Accuracy of Body Mass Index in Diagnosing Adiposity Compared to Body Fat Percentage Measured by Bioelectrical Impedance in Adults at Al-Najaf Governorate
Received: 23-Oct-2018 / Accepted Date: 20-Jan-2019 / Published Date: 31-Jan-2019 DOI: 10.4172/2161-0711.1000645
Abstract
Background: Obesity is defined as an excess of body fat that is sufficient to affect adversely on health. The Body Fat Percentage (BFP) of a human or other living being is the total mass of fat divided by total body mass. Bioelectrical impedance analysis has been shown to be more precise for determining lean or fat mass in humans, In comparison with body mass index, anthropometric and skin fold methods, Bioelectrical Impedance Analysis (BIA) is commonly used method for estimating body composition, and in particular Percentage of body fat . BIA is known to provide a rapid, non-invasive and relatively accurate measurement of body composition
Aim: To evaluate the accuracy of body mass index in diagnosing overweight and obesity compared to percentage of body fat measured by bioelectrical impedance.
Subjects and methods: Cross sectional study for adult population age of them range from (18-65) years of both gender selected by using systematic sampling technique from private clinic during the period (1st of March to the 1st November) 2017. For each person measured the height with a regularly calibrated Stadiometer. And according to weight was recorded by bioelectrical impedance machine then enter manually age, gender and height to bioelectrical impedance technique (in body 370 machine) that is used for estimating body composition. The data will be coded and entered into Statistical Package for Social Sciences (SPSS) version 20.
Result: A total of 711 subjects had been included in this study, the male: female ratio was 0.35: 1, the mean age of subjects was 31.4 ± 10.58 range (18-65) years there was shows high validity of BMI in detecting excess of body fat as compared to Body Fat percent as a reference test. The sensitivity was 94% and specificity was 96%. For both gender. Also There was a strong and significant positive correlation between percentage of body fat and Body Mass Index and mineral density and Body Fat Mass when p<0.001 in males and females. A positive correlation also was detected between percentage of body fat and age in both genders with waist hip ratio.
Conclusion: There is high validity of body mass index in diagnosing overweight and obesity but still there are false positive and false negative cases that should be encountered.
Keywords: Obesity; Body mass index; Bio-electrical impedance
Introduction
Obesity is defined as an excess of body fat that is sufficient to affect adversely on health [1]. It results from an increase intake of energy from food consumption over energy expenditure and thus both an increase in intake and a decrease in expenditure will lead to excess calories being stored as fat and ultimately to obesity. So it is more prevalent in the lower socio-economic people in developed countries and the reason for this is unknown, but may reflect food availability and marketing practices. Ethnicity of subjects also plays role in the prevalence of obesity; white men and black women are more obese than their corresponding counterparts [2]. Obesity tends to run in families but shared environmental factors (meals and level of activity) probably contribute more to obesity than common genetic factors and the current, rapid increase in obesity prevalence cannot be explained by the gene pool changing so quickly. Some individuals are genetically more susceptible to the effects of an obesogenic environment and it is associated with a higher risk of death and morbidity [3]. Obesity is like many other medical conditions, the result of an interplay factor [4]. Polymorphisms in various genes controlling appetite and metabolism predispose to obesity when sufficient food energy is present. As of 2006, more than 41 of these sites on the human genome have been linked to the development of obesity when a favorable environment is present [5]. The ideal healthy body weight for a particular person is dependent on many things, such as frame size, sex, muscle mass, bone density, age, and height additionally dependent on cultural factors and the mainstream societal advertisement of beauty [6]. Due to difficulty of measuring body fat under field conditions the practical definition of obesity for adults is based on Body Mass Index (BMI). Body mass index, also known as Quetelet’s index, is calculated as an individual’s weight by (kg) on square length by (m2) [7]. Body Mass Index (BMI) was devised in year of 1830 by Lambert Adolphe Jacques quetelet, a Belgian mathematician. No specialized equipment is required; so it is easy to measure accurately and consistently across different countries and regions. Therefore, it has been accepted as an international standard for the measurement of obesity [8]. Although the two terms overweight and obese are often used interchangeably and considered as gradations of the same thing, they denote different things [6]. National Institute of Clinical Excellence (NICE) has recommended the use of BMI measures in the management of overweight and obesity in adults [9]. According to WHO classification BMI (kg/m2) associated health risks underweight less than (18.5) consider low (but risk of other clinical problems increased) normal range is between (18.5-24.9), overweight is between (25.0-29.9) risk is Increased, obese class I is between (30.0–34.9) moderately increased risk, obese class II is between (35.0-39.9) severely increased risk, obese class III is 40 or higher very severely increased risk [10]. The life expectancy of men and women with a BMI of >45 kg/m2, 13 years for age of 20 and 8 years for age of 30 lower than that of those with a BMI of 24 kg/m2 [3]. The health risks associated with increasing BMI are continuous and the interpretation of BMI gradings in relation to risk may differ for different populations. BMI values are age-independent and the same for both sexes. However, BMI may not correspond to the same degree of fatness in different populations due, in part, to different body proportions [10]. Central or visceral adiposity is a particularly strong predictor for the development of hypertension and this association is independent of BMI [11,12]. Organs like kidney, liver, and heart are more severely affected by abdominal fat than by the fat around the bottom or hips. In addition, very high muscle mass skews the BMI measures. Muscle is denser and weighs more than fat (a cubic centimeter of muscle weighs more than a cubic centimeter of fat therefore, well built, athletic people will inevitably be classed as fatter, by BMI, than they really are [13].
