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Obesity Determined by Different Measures and its Impact on the Health-Related Quality of Life of Young-Adult Nigerians



Chukwunonso E.C.C. Ejike, Chinwendu E. Ikwuegu and Ruth C. Abalogu
 
ABSTRACT

The prevalence of overweight/obesity is rising in developing countries yet their effects on the Quality Of Life (QOL) of Nigerians have not been assessed. This study therefore investigated the prevalence of obesity determined by different tools and their effects on the QOL of young Nigerians. Internationally accepted protocols were employed for the anthropometric diagnosis of obesity while the percentage body fat was estimated using a bioelectrical impedance analysis device. The QOL of the subjects were assessed by subjective self-report. Appropriate statistical analyses were performed on the data generated. A total of 2000 young-adult Nigerians (50% females) were studied. The results show that a total of 17, 3.9, 0.4, 11.9 and 18% of the population were overweight/obese, when BMI, WC, WHpR, WHtR and percentage body fat, respectively, were used as diagnostic criteria. There was a clear female preponderance of obesity in the population. As much as 12.4% of the population were completely dissatisfied with their QOL and the QOL of the subjects was negatively affected by overweight/obesity irrespective of the diagnostic used. The QOL was worse in subjects with visceral obesity and very high percentage body fat compared to those with generalized obesity. Only the anthropometric measures of obesity were positively correlated with QOL in this population. The prevalence of overweight/obesity in this population is lower than previous reports from similar populations in Nigeria but not reports from outside Nigeria. The impact of obesity on QOL appears to be driven by the functional impairment and physical discomfort that arise from it.

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Chukwunonso E.C.C. Ejike, Chinwendu E. Ikwuegu and Ruth C. Abalogu, 2015. Obesity Determined by Different Measures and its Impact on the Health-Related Quality of Life of Young-Adult Nigerians. Asian Journal of Clinical Nutrition, 7: 64-75.

DOI: 10.3923/ajcn.2015.64.75

URL: https://scialert.net/abstract/?doi=ajcn.2015.64.75
 
Received: September 16, 2015; Accepted: October 22, 2015; Published: November 13, 2015

INTRODUCTION

Overweight/obesity, i.e., a Body Mass Index (BMI) ≥25 kg m–2 is currently a global public health problem. It is linked to chronic diseases such as heart disease, type II diabetes, hypertension and prostatic diseases (Ejike and Ezeanyika, 2008; Bastien et al., 2014). Popkin et al. (2012) estimated from trend analysis that there are already more than 2 billion obese individuals globally and the prevalence is rising steeply in developing countries. The World Health Organisation (WHO) estimated that the prevalence of overweight and obesity among those aged 15 years and above living in sub-Saharan Africa in 2010 exceeded 60% and 70% for men and women, respectively. The same survey reported that the prevalence of overweight/obesity in Nigeria was 29 and 45.1% in men and women respectively (Ono et al., 2012). A recent study in young adult Nigerians reported a prevalence of 28.4 and 20.7% for overweight/obesity, using the BIA and BMI methods, respectively (Ejike and Ijeh, 2012). There is therefore unequivocal evidence that obesity is currently a public health challenge, even in Nigeria.

The BMI is the most widely used index for the diagnosis of obesity, especially in epidemiological research. Its strengths include its simplicity, wide acceptability and no requirement for elaborate equipment. It is however limited in utility when quantifying body composition as it does not distinguish between the lean muscle and fat mass and does not account for important contributors to weight such as bone density and blood volume (Bogin and Varela-Silva, 2012). As a result, very often otherwise healthy adults with more lean muscle mass are erroneously classified as either obese or overweight. Consequently, other anthropometric indices, namely Waist Circumference (WC), waist-to-hip ratio (WHpR) and waist-to-height ratio (WHtR) are used as surrogates for visceral adiposity and reportedly predict obesity-related health risks better than BMI (Snijder et al., 2006). Though technologies such as underwater weighing, dual-energy X-ray absorptiometry (DXA), etc., define adiposity better than the anthropometric indices in current use, they are not widely used in epidemiological studies due to cost and complexity of operation constraints (Mei et al., 2002). A non-invasive multi frequency Bioelectrical Impedance Analysis (BIA) device for the direct estimation of visceral fat area has been shown to be useful in assessing fat mass in healthy adults (Malavolti et al., 2003) even in Nigeria (Ejike and Ijeh, 2012).

