Anthropometric indices as predictors of 10-year cardiovascular risk among Sub-Sahara Africans with type 2 diabetes
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Abstract
Background: Body mass index, waist circumference, waist-hip ratio and waist-height ratio are simple clinical tools for determining obesity. Type 2 diabetes mellitus is often associated with multiple cardiovascular risk factors and increased cardiovascular death. The study was aimed at determining the relationship between these anthropometric indices and 10-year cardiovascular risk among sub-saharan Africans with type 2 diabetes mellitus.
Methods: It was a cross-sectional study involving 67 adults (with 50.7% females) managed for type 2 diabetes mellitus in a referral hospital in Nigeria. Ethical approval was obtained at the institution review board and the participants also gave written consent. Anthropometric indices were determined using standard protocols. Fasting lipid profile, fasting plasma glucose, glycated haemoglobin and plasma creatinine were assayed using standard laboratory techniques. Atherogenic index of plasma, estimated glomerular filtration rate and the WHO-ISH cardiovascular risk score were also determined. Data was analyzed with SPSS version 22. Pearson correlation coefficient, Students’ t test, Chi square test, ROC curve analysis were performed as appropriate.
Results: The mean age was 54.12±9.03 years. Obesity was found in 37.3%, 66.5%, 70.1% and 95.5% of the participants using BMI, WHR, WC and WHtR respectively. Intermediate/high cardiovascular risk was found in 38.2% and 24.2% of the males and females respectively. BMI and WC significantly correlated with blood pressure. There was no significant correlation between anthropometric indices and other cardiovascular risk factors studied. Using ROC curve analysis, BMI and WHtR had the highest AUC of 0.613 and 0.577 respectively.
Conclusion: Among sub-sahara Africans with type 2 diabetes mellitus, there is a significant association between WC and BMI with the blood pressure. BMI and WHtR have the highest 10-year cardiovascular risk predictability among the anthropometric indices in this cohort of individuals. Larger and prospective studies are needed to validate these findings.
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Woldegebriel AG, Fenta KA, Aregay AB, Aregay AD, Mamo NB, et al. (2020) Effectiveness of Anthropometric Measurements for Identifying Diabetes and Prediabetes among Civil Servants in a Regional City of Northern Ethiopia: A Cross-Sectional Study. J Nutr Metab 2020: e8425912. Link: https://bit.ly/3oqfqyI
Jensen MD (2008) Role of Body Fat Distribution and the Metabolic Complications of Obesity. J Clin Endocrinol Metab 93: S57–S63. Link: https://bit.ly/3H9KX0B
Haregu TN, Oti S, Egondi T, Kyobutungi C (2016) Measurement of overweight and obesity an urban slum setting in sub-Saharan Africa: a comparison of four anthropometric indices. BMC Obes 3: 46. Link: https://bit.ly/3FbupmS
Ettarh R, Van de Vijver S, Oti S, Kyobutungi C (2013) Overweight, Obesity, and Perception of Body Image Among Slum Residents in Nairobi, Kenya, 2008–2009. Prev Chronic Dis [Internet] 10. Link: https://bit.ly/3kxcsax
Yuyun MF, Sliwa K, Kengne AP, Mocumbi AO, Bukhman G (2020) Cardiovascular Diseases in Sub-Saharan Africa Compared to High-Income Countries: An Epidemiological Perspective. Global Heart 15: 15. Link: https://bit.ly/3ogdZ60
Nuttall FQ (2015) Body Mass Index. Nutr Today 50: 117–28. Link: https://bit.ly/31ULwLA
Mohammadifard N, Nazem M, Sarrafzadegan N, Nouri F, Sajjadi F, et al. (2013) Body Mass Index, Waist-circumference and Cardiovascular Disease Risk Factors in Iranian Adults: Isfahan Healthy Heart Program. J Health Popul Nutr. 31: 388–397. Link: https://bit.ly/3Ce4k4F
Tran NTT, Blizzard CL, Luong KN, Truong NLV, Tran BQ, et al. (2018) The importance of waist circumference and body mass index in cross-sectional relationships with risk of cardiovascular disease in Vietnam. PLOS ONE 13: e0198202. Link: https://bit.ly/3Cad5N6
Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J (2010) Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk—a review of the literature. Eur J Clin Nutr 64: 16–22. Link: https://bit.ly/30fcSLF
Asia Pacific Cohort Studies Collaboration (2004) Body mass index and cardiovascular disease in the Asia-Pacific Region: an overview of 33 cohorts involving 310 000 participants. International Journal of Epidemiology 33: 751–758.