Waist circumference is a better appraiser of metabolic risk than BMI because it is more directly proportional to total body fat and the amount of metabolically active visceral fat [14]. It is at least a good indicator of total body fat as BMI or skin fold thicknesses, and is also the best anthropometric predictor of visceral fat [15]. A raised waist circumference is defined as greater than 102 cm in men and greater than 88 cm in women [16]. People with increased fat around the abdomen or wasting of large muscle groups, or both, tend to have a large waist circumference relative to that of the hips (high waist to hip ratio).Waist circumference alone gives a better prediction of visceral and total fat and of disease risks than waist to hip ratio Waist circumference is minimally related to height, so correction for height (as in waist to height ratio) does not improve its relation with intraabdominal fat or ill health [15]. Waist Circumference (WC) is a valid measure of abdominal fat mass and disease risk in individuals with a BMI less than 35 kg/m2. If BMI is 35 kg/m2 or more, WC adds little to the absolute measure of risk provided by BMI. Therefore, WC and WHR are not routinely measured in patients with BMI greater than 35 kg/m2 [16]. Increased body mass associated with obesity places a greater load on skeletal muscle and therefore imparts a training effect (analogous to resistance training) to increase muscle mass and strength. Evidence in support of this is that maximal leg and trunk strength, but not handgrip and arm strength, has been shown to be greater in obese than in lean individuals [17,18]. The Body Fat Percentage (BFP) of a human or other living being is the total mass of fat divided by total body mass. The body fat includes two types: essential and storage body fat, Essential body fat that is necessary to maintain life and a reproductive functions and storage fat that consist of fat accumulation in adipose tissue, part of which protects internal organs in the chest and abdomen [19]. Physically, differences in stature and shape are the first indicators that allow us to distinguish between men and women and we are biologically programmed to be acutely sensitive to these markers. In addition to size differences, at any given Body Mass Index (BMI), women generally have higher levels of total body fat and lower levels of fat-free mass and they weigh less and are shorter [20]. This gender difference reflects the differences in body fat content; body fat is practically water-free. This also means that if a person gains weight in the form of fat the percentage of total body water content declines [21]. The normal range percentage of essential fat is 2-5% in men, and 10%-13% in women [19]. Bioelectrical Impedance Analysis (BIA) is commonly used method for estimating body composition, and in particular body fat% [22]. BIA is known to provide a rapid, non-invasive and relatively accurate measurement of body composition [23]. Actually determines the electrical impedance to the flow of an electric current through body tissues which can then be used to estimate Total Body Water (TBW), which can be used to estimate fat-free body mass and, by difference with body weight, body fat [24]. Bioelectrical Impedance Analysis (BIA) is a good alternative for measurement of percentage body fat (%BF) when subjects are within normal body fat range [25]. This device is based on the fact that fat slows down the passage of electricity through the body. When a small amount of electricity is passed through the body, the rate at which it travels is used to determine body composition [6]. The studies on the electrical properties of biological tissues have been going on since the late 18th century [26] and categorized according to the source of the electricity like active and passive response. Active response (bioelectricity) occurs when biological tissue provokes electricity from ionic activities inside cells, as in Electrocardiograph (ECG) signals from the heart and Electroencephalograph (EEG) signals from the brain. Passive response occurs when biological tissues are simulated through an external electrical current source [22].
