Skip to main content
Top
Published in: Journal of Translational Medicine 1/2021

Open Access 01-12-2021 | Research

Trajectories of cardiovascular disease risk and their association with the incidence of cardiovascular events over 18 years of follow-up: The Tehran Lipid and Glucose study

Authors: Fatemeh Koohi, Nooshin Ahmadi, Farzad Hadaegh, Siavash Safiee, Fereidoun Azizi, Davood Khalili

Published in: Journal of Translational Medicine | Issue 1/2021

Login to get access

Abstract

Background

Understanding long-term patterns (trajectories) of cardiovascular diseases (CVD) risk and identifying different sub-groups with the same underlying risk patterns could help facilitate targeted cardiovascular prevention programs.

Methods

A total of 3699 participants of the Tehran Lipid and Glucose Study (TLGS) (43% men, mean age = 53.2 years), free of CVD at baseline in 1999–2001 and attending at least one re-examination cycle between the second (2002–2005) and fourth cycles (2009–2011) were included. We examined trajectories of CVD risk, based on the ACC/AHA pooled cohort equation, over ten years and subsequent risks of incident CVD during eight years later. We estimated trajectories of CVD risk using group-based trajectory modeling. The prospective association of identified trajectories with CVD was examined using Cox proportional hazard model.

Results

Three distinct trajectories were identified (low-low, medium-medium, and high-high risk). The high-high and medium-medium CVD risk trajectories had an increasing trend of risk during the time; still, this rising trend was disappeared after removing the effect of increasing age. Upon a median 8.4 years follow-up, 146 CVD events occurred. After adjusting for age, the medium-medium and high-high trajectories had a 2.4-fold (95% CI 1.46–3.97) and 3.46-fold (95% CI 1.56–7.70) risk of CVD compared with the low-low group, respectively. In all trajectory groups, unfavorable increasing in fasting glucose, but favorable raising in HDL and decreasing smoking and total cholesterol happened over time.