Given that obesity affects negatively the individual’s self-esteem, self-image and ultimately Quality Of Life (QOL) (Soltoft et al., 2009; Dey et al., 2013) and the reported variations in the prevalence of obesity determined by different tools (Ejike and Ijeh, 2012), this study sought to determine how much the QOL of young Nigerians was affected by obesity and the impact of the different diagnostic tools on the QOL of the population. Put differently, three questions were asked: (1) What is the prevalence of obesity in this population and how does it vary by the diagnostic tools used, (2) What is the effect of obesity on the QOL of the population and (3) Does the QOL of the population vary significantly if the measure of obesity varies? The answers are hoped to contribute to the knowledge gaps existing in this direction and would help public health research and policy formulation.

MATERIALS AND METHODS

Subjects: Students of Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria (mean age, 21.7±2.6 years) were randomly recruited for this study. The purpose and rationale of the study was explained to them and those who gave an informed consent were recruited if they met the other inclusion criteria. Exclusion criteria included apparent ill-health, use of weight-loss medication, current cigarette smoking, drinking of alcoholic beverages in excess of four 600 mL bottles a week, history or present ‘Substance’ abuse and pregnancy (in females). These conditions were assessed by self-report. A total of 2000 students (50% females) who met the inclusion criteria were ultimately recruited. No honoraria were paid to participants. They were nonetheless offered some health education based on their nutritional status as determined in the study.

Methods: Self-reported age at last birthday for each subject was recorded. The heights of the subjects were measured using an improvised stadiometer with the subject standing on bare feet to the nearest 0.1 cm. The subject’s weights were measured using a BIA machine (Omron BF-400, Omron Healthcare Europe BV, Hoofddorp, The Netherlands) with the subject dressed in light clothing, without shoes/sandals to the nearest 0.1 kg. The Waist Circumference (WC) of each participant, as well as the Hip Circumference (HC) was measured using a non-elastic measuring tape to the nearest 0.1 cm. Waist circumference was measured midway between the lowest rib and the superior border of the iliac crest with the subject breathing minimally. The HC was measured at the widest circumference over the buttocks. From the data obtained from the height and weight measurements, the BMI for each subject was calculated as:

From the data obtained from the waist, hip and height measurements, WHpR was calculated as WC/HC while WHtR was calculated as WC/Height.

Body fat percentage was measured with the BIA device mentioned above, following the manufacturer’s instructions. The device sends an extremely weak electrical current of 50 kHz and less than 500 μA through the subject’s body. It then combines the electrical resistance with the distance of electricity conducted and the pre-entered particulars of the subject (age, sex, weight and height) to give the body fat percentage using an in-built formula which the manufacturer did not disclose.

The QOL of the subjects were accessed by a single question: If you were to spend the rest of your life with your weight status just the way it is now, how would you feel about that? The responses ranged from 0-6 denoting delighted to terrible.

Definitions: Overweight and obesity were defined by multiple definitions depending on the diagnostic tool employed, viz: (1) BMI≥25 but < 30 (overweight) and BMI≥30 (obese) (WHO, 1998), (2) WC≥80 for women and ≥94 for men (Grundy et al., 2005), (3) WHpR≥1 (WHO, 2000), (4) WHtR≥0.5 (Ashwell and Hsieh, 2005) and (5) percentage body fat≥32.0% (overweight) and ≥37.1% (obese) in black females and ≥21.7% (overweight) and ≥28.3% (obese) in black males (Zhu et al., 2003). Based on the QOL scores of the subject, they were described as being satisfied (0-2), mixed, i.e., satisfied/dissatisfied (3) and dissatisfied (4-6).