Bioelectrical impedance analysis has been shown to be more precise for determining lean or fat mass in humans [27]. Not all obese individuals have increased amounts of muscle mass. In fact, 5%-10% of the elderly are both obese and have low levels of muscle mass [28,29] a condition referred to as sarcopenic obesity. These individuals are at much greater risk for disability [30] and also mortality [31,32]. Unfortunately, individuals with sarcopenic obesity are not easily identified because they may have normal or near-normal body mass index [33]. So we studied sub population at Al-Najaf governorate to determine the accuracy of BMI in detecting obesity compared to BF %, as reference test. As obesity defined an excess of body fat and also we tried to find the effects of age and gender on this relationship.
Subject and Methods
Study design: Cross sectional study.
Setting of the study: The study were done in a private clinic at Al- Najaf governorate during the period (1st of March to the 1st of November) 2017.
Sampling technique: Systematic sampling technique (between one and another with taking the subsequent one in case of the patient was child), nearly 3 days per week.
Inclusion criteria: Subjects between (18-65) years old, both gender.
Exclusion criteria: Pregnant woman, Patient cannot stand, Woman with menstrual cycle, Person take diuretic within 7 days from time to test done.
Ethical considerations: Verbal consent had been taken from all participants.
Sample size determination
The sample was estimate according to the following equation [34]:
n=Z2 P(1-p)/d2
n=(1.96)2×0.6×0.553/(0.05)2
Minimal sample size required
n=397
n=Sample size
Z=Confidence interval according to the standard normal distribution 95% (1.96)
P=Estimated proportion of the participant 0.553 [35]
d=Tolerated margin of error which was selected to be 0.05
Anthropometry
For each person measures to him the height with a regularly calibrated Stadiometer. By using a horizontal arm that moves vertically on a calibrated scale, person should be stand erect in bare feet that are kept together without shoes against a straight surface with the head level. The head level should be with a horizontal Frankfort plane (an imaginary line from lower border of the eye orbit to the auditor Meatus) and according to weight was recorded by analysis then enter manually age, gender and height that measuring it beforehand [15].
Bioelectrical impedance derived percentage of body fat
In body 370 machines (In Body Bldg., 54, Nonhyeon-ro2-gil, Gangnam-gu, Seoul 06313 Korea) that is used method to estimating body composition person was measured while standing erect, in bare feet on the footpads analyzer and wearing thin clothes. Not eat or drink within 4 hrs and no exercise within 12 hrs of the test done and gently grasped the two handgrips with arms held straight forward at 90 degrees. The machine of 15 impedance measurements by using 3 different frequencies (5 kHz, 50 kHz, 250 kHz) at each 5 segment of the body (Right arm, Left arm, Trunk, Right leg, Left leg) it consists of tetra polar 8 point tactile electrode system stainless steel foot [36].
Statistical analysis
The data has been coded and entered into Statistical Package for Social Sciences (SPSS) version 20 and analyzed by using chi square, Pearson correlation coefficient in addition to calculation of Sensitivity, Specificity, PPV, NPV, LR+, LR-.
Result
A total of 711 subjects had been included in this study, the male: female ratio was 0.35: 1; the mean age of subjects was 31.4 ± 10.58 years (range 18-65). There were 188 (26.4%) males and 523 (73.6%) females (Table 1).
| Variable | Frequency | Percentage | |
|---|---|---|---|
| Gender | Male | 188 | 26.4 |
| Female | 523 | 73.6 | |
| Age/years | 18-30 | 379 | 53.3 |
| 31-40 | 183 | 25.7 | |
| 41-50 | 116 | 16.3 | |
| >50 | 33 | 4.6 | |
| Diabetes mellitus | 47 | 6.6 | |
| Hypertension | 82 | 11.5 | |
| Asthma | 12 | 1.7 | |
Table 1: General demographic characteristics.
Table 2 shows high validity of BMI in detecting excess of male body fat as compared to BF% as a reference test. The sensitivity was 92% and specificity was 95%.
| PBF | Total | |||
|---|---|---|---|---|
| High | Normal | |||
| BMI Kg/m2 | ≥ 25 | 99 | 4 | 103 |
| 96.1% | 3.9% | 100.0% | ||
| ≤ 25 | 8 | 77 | 85 | |
| 9.4% | 90.6% | 100.0% | ||
| Total | 107 | 81 | 188 | |
| 56.9% | 43.1% | 100.0% | ||
| BMI: Body Mass Index; PBF: Percentage of Body Fat; Sensitivity:=92%; Specificity=95; Positive predictive value=96%; Negative predictive value=90%; Positive Likelihood Ratio=18.736; Negative Likelihood Ratio=0.079 | ||||
Table 2: Accuracy of body mass index compared to percentage of body fat among males.