Conclusions

Although the risk trajectories were stable during the time, different risk factors varied differently in each trajectory. These findings emphasize the importance of attention to each risk factor separately and implementing preventive strategies that optimize CVD risk factors besides the CVD risk.
Appendix
Available only for authorised users
Literature
2.
go back to reference Lallukka T, Millear A, Pain A, Cortinovis M, Giussani G. GBD 2015 mortality and causes of death collaborators. Global, regional, and national life expectancy, all-cause mortality, and cause-specifi c mortality for 249 causes of death, 1980–2015: a systematic analysis for the global burden of disease study 2015 (vol 388, pg 1459, 2016). Lancet. 2017;389(10064):E1-E. Lallukka T, Millear A, Pain A, Cortinovis M, Giussani G. GBD 2015 mortality and causes of death collaborators. Global, regional, and national life expectancy, all-cause mortality, and cause-specifi c mortality for 249 causes of death, 1980–2015: a systematic analysis for the global burden of disease study 2015 (vol 388, pg 1459, 2016). Lancet. 2017;389(10064):E1-E.
3.
go back to reference Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet (London, England). 2004;364(9438):937.CrossRef Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet (London, England). 2004;364(9438):937.CrossRef
4.
go back to reference Erhardt LR. Rationale for multiple risk intervention: the need to move from theory to practice. Vascular Health Risk Management. 2007;3(6):985.PubMedPubMedCentral Erhardt LR. Rationale for multiple risk intervention: the need to move from theory to practice. Vascular Health Risk Management. 2007;3(6):985.PubMedPubMedCentral
5.
go back to reference Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’agostino RB, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am College Cardiol. 2014;63(25 Part B):2935–59.CrossRef Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’agostino RB, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am College Cardiol. 2014;63(25 Part B):2935–59.CrossRef
6.
go back to reference Sheridan SL, Crespo E. Does the routine use of global coronary heart disease risk scores translate into clinical benefits or harms? A systematic review of the literature. BMC Health Serv Res. 2008;8(1):1–14.CrossRef Sheridan SL, Crespo E. Does the routine use of global coronary heart disease risk scores translate into clinical benefits or harms? A systematic review of the literature. BMC Health Serv Res. 2008;8(1):1–14.CrossRef
7.
go back to reference Willis A, Davies M, Yates T, Khunti K. Primary prevention of cardiovascular disease using validated risk scores: a systematic review. J R Soc Med. 2012;105(8):348–56.CrossRef Willis A, Davies M, Yates T, Khunti K. Primary prevention of cardiovascular disease using validated risk scores: a systematic review. J R Soc Med. 2012;105(8):348–56.CrossRef
8.
go back to reference Usher-Smith JA, Silarova B, Schuit E, Moons KG, Griffin SJ. Impact of provision of cardiovascular disease risk estimates to healthcare professionals and patients: a systematic review. BMJ Open. 2015;5(10):e008717.CrossRef Usher-Smith JA, Silarova B, Schuit E, Moons KG, Griffin SJ. Impact of provision of cardiovascular disease risk estimates to healthcare professionals and patients: a systematic review. BMJ Open. 2015;5(10):e008717.CrossRef
9.
go back to reference Studziński K, Tomasik T, Krzysztoń J, Jóźwiak J, Windak A. Effect of using cardiovascular risk scoring in routine risk assessment in primary prevention of cardiovascular disease: an overview of systematic reviews. BMC Cardiovasc Disord. 2019;19(1):11.CrossRef Studziński K, Tomasik T, Krzysztoń J, Jóźwiak J, Windak A. Effect of using cardiovascular risk scoring in routine risk assessment in primary prevention of cardiovascular disease: an overview of systematic reviews. BMC Cardiovasc Disord. 2019;19(1):11.CrossRef
11.
go back to reference Brindle P, Beswick A, Fahey T, Ebrahim S. Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review. Heart. 2006;92(12):1752–9.CrossRef Brindle P, Beswick A, Fahey T, Ebrahim S. Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review. Heart. 2006;92(12):1752–9.CrossRef
12.
go back to reference Chamnan P, Simmons RK, Sharp SJ, Khaw K-T, Wareham NJ, Griffin SJ. Repeat cardiovascular risk assessment after four years: Is there improvement in risk prediction? PLoS ONE. 2016;11(2):e0147417.CrossRef Chamnan P, Simmons RK, Sharp SJ, Khaw K-T, Wareham NJ, Griffin SJ. Repeat cardiovascular risk assessment after four years: Is there improvement in risk prediction? PLoS ONE. 2016;11(2):e0147417.CrossRef
13.
go back to reference Azizi F, Rahmani M, Emami H, Mirmiran P, Hajipour R, Madjid M, et al. Cardiovascular risk factors in an Iranian urban population: Tehran lipid and glucose study (phase 1). Sozial-und präventivmedizin. 2002;47(6):408–26.CrossRef Azizi F, Rahmani M, Emami H, Mirmiran P, Hajipour R, Madjid M, et al. Cardiovascular risk factors in an Iranian urban population: Tehran lipid and glucose study (phase 1). Sozial-und präventivmedizin. 2002;47(6):408–26.CrossRef
14.
go back to reference Azizi F, Zadeh-Vakili A, Takyar M. Review of rationale, design, and initial findings: Tehran Lipid and Glucose Study. Int J Endocrinol Metab. 2018;16(4 Suppl):e84777.PubMedPubMedCentral Azizi F, Zadeh-Vakili A, Takyar M. Review of rationale, design, and initial findings: Tehran Lipid and Glucose Study. Int J Endocrinol Metab. 2018;16(4 Suppl):e84777.PubMedPubMedCentral
15.
go back to reference Khalili D, Azizi F, Asgari S, Zadeh-Vakili A, Momenan AA, Ghanbarian A, et al. Outcomes of a longitudinal population-based cohort study and pragmatic community trial: findings from 20 years of the Tehran Lipid and Glucose Study. Int J Endocrinol Metab. 2018;16(4 Suppl):e84748.PubMedPubMedCentral Khalili D, Azizi F, Asgari S, Zadeh-Vakili A, Momenan AA, Ghanbarian A, et al. Outcomes of a longitudinal population-based cohort study and pragmatic community trial: findings from 20 years of the Tehran Lipid and Glucose Study. Int J Endocrinol Metab. 2018;16(4 Suppl):e84748.PubMedPubMedCentral
