committee of Sohag University Hospital, Egypt under approval no. IBR#S19-66.


No Animals were used in this research. All human research procedures followed were in accordance with the ethical standards of the committee responsible for human experimentation (institutional and national), and with the Helsinki Declaration of 1975, as revised in 2013.


Informed consent was taken from all the participants when they were enrolled.


The data sets used during the current study can be provided from the corresponding author [A.H.K.A], upon reasonable request.




The authors declare no conflict of interest, financial or otherwise.


Declared none.


Beigh SH, Jain S. Prevalence of metabolic syndrome and gender differences. Bioinformation 2012; 8(13): 613-.
Eckel RH, Alberti KG, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet 2010; 375(9710): 181-3.
Galisteo M, Duarte J, Zarzuelo A. Effects of dietary fibers on disturbances clustered in the metabolic syndrome. J Nutr Biochem 2008; 19(2): 71-84.
Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 1988; 37(12): 1595-607.
The IDF consensus worldwide definition of the metabolic syndrome 2008.
Alberti KG, Zimmet P, Shaw J. The metabolic syndrome: A new worldwide definition. Lancet 2005; 366(9491): 1059-62.
Gershon AS, Wang C, Guan J, To T. Burden of comorbidity in individuals with asthma. Thorax 2010; 65(7): 612-8.
Ribisl PM, Lang W, Jaramillo SA, et al. Exercise capacity and cardiovascular/metabolic characteristics of overweight and obese individuals with type 2 diabetes: the Look AHEAD clinical trial. Diabetes Care 2007; 30(10): 2679-84.
Ford ES, Li C. Physical activity or fitness and the metabolic syndrome. Expert Rev Cardiovasc Ther 2006; 4(6): 897-915.
Poirier P, Despre´s JP. Waist circumference, visceral obesity, and cardiovascular risk. J Cardiopulm Rehabil 2003; 23: 161-9.
International Diabetes Federation. The IDF consensus worldwide definition of the metabolic syndrome 2015.
Pemminati S, Prabha Adhikari MR, Pathak R, Pai MR. Prevalence of metabolic syndrome (METS) using IDF 2005 guidelines in a semi urban south Indian (Boloor Diabetes Study) population of Mangalore. J Assoc Physicians India 2010; 58: 674-7.
Adeyeye OO, Ogbera AO, Ogunleye OO, et al. Understanding asthma and the metabolic syndrome - A Nigerian report. Int Arch Med 2012; 5(1): 20.
Uzunlulu M, Oguz A, Gedik C, Asian G, Arik S. Is prevalence of metabolic syndrome high in patients with asthma? Acta Clin Belg 2011; 66(1): 49-52.
Beuther DA, Sutherland ER. Overweight, obesity, and incident asthma: A meta-analysis of prospective epidemiologic studies. Am J Respir Crit Care Med 2007; 175(7): 661-6.
Leone N, Courbon D, Thomas F, et al. Lung function impairment and metabolic syndrome: The critical role of abdominal obesity. Am J Respir Crit Care Med 2009; 179(6): 509-16.
Park HW, Kwon HS, Kim TB, et al. Differences between asthma in young and elderly: Results from the COREA study. Respir Med 2013; 107(10): 1509-14.
Stockley RA. Progression of chronic obstructive pulmonary disease: Impact of inflammation, comorbidities and therapeutic intervention. Curr Med Res Opin 2009; 25(5): 1235-45.
Walter RE, Beiser A, Givelber RJ, O’Connor GT, Gottlieb DJ. Association between glycemic state and lung function: The Framingham Heart Study. Am J Respir Crit Care Med 2003; 167(6): 911-6.
Leone N, Courbon D, Thomas F, et al. Lung function impairment and metabolic syndrome: the critical role of abdominal obesity. Am J Respir Crit Care Med 2009; 179(6): 509-16.
Koo HK, Kim DK, Chung HS, Lee CH. Association between metabolic syndrome and rate of lung function decline: A longitudinal analysis. Int J Tuberc Lung Dis 2013; 17(11): 1507-14.
Engström G, Wollmer P, Valind S, Hedblad B, Janzon L. Blood pressure increase between 55 and 68 years of age is inversely related to lung function: Longitudinal results from the cohort study ‘Men born in 1914’. J Hypertens 2001; 19(7): 1203-8.
Yeh HC, Punjabi NM, Wang NY, Pankow JS, Duncan BB, Brancati FL. Vital capacity as a predictor of incident type 2 diabetes: The atherosclerosis risk in communities study. Diabetes Care 2005; 28(6): 1472-9.