Table 3 shows high validity of BMI in detecting excess of female body fat as compared to BF% as reference test. The sensitivity was 95% and specificity was 97%.
| PBF | Total | |||
|---|---|---|---|---|
| High | Normal | |||
| BMI | ≥ 25 | 341 | 4 | 345 |
| 98.8% | 1.2% | 100.0% | ||
| £ 25 | 16 | 162 | 178 | |
| 9.0% | 91.0% | 100.0% | ||
| Total | 357 | 166 | 523 | |
| 68.3% | 31.7% | 100.0% | ||
| Sensitivity=95%; Specificity=97%; Positive predictive value=98%; Negative predictive value=91% Positive Likelihood Ratio=39.640; Negative Likelihood Ratio=0.046 | ||||
Table 3: Accuracy of body mass index compared to percentage of body fat among Females.
There is high validity of BMI in detecting excess of body fat as compared to BF% when the sensitivity was 94% and specificity was 96% as shown in Table 4.
| PBF | Total | |||
|---|---|---|---|---|
| High | Normal | |||
| BMI | ≥25 | 440 | 8 | 448 |
| 194.9% | 5.1% | 200% | ||
| <25 | 24 | 239 | 263 | |
| 18.4% | 181.6% | 200% | ||
| Total | 464 | 247 | 711 | |
| 125.2% | 74.8% | 200% | ||
| Sensitivity=94%; Specificity=96%; Positive predictive value=98%; Negative predictive value=90% Positive Likelihood Ratio=29.278; Negative Likelihood Ratio=0.053 | ||||
Table 4: Accuracy of body mass index compared to percentage of body fat for all participants.
Table 5 Shows significant association between age of the subject and BF%, when p<0.001
| PBF male | Total | X2 | p value | |||
|---|---|---|---|---|---|---|
| High | Normal | |||||
| Age/years | 18-30 | 20 | 59 | 79 | 68.439 | <0.001 |
| 25.3% | 74.7% | 100.0% | ||||
| 31-40 | 51 | 8 | 59 | |||
| 86.4% | 13.6% | 100.0% | ||||
| 41-50 | 16 | 14 | 30 | |||
| 53.3% | 46.7% | 100.0% | ||||
| >50 | 20 | 0 | 20 | |||
| 100.0% | 0.0% | 100.0% | ||||
| Total | 107 | 81 | 188 | |||
| 56.9% | 43.1% | 100.0% | ||||
Table 5: Relation between age and PBF among males.
There is significant association between age of the subject and BF% (p<0.001) as shown in Table 6. Table 7 Shows significant association between age of the subject and BMI when (p<0.001).
| PBF female | Total | X2 | p value | |||
|---|---|---|---|---|---|---|
| High | Normal | |||||
| Age/years | 18-30 | 157 | 143 | 300 | 84.462 | <0.001 |
| 52.3% | 100.0% | |||||
| 31-40 | 107 | 17 | 124 | |||
| 86.3% | 13.7% | 100.0% | ||||
| 41-50 | 82 | 4 | 86 | |||
| 95.3% | 4.7% | 100.0% | ||||
| >50 | 11 | 2 | 13 | |||
| 84.6% | 15.4% | 100.0% | ||||
| Total | 357 | 166 | 523 | |||
| 68.3% | 31.7% | 100.0% | ||||
Table 6: Relation between age and PBF among Female.
| BMI | Total | X2 | p value | |||
|---|---|---|---|---|---|---|
| BMI ≥ 25 | <25 | |||||
| Age/years | 18-30 | 20 | 59 | 79 | 58.966 | <0.001 |
| 25.3% | 74.7% | 100.0% | ||||
| 31-40 | 47 | 12 | 59 | |||
| 79.7% | 20.3% | 100.0% | ||||
| 41-50 | 16 | 14 | 30 | |||
| 53.3% | 46.7% | 100.0% | ||||
| >50 | 20 | 0 | 20 | |||
| 100.0% | 0.0% | 100.0% | ||||
| Total | 103 | 85 | 188 | |||
| 54.8% | 45.2% | 100.0% | ||||
Table 7: Relation between age and BMI among males.
