Original Article

Anthropometric Indices as Predictors of Coronary Heart Disease Risk: Joint Modeling of Longitudinal Measurements and Time to Event

Abstract

Background: The prevalence of overweight and obesity have increased dramatically worldwide and together they constitute a major risk factor for coronary heart disease (CHD). The aim of this study was to assess the repeated measurements of body mass index (BMI), waist circumference (WC), waist to hip ratio (WHR) and waist to height ratio (WHtR) in predicting CHD incidence.

Methods: This longitudinal study was conducted within the framework of the Tehran Lipid and Glucose Study between 1999–2011, on 1959 women and 1371 men participants’ ages ≥30 yr, without a history of CVD. A joint modeling approach was utilized for data analysis using R software. The resulting joint model allowed measuring α (quantifies the association between anthropometric indices up to time t and the hazard for CHD event at the same time point).

Results: About 9% of the participants (7.1% of the women and 11.7% of the men) experienced CHD event during follow-up. The results indicated a significant linear increasing trend in BMI, WC, WHR, and WHtR over time (P<0.001). The increased risk of CHD event in females increases with the values of BMI (α= 0.004, P=0.023), WC (α= 0.018, P=0.009), WHR (α= 0.067, P=0.014) and WHtR (α= 0.106, P=0.002). Furthermore, in males the risk of CHD risk increases by the values of BMI (α= 0.005, P=0.032), WC (α= 0.019, P=0.008), WHR (α= 0.043, P=0.015) and WHtR (α= 0.096, P=0.002).

Conclusion: By jointly modeling longitudinal data with time-to-event outcomes, our study revealed that WHtR is superior to other indices in predicting CHD incidence.

 

 

Benedetto C, Salvagno F, Canuto EM, Gennarelli G (2015). Obesity and Female Malignancies. Best Pract Res Clin Obstet Gynaecol, 29:528-540.

Lavie CJ, McAuley PA, Church TS, Milani RV, Blair SN (2014). Obesity and cardiovascular diseases: implications regarding fitness, fatness, and severity in the obesity paradox. J Am Coll Cardiol, 63:1345-1354.

Bastien M, Poirier P, Lemieux I, Després JP (2014). Overview of epidemiology and contribution of obesity to cardiovascular disease. Prog Cardiovasc Dis, 56:369-381.

Mann DL, Zipes DP, Libby P, Bonow RO (2014). Braunwald's heart disease: a textbook of cardiovascular medicine. ed. Elsevier Health Sciences.

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.

Luksiene D, Tamosiunas A, Virviciute D, Bernotiene G, Peasey A (2015). Anthropometric trends and the risk of cardiovascular disease mortality in a Lithuanian urban population aged 45–64 years. Scand J Public Health, 43(8): 882–889.

Jensen MD, Ryan DH, Apovian CM et al (2014). 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation, 24;129(25 Suppl 2):S102-38.

De Schutter A, Lavie CJ, Milani RV (2014). The impact of obesity on risk factors and prevalence and prognosis of coronary heart disease-the obesity paradox. Prog Cardiovasc Dis, 56:401-408.

Mbanya V, Kengne A, Mbanya J, Akhtar H (2015). Body mass index, waist circumference, Hip circumference, waist-hip-ratio and waist-height-ratio: which is the better discriminator of prevalent screen-detected diabetes in a Cameroonian population? Diabetes Res Clin Pract,108:23-30.

Collaboration ERF , Wormser D, Kaptoge S et al (2011). Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. Lancet, 377:1085-1095.

Cragg J, Ravensbergen HR, Borisoff J, Claydon V (2015). Optimal scaling of weight and waist circumference to height for adiposity and cardiovascular disease risk in individuals with spinal cord injury. Spinal Cord, 53:64-68.

Ravensbergen HJC, Lear SA, Claydon VE (2014). Waist circumference is the best index for obesity-related cardiovascular disease risk in individuals with spinal cord injury. J Neurotrauma, 31:292-300.

Jackson A, Stanforth P, Gagnon J, Rankinen T, Leon A, Rao D, Skinner J, Bouchard C, Wilmore J (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.

Hadaegh F, Zabetian A, Sarbakhsh P, Khalili D, James W, Azizi F (2009). Appropriate cutoff values of anthropometric variables to predict cardiovascular outcomes: 7.6 years follow-up in an Iranian population. Int J Obes (Lond), 33:1437-1445.

Mirmiran P, Esmaillzadeh A, Azizi F (2004). Detection of cardiovascular risk factors by anthropometric measures in Tehranian adults: receiver operating characteristic (ROC) curve analysis. Eur J Clin Nutr, 58:1110-1118.

Esmaillzadeh A, Mirmiran P, Azizi F (2006). Comparative evaluation of anthropometric measures to predict cardiovascular risk factors in Tehranian adult women. Public Health Nutr, 9:61-69.

Rizopoulos D (2012). Joint models for longitudinal and time-to-event data: With applications in R. ed. CRC Press.

Khalili D, Sheikholeslami FH, Bakhtiyari M, Azizi F, Momenan AA, Hadaegh F (2014). The Incidence of Coronary Heart Disease and the Population Attributable Fraction of Its Risk Factors in Tehran: A 10-Year Population-Based Cohort Study. PLoS One, 9:e105804.

