Body Mass Index and Associated Factors of Obesity and Underweight in a General Out-patient Population in a Tertiary Hospital in Nigeria

There is increasing incidence of abnormal weight changes in Sub-Saharan Africa, including Nigeria. Factors which influence body mass index (BMI) in developing countries have not been completely identified. Weight changes may be observed even in general out patients, unrelated to the major complaints for which the patients are presenting in clinics. This study was set out to evaluate BMI and potential risk factors of underweight and obesity in subjects attending the general outpatient clinic in Federal Medical Centre, Owerri, Nigeria. This was a cross-sectional study conducted on general out patients. Body mass index was determined. Relevant investigations were performed. Association of weight changes with their potential risk factors and the strength of variables to predict BMI, underweight and obesity were determined. The mean BMI of the subjects was 25.5± 6.5kg/m 2 . Underweight was present in 14(10.3%), normal weight in 57(41.9%), overweight in 24(17.6%) and obesity in 41(30.1%) of the subjects. There was significant association between BMI and hemoglobin (Hb), 24-hour urine osmolality (24HUOsm), serum cholesterol, serum low density lipoprotein cholesterol (LDL), serum high density lipoprotein cholesterol (HDL), as well as serum triglyceride. There was significant but poor correlation between BMI and spot urine protein (SUP), spot urine creatinine (SUCr), spot urine osmolality (SUOsm), serum cholesterol, serum triglyceride, serum HDL, serum LDL, as well as Hb. Spot urine protein, SUOsm, and Hb predicted BMI. Furthermore, SUOsm, serum cholesterol, serum HDL and serum LDL predicted obesity. The prevalence of underweight (10.3%) and obesity (30.1%) were high in the general out patients. Abnormalities of serum lipids, proteinuric renal disease with dilute urine were common in these subjects. There is a need for clinicians to routinely assess BMI and further search for anemia, dyslipidemia and renal damage in subjects with underweight and obesity attending the general out-patient clinics.


Introduction
Worldwide, obesity is an increasing healthcare problem.[1,2] Obesity has previously been an apparent problem of developed nations, associated with diseases like diabetes mellitus, hypertension and high socioeconomic status, while underweight has been seemingly consigned to developing countries associated with under-nutrition and infections.[3,4,5,6] Studies have shown a trend in the epidemiology of obesity: the low and middle income groups have high incidence of obesity in the developed nations while in the developing countries the incidence is rising among people of high economic status, urban dwellers and even rural dwellers.[7,8,9] Under-nutrition is high among rural dwellers in developing countries.[10] The prevalence of overweight was 62% and obesity 26% for both sexes in the World Health Organization (WHO) Population in a Tertiary Hospital in Nigeria region of the Americas, compared to overweight of 14% and obesity 3% in South East Asia.[11] In Nigeria, the prevalence of overweight ranged from 20.3% -35/1% and obesity 8.1% -22.3% in adults.[12,13,14,15] Two Nigerian studies reported underweight prevalence of 2.5% and 5.0%.[13,16] Factors associated with obesity included: sex, leisure-time, physical exercise, (and educational level appear to influence obesity) low physical activity, high wealth index, no livestock, low animal fat consumption, high n-6 polyunsaturated fat consumption, television ownership, time spent watching television, low rurality index, high caste, older age, rural living, current smoking, lower systolic pressure, urbanization, higher systolic pressure, and diabetes.[10,13,17,18] On the other hand, studies have shown that underweight was associated with low wealth index, high rurality index, low intake of n-6 PUFAs and less urbanization.[10,18] There is a paucity of studies on the associated factors of underweight, overweight and obesity in the general out patients in Nigeria.This has prompted us to evaluate BMI and the factors which might be associated with underweight and obesity in this group of people.This will help in identifying factors that may influence underweight and obesity with a view to instituting early interventions that will whittle down adverse outcomes in patients with underweight and obesity attending the general out-patient clinics.