Table 8 shows significant association between age of the subject and BMI (p<0.001). There was a strong and significant positive correlation between BF% and BMI and mineral density and BFM when p<0.001 in females as shown in Table 9. In Table 10 there was a strong and significant positive correlation between BF% and BMI and mineral density and BFM when p<0.001 in males.
| BMI Kg/m2 | Total | X2 | p value | |||
|---|---|---|---|---|---|---|
| ≥ 25 | <25 | |||||
| Age/years | 18-30 | 149 | 151 | 300 | 87.97 | <0.001 |
| 49.7% | 50.3% | 100.0% | ||||
| 31-40 | 105 | 19 | 124 | |||
| 84.7% | 15.3% | 100.0% | ||||
| 41-50 | 82 | 4 | 86 | |||
| 95.3% | 4.7% | 100.0% | ||||
| >50 | 9 | 4 | 13 | |||
| 69.2% | 30.8% | 100.0% | ||||
| Total | 345 | 178 | 523 | |||
| 66.0% | 34.0% | 100.0% | ||||
Table 8: Relation between age and BMI among females.
| Variable | r | p value |
|---|---|---|
| BMI Kg/m2 | 0.844 | <0.001 |
| BFM kg | 0.803 | <0.001 |
| r=correlation coefficient; BFM=Body Fat Mass | ||
Table 9: Correlation between Percentage of Body Fat and BMI, BFM among males.
| Variable | r | p value |
|---|---|---|
| BMI Kg/m2 | 0.779 | <0.001 |
| r=correlation coefficient; BFM=Body Fat Mass | ||
Table 10: Correlation between percentage of body fat among females and different variable’s.
Discussion
Overweight and obesity are the fifth leading risk for global deaths this leading to at least 2.8 million adult deaths each year [37]. An increase in the prevalence of overweight and obesity is expected over the next two decades, also the prevalence of obesity has increased in high and low income countries but high income countries, obesity affects mainly the less advantaged population [38,39]. And in developing countries, obesity prevalence is greater in the higher income population [40]. The use of BMI as a measure of excess body weight may lead to some misclassification as it does not distinguish between fat and muscle mass [41]. The body fat percentage is a measure level; it is the only body fitness of measurement which directly calculates a person's relative body composition without regard to height or weight [42]. So in our study we use bioelectrical impedance which is a simple instrument for estimating body fat [22]. BIA is reasonably accurate for measuring groups in an individual over a period of time [43]. The present study shows the frequency of obesity and overweight according to socio-demographic variables related to gender and age group and some chronic morbidity will be used as baseline to analyze the evolution of this morbidity for 711 adult from population living at Al-Najaf governorate. Female to male ratio was (0.35:1), age of them range from (18-65 yrs). In the current study, there was high accuracy of body mass index which was observed in males, that shows the sensitivity was 92% and the specificity was 95% and in female also shows high validity of BMI in detecting obesity and overweight as compared to BF%. The sensitivity was 95% and specificity was 97% and when take over the all males and females we see also high validity of BMI, the sensitivity was 94% and the specificity was 96% this study had some differences when compare with study of Singapore on 2012 represents a BMI had overall poor sensitivity (48.7%) and good specificity (95.7%) to detect BFP-defined obesity. After stratifying by gender, it was found that BMI had good sensitivity in males (82.6%), but only 34% sensitivity in females. On the other hand, the specificity was good for both genders, being 92.6% and 100.0% for males and females, respectively due to BMI standards in Singapore, which were revised in 2005, overweigh was BMI >23.0 kg/m2 and obesity was BMI >27.5 kg/m2 [ 44 ].