Saki Malehi A, Hajizadeh E, Ahmadi KA, Mansouri P (2015). Joint modelling of longitudinal biomarker and gap time between recurrent events: copula-based dependence. Journal of Applied Statistics, 42:1931-1945.

Rizopoulos D (2010). JM: An R package for the joint modelling of longitudinal and time-to-event data. Journal of Statistical Software, 35:1-33.

Team RC (2015). R: A Language and Environment for Statistical Computing (2014) Available at: http://www. r-project. org. Accessed February, 16.

Crowther MJ (2015) Development and application of methodology for the parametric analysis of complex survival and joint longitudinal-survival data in biomedical research. Diss, Department of Health Sciences.

Ekinci EI, Moran JL, Thomas MC, Cheong K, Clarke S, Chen A, Dobson M, Leong A, MacIsaac RJ, Jerums G (2014). Relationship Between Urinary Sodium Excretion Over Time and Mortality in Type 2 Diabetes. Diabetes Care, 37:e62-e63.

Njagi EN, Rizopoulos D, Molenberghs G, Dendale P, Willekens K (2013). A joint survival-longitudinal modelling approach for the dynamic prediction of rehospitalization in telemonitored chronic heart failure patients. Stat Modelling, 13:179-198.

Gracitelli CP, Abe RY, Tatham AJ, Rosen PN, Zangwill LM, Boer ER, Weinreb RN, Medeiros FA (2015). Association Between Progressive Retinal Nerve Fiber Layer Loss and Longitudinal Change in Quality of Life in Glaucoma. JAMA Ophthalmol, 133:384-390.

Andrinopoulou E-R, Rizopoulos D, Geleijnse ML, Lesaffre E, Bogers AJ, Takkenberg JJ (2015). Dynamic prediction of outcome for patients with severe aortic stenosis: application of joint models for longitudinal and time-to-event data. BMC Cardiovasc Disord, 15:28.

Barrett J, Diggle P, Henderson R, Taylor‐Robinson D (2015). Joint modelling of repeated measurements and time‐to‐event outcomes: flexible model specification and exact likelihood inference. J R Stat Soc Series B Stat Methodol, 77:131-148.

Ashwell M, Gunn P, Gibson S (2012). Waist‐to‐height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta‐analysis. Obes Rev, 13:275-286.

Song X, Jousilahti P, Stehouwer C et al (2013). Comparison of various surrogate obesity indicators as predictors of cardiovascular mortality in four European populations. Eur J Clin Nutr, 67:1298-1302.

Gelber RP, Gaziano JM, Orav EJ, Manson JE, Buring JE, Kurth T (2008). Measures of obesity and cardiovascular risk among men and women. J Am Coll Cardiol, 52:605-615.

Rausch JC, Perito ER, Hametz P (2011). Obesity prevention, screening, and treatment: practices of pediatric providers since the 2007 expert committee recommendations. Clin Pediatr (Phila), 50:434-441.

Zalesin KC, Franklin BA, Miller WM, Peterson ED, McCullough PA (2008). Impact of obesity on cardiovascular disease. Endocrinol Metab Clin North Am, 37(3):663-84.

Bener A, Yousafzai MT, Darwish S, Al-Hamaq AO, Nasralla EA, Abdul-Ghani M (2013). Obesity index that better predict metabolic syndrome: body mass index, waist circumference, waist hip ratio, or waist height ratio. J Obes, 2013;2013:269038.

Tchernof A, Després JP (2013). Pathophysiology of human visceral obesity: an update. Physiol Rev, 93:359-404.

Savva S, Tornaritis M, Savva M, Kourides Y, Panagi A, Silikiotou N, Georgiou C, Kafatos A (2000). Waist circumference and waist-to-height ratio are better predictors of cardiovascular disease risk factors in children than body mass index. Int J Obes Relat Metab Disord, 24:1453-1458.

Brambilla P, Bedogni G, Heo M, Pietrobelli A (2013). Waist circumference-to-height ratio predicts adiposity better than body mass index in children and adolescents. Int J Obes (Lond), 37:943-946.

Lu Y, Hajifathalian K, Ezzati M, Woodward M, Rimm EB, Danaei G (2014). Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1· 8 million participants. Lancet, 383:970-983.

Wildman RP, Gu D, Reynolds K, Duan X, Wu X, He J (2005). Are waist circumference and body mass index independently associated with cardiovascular disease risk in Chinese adults? Am J Clin Nutr, 82:1195-1202.

Files
IssueVol 46 No 11 (2017) QRcode
SectionOriginal Article(s)
Keywords
Coronary heart disease Body mass index Waist circumference Waist hip ratio Height Joint model

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
1.
GILANI N, KAZEMNEJAD A, ZAYERI F, HADAEGH F, AZIZI F, KHALILI D. Anthropometric Indices as Predictors of Coronary Heart Disease Risk: Joint Modeling of Longitudinal Measurements and Time to Event. Iran J Public Health. 2017;46(11):1546-1554.