Ashwell M, Hsieh SD (2005) Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr. 56: 303–307. Link: https://bit.ly/3kxegQR
Welborn TA, Dhaliwal SS (2007) Preferred clinical measures of central obesity for predicting mortality. Eur J Clin Nutr. 61: 1373–1379. Link: https://bit.ly/3oql7fY
Klein S, Allison DB, Heymsfield SB, Kelley DE, Leibel RL, et al. (2007) Waist Circumference and Cardiometabolic Risk: A Consensus Statement from Shaping America’s Health: Association for Weight Management and Obesity Prevention; NAASO, The Obesity Society; the American Society for Nutrition; and the American Diabetes Association. Am J Clin Nutr 85: 1197-1202. Link: https://bit.ly/3c33a1o
Chan DC, Watts GF, Barrett PHR, 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. Link: https://bit.ly/3ClCRyp
Janssen I, Heymsfield SB, Allison DB, Kotler DP, Ross R (2002) Body mass index and waist circumference independently contribute to the prediction of nonabdominal, abdominal subcutaneous, and visceral fat. Am J Clin Nutr Apr 75: 683–688. Link: https://bit.ly/2YF54SV
Siren R, Eriksson JG, Vanhanen H (2012) Waist circumference a good indicator of future risk for type 2 diabetes and cardiovascular disease. BMC Public Health 12: 631. Link: https://bit.ly/3olPF2K
Ross R, Neeland IJ, Yamashita S, Shai I, Seidell J, et al. (2020) Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol 16: 177–189. Link: https://bit.ly/3qwuVrF
Adegbija O, Hoy WE, Dong B, Wang Z (2017) Body mass index and waist circumference as predictors of all-cause mortality in an Aboriginal Australian community. Obes Res Clin Pract 11: 19–26. Link: https://bit.ly/3c7fc9W
Cao Q, Yu S, Xiong W, Li Y, Li H, et al. (2018) Waist-hip ratio as a predictor of myocardial infarction risk: A systematic review and meta-analysis. Medicine 97: e11639. Link: https://bit.ly/3D58wVP
Yusuf S, Hawken S, Ôunpuu S, Bautista L, Franzosi MG, et al. (2005) Obesity and the risk of myocardial infarction in 27 000 participants from 52 countries: a case-control study. Lancet 366: 1640–1649. Link: https://bit.ly/3ky2VQz
Czernichow S, Kengne A-P, Huxley RR, Batty GD, de Galan B, et al. (2011) Comparison of waist-to-hip ratio and other obesity indices as predictors of cardiovascular disease risk in people with type 2 diabetes: a prospective cohort study from ADVANCE. Eur J Cardiovasc Prev Rehabil 18: 312–319. Link: https://bit.ly/2YF65ud
Son YJ, Kim J, Park H-J, Park SE, Park C-Y, et al. (2016) Association of Waist-Height Ratio with Diabetes Risk: A 4-Year Longitudinal Retrospective Study. Endocrinol Metab (Seoul) 31: 127–133. Link: https://bit.ly/3F7G1r8
Browning LM, Hsieh SD, Ashwell M (2010) A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0·5 could be a suitable global boundary value. Nutr Res Rev 23: 247–269. Link: https://bit.ly/2YHOOAA
Peng Y, Li W, Wang Y, Bo J, Chen H (2015) The Cut-Off Point and Boundary Values of Waist-to-Height Ratio as an Indicator for Cardiovascular Risk Factors in Chinese Adults from the PURE Study. PLOS ONE 10: e0144539. Link: https://bit.ly/3H9Pz6V
Mc Namara K, Alzubaidi H, Jackson JK (2019) Cardiovascular disease as a leading cause of death: how are pharmacists getting involved? Integr Pharm Res Pract 8:1–11. Link: https://bit.ly/30c9y3M
Hamid S, Groot W, Pavlova M (2019) Trends in cardiovascular diseases and associated risks in sub-Saharan Africa: a review of the evidence for Ghana, Nigeria, South Africa, Sudan and Tanzania. Aging Male 22: 169–176. Link: https://bit.ly/3oqkP8Y
Petrie JR, Guzik TJ, Touyz RM (2018) Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms. Can J Cardiol 34: 575–584. Link: https://bit.ly/3HfvAnq
Regufe VMG, Pinto CMCB, Perez PMVHC (2020) Metabolic syndrome in type 2 diabetic patients: a review of current evidence. Porto Biomed J 5: e101. Link: https://bit.ly/3omEUgt
Azeez TA (2021) Association between lipid indices and 10-year cardiovascular risk of a cohort of black Africans living with type 2 diabetes mellitus. Journal of Advances in Internal Medicine 10: 38–42. Link: https://bit.ly/3HgF819
Sibilitz KL, Benn M, Nordestgaard BG (2014) Creatinine, eGFR and association with myocardial infarction, ischemic heart disease and early death in the general population. Atherosclerosis 237: 67–75. Link: https://bit.ly/30aoW0x
Babatunde OA, Olarewaju SO, Adeomi AA, Akande JO, Bashorun A, et al. (2020) 10-year risk for cardiovascular diseases using WHO prediction chart: findings from the civil servants in South-western Nigeria. BMC Cardiovasc Disord 20: 154. Link: https://bit.ly/3n7V0Ly
Pedro JM, Brito M, Barros H (2018) Cardiovascular Risk Assessment in Angolan Adults: A Descriptive Analysis from CardioBengo, a Community-Based Survey. Int J Hypertens e2532345. Link: https://bit.ly/3H9Rkkx
Adje DU, Oparah AC, Megbolu T (2020) Cardiovascular risk assessment in Community Pharmacies in Warri, Nigeria. Journal of Pharmacy & Bioresources 17: 105–111. Link: https://bit.ly/3oh5rMa
Edward AO, Oladayo AA, Omolola AS, Adetiloye AA, Adedayo PA (2013) Prevalence of traditional cardiovascular risk factors and evaluation of cardiovascular risk using three risk equations in Nigerians living with human immunodeficiency virus. N Am J Med Sci 5: 680-688. Link: https://bit.ly/3ooYFnK
Anurhada R, Hemacnandran S, Ruma D (2012) The Waist Circumference Measurement: A Simple Method for Assessing the Abdominal Obesity. J Clin Diagn Res 6: 1510–1513. Link: https://bit.ly/3qLbBar
Gierach M, Gierach J, Ewertowska M, Arndt A, Junik R (2014) Correlation between Body Mass Index and Waist Circumference in Patients with Metabolic Syndrome. ISRN Endocrinol. Link: https://bit.ly/3wD7s9k
World Health Organization (2011) Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8-11 December 2008. Geneva: World Health Organization. Link: https://bit.ly/3oqnpfa
Stergiou GS, Palatini P, Parati G, O’Brien E, Januszewicz A, et al. (2021) European Society of Hypertension practice guidelines for office and out-of-office blood pressure measurement. J Hypertens 39: 1293-1302. Link: https://bit.ly/3Fb3wiY
Kumar BV, Mohan T (2017) Retrospective Comparison of Estimated GFR using 2006 MDRD, 2009 CKD-EPI and Cockcroft-Gault with 24 Hour Urine Creatinine Clearance. J Clin Diagn Res 11: BC09-BC12. Link: https://bit.ly/3F7THT1
WHO. Cardiovascular diseases [Internet]. [cited 2021 May 7].