Also when we measure it using correlation the result has been showed there was a strong and significant positive correlation between BF% and BMI in males was (r=0.844, p<0.001) and in female was ( r=0.779, p<0.001) this result was similar to study in Siri Lanka 2013 in this research they depend on correlation and the study has been showed a strong and significant positive correlation between BMI-BF %, in males were (r=0.75, p<0.01 ) and also in females were ( r=0.82, p<0.01 ).And in both male and females correlations calculated for the three different age groups of adults separately, also showed significant positive correlation when (p<0.01). They were, r =0.79/ 0.84 in (young), r=0.71/0.70 in (middle age), and in r =0.59/0.075 in (elderly) respectively [45]. Also our study was similar to result of study in a group of Saudi Arabian Adults in 2017 has been showed a significant positive correlation was observed between BMI-BF%, in males (r=1, p<0.05 ) and in females (r=0.9, p<0.05 ) of all ages [46]. Also see that study of Iran on 2013 that deal with women with multi age group has been showed BMI and Total body fat percent were strongly and significantly correlated when (Beta=0.194, p-value<0.00) [47]. When measure relationship between body fat percent and age group shows significant association between them so with increase age there is increase in body fat percent in male (p<0.001, χ2=68.439) and in female (p<0.001, χ2=84.462) this result is similar to study in Siri Lanka 2013 has been showed In both males and females BF% showed an increase with age with a positive linear correlation (males r=0.47, females r=0.64; p<0.000). Females of all ages had significantly higher total body fat than males (p<0.001) the mean difference in BF% between females and males was 10.44 [45]. And when see the study that occur in Iran on 2013 which deal with women with multiage group, we see correlation study of age with body fat percentage showed a weak but significant association (Beta=0.194; p value=0.00) [47]. And this result supported by study of Vietnam on 2015 that showed age and gender were also statistically associated with Percentage of body fat [48]. Also regard to relation between age and Body mass index shows significant association between age of the subject and BMI so with increase age BMI increase for both males and females, in male (p<0.001 and χ2=58.966 ) and more in female ( p<0.001 and χ2=87.970) this result similar to study of city in Northeastern Brazil in 2006 has been showed overweight (BMI ≥ 25 kg/m2 ) and obesity (BMI ≥ 30 kg/m2) increased with age group which more prevalent [49]. In addition, similar to study of Iran on 2013 of women with multiage group, when evaluate of the correlation between age and Body Mass Index showed that BMI of women increased (p value=0.002) for each year of increment in age [47]. The prevalence of overweight and obesity measuring by BMI was increased with age in both genders that result of study of Vietnam on 2015 [48]. The relationship between body fat percent and bone mineral density, in our study shows there was a strong and significant positive correlation between BF% and mineral density in males ( r=0.771, p<0.001 ) and in female ( r=0.175, p<0.001 ) and also according to relationship between BF% and BFM, in males (r=0.803, p<0.001) and in females (r=0.736, p<0.001 ) this result was similar to study in china 2015 has been showed Increase Fat mass and BF% were positively associated with arm, trunk, and pelvic body mineral density in Chinese obese females. And Increased FM was positively associated with total, rib, and trunk BMD in Chinese obese males [50]. Regarding to correlation between Body fat percentage and Waist hip ratio in our study for males show significant positive linear correlation between them (r=0.841, p<0.001 ) and also in females (r=0.696, p<0.001 ) this result was similar to study in stanbul, Turkey 1999 the study shows PBF was positively correlated with Waist Circumference( WC) (r=0.73, p>0.000), and WHR (r=0.45, p>0.000) in adult females to know the fact Both WHR and waist circumference have a stronger correlation with cardiovascular risk factors [51]. Also similar to study of Brazil 2010 related to relation between Anthropometric Indicators and Risk Factors for Cardiovascular Disease has been showed positive Correlation between body fat percentage and Waist hip ratio in males (r=0.619, p<0.001) and in female (r=0.664, p<0.001) [51].
Conclusion
• There is high validity of Body mass index compared to percentage of body fat as reference sensitivity and specificity which influenced by age and gender.
• There was a strong and significant positive correlation between PBF and mineral density and Body fat mass for both gender.
• The Waist hip ratio was positive correlate with body fat percent that influenced by age and gender.
References
- Warrell DA, Cox TM, John D (2010) Obesity oxford textbook of medicine. (5th edn). United States: (Oxford.university.press)
- Seetho I, Wilding J (2013) How to approach endocrine assessment in severe obesity?. Clin Endocrinol 79: 163-167.
- Webster J, Joan G, Angela M, Michelle HW (2012) Oxford handbook of nutrition and dietetics. (2nd edn). Oxford University Press.
- Albuquerque D, Nóbrega C, Manco L, Padez C (2017) The contribution of genetics and environment to obesity. Br Med Bull 123: 159-173.
- Poirier P, Giles TD, Bray GA, Hong Y, Stern JS, et al. (2006) Obesity and cardiovascular disease: Pathophysiology, evaluation, and effect of weight loss: An update of the 1997 American heart association scientific statement on obesity and heart disease from the obesity committee of the council on nutrition, physical activity, and metabolism. Circulation 26: 898- 918.