Statistical analyses: The data generated were subjected to descriptive statistics. Continuous data are reported as means±standard deviations while categorical data are presented as percentages. Differences between means were separated by One Way ANOVA for continuous variables and the chi-square test for categorical variables. The correlations between the QOL of the subjects and the measures of obesity were assessed using the pearson’s product moment correlation coefficients. The significant threshold was fixed at p<0.05 for all analyses. Data analyses were done using IBM-SPSS version 20.0 (IBM Corp. Atlanta, GA).

RESULTS

The mean age of the participants was 21.7±2.6 years. The males were however, significantly older than the females (22.2±3.0 years vs 21.2±2.2 years, p<0.001). Similarly, the males were significantly (p<0.001) taller and heavier than the females. In the same vein, the males had significantly (p<0.001) higher WHpR compared to the females. Conversely, the females had significantly (p<0.001) higher HC, WHtR and percentage fat mass% relative to the males. The BMI, WC and QOL scores of the males and females were statistically similar (p>0.001) (Table 1).

Using BMI as a diagnostic criterion, a total of 17% of the population (13% for males and 20.9% for females) were overweight/obese while 4.1% (1.8% for males and 6.4% for females) were underweight (Fig. 1). Visceral obesity (as diagnosed by WC) was found in 3.9% of the population (0.2% for males and 7.5% for females).

Fig. 1:Weight status of the population determined by BMI, *Significant difference (Chi-squared test) between the sexes

Table 1:Relevant characteristics of the studied population
BMI: Body mass index, QOL: Quality of life, FM: Fat mass, HC: Hip circumference, WC: Waist circumference, WhpR: Waist-to-hip ratio, WhtR: Waist to height ratio

Based on WHpR, only 0.4% of the population (0.2% for males and 0.6% for females) were obese. However, data for WHtR show that 11.9% of the population (5.6% for males and 23.3% for females) were obese (Fig. 2). The BIA determined percentage body fat shows on the one hand, that 18% of the population (6.7 for males and 29.3% for females) had high/very high body fat, corresponding to overweight/obese. On the other hand, 10.1% of the population had low body fat akin to underweight (Fig. 3).

As much as 28.1% of the population were not entirely satisfied with their QOL and the proportion did not vary significantly by sex. However, only 12.4% of the population were completely dissatisfied with their QOL (Fig. 4). Of all those who were satisfied with their QOL, 2.7, 83.4, 12.7 and 1.2% were underweight, normal weight, overweight and obese respectively, based on BMI standards. On the other hand, of the subjects who were dissatisfied with their QOL, 10.0, 63.1, 22.1 and 4.4% were underweight, normal weight, overweight and obese respectively. Among obese and thin subjects, significantly more subjects were dissatisfied with their QOL whereas among normal weight subjects, the reverse was the case (Table 2).

When overweight/obesity is defined using WC, WHpR and WHtR, it was found that irrespective of which of the three diagnostics was used, significantly more overweight/obese subjects were dissatisfied with their QOL compared to those who were satisfied with their QOL.

Fig. 2:Weight status of the population determined by relevant anthropometric indices, *Significant difference (Chi-square test) between the sexes

Fig. 3:Weight status of the population determined by body fat percentage, *Significant difference (Chi-square test) between the sexes

Table 2:Proportion of the population (stratified by BMI determined weight status) with the three QOL statuses
*Significant differences between the sexes but within each QOL classification and along each column, QOL: Quality of life, BMI: Body mass index

Fig. 4:Self-reported quality of life of the subjects

Table 3:Proportion of the population (stratified by varying anthropometric indices) with the three QOL statuses
*Significant differences between the sexes, for each anthropometric indicator of weight status, along each column, QOL: Quality of life, WC: Waist circumference, WHpR: Waist-to-hip ratio, WHtR: Waist-to-height ratio

Table 4:Proportion of the population (stratified by body fat percentage determined weight status) with the three QOL statuses
*Significant differences between the sexes but within each QOL classification and along each column, QOL: Quality of life, FM: Fat mass

Interestingly, all those who reported having mixed QOL had normal weight by WHpR standards (Table 3).

Table 5:Correlations between QOL scores and the measures of obesity in the studied population
QOL: Quality of life, BMI: Body mass index, WC: Waist circumference, WHpR: Waist-to-hip ratio, WHtR: Waist-to-height ratio, FM: Fat mass ratio

From Table 4, it is seen that the proportion of the population that were dissatisfied with their QOL was exponentially higher among those who had low, high and very high percentage body fat content, compared to their counterparts with normal percentage body fat.

Irrespective of sex, QOL scores were significantly and positively correlated with all the measures of obesity (r = +0.064 - +0.154, p<0.01) except percentage fat mass. These significant correlations were however driven by the strength of the correlations in the females as the correlation between QOL and any given measure of obesity in the males was not significant (Table 5).

DISCUSSION

Obesity is characterized by excess adiposity which is often seen in increased body weight. Though scientists agree that adiposity, not simply body weight is the real predictor of adverse health risks and outcomes, the measurement of adiposity remains challenging (Bogin and Varela-Silva, 2012). The finding of BMI-metabolic risk sub-phenotypes has however warranted a rethink of how far the BMI can be used in (at least) individuals. Interestingly, the prevalence of "Metabolically Obese Normal Weight" (MONW) and/or "Metabolically Healthy Obese" (MHO) phenotypes in Nigeria, is high (Ejike et al., 2009; Ijeh et al., 2010). For this and other reasons, other measures of obesity such as WC (Grundy et al., 2005), WHpR (WHO, 2000) and WHtR (Ashwell and Hsieh, 2005) which define visceral fat distribution (apparently better than BMI) have been suggested to be better predictors of cardio-metabolic health (Huxley et al., 2010). As is in all diagnostics, these indices and the references suggested for body fat percentage (Zhu et al., 2003) are not without limitations and criticisms.

Using BMI as a reference diagnostic, the 17% prevalence of overweight/obesity in this population (13% for males and 20.9% for females) is lower than previous reports from Nigeria. An earlier study in young-adult Nigerians found overweight/obesity in 20.7% of the population (Ejike and Ijeh, 2012). An earlier study of undergraduates in a different Nigerian university had reported a prevalence of 27.3% for overweight/obesity (Oghagbon et al., 2009). One must keep in mind however that a 2010 WHO report indicates that 29 and 45.1% of adult male and female Nigerians respectively were overweight/obese (Ono et al., 2012). Mogre et al. (2014) reported that the prevalence of general overweight/obesity among University students in Tamale, Ghana was found to be 12.2%. A study of medical students in Delhi, India gave a prevalence of 16.4% for overweight/obesity (Chhabra et al., 2006), while 10.4% of university students in Malaysia were found to be overweight (Hazizi et al., 2012).

Clearly the prevalence of overweight/obesity in this population is lower than previous reports from Nigeria but higher than results from similar populations in West Africa and Asia. Obesity in the studied age-bracket is however expectedly lower than in older populations because obesity is known to increase with age (Flegal et al., 2007). Young people are also known to be more conscious of their body shape, such that ‘Weight-watching’ is more common among them. This coupled with the rigours of undergraduate work and the often widespread poverty appears to be responsible (at least in part) for the low prevalence of obesity in this population. Though 4.1% (1.8% for males and 6.4% for females) were underweight and apparently have mild nutrient deficiency, the WHO expert committee notes that there is little experimental support for the use of 18.5 as cut off for underweight (WHO., 1995). The maintenance of the recommended threshold for a healthy body habitus is very important especially for people who would soon be engaged in procreation and should therefore be encouraged.