16.
go back to reference Khalili D, Asgari S, Hadaegh F, Steyerberg EW, Rahimi K, Fahimfar N, et al. A new approach to test validity and clinical usefulness of the 2013 ACC/AHA guideline on statin therapy: a population-based study. Int J Cardiol. 2015;184:587–94.CrossRef Khalili D, Asgari S, Hadaegh F, Steyerberg EW, Rahimi K, Fahimfar N, et al. A new approach to test validity and clinical usefulness of the 2013 ACC/AHA guideline on statin therapy: a population-based study. Int J Cardiol. 2015;184:587–94.CrossRef
17.
go back to reference Jones BL, Nagin DS. A Stata plugin for estimating group-based trajectory models. Research Showcase@ CMU Carnegie Mellon University Retrieved on July. 2012;10:2015. Jones BL, Nagin DS. A Stata plugin for estimating group-based trajectory models. Research Showcase@ CMU Carnegie Mellon University Retrieved on July. 2012;10:2015.
18.
go back to reference Jones BL, Nagin DS, Roeder K. A SAS procedure based on mixture models for estimating developmental trajectories. Sociol Methods Res. 2001;29(3):374–93.CrossRef Jones BL, Nagin DS, Roeder K. A SAS procedure based on mixture models for estimating developmental trajectories. Sociol Methods Res. 2001;29(3):374–93.CrossRef
19.
go back to reference Jones BL, Nagin DS. A note on a Stata plugin for estimating group-based trajectory models. Sociol Methods Res. 2013;42(4):608–13.CrossRef Jones BL, Nagin DS. A note on a Stata plugin for estimating group-based trajectory models. Sociol Methods Res. 2013;42(4):608–13.CrossRef
20.
go back to reference Nagin DS, Nagin D. Group-based modeling of development. Cambridge: Harvard University Press; 2005.CrossRef Nagin DS, Nagin D. Group-based modeling of development. Cambridge: Harvard University Press; 2005.CrossRef
21.
go back to reference Guo L, Zhang S. Association between ideal cardiovascular health metrics and risk of cardiovascular events or mortality: a meta-analysis of prospective studies. Clin Cardiol. 2017;40(12):1339–46.CrossRef Guo L, Zhang S. Association between ideal cardiovascular health metrics and risk of cardiovascular events or mortality: a meta-analysis of prospective studies. Clin Cardiol. 2017;40(12):1339–46.CrossRef
22.
go back to reference Wu S, An S, Li W, Lichtenstein AH, Gao J, Kris-Etherton PM, et al. Association of trajectory of cardiovascular health score and incident cardiovascular disease. JAMA Netw Open. 2019;2(5):e194758-e. Wu S, An S, Li W, Lichtenstein AH, Gao J, Kris-Etherton PM, et al. Association of trajectory of cardiovascular health score and incident cardiovascular disease. JAMA Netw Open. 2019;2(5):e194758-e.
23.
go back to reference van Sloten TT, Tafflet M, Périer M-C, Dugravot A, Climie RE, Singh-Manoux A, et al. Association of change in cardiovascular risk factors with incident cardiovascular events. JAMA. 2018;320(17):1793–804.CrossRef van Sloten TT, Tafflet M, Périer M-C, Dugravot A, Climie RE, Singh-Manoux A, et al. Association of change in cardiovascular risk factors with incident cardiovascular events. JAMA. 2018;320(17):1793–804.CrossRef
24.
go back to reference Dhingra R, Vasan RS. Age as a risk factor. Med Clin North Am. 2012;96(1):87–91.CrossRef Dhingra R, Vasan RS. Age as a risk factor. Med Clin North Am. 2012;96(1):87–91.CrossRef
25.
go back to reference Lind L, Sundström J, Ärnlöv J, Lampa E. Impact of aging on the strength of cardiovascular risk factors: a longitudinal study over 40 years. J Am Heart Assoc Cardiovasc Cerebrovasc Dis. 2018;7(1):e007061. Lind L, Sundström J, Ärnlöv J, Lampa E. Impact of aging on the strength of cardiovascular risk factors: a longitudinal study over 40 years. J Am Heart Assoc Cardiovasc Cerebrovasc Dis. 2018;7(1):e007061.
26.
go back to reference Khalili D, Sheikholeslami FH, Bakhtiyari M, Azizi F, Momenan AA, Hadaegh F. 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. 2014;9(8):e105804.CrossRef Khalili D, Sheikholeslami FH, Bakhtiyari M, Azizi F, Momenan AA, Hadaegh F. 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. 2014;9(8):e105804.CrossRef
27.
go back to reference Bress AP, Colantonio LD, Booth JN, Spruill TM, Ravenell J, Butler M, et al. Modifiable risk factors versus age on developing high predicted cardiovascular disease risk in blacks. J Am Heart Assoc. 2017;6(2):e005054.CrossRef Bress AP, Colantonio LD, Booth JN, Spruill TM, Ravenell J, Butler M, et al. Modifiable risk factors versus age on developing high predicted cardiovascular disease risk in blacks. J Am Heart Assoc. 2017;6(2):e005054.CrossRef
28.
go back to reference Seals DR, Brunt VE, Rossman MJ. Keynote lecture: strategies for optimal cardiovascular aging. Am J Physiol Heart Circul Physiol. 2018;315(2):H183–8.CrossRef Seals DR, Brunt VE, Rossman MJ. Keynote lecture: strategies for optimal cardiovascular aging. Am J Physiol Heart Circul Physiol. 2018;315(2):H183–8.CrossRef
29.
go back to reference Collaboration APCS. The impact of cardiovascular risk factors on the age-related excess risk of coronary heart disease. Int J Epidemiol. 2006;35(4):1025–33.CrossRef Collaboration APCS. The impact of cardiovascular risk factors on the age-related excess risk of coronary heart disease. Int J Epidemiol. 2006;35(4):1025–33.CrossRef
30.
go back to reference Fuchs FD, Whelton PK. High blood pressure and cardiovascular disease. Hypertension. 2020;75(2):285–92.CrossRef Fuchs FD, Whelton PK. High blood pressure and cardiovascular disease. Hypertension. 2020;75(2):285–92.CrossRef
Metadata
Title
Trajectories of cardiovascular disease risk and their association with the incidence of cardiovascular events over 18 years of follow-up: The Tehran Lipid and Glucose study
Authors
Fatemeh Koohi
Nooshin Ahmadi
Farzad Hadaegh
Siavash Safiee
Fereidoun Azizi
Davood Khalili
Publication date
01-12-2021
Publisher
BioMed Central
Published in
Journal of Translational Medicine / Issue 1/2021
Electronic ISSN: 1479-5876
DOI
https://doi.org/10.1186/s12967-021-02984-2

Other articles of this Issue 1/2021

Journal of Translational Medicine 1/2021 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine