Body Mass Index versus Other Adiposity Traits: Best Predictor of Cardiometabolic Risk

  • Muhammad SAQLAIN Department of Biochemistry, PMAS–Arid Agriculture University, Rawalpindi, Pakistan
  • Zainab AKHTAR Department of Biochemistry, PMAS–Arid Agriculture University, Rawalpindi, Pakistan
  • Raheela KARAMAT Department of Biochemistry, PMAS–Arid Agriculture University, Rawalpindi, Pakistan
  • Samra MUNAWAR Department of Biochemistry, PMAS–Arid Agriculture University, Rawalpindi, Pakistan
  • Maria IQBAL Department of Biochemistry, PMAS–Arid Agriculture University, Rawalpindi, Pakistan
  • Muhammad FIAZ Department of Pathology, Shaheed Zulfiqar Ali Bhutto Medical University, Islamabad, Pakistan
  • Muhammad Mubeen ZAFAR Department of Biochemistry, PMAS–Arid Agriculture University, Rawalpindi, Pakistan
  • Sadia SAEED Department of Biochemistry, PMAS–Arid Agriculture University, Rawalpindi, Pakistan
  • Muhammad Farooq NASIR Department of Entomology, PMAS–Arid Agriculture University, Rawalpindi, Pakistan
  • S.M. Saqlan NAQVI Department of Biochemistry, PMAS–Arid Agriculture University, Rawalpindi, Pakistan
  • Ghazala Kaukab RAJA Department of Biochemistry, PMAS–Arid Agriculture University, Rawalpindi, Pakistan
Obesity, Body mass index, Waist circumference, Pakistan


Background: A number of anthropometric indices have been used in different world populations as markers to estimate obesity and its related health risks. The present study is large population based study dealing with five anthropometric obesity scales; Body mass index (BMI), waist circumference (WC), waist to hip ratio (WHR), basal adiposity index (BAI), and Visceral adiposity index (VAI) to identify common adiposity trait(s) that best predict obesity and associated health complication(s).

Methods: A total of 4000 subjects including 1000 in each category of BMI from four provinces (Punjab, Sindh, Kahyber pakhtoonkha and Balochistan) of Pakistan from 2012-2017 were collected. Complete anthropometric measurementswere obtained and blood samples were collected and Biochemical profiling was performed. Descriptive statistics, linear regression, binary and multiple regression analysis was done.

Results: Our data analysis explored the relationships of obesity five indices; BMI, WC, WHR, BAI, and VAI with common metabolic health complications. Effect size analysis clearly indicates that a unit increase in BMI significant raised all anthropometric and clinical parameters. General and sex specific association analysis of adiposity traits with risk phenotypes (hypertension, hyperglycemia and dyslipidemia) indicated significant associations of WC with all three metabolic risks. Varying degrees of correlations of other adiposity traits with metabolic risks were observed. Frequency of different obesity classes among obese population group were as follows; 55.7% class I, 28.50% Class II and 15.80% Class III.

Conclusion: WC is the strong predictor of obesity associated metabolic health issues in Pakistani populations. While BMI has significant increasing effect on other obesity indices like WHR, VAI and BAI.