We found variations in prevalence of overweight/obesity when the different anthropometric indices were applied in diagnosis. This is in consonance with the report of Ejike and Ijeh (2012) who earlier reported a similar phenomenon in Nigerian young-adults. Using WC, WHpR, WHtR and BMI, obesity was found in 3.9% of the population (0.2% for males and 7.5% for females), 0.4% of the population (0.2% for males and 0.6% for females), 11.9% of the population (5.6% for males and 23.3% for females) and 1.9% of the population (0.8% for males and 2.9% for females), respectively as against 4.6% of the population (1.7% for males and 7.5% for females) diagnosed to be obese by BIA standard. Using BIA as a reference standard, WHtR misdiagnosed obesity the most, followed by WHpR, BMI and WC. The WC was comparable to BIA especially in the female population but diagnosed fewer males as obese. The BMI under diagnosed obesity in both males and females. The discrepancy observed, especially with reference to BMI poses a lot of challenges for population and individual health as it could result in missed intervention opportunities that otherwise could have been useful in reducing the negative consequences associated with excess adiposity. Though Ejike and Ijeh (2012) had reported that BMI had the least discrepancy when compared to BIA data, their conclusion may have been borne out their lumping of overweight and obese data together in making the comparison as against our comparing data for only obese subjects. In fact, a re-analysis of the present data with pooled data on overweight and obese subjects corroborates the report by Ejike and Ijeh (2012). Kennedy et al. (2009) had earlier reported that BMI had the poorest ability to predict true adiposity. The WC therefore, may be a reliable anthropometric indicator of adiposity in this population and its use should be encouraged.

Though anthropometry is accepted universally in the diagnosis of overweight/obesity, there are still concerns about its ability to define adiposity status accurately (Prentice and Jebb, 2001). This and other studies have shown that a significant proportion of subjects with a BMI <30 kg m–2 were actually obese when classified by BIA derived percentage fat mass (Frankenfield et al., 2001). The BMI is no doubt an important tool used by clinicians and epidemiologists and has become the most commonly used measure of weight status due to its simplicity of calculation when collecting data for large population surveys (Shah and Braverman, 2012). The BMI’s inability to identify differences in body composition and body fat distribution remain its greatest challenge (Bogin and Varela-Silva, 2012). The WC though proposed to be better than BMI in measuring obesity, because it captures abdominal obesity does not reflect fat mass that may be distributed in non-abdominal tissues-a major demerit of the tool (Lean et al., 1995). The WHtR has been shown to be a good predictor of adiposity-related disorders such as the metabolic syndrome (Shao et al., 2010). It is nonetheless limited because "The WC measurement assesses only visceral adiposity such that dividing it by the subject’s height wrongly distributes the fatness localized around the abdomen to the entire body" (Ejike and Ijeh, 2012). The discordance between WHtR and percentage body fat in the diagnosis of obesity in this population is therefore understandable. The WHpR has a couple of advantages over BMI, WC and WHtR as a measure of adiposity. This is owing to the distinct physiologic characteristics of fat depots in different parts of the body. It has been shown that visceral fat has a lower threshold for lipolysis relative to subcutaneous fat. Free fatty acids released by lipolysis result in a wide array of metabolic consequences especially as they have direct access to the liver (Snijder et al., 2006). Bioactive peptides and compounds with local and systemic adverse effects are also synthesized in visceral adipose tissues in rates that are proportional to the quantity of visceral fat present (Qatanani and Lazar, 2007). Sub-cutaneous fat on the other hand appears to act as a sink for free-fatty acids (Snijder et al., 2006), such that in older subjects, higher subcutaneous fat is known to be associated with metabolic benefits (Gavi et al., 2007). The fact that the body fat content of the entire body is not just localized around the waist and hip attenuates the benefits of the WHpR and may explain the discrepancy observed between percentage body fat and WHpR in this study. The high degree of discordance between obesity diagnosed by BIA and that determined by the studied anthropometric variables may account (at least in part) for the BMI-metabolic risk sub-phenotype observed in populations in Nigeria (Ejike et al., 2009; Ijeh et al., 2010).