Materials and Methods
This was a three-month cross-sectional study involving 136 subjects consecutively recruited from the general outpatients clinic in FMC, Owerri, Southeast Nigeria., conducted in 2011.Those included in the study were between 16-65 years.Those who were pregnant, or have known pituitary, adrenal, renal or terminal illness were excluded from the study.Informed written consent was obtained from all the subjects.Approval for this study was given by the Ethics Research Committee of the hospital.
With the aid of a questionnaire, demographic and anthropometric data were obtained.Our laboratory technicians administered the questionnaire and obtained the relevant data.Because the study was hospital-based, it was not pre-tested, as data collection was not difficult.In both English and our native languages, the aim of the study was explained to the subjects.The place of domicile and origin, age and gender were obtained.Height and weight were measured and BMI determined as the ratio of weight/height 2 (kg/m 2 ).
Clear instructions were given to all the subjects on how to collect 24-hour urine sample.A day-time random spot urine sample and blood samples were collected at the end of the 24-hour urine sample collection.[19,20] From the random spot urine samples collected, spot urine protein (SUP), spot urine creatinine (SUCr) and spot urine osmolality (SUOsm) were performed.Also from the 24hour urine samples collected, 24-hour urine protein (24HUP), 24-hour urine creatinine (24HUCr) and 24-hour urine osmolality (24HUOsm) were performed.Hemoglobin (Hb) and serum creatinine were performed on the blood samples collected.Other tests done from the blood samples were HIV screening test, fasting serum lipid profile (FSLP) (total cholesterol, triglyceride, high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol (LDL).Osmolality was determined by freezing point depression method using Precision Osmette 5002 osmometer, creatinine by modified Jeff's method and protein by photometric method.Creatinine clearance (ClCr) was determined.[19,20,21] Statistical Analyses: SPSS version 17.0 (SPSS Int.Chicago, II, USA) was used in analyzing the data.The distribution and characterization of clinical and laboratory features among subjects with different levels of BMI were analyzed using cross-tabulation, while the statistical significance of association between these variables and different levels of BMI was determined using student t-test.Multivariate linear regression analyses were used to determine the strength of variables to predict BMI, underweight and obesity.P≤0.05 was taken as statistically significant.
Overall, in this study, anemia was defined as Hb ≤12.0g/dl.

