DIAGNOSIS OF OBESITY

Introduction

Obesity is projected to the third greatest booster to overall burden of all ailments globally and especially in United States and Australia respectively. This is mostly evaluated and analyzed using the body mass index approximating 30 grams per meter square or higher. The disadvantages of obesity have been outlined in this paper. The scope of this paper discusses beyond the use of body mass index to evaluate obesity to the real measurements of body fat amounts. Although, techniques like underwater weighing, dual energy x ray absorption and biometrical impedance evaluation only measure the total mass of fat in the body are very expensive. These methods are also very time-consuming to apply in a large set of population (lam, et al, 2015).

In regards to this, the anthropometric technique which recognizes high risk adiposity correctly is still under debate. Several other alternatives have been used to rectify the assessment of high risk adiposity hence provide an effective measurements. Some of these techniques include the, higher waist circumference, waist hip ratio, and the waist height ratio. High waist circumference has been used over the years as the best predictor of metabolic diseases compared to body mass index. This because waist circumference easily identifies health risk through evaluation of total body fat and the central adiposity

Recent researches and analysis of data have revealed out that, 30 to 60 percent of the population categorized as possessing high risk waist circumference has a body mass index which is below the obese level. Studies have also shown out that a substantial portion of the population who have large waist circumference do not possess obese body mass index. This research is aimed at find out whether those without obese mass index but with large waist circumference boosts the analysis of adiposity associated metabolic outputs. (Rodríguez,  et al, 2018).

Methods

Measuring waist circumference and body mass index

Height dimensions were captured to the nearest half centimeter, shoeless using a stadiometer. Body weights were taken without shoes and excessive cloth ware to nearest 0.1 kilogram using a beam balance. The body mass index was computed as weight (kg) divided by height (m) ². This Data was classified as (a) none obese less than 30 kg/m2 and ( b) obese greater than or equal to 30kg/m2.waist circumferences were taken at the midway points between the iliac crests and the costal margin, then the mean of the two was computed. Waist circumference was classified as following:(a)non-obese less than 100 cm for men ,less than 88cm for females and obese if greater than 100cm for males and greater than 88cm for females. Adiposity divisions were formed using the integration of body mass index and waist circumference as follows; (a) BMI^N/WC^N; (b) BMI^N/WC^O; (c) BMI^O/WC^N; and (d) BMI^O/WC^O, (N = non-obese and O = obese.) ( lessi, et al,2017) Estimating metabolic outcomes

In all the regions blood pressure was estimated using a Dina map oscillate-metric blood pressure sensor.in Virginia blood pressure was estimated using a standard mercury sphygmomanometer and fine-tuned accurately. Blood samples were taken by venipuncture after the participants fasting overnight (Javed, et al, 2015). All the blood samples were super filtered to separate the plasma from the serum, and then transferred to the nearest laboratory as soon as possible. Obesity was defined on the basis of fasting plasma glucose if less or 7.0 mmol/l or two-hour plasma glucose ≥11.1 mmol/l, or current medication using insulin or oral hypo-glycaemic components. Dyslipidemia was identified as triglycerides >2.0 mmol/l or maximum density lipoprotein (HDL) cholesterol <1.0 mmol/l. Cardiovascular ailment status was self-reported and was identified as former angina, stroke, or heart artery ailments (ford, et al,2014)

Estimation of covariates

Covariate information and data was collected using interview conducted questionnaires. Educational level was classified as lower, which include high school level, middle (certificate, or diploma) and high including (bachelor’s degree or a post graduate diploma).physical exercise was reported in person and was classified as follows inactive if 0 hours, insufficient if less or equal to 150 hours, and sufficient if greater than 150 hours. Smoking status were classified as, (a) regular smoker, (b) non-smoker, and (c)  x-smokers (Nazale, et al ,2015).

Ten thousand, three hundred and seventy six participants were involved in this exercise .the activity took a period of 8 weeks. The data captured from this sub portion of the population included height, weight and the waist circumference. Adiposity categories were classified as BMI^N/WC^N, BMI^N/WC^O, BMI^O/WC^N, and BMI^O/WC^O (N = non-obese and O = obese). Population attributable fraction, area under the receiver operating characteristic curve (AUC), and odds ratios (OR) were calculated (Diet al, 2016).

.

Results

All the Members were approximately 50 years of Age and half of them were male. The proportions of BMI^N/WC^N, BMI^N/WC^O, BMI^O/WC^N and BMI^O/WC^O were 67, 11, 1 and 18 %, respectively. A lower proportion of diabetes was attributable to obesity defined using body mass index as compared to body mass index and waist circumference integrated (32 percent and 47 percent). Risks for diabetes were also lower when obesity was identified using BMI alone (0.62 vs. 0.66). Same observations were recorded for all outputs (kjaer, et al, 2015). The chances for hypertension, dyslipidemia, and diabetes were higher for those with BMI^N/WC^O

Evidence Table Worksheet

1. PICOT Question:

plus

1. Will you have a comparison group or will subjects be their own controls?
2. Is a ‘time’ appropriate with your question—why or why not?

II.   Evidence Synthesis

• Evaluation Table

References

Ford, E. S., Maynard, L. M., &  Li, C. (2014).  Trends in mean waist circumference and abdominal obesity among US adults,  1999-2012. Jama, 312(11), 1151-1153.

Rodríguez, N. V., Fernandez-Britto, J. E., Martinez, T. P., Martinez, R. G., Castañeda, C. G., Garriga, M. R., … & Blanco, F. A. (2018). Waist-height ratio in  children of 7 to 11 years with high weight at birth and its relationship with gender, age and diet. Clinica e investigacion en arteriosclerosis: publicacion oficial de la Sociedad Espanola de Arteriosclerosis.

Lee, S., Kuk, J. L., Boesch, C., & Arslanian, S. (2017). Waist circumference is associated with liver fat in black and white adolescents. Applied physiology,  nutrition, and metabolism, 42(8), 829-833.

Nazare, J. A., Smith, J., Borel, A. L., Aschner, P., Barter, P., Van Gaal, L., … & Ross, R. (2015). Usefulness of measuring both body mass index and waist circumference for the estimation of visceral adiposity and related cardiometabolic risk profile (from the INSPIRE ME IAA study). The American j ournal of cardiology, 115(3), 307-315.

Kim, D. W., Kim, J. Y., & Jeong, H. (2017). Comparison of Body Mass Index, Waist Circumference, and Waist-to-Height Ratio as a Predictors of Abdominal Fat Distribution in Male Examinees from the Health Promotion Center. Korean Journal of Family Practice, 7(4), 596-599.

Lam, B. C. C., Koh, G. C. H., Chen, C., Wong, M. T. K., & Fallows, S. J. (2015). Comparison of body mass index (BMI), body adiposity index (BAI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) as predictors of cardiovascular disease risk factors in an adult population in Singapore. PLoS One, 10(4), e0122985.

Kjær, I. G. H., Kolle, E., Hansen,  B. H., Anderssen, S. A., & Torstveit, M. K. (2015). Obesity prevalence in N orwegian adults assessed by body mass index, waist circumference and fat mass percentage. Clinical obesity, 5(4), 211-218.

Javed, A., Jumean, M., Murad, M. H., Okorodudu, D., Kumar, S., Somers, V. K., … & Lopez‐Jimenez, F. (2015). Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta‐analysis. Pediatric obesity, 10(3), 234-244.

Di Angelantonio, E., Bhupathiraju, S. N., Wormser, D., Gao, P., Kaptoge, S., de Gonzalez, A. B., … & Lewington, S. (2016). Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. The Lancet, 388(10046), 776-786.