Olokoba AB, Obateru OA, Olokoba LB (2012) Type 2 Diabetes Mellitus: A Review of Current Trends. Oman Med J 27: 269–273. Link: https://bit.ly/3or6lWF
Balogun WO, Onasanya AS, Azeez TA (2020) Prevalence and clinical characteristics of Nigerian patients with early-onset Type 2 diabetes. Nigerian Journal of Medicine 29: 49–54. Link: https://bit.ly/3CalJv2
Association AD (2021) 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2021. Diabetes Care 44: S15–S33. Link: https://bit.ly/3kvk7X7
Zwar N, Hasan I, Hermiz O, Vagholkar S, Comino E, et al. (2008) Multidisciplinary care plans and diabetes--benefits for patients with poor glycaemic control. Aust Fam Physician 37: 960–962. Link: https://bit.ly/3C4N6qp
Tourkmani AM, Abdelhay O, Alkhashan HI, Alaboud AF, Bakhit A, et al. (2018) Impact of an integrated care program on glycemic control and cardiovascular risk factors in patients with type 2 diabetes in Saudi Arabia: an interventional parallel-group controlled study. BMC Fam Pract. Link: https://bit.ly/3C7lqkP
Tauqeer Z, Gomez G, Stanford FC (2018) Obesity in Women: Insights for the Clinician. J Womens Health (Larchmt) 27: 444–457. Link: https://bit.ly/3FfDNpT
Garawi F, Devries K, Thorogood N, Uauy R (2014) Global differences between women and men in the prevalence of obesity: is there an association with gender inequality? Eur J Clin Nutr 68: 1101–1106. Link: https://bit.ly/30h2bsn
Karastergiou K, Smith SR, Greenberg AS, Fried SK (2012) Sex differences in human adipose tissues – the biology of pear shape. Biol Sex Differ 3: 13. Link: https://bit.ly/3om53w3
Jackson AS, Stanforth PR, Gagnon J, Rankinen T, Leon AS, et al. (2002) The effect of sex, age and race on estimating percentage body fat from body mass index: The Heritage Family Study. Int J Obes Relat Metab Disord 26: 789–796. Link: https://bit.ly/3Da7cBd
Kim K-B, Shin Y-A (2020) Males with Obesity and Overweight. J Obes Metab Syndr 29: 18–25. Link: https://bit.ly/3qBIxC9
Alshamiri MQ, Mohd A Habbab F, AL-Qahtani SS, Alghalayini KA, Al-Qattan OM, et al. (2020) Waist-to-Height Ratio (WHtR) in Predicting Coronary Artery Disease Compared to Body Mass Index and Waist Circumference in a Single Center from Saudi Arabia. Cardiol Res Pract. Link: https://bit.ly/3wGtlUY
Mabchour AE, Delisle H, Vilgrain C, Larco P, Sodjinou R,et al. (2015) Specific cut-off points for waist circumference and waist-to-height ratio as predictors of cardiometabolic risk in Black subjects: a cross-sectional study in Benin and Haiti. Diabetes Metab Syndr Obes 8: 513–523. Link: https://bit.ly/3om7bUz
Matsha TE, Kengne A-P, Yako YY, Hon GM, Hassan MS, et al. (2013) Optimal Waist-to-Height Ratio Values for Cardiometabolic Risk Screening in an Ethnically Diverse Sample of South African Urban and Rural School Boys and Girls. PLOS ONE 8: e71133. Link: https://bit.ly/3quRjBG
Wakabayashi I (2017) Gender differences in cardiovascular risk factors in patients with coronary artery disease and those with type 2 diabetes. J Thorac Dis 9: E503–E506. Link: https://bit.ly/3okgZyo
Bots SH, Peters SAE, Woodward M (2017) Sex differences in coronary heart disease and stroke mortality: a global assessment of the effect of ageing between 1980 and 2010. BMJ Global Health 2: e000298. Link: https://bit.ly/3n8o99v
Shuger SL, Sui X, Church TS, Meriwether RA, Blair SN (2008) Body mass index as a predictor of hypertension incidence among initially healthy normotensive women. Am J Hypertens 21: 613–619. Link: https://bit.ly/3DcpDVB
Gelber RP, Gaziano JM, Manson JE, Buring JE, Sesso HD (2007) A prospective study of body mass index and the risk of developing hypertension in men. Am J Hypertens 20: 370–377. Link: https://bit.ly/3HipJ0D
Drøyvold WB, Midthjell K, Nilsen TIL, Holmen J (2005) Change in body mass index and its impact on blood pressure: a prospective population study. Int J Obes (Lond) 29: 650–655. Link: https://bit.ly/3Caq1Ta
Harsha DW, Bray GA (2008) Weight Loss and Blood Pressure Control (Pro). Hypertension 51: 1420–1425. Link: https://bit.ly/3c5q6x3
Andeansah M, Afiatin M, Sukandar H (2015) Correlation between Waist Circumference and Hypertension in Jatinangor. Journal of Hypertension 33: e14. Link: https://bit.ly/3Ce0bxM
Okafor CI, Raimi TH, Gezawa ID, Sabir AA, Enang O, et al. (2016) Performance of waist circumference and proposed cutoff levels for defining overweight and obesity in Nigerians. Ann Afr Med 15: 185–193. Link: https://bit.ly/3wMef0n