As much as 28.1% of the population were not entirely satisfied with their QOL and the proportion did not vary significantly by sex. Interestingly, among obese and thin subjects, significantly more subjects were dissatisfied with their QOL whereas among normal weight subjects, the reverse was the case. This is not surprising because obesity is known to affect QOL adversely (Soltoft et al., 2009; Mannucci et al., 2010; Dey et al., 2013). The negative effect obesity has on QOL is thought to be driven by the presence of concurrent somatic diseases and psychopathological disturbances in morbidly obese patients, relative to those lesser degrees of obesity (Mannucci et al., 2010). In fact, it has been reported that the sex difference found in the impact of obesity on QOL may be due to the higher incidence of psychopathology among women (Sullivan et al., 1993). It is therefore possible that because of the young age of the studied population, somatic and psychopathological diseases may be minimal (though not assessed in this study) thereby abrogating the sex differences reported in other studies.

Using WC, WHpR and WHtR as diagnostics for obesity, it was found that irrespective of which of the three diagnostics was used significantly more overweight/obese subjects were dissatisfied with their QOL compared to those who were satisfied with their QOL. Furthermore, a greater proportion of the subjects with visceral obesity and very high percentage body fat (compared to generalized obesity) were dissatisfied with their QOL. This is indicative that QOL in this population may have affected more by body shape dissatisfaction and not somatic diseases. Not surprisingly, only the anthropometric measures of obesity and not the BIA determined percentage body fat which is a more precise measure of adiposity which is implicated in somatic diseases (Ejike and Ijeh, 2012) were positively correlated with QOL. Mannucci et al. (2010) had argued that a higher BMI affected largely the physical, rather than the psychosocial, components of QOL. Implicit in that is that the functional impairment and physical discomfort that arise due to overweight and obesity are the major factors driving their negative effect on QOL. Addressing this weight driven challenges may therefore improve the QOL of the subjects.

This study may be limited by its small sample size and the fact that we studied a convenient sample that may not represent the same age bracket in the general population. The sample size may not therefore permit robust and unequivocal statistical inferences to be drawn from the data. However, in the light of the daunting cultural and financial challenges that this type of epidemiological study often faces, the sample size should be acceptable. The differences in the prevalence of obesity determined by the different tools should be interpreted with caution especially as there is, as yet no consensus on the notion that percentage body fat is an improved phenotypic indicator of morbidity and mortality over BMI, WC etc. Furthermore, in morbidly obese individuals, an increased amount of body water and extracellular water often leads to an under-estimation of body fat percentage using the BIA technique. This may however not have affected our data since the prevalence of obesity in this population was low and we did have morbidly obese subjects. Finally, a single question was used to assess the QOL of the subjects instead of more sophisticated instruments such as the SF-36, the impact of weight on QOL questionnaire, the obesity-related psychosocial scale, etc. This was however warranted by the lack of validation of these scales in our clime and the need to keep the assessment simple and straightforward. The strengths of this study are in its novelty and originality as it is the first to assess the impact of obesity determined by different methods on the QOL of young-adult Nigerians.

CONCLUSION

In conclusion, this study investigated the prevalence of obesity determined by different tools and their effects on the QOL of young Nigerians. The results show that: (1) Using BMI, WC, WHpR, WHtR and body fat percentage as a diagnostic criteria, a total of 17, 3.9, 0.4, 11.9 and 18% of the population respectively were overweight/obese, (2) as much as 12.4% of the population were completely dissatisfied with their QOL and the QOL of the subjects was negatively affected by overweight/obesity irrespective of the diagnostics used, (3) QOL was worse in subjects with visceral obesity and very high body fat percentage compared to those with generalized obesity.

ACKNOWLEDGMENTS

The wilful participation of our subjects, despite the absence of honoraria is hereby heartily acknowledged.

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