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Bharathi, Kannan Kumar, and Niruba: Correlation between PEFR and regional body fat in the middle aged male subjects – A cross sectional study


Obesity and its related morbidities has reached epidemic proportions with reports from World Health Organization showing 1.9 billion adults being overweight, among which 600 million people are obese.1 Obesity induced insulin resistance produces low-grade inflammatory condition and proinflammatory cytokines Which leads to systemic inflammation that can impair lung functions.2,3,4,5,6,7,8 In the disease process visceral fat is found to be important for assessing morbidity and mortality due to overweight and obesity. Current clinical guidelines recommend regional fat distribution as major risk factor than body mass index (BMI).9 Studies show increase in percentage of body fat and central pattern of fat distribution may affect the pulmonary function tests by altering lung compliance, work of breathing and the elastic recoil.10 Upper body obesity severely compromise lung volumes than lower body obesity.11,12,13 To assess quantitative and qualitative estimation of pulmonary functions PEFR remains as a convenient tool which can be easily measured by peak flow meter.6

Rationale of the study

We studied regional fat influence over pulmonary functions as there is paucity of data imposing the importance of areas of fat distribution on pulmonary functions.


To study the correlation and identify the better predictor between BMI and regional body fat percentage on pulmonary functions.

Materials and Methods

Study design

All participants gave a written informed consent to participate in this study. Information details about socio demographic characteristics, family history (Diabetes, Hypertension), respiratory illness, alcohol consumption, cigarette smoking, drug intake, diet history, exercise history, occupational history and history of recent illness were obtained by a structured questionnaire. Institutional ethical committee clearance was obtained.

Inclusion criteria

100 male subjects in the age group - 35-45 yrs with BMI between 18.5 to < or equal to 30 were included.

Exclusion criteria

Diabetics, hypertensives, alcoholics, smokers, subjects with any respiratory and other illness in past few days were excluded.

BC 601 TANITA segmental body composition analyser is used for Bioelectric Impedance Analysis (BIA) to measure visceral fat and subcutaneous fat.14

Preparations to do bio impedance analysis

After detailed diet and exercise history data was measured. Measurement is taken 3 times and average is taken. BIA measures the impedance or resistance to the signal as it travels through the water that is found in muscle and fat. BIA introduce into the body a known amount of current (I), of about 800 μ A, most often at a frequency of 50 kHz. The current passes between two electrodes, often called the source and sink (or detector), and generates voltages between different points in the body volume according to Ohm’s law. As living tissue constitutes a volume conductor the current flows through all conducting material present in the body in the path between the source and sink electrodes. Results thus obtained are analysed. BMI was calculated from height and weight of the subjects. PEFR was expressed in litres per minute (L/min).15,16,17,18,19 The PEFR was recorded with Wright’s portable peak flow meter in a standing position with pinched nostrils. At intervals subjects were asked to perform deep inspiration and expiration as strong as possible into the instrument. At least three readings were obtained under supervision and the high est of the three was recorded.20,21

Table 1
Age Mean ±SD (in years) PEFR (L/min) Visceral fat Mean ± SD (%) Subcutaneous fat Mean ±SD (%) BMI kg/m2
39 ± 4 272.16 ± 6 8.18 ± 2 27.18 ± 4 28.42 ± 4.5

Description of Age, BMI, visceral fat, subcutaneous fat and PEFR

Table 2
PEFR L/min
Visceral fat % r value - 0.576
Subcutaneous fat% r value - 0 .409
BMI kg/m2 r value - 0 .203

Correlation between visceral fat, subcutaneous fat and BMI with PEFR


Statistical Analysis was done using SPSS software 16.0 and Pearson correlation was applied. Descriptive statistics of age, visceral fat, subcutaneous fat, PEFR values and BMI of 100 middle aged male subjects mentioned in Table 1 and Table 2 (enclosed) shows Pearson cor relation of PEFR with BMI, subcutaneous fat and visceral fat.


Studies have established link between obesity and respiratory complications like impaired pulmonary function and airway hyper responsiveness20,21 both in children and adults which leads to reduced lung volumes and pulmonary morbidities.22,23 Among the regional fat distribution, truncal obesity and increased subcutaneous fat over the chest may alter pulmonary function by reducing compliance of chest wall, respiratory muscle function and size of peripheral airway and limits diaphragm descent during breathing.24,25

Hence in our study we intended to correlate PEFR with BMI and regional body fat distribution and results obtained showed significant negative correlation of PEFR with visceral fat and subcutaneous fat compared to BMI.

As the BMI considers only height and weight, its value on the distribution between adipose tissue and lea n mass are not always accurate.26 Study by Banerjee J et al also demonstrate compared to BMI, body fat% as a better index for determination of lung function impairments in obese subjects.27 Collins et al indicated upper body obesity has a more severe impact on the lung volumes as compared to obese patients with lower body obesity.28

Study by Cotes et al, says that pulmonary functions are not under the influence of body weight but muscularity and fat distribution plays a significant role.29 Negative correlation was observed by Bilgin et al between fat% and FVC, MVV, FEV1, and PEFR.30

Study by Lazarus et al, observed no effect of central pattern of fat distribution on pu lmonary flow rates but subcutaneous fat was significantly associated with the flow rate31 which contradicts our study.

Our findings were similar to Saxena et al32 who reported PEFR is affected by both overall and abdominal adiposity markers among which WHR(waist hip ratio) showed significant negative association with PEFR.


This study implies that life style modification and weight reduction can improve pulmonary functions and reduce the mortality and morbidity in obese individuals.


The major limitation of our study is smaller sample size. And we could not record all the lung function test parameters and lung volumes. Future longitudinal studies incorporating a larger sample size in different grades of obesity on lung functions can be done.


Our study concludes visceral fat and subcutaneous fat are the better predictors than BMI in assessing pulmonary related morbidities.

Source of funding


Conflicts of interest




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