- Zimmerman M, Snow B (2012) Energy balance and body weight, indicators of health: Body mass index, body fat and content, fat distribution p: 557.
- Björntropn P (2005) International textbook of obesity. Chichester : John Wiley&Sons.
- Campbell IW, Haslam D (2005) Obesity: Your questions answered. Edinburgh: Churchill Livingstone.
- de Gonzalez AB, Hartge P, Cerhan JR, Flint AJ HannanL, et al. (2010) Body-mass index and mortality among 1.46 million white adults. N Eng l J Med 363: 2211-2219.
- Hayashi T, Boyko EJ, Leonetti DL, McNeely MJ, Newell-Morris L, et al. (2004) Visceral adiposity is an independent predictor of incident hypertension in Japanese Americans. Ann Intern Med 140: 992-1000.
- Da Silva AA, Do Carmo J, Dubinion J, Hall JE (2009) The role of the sympathetic nervous system in obesity-related hypertension. Curr Hypertens Rep 11: 206-211.
- Nightingale CM, Rudnick a AR, Owen CG, Cook DG, Whin PH (2011) Patterns of body size and adiposity among UK children of south asian black African-caribbean and white European origin: Child Heart And health Study I England. Int J Epidemiol 40: 33–44.
- Han TS (2006) Assessment of obesity and its clinical implications. BMJ 333: 695 -698.
- Health and Social Care Information Center (2018) Statistics on obesity, physical activity and diet-England, 2014. London.
- Hulens M, Vansant G, Lysens R, Claessens AL, Muls E, et al. (2001) Study of differences in peripheral muscle strength of lean versus obese women: An allometric approach. Int J Obes Relat Metab Disord 25: 676-681.
- Lafortuna CL, Maffiuletti NA, Agosti F, Sartorio A (2005) Gender variations of obesity body composition, muscle strength and power output in morbid OBESITY . Int J Obes (Lond) 29: 833-841.
- ACE (American Council on Exercise) (2009) What are the guidelines for percentage of body fat loss?.
- Lovejoy JC, Sainsbury A, The Stock Conference 2008 Working Group (2009) Sex differences in obesity and the regulation of energy homeostatic. Obes Rev 10: 154–167.
- Zimmerman M, Snow B, (2012) nutrientient important to fluid and electrolyte balance, overview of fluid and electrolyte balance.
- Kyle UG, Bosaeus I, De Lorenzo AD, Paul D, Elia M, et al. (2004) Bioelectrical impedan analysis part I: Review of principle and methods. Clinical Nutrit 23: 1226-1243.
- Sharma AM (2010) Obesity and cardiovascular risk. Growth Horm IGF Res 13 Suppl: S10–17.
- Sun G, French CR, Martin GR, Younghusband B, Green RC, et al. (2005) Comparison of multi frequency bioelectrical impedance analysis with dual-energy X-ray absorptiometry for assessment of percentage body fat in a large, healthy population. Am J Clin Nutr 81: 74-78.
- Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, et al. (2004) Bioelectrical impedance analysis-Part ІІ: Utilization in clinical practice. Clin Nutr 23: 1430-1453.
- Kyle UG, Pichard C (2000) Dynamic assessment of fat-free mass during catabolism and recovery. Curr Opin Clin Nutr Metab Care 3: 317-322.
- Baumgartner RN (2000) Body composition in healthy aging. Ann N YAcad Sci 904: 437-48. 42.
- Castillo EM, Gruen DG, Silverstein DK, Morton DJ, Wingard DL, et al. (2003) Sarcopenia in elderly men and women: The rancho bernardo study. Am J Prev Med 25: 226-231.
- Baumgartner RN, Wayne SJ, Waters DL, Janssen I, Gallagher D, et al. (2004) Sarcopenic obesteteic predicts instrumental activities of daily living disability in the elderly. Obese Res 12: 1995-2004.
- Honda H, Qureshi AR, Axelsson J, Heimburger O, Suliman ME, et al. (2007) Obese sarcopenia in patients with end - stage renal disease is associated with inflammation and rise mortality. Am J Clin Nutr 86: 633-638.
- Tan BH, Birdsell LA, Martin L, Baracos VE, Fearon KC (2009) Sarcopenia in an overweight or obeses patient is an adverse prognostic factor in pancreatic cancer. Clin Cancer Res 15: 6973-6979.