1. O'Rahilly S, Farooqi IS (2006). Genetics of obesity. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 361:1095-1105.
2. Yazdi FT, Clee SM, Meyre D (2015). Obesity genetics in mouse and human: back and forth, and back again. PeerJ, 3:e856.
3. Brown R, Kuk J (2015). Consequences of obesity and weight loss: a devil's advocate position. Obes Rev, 16:77-87.
4. Lee SY, Gallagher D (2008). Assessment methods in human body composition. Curr Opin Clin Nutr Metab Care, 11:566-72.
5. Liu Y, Tong G, Tong W, Lu L, Qin X (2011). Can body mass index, waist circumference, waist-hip ratio and waist-height ratio predict the presence of multiple metabolic risk factors in Chinese subjects? BMC Public Health, 11:35.
6. Locke AE, Kahali B, Berndt SI et al (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518:197-206.
7. Ashwell M, Gibson S (2016). Waist-to-height ratio as an indicator of ‘early health risk’: simpler and more predictive than using a ‘matrix’based on BMI and waist circumference. BMJ open, 6:e010159.
8. Sardinha LB, Santos DA, Silva AM et al (2016). A Comparison between BMI, Waist Circumference, and Waist-To-Height Ratio for Identifying Cardio-Metabolic Risk in Children and Adolescents. PloS one, 11:e0149351.
9. Katzmarzyk PT, Bray GA, Greenway FL et al (2011). Ethnic‐Specific BMI and Waist Circumference Thresholds. Obesity, 19:1272-1278.
10. Vogel P, Stein A, Marcadenti A (2016). Visceral adiposity index and prognosis among patients with ischemic heart failure. Sao Paulo Med J, 34(3):211-8.
11. Munusamy V, George M, Jena A et al (2015). Comparison of Visceral Adiposity Index with Other Indices of Adiposity in Patients with Acute Myocardial Infarction. Pharmacol, Toxicol and Biomed Reports, 1.
12. Lichtash CT, Cui J, Guo X et al (2013). Body adiposity index versus body mass index and other anthropometric traits as correlates of cardiometabolic risk factors. PLoS One, 8(6):e65954.
13. Millar SR, Perry IJ, Phillips CM (2015). Assessing cardiometabolic risk in middle-aged adults using body mass index and waist–height ratio: are two indices better than one? A cross-sectional study. Diabetol metab syndr, 7:73.
14. Abulmeaty MM, Almajwal AM, Almadani NK et al (2017). Anthropometric and central obesity indices as predictors of long-term cardiometabolic risk among Saudi young and middle-aged men and women. Saudi Med J, 38:372-380.
15. Zhang Z-q, Deng J, He L-p et al (2013). Comparison of various anthropometric and body fat indices in identifying cardiometabolic disturbances in Chinese men and women. PloS one, 8(8):e70893.
16. Warraich HJ, Javed F, Faraz-ul-Haq M, Khawaja FB, Saleem S (2009). Prevalence of obesity in school-going children of Karachi. PLoS One, 4(3):e4816.
17. Runge CF (2007). Economic consequences of the obese. Diabetes, 56(11):2668-2672.
18. Narkiewicz K (2006). Obesity and hypertension—the issue is more complex than we thought. Nephrol dia transpl, 21:264-267.
19. Bastien M, Poirier P, Lemieux I, Després J-P (2014). Overview of epidemiology and contribution of obesity to cardiovascular disease. Prog cardiovas dis, 56:369-381.
20. Raja G, Sarzynski M, Katzmarzyk P et al (2014). Commonality versus specificity among adiposity traits in normal-weight and moderately overweight adults. Int j obesity (Lond), 38:719-23.
21. Stępień A, Stępień M, Wlazeł RN et al (2014). Assessment of the relationship between lipid parameters and obesity indices in non-diabetic obese patients: a preliminary report. Med Sci Monitor, 20:2683-2688.
22. Wang J, Zhu Y, Jing J et al (2015). Relationship of BMI to the incidence of hypertension: a 4 years’ cohort study among children in Guangzhou, 2007–2011. BMC Public Health, 15:782.
23. Flores-Huerta S, Klünder-Klünder M, de la Cruz LR, Santos JI (2009). Increase in body mass index and waist circumference is associated with high blood pressure in children and adolescents in Mexico City. Arch Med Res, 40:208-215.
24. Barreira TV, Harrington DM, Staiano AE et al (2011). Body adiposity index, body mass index, and body fat in white and black adults. Jama, 306:828-830.
25. Jafar T, Levey A, White F et al (2004). Ethnic differences and determinants of diabetes and central obesity among South Asians of Pakistan. Diabet Med, 21:716-723.
26. Fiaz M, Rani F, Saqlain M et al (2016). Identification of Population Specific Risk Phenotypes Contributing Towards Development of Metabolic Syndrome. Pakistan J Zool, 48:949-955.
27. WHO EC (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet, 363:157-63.
28. Amato M, Giordano C (2013). Clinical indications and proper use of Visceral Adiposity Index. Nutrition, Metabol Card Dis, 23:e31-e32.
29. Yang M, Xu Y, Liang L et al (2014). The effects of genetic variation in FTO rs9939609 on obesity and dietary preferences in Chinese Han children and adolescents. PloS one, 9(8):e104574.
30. Al‐Daghri NM, Al‐Attas OS, Alokail MS et al (2013). Visceral adiposity index is highly associated with adiponectin values and glycaemic disturbances. Eur J Clin Invest, 43:183-189.
31. Chan D, Watts G, Barrett P, Burke V (2003). Waist circumference, waist-to-hip ratio and body mass index as predictors of adipose tissue compartments in men. Qjm, 96:441-447.
32. Mousavi S, Mohebi R, Mozaffary A et al (2015). Changes in body mass index, waist and hip circumferences, waist to hip ratio and risk of all-cause mortality in men. European J Clin Nut, 69:927-932.
How to Cite
SAQLAIN M, AKHTAR Z, KARAMAT R, MUNAWAR S, IQBAL M, FIAZ M, ZAFAR MM, SAEED S, NASIR MF, NAQVI SS, RAJA GK. Body Mass Index versus Other Adiposity Traits: Best Predictor of Cardiometabolic Risk. Iran J Public Health. 48(12):2224-2231.
Original Article(s)