Discussion
This study showed that the prevalence of underweight was 10.3%, overweight 17.6% and obesity 30.1% in the subjects.There was significant association between BMI and Hb, 24HUOsm, serum cholesterol, serum LDL, serum HDL, as well as serum triglyceride.There was significant but poor correlation between BMI and SUP, SUCr, SUOsm, serum cholesterol, serum triglyceride, serum HDL, serum LDL, Hb.Spot urine protein, SUOsm, and Hb were predictors of BMI, while SUOsm, serum cholesterol, serum HDL, and serum LDL were predictors of obesity.
The prevalence 10.3% of underweight in subjects attending the general out-patient clinic observed in this study is similar to 6.1% reported by Anyabolu EN [25] in treatment-naïve HIV subjects despite the difference in the HIV status of the subjects in this study.In contrast, this is higher than the 2.5% documented by Chukwuonye et al [16] in Abia State, Nigeria.Both studies were done in the same Southeast geopolitical zone of Nigeria.However, the difference in prevalence might be explained by the slight difference in study design.Their study subjects were urban and rural dwellers drawn from the communities, while ours comprised of subjects attending the general out-patient clinic.In addition, factors related to illnesses for which the patients presented in out-patient clinic might account for the higher prevalence observed in our study.At the other pole, one study reported a much higher underweight prevalence of 22.7% in rural dwellers in India, among a low-resource income population.[10] Our study did not evaluate the association of underweight with socioeconomic status.
It was observed in this study that the prevalence 17.6% of overweight was similar to that in a multi-center, Nigerian study of urban dwellers.[9] It was, however, lower than the 28.2% and 38.4% reported in two Nigerian studies.[16,25] The first study was community-based, the second HIV clinicbased, while ours was hospital-based: this might be the reason for the difference in the prevalence of overweight compared to that noted in our study.
This study observed a prevalence 30.1% of obesity, a value much higher than those reported in Nigeria and India.[9,10,16,20] Unlike our study which was carried out in an outpatient clinic, these studies were community-based with much larger population sizes.
This study found a significant association between BMI and Hb.Hemoglobin was a predictor of BMI, but did not predict isolated underweight or obesity.Nonetheless, it demonstrated that the prevalence of anemia increased with underweight but declined with obesity.This agrees with the reports that demonstrated an association between anemia and weight changes in HIV subjects.[25,26] This demonstrated that anemia was a risk factor of underweight irrespective of the setting in which underweight occurred.
In this study, significant association was observed between BMI and 24HUOsm, (df=3, p=0.002).It further demonstrated that the prevalence of dilute urine declined with underweight and obesity.It was also observed that SUOsm was a predictor of obesity.Dilute urine may be seen in renal damage involving the interstitial compartment and the tubules, suggesting that among our study subjects, those with obesity were more likely to have renal damage.Two studies, however, reported an association between 24HUOsm and BMI, though one was in HIV subjects.[21,25] It was noted in this study that serum cholesterol has significant association with BMI, (df=6, p<0.001).It also demonstrated that the prevalence of hypercholesterolemia declined with underweight but increased with obesity.It further showed that serum cholesterol predicted obesity.This agrees with the studies that associated hypercholesterolemia with obesity.[27,28] This study found a significant association between BMI and serum LDL, (df=6, p<0.001), and further demonstrated that the prevalence of LDL hypercholesterolemia declined with underweight but increased with obesity.It was also observed that it predicted obesity.This is in agreement with reports that have shown an association between obesity and LDL hypercholesterolemia. [29,30] Serum LDL is the major determinant of serum total cholesterol, and therefore increases as serum total cholesterol increases.[30] The association between BMI and serum HDL was significant, (df=3, p<0.001), as found in this study.It was also observed that abnormal serum HDL occurred more in underweight subjects, contrary to the report documented in another study that showed an association between abnormal HDL and obesity.[30] Serum HDL predicted obesity in this study.Perhaps, our study subjects might have other morbidities that could account for this contrast, as they were patients presenting in the out-patient clinic.
This study showed that serum triglyceride and BMI have significant association.It was further observed that hypertriglyceridemia was absent in underweight subjects but prevalent in those obese.Our study also noted that serum triglyceride was a predictor of obesity.This contrasts with another study that found no association between serum triglyceride and BMI.[25] On the contrary, one study observed that serum triglyceride increased with obesity.[31] Spot urine protein was a predictor of BMI in this study.This implied that proteinuria would increase with increasing weight but decline with decreasing weight.A study demonstrated a similar observation that the prevalence of proteinuria increased with underweight as well as with obesity.[25] This held sway despite the difference in the study population; ours were out patients that presented in an out-patient clinic, while theirs were HIV subjects presenting in the early stage of the disease in an HIV clinic.

Conclusion
The prevalence of underweight (10.3%) and obesity (30.1%) were high in the general out patients.Abnormalities of serum lipids, proteinuric renal disease with dilute urine were common in these subjects.There is a need for clinicians to routinely assess BMI and further search for anemia, lipid and proteinuric renal abnormalities in subjects with underweight and obesity attending the general out-patient clinics.

Limitations
The study population was small.A much larger study sample size would have been better as it would averted the skewed colinearity variance that voided the regression analysis of underweight with its potential risk factors.In addition, some elements of association of BMI with concentrated urine would have been observed.

Figure 1 .
Figure 1.Association between BMI and Anemia

Figure 3 .
Figure 3. Association between BMI and serum LDL.

Table 1 .
Variables in Study subjects n=136.

Table 2 .
Distribution and characterization of potential risk factors of BMI among subjects with different levels of BMI (n=136).

Table 3 .
Correlation of BMI with potential risk factors in study subjects (n=136).Multivariate linear regression analysis of BMI with potential risk factors showed that SUP, SUOsm, and Hb predicted BMI while SUCr, serum cholesterol, serum triglyceride, serum HDL, and serum HDL did not (Table4).

Table 4 .
Multivariate linear regression of BMI with potential risk factors in study subjects n=136.