- Gallagher D, Ruts E, Visser M, Heshka S, Baumgartner RN, et al. (2000) Weight stability masks sarcopenia in elderly men and women. Am J Physiol Endocrinol Metab 279: E366-75.
- Charan J, Biswas T (2013) How to calculate sample size for different study design in medical research? Indian J Psychol Med 35: 121-126.
- Mohammed SJ, Hamid HG (2012) Obesity and overweight among medical teachers in college of medicine in university of Kufa. Kufa Med J 15: 285-279.
- Finkelstein EA, Khavjou OA, Thompson H, Trogdon JG, Pan L, et al. (2012) Obesity and severe obesity forecasts through 2030. Am J Prevent Med 42: 563-570.
- Sobal J, Rauschenbach B, Frongillo EA (2003) Marital status changes and body weight changes: A US longitudinal analysis. Soc Sci Med 56: 1543-1555.
- Monteiro CA, Moura EC, Conde WL, Popkin BM (2004) Socioeconomic status and obesity in adult populations of developing countries: A review. Bull World Health Organ 82: 940-946.
- Morgan K, McGee H, Watson D, Perry I, Barry M (2007) Survey of lifestyle, attitudes & nutrition in Ireland: Main report. Department of Health Children. The Stationery Office, Dublin.
- Gambineri A, Pelusi C, Vicennati V, Pagotto U, Pasquali R (2002) Obesity and the polycystic ovary syndrome. Int J Obes Relat Metab Disord 26: 883-896.
- Buchholz AC, Bartok C, Schoeller D (2004) The validity of bioelectrical impedance models in clinical populations. Nutr Clin Pract 19: 433-446.
- Goonasegaran AR, Nabila FN, Shuhada NS (2012) Comparison of the effectiveness of body mass index and body fat percentage in defining body composition. Singapore Med J 53: 403-408.
- Ranasinghe C, Gamage P, Katulanda P, Andraweera N, Thilakarathne S, et al. ( 2013) Relationship between body mass index (BMI) and body fat percentage, estimated by bioelectrical impedance, in a group of Sri Lankan adults: A cross sectional study. BMC Public Health 13: 797.
- Nasr Eldeen SK, Al-Buni R, Al Yami A, Huda Alali (2017) Relationship between body mass index (BMI) and body fat percentage in a Group of Saudi Arabian adults. Ann Public Health Res 4: 1059.
- Abolhasani M, Sahar D, Tahereh Y, Ali VF, Mojtaba S, et al. (2013) Does body fat percentage associate with age and body mass index? Int Res J Appl Bas Sci 5: 1307-1311.
- Lan T, Ho-Pham, Thai QL, Mai T (2015) Relationship between body mass index and percent body fat in Vietnamese: Implications for the diagnosis of obesity. PLoS One 10: e0127198.
- Denise PG, Moura EC, Luciana MVS (2009) Prevalence of overweight and obesity and associated factors , Brazil, 2006. Rev Saúde Pública 43 : 83-89.
- Zhang J, Jin Y, Xu S, Zheng J, Zhang Q, et al. ( 2014) Associations of fat mass and fat distribution with bone mineral density in Chinese obese population. J Clin Densitom 18: 44-49.
- Sabuncu T, Ankan E, Tasan E, Hatemi H (1999) Comparison of the associations of body mass index, percentage body fat, waist circumference and waist/hip ratio with hypertension and other cardiovascular risk factors. Turkish J Endocrinol Metab 3: 137-142.
- Oliveira MAM, Moreira EAM, Trindade EBSM, Carvalho T, Fagundes RLM (2010) Relation between anthropometric indication and risk factors for cardiovascular disease. Brazil Arq Bras Cardiol 94: 451-457.
Citation: Mohammed SJ, Mohammad Hussein LA, Hameed HG (2019) Accuracy of Body Mass Index in Diagnosing Adiposity Compared toBody Fat Percentage Measured by Bioelectrical Impedance in Adults at Al-Najaf Governorate. J Community Med Health Educ 9: 645. DOI: 10.4172/2161-0711.1000645
Copyright: ©2019 Mohammed SJ, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Share This Article
Recommended Journals
Open Access Journals
Article Tools
Article Usage
- Total views: 3311
- [From(publication date): 0-2019 - Dec 23, 2024]
- Breakdown by view type
- HTML page views: 2682
- PDF